disaster-tweet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e73be295",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:47.162212Z",
     "iopub.status.busy": "2023-08-12T22:10:47.161803Z",
     "iopub.status.idle": "2023-08-12T22:10:47.176492Z",
     "shell.execute_reply": "2023-08-12T22:10:47.175412Z"
    },
    "papermill": {
     "duration": 0.033246,
     "end_time": "2023-08-12T22:10:47.179084",
     "exception": false,
     "start_time": "2023-08-12T22:10:47.145838",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/nlp-getting-started/sample_submission.csv\n",
      "/kaggle/input/nlp-getting-started/train.csv\n",
      "/kaggle/input/nlp-getting-started/test.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import re\n",
    "import string\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "        \n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c4c98b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:47.205656Z",
     "iopub.status.busy": "2023-08-12T22:10:47.205010Z",
     "iopub.status.idle": "2023-08-12T22:10:48.765224Z",
     "shell.execute_reply": "2023-08-12T22:10:48.764157Z"
    },
    "papermill": {
     "duration": 1.57654,
     "end_time": "2023-08-12T22:10:48.767908",
     "exception": false,
     "start_time": "2023-08-12T22:10:47.191368",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id keyword location                                               text  \\\n",
       "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
       "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
       "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
       "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
       "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
       "\n",
       "   target  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import necessary libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#Import data\n",
    "df_train = pd.read_csv ('/kaggle/input/nlp-getting-started/train.csv')\n",
    "df_test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')\n",
    "submission = pd.read_csv('../input/nlp-getting-started/sample_submission.csv')\n",
    "df_test = pd.merge(df_test, submission, on='id', how='left')\n",
    "df_train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a7f97d42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:48.795811Z",
     "iopub.status.busy": "2023-08-12T22:10:48.795379Z",
     "iopub.status.idle": "2023-08-12T22:10:48.807817Z",
     "shell.execute_reply": "2023-08-12T22:10:48.806992Z"
    },
    "papermill": {
     "duration": 0.028828,
     "end_time": "2023-08-12T22:10:48.810102",
     "exception": false,
     "start_time": "2023-08-12T22:10:48.781274",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Just happened a terrible car crash</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Heard about #earthquake is different cities, s...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id keyword location                                               text  \\\n",
       "0   0     NaN      NaN                 Just happened a terrible car crash   \n",
       "1   2     NaN      NaN  Heard about #earthquake is different cities, s...   \n",
       "2   3     NaN      NaN  there is a forest fire at spot pond, geese are...   \n",
       "3   9     NaN      NaN           Apocalypse lighting. #Spokane #wildfires   \n",
       "4  11     NaN      NaN      Typhoon Soudelor kills 28 in China and Taiwan   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca310fe",
   "metadata": {
    "papermill": {
     "duration": 0.01301,
     "end_time": "2023-08-12T22:10:48.836260",
     "exception": false,
     "start_time": "2023-08-12T22:10:48.823250",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# 📊 **EDA**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e51b7e7",
   "metadata": {
    "papermill": {
     "duration": 0.012603,
     "end_time": "2023-08-12T22:10:48.861812",
     "exception": false,
     "start_time": "2023-08-12T22:10:48.849209",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "✅ **Checking for missing value**[](http://)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a5b68e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:48.889808Z",
     "iopub.status.busy": "2023-08-12T22:10:48.889125Z",
     "iopub.status.idle": "2023-08-12T22:10:48.905752Z",
     "shell.execute_reply": "2023-08-12T22:10:48.904578Z"
    },
    "papermill": {
     "duration": 0.033367,
     "end_time": "2023-08-12T22:10:48.908093",
     "exception": false,
     "start_time": "2023-08-12T22:10:48.874726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id             0\n",
       "keyword       61\n",
       "location    2533\n",
       "text           0\n",
