yogeshchappewar/Logistic Regression_updated_main.ipynb

## Logistic Regression_updated_main.ipynb
{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:14:58.494689Z",
          "end_time": "2023-02-02T10:15:02.194409Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\nimport seaborn as sb\nimport matplotlib.pyplot as plt  ",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:02.194409Z",
          "end_time": "2023-02-02T10:15:02.636875Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.linear_model import LogisticRegression\n#from sklearn.model_selection import train_test_split # train and test \nfrom sklearn import metrics\n#from sklearn import preprocessing\nfrom sklearn.metrics import classification_report  ",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:02.636875Z",
          "end_time": "2023-02-02T10:15:02.694728Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# loading claimants data \nclaimants = pd.read_csv(\"claimants.csv\")\nclaimants ",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "      CASENUM  ATTORNEY  CLMSEX  CLMINSUR  SEATBELT  CLMAGE    LOSS\n0           5         0     0.0       1.0       0.0    50.0  34.940\n1           3         1     1.0       0.0       0.0    18.0   0.891\n2          66         1     0.0       1.0       0.0     5.0   0.330\n3          70         0     0.0       1.0       1.0    31.0   0.037\n4          96         1     0.0       1.0       0.0    30.0   0.038\n...       ...       ...     ...       ...       ...     ...     ...\n1335    34100         1     0.0       1.0       0.0     NaN   0.576\n1336    34110         0     1.0       1.0       0.0    46.0   3.705\n1337    34113         1     1.0       1.0       0.0    39.0   0.099\n1338    34145         0     1.0       0.0       0.0     8.0   3.177\n1339    34153         1     1.0       1.0       0.0    30.0   0.688\n\n[1340 rows x 7 columns]",
            "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>CASENUM</th>\n      <th>ATTORNEY</th>\n      <th>CLMSEX</th>\n      <th>CLMINSUR</th>\n      <th>SEATBELT</th>\n      <th>CLMAGE</th>\n      <th>LOSS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>50.0</td>\n      <td>34.940</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>0.891</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>66</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.330</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>70</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>31.0</td>\n      <td>0.037</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>96</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>30.0</td>\n      <td>0.038</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1335</th>\n      <td>34100</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.576</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>34110</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>46.0</td>\n      <td>3.705</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>34113</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>39.0</td>\n      <td>0.099</td>\n    </tr>\n    <tr>\n      <th>1338</th>\n      <td>34145</td>\n      <td>0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>3.177</td>\n    </tr>\n    <tr>\n      <th>1339</th>\n      <td>34153</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>30.0</td>\n      <td>0.688</td>\n    </tr>\n  </tbody>\n</table>\n<p>1340 rows × 7 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:02.694728Z",
          "end_time": "2023-02-02T10:15:02.710394Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "claimants.head(10)\nclaimants.isnull().sum() ",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "CASENUM       0\nATTORNEY      0\nCLMSEX       12\nCLMINSUR     41\nSEATBELT     48\nCLMAGE      189\nLOSS          0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:02.710394Z",
          "end_time": "2023-02-02T10:15:02.750414Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "claimants.describe()  ",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "            CASENUM     ATTORNEY       CLMSEX     CLMINSUR     SEATBELT  \\\ncount   1340.000000  1340.000000  1328.000000  1299.000000  1292.000000   \nmean   11202.001493     0.488806     0.558735     0.907621     0.017028   \nstd     9512.750796     0.500061     0.496725     0.289671     0.129425   \nmin        0.000000     0.000000     0.000000     0.000000     0.000000   \n25%     4177.000000     0.000000     0.000000     1.000000     0.000000   \n50%     8756.500000     0.000000     1.000000     1.000000     0.000000   \n75%    15702.500000     1.000000     1.000000     1.000000     0.000000   \nmax    34153.000000     1.000000     1.000000     1.000000     1.000000   \n\n            CLMAGE         LOSS  \ncount  1151.000000  1340.000000  \nmean     28.414422     3.806307  \nstd      20.304451    10.636903  \nmin       0.000000     0.000000  \n25%       9.000000     0.400000  \n50%      30.000000     1.069500  \n75%      43.000000     3.781500  \nmax      95.000000   173.604000  ",
            "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>CASENUM</th>\n      <th>ATTORNEY</th>\n      <th>CLMSEX</th>\n      <th>CLMINSUR</th>\n      <th>SEATBELT</th>\n      <th>CLMAGE</th>\n      <th>LOSS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1340.000000</td>\n      <td>1340.000000</td>\n      <td>1328.000000</td>\n      <td>1299.000000</td>\n      <td>1292.000000</td>\n      <td>1151.000000</td>\n      <td>1340.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>11202.001493</td>\n      <td>0.488806</td>\n      <td>0.558735</td>\n      <td>0.907621</td>\n      <td>0.017028</td>\n      <td>28.414422</td>\n      <td>3.806307</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>9512.750796</td>\n      <td>0.500061</td>\n      <td>0.496725</td>\n      <td>0.289671</td>\n      <td>0.129425</td>\n      <td>20.304451</td>\n      <td>10.636903</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>4177.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>9.000000</td>\n      <td>0.400000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>8756.500000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>30.000000</td>\n      <td>1.069500</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>15702.500000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>43.000000</td>\n      <td>3.781500</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>34153.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>95.000000</td>\n      <td>173.604000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:02.750414Z",
          "end_time": "2023-02-02T10:15:02.959135Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "\nsb.boxplot(x=\"ATTORNEY\",y=\"CLMAGE\",data=claimants,palette=\"hls\")  ",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='ATTORNEY', ylabel='CLMAGE'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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pfsWMZVlq06aNxowZo8GDByss7Ko+QBgAACBg/KqRr776Snl5eVq2bJkWLVqkUaNGaezYserYsWN9zQcAAHBJfr0AOC4uTjNmzNCePXv09ttvq7S0VD179tTPfvYz5ebmqrq6ur7mBAAAuKirfjfTnXfeqSVLlmj//v2KiorSxIkTdeLEiQCOBgAAcHlXHTOFhYUaN26c2rVrp5MnT+qNN95Qs2bNAjgaAADA5fn1mpljx45p+fLlWrp0qUpLSzVy5EgVFhbq5ptvrq/5AAAALsmvmGnbtq3i4+M1evRo3XfffQoPD1dVVZV27drlc7tbbrkloEMCAADUxq+YOXfunA4fPqyXX35Zv/nNbyT97/NmzuNzZgAAQEPyK2YOHjxYX3MAAABcFb9PM11KaWmp1q5dq1/+8pd1Gsoktm3L4/E4PQYknz8H/kyCh8vl8vmkcJNwfAcPju/gFCzHt2X/8DxRHezcuVNdu3YNqtNM5eXlio2NVVlZmWJiYgL++N99953Gjx8f8McFQkVubq4iIiKcHuOqcHwDl1afx7c/f38H5FuzAQAAnMKXKwXQ+NgohTu/2vajZdu2zv335zApKJY+f6zO2lJu2WmnxwioqNHjpfBwp8f40bJtWzr33yM8LIzj20lnz+p0Xq7TU/ggZgIo3JLCOcCcY1m6xukZ8F8BO3sdPMLDZREzjrEk6RqO8GAQjEe3XzHz+uuvX3L/119/XadhAAAA/OVXzMybN++yt7n++uuvehgAAAB/8TkzAADAaH69m+mDDz7QTTfdpPLy8hr7ysrKdPPNN2vz5s0BGw4AAOBy/IqZ7OxsjR8//qLv946NjdWECRM0d+7cqxokKytLlmUpNTXVu822bWVkZCg+Pl6RkZHq27evdu/efVWPDwAAQpNfMbNz507179+/1v0pKSkqKirye4itW7dq8eLFNb6gcs6cOZo7d65ycnK0detWxcXFKTk5WRUVFX7/DgAAEJr8iplvvvlG4Zd4a2JYWJj+85//+DXAyZMnNXLkSOXm5qp58+be7bZtKzs7W+np6Ro6dKg6deqkvLw8nT59Wvn5+bU+nsfjUXl5uc8FAACELr9i5rrrrtPnn39e6/5du3apdevWfg3w1FNPaeDAgerXr5/P9oMHD6q4uFgpKSnebS6XS3369FFhYWGtj5eVlaXY2FjvJSEhwa95AACAWfyKmXvvvVczZ87Ud999V2PfmTNn9NJLL2nQoEFX/HhvvfWWioqKlJWVVWNfcXGxJMntdvtsd7vd3n0Xk5aWprKyMu/lyJEjVzwPAAAwj19vzX7hhRf0zjvvqF27dpo8ebLat28vy7K0Z88evfHGG6qqqlJ6evoVPdaRI0c0depUbdy48ZJfUvXDj6y2bfuSH2Ptcrnkcrmu7AkBAADj+RUzbrdbhYWFevLJJ5WWlqbzX7htWZbuueceLViwoMZKSm2KiopUUlKibt26ebdVVVXp448/Vk5Ojvbu3Svp+xWaC09dlZSUXPHvAAAAoc/v72Zq27at1q9fr9LSUh04cEC2bSspKcnnxbtX4u67767x+ptf/epX6tChg2bMmKEbb7xRcXFxKigoUJcuXSRJlZWV2rRpk2bPnu3v2AAAIERd9RdNNm/eXD169LjqXxwdHa1OnTr5bGvSpIlatmzp3Z6amqrMzEwlJSUpKSlJmZmZioqK0ogRI6769wIAgNAS1N+aPX36dJ05c0aTJk1SaWmpevbsqY0bNyo6Otrp0QAAQJAIqpj56KOPfK5blqWMjAxlZGQ4Mg8AAAh+fr01GwAAINgQMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoYU4PAADByrbt//189qyDkwDB48Jj4cJjxEnEDADUorKy0vvzmbxcBycBglNlZaUiIyOdHoPTTAAAwGyszABALa655hrvz5Gjx8sKD3dwGiA42GfPelcqLzxGnETM1NGF5wvPBsm5Q8BpFx4LwXJO/WpYlvW/n8PDiRngBy48RpxEzNTRhefUc8vOODgJEJyC5Zw6gNDFa2YAAIDRWJmpowvPF46PjVR4kCy5AU46a9velcpgOacOIHQRM3V04fnCcMsiZoAfCJZz6gBCF6eZAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRHI2ZrKws9ejRQ9HR0WrVqpWGDBmivXv3+tzGtm1lZGQoPj5ekZGR6tu3r3bv3u3QxAAAINg4GjObNm3SU089pU8//VQFBQU6d+6cUlJSdOrUKe9t5syZo7lz5yonJ0dbt25VXFyckpOTVVFR4eDkAAAgWIQ5+cv/9re/+VxfunSpWrVqpaKiIvXu3Vu2bSs7O1vp6ekaOnSoJCkvL09ut1v5+fmaMGFCjcf0eDzyeDze6+Xl5fX7JAAAgKOC6jUzZWVlkqQWLVpIkg4ePKji4mKlpKR4b+NyudSnTx8VFhZe9DGysrIUGxvrvSQkJNT/4AAAwDFBEzO2bWvatGm688471alTJ0lScXGxJMntdvvc1u12e/f9UFpamsrKyryXI0eO1O/gAADAUY6eZrrQ5MmTtWvXLn3yySc19lmW5XPdtu0a285zuVxyuVz1MiMAAAg+QbEyM2XKFK1Zs0Yffvih2rRp490eFxcnSTVWYUpKSmqs1gAAgB8nR2PGtm1NnjxZ77zzjj744AMlJib67E9MTFRcXJwKCgq82yorK7Vp0yb16tWroccFAABByNHTTE899ZTy8/P1l7/8RdHR0d4VmNjYWEVGRsqyLKWmpiozM1NJSUlKSkpSZmamoqKiNGLECCdHBwAAQcLRmFm4cKEkqW/fvj7bly5dqjFjxkiSpk+frjNnzmjSpEkqLS1Vz549tXHjRkVHRzfwtAAAIBg5GjO2bV/2NpZlKSMjQxkZGfU/EAAAME5QvAAYAADgahEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaGFODxBKztqSZDs9xo+Wbds699+fwyRZluXkOD9qZzkMADQgYiaAcstOOz0CAAA/OpxmAgAARmNlpo5cLpdyc3OdHgOSPB6PJk+eLEnKycmRy+VyeCJICp0/h7NnOYnsINu2pXP/PZEcFsZpZCedPev0BDUQM3VkWZYiIiKcHgM/4HK5+HNBQJ3O439agGDFaSYAAGA0VmYAoBacRg4enEYOTsHy50DMAEAtOI0cnDiNjB/iNBMAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMAAAwGjEDAACMRswAAACjETMAAMBoxAwAADCaETGzYMECJSYmKiIiQt26ddPmzZudHgkAAASJoI+ZFStWKDU1Venp6dq+fbvuuusuDRgwQIcPH3Z6NAAAEATCnB7gcubOnauxY8dq3LhxkqTs7Gxt2LBBCxcuVFZWlsPThQbbtuXxeJweo84ufA6h8HwkyeVyybIsp8eAwTi+gxfHd+AEdcxUVlaqqKhIzz33nM/2lJQUFRYWXvQ+Ho/H51/08vLyep0xFHg8Ho0fP97pMQJq8uTJTo8QELm5uYqIiHB6DBiM4zt4cXwHTlCfZvr2229VVVUlt9vts93tdqu4uPii98nKylJsbKz3kpCQ0BCjAgAAhwT1ysx5P1yGs2271qW5tLQ0TZs2zXu9vLycoLkMl8ul3Nxcp8cIiPOrci6Xy+FJAiNUngecw/EdvELleQSDoI6Za6+9Vo0bN66xClNSUlJjteY8l8vFvyB+siwrZJY6Q+V5AIHC8Y0fg6A+zXTNNdeoW7duKigo8NleUFCgXr16OTQVAAAIJkG9MiNJ06ZN02OPPabu3bvr9ttv1+LFi3X48GFNnDjR6dEAAEAQCPqYGTZsmI4fP65Zs2bp2LFj6tSpk9avX6+2bds6PRoAAAgClm3bttND1Kfy8nLFxsaqrKxMMTExTo8DAACugD9/fwf1a2YAAAAuh5gBAABGI2YAAIDRiBkAAGA0YgYAABiNmAEAAEYjZgAAgNGIGQAAYDRiBgAAGC3ov86grs5/wHF5ebnDkwAAgCt1/u/tK/migpCPmYqKCklSQkKCw5MAAAB/VVRUKDY29pK3CfnvZqqurtbRo0cVHR0ty7KcHgf1rLy8XAkJCTpy5AjfxQWEGI7vHxfbtlVRUaH4+Hg1anTpV8WE/MpMo0aN1KZNG6fHQAOLiYnhP3ZAiOL4/vG43IrMebwAGAAAGI2YAQAARiNmEFJcLpdeeukluVwup0cBEGAc36hNyL8AGAAAhDZWZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmEDIWLFigxMRERUREqFu3btq8ebPTIwEIgI8//liDBw9WfHy8LMvS6tWrnR4JQYaYQUhYsWKFUlNTlZ6eru3bt+uuu+7SgAEDdPjwYadHA1BHp06d0q233qqcnBynR0GQ4q3ZCAk9e/ZU165dtXDhQu+2jh07asiQIcrKynJwMgCBZFmW3n33XQ0ZMsTpURBEWJmB8SorK1VUVKSUlBSf7SkpKSosLHRoKgBAQyFmYLxvv/1WVVVVcrvdPtvdbreKi4sdmgoA0FCIGYQMy7J8rtu2XWMbACD0EDMw3rXXXqvGjRvXWIUpKSmpsVoDAAg9xAyMd80116hbt24qKCjw2V5QUKBevXo5NBUAoKGEOT0AEAjTpk3TY489pu7du+v222/X4sWLdfjwYU2cONHp0QDU0cmTJ3XgwAHv9YMHD2rHjh1q0aKFrr/+egcnQ7DgrdkIGQsWLNCcOXN07NgxderUSfPmzVPv3r2dHgtAHX300Uf6+c