       "target         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b83c142f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:48.937086Z",
     "iopub.status.busy": "2023-08-12T22:10:48.936380Z",
     "iopub.status.idle": "2023-08-12T22:10:48.949318Z",
     "shell.execute_reply": "2023-08-12T22:10:48.948077Z"
    },
    "papermill": {
     "duration": 0.029795,
     "end_time": "2023-08-12T22:10:48.951749",
     "exception": false,
     "start_time": "2023-08-12T22:10:48.921954",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id             0\n",
       "keyword       26\n",
       "location    1105\n",
       "text           0\n",
       "target         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb5d1e8",
   "metadata": {
    "papermill": {
     "duration": 0.012741,
     "end_time": "2023-08-12T22:10:48.977821",
     "exception": false,
     "start_time": "2023-08-12T22:10:48.965080",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "✅**Handling missing value**[](http://)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6b78cd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:49.006263Z",
     "iopub.status.busy": "2023-08-12T22:10:49.005857Z",
     "iopub.status.idle": "2023-08-12T22:10:49.028465Z",
     "shell.execute_reply": "2023-08-12T22:10:49.027301Z"
    },
    "papermill": {
     "duration": 0.039253,
     "end_time": "2023-08-12T22:10:49.030631",
     "exception": false,
     "start_time": "2023-08-12T22:10:48.991378",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>10869</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Two giant cranes holding a bridge collapse int...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>10870</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>@aria_ahrary @TheTawniest The out of control w...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>10871</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>10872</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Police investigating after an e-bike collided ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>10873</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>The Latest: More Homes Razed by Northern Calif...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id     keyword     location  \\\n",
       "0         1  no_keyword  no_location   \n",
       "1         4  no_keyword  no_location   \n",
       "2         5  no_keyword  no_location   \n",
       "3         6  no_keyword  no_location   \n",
       "4         7  no_keyword  no_location   \n",
       "...     ...         ...          ...   \n",
       "7608  10869  no_keyword  no_location   \n",
       "7609  10870  no_keyword  no_location   \n",
       "7610  10871  no_keyword  no_location   \n",
       "7611  10872  no_keyword  no_location   \n",
       "7612  10873  no_keyword  no_location   \n",
       "\n",
       "                                                   text  target  \n",
       "0     Our Deeds are the Reason of this #earthquake M...       1  \n",
       "1                Forest fire near La Ronge Sask. Canada       1  \n",
       "2     All residents asked to 'shelter in place' are ...       1  \n",
       "3     13,000 people receive #wildfires evacuation or...       1  \n",
       "4     Just got sent this photo from Ruby #Alaska as ...       1  \n",
       "...                                                 ...     ...  \n",
       "7608  Two giant cranes holding a bridge collapse int...       1  \n",
       "7609  @aria_ahrary @TheTawniest The out of control w...       1  \n",
       "7610  M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...       1  \n",
       "7611  Police investigating after an e-bike collided ...       1  \n",
       "7612  The Latest: More Homes Razed by Northern Calif...       1  \n",
       "\n",
       "[7613 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "for df in [df_train, df_test]:\n",
    "    for col in ['keyword', 'location']:\n",
    "        df[col] = df[col].fillna(f'no_{col}')\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d0ff6a65",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:49.059544Z",
     "iopub.status.busy": "2023-08-12T22:10:49.059142Z",
     "iopub.status.idle": "2023-08-12T22:10:49.074841Z",
     "shell.execute_reply": "2023-08-12T22:10:49.073789Z"
    },
    "papermill": {
     "duration": 0.033074,
     "end_time": "2023-08-12T22:10:49.077190",
     "exception": false,
     "start_time": "2023-08-12T22:10:49.044116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Just happened a terrible car crash</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Heard about #earthquake is different cities, s...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>10861</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259</th>\n",
       "      <td>10865</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Storm in RI worse than last hurricane. My city...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>10868</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>Green Line derailment in Chicago http://t.co/U...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3261</th>\n",
       "      <td>10874</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>MEG issues Hazardous Weather Outlook (HWO) htt...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3262</th>\n",
       "      <td>10875</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>#CityofCalgary has activated its Municipal Eme...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3263 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id     keyword     location  \\\n",
       "0         0  no_keyword  no_location   \n",
       "1         2  no_keyword  no_location   \n",
       "2         3  no_keyword  no_location   \n",
       "3         9  no_keyword  no_location   \n",
       "4        11  no_keyword  no_location   \n",
       "...     ...         ...          ...   \n",
       "3258  10861  no_keyword  no_location   \n",
       "3259  10865  no_keyword  no_location   \n",
       "3260  10868  no_keyword  no_location   \n",
       "3261  10874  no_keyword  no_location   \n",
       "3262  10875  no_keyword  no_location   \n",
       "\n",
       "                                                   text  target  \n",
       "0                    Just happened a terrible car crash       0  \n",
       "1     Heard about #earthquake is different cities, s...       0  \n",
       "2     there is a forest fire at spot pond, geese are...       0  \n",
       "3              Apocalypse lighting. #Spokane #wildfires       0  \n",
       "4         Typhoon Soudelor kills 28 in China and Taiwan       0  \n",
       "...                                                 ...     ...  \n",
       "3258  EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...       0  \n",
       "3259  Storm in RI worse than last hurricane. My city...       0  \n",
       "3260  Green Line derailment in Chicago http://t.co/U...       0  \n",
       "3261  MEG issues Hazardous Weather Outlook (HWO) htt...       0  \n",
       "3262  #CityofCalgary has activated its Municipal Eme...       0  \n",
       "\n",
       "[3263 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca6109e",
   "metadata": {
    "papermill": {
     "duration": 0.014245,
     "end_time": "2023-08-12T22:10:49.105551",
     "exception": false,
     "start_time": "2023-08-12T22:10:49.091306",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "✅**Balanced/Imbalanced**[](http://)\n",
    "\n",
    "We reach that classes are almost equally separated ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a93c723",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:49.135592Z",
     "iopub.status.busy": "2023-08-12T22:10:49.135171Z",
     "iopub.status.idle": "2023-08-12T22:10:49.362281Z",
     "shell.execute_reply": "2023-08-12T22:10:49.361139Z"
    },
    "papermill": {
     "duration": 0.245239,
     "end_time": "2023-08-12T22:10:49.364866",
     "exception": false,
     "start_time": "2023-08-12T22:10:49.119627",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_train.target.hist()\n",
    "#df_test.target.hist()\n",
    "sns.countplot(x='target', data=df_train)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9268c857",
   "metadata": {
    "papermill": {
     "duration": 0.013695,
     "end_time": "2023-08-12T22:10:49.393233",
     "exception": false,
     "start_time": "2023-08-12T22:10:49.379538",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "📈**Discover trend of dataset**[](http://)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12562e7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:49.423564Z",
     "iopub.status.busy": "2023-08-12T22:10:49.422727Z",
     "iopub.status.idle": "2023-08-12T22:10:49.781989Z",
     "shell.execute_reply": "2023-08-12T22:10:49.780845Z"
    },
    "papermill": {
     "duration": 0.377161,
     "end_time": "2023-08-12T22:10:49.784517",
     "exception": false,
     "start_time": "2023-08-12T22:10:49.407356",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>48</td>\n",
       "      <td>ablaze</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>@bbcmtd Wholesale Markets ablaze http://t.co/l...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>49</td>\n",
       "      <td>ablaze</td>\n",
       "      <td>Est. September 2012 - Bristol</td>\n",
       "      <td>We always try to bring the heavy. #metal #RT h...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>50</td>\n",
       "      <td>ablaze</td>\n",
       "      <td>AFRICA</td>\n",