9/XmP76NGjtWzZsoYfCEGHmAEAAEbjNTMAAMBoxAwAADAaMQMAAIxGzAAAAKMRMwAAwGjEDAAAMBoxAwAAjEbMAAAAoxEzAADAaMQMgKtSWFioxo0bq3///pKkMWPGyLKsOl3OfzR9VVWV5s2bp1tuuUURERFq1qyZBgwYoH/84x8+Myxbtszn/m63W4MHD9bu3bt9bnd+tt/+9rc+21evXi3LsrzXP/roo1pnKy4u1tixY9W5c2dVVlb6PM769esVHh6uzz77LFD/eAH4gZgBcFXefPNNTZkyRZ988okOHz6s3//+9zp27Jj3IklLly71Xj906JDP/kceeUT9+/f32TZs2DDZtq3hw4dr1qxZevrpp7Vnzx5t2rRJCQkJ6tu3r1avXu0zR0xMjI4dO6ajR4/qr3/9q06dOqWBAwfWCI6IiAjNnj1bpaWll31ue/fu9Znr2LFjatWqlbKzs1VRUaGXXnrJe9sTJ07oiSeeUHp6urp37173f7AA/Ma3ZgPw26lTp7Ry5Upt3bpVxcXFWrZsmWbOnKnY2Fif2zVr1kxxcXEXfYzIyEh5PJ4a+1esWKG3335ba9as0eDBg73bFy9erOPHj2vcuHFKTk5WkyZNJEmWZXkfo3Xr1nrmmWd03333ae/evercubP3/v369dOBAweUlZWlOXPmXPL5tWrVSs2aNauxPTo6WsuWLVNKSoqGDBminj17KjU1Va1bt9YLL7xwyccEUH9YmQHgtxUrVqh9+/Zq3769Ro0apaVLlypQ31mbn5+vdu3a+YTMec8++6yOHz+ugoKCi973xIkTys/PlySFh4f77GvcuLEyMzM1f/58ffXVV1c9X9++fTVp0iSNHj1aq1at0sqVK7V8+XKFhfH/hoBTiBkAfluyZIlGjRolSerfv79Onjyp999/PyCPvW/fPnXs2PGi+85v37dvn3dbWVmZmjZtqiZNmqh58+Z66623dN9996lDhw417v/AAw/otttu8zlNdDFt2rRR06ZNvZf27dv77M/KypJlWRo+fLgyMzNrnRdAwyBmAPhl79692rJli4YPHy5JCgsL07Bhw/Tmm2822AwXvmg3OjpaO3bsUFFRkRYtWqSf/OQnWrRoUa33nT17tvLy8vTFF1/UepvNmzdrx44d3suGDRt89kdGRurZZ59VVFSUpk6dWvcnBKBOWBcF4JclS5bo3Llzuu6667zbbNtWeHi4SktL1bx58zo9frt27WoNjT179kiSkpKSvNsaNWqkn/70p5KkDh06qLi4WMOGDdPHH3980cfo3bu37rnnHj3//PMaM2bMRW+TmJh40dfMXCgsLEyNGzf2CSsAzmBlBsAVO3funJYvX67XXnvNZ+Vi586datu2rf70pz/V+XcMHz5c+/fv19q1a2vse+2119SyZUslJyfXev9nnnlGO3fu1LvvvlvrbbKysrR27VoVFhbWeV4AzmNlBsAVW7dunUpLSzV27Nga71x66KGHtGTJEk2ePLlOv2P48OFatWqVRo8erVdffVV33323ysvL9cYbb2jNmjVatWqV951MFxMTE6Nx48bppZde0pAhQy66cnLLLbdo5MiRmj9//kUfo6SkRN99953PtpYtW9Z4UTGA4MDKDIArtmTJEvXr169GyEjSgw8+qB07dmjbtm11+h2WZWnlypVKT0/XvHnz1KFDB9111106dOiQPvzwQw0ZMuSyjzF16lTt2bNHq1atqvU2L7/8cq3vwGrfvr1at27tcykqKrrapwSgnll2oN5PCQAA4ABWZgAAgNGIGQAAYDRiBgAAGI2YAQAARiNmAACA0YgZAABgNGIGAAAYjZgBAABGI2YAAIDRiBkAAGA0YgYAABjt/wFsjicb4PitQQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:02.959135Z",
          "end_time": "2023-02-02T10:15:02.975186Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Droping first column \nclaimants.drop([\"CASENUM\"],inplace=True,axis = 1)\n#cat_cols = [\"ATTORNEY\",\"CLMSEX\",\"SEATBELT\",\"CLMINSUR\"]\n#cont_cols = [\"CLMAGE\",\"LOSS\"]\n\n# Getting the barplot for the categorical columns  ",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:02.975186Z",
          "end_time": "2023-02-02T10:15:03.007359Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "claimants   ",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 8,
          "data": {
            "text/plain": "      ATTORNEY  CLMSEX  CLMINSUR  SEATBELT  CLMAGE    LOSS\n0            0     0.0       1.0       0.0    50.0  34.940\n1            1     1.0       0.0       0.0    18.0   0.891\n2            1     0.0       1.0       0.0     5.0   0.330\n3            0     0.0       1.0       1.0    31.0   0.037\n4            1     0.0       1.0       0.0    30.0   0.038\n...        ...     ...       ...       ...     ...     ...\n1335         1     0.0       1.0       0.0     NaN   0.576\n1336         0     1.0       1.0       0.0    46.0   3.705\n1337         1     1.0       1.0       0.0    39.0   0.099\n1338         0     1.0       0.0       0.0     8.0   3.177\n1339         1     1.0       1.0       0.0    30.0   0.688\n\n[1340 rows x 6 columns]",
            "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>ATTORNEY</th>\n      <th>CLMSEX</th>\n      <th>CLMINSUR</th>\n      <th>SEATBELT</th>\n      <th>CLMAGE</th>\n      <th>LOSS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>50.0</td>\n      <td>34.940</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0</td>\n      <td>0.891</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.330</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>31.0</td>\n      <td>0.037</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>30.0</td>\n      <td>0.038</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1335</th>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.576</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>46.0</td>\n      <td>3.705</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>39.0</td>\n      <td>0.099</td>\n    </tr>\n    <tr>\n      <th>1338</th>\n      <td>0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>3.177</td>\n    </tr>\n    <tr>\n      <th>1339</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>30.0</td>\n      <td>0.688</td>\n    </tr>\n  </tbody>\n</table>\n<p>1340 rows × 6 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.007359Z",
          "end_time": "2023-02-02T10:15:03.120459Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sb.countplot(x=\"ATTORNEY\",data=claimants,palette=\"hls\") \n ",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='ATTORNEY', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.120459Z",
          "end_time": "2023-02-02T10:15:03.152929Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.crosstab(claimants.ATTORNEY,claimants.CLMINSUR)  ",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "CLMINSUR  0.0  1.0\nATTORNEY          \n0          76  585\n1          44  594",
            "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>CLMINSUR</th>\n      <th>0.0</th>\n      <th>1.0</th>\n    </tr>\n    <tr>\n      <th>ATTORNEY</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>76</td>\n      <td>585</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>44</td>\n      <td>594</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.152929Z",
          "end_time": "2023-02-02T10:15:03.321513Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.crosstab(claimants.ATTORNEY,claimants.CLMINSUR).plot(kind = 'bar')  ",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='ATTORNEY'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.321513Z",
          "end_time": "2023-02-02T10:15:03.430648Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sb.countplot(x=\"SEATBELT\",data=claimants,palette=\"hls\")\n ",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='SEATBELT', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.433525Z",
          "end_time": "2023-02-02T10:15:03.561263Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sb.countplot(x=\"CLMINSUR\",data=claimants,palette=\"hls\") ",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='CLMINSUR', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.561263Z",
          "end_time": "2023-02-02T10:15:03.705561Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.crosstab(claimants.SEATBELT,claimants.CLMINSUR).plot(kind=\"bar\")  ",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='SEATBELT'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.705561Z",
          "end_time": "2023-02-02T10:15:03.818122Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sb.countplot(x=\"CLMSEX\",data=claimants,palette=\"hls\")  ",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='CLMSEX', ylabel='count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.818857Z",
          "end_time": "2023-02-02T10:15:03.970663Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.crosstab(claimants.CLMSEX,claimants.CLMINSUR).plot(kind=\"bar\") ",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='CLMSEX'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.971998Z",
          "end_time": "2023-02-02T10:15:03.987045Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "claimants.isnull().sum()\n#claimants.shape # 1340 6 => Before dropping null values\n# To drop null values ( dropping rows)\n#claimants.dropna().shape # 1096 6 => After dropping null values ",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "ATTORNEY      0\nCLMSEX       12\nCLMINSUR     41\nSEATBELT     48\nCLMAGE      189\nLOSS          0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:03.987045Z",
          "end_time": "2023-02-02T10:15:04.003349Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Fill nan values with mode of the categorical column \nclaimants[\"CLMSEX\"].fillna(1,inplace=True) # claimants.CLMSEX.mode() = 1\nclaimants[\"CLMINSUR\"].fillna(1,inplace=True) # claimants.CLMINSUR.mode() = 1\nclaimants[\"SEATBELT\"].fillna(0,inplace=True) # claimants.SEATBELT.mode() = 0 ",
      "execution_count": 18,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.005198Z",
          "end_time": "2023-02-02T10:15:04.019740Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "claimants.CLMSEX.mode()\nclaimants.CLMINSUR.mode()\nclaimants.SEATBELT.mode()\nclaimants.CLMAGE.mean() ",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 19,
          "data": {
            "text/plain": "28.414422241529106"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.019740Z",
          "end_time": "2023-02-02T10:15:04.035947Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "claimants.CLMAGE.fillna(28.4144,inplace=True) # claimants.CLMAGE.mean() = 28.4 ",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.035947Z",
          "end_time": "2023-02-02T10:15:04.051855Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "claimants.isnull().sum()  ",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "ATTORNEY    0\nCLMSEX      0\nCLMINSUR    0\nSEATBELT    0\nCLMAGE      0\nLOSS        0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.051855Z",
          "end_time": "2023-02-02T10:15:04.067744Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Model building \nfrom sklearn.linear_model import LogisticRegression\nclaimants.shape   ",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": "(1340, 6)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.067744Z",
          "end_time": "2023-02-02T10:15:04.115757Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "X = claimants.iloc[:,[1,2,3,4,5]]\nY = claimants.iloc[:,0]\nclassifier = LogisticRegression()\nclassifier.fit(X,Y) ",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "LogisticRegression()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.115757Z",
          "end_time": "2023-02-02T10:15:04.136643Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "classifier.coef_  # coefficients of features   ",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "array([[ 0.31711334,  0.5055713 , -0.52814335,  0.0066041 , -0.3224325 ]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.136643Z",
          "end_time": "2023-02-02T10:15:04.152665Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "classifier.predict_proba (X) # Probability values   ",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "array([[9.99974075e-01, 2.59253820e-05],\n       [4.95386951e-01, 5.04613049e-01],\n       [4.25082812e-01, 5.74917188e-01],\n       ...,\n       [2.85349951e-01, 7.14650049e-01],\n       [6.86685509e-01, 3.13314491e-01],\n       [3.38781941e-01, 6.61218059e-01]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.152665Z",
          "end_time": "2023-02-02T10:15:04.176668Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_pred = classifier.predict(X)\nclaimants[\"y_pred\"] = y_pred\nclaimants   ",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "      ATTORNEY  CLMSEX  CLMINSUR  SEATBELT   CLMAGE    LOSS  y_pred\n0            0     0.0       1.0       0.0  50.0000  34.940       0\n1            1     1.0       0.0       0.0  18.0000   0.891       1\n2            1     0.0       1.0       0.0   5.0000   0.330       1\n3            0     0.0       1.0       1.0  31.0000   0.037       1\n4            1     0.0       1.0       0.0  30.0000   0.038       1\n...        ...     ...       ...       ...      ...     ...     ...\n1335         1     0.0       1.0       0.0  28.4144   0.576       1\n1336         0     1.0       1.0       0.0  46.0000   3.705       0\n1337         1     1.0       1.0       0.0  39.0000   0.099       1\n1338         0     1.0       0.0       0.0   8.0000   3.177       0\n1339         1     1.0       1.0       0.0  30.0000   0.688       1\n\n[1340 rows x 7 columns]",