       "      <td>#AFRICANBAZE: Breaking news:Nigeria flag set a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>52</td>\n",
       "      <td>ablaze</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>Crying out for more! Set me ablaze</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>53</td>\n",
       "      <td>ablaze</td>\n",
       "      <td>London, UK</td>\n",
       "      <td>On plus side LOOK AT THE SKY LAST NIGHT IT WAS...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7575</th>\n",
       "      <td>10826</td>\n",
       "      <td>wrecked</td>\n",
       "      <td>TN</td>\n",
       "      <td>On the bright side I wrecked http://t.co/uEa0t...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7577</th>\n",
       "      <td>10829</td>\n",
       "      <td>wrecked</td>\n",
       "      <td>#NewcastleuponTyne #UK</td>\n",
       "      <td>@widda16 ... He's gone. You can relax. I thoug...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7579</th>\n",
       "      <td>10831</td>\n",
       "      <td>wrecked</td>\n",
       "      <td>Vancouver, Canada</td>\n",
       "      <td>Three days off from work and they've pretty mu...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7580</th>\n",
       "      <td>10832</td>\n",
       "      <td>wrecked</td>\n",
       "      <td>London</td>\n",
       "      <td>#FX #forex #trading Cramer: Iger's 3 words tha...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7581</th>\n",
       "      <td>10833</td>\n",
       "      <td>wrecked</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>@engineshed Great atmosphere at the British Li...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5080 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  keyword                       location  \\\n",
       "31       48   ablaze                     Birmingham   \n",
       "32       49   ablaze  Est. September 2012 - Bristol   \n",
       "33       50   ablaze                         AFRICA   \n",
       "34       52   ablaze               Philadelphia, PA   \n",
       "35       53   ablaze                     London, UK   \n",
       "...     ...      ...                            ...   \n",
       "7575  10826  wrecked                             TN   \n",
       "7577  10829  wrecked         #NewcastleuponTyne #UK   \n",
       "7579  10831  wrecked              Vancouver, Canada   \n",
       "7580  10832  wrecked                        London    \n",
       "7581  10833  wrecked                        Lincoln   \n",
       "\n",
       "                                                   text  target  \n",
       "31    @bbcmtd Wholesale Markets ablaze http://t.co/l...       1  \n",
       "32    We always try to bring the heavy. #metal #RT h...       0  \n",
       "33    #AFRICANBAZE: Breaking news:Nigeria flag set a...       1  \n",
       "34                   Crying out for more! Set me ablaze       0  \n",
       "35    On plus side LOOK AT THE SKY LAST NIGHT IT WAS...       0  \n",
       "...                                                 ...     ...  \n",
       "7575  On the bright side I wrecked http://t.co/uEa0t...       0  \n",
       "7577  @widda16 ... He's gone. You can relax. I thoug...       0  \n",
       "7579  Three days off from work and they've pretty mu...       0  \n",
       "7580  #FX #forex #trading Cramer: Iger's 3 words tha...       0  \n",
       "7581  @engineshed Great atmosphere at the British Li...       0  \n",
       "\n",
       "[5080 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "special_indices = df_train[(df_train['location'] == 'no_location')|(df_train['keyword'] == 'no_keyword')].index\n",
    "df_train_clean = df_train.drop(special_indices)\n",
    "\n",
    "#the top 10 most frequent values in the 'keyword' column\n",
    "sns.barplot(y=df_train_clean['keyword'].value_counts()[:10].index,x=df_train_clean['keyword'].value_counts()[:10],\n",
    "            orient='h',palette='cool')\n",
    "df_train_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2fbe94c2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:49.817246Z",
     "iopub.status.busy": "2023-08-12T22:10:49.816770Z",
     "iopub.status.idle": "2023-08-12T22:10:50.097449Z",
     "shell.execute_reply": "2023-08-12T22:10:50.096411Z"
    },
    "papermill": {
     "duration": 0.300164,
     "end_time": "2023-08-12T22:10:50.099914",
     "exception": false,
     "start_time": "2023-08-12T22:10:49.799750",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='location'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Replacing the ambigious locations name with Standard names\n",
    "df_train_clean['location'].replace({'United States':'USA',\n",
    "                           'New York':'USA',\n",
    "                            \"London\":'UK',\n",
    "                            \"Los Angeles, CA\":'USA',\n",
    "                            \"Washington, D.C.\":'USA',\n",
    "                            \"California\":'USA',\n",
    "                             \"Chicago, IL\":'USA',\n",
    "                             \"Chicago\":'USA',\n",
    "                            \"New York, NY\":'USA',\n",
    "                            \"California, USA\":'USA',\n",
    "                            \"FLorida\":'USA',\n",
    "                            \"Nigeria\":'Africa',\n",
    "                            \"Kenya\":'Africa',\n",
    "                            \"Everywhere\":'Worldwide',\n",
    "                            \"San Francisco\":'USA',\n",
    "                            \"Florida\":'USA',\n",
    "                            \"United Kingdom\":'UK',\n",
    "                            \"Los Angeles\":'USA',\n",
    "                            \"Toronto\":'Canada',\n",
    "                            \"San Francisco, CA\":'USA',\n",
    "                            \"NYC\":'USA',\n",
    "                            \"Seattle\":'USA',\n",
    "                            \"Earth\":'Worldwide',\n",
    "                            \"Ireland\":'UK',\n",
    "                            \"London, England\":'UK',\n",
    "                            \"New York City\":'USA',\n",
    "                            \"Texas\":'USA',\n",
    "                            \"London, UK\":'UK',\n",
    "                            \"Atlanta, GA\":'USA',\n",
    "                            \"Mumbai\":\"India\"},inplace=True)\n",
    "#the top 5 most frequent values in the 'location' column\n",
    "sns.barplot(y=df_train_clean['location'].value_counts()[:5].index,x=df_train_clean['location'].value_counts()[:5],\n",
    "            orient='h',palette='cool')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96c7199f",
   "metadata": {
    "papermill": {
     "duration": 0.016471,
     "end_time": "2023-08-12T22:10:50.132077",
     "exception": false,
     "start_time": "2023-08-12T22:10:50.115606",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **✂️ Preprocessing**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29554f40",
   "metadata": {
    "papermill": {
     "duration": 0.015268,
     "end_time": "2023-08-12T22:10:50.163644",
     "exception": false,
     "start_time": "2023-08-12T22:10:50.148376",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Difference between EDA and preprocessing :**\n",
    "\n",
    "* EDA : is the initial step in data analysis where you explore and understand the dataset\n",
    "* Preprocessing : The steps required to go from raw data to a fprmat suitable to input to ypur ML model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e3e4d1",
   "metadata": {
    "papermill": {
     "duration": 0.015547,
     "end_time": "2023-08-12T22:10:50.195441",
     "exception": false,
     "start_time": "2023-08-12T22:10:50.179894",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Text Preprocessing:**\n",
    "* Text cleaning: remove unnecessary characters, HTML tags, special symbols\n",
    "* Stop words removal: common words that don't carry much information\n",
    "* Normalization: convert all words to lowercase to ensure consistency\n",
    "* Stemming or Lemmatization: reduce words to their root\n",
    "* Tokenization: split the text into individual words or tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6047464",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:50.229380Z",
     "iopub.status.busy": "2023-08-12T22:10:50.228943Z",
     "iopub.status.idle": "2023-08-12T22:10:52.800167Z",
     "shell.execute_reply": "2023-08-12T22:10:52.799092Z"
    },
    "papermill": {
     "duration": 2.59128,
     "end_time": "2023-08-12T22:10:52.802488",
     "exception": false,
     "start_time": "2023-08-12T22:10:50.211208",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>our deeds are the reason of this earthquake ma...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>forest fire near la ronge sask canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>all residents asked to shelter in place are be...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>people receive wildfires evacuation orders in...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>just got sent this photo from ruby alaska as s...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>10869</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>two giant cranes holding a bridge collapse int...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>10870</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>ariahrary thetawniest the out of control wild ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>10871</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>s of volcano hawaii</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>10872</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>police investigating after an ebike collided w...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>10873</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>the latest more homes razed by northern califo...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id     keyword     location  \\\n",