            "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>ATTORNEY</th>\n      <th>CLMSEX</th>\n      <th>CLMINSUR</th>\n      <th>SEATBELT</th>\n      <th>CLMAGE</th>\n      <th>LOSS</th>\n      <th>y_pred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>50.0000</td>\n      <td>34.940</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0000</td>\n      <td>0.891</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0000</td>\n      <td>0.330</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>31.0000</td>\n      <td>0.037</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>30.0000</td>\n      <td>0.038</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1335</th>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>28.4144</td>\n      <td>0.576</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>46.0000</td>\n      <td>3.705</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>39.0000</td>\n      <td>0.099</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1338</th>\n      <td>0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0000</td>\n      <td>3.177</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1339</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>30.0000</td>\n      <td>0.688</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>1340 rows × 7 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.176668Z",
          "end_time": "2023-02-02T10:15:04.208672Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_prob = pd.DataFrame(classifier.predict_proba(X.iloc[:,:]))\nnew_df = pd.concat([claimants,y_prob],axis=1)\nnew_df  ",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "      ATTORNEY  CLMSEX  CLMINSUR  SEATBELT   CLMAGE    LOSS  y_pred         0  \\\n0            0     0.0       1.0       0.0  50.0000  34.940       0  0.999974   \n1            1     1.0       0.0       0.0  18.0000   0.891       1  0.495387   \n2            1     0.0       1.0       0.0   5.0000   0.330       1  0.425083   \n3            0     0.0       1.0       1.0  31.0000   0.037       1  0.490007   \n4            1     0.0       1.0       0.0  30.0000   0.038       1  0.363271   \n...        ...     ...       ...       ...      ...     ...     ...       ...   \n1335         1     0.0       1.0       0.0  28.4144   0.576       1  0.406789   \n1336         0     1.0       1.0       0.0  46.0000   3.705       0  0.549435   \n1337         1     1.0       1.0       0.0  39.0000   0.099       1  0.285350   \n1338         0     1.0       0.0       0.0   8.0000   3.177       0  0.686686   \n1339         1     1.0       1.0       0.0  30.0000   0.688       1  0.338782   \n\n             1  \n0     0.000026  \n1     0.504613  \n2     0.574917  \n3     0.509993  \n4     0.636729  \n...        ...  \n1335  0.593211  \n1336  0.450565  \n1337  0.714650  \n1338  0.313314  \n1339  0.661218  \n\n[1340 rows x 9 columns]",
            "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>ATTORNEY</th>\n      <th>CLMSEX</th>\n      <th>CLMINSUR</th>\n      <th>SEATBELT</th>\n      <th>CLMAGE</th>\n      <th>LOSS</th>\n      <th>y_pred</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>50.0000</td>\n      <td>34.940</td>\n      <td>0</td>\n      <td>0.999974</td>\n      <td>0.000026</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>18.0000</td>\n      <td>0.891</td>\n      <td>1</td>\n      <td>0.495387</td>\n      <td>0.504613</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>5.0000</td>\n      <td>0.330</td>\n      <td>1</td>\n      <td>0.425083</td>\n      <td>0.574917</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>31.0000</td>\n      <td>0.037</td>\n      <td>1</td>\n      <td>0.490007</td>\n      <td>0.509993</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>30.0000</td>\n      <td>0.038</td>\n      <td>1</td>\n      <td>0.363271</td>\n      <td>0.636729</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1335</th>\n      <td>1</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>28.4144</td>\n      <td>0.576</td>\n      <td>1</td>\n      <td>0.406789</td>\n      <td>0.593211</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>46.0000</td>\n      <td>3.705</td>\n      <td>0</td>\n      <td>0.549435</td>\n      <td>0.450565</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>39.0000</td>\n      <td>0.099</td>\n      <td>1</td>\n      <td>0.285350</td>\n      <td>0.714650</td>\n    </tr>\n    <tr>\n      <th>1338</th>\n      <td>0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0000</td>\n      <td>3.177</td>\n      <td>0</td>\n      <td>0.686686</td>\n      <td>0.313314</td>\n    </tr>\n    <tr>\n      <th>1339</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>30.0000</td>\n      <td>0.688</td>\n      <td>1</td>\n      <td>0.338782</td>\n      <td>0.661218</td>\n    </tr>\n  </tbody>\n</table>\n<p>1340 rows × 9 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.208672Z",
          "end_time": "2023-02-02T10:15:04.224673Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import confusion_matrix\nconfusion_matrix = confusion_matrix(Y,y_pred)\nprint (confusion_matrix) \n",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[[435 250]\n [147 508]]\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.224673Z",
          "end_time": "2023-02-02T10:15:04.256676Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.crosstab(y_pred,Y)  ",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 29,
          "data": {
            "text/plain": "ATTORNEY    0    1\nrow_0             \n0         435  147\n1         250  508",
            "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>ATTORNEY</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>row_0</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>435</td>\n      <td>147</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>250</td>\n      <td>508</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.256676Z",
          "end_time": "2023-02-02T10:15:04.272678Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#type(y_pred)\naccuracy = sum(Y==y_pred)/claimants.shape[0]\naccuracy ",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/plain": "0.7037313432835821"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.272678Z",
          "end_time": "2023-02-02T10:15:04.288679Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import classification_report \nprint (classification_report (Y, y_pred))  ",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": "              precision    recall  f1-score   support\n\n           0       0.75      0.64      0.69       685\n           1       0.67      0.78      0.72       655\n\n    accuracy                           0.70      1340\n   macro avg       0.71      0.71      0.70      1340\nweighted avg       0.71      0.70      0.70      1340\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.288679Z",
          "end_time": "2023-02-02T10:15:04.304682Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import roc_curve\nLogit_roc_score=roc_auc_score(Y,classifier.predict(X))\nLogit_roc_score  ",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": "0.7053045077171672"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.304682Z",
          "end_time": "2023-02-02T10:15:04.477473Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fpr, tpr, thresholds = roc_curve(Y,classifier.predict_proba(X)[:,1]) \nplt.plot(fpr, tpr, label='Logistic Regression (area=%0.2f)'% Logit_roc_score)\nplt.plot([0, 1], [0, 1],'r--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic')\nplt.legend(loc=\"lower right\")\nplt.show()   ",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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2bFmGDRvGqlWraNy4McOHD6dy5cqYzWbCw8P5448/ePPNN6lTp85DY+zXrx+vvvoqV65coX79+pYWqRQTJ05kw4YN1K9fn6FDh1KmTBkSEhI4f/48a9euZe7cuY/VzN+yZUtat27NyJEjiYmJoUGDBpZZY9WqVaNnz55WxwcGBtKxY0fGjx9PQEAA33zzDRs2bOB///sfbm5uAJnyebRv356pU6fSvXt3XnnlFa5fv86nn36a5kyeSpUqsWLFClauXEnx4sVxcXGhUqVKGf4s7tWmTRsaNGjAm2++SUxMDDVq1GDnzp0sWbIEwGqGX1qGDRvGypUreeaZZxg1ahS1a9cmPj6ezZs306FDB5o2bZruWLy8vGjcuDFTpkyhYMGCBAcHs3nzZhYuXGjVkvMoHTp0YPHixZQtW5bKlSuzb98+pkyZkurnJj2xe3p6EhQUxM8//0zz5s3x8fGxxDZjxgwaNmxIo0aNeO211wgODub27ducOXOGX3755bGSw0OHDjFkyBCef/55SpUqhZOTE3///TeHDh2yaiXLyM/C1KlTadiwIXXq1GHUqFGULFmSf//9lzVr1jBv3jzLH0PCxuk7VluIjEmZMXP/bCWllIqPj1fFihVTpUqVsszQOXjwoHrhhReUr6+vcnR0VP7+/qpZs2Zq7ty5Vs+9ePGi6tevn/L391eOjo4qMDBQvfDCC+rff/+1HHPnzh313nvvqTJlyignJyfl7e2tKlWqpIYPH64iIyMtx90/ayxFdHS0cnV1VYBasGBBmu/v2rVraujQoSokJEQ5OjoqHx8fVaNGDTVmzBh1584dpdTd2TFTpkxJ9+cWHx+vRo4cqYKCgpSjo6MKCAhQr732mrp586bVcUFBQap9+/bqhx9+UBUqVFBOTk4qODhYTZ06NdU50/t5AGrw4MFpxvXVV1+pMmXKKGdnZ1W8eHE1efJktXDhQgWosLAwy3Hnz59XrVq1Up6engpQQUFBVp9FWrPGrl27ZvVaKT879573xo0bqm/fvipfvnzKzc1NtWzZUu3atUsBVjO1HuTmzZvqjTfeUMWKFVOOjo7K19dXtW/fXp04ccIqvrSuFffNyLp06ZLq0qWLyp8/v/L09FRt2rRRR44cSfXz9LD/Azdv3lT9+/dXvr6+ys3NTTVs2FBt3bpVNWnSJNVMq0fFrpRSf/75p6pWrZpydnZWgFUcYWFhql+/fqpw4cLK0dFRFSpUSNWvX19NmjTJckzKzLDvv/8+Vaz3zxr7999/VZ8+fVTZsmWVu7u78vDwUJUrV1bTpk2zmnGXkZ8FpZQ6duyYev7551WBAgWUk5OTKlasmOrTp49KSEhIFZOwTQal7lm5TAhh04KDg6lYsSK//vqr3qHoZvny5fTo0YPt27dTv359vcMRQmQx6RoTQtisb7/9lsuXL1OpUiXs7OzYtWsXU6ZMoXHjxpIECWEjJBESQtgsT09PVqxYwaRJk4iNjSUgIIA+ffowadIkvUMTQmQT6RoTQgghhM2S6fNCCCGEsFmSCAkhhBDCZkkiJIQQQgibZXODpc1mM1euXMHT0zPdy74LIYQQQl9KKW7fvk1gYOAjFzzNCJtLhK5cuULRokX1DkMIIYQQj+HixYuZupmuzSVCKUuqX7x4ES8vL52jEUIIIUR6xMTEULRo0UzfGsXmEqGU7jAvLy9JhIQQQohcJrOHtchgaSGEEELYLEmEhBBCCGGzJBESQgghhM2SREgIIYQQNksSISGEEELYLEmEhBBCCGGzJBESQgghhM2SREgIIYQQNksSISGEEELYLEmEhBBCCGGzdE2EtmzZwtNPP01gYCAGg4Gffvrpkc/ZvHkzNWrUwMXFheLFizN37tysD1QIIYQQeZKuiVBsbCxVqlTh888/T9fxYWFhtGvXjkaNGhEaGsq7777L0KFDWbVqVRZHKoQQQoi8SNdNV9u2bUvbtm3TffzcuXMpVqwY06dPB6BcuXLs3buXTz/9lC5dumRRlEIIIYTIq3LV7vM7d+6kVatWVnWtW7dm4cKFJCcn4+joqFNkQgghhMhsRy5Hc+lmHABxN29kyWvkqkQoMjISPz8/qzo/Pz+MRiNRUVEEBASkek5iYiKJiYmWckxMTJbHKYQQQuRGySYzBy/ewmhWlrqdZ6+z/mgkLo722RrL2Wt3uJ1gtJRnfjc2S14nVyVCAAaDwaqslEqzPsXkyZOZMGFClsclhBBC5EZKKcJvxJFoNDNq1SH2h9/SO6RUagbl569O/WDa/kw/d65KhPz9/YmMjLSqu3r1Kg4ODhQoUCDN54wePZoRI0ZYyjExMRQtWjRL4xRCCCFyqpuxSSSbzQDEJ5loMmVTmseVKORuuR+TYGRI05IUzueaHSEC4HrxAt5HQgka3A9PF0diYioyc9qQTH+dXJUI1atXj19++cWq7o8//qBmzZoPHB/k7OyMs7NzdoQnhBBC5Bgms8KslFXdFxvPMP3P0w98TgF3Jwp5OvN1v9r4eblkdYhpUwqWLIEhQyAxEZrXgapVs+zldE2E7ty5w5kzZyzlsLAwDhw4gI+PD8WKFWP06NFcvnyZJUuWADBw4EA+//xzRowYwcsvv8zOnTtZuHAh3377rV5vQQghhMhxdpyNYsDXe4lLMj3wGLv/RpSYFVQpmo+Vr9TN9nFAqdy8Ca++Ct9/r5UbNYL8+bP0JXVNhPbu3UvTpk0t5ZQurN69e7N48WIiIiIIDw+3PB4SEsLatWsZPnw4X3zxBYGBgcycOVOmzgshhBDArbgkfj5whXFrjj7wGA9nB354rR5l/b2yMbJ02LQJevaES5fAwQEmTICRI8E+a5Mzg1L3tZvlcTExMXh7exMdHY2XVw77IRBCCCHS4czV27yx4gDR8clW9ZduxluVR7ctS7faxazqXBztcHbQueXnfmPHwqRJWrdYqVKwbBnUqmV1SFZ9f+eqMUJCCCGELYiIjifsWiwAJyJv880/F3B3uvuVffhy9EOf7+5kz5j25elep9hDj8sxPD21JOjll2HqVPDwyLaXlkRICCGE0NHhS9F8uPYYjvbarlcxCUYOXryVruc2KV2I4S1LW9X5uDlRrIBbZoeZuZSCqCgoVEgrv/mm1gL01FPZHookQkIIIUQ2uHo7AdN/CxV+veMCEdHxRN1JZPuZ6w98Thk/TwDuJBrpWqsolQp7Wx5zdrSjVrCPJYHKNa5dg/794dQp2L8f3NzAzk6XJAgkERJCCCEyXUKy9WytKetPsnBb2EOf81yNItQvcXdNvNohPhTJn8NbdjJq3Tro2xciI8HJCXbsgBYtdA1JEiEhhBAiEyil+HH/Zd764SAPm4bkZG+HQpFsUrzfoTwGoGlZX0IKuj/4SbldQoI2A2zmTK1cvjwsXw5VqugbF5IICSGEEA908UYcm05exXxfYvNvTAKzN53FxfFut1RCsvmh5/JyceCnwQ0oXij7BgLnCIcPQ/fucOSIVh4yBD75BFyzb5Xqh5FESAghhLhHTEIyHWZuI9lkJiI64aHHPij5Gdq8FP0bhmBvd3cfTGcHu9w3niczvPuulgT5+sKiRdCund4RWZFESAghhPjPttNRvLTwn1T11YvlI+C+fbbMZkWrCn7UDPKx1NnZGQj0dnngRuA2ae5crVts6lQtGcphJBESQgiR50VGJ3DtdiLX7iQwZ9NZnBxSt8xcv5PEicjblrK/lwsLetXE39uFQp6yZ2W6/fwz7NwJH3+slQsXhm++0Temh5BESAghRJ50PiqWKX+c5GTkbc5cvZOh537QqSI96wZlUWR5VGwsjBgB8+dr5WbNoFUrfWNKB0mEhBBC5Dk/hV5m2MoDqeoDvF2ISzLRuHQhWpRL3U1jMBioE+Kj387rudW+fdqA6FOnwGCAt96CJk30jipdJBESQgiRK8UmGvl2d3iq/bYAZv19xnK/SelCtCzvR8vyfpLgZDaTCaZMgfffB6NR6wZbskRrDcolJBESQgiRa5jMirWHI7h2O5Hlu8Mf2eW1uG8tniqT8wbo5hldu8KqVdr9Ll20bjEfn4c/J4eRREgIIUSOFn49jn3hNwDYfPIaPx24kuqY3vVSj+cpH+glSVBWe+klWL9eWyixTx+tWyyXkURICCFEjrTz7HXe/O4AVx6wlk/HKoG4ONrxSuMSlPS1sUUK9RITAydOQO3aWrlTJzh37u7mqbmQJEJCCCF0cSsuifPX4zCZFf9bd4J72xKi45OtprID1ArOj4ujPY72dgxoFEL9EgWzN2Bbt3Mn9OgB0dHaatGBgVp9Lk6CQBIhIYQQOrh4I45Gn2xM17FDmpakZ70gGeisF6MRJk3SbiYTBAfDv//eTYRyOUmEhBBCPBazWXE70Zih55jMivYzt1ptXVEkvytms8Lf24V+DUOsjq8VLFPZdXXunDYOaOdOrfzSS/D55+DtrW9cmUgSISGEEOly+VY83++9SJLRjALmbDr7xOfsVqsoH3ep/OTBicz39dfaBql37miJz5w58OKLekeV6SQREkIIYXHq39tsPnkNgA/XHsdgwDJ25/4d2J9EQQ8n/n7rKbxcHDPvpCJz7dqlJUGNGsHSpRCUN1falkRICCHysMXbw5j46zF83J3SdXzUnSSrslJwf/5TpWg+ahTLD0C1YvloXcE/QzEZDNjmLuy5gdEIDv+lBp99BhUrwsCBYG+vb1xZSBIhIYTIYyKjE3hjRSgHL90iIdkMpE5wHqVFOV+8XBwJLuhOt1pFLfVuzg54OMtXR56TlARjx2pbZaxfD3Z24OYGgwfrHVmWk59mIYTIY9rN3MqNWOvEZ9aL1Sjt55mu5/t7u+DtKl1WNuPECW1a/P79WvmPP6BNG31jykaSCAkhRB6QZDSz7cw1pm04bUmCShRy56POlahcJB+uTnm3a0M8JqVg3jxtx/j4eG1rjC+/tKkkCCQREkKIXOtOopGrMQl8tT2Mb3aFp3r819cbSQIk0nbtGvTvD7/8opVbtNBmieWRtYEyQhIhIYTIZZKMZp6etY2T/95O8/Eu1Yvw2lMlJAkSD9atG/z9Nzg5weTJMGyYNi7IBkkiJIQQOYzZrFhz8IrVooMpZm86w+2E1IsYlvHzZNzT5alfUradEOnw2WfQrx8sWgRVqugdja4kERJCiBzmn7AbDFt54JHHuTvZs2N0czydHbCzy327fotsdPgwhIZCr15auWpVbYZYLtwtPrNJIiSEENkoLsnIC/N2cifBiOEBX0J3/tu2ooC7E03L+qZ63NPFgSFNS1LAwzlLYxV5gNkMs2bByJHa/YoVoXp17TFJggBJhIQQItvsu3CTLnN2pPv4JmUK8enztt1tIZ5ARAT06aNNhwdo3x6KFNE1pJxIEiEhhMhiUXcSOXctlhfm7bTUOTnYsWxAnQc+x97OQKXCeWdjS5HNfv5ZmxV2/Tq4uMDUqdoK0dIKlIokQkIIkYWGfhvKmoNXrOrebl2GQU+VeGDXmBBP5I03YOZM7X7VqrB8OZQrp2tIOZltzpUTQogsYjYrjCYzRpOZN787aJUE+bg7MeipEgxuWlKSIJF1goO1f996S9s4VZKgh5IWISGEeEI3Y5P4MfQy+y7cYO3hyDSP2Tm6GQHertkcmbAJJhNERkLhwlr5jTegYUOoVUvfuHIJSYSEEOIx7DgTxQe/HefctTskGs0PPM7b1ZF1wxpJEiSyxsWL0LOnNjB6/35wd9cWRpQkKN0kERJCiHRSSrE77AYvL9lLTBqLGhZwd6JVBT+eq1GEEoU8AHBzcsDJQUYhiCywcqU2APrWLS0BCg3VWoJEhkgiJIQQ6ZCQbGLmX6eZvemsVX27Sv683qwUBTyc8PV00Sk6YVNiYuD112HJEq1cuzYsWwYlS+obVy4liZAQQqTD4GX7+evEVUu5QckCLOxdCxdH2c9LZKOdO6FHDwgL07rAxoyB998HR0e9I8u1JBESQoiHCIuK5dvd4VZJ0Je9atKivJ+OUQmbNWmSlgQFB8PSpdIVlgkkERJC2Kxz1+6wYs9Fkh4w2HnxjvOp6n5/oxHlAryyODIhHuDLL+GDD7Qd471lwc3MIImQEMIm/XE0kleW7kv38W+3LkNJXw/K+ntmYVRC3EMprdUnNBSmTdPqAgJg9mx948pjJBESQuRpZ67eYc6ms6zafwk3J208T1ySyeqYIvldeaZqYJrPL+ThTPc6QTLzS2Svmze1GWHffaeVO3SA5s31jSmPkkRICJGnDfh6D+evxwGpEyCAoc1K8mqTErg7y69DkUNs2qStDXTpEjg4wIQJ8NRTekeVZ8n/fCFEnhOfZCI0/Cbbz0ZZkqBSvh6MaV/Osr4PQGA+V+ztZKsLkUMkJcHYsfDJJ1q3WKlS2rR4WRwxS0kiJITIM+KTTJy9dof+X+/h35hEq8d+GFgfbzeZYixysE6d4PfftfsDBmjjgjw8HvoU8eQkERJC5AkHLt6i0xfbU9UXye/Kp89XkSRI5HyvvQa7d8OCBdC5s97R2AxJhIQQuYZSivjku+N8Lt2M59vd4ZjMiiU7L1jqvVwcKOPvyeK+tWXsj8i5rl2DEyegUSOt/PTTcO4ceMnyDNlJfkMIIXK8dUciORYRw8y/Tj/y2NeeKsHINmWzISohnsD69dCnDyQmwqFDUKSIVi9JULaTREgIkSMdvHiL/eE3ibqTyBcbzz702PolClAz2IdCns50rVk0myIU4jEkJMCoUTBjhlYuXx5u39Y3JhsniZAQIkc5diWGId/u59y12FSP9aoXRClfD7rUKGKpc7CzkzV+RO5w+DB07w5HjmjlIUO0GWKurvrGZeMkERJC5Ch/n/jXKglqXykAOzsDbSr4075ygI6RCfEEZsyAkSO1rjBfX1i0CNq10zsqgSRCQgidJZvMDF62n9sJRgAu3dLW/alcxJtvX64rg51F3nDqlJYEtW8PX32lJUMiR5DfMEKIbHXpZhyT155AoQBYezgyzeOalvGVJEjkbomJ4Oys3Z8yBerU0VaMNsginjmJ/JYRQmSba7cTafi/jQ98fNaL1QBwdbSnYamC2RWWEJkrNhbefBNOnoQ//wR7e3Bzg1699I5MpEESISFEtvh+70Xe/uGQpVy/RAHaVvQHoIiPG03LSFeByAP27YMePbQkCGDLFmjaVN+YxENJIiSEyFTbz0RxPCImVf0vB69Y7j9XowifPl8lO8MSImuZTPDpp/Dee2A0QuHC8PXXkgTlApIICSEe29JdF5iw5ij53JwAuBmXhMmsHvqc0W3L8mqTEtkRnhDZ4+JFbezP5s1auUsXmDcPChTQNy6RLpIICSHS5WTkbS7djOPDtcexNxi4fCueuCRtu4uoO4mpju9UNTBVnberI8/dswaQEHlC9+6wbRu4u8PMmdC3rwyIzkUkERJCPJTJrOj0xXYOX45+4DEzulWljL+npVy8oIcscihsx+efw+uva9PiS5bUOxqRQZIICSHSZDIrDly8SZc5O63qKxfxpoC7Ey83Lo4BA1WKeuPmJL9KhA3ZuVNbHfrll7VylSpat5i0AuVKuv/JNnv2bEJCQnBxcaFGjRps3br1occvW7aMKlWq4ObmRkBAAH379uX69evZFK0QtuPlJXtTJUEHx7ZizZCGLOpbm/olClKvRAFJgoTtMBph/Hhtt/hBg7QZYikkCcq1dE2EVq5cybBhwxgzZgyhoaE0atSItm3bEh4enubx27Zto1evXvTv35+jR4/y/fffs2fPHgYMGJDNkQuR9x29crcr7LkaRTj3UTu83Rx1jEgIHZ07B40bw4QJ2gyxrl2lGyyP0DURmjp1Kv3792fAgAGUK1eO6dOnU7RoUebMmZPm8bt27SI4OJihQ4cSEhJCw4YNefXVV9m7d282Ry5E3vbroSv8G6MNgP5taEM+fb4KdnbyF6+wQUrBkiVa99fOneDlBcuWwTffgLe33tGJTKBbIpSUlMS+ffto1aqVVX2rVq3YsWNHms+pX78+ly5dYu3atSil+Pfff/nhhx9o3779A18nMTGRmJgYq5sQ4sEioxMYsjzUUi7m46ZjNELorG9f6N0b7tyBhg3h4EFtlpjIM3RLhKKiojCZTPj5+VnV+/n5ERmZ9t5D9evXZ9myZXTt2hUnJyf8/f3Jly8fs2bNeuDrTJ48GW9vb8utaNGimfo+hMhr2s7YYrk/96UaeLpId5iwYdWqgYMDfPghbNoEwcF6RyQyme6DpQ33DTBTSqWqS3Hs2DGGDh3K2LFj2bdvH+vWrSMsLIyBAwc+8PyjR48mOjracrt48WKmxi9EXnL9TiI345IBaFy6EG3+2wJDCJuRlATnz98tv/661gr07rvanmEiz9FtukfBggWxt7dP1fpz9erVVK1EKSZPnkyDBg14++23AahcuTLu7u40atSISZMmERAQkOo5zs7OOKfs/iuEeKheX+223J/fs4aOkQihg5MntW6vmBgIDQUPD7Czg/Ll9Y5MZCHdEiEnJydq1KjBhg0b6Ny5s6V+w4YNPPPMM2k+Jy4uDgcH65Dt/8vQlXr4sv5CiNQWbw/jyBVt3NxvhyKIT9ZWii5RyB0XR/nrV9gIpWD+fBg+HOLjwccHjh+HWrX0jkxkA10XABkxYgQ9e/akZs2a1KtXj/nz5xMeHm7p6ho9ejSXL19myZIlADz99NO8/PLLzJkzh9atWxMREcGwYcOoXbs2gYGpl/MXwpaZ79nz6+8TV/knzHq9rciYRKuNUO/1w8D6WRqbEDnGtWswYACsWaOVW7SAxYu1TVOFTdA1EeratSvXr19n4sSJREREULFiRdauXUtQUBAAERERVmsK9enTh9u3b/P555/z5ptvki9fPpo1a8b//vc/vd6CEDlKRHQ8vx6MYMOxf9l9/ka6nzeyTVkA3J3teaZqYbxdZYC0sAHr10OfPhAZCU5OMHkyDBumdYcJm2FQNtanFBMTg7e3N9HR0Xh5eekdjhCZ5ptdF3jvpyNpPuZob6B1BX8K53NN9dhTZXypV0J2yRY2Rino0AHWroVy5WD5cqhaVe+oxENk1fe3rI0vRC6mlGJ/+C2GrzxA+I04S325AC+qFs1H7/pB+Hm64OJoj6uTjPkRwsJggIULYdo0bdsM19R/JAjbIImQELnU2sMRDFq2P1X98gF1qF+yoA4RCZGDmc0wa5Y2M2z2bK3O3x9kaIXNk0RIiFxqwdZzVuWaQflZ0r+2bIIqxP0iIrQVotev18pdu0KTJvrGJHIM+Y0pRA5zMzaJZJPZUg6/Ecfyf1JvRBwWFQtAr3pBjG5bTrq+hEjLzz9rs8KiosDFBaZO1TZPFeI/kggJkQMYTWbMChbvCOOjtScy9NxnqhaWJEiI+8XGwptvwrx5WrlqVW1AdLlyuoYlch5JhITQ2d8n/mXQsv0kJJut6u/d7N2soGV5P2oH+1gdE5DPherF8mVDlELkIkpBu3aw5b99895+Gz74AGSXAZEGSYSE0FF0XDL9Fu+1qnN1tOebAXWoEZRfp6iEyOUMBhg5Es6eha+/hubN9Y5I5GCSCAmhA5NZ8d5Ph/l2991NgCd1qsjTVQJxdrCT7S2EyKiLF+HUqbtJT7t2cPq0TIsXjySJkBA6WHs4wioJqh3iw0t1g3SMSIhcbOVK+G9rJg4ehGLFtPuSBIl0kERIiGx0NSaBa3cSef3bUEvd0v61qVtcVnYWIsNiYuD11+G//SipXRtMJn1jErmOJEJCZAOlFJtOXaPvoj1W9S83CqFRqUI6RSVELrZzJ/ToAWFh2t5gY8bA+++Do+yTJzJGEiEhssF7Px1h2T1rARX0cMbX05nhLUvrGJUQuZBS2gywiRO11p/gYFi6FBo21DsykUtJIiREFlJK8eZ3B/kx9LKlbvzT5enTIETHqITIxQwGuH5dS4Jeegk+/xy8vfWOSuRikggJkYV+ORRhlQTtHN2MAG8ZwClEhiilLZDo4aGVP/4YmjWDZ57RNy6RJ9jpHYAQednQewZFbxvZVJIgITLq5k1tb7B27e4OhHZ1lSRIZBppERIiiyQZ764UPalTRYrkd9MxGiFyoY0boVcvuHQJHBzgn3+gfn29oxJ5jLQICZEFou4kUvq93y3lpmV9dYxGiFwmKUlbGbp5cy0JKlUKduyQJEhkCWkREiIL/HYownI/wNuFQG8XHaMRIhc5cUKbFr9/v1Z++WVtx/iU8UFCZDJJhITIZH8e+5dxa44C4OniwPaRzTAYDI94lhACpaBPHy0J8vGBL7+Ezp31jkrkcZIICZFJTGZFr6/+YfuZ65a6ic9UwM5OkiAh0sVg0JKf0aNh3jwIDNQ7ImEDJBESIpPsDrthlQR93a82TUrLqtFCPNT69dpmqa+/rpUrVoRfftE3JmFTJBES4glcuhnH1zvOs3jHedyc7v532vteCwp6OOsYmRA5XEKCNiB65kywt4d69aBmTb2jEjZIEiEhnkDfRXs4ffUOANHxyQC8ULOIJEFCPMzhw9C9Oxw5opVfew0qVNA3JmGzJBES4jEppSxJUEEPZ0a3LUvFwt6U8pXZLUKkyWyGWbO0lqDERPD1hUWLtMUShdCJJEJCPKYJvxyz3F/xSl1KSgIkxIMpBc8+Cz//rJU7dICFC7VkSAgdyYKKQjyGHWejWLzjvKUcUtBdv2CEyA0MBmjRAlxcYPZsWLNGkiCRI0iLkBDpZDYrvt0TzpjVR6zq1w9rjL1MkRcitdhYuHwZSpfWyoMHQ/v2EBKib1xC3ENahIRIpwOXbqVKgsY9XZ4y/p46RSREDrZvH1SvDm3bwu3bWp3BIEmQyHGkRUiIRzCazGw8eY3dYXfXCBrTrhx9GgTjaC9/SwhhxWSCKVPg/ffBaITChSEsDCpX1jsyIdL0WImQ0Whk06ZNnD17lu7du+Pp6cmVK1fw8vLCQ/aDEXmAUoojl2P4NyaBnw9e4ZeDVyyPVSnizcuNi+sYnRA5VHi4tlv85s1auUsXmD9f2y5DiBwqw4nQhQsXaNOmDeHh4SQmJtKyZUs8PT355JNPSEhIYO7cuVkRpxDZan/4TbrM2ZmqvnlZX3rULaZDRELkcCtXwquvQnQ0uLtr0+T79NG6w4TIwTKcCL3xxhvUrFmTgwcPUqBAAUt9586dGTBgQKYGJ4RePvj1OAAezg6U9PXA2cGOt1uXoWaw/GUrRCpKwTffaElQ7dqwbBmULKl3VEKkS4YToW3btrF9+3acnJys6oOCgrh8+XKmBSaEXmITjRy4eAuARqUKMuelGvoGJEROpZTW4mMwaGsCffklvP02ODrqHZkQ6ZbhkZ5msxmTyZSq/tKlS3h6yuwZkfsZzcpyf3xHWfZfiFSMRhg/Hvr3v1vn6wvvvitJkMh1MpwItWzZkunTp1vKBoOBO3fuMG7cONrJMukij/Fxd3r0QULYknPnoHFjmDBB2x5jxw69IxLiiWS4a2zatGk0bdqU8uXLk5CQQPfu3Tl9+jQFCxbk22+/zYoYhchWi7ef1zsEIXIepWDJEhgyBO7cAS8vmDMH6tfXOzIhnkiGE6HAwEAOHDjAihUr2LdvH2azmf79+9OjRw9cXV2zIkYhss3RK9FM+/OUpewgK0YLATdvajPCvv9eKzdqBEuXQlCQvnEJkQkMSin16MPu2rJlC/Xr18fBwTqHMhqN7Nixg8aNG2dqgJktJiYGb29voqOj8fLy0jsckQMopfh+7yV+2HeJ3edvWOp/fb0hFQt76xiZEDmAUlCnDuzZAw4OWpfYyJFgb693ZMLGZNX3d4bHCDVt2pQbN26kqo+OjqZp06aZEpQQ2emfsBu8s+qQVRL0Ut1ikgQJAdqMsIkTtf3CduzQBkRLEiTykAx3jSmlMKSxQNb169dxd5cduEXukJBs4qO1x7mTaOTctVgACrg78XGXyrg52VMnRNYLEjbsxAltW4y2bbVymzZw5IjMCBN5UroToWeffRbQZon16dMHZ2dny2Mmk4lDhw5RXwbNiVzAZFaUfX9dqvqW5f1oWd5Ph4iEyCGU0rbEGD5cS3oOHoTgYO0xSYJEHpXuRMjbW+smUErh6elpNTDaycmJunXr8vLLL2d+hEI8oS2nrnH4cjQAe87fYNPJa5bHnB3sGNGyNE4OdnSoHKhXiELo79o1GDAA1qzRyg0agJMsHyHyvnQnQosWLQIgODiYt956S7rBRK5wOyGZfov3WC2SmKJSYW9+HtwAO5kZJmzdunXQty9ERmrJz+TJMGwY2GV4GKkQuU6GxwiNGzcuK+IQIkvEJ5ksSVC3WkUBsLMz0L12McoHeEkSJGybUjBiBKQsklu+PCxfDlWq6BqWENkpw4kQwA8//MB3331HeHg4SUlJVo/t378/UwIT4kkppTh4SesSszPAx10q6xyREDnMvRNfhgyBTz4BWQ9O2JgMt3vOnDmTvn374uvrS2hoKLVr16ZAgQKcO3eOtikzDITIAb7ZdYGXl+wFwC6NmY5C2CSzGW7duluePBn++gtmzZIkSNikDCdCs2fPZv78+Xz++ec4OTnxzjvvsGHDBoYOHUp0dHRWxCjEY7l4M95y/7WnSugYiRA5REQEtGsHHTpoG6cCuLhAs2b6xiWEjjKcCIWHh1umybu6unL79m0AevbsKXuNiRzj7xP/Mn/LOQBebVycN1uV0TkiIXT2889QuTKsXw/79kFoqN4RCZEjZDgR8vf35/r16wAEBQWxa9cuAMLCwsjgbh1CZLr4JBNf7zhPv8V7LXUezo81FE6IvCE2FgYOhE6dICoKqlbVEqFatfSOTIgcIcPfEM2aNeOXX36hevXq9O/fn+HDh/PDDz+wd+9ey6KLQughyWim3FjrhRIHPVWCnvVkY0hho/btg+7d4dR/Gwm/9RZMmgT3LIgrhK3LcCI0f/58zGYzAAMHDsTHx4dt27bx9NNPM3DgwEwPUIhHOXftDkeuxBAZHW9V/+nzVXiuRhGdohJCZ0rBa69pSVDhwvD119C8ud5RCZHjZHj3+Ye5fPkyhQsXzqzTZQnZfT5vSTSaqPnBn9xONFrVn/+4vU4RCZGDHDsGH36ozQjzkf3zRO6WY3afT0tkZCSvv/46JUuWzIzTCZFucYkmSxJUJ8SHesUL8L8ulXSOSgidrFwJn312t1y+PCxbJkmQEA+R7kTo1q1b9OjRg0KFChEYGMjMmTMxm82MHTuW4sWLs2vXLr766qusjFWIVEb/eNhyf2n/Onz7Sl261iqmY0RC6CAmBnr3hm7dYORIkIVthUi3dI8Revfdd9myZQu9e/dm3bp1DB8+nHXr1pGQkMDvv/9OkyZNsjJOIdIUEZMAQKC3C04Osi+SsEE7d0KPHhAWpu0N9u67UElaRYVIr3QnQr/99huLFi2iRYsWDBo0iJIlS1K6dGmmp+xRI0Q2i0lI5uDFWwB80KmivsEIkd2MRm0G2KRJYDJBcDAsXQoNG+odmRC5SroToStXrlC+fHkAihcvjouLCwMGDMiywIR4mNsJyVQe/4el7Opkr2M0QmQzpaBNG21rDICXXoLPPwdvb33jEiIXSnciZDabcXR0tJTt7e1xd3fPkqCESMvV2wnsPHsdpbQp8ylK+3lQO1gGgwobYjBAly6wZw/MmaOtFSSEeCzpToSUUvTp0wfn/xbiSkhIYODAgamSoR9//DFzIxQCMJkVdT/6C/N9iz0U9HDij+EyPk3YgJs34fJlqPhfN/DAgdC5M/j76xuXELlcukeX9u7dG19fX7y9vfH29uall14iMDDQUk65ZdTs2bMJCQnBxcWFGjVqsHXr1ocen5iYyJgxYwgKCsLZ2ZkSJUrIbLU8zGRW7D1/gxLvrrUkQR7ODjQqVZDGpQsx8RkZGyRswKZN2j5hTz+tzRADrVVIkiAhnli6W4QWLVqU6S++cuVKhg0bxuzZs2nQoAHz5s2jbdu2HDt2jGLF0p4C/cILL/Dvv/+ycOFCSpYsydWrVzEajWkeK3K/wcv2s+5opFXd/vdbygwxYRuSkmDsWPjkE21cUMmS2g7yshisEJkmU1eWzqg6depQvXp15syZY6krV64cnTp1YvLkyamOX7duHd26dePcuXP4POYCYbKydM5mNiuOXolh5d5wNp64xuVbd7fNaF8pgBndquJgL0mQsAEnT2pjf1LWBBowAKZNAw8PfeMSQidZ9f2t27bcSUlJ7Nu3j1GjRlnVt2rVih07dqT5nDVr1lCzZk0++eQTli5diru7Ox07duSDDz7A1dU1O8IWWchoMtNh1jZORN5O9diu0c3x93bRISohsplSMH8+DB8O8fHaqtALFoBsai1EltAtEYqKisJkMuHn52dV7+fnR2RkZJrPOXfuHNu2bcPFxYXVq1cTFRXFoEGDuHHjxgPHCSUmJpKYmGgpx6T0r4scZevpa/RcuNuqrkU5X2qH+NClehEKeMhu2cKGrF2rJUEtWsDixdqmqUKILKFbIpTCYDBYlZVSqepSmM1mDAYDy5YtswzMnjp1Ks899xxffPFFmq1CkydPZsKECZkfuMhUW09HWe5XLuLN6kENsLdL++dAiDzJbNZWhjYY4MsvtX3DBg3S6oQQWUa3/2EFCxbE3t4+VevP1atXU7USpQgICKBw4cJWs9PKlSuHUopLly6l+ZzRo0cTHR1tuV28eDHz3oR4YgnJJp6bs4P5W84B0KlqID9JEiRsSUICDBum7RWWolAhGDJEkiAhssFj/S9bunQpDRo0IDAwkAsXLgAwffp0fv7553Sfw8nJiRo1arBhwwar+g0bNlC/fv00n9OgQQOuXLnCnTt3F9M7deoUdnZ2FClSJM3nODs74+XlZXUTOUefRbvZe+GmpVwj2Ac7SYKErTh8GGrVghkz4JtvYO9evSMSwuZkOBGaM2cOI0aMoF27dty6dQuTyQRAvnz5Mrzv2IgRI/jyyy/56quvOH78OMOHDyc8PJyBAwcCWmtOr169LMd3796dAgUK0LdvX44dO8aWLVt4++236devnwyWzmVMZsVX28LYde6Gpe6XIQ15qY7sHC9sgNmsJT+1asGRI+DrC7/9BjVr6h2ZEDYnw2OEZs2axYIFC+jUqRMff/yxpb5mzZq89dZbGTpX165duX79OhMnTiQiIoKKFSuydu1agoKCAIiIiCA8PNxyvIeHBxs2bOD111+nZs2aFChQgBdeeIFJkyZl9G0Inb305T/sPHfdUv7n3eb4ecmsMGEDIiKgb19Yv14rt28PX32lJUNCiGyX4XWEXF1dOXHiBEFBQXh6enLw4EGKFy/O6dOnqVy5MvHx8Y8+iY5kHSH9fb/3Im//cMhSnvtSddpUDNAxIiGyiVJQtSocOgQuLjB1qrZVxgMmiAgh7sqq7+8Md42FhIRw4MCBVPW///67ZXd6IR7k4o04qyTo2MTWkgQJ22EwwKefQrVqsG8fvPaaJEFC6CzDXWNvv/02gwcPJiEhAaUUu3fv5ttvv2Xy5Ml8+eWXWRGjyEMOXrpluf9N/zq4Oem+goMQWWvfPrh0CZ55Riu3bAnNm8uMMCFyiAx/C/Xt2xej0cg777xDXFwc3bt3p3DhwsyYMYNu3bplRYwiD7l8U+s6rVTYm4alCuocjRBZyGTSWn/eew9cXeHgQQgJ0R6TJEiIHOOx/hx/+eWXefnll4mKisJsNuMrg/xEOsQlGZn8+wlA20FeiDzr4kXo2RM2b9bKrVrJRqlC5FAZ/rNkwoQJnD17FtAWRZQkSKTX0G8PWO73aRCsWxxCZKmVK6FyZS0JcneHhQvh+++hQAG9IxNCpCHDidCqVasoXbo0devW5fPPP+fatWtZEZfIg67d0fZ8CynoTusK/jpHI0QmU0qbFt+tG9y6BbVrw4ED0K+fDIgWIgfLcCJ06NAhDh06RLNmzZg6dSqFCxemXbt2LF++nLi4uKyIUeQxY9qV0zsEITKfwQAFC2rjf957D7Ztg5Il9Y5KCPEIjzVir0KFCnz00UecO3eOjRs3EhISwrBhw/D3l7/yhRA2xGiEe1vFJ02CHTvggw/A0VG/uIQQ6fbEUxfc3d1xdXXFycmJ5OTkzIhJCCFyvnPnoHFjbVq80ajVOTtDnTr6xiWEyJDHSoTCwsL48MMPKV++PDVr1mT//v2MHz8+1U7yQgiR5ygFS5ZoK0Tv3AlHj8KxY3pHJYR4TBmew1yvXj12795NpUqV6Nu3r2UdISEe5szVOxy8eEvvMIR4MjdvaltifPedVm7YEJYuheBgXcMSQjy+DCdCTZs25csvv6RChQpZEY/Io349dMVyPyCfbK4qcqFNm7S1gS5dAgcHmDABRo4Ee3u9IxNCPIEMJ0IfffRRVsQh8jCzWTH9z9MA1A72oUKgt84RCZFBZjO8/baWBJUqBcuWQa1aekclhMgE6UqERowYwQcffIC7uzsjRox46LFTp07NlMBE3nA+KpYxPx22lOuWkEXlRC5kZ6eNC5o1Cz75BDw89I5ICJFJ0pUIhYaGWmaEhYaGZmlAIm+Z+ddptp+5bikPaBSiYzRCpJNSsGABREXBu+9qdeXKwezZ+sYlhMh06UqENm7cmOZ9IR4lNkmbVuzj7sSSfrXxcpG1VUQOd+0avPwy/Pyz1hLUrp02Q0wIkSdlePp8v379uH37dqr62NhY+vXrlylBibzh4o041h/9F4A3W5WmYmEZGyRyuPXrtX3Cfv4ZnJy03eMrV9Y7KiFEFspwIvT1118THx+fqj4+Pp4lS5ZkSlAi91NK0fTTTZZySAF3/YIR4lESEmD4cGjTBiIjoXx52L1bq7N74nVnhRA5WLpnjcXExKCUQinF7du3cXG5OwXaZDKxdu1a2YleWLz30xGMZgVAi3J+1C9ZUOeIhHgAsxmaNoVdu7TykCHagGhXV33jEkJki3QnQvny5cNgMGAwGChdunSqxw0GAxMmTMjU4ETuE59kotzYdVZ1s3tU1ykaIdLBzg769NG2zFi0SBsTJISwGelOhDZu3IhSimbNmrFq1Sp8fHwsjzk5OREUFERgYGCWBClyB6VUqiTozxFNcHKQrgWRw0REaF1g1app5Vdegeefh3t+rwkhbEO6E6EmTZoA2j5jxYoVw2AwZFlQIndKWTQRoHA+V/4c0QRXJ1l1V+QwP/8M/fuDuzscPAj58oHBIEmQEDYqXYnQoUOHqFixInZ2dkRHR3P48OEHHltZZljYnIRkE13m7ODolRhL3dZ3mmJnJ8myyEFiY+HNN2HePK1ctCjcuqUlQkIIm5WuRKhq1apERkbi6+tL1apVMRgMKKVSHWcwGDCZTJkepMjZzly9Y5UE/TG8sSRBImfZtw+6d4dTp7TWn7fegg8+AGdnvSMTQugsXYlQWFgYhQoVstwX4l4x8cmW+4fHt8JTFk0UOYXZDFOmwHvvgdEIhQtrW2U0a6Z3ZEKIHCJdiVBQUFCa94U4cPEW3b/8B4BCns6SBImcxWCAf/7RkqAuXWD+fBkLJISw8lgLKv7222+W8jvvvEO+fPmoX78+Fy5cyNTgRM6WaDTx0drjlnL32sV0jEaIe/y3NyIGg7Zn2JIl8P33kgQJIVLJcCL00Ucf4frfQmM7d+7k888/55NPPqFgwYIMHz480wMUOVOyyUzzzzazO+wGAM/VKMLwlqnXlxIiW8XEQO/e0LOntnEqQIECWllmugoh0pDu6fMpLl68SMmSJQH46aefeO6553jllVdo0KABTz31VGbHJ3Kom3FJXLqpbbUS6O1C/4ayq7zQ2Y4d8NJLEBamLZI4ejRUqaJ3VEKIHC7DLUIeHh5cv34dgD/++IMWLVoA4OLikuYeZCJvmvmXtmaQwQA7RjenXICXzhEJm2U0wvjx0KiRlgQFB8PmzZIECSHSJcMtQi1btmTAgAFUq1aNU6dO0b59ewCOHj1KcHBwZscncqBEo4lvdoUDYCfdDUJPZ89qrUAp+4S99BJ8/jl4e+sblxAi18hwi9AXX3xBvXr1uHbtGqtWraJAgQIA7Nu3jxdffDHTAxQ5z5IddwfFL+1fW8dIhE0zm6FDBy0J8vaG5cth6VJJgoQQGWJQaa2MmIfFxMTg7e1NdHQ0Xl7SnZNRiUYTZd67u5/YqUltZS8xoZ+//4aJE+Hrr0GW9hAiT8uq7+8Md40B3Lp1i4ULF3L8+HEMBgPlypWjf//+eMtfYnne6B/vbq/yfofykgSJ7LVxI0RFaRukgrYwYtOmMiNMCPHYMvwttnfvXkqUKMG0adO4ceMGUVFRTJs2jRIlSrB///6siFHkAEaTmei4ZH7cf9lS16ue/AUusklSEowaBc2bQ79+2tigFJIECSGeQIZbhIYPH07Hjh1ZsGABDg7a041GIwMGDGDYsGFs2bIl04MU+kpINtFi6mbLdHmANUMa4GgvrUEiG5w4AT16QMofWt26gZ+fvjEJIfKMDCdCe/futUqCABwcHHjnnXeoWbNmpgYncoZLN+OskqAqRbypECjdoCKLKaXtFD9iBMTHa6tCf/kldO6sd2RCiDwkw4mQl5cX4eHhlC1b1qr+4sWLeHp6ZlpgImfYePIqfRftASCfmyN7xrTAwc6AQbojRFYym7W9wX76SSu3aKENiA4M1DUsIUTek+G+ja5du9K/f39WrlzJxYsXuXTpEitWrGDAgAEyfT6PuRGbZEmCAMr6e+JobydJkMh6dnZQpgw4OcHUqbB+vSRBQogskeEWoU8//RSDwUCvXr0wGo0AODo68tprr/Hxxx9neoBCP/+cu265P7R5KV5vVlLHaESel5AAN27cTXgmTtT2CKtQQd+4hBB52mOvIxQXF8fZs2dRSlGyZEnc3NwyO7YsIesIpd9vhyIYvHw/+dwcCX2/pbQEiaxz+DB07w5ubrBtGzg66h2RECKHyarv73R3jcXFxTF48GAKFy6Mr68vAwYMICAggMqVK+eaJEikT5LRzI3YJL7cdg6AMn6ekgSJrGE2w4wZUKsWHDkC58/DmTN6RyWEsCHp7hobN24cixcvpkePHri4uPDtt9/y2muv8f3332dlfCKb3YxNovnUzdyITbLUeTg/1rqbQjxcRAT06QN//KGV27eHr74CX19dwxJC2JZ0f8P9+OOPLFy4kG7dugHw0ksv0aBBA0wmE/b29lkWoMheZ6/dsUqCACY8I2M0RCb7+Wfo3x+uXwcXF21A9MCBsjiiECLbpTsRunjxIo0aNbKUa9eujYODA1euXKFo0aJZEpzIPkaTmeHfHeSXg1cACC7gxsa3npIuMZH5TCaYNElLgqpW1TZLLVdO76iEEDYq3YmQyWTCycnJ+skODpaZYyL3UkpRbeIGbifevZblA70kCRJZw94eli3TusEmTABnZ70jEkLYsHQnQkop+vTpg/M9v7QSEhIYOHAg7u7ulroff/wxcyMUWe7yrXirJGjVa/WpVjSffgGJvMVkgilTtOnx48drdaVLgyy3IYTIAdKdCPXu3TtV3UsvvZSpwQh93LuAwrmP2mFnJy1BIpNcvKitBbR5szb+57nnoGJFvaMSQgiLdCdCixYtyso4RA7g6mgvSZDIPCtXagOgb90Cd3eYNUsWRxRC5DgyL9rG/RuTwKd/nNQ7DJGXxMTA66/DkiVauXZtbUxQSVmZXAiR80giZMP+jUmgzkd/WcoeLvLjIJ6QyQQNG2orRdvZwZgx8P77slK0ECLHyvCmqyLvaDVti+V++QAvPn+xmo7RiDzB3h7eeAOCg7VxQRMnShIkhMjRpAnARiUZzUTHJwPQuoIf83rW1DkikWudOwdRUVoXGEC/ftCtmzYuSAghcjhpEbJBSin+PP6vpfzZC1X1C0bkXkpp44CqVIEuXeDmTa3eYJAkSAiRazxWIrR06VIaNGhAYGAgFy5cAGD69On8/PPPmRqcyBrf/BPOoGX7AWhYsqDsJSYy7uZNrdWnd2+4c0frCouL0zsqIYTIsAwnQnPmzGHEiBG0a9eOW7duYTKZAMiXLx/Tp0/P7PhEJlJKsfHEVd7/6Yil7n/PVdYxIpErbdoElSvDd9+BgwN8+KFWV7iw3pEJIUSGZTgRmjVrFgsWLGDMmDFWm63WrFmTw4cPZ2pwInMt2HqOvov3WMpf96tN4XyuOkYkchWTCUaNgmbN4NIlKFUKduyAd9/VBkkLIUQulOFEKCwsjGrVUs8ucnZ2JjY2NlOCElnj6JUYAGqH+PD7G41oUrqQzhGJXMXODs6e1cYGDRgA+/dDrVp6RyWEEE8kw4lQSEgIBw4cSFX/+++/U758+cyISWQBk1nx+5FIAFqV96NcgJfOEYlcQSmIj9fuGwwwbx78/DMsWAAeHvrGJoQQmSDDo2TffvttBg8eTEJCAkopdu/ezbfffsvkyZP58ssvsyJGkQm2nLpGktEMgLODTBYU6XDtmtby4+SkjQcyGMDHBzp21DsyIYTINBn+Ruzbty/jxo3jnXfeIS4uju7duzN37lxmzJhBt27dMhzA7NmzCQkJwcXFhRo1arB169Z0PW/79u04ODhQtWrVDL+mLboRm2S5365SgI6RiFxh3TptQPSaNdrt+HG9IxJCiCzxWE0DL7/8MhcuXODq1atERkZy8eJF+vfvn+HzrFy5kmHDhjFmzBhCQ0Np1KgRbdu2JTw8/KHPi46OplevXjRv3vxxwrc5RpOZYxHa+KAmpQtRwMNZ54hEjpWQoK0M3bYtREZC+fKwe7f2rxBC5EFP1EdSsGBBfH19H/v5U6dOpX///gwYMIBy5coxffp0ihYtypw5cx76vFdffZXu3btTr169x35tW9J70W4WbgsDtN4NIdJ0+LA2+HnmTK08ZAjs3astmCiEEHlUhscIhYSEYHjIt+m5c+fSdZ6kpCT27dvHqFGjrOpbtWrFjh07Hvi8RYsWcfbsWb755hsmTZr0yNdJTEwkMTHRUo6JiUlXfHnFLwevsP3MdUv52epFdIxG5FgmEzz7LJw5A76+sGgRtGund1RCCJHlMpwIDRs2zKqcnJxMaGgo69at4+233073eaKiojCZTPj5+VnV+/n5ERkZmeZzTp8+zahRo9i6dSsODukLffLkyUyYMCHdceUlu85d5/VvQy3lg2Nb4e0mG2CKNNjbazPBpk6FL7/UkiEhhLABGU6E3njjjTTrv/jiC/bu3ZvhAO5vXVJKpdniZDKZ6N69OxMmTKB06dLpPv/o0aMZMWKEpRwTE0PRokUzHGducvRKNP0W7+HfmLstYW80LyVJkLC2Zo22PUb37lr5qae0mxBC2JBMm0fdtm1bVq1ale7jCxYsiL29farWn6tXr6ZqJQK4ffs2e/fuZciQITg4OODg4MDEiRM5ePAgDg4O/P3332m+jrOzM15eXla3vG7XuRtWSdArjYvTo24xHSMSOUpsLAwcCM88A6+8oi2SKIQQNirTdtv84Ycf8PHxSffxTk5O1KhRgw0bNtC5c2dL/YYNG3jmmWdSHe/l5ZVqC4/Zs2fz999/88MPPxASEvL4wecxF29om1/WCs7PsgF1cZJ1g0SKffu0FqBTp7Tya69BERk3JoSwXRlOhKpVq2bVdaWUIjIykmvXrjF79uwMnWvEiBH07NmTmjVrUq9ePebPn094eDgDBw4EtG6ty5cvs2TJEuzs7KhYsaLV8319fXFxcUlVb8tuxSWxeMd5AIrkd5MkSGhMJpgyBd5/H4xGbYPUr78GWYJCCGHjMpwIderUyapsZ2dHoUKFeOqppyhbtmyGztW1a1euX7/OxIkTiYiIoGLFiqxdu5agoCAAIiIiHrmmkLC2Ys9Fy/2utfL2WCiRTkYjtG4NKd3HXbrA/PnaKtFCCGHjDEopld6DjUYjy5Yto3Xr1vj7+2dlXFkmJiYGb29voqOj8+R4oaafbiIsKpZnqxdm6gtV9Q5H5BTvvqutDzRrFvTpIwtKCSFynaz6/s5Qv4mDgwOvvfaa1bo8Iue4GpNAWFQsAH3qB+sbjNBXTAzc25o6YQIcOgR9+0oSJIQQ98jwAJI6deoQGhr66ANFtpu35e5ilu7OmTYOXuQ2O3dC1apaF1hyslbn6AjFi+salhBC5EQZ/rYcNGgQb775JpcuXaJGjRq4u7tbPV65cuVMC06k39XbCZZtNAK9XShRyEPniES2Mxph0iTtZjKB2ay1CpUooXdkQgiRY6U7EerXrx/Tp0+na9euAAwdOtTymMFgsCyEaDKZMj9K8Uh7wm5a7o/rWEHHSIQuzp2Dl17SWoMAevSAL74Ab2994xJCiBwu3YnQ119/zccff0xYWFhWxiMeU3S81gVS1MeV1hVy50B28RiUgiVLtA1S79wBLy+YM+fuatFCCCEeKt2JUMrkspSp7SLnMJrMvLtaW2wy0NtV52hEtjKZYPZsLQlq1AiWLgX5PyqEEOmWoTFCD9t1XugnPvlud2SXGrJKsE1QSpv95eAA33wDq1bB229rm6cKIYRItwwlQqVLl35kMnTjxo0nCkg8mWeqBuodgshKSUkwdqyW8Hz4oVZXqhSMGqVvXEIIkUtlKBGaMGEC3jL4Ugh9nDypjf3Zv19rDerVC8qU0TsqIYTI1TKUCHXr1g1fX9+sikUIkRaltC0xhg+H+Hhta4wFCyQJEkKITJDuREjGBwmhg2vXYMAAWLNGK7doAYsXa5umCiGEeGIZnjUmhMgmRiM0aACnT4OTE0yeDMOGgV2GF4QXQgjxAOn+jWo2m6VbTIjs5OCgbZZarhz88w+MGCFJkBBCZDL5rSpETnL4MOzYcbfcuzeEhmp7hwkhhMh0kggJkROYzTBjBtSqBS+8ACnLUBgM4Oysb2xCCJGHyRblQugtIgL69oX167VylSraitFCCCGynLQI5QEXb8TrHYJ4XD//DJUra0mQi4u2Ueqvv0KhQnpHJoQQNkFahPKA6X+eAiCkoDtO9pLb5gpGo7ZR6rx5WrlqVVi2DMqX1zUsIYSwNfKtmQdcvZ0IwCuNi8t6T7mFvT3cvKndf+st2LVLkiAhhNCBtAjlIQU9ZFBtjmYyaStDe3hog6DnzoWBA6FpU70jE0IImyUtQrmY2ay4GpNAssmsdyjiUS5e1FaF7tVL2zIDIH9+SYKEEEJn0iKUi/VetJutp6P0DkM8ynffwauvwq1b4O4OZ85oO8YLIYTQnbQI5WL7LmhjTOwMUDifK1WKeusckbASEwN9+kDXrloSVLu2tjiiJEFCCJFjSItQLnPxRhyLtp/nq+1hlrrNbzelqI+bjlGJVHbuhB49ICxM2xbj3Xdh7FhwdNQ7MiGEEPeQRCgX2R12gxfm7bSq8/V0ppCnDJLOUYxGeOklLQkKCoJvvoGGDfWOSgghRBokEcolPl1/ks83nrGUKxfx5tXGJWhezhcXR3sdIxOpODjAokWwYAF8/jl4S5elEELkVJII5QJGk9kqCXqzZWleby7jTHIMpWDpUu3f3r21usaNtZsQQogcTRKhXGB/+C3L/UV9a9GklGy/kGPcvKmtBfTdd+DmBo0aQfHiekclhBAinSQRyoGUUiQk310baMZfpyz36xUvgJ2drB6dI2zaBD17wqVLWnfYe+9pY4KEEELkGpII5TAnI2/TevqWNB/r1yBExgPlBElJ2gywTz7RusNKldL2CatVS+/IhBBCZJAkQjnMm98fSLM+n5sjvetLa4PukpO1GWB79mjlAQNg2jRt2wwhhBC5jiRCOUzKbhnP1yjChGcqWOqd7O1wkJ3l9efoCK1bw9mz2qywZ5/VOyIhhBBPQBKhHKpj1UDcnOTy5AjXrsHt23cHQY8dC4MGQUCAvnEJIYR4YtLEkEPEJRn5KfQyxyNi9A5F3GvdOqhcGZ5/XhsbBFqrkCRBQgiRJ0iTQw5wMzaJ+h//TXyyyVLn6+miY0SChAQYNQpmzNDKPj4QGQnFiukblxBCiEwliZCOdpyNYuzPRzlz9Y5V/Xvty1HG31OnqASHD0P37nDkiFZ+/XX43//A1VXfuIQQQmQ6SYR09N5PRzh3LdZSbliyIEv61ZZ1gvRiNsOsWTByJCQmgq+vtlVGu3Z6RyaEECKLSCKkA5NZsfZwhCUJal85gHEdyuPrJd1hujKbYcUKLQnq0AEWLtSSISGEEHmWJEI62HXuOq9/G2opv9G8lCRBelIKDAZtdehvvoENG+DVV7U6IYQQeZokQjq4FZdsuT+iZWlK+cpifLqIjYURI7Td4T/5RKsrUUK7CSGEsAmSCOmodogPQ2UXeX3s3Qs9esCpU2Bnp7UASQIkhBA2R9YRErbFZIKPP4Z69bQkqHBhrStMkiAhhLBJ0iKkA7NSeodgm8LDoVcv2LxZK3fpAvPna2sECSGEsEmSCGWzU//evjtQWvKh7JOUBI0aacmQu7s2Tb5PHxkQLYQQNk66xrJRXJKRVtO2WMqNSxfUMRob4+QEkyZB7dpw4AD07StJkBBCCEmEstOK3Rct93vWDWJIMxkonaV27LjbDQbw0kuwfTuULKlfTEIIIXIUSYSyybXbifyw75Kl/H6H8jpGk8cZjTB+vNYV9uKLcP26Vp+yVpAQQgjxH/lWyAYms6LWh39ayn3qB+PkIDloljh3TpsWv2uXVm7eXJIfIYQQDyTfEFnIbFaM/vEwK/fe7RILKuDGczWK6BhVHqUULFkCQ4bAnTvaIolz5mgtQkIIIcQDSCKUhXp8+Q87z123lN2c7Nn01lMYZJBu5kpKgp494bvvtHKjRrB0KQQF6RuXEEKIHE8SoSz0b0yC5f78njVoVtZXkqCs4OSkdX85OMCECdru8fb2ekclhBAiF5BEKBt8P7AetYJl0b5MlZQE8fFaFxjA7NkwfDjUrKlvXEIIIXIVGbErcp8TJ7QtMnr31sYGgZYQSRIkhBAigyQRErmHUjBvHlSvDvv3w9atcP683lEJIYTIxSQRErnDtWvQqRMMHKh1ibVoAYcPQ0iI3pEJIYTIxSQREjnfunVQuTKsWaMNjJ46Fdavh8BAvSMTQgiRy8lg6SyQZDQz469TnIuK1TuU3C8pCQYNgshIKF8eli+HKlX0jkoIIUQeIYlQFpj02zGW7LxgKedzddQxmlzOyUlbE2jlSvjf/8DVVe+IhBBC5CGSCGWyLzaesUqC5r5Ug1J+njpGlMuYzTBrFri7w4ABWl2DBtpNCCGEyGS6jxGaPXs2ISEhuLi4UKNGDbZu3frAY3/88UdatmxJoUKF8PLyol69eqxfvz4bo324JKOZuZvPWsrrhzWmTUV/HSPKZSIioG1bGDYMhg6VGWFCCCGynK6J0MqVKxk2bBhjxowhNDSURo0a0bZtW8LDw9M8fsuWLbRs2ZK1a9eyb98+mjZtytNPP01oaGg2R562NQevcDvBCMCfI5pQxl9agtLt55+hUiX44w9wcYHPPpMtMoQQQmQ5g1IpK9Jlvzp16lC9enXmzJljqStXrhydOnVi8uTJ6TpHhQoV6Nq1K2PHjk3X8TExMXh7exMdHY2Xl9djxf0gY1YfZtk/4TxVphCL+9bO1HPnWbGx8Oab2vpAAFWragOiy5XTNSwhhBA5S1Z9f+s2RigpKYl9+/YxatQoq/pWrVqxY8eOdJ3DbDZz+/ZtfHwevH1FYmIiiYmJlnJMTMzjBfwIUXcSWfaP1pLl5+mSJa+R5yQmQu3acOyYVn77bfjgA3B21jcuIYQQNkO3rrGoqChMJhN+fn5W9X5+fkRGRqbrHJ999hmxsbG88MILDzxm8uTJeHt7W25FixZ9orgfJDL67garXWoUyZLXyHOcneH556FwYfjzT/jkE0mChBBCZCvdB0vfvxu7UipdO7R/++23jB8/npUrV+Lr6/vA40aPHk10dLTldvHixSeO+WH8vVyoHSIbrD7QxYtw6tTd8nvvwaFD0Ly5fjEJIYSwWbp1jRUsWBB7e/tUrT9Xr15N1Up0v5UrV9K/f3++//57WrRo8dBjnZ2dcc6GVobDl6Oz/DVyvZUrtS0ygoLgn3+01h8HB3hI16YQQgiRlXRrEXJycqJGjRps2LDBqn7Dhg3Ur1//gc/79ttv6dOnD8uXL6d9+/ZZHWa6ffjbcQCMZrPOkeRAMTHaTvHdusGtW9qssJs39Y5KCCGE0HdBxREjRtCzZ09q1qxJvXr1mD9/PuHh4QwcOBDQurUuX77MkiVLAC0J6tWrFzNmzKBu3bqW1iRXV1e8vb2zPX6lFENXHGDf+RvEJmnT5l9vVirb48jRduyAl16CsDCws4MxY+D998FRVtsWQgihP10Toa5du3L9+nUmTpxIREQEFStWZO3atQT9t35MRESE1ZpC8+bNw2g0MnjwYAYPHmyp7927N4sXL87u8Lkem8QvB69Yym5O9nSqVjjb48iRjEaYNEmbBWY2Q3CwtlVGw4Z6RyaEEEJY6LqOkB4ycx2Ca7cTqfXhnwD8MqQhRfK7kt/dKTPCzP2MRmjS5G6L0Oefgw6tdkIIIfKGPLeOUF6w42yU5X6lIvIlj1Ja64+9vTYI+ptvYNcuePFFvSMTQggh0iSJ0BOYs+nsow+yFTdvajPCCheGqVO1upAQ7SaEEELkUJIIPYGU9Y5Gty2rcyQ627QJevaES5e0QdDDhkGxYnpHJYQQQjyS7gsq5gXlAzN3z7JcIykJRo2CZs20JKhUKdi+XZIgIYQQuYa0CInHc+IE9OgB+/dr5QEDYNo08PDQNy4hhBAiAyQRekzJJjPno2L1DkMfiYlaK1BEhLYq9JdfQufOekclhBBCZJh0jT2GRKOJsu+vIz7ZBECQj7vOEWUzZ2f49FNo0QIOH5YkSAghRK4lLUKPYe/5m5jM2vJL3esUo1gBN50jygbr1mkDoVM2R+3eXZsWn44NcoUQQoicSlqEHkPPhf9Y7o9/uoKOkWSDhAR44w1o21ZbGPHatbuPSRIkhBAil5MWoQyISzKybFc4/zUG0ad+ME4OeTiXPHxYa/k5ckQrP/ecDIYWQgiRp+Thb/HM9+uhCD5cq+0yb2eA4S1L6xxRFjGbYcYMqFVLS4J8feG332DWLHB11Ts6IYQQItNIi1A6GU1m3vnhkKU8rWtVvF3z4A7qCQnQqROsX6+V27eHr77SkiEhhBAij5EWoXQ6fz3Ocv/t1mV4pmoe3WXexUVLelxcYPZs+OUXSYKEEELkWZIIPYZBT5XQO4TMFRsLN27cLX/+ubZQ4muvyYBoIYQQeZokQhmUz83RssdYnrBvH1SvDr17a7vHA3h5Qbly+sYlhBBCZANJhGyVyQT/+x/UrQunTkFoKFy+rHdUQgghRLaSRMgWXbyoLYw4ahQYjdClCxw8CEWK6B2ZEEIIka1k1thDXLwRx8JtYSzecV7vUDLPypUwcCDcugXu7jBzJvTtK2OBhBBC2CRJhB7gwMVbdPpie6r6mkE+OkSTSRIS4N13tSSodm1YtgxKltQ7KiGEEEI3kgg9wMYTVy33qxfLx5BmJalcJB8F3J10jOoJubhoyc9vv8HYsdreYUIIIYQNk0ToETpUDuDz7tX1DuPxGI0waRL4+WlT4UEbHF23rr5xCSGEEDmEJEIPEJOQDEB+t1zaAnTunLZJ6s6dWkvQ00/LYGghhBDiPjJrLA3XbieyaPt5vcN4PErBkiVQpYqWBHl5wcKFkgQJIYQQaZAWoTSE34i13G9dwV/HSDLo5k1tRth332nlRo1g6VIICtI3LiGEECKHkkQoDW9+dxCAoAJuNCxVUOdo0ik+HmrUgLAwcHCACRNg5Eiwt9c7MiGEECLHkq6xNFy9nQhA0fxuOkeSAa6u2npApUrBjh3aNHlJgoQQQoiHkkQoDSlLC37UuZKucTzSyZNw/Pjd8ujR2maptWrpF5MQQgiRi0gilBspBfPmQbVq0LWrtlAiaF1iHh76xiaEEELkIjJGKLe5dg0GDIA1a7Syry/cuaNNkRdCCCFEhkiLUBpik0x6h5C29euhcmUtCXJygs8+gz/+gIK5ZEC3EEIIkcNIi9B95m0+q3cIqSUlaTPApk/XyuXLw/Ll2lpBQgghhHhskgjdJzT8luV+YL4c0t1kbw/79mn3hwyBTz7RZokJkYMppTAajZhMObSFVQiR4zg6OmKfzTOeJRF6gA86VcTBXseeQ7MZTCZtY1R7e21hxKNHoV07/WISIp2SkpKIiIggLi5O71CEELmIwWCgSJEieGTjxB9JhHKiiAhtTaCyZe92hwUFyQrRIlcwm82EhYVhb29PYGAgTk5OGAyGRz9RCGHTlFJcu3aNS5cuUapUqWxrGZJEKKf5+WdtVlhUFGzdCu+8A4GBekclRLolJSVhNpspWrQobm65aFFSIYTuChUqxPnz50lOTs62REhmjeUUsbHaPmGdOmlJUNWqsHevJEEi17Kzk18vQoiM0aP1WH5T5QT79kH16toiiQBvvQW7dkG5cvrGJYQQQuRxkgjpLS4O2raFU6egcGH480+YMgWcnfWOTAiRBYKDg5meMvbvMSxevJh8+fJlWjx5yVNPPcWwYcOy5bXef/99XnnllWx5rbzkueeeY+rUqXqHYUUSIb25ucHMmdClCxw8CM2b6x2REDarT58+dOrUKUtfY8+ePen+Ak0raeratSunTp167NdfvHgxBoPBcvPz8+Ppp5/m6NGjj33OnOLHH3/kgw8+yPLX+ffff5kxYwbvvvtulr9WVpk9ezYhISG4uLhQo0YNtm7d+tDj+/TpY/Vzk3KrUKGC5ZijR4/SpUsXgoODMRgMaSb8Y8eO5cMPPyQmJiaz39Jjk0ToHskmM+uORmb9C333nbZKdIpu3eD776FAgax/bSGErgoVKvREg8hdXV3x9fV9ohi8vLyIiIjgypUr/Pbbb8TGxtK+fXuSkpKe6LyPkpycnKXn9/HxwdPTM0tfA2DhwoXUq1eP4ODgJzpPVn8eD7Jy5UqGDRvGmDFjCA0NpVGjRrRt25bw8PAHPmfGjBlERERYbhcvXsTHx4fnn3/eckxcXBzFixfn448/xt/fP83zVK5cmeDgYJYtW5bp7+txSSL0H7NZUWHs3eTEOSvWEIqJgT59tI1Se/XS9g1LIdOLhcjxNm/eTO3atXF2diYgIIBRo0ZhNBotj9++fZsePXrg7u5OQEAA06ZNS9Vdc38rz/jx4ylWrBjOzs4EBgYydOhQQOvmuXDhAsOHD7f89Q1pd42tWbOGmjVr4uLiQsGCBXn22Wcf+j4MBgP+/v4EBARQs2ZNhg8fzoULFzh58qTlmB07dtC4cWNcXV0pWrQoQ4cOJTY21vJ4REQE7du3x9XVlZCQEJYvX57qvRkMBubOncszzzyDu7s7kyZNAuCXX36hRo0auLi4ULx4cSZMmGD1OT7oMwGtJaNUqVK4uLjg5+fHc889Z3ns/s/65s2b9OrVi/z58+Pm5kbbtm05ffq05fGUz3L9+vWUK1cODw8P2rRpQ0RExEM/vxUrVtCxY0erunXr1tGwYUPy5ctHgQIF6NChA2fP3t2p4Pz58xgMBr777jueeuopXFxc+OabbwBYtGgR5cqVw8XFhbJlyzJ79myrc48cOZLSpUvj5uZG8eLFef/9958oiZo6dSr9+/dnwIABlCtXjunTp1O0aFHmzJnzwOd4e3vj7+9vue3du5ebN2/St29fyzG1atViypQpdOvWDeeHDO/o2LEj33777WPHn9kkEfpPREwCSSazpdy6QtrZ7GPbuVObCfb112BnB6++CtLPL2yEUoq4JGO235RSmfYeLl++TLt27ahVqxYHDx5kzpw5LFy40PLlDjBixAi2b9/OmjVr2LBhA1u3bmX//v0PPOcPP/zAtGnTmDdvHqdPn+ann36iUqVKgNbNU6RIESZOnGj5Kzwtv/32G88++yzt27cnNDSUv/76i5o1a6b7fd26dYvly5cD2qq+AIcPH6Z169Y8++yzHDp0iJUrV7Jt2zaGDBlieV6vXr24cuUKmzZtYtWqVcyfP5+rV6+mOv+4ceN45plnOHz4MP369WP9+vW89NJLDB06lGPHjjFv3jwWL17Mhx9++MjPZO/evQwdOpSJEydy8uRJ1q1bR+PGjR/43vr06cPevXtZs2YNO3fuRClFu3btrJKIuLg4Pv30U5YuXcqWLVsIDw/nrbfeeuA5b968yZEjR1J9xrGxsYwYMYI9e/bw119/YWdnR+fOnTGbzVbHjRw5kqFDh3L8+HFat27NggULGDNmDB9++CHHjx/no48+4v333+frr7+2PMfT05PFixdz7NgxZsyYwYIFC5g2bZrl8a1bt+Lh4fHQ20cffQRoy1vs27ePVq1aWcXVqlUrduzY8cD3fb+FCxfSokULgh5jfbvatWuze/duEhMTM/zcrCDrCP3n3l+YYZPbZd4UPqMRJk3SbiaTtijiN99Aw4aZc34hcoH4ZBPl72lxzS7HJrbGzSlzfs3Nnj2bokWL8vnnn2MwGChbtixXrlxh5MiRjB07ltjYWL7++muWL19O8//G+i1atIjAhyyBER4ejr+/Py1atMDR0ZFixYpRu3ZtQOvmsbe3x9PT84HdDAAffvgh3bp1Y8KECZa6Ko/YhzA6OhoPDw8tQf1v9e+OHTtStmxZAKZMmUL37t0trSulSpVi5syZNGnShDlz5nD+/Hn+/PNP9uzZY0kIvvzyS0qVKpXqtbp3706/fv0s5Z49ezJq1Ch69+4NQPHixfnggw945513GDdu3EM/k/DwcNzd3enQoQOenp4EBQVRrVq1NN/j6dOnWbNmDdu3b6d+/foALFu2jKJFi/LTTz9ZunSSk5OZO3cuJUqUAGDIkCFMnDjxgZ/dhQsXUEqluq5dunSxKi9cuBBfX1+OHTtGxYoVLfXDhg2zarH74IMP+Oyzzyx1ISEhlgQx5TN67733LMcHBwfz5ptvsnLlSt555x0AatasyYEDBx4YM2g/TwBRUVGYTCb8/PysHvfz8yMyMn1DQyIiIvj9998tCXRGFS5cmMTERCIjIx8rkcpskgjdx8XRLvOSoLg4aNFCaw0C6NEDvvgCvL0z5/xCiGxz/Phx6tWrZ/X7oUGDBty5c4dLly5x8+ZNkpOTLV/aoHUnlClT5oHnfP7555k+fTrFixenTZs2tGvXjqeffhoHh/T/aj5w4AAvv/xyht6Lp6cn+/fvx2g0snnzZqZMmcLcuXMtj+/bt48zZ85YjeNQSllWDT916hQODg5Ur17d8njJkiXJnz9/qte6v+Vk37597Nmzx9ICBGAymUhISCAuLu6hn0nLli0JCgqyPNamTRs6d+6c5pir48eP4+DgQJ06dSx1BQoUoEyZMhw/ftxS5+bmZkmCAAICAtJs2UoRHx8PgIuL9V6UZ8+e5f3332fXrl1ERUVZWoLCw8OtEqF7P49r165x8eJF+vfvb3UNjUYj3vd8T/zwww9Mnz6dM2fOcOfOHYxGI15eXpbHXV1dKVmy5ANjTsv933NKqXR/96V0KT7uxALX//bKzClb8EgilJXc3KB0aW2PsDlzoHt3vSMSQheujvYcm9hal9fNLGl9UaS0JBsMBqv7aR2TlqJFi3Ly5Ek2bNjAn3/+yaBBg5gyZQqbN2+2dFM9iutjbMBsZ2dn+eIsW7YskZGRdO3alS1btgDaNimvvvqq1dicFMWKFbMaS3SvtN6ru7u7VdlsNjNhwoQ0xzG5uLg89DNJSeA2bdrEH3/8wdixYxk/fjx79uxJNW7qQZ/7/dfx/s/53muZloIFCwJaF1mhQoUs9U8//TRFixZlwYIFBAYGYjabqVixYqoB6Pd+HinJ0oIFC6wSNsCyqvKuXbssLX6tW7fG29ubFStW8Nlnn1mO3bp1K23btn1gzADvvvsu7777LgULFsTe3j5V68/Vq1dTtRKlRSnFV199Rc+ePXFycnrk8Wm5ceMGgNXnpydJhDLbzZtad1jKBZ41C8aPhyecXSBEbmYwGDKti0ov5cuXZ9WqVVZfpDt27MDT05PChQuTL18+HB0d2b17N0WLFgUgJiaG06dP06RJkwee19XVlY4dO9KxY0cGDx5M2bJlOXz4MNWrV8fJyQmTyfTQuCpXrsxff/1lNWg1o4YPH87UqVNZvXo1nTt3pnr16hw9evSBrQxly5bFaDQSGhpKjRo1ADhz5gy3bt165GtVr16dkydPPrQF42GfiYODAy1atKBFixaMGzeOfPny8ffff6dKrMqXL4/RaOSff/6xdI1dv36dU6dOUe4JFqstUaIEXl5eHDt2jNKlS1vOe/z4cebNm0ejRo0A2LZt2yPP5efnR+HChTl37hw9evRI85jt27cTFBTEmDFjLHUXLlywOiYjXWNOTk7UqFGDDRs20LlzZ8vjGzZs4JlnnnlkzJs3b+bMmTP079//kcc+yJEjRyhSpIglqdRb7v7NlIkuXM+EJrpNm6BnT6hcGX79VZsJ5ump3YQQuUJ0dHSqLxUfHx8GDRrE9OnTef311xkyZAgnT55k3LhxjBgxAjs7Ozw9Penduzdvv/02Pj4++Pr6Mm7cOOzsHtzdvnjxYkwmE3Xq1MHNzY2lS5fi6upqGTcRHBzMli1bLLNw0vriGDduHM2bN6dEiRJ069YNo9HI77//bhk/kh5eXl4MGDCAcePG0alTJ0aOHEndunUZPHgwL7/8Mu7u7hw/fpwNGzYwa9YsypYtS4sWLXjllVeYM2cOjo6OvPnmm7i6uj6ye2Xs2LF06NCBokWL8vzzz2NnZ8ehQ4c4fPgwkyZNeuhn8uuvv3Lu3DkaN25M/vz5Wbt2LWazOc3ux1KlSvHMM8/w8ssvM2/ePDw9PRk1ahSFCxdO1xf+g9jZ2dGiRQu2bdtm6RrKnz8/BQoUYP78+QQEBBAeHs6oUaPSdb7x48czdOhQvLy8aNu2LYmJiZYZWSNGjKBkyZKEh4ezYsUKatWqxW+//cbq1autzpHRrrERI0bQs2dPatasSb169Zg/fz7h4eEMHDjQcszo0aO5fPkyS5YssXruwoULqVOnjlV3X4qkpCSOHTtmuX/58mUOHDiAh4eHVXxbt25NNVhbV8rGREdHK0BFR0db1Tf7dKMKGvmrChr5a8ZPmpio1MiRShkMSoFSpUopFRGRSRELkbvEx8erY8eOqfj4eL1DybDevXsrINWtd+/eSimlNm3apGrVqqWcnJyUv7+/GjlypEpOTrY8PyYmRnXv3l25ubkpf39/NXXqVFW7dm01atQoyzFBQUFq2rRpSimlVq9ererUqaO8vLyUu7u7qlu3rvrzzz8tx+7cuVNVrlxZOTs7q5Rf14sWLVLe3t5Wca9atUpVrVpVOTk5qYIFC6pnn332ge8xrecrpdSFCxeUg4ODWrlypVJKqd27d6uWLVsqDw8P5e7uripXrqw+/PBDy/FXrlxRbdu2Vc7OziooKEgtX75c+fr6qrlz51qOAdTq1atTvda6detU/fr1laurq/Ly8lK1a9dW8+fPf+RnsnXrVtWkSROVP39+5erqqipXrmyJVymlmjRpot544w1L+caNG6pnz57K29tbubq6qtatW6tTp0499LNYvXq1etRX47p161ThwoWVyWSy1G3YsEGVK1dOOTs7q8qVK6tNmzZZvf+wsDAFqNDQ0FTnW7ZsmeX65c+fXzVu3Fj9+OOPlsfffvttVaBAAeXh4aG6du2qpk2bluY1zIgvvvhCBQUFKScnJ1W9enW1efNmq8d79+6tmjRpYlV369Yt5erqarlW90t5j/ff7j1PfHy88vLyUjt37kzzHA/7/fGg7+8nZVAqE+eX5gIxMTF4e3sTHR1tNdis2aebOBcVS5/6wYzvWOEhZ7jPyZPa2J+UKbIDBsC0aeDhkcmRC5E7JCQkEBYWZlm11pbFxsZSuHBhPvvssyfqSsgNLl26RNGiRfnzzz8ts+byKqUUdevWZdiwYbz44ot6h5OrfPHFF/z888/88ccfaT7+sN8fD/r+flLSNXafdpUC0negUjB/PgwfDvHx4OMDCxbAIxYyE0LkXaGhoZw4cYLatWsTHR1tmYb9JF0xOdXff//NnTt3qFSpEhEREbzzzjsEBwc/dF2fvMJgMDB//nwOHTqkdyi5jqOjI7NmzdI7DCuSCAFhUbGci4p99IH3io/XNkeNj9emyC9erG2aKoSwaZ9++iknT560DErdunVrjhkUmpmSk5N59913OXfuHJ6entSvX59ly5ale7ZbblelSpVHrtckUsuJG9XafCKUZDTT9NNNlrKPezqnA7q5wbJlsG2b1ipkJ4t0C2HrqlWrxr59+/QOI1u0bt2a1q2zf0kEITKbTSdCZ6/doflnmy3lp8oUoqTvA8b2JCTAyJFQogSkrK1Rp452E0IIIUSuZNOJ0Ee/3V1d1NvVkc+7V0/7wMOHtQHRR46Aiwu88AI8ZMl7IYQQQuQONtufcyIihr9OaMuot6ngz8FxrfBwvi8vNJth+nSoWVNLgnx9YdUqSYKESAcbm5AqhMgEevzesNkWoaW77q7MObR56o0CiYiAPn0gZYpfhw6wcKGWDAkhHihlsGxcXNxjbf8ghLBdKVuSpGwxkh1sNhH6+cAV7JzdaFnej/KB961HEBsLNWpoyZCLC0ydCgMHaitFCyEeyt7ennz58lk2rnRzc8u8jYyFEHmW2Wzm2rVruLm5ZWjj4Sdls4lQikFPlUhd6e4Or78O330Hy5fDE+xLI4Qt8v+v+/hhu3gLIcT97OzsKFasWLb+8WTTiZCflzPViuXXCvv2gbMzpOyf8s47MGKEVieEyBCDwUBAQAC+vr4kJyfrHY4QIpdwcnLCLpuXo9E9EZo9ezZTpkwhIiKCChUqMH36dMvuvWnZvHkzI0aM4OjRowQGBvLOO+9YbRSXYSaTtjDi++9DmTKwZw+4uoK9vXYTQjw2e3v7bO3rF0KIjNJ11tjKlSsZNmwYY8aMITQ0lEaNGtG2bVvCw8PTPD4sLIx27drRqFEjQkNDeffddxk6dCirVq16rNf3vXUVmjeH0aPBaISyZeG/gVpCCCGEyPt03XS1Tp06VK9enTlz5ljqypUrR6dOnZg8eXKq40eOHMmaNWs4fvzu+j8DBw7k4MGD7Ny5M12vmbJp28tt32DqpoV4xN/RxgTNmqXNEpNBnUIIIUSOk1WbrurWIpSUlMS+ffto1aqVVX2rVq3YsWNHms/ZuXNnquNbt27N3r17MzwO4dPfZ2hJUO3acOAA9O0rSZAQQghhY3QbIxQVFYXJZMLPz8+q3s/Pj8jIyDSfExkZmebxRqORqKgoAgJS7xyfmJhIYmKipRwdHQ3ATUC9/TaGkSPB0RFiYp7wHQkhhBAiq8T89z2d2R1Zug+Wvn+KnFLqodPm0jo+rfoUkydPZsKECanqg0EbJD1lSobiFUIIIYR+rl+/jre3d6adT7dEqGDBgtjb26dq/bl69WqqVp8U/v7+aR7v4OBAgQIF0nzO6NGjGTFihKV869YtgoKCCA8Pz9QPUjyemJgYihYtysWLFzO1z1dknFyLnEOuRc4h1yLniI6OplixYvj4+GTqeXVLhJycnKhRowYbNmygc+fOlvoNGzbwzDPPpPmcevXq8csvv1jV/fHHH9SsWdOyrP/9nJ2dcU5jLSBvb2/5oc5BvLy85HrkEHItcg65FjmHXIucI7PXGdJ1+vyIESP48ssv+eqrrzh+/DjDhw8nPDzcsi7Q6NGj6dWrl+X4gQMHcuHCBUaMGMHx48f56quvWLhwIW+99ZZeb0EIIYQQuZiuY4S6du3K9evXmThxIhEREVSsWJG1a9cSFBQEQEREhNWaQiEhIaxdu5bhw4fzxRdfEBgYyMyZM+nSpYteb0EIIYQQuZjug6UHDRrEoEGD0nxs8eLFqeqaNGnC/v37H/v1nJ2dGTduXJrdZSL7yfXIOeRa5BxyLXIOuRY5R1ZdC10XVBRCCCGE0JOuY4SEEEIIIfQkiZAQQgghbJYkQkIIIYSwWZIICSGEEMJm5clEaPbs2YSEhODi4kKNGjXYunXrQ4/fvHkzNWrUwMXFheLFizN37txsijTvy8i1+PHHH2nZsiWFChXCy8uLevXqsX79+myMNu/L6P+NFNu3b8fBwYGqVatmbYA2JKPXIjExkTFjxhAUFISzszMlSpTgq6++yqZo87aMXotly5ZRpUoV3NzcCAgIoG/fvly/fj2bos27tmzZwtNPP01gYCAGg4Gffvrpkc/JlO9vlcesWLFCOTo6qgULFqhjx46pN954Q7m7u6sLFy6kefy5c+eUm5ubeuONN9SxY8fUggULlKOjo/rhhx+yOfK8J6PX4o033lD/+9//1O7du9WpU6fU6NGjlaOjo9q/f382R543ZfR6pLh165YqXry4atWqlapSpUr2BJvHPc616Nixo6pTp47asGGDCgsLU//884/avn17NkadN2X0WmzdulXZ2dmpGTNmqHPnzqmtW7eqChUqqE6dOmVz5HnP2rVr1ZgxY9SqVasUoFavXv3Q4zPr+zvPJUK1a9dWAwcOtKorW7asGjVqVJrHv/POO6ps2bJWda+++qqqW7dulsVoKzJ6LdJSvnx5NWHChMwOzSY97vXo2rWreu+999S4ceMkEcokGb0Wv//+u/L29lbXr1/PjvBsSkavxZQpU1Tx4sWt6mbOnKmKFCmSZTHaovQkQpn1/Z2nusaSkpLYt28frVq1sqpv1aoVO3bsSPM5O3fuTHV869at2bt3L8nJyVkWa173ONfifmazmdu3b2f6Bnu26HGvx6JFizh79izjxo3L6hBtxuNcizVr1lCzZk0++eQTChcuTOnSpXnrrbeIj4/PjpDzrMe5FvXr1+fSpUusXbsWpRT//vsvP/zwA+3bt8+OkMU9Muv7W/eVpTNTVFQUJpMp1e71fn5+qXatTxEZGZnm8UajkaioKAICArIs3rzsca7F/T777DNiY2N54YUXsiJEm/I41+P06dOMGjWKrVu34uCQp35V6OpxrsW5c+fYtm0bLi4urF69mqioKAYNGsSNGzdknNATeJxrUb9+fZYtW0bXrl1JSEjAaDTSsWNHZs2alR0hi3tk1vd3nmoRSmEwGKzKSqlUdY86Pq16kXEZvRYpvv32W8aPH8/KlSvx9fXNqvBsTnqvh8lkonv37kyYMIHSpUtnV3g2JSP/N8xmMwaDgWXLllG7dm3atWvH1KlTWbx4sbQKZYKMXItjx44xdOhQxo4dy759+1i3bh1hYWGWzcJF9sqM7+889WdewYIFsbe3T5XJX716NVXWmMLf3z/N4x0cHChQoECWxZrXPc61SLFy5Ur69+/P999/T4sWLbIyTJuR0etx+/Zt9u7dS2hoKEOGDAG0L2OlFA4ODvzxxx80a9YsW2LPax7n/0ZAQACFCxfG29vbUleuXDmUUly6dIlSpUplacx51eNci8mTJ9OgQQPefvttACpXroy7uzuNGjVi0qRJ0ouQjTLr+ztPtQg5OTlRo0YNNmzYYFW/YcMG6tevn+Zz6tWrl+r4P/74g5o1a+Lo6JhlseZ1j3MtQGsJ6tOnD8uXL5c+90yU0evh5eXF4cOHOXDggOU2cOBAypQpw4EDB6hTp052hZ7nPM7/jQYNGnDlyhXu3LljqTt16hR2dnYUKVIkS+PNyx7nWsTFxWFnZ/3VaW9vD9xtjRDZI9O+vzM0tDoXSJkKuXDhQnXs2DE1bNgw5e7urs6fP6+UUmrUqFGqZ8+eluNTpt8NHz5cHTt2TC1cuFCmz2eSjF6L5cuXKwcHB/XFF1+oiIgIy+3WrVt6vYU8JaPX434yayzzZPRa3L59WxUpUkQ999xz6ujRo2rz5s2qVKlSasCAAXq9hTwjo9di0aJFysHBQc2ePVudPXtWbdu2TdWsWVPVrl1br7eQZ9y+fVuFhoaq0NBQBaipU6eq0NBQy1IGWfX9necSIaWU+uKLL1RQUJBycnJS1atXV5s3b7Y81rt3b9WkSROr4zdt2qSqVaumnJycVHBwsJozZ042R5x3ZeRaNGnSRAGpbr17987+wPOojP7fuJckQpkro9fi+PHjqkWLFsrV1VUVKVJEjRgxQsXFxWVz1HlTRq/FzJkzVfny5ZWrq6sKCAhQPXr0UJcuXcrmqPOejRs3PvQ7IKu+vw1KSVueEEIIIWxTnhojJIQQQgiREZIICSGEEMJmSSIkhBBCCJsliZAQQgghbJYkQkIIIYSwWZIICSGEEMJmSSIkhBBCCJsliZAQwsrixYvJly+f3mE8tuDgYKZPn/7QY8aPH0/VqlWzJR4hRM4miZAQeVCfPn0wGAypbmfOnNE7NBYvXmwVU0BAAC+88AJhYWGZcv49e/bwyiuvWMoGg4GffvrJ6pi33nqLv/76K1Ne70Huf59+fn48/fTTHD16NMPnyc2JqRA5nSRCQuRRbdq0ISIiwuoWEhKid1iAtqlrREQEV65cYfny5Rw4cICOHTtiMpme+NyFChXCzc3tocd4eHhkaHfqx3Xv+/ztt9+IjY2lffv2JCUlZflrCyHSRxIhIfIoZ2dn/P39rW729vZMnTqVSpUq4e7uTtGiRRk0aJDVrub3O3jwIE2bNsXT0xMvLy9q1KjB3r17LY/v2LGDxo0b4+rqStGiRRk6dCixsbEPjc1gMODv709AQABNmzZl3LhxHDlyxNJiNWfOHEqUKIGTkxNlypRh6dKlVs8fP348xYoVw9nZmcDAQIYOHWp57N6useDgYAA6d+6MwWCwlO/tGlu/fj0uLi7cunXL6jWGDh1KkyZNMu191qxZk+HDh3PhwgVOnjxpOeZh12PTpk307duX6OhoS8vS+PHjAUhKSuKdd96hcOHCuLu7U6dOHTZt2vTQeIQQqUkiJISNsbOzY+bMmRw5coSvv/6av//+m3feeeeBx/fo0YMiRYqwZ88e9u3bx6hRo3B0dATg8OHDtG7dmmeffZZDhw6xcuVKtm3bxpAhQzIUk6urKwDJycmsXr2aN954gzfffJMjR47w6quv0rdvXzZu3AjADz/8wLRp05g3bx6nT5/mp59+olKlSmmed8+ePQAsWrSIiIgIS/leLVq0IF++fKxatcpSZzKZ+O677+jRo0emvc9bt26xfPlyAMvnBw+/HvXr12f69OmWlqWIiAjeeustAPr27cv27dtZsWIFhw4d4vnnn6dNmzacPn063TEJISBP7j4vhK3r3bu3sre3V+7u7pbbc889l+ax3333nSpQoIClvGjRIuXt7W0pe3p6qsWLF6f53J49e6pXXnnFqm7r1q3Kzs5OxcfHp/mc+89/8eJFVbduXVWkSBGVmJio6tevr15++WWr5zz//POqXbt2SimlPvvsM1W6dGmVlJSU5vmDgoLUtGnTLGVArV692uqYcePGqSpVqljKQ4cOVc2aNbOU169fr5ycnNSNGzee6H0Cyt3dXbm5uVl20u7YsWOax6d41PVQSqkzZ84og8GgLl++bFXfvHlzNXr06IeeXwhhzUHfNEwIkVWaNm3KnDlzLGV3d3cANm7cyEcffcSxY8eIiYnBaDSSkJBAbGys5Zh7jRgxggEDBrB06VJatGjB888/T4kSJQDYt28fZ86cYdmyZZbjlVKYzWbCwsIoV65cmrFFR0fj4eGBUoq4uDiqV6/Ojz/+iJOTE8ePH7ca7AzQoEEDZsyYAcDzzz/P9OnTKV68OG3atKFdu3Y8/fTTODg8/q+zHj16UK9ePa5cuUJgYCDLli2jXbt25M+f/4nep6enJ/v378doNLJ582amTJnC3LlzrY7J6PUA2L9/P0opSpcubVWfmJiYLWOfhMhLJBESIo9yd3enZMmSVnUXLlygXbt2DBw4kA8++AAfHx+2bdtG//79SU5OTvM848ePp3v37vz222/8/vvvjBs3jhUrVtC5c2fMZjOvvvqq1RidFMWKFXtgbCkJgp2dHX5+fqm+8A0Gg1VZKWWpK1q0KCdPnmTDhg38+eefDBo0iClTprB582arLqeMqF27NiVKlGDFihW89tprrF69mkWLFlkef9z3aWdnZ7kGZcuWJTIykq5du7Jlyxbg8a5HSjz29vbs27cPe3t7q8c8PDwy9N6FsHWSCAlhQ/bu3YvRaOSzzz7Dzk4bIvjdd9898nmlS5emdOnSDB8+nBdffJFFixbRuXNnqlevztGjR1MlXI9yb4Jwv3LlyrFt2zZ69eplqduxY4dVq4urqysdO3akY8eODB48mLJly3L48GGqV6+e6nyOjo7pmo3WvXt3li1bRpEiRbCzs6N9+/aWxx73fd5v+PDhTJ06ldWrV9O5c+d0XQ8nJ6dU8VerVg2TycTVq1dp1KjRE8UkhK2TwdJC2JASJUpgNBqZNWsW586dY+nSpam6au4VHx/PkCFD2LRpExcuXGD79u3s2bPHkpSMHDmSnTt3MnjwYA4cOMDp06dZs2YNr7/++mPH+Pbbb7N48WLmzp3L6dOnmTp1Kj/++KNlkPDixYtZuHAhR44csbwHV1dXgoKC0jxfcHAwf/31F5GRkdy8efOBr9ujRw/279/Phx9+yHPPPYeLi4vlscx6n15eXgwYMIBx48ahlErX9QgODubOnTv89ddfREVFERcXR+nSpenRowe9evXixx9/JCwsjD179vC///2PtWvXZigmIWyengOUhBBZo3fv3uqZZ55J87GpU6eqgIAA5erqqlq3bq2WLFmiAHXz5k2llPXg3MTERNWtWzdVtGhR5eTkpAIDA9WQIUOsBgjv3r1btWzZUnl4eCh3d3dVuXJl9eGHHz4wtrQG/95v9uzZqnjx4srR0VGVLl1aLVmyxPLY6tWrVZ06dZSXl5dyd3dXdevWVX/++afl8fsHS69Zs0aVLFlSOTg4qKCgIKVU6sHSKWrVqqUA9ffff6d6LLPe54ULF5SDg4NauXKlUurR10MppQYOHKgKFCigADVu3DillFJJSUlq7NixKjg4WDk6Oip/f3/VuXNndejQoQfGJIRIzaCUUvqmYkIIIYQQ+pCuMSGEEELYLEmEhBBCCGGzJBESQgghhM2SREgIIYQQNksSISGEEELYLEmEhBBCCGGzJBESQgghhM2SREgIIYQQNksSISGEEELYLEmEhBBCCGGzJBESQgghhM2SREgIIYQQNuv/1QZQhXRINYYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.477473Z",