       "0         1  no_keyword  no_location   \n",
       "1         4  no_keyword  no_location   \n",
       "2         5  no_keyword  no_location   \n",
       "3         6  no_keyword  no_location   \n",
       "4         7  no_keyword  no_location   \n",
       "...     ...         ...          ...   \n",
       "7608  10869  no_keyword  no_location   \n",
       "7609  10870  no_keyword  no_location   \n",
       "7610  10871  no_keyword  no_location   \n",
       "7611  10872  no_keyword  no_location   \n",
       "7612  10873  no_keyword  no_location   \n",
       "\n",
       "                                                   text  target  \n",
       "0     our deeds are the reason of this earthquake ma...       1  \n",
       "1                 forest fire near la ronge sask canada       1  \n",
       "2     all residents asked to shelter in place are be...       1  \n",
       "3      people receive wildfires evacuation orders in...       1  \n",
       "4     just got sent this photo from ruby alaska as s...       1  \n",
       "...                                                 ...     ...  \n",
       "7608  two giant cranes holding a bridge collapse int...       1  \n",
       "7609  ariahrary thetawniest the out of control wild ...       1  \n",
       "7610                               s of volcano hawaii        1  \n",
       "7611  police investigating after an ebike collided w...       1  \n",
       "7612  the latest more homes razed by northern califo...       1  \n",
       "\n",
       "[7613 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying a first round of text cleaning techniques\n",
    "\n",
    "def clean_text(tweet):\n",
    "    '''Make text lowercase, remove text in square brackets,remove links,remove punctuation\n",
    "    and remove words containing numbers.'''\n",
    "    ## Use re.sub to replace matched characters with an empty string\n",
    "    #cleaned_text = re.sub(pattern, '', text)\n",
    "    tweet = tweet.lower()\n",
    "    tweet = re.sub('\\[.*?\\]', '', tweet)\n",
    "    tweet = re.sub('https?://\\S+|www\\.\\S+', '', tweet)\n",
    "    tweet = re.sub('<.*?>+', '', tweet)\n",
    "    tweet = re.sub('[%s]' % re.escape(string.punctuation), '', tweet)\n",
    "    tweet = re.sub('\\n', '', tweet)\n",
    "    tweet = re.sub('\\w*\\d\\w*', '', tweet)\n",
    "    tweet = re.sub('ûó', '', tweet)\n",
    "    \n",
    "    # Special characters\n",
    "    \n",
    "    tweet = re.sub(r\"ûª\", \"\", tweet)\n",
    "    tweet = re.sub(r\"ûòåê\", \"\", tweet)\n",
    "    tweet = re.sub(r\"ûò\", \"\", tweet)\n",
    "    tweet = re.sub(r\"aaa\", \"a\", tweet)\n",
    "    tweet = re.sub(r\"ûï\", \"\", tweet)\n",
    "    tweet = re.sub(r\"aa\", \"a\", tweet)\n",
    "    tweet = re.sub(r\"aaallll\", \"al\", tweet)\n",
    "    tweet = re.sub(r\"ûòthe\", \"the\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Û_\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89ÛÒ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89ÛÓ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89ÛÏWhen\", \"When\", tweet)\n",
    "    tweet = re.sub(r\"\\x89ÛÏ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"China\\x89Ûªs\", \"China's\", tweet)\n",
    "    tweet = re.sub(r\"let\\x89Ûªs\", \"let's\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Û÷\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Ûª\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Û\\x9d\", \"\", tweet)\n",
    "    tweet = re.sub(r\"å_\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Û¢\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Û¢åÊ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"fromåÊwounds\", \"from wounds\", tweet)\n",
    "    tweet = re.sub(r\"åÊ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"åÈ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"JapÌ_n\", \"Japan\", tweet)    \n",
    "    tweet = re.sub(r\"Ì©\", \"e\", tweet)\n",
    "    tweet = re.sub(r\"å¨\", \"\", tweet)\n",
    "    tweet = re.sub(r\"SuruÌ¤\", \"Suruc\", tweet)\n",
    "    tweet = re.sub(r\"åÇ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"å£3million\", \"3 million\", tweet)\n",
    "    tweet = re.sub(r\"åÀ\", \"\", tweet)\n",