          "end_time": "2023-02-02T10:15:04.493475Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_prob1 = pd.DataFrame(classifier.predict_proba(X)[:,1]) ",
      "execution_count": 34,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.493475Z",
          "end_time": "2023-02-02T10:15:04.509479Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "y_prob1 ",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "text/plain": "             0\n0     0.000026\n1     0.504613\n2     0.574917\n3     0.509993\n4     0.636729\n...        ...\n1335  0.593211\n1336  0.450565\n1337  0.714650\n1338  0.313314\n1339  0.661218\n\n[1340 rows x 1 columns]",
            "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>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.000026</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.504613</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.574917</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.509993</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.636729</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1335</th>\n      <td>0.593211</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>0.450565</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>0.714650</td>\n    </tr>\n    <tr>\n      <th>1338</th>\n      <td>0.313314</td>\n    </tr>\n    <tr>\n      <th>1339</th>\n      <td>0.661218</td>\n    </tr>\n  </tbody>\n</table>\n<p>1340 rows × 1 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:04.509479Z",
          "end_time": "2023-02-02T10:15:05.525907Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import statsmodels.api as sm   ",
      "execution_count": 36,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:05.527713Z",
          "end_time": "2023-02-02T10:15:05.575841Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "logit = sm.Logit(Y, X)   ",
      "execution_count": 37,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:05.578015Z",
          "end_time": "2023-02-02T10:15:05.609600Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "logit.fit().summary()  ",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Optimization terminated successfully.\n         Current function value: 0.609294\n         Iterations 7\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 38,
          "data": {
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                           Logit Regression Results                           \n==============================================================================\nDep. Variable:               ATTORNEY   No. Observations:                 1340\nModel:                          Logit   Df Residuals:                     1335\nMethod:                           MLE   Df Model:                            4\nDate:                Thu, 02 Feb 2023   Pseudo R-squ.:                  0.1207\nTime:                        15:45:05   Log-Likelihood:                -816.45\nconverged:                       True   LL-Null:                       -928.48\nCovariance Type:            nonrobust   LLR p-value:                 2.515e-47\n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nCLMSEX         0.3005      0.116      2.591      0.010       0.073       0.528\nCLMINSUR       0.4167      0.124      3.364      0.001       0.174       0.660\nSEATBELT      -0.6828      0.522     -1.308      0.191      -1.706       0.341\nCLMAGE         0.0059      0.003      1.951      0.051   -2.58e-05       0.012\nLOSS          -0.3262      0.029    -11.234      0.000      -0.383      -0.269\n==============================================================================\n\"\"\"",
            "text/html": "<table class=\"simpletable\">\n<caption>Logit Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>       <td>ATTORNEY</td>     <th>  No. Observations:  </th>  <td>  1340</td>  \n</tr>\n<tr>\n  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  1335</td>  \n</tr>\n<tr>\n  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n</tr>\n<tr>\n  <th>Date:</th>            <td>Thu, 02 Feb 2023</td> <th>  Pseudo R-squ.:     </th>  <td>0.1207</td>  \n</tr>\n<tr>\n  <th>Time:</th>                <td>15:45:05</td>     <th>  Log-Likelihood:    </th> <td> -816.45</td> \n</tr>\n<tr>\n  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -928.48</td> \n</tr>\n<tr>\n  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>2.515e-47</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n</tr>\n<tr>\n  <th>CLMSEX</th>   <td>    0.3005</td> <td>    0.116</td> <td>    2.591</td> <td> 0.010</td> <td>    0.073</td> <td>    0.528</td>\n</tr>\n<tr>\n  <th>CLMINSUR</th> <td>    0.4167</td> <td>    0.124</td> <td>    3.364</td> <td> 0.001</td> <td>    0.174</td> <td>    0.660</td>\n</tr>\n<tr>\n  <th>SEATBELT</th> <td>   -0.6828</td> <td>    0.522</td> <td>   -1.308</td> <td> 0.191</td> <td>   -1.706</td> <td>    0.341</td>\n</tr>\n<tr>\n  <th>CLMAGE</th>   <td>    0.0059</td> <td>    0.003</td> <td>    1.951</td> <td> 0.051</td> <td>-2.58e-05</td> <td>    0.012</td>\n</tr>\n<tr>\n  <th>LOSS</th>     <td>   -0.3262</td> <td>    0.029</td> <td>  -11.234</td> <td> 0.000</td> <td>   -0.383</td> <td>   -0.269</td>\n</tr>\n</table>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:05.610104Z",
          "end_time": "2023-02-02T10:15:05.624718Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fpr ",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 39,
          "data": {
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0.13430657,\n       0.13430657, 0.13576642, 0.13576642, 0.13722628, 0.13722628,\n       0.14014599, 0.14014599, 0.14160584, 0.14160584, 0.14306569,\n       0.14306569, 0.1459854 , 0.1459854 , 0.1459854 , 0.15036496,\n       0.15036496, 0.15182482, 0.15182482, 0.15328467, 0.15328467,\n       0.15620438, 0.15620438, 0.15620438, 0.16058394, 0.16058394,\n       0.1620438 , 0.1620438 , 0.16350365, 0.16350365, 0.1649635 ,\n       0.1649635 , 0.16642336, 0.16642336, 0.16934307, 0.16934307,\n       0.17080292, 0.17080292, 0.17226277, 0.17226277, 0.17518248,\n       0.17518248, 0.17664234, 0.17664234, 0.17810219, 0.17810219,\n       0.17956204, 0.17956204, 0.1810219 , 0.1810219 , 0.18248175,\n       0.18248175, 0.18394161, 0.18394161, 0.18540146, 0.18540146,\n       0.18832117, 0.18832117, 0.18978102, 0.18978102, 0.18978102,\n       0.19124088, 0.19124088, 0.19270073, 0.19270073, 0.19416058,\n       0.19416058, 0.19562044, 0.19562044, 0.19708029, 0.19708029,\n       0.2       , 0.2       , 0.20145985, 0.20145985, 0.20291971,\n       0.20291971, 0.20583942, 0.20583942, 0.20875912, 0.20875912,\n       0.21167883, 0.21167883, 0.21605839, 0.21605839, 0.21751825,\n       0.21751825, 0.22043796, 0.22043796, 0.22189781, 0.22189781,\n       0.22335766, 0.22335766, 0.23065693, 0.23065693, 0.23211679,\n       0.23211679, 0.23357664, 0.23357664, 0.2350365 , 0.2350365 ,\n       0.2350365 , 0.24087591, 0.24087591, 0.24233577, 0.24233577,\n       0.24525547, 0.24525547, 0.24817518, 0.24817518, 0.25109489,\n       0.25109489, 0.25839416, 0.25839416, 0.26131387, 0.26131387,\n       0.26277372, 0.26277372, 0.26861314, 0.26861314, 0.26861314,\n       0.27007299, 0.27445255, 0.27445255, 0.27591241, 0.27591241,\n       0.27737226, 0.27737226, 0.27883212, 0.27883212, 0.28467153,\n       0.28467153, 0.28905109, 0.28905109, 0.28905109, 0.28905109,\n       0.2919708 , 0.2919708 , 0.29489051, 0.29489051, 0.29635036,\n       0.29635036, 0.29781022, 0.29781022, 0.30072993, 0.30072993,\n       0.30218978, 0.30510949, 0.30510949, 0.30656934, 0.30656934,\n       0.3080292 , 0.3080292 , 0.30948905, 0.30948905, 0.31094891,\n       0.31094891, 0.31240876, 0.31240876, 0.31386861, 0.31386861,\n       0.31532847, 0.31532847, 0.31678832, 0.31678832, 0.31824818,\n       0.31824818, 0.32116788, 0.32116788, 0.32554745, 0.32554745,\n       0.3270073 , 0.3270073 , 0.32846715, 0.32846715, 0.33138686,\n       0.33138686, 0.33284672, 0.33284672, 0.33868613, 0.33868613,\n       0.34890511, 0.34890511, 0.36058394, 0.36058394, 0.36350365,\n       0.36350365, 0.36642336, 0.36642336, 0.36788321, 0.36788321,\n       0.36934307, 0.36934307, 0.37080292, 0.37080292, 0.37518248,\n       0.37518248, 0.3810219 , 0.38248175, 0.39416058, 0.39416058,\n       0.40145985, 0.40145985, 0.40291971, 0.40291971, 0.40437956,\n       0.40437956, 0.40583942, 0.40583942, 0.40729927, 0.40729927,\n       0.40875912, 0.40875912, 0.41167883, 0.41167883, 0.41313869,\n       0.41313869, 0.41459854, 0.41459854, 0.41605839, 0.41605839,\n       0.42043796, 0.42043796, 0.42627737, 0.42627737, 0.42773723,\n       0.42773723, 0.43649635, 0.43649635, 0.44963504, 0.44963504,\n       0.45255474, 0.45255474, 0.4540146 , 0.4540146 , 0.46131387,\n       0.46131387, 0.46277372, 0.46277372, 0.46423358, 0.46715328,\n       0.47007299, 0.47007299, 0.4729927 , 0.4729927 , 0.47883212,\n       0.47883212, 0.48321168, 0.48321168, 0.4919708 , 0.4919708 ,\n       0.49781022, 0.49781022, 0.49927007, 0.49927007, 0.50364964,\n       0.50364964, 0.5080292 , 0.5080292 , 0.50948905, 0.50948905,\n       0.51240876, 0.51240876, 0.52116788, 0.52116788, 0.52408759,\n       0.52408759, 0.54014599, 0.54014599, 0.54160584, 0.54306569,\n       0.5459854 , 0.5459854 , 0.54890511, 0.54890511, 0.55182482,\n       0.55182482, 0.55328467, 0.55328467, 0.55620438, 0.55620438,\n       0.56058394, 0.56058394, 0.5649635 , 0.5649635 , 0.57226277,\n       0.57226277, 0.57372263, 0.57372263, 0.57518248, 0.57518248,\n       0.57956204, 0.57956204, 0.5810219 , 0.5810219 , 0.58248175,\n       0.58248175, 0.58394161, 0.58394161, 0.60145985, 0.60145985,\n       0.61459854, 0.61459854, 0.61751825, 0.61751825, 0.6189781 ,\n       0.6189781 , 0.62335766, 0.62335766, 0.62627737, 0.62627737,\n       0.62919708, 0.62919708, 0.63211679, 0.63211679, 0.63357664,\n       0.63357664, 0.6379562 , 0.6379562 , 0.63941606, 0.63941606,\n       0.66277372, 0.66277372, 0.66861314, 0.66861314, 0.67737226,\n       0.67737226, 0.68467153, 0.68467153, 0.71240876, 0.71240876,\n       0.71386861, 0.71386861, 0.71678832, 0.71678832, 0.72262774,\n       0.72262774, 0.7270073 , 0.72992701, 0.73284672, 0.73284672,\n       0.73430657, 0.73430657, 0.73722628, 0.73722628, 0.74452555,\n       0.74452555, 0.75036496, 0.75036496, 0.76350365, 0.76350365,\n       0.7649635 , 0.7649635 , 0.76642336, 0.76642336, 0.77080292,\n       0.77080292, 0.77664234, 0.77664234, 0.79124088, 0.79124088,\n       0.79270073, 0.79270073, 0.79416058, 0.79416058, 0.79562044,\n       0.79562044, 0.8       , 0.8       , 0.81313869, 0.81313869,\n       0.82335766, 0.82335766, 0.82627737, 0.82627737, 0.83649635,\n       0.83649635, 0.84379562, 0.84379562, 0.84963504, 0.84963504,\n       0.85109489, 0.85109489, 0.85985401, 0.85985401, 0.86715328,\n       0.86715328, 0.87153285, 0.87445255, 0.87737226, 0.87737226,\n       0.88905109, 0.88905109, 0.8919708 , 0.8919708 , 0.89343066,\n       0.89343066, 0.90510949, 0.90510949, 0.91386861, 0.91386861,\n       0.91532847, 0.91532847, 0.92116788, 0.92116788, 0.93138686,\n       0.93138686, 0.99270073, 0.99270073, 1.        ])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:05.625723Z",
          "end_time": "2023-02-02T10:15:05.641047Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "tpr  ",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": "array([0.        , 0.00152672, 0.00305344, 0.00305344, 0.00763359,\n       0.00763359, 0.01984733, 0.01984733, 0.02442748, 0.02442748,\n       0.02900763, 0.02900763, 0.03053435, 0.03053435, 0.03206107,\n       0.03206107, 0.0351145 , 0.0351145 , 0.04580153, 0.04580153,\n       0.06412214, 0.06412214, 0.06564885, 0.06564885, 0.06870229,\n       0.07480916, 0.0870229 , 0.09007634, 0.09312977, 0.09312977,\n       0.09618321, 0.10076336, 0.10992366, 0.10992366, 0.11603053,\n       0.11603053, 0.12671756, 0.12671756, 0.12824427, 0.12824427,\n       0.12977099, 0.12977099, 0.14656489, 0.14656489, 0.15114504,\n       0.15114504, 0.15267176, 0.15572519, 0.15572519, 0.16030534,\n       0.16030534, 0.16183206, 0.16335878, 0.17709924, 0.17709924,\n       0.18625954, 0.18625954, 0.19083969, 0.19083969, 0.20152672,\n       0.20152672, 0.20763359, 0.20916031, 0.21221374, 0.21221374,\n       0.21374046, 0.21374046, 0.21679389, 0.21679389, 0.2259542 ,\n       0.23206107, 0.23816794, 0.23816794, 0.23969466, 0.23969466,\n       0.24122137, 0.24122137, 0.24427481, 0.24427481, 0.24732824,\n       0.25038168, 0.25038168, 0.25343511, 0.25343511, 0.25496183,\n       0.25496183, 0.27022901, 0.27022901, 0.27633588, 0.27633588,\n       0.2778626 , 0.2778626 , 0.28091603, 0.28091603, 0.28244275,\n       0.28244275, 0.29312977, 0.29312977, 0.29465649, 0.29465649,\n       0.29465649, 0.29465649, 0.29923664, 0.29923664, 0.30534351,\n       0.30534351, 0.30839695, 0.30839695, 0.30992366, 0.30992366,\n       0.31145038, 0.31145038, 0.31755725, 0.31755725, 0.32977099,\n       0.33129771, 0.35267176, 0.35572519, 0.36335878, 0.36335878,\n       0.37099237, 0.37099237, 0.37557252, 0.37557252, 0.38167939,\n       0.38320611, 0.38625954, 0.38625954, 0.39389313, 0.39389313,\n       0.39541985, 0.39541985, 0.4       , 0.4       , 0.40305344,\n       0.40305344, 0.41221374, 0.41221374, 0.41984733, 0.42137405,\n       0.42290076, 0.42290076, 0.4259542 , 0.4259542 , 0.42900763,\n       0.43358779, 0.43358779, 0.43664122, 0.43664122, 0.43816794,\n       0.43816794, 0.45038168, 0.45038168, 0.4519084 , 0.4519084 ,\n       0.45343511, 0.45343511, 0.45648855, 0.45954198, 0.45954198,\n       0.4610687 , 0.4610687 , 0.47022901, 0.47022901, 0.47328244,\n       0.47328244, 0.47480916, 0.47938931, 0.47938931, 0.48396947,\n       0.48396947, 0.4870229 , 0.4870229 , 0.49160305, 0.49160305,\n       0.49618321, 0.49618321, 0.50229008, 0.50229008, 0.50687023,\n       0.50687023, 0.50839695, 0.50839695, 0.51603053, 0.51603053,\n       0.51908397, 0.51908397, 0.5221374 , 0.5221374 , 0.52366412,\n       0.52366412, 0.52519084, 0.52519084, 0.52671756, 0.52671756,\n       0.52977099, 0.52977099, 0.53282443, 0.53282443, 0.54045802,\n       0.54045802, 0.54351145, 0.54351145, 0.54656489, 0.54961832,\n       0.54961832, 0.56793893, 0.56793893, 0.57099237, 0.57099237,\n       0.57862595, 0.57862595, 0.58167939, 0.58167939, 0.58473282,\n       0.58473282, 0.58778626, 0.58778626, 0.59083969, 0.59083969,\n       0.59236641, 0.59236641, 0.59847328, 0.59847328, 0.60152672,\n       0.60152672, 0.60305344, 0.60305344, 0.60458015, 0.60458015,\n       0.60763359, 0.60763359, 0.60916031, 0.60916031, 0.61068702,\n       0.61068702, 0.61374046, 0.61374046, 0.61832061, 0.61832061,\n       0.61984733, 0.61984733, 0.62748092, 0.62748092, 0.63053435,\n       0.63358779, 0.63358779, 0.63816794, 0.63816794, 0.63969466,\n       0.63969466, 0.64274809, 0.64274809, 0.65038168, 0.65038168,\n       0.65954198, 0.65954198, 0.6610687 , 0.6610687 , 0.66412214,\n       0.66412214, 0.66564885, 0.66564885, 0.66870229, 0.67022901,\n       0.67175573, 0.67175573, 0.67480916, 0.67480916, 0.6778626 ,\n       0.6778626 , 0.68854962, 0.68854962, 0.69312977, 0.69312977,\n       0.69618321, 0.69618321, 0.69770992, 0.70076336, 0.70229008,\n       0.70229008, 0.70381679, 0.70381679, 0.70687023, 0.70687023,\n       0.70992366, 0.70992366, 0.71603053, 0.71603053, 0.71755725,\n       0.71755725, 0.71755725, 0.7221374 , 0.7221374 , 0.72366412,\n       0.72366412, 0.72519084, 0.72519084, 0.72671756, 0.72671756,\n       0.73129771, 0.73129771, 0.73282443, 0.73282443, 0.7389313 ,\n       0.7389313 , 0.74198473, 0.74198473, 0.74351145, 0.74351145,\n       0.74656489, 0.74656489, 0.74961832, 0.74961832, 0.75114504,\n       0.75114504, 0.75572519, 0.75572519, 0.75725191, 0.75725191,\n       0.76030534, 0.76030534, 0.76335878, 0.76335878, 0.76641221,\n       0.76641221, 0.76793893, 0.76793893, 0.7740458 , 0.7740458 ,\n       0.77557252, 0.77557252, 0.77709924, 0.77709924, 0.77862595,\n       0.77862595, 0.78015267, 0.78015267, 0.78167939, 0.78167939,\n       0.78320611, 0.78320611, 0.78473282, 0.78473282, 0.78778626,\n       0.78778626, 0.78931298, 0.78931298, 0.79236641, 0.79236641,\n       0.79694656, 0.79694656, 0.8       , 0.8       , 0.80305344,\n       0.80305344, 0.80916031, 0.80916031, 0.81068702, 0.81068702,\n       0.81221374, 0.81221374, 0.81374046, 0.81374046, 0.81526718,\n       0.81526718, 0.81679389, 0.81679389, 0.81832061, 0.81832061,\n       0.81984733, 0.81984733, 0.82137405, 0.82137405, 0.8259542 ,\n       0.8259542 , 0.82900763, 0.82900763, 0.83053435, 0.83053435,\n       0.83206107, 0.83206107, 0.83358779, 0.83358779, 0.83358779,\n       0.83358779, 0.83969466, 0.83969466, 0.84122137, 0.84122137,\n       0.84427481, 0.84427481, 0.84580153, 0.84580153, 0.84732824,\n       0.84732824, 0.85038168, 0.85038168, 0.8519084 , 0.8519084 ,\n       0.85496183, 0.85496183, 0.85648855, 0.85648855, 0.8610687 ,\n       0.8610687 , 0.86259542, 0.86259542, 0.86412214, 0.86412214,\n       0.86717557, 0.86717557, 0.86870229, 0.86870229, 0.87022901,\n       0.87022901, 0.87480916, 0.87480916, 0.8778626 , 0.8778626 ,\n       0.87938931, 0.87938931, 0.88091603, 0.88091603, 0.88244275,\n       0.88244275, 0.88396947, 0.88396947, 0.88549618, 0.88549618,\n       0.88854962, 0.88854962, 0.89007634, 0.89007634, 0.89160305,\n       0.89160305, 0.89465649, 0.89465649, 0.89618321, 0.89618321,\n       0.89770992, 0.89770992, 0.89923664, 0.89923664, 0.90381679,\n       0.90381679, 0.90534351, 0.90534351, 0.90687023, 0.90687023,\n       0.90839695, 0.90839695, 0.90992366, 0.90992366, 0.91145038,\n       0.91145038, 0.9129771 , 0.9129771 , 0.91603053, 0.91603053,\n       0.91908397, 0.91908397, 0.92061069, 0.92061069, 0.9221374 ,\n       0.9221374 , 0.92366412, 0.92366412, 0.92519084, 0.92519084,\n       0.92671756, 0.92671756, 0.92824427, 0.92824427, 0.92977099,\n       0.92977099, 0.93129771, 0.93129771, 0.93435115, 0.93435115,\n       0.93587786, 0.93587786, 0.93587786, 0.93587786, 0.93740458,\n       0.93740458, 0.9389313 , 0.9389313 , 0.94045802, 0.94045802,\n       0.94351145, 0.94351145, 0.94656489, 0.94656489, 0.94961832,\n       0.94961832, 0.95114504, 0.95114504, 0.95267176, 0.95267176,\n       0.95419847, 0.95419847, 0.95572519, 0.95572519, 0.95725191,\n       0.95725191, 0.95877863, 0.95877863, 0.96030534, 0.96030534,\n       0.96183206, 0.96183206, 0.96335878, 0.96335878, 0.9648855 ,\n       0.9648855 , 0.96641221, 0.96641221, 0.96793893, 0.96793893,\n       0.97099237, 0.97099237, 0.97251908, 0.97251908, 0.9740458 ,\n       0.9740458 , 0.97557252, 0.97557252, 0.97709924, 0.97709924,\n       0.97862595, 0.97862595, 0.97862595, 0.97862595, 0.98167939,\n       0.98167939, 0.98320611, 0.98320611, 0.98473282, 0.98473282,\n       0.98778626, 0.98778626, 0.99083969, 0.99083969, 0.99236641,\n       0.99236641, 0.99389313, 0.99389313, 0.99694656, 0.99694656,\n       0.99847328, 0.99847328, 1.        , 1.        ])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:05.641047Z",
          "end_time": "2023-02-02T10:15:06.388160Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sklearn.metrics import accuracy_score\naccuracy_ls = []\nfor thres in thresholds:\n    y_pred = np.where(classifier.predict_proba(X)[:,1]>thres,1,0)\n    accuracy_ls.append(accuracy_score(Y, y_pred, normalize=True))\n    \naccuracy_ls = pd.concat([pd.Series(thresholds), pd.Series(accuracy_ls)],\n                        axis=1)\naccuracy_ls.columns = ['thresholds', 'accuracy']\naccuracy_ls.sort_values(by='accuracy', ascending=False, inplace=True)\naccuracy_ls  ",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "       thresholds  accuracy\n317  5.324383e-01  0.713433\n321  5.285764e-01  0.713433\n322  5.250800e-01  0.713433\n310  5.392288e-01  0.712687\n312  5.370079e-01  0.712687\n..            ...       ...\n0    1.769008e+00  0.511194\n1    7.690085e-01  0.511194\n551  1.432688e-11  0.492537\n552  7.980210e-12  0.491791\n553  1.326704e-24  0.489552\n\n[554 rows x 2 columns]",
            "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>thresholds</th>\n      <th>accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>317</th>\n      <td>5.324383e-01</td>\n      <td>0.713433</td>\n    </tr>\n    <tr>\n      <th>321</th>\n      <td>5.285764e-01</td>\n      <td>0.713433</td>\n    </tr>\n    <tr>\n      <th>322</th>\n      <td>5.250800e-01</td>\n      <td>0.713433</td>\n    </tr>\n    <tr>\n      <th>310</th>\n      <td>5.392288e-01</td>\n      <td>0.712687</td>\n    </tr>\n    <tr>\n      <th>312</th>\n      <td>5.370079e-01</td>\n      <td>0.712687</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>1.769008e+00</td>\n      <td>0.511194</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>7.690085e-01</td>\n      <td>0.511194</td>\n    </tr>\n    <tr>\n      <th>551</th>\n      <td>1.432688e-11</td>\n      <td>0.492537</td>\n    </tr>\n    <tr>\n      <th>552</th>\n      <td>7.980210e-12</td>\n      <td>0.491791</td>\n    </tr>\n    <tr>\n      <th>553</th>\n      <td>1.326704e-24</td>\n      <td>0.489552</td>\n    </tr>\n  </tbody>\n</table>\n<p>554 rows × 2 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:06.390318Z",
          "end_time": "2023-02-02T10:15:06.403835Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from numpy import argmax\nJ = tpr - fpr\nix = argmax(J)\nbest_thresh = thresholds[ix]\nprint('Best Threshold=%f' % (best_thresh)) ",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Best Threshold=0.525080\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-02-02T10:15:06.404841Z",
          "end_time": "2023-02-02T10:15:06.429363Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "threshold = 0.525080\npreds = np.where(classifier.predict_proba(X)[:,1] > threshold, 1, 0)\nprint(classification_report(Y,preds))   ",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": "              precision    recall  f1-score   support\n\n           0       0.75      0.67      0.70       685\n           1       0.69      0.76      0.72       655\n\n    accuracy                           0.71      1340\n   macro avg       0.72      0.71      0.71      1340\nweighted avg       0.72      0.71      0.71      1340\n\n",
          "name": "stdout"
        }
      ]
    }
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