    "    \n",
    "    # Contractions\n",
    "    tweet = re.sub(r\"he's\", \"he is\", tweet)\n",
    "    tweet = re.sub(r\"there's\", \"there is\", tweet)\n",
    "    tweet = re.sub(r\"We're\", \"We are\", tweet)\n",
    "    tweet = re.sub(r\"That's\", \"That is\", tweet)\n",
    "    tweet = re.sub(r\"won't\", \"will not\", tweet)\n",
    "    tweet = re.sub(r\"they're\", \"they are\", tweet)\n",
    "    tweet = re.sub(r\"Can't\", \"Cannot\", tweet)\n",
    "    tweet = re.sub(r\"wasn't\", \"was not\", tweet)\n",
    "    tweet = re.sub(r\"don\\x89Ûªt\", \"do not\", tweet)\n",
    "    tweet = re.sub(r\"aren't\", \"are not\", tweet)\n",
    "    tweet = re.sub(r\"isn't\", \"is not\", tweet)\n",
    "    tweet = re.sub(r\"What's\", \"What is\", tweet)\n",
    "    tweet = re.sub(r\"haven't\", \"have not\", tweet)\n",
    "    tweet = re.sub(r\"hasn't\", \"has not\", tweet)\n",
    "    tweet = re.sub(r\"There's\", \"There is\", tweet)\n",
    "    tweet = re.sub(r\"He's\", \"He is\", tweet)\n",
    "    tweet = re.sub(r\"It's\", \"It is\", tweet)\n",
    "    tweet = re.sub(r\"You're\", \"You are\", tweet)\n",
    "    tweet = re.sub(r\"I'M\", \"I am\", tweet)\n",
    "    tweet = re.sub(r\"shouldn't\", \"should not\", tweet)\n",
    "    tweet = re.sub(r\"wouldn't\", \"would not\", tweet)\n",
    "    tweet = re.sub(r\"i'm\", \"I am\", tweet)\n",
    "    tweet = re.sub(r\"I\\x89Ûªm\", \"I am\", tweet)\n",
    "    tweet = re.sub(r\"I'm\", \"I am\", tweet)\n",
    "    tweet = re.sub(r\"Isn't\", \"is not\", tweet)\n",
    "    tweet = re.sub(r\"Here's\", \"Here is\", tweet)\n",
    "    tweet = re.sub(r\"you've\", \"you have\", tweet)\n",
    "    tweet = re.sub(r\"you\\x89Ûªve\", \"you have\", tweet)\n",
    "    tweet = re.sub(r\"we're\", \"we are\", tweet)\n",
    "    tweet = re.sub(r\"what's\", \"what is\", tweet)\n",
    "    tweet = re.sub(r\"couldn't\", \"could not\", tweet)\n",
    "    tweet = re.sub(r\"we've\", \"we have\", tweet)\n",
    "    tweet = re.sub(r\"it\\x89Ûªs\", \"it is\", tweet)\n",
    "    tweet = re.sub(r\"doesn\\x89Ûªt\", \"does not\", tweet)\n",
    "    tweet = re.sub(r\"It\\x89Ûªs\", \"It is\", tweet)\n",
    "    tweet = re.sub(r\"Here\\x89Ûªs\", \"Here is\", tweet)\n",
    "    tweet = re.sub(r\"who's\", \"who is\", tweet)\n",
    "    tweet = re.sub(r\"I\\x89Ûªve\", \"I have\", tweet)\n",
    "    tweet = re.sub(r\"y'all\", \"you all\", tweet)\n",
    "    tweet = re.sub(r\"can\\x89Ûªt\", \"cannot\", tweet)\n",
    "    tweet = re.sub(r\"would've\", \"would have\", tweet)\n",
    "    tweet = re.sub(r\"it'll\", \"it will\", tweet)\n",
    "    tweet = re.sub(r\"we'll\", \"we will\", tweet)\n",
    "    tweet = re.sub(r\"wouldn\\x89Ûªt\", \"would not\", tweet)\n",
    "    tweet = re.sub(r\"We've\", \"We have\", tweet)\n",
    "    tweet = re.sub(r\"he'll\", \"he will\", tweet)\n",
    "    tweet = re.sub(r\"Y'all\", \"You all\", tweet)\n",
    "    tweet = re.sub(r\"Weren't\", \"Were not\", tweet)\n",
    "    tweet = re.sub(r\"Didn't\", \"Did not\", tweet)\n",
    "    tweet = re.sub(r\"they'll\", \"they will\", tweet)\n",
    "    tweet = re.sub(r\"they'd\", \"they would\", tweet)\n",
    "    tweet = re.sub(r\"DON'T\", \"DO NOT\", tweet)\n",
    "    tweet = re.sub(r\"That\\x89Ûªs\", \"That is\", tweet)\n",
    "    tweet = re.sub(r\"they've\", \"they have\", tweet)\n",
    "    tweet = re.sub(r\"i'd\", \"I would\", tweet)\n",
    "    tweet = re.sub(r\"should've\", \"should have\", tweet)\n",
    "    tweet = re.sub(r\"You\\x89Ûªre\", \"You are\", tweet)\n",
    "    tweet = re.sub(r\"where's\", \"where is\", tweet)\n",
    "    tweet = re.sub(r\"Don\\x89Ûªt\", \"Do not\", tweet)\n",
    "    tweet = re.sub(r\"we'd\", \"we would\", tweet)\n",
    "    tweet = re.sub(r\"i'll\", \"I will\", tweet)\n",
    "    tweet = re.sub(r\"weren't\", \"were not\", tweet)\n",
    "    tweet = re.sub(r\"They're\", \"They are\", tweet)\n",
    "    tweet = re.sub(r\"Can\\x89Ûªt\", \"Cannot\", tweet)\n",
    "    tweet = re.sub(r\"you\\x89Ûªll\", \"you will\", tweet)\n",
    "    tweet = re.sub(r\"I\\x89Ûªd\", \"I would\", tweet)\n",
    "    tweet = re.sub(r\"let's\", \"let us\", tweet)\n",
    "    tweet = re.sub(r\"it's\", \"it is\", tweet)\n",
    "    tweet = re.sub(r\"can't\", \"cannot\", tweet)\n",
    "    tweet = re.sub(r\"don't\", \"do not\", tweet)\n",
    "    tweet = re.sub(r\"you're\", \"you are\", tweet)\n",
    "    tweet = re.sub(r\"i've\", \"I have\", tweet)\n",
    "    tweet = re.sub(r\"that's\", \"that is\", tweet)\n",
    "    tweet = re.sub(r\"i'll\", \"I will\", tweet)\n",
    "    tweet = re.sub(r\"doesn't\", \"does not\", tweet)\n",
    "    tweet = re.sub(r\"i'd\", \"I would\", tweet)\n",
    "    tweet = re.sub(r\"didn't\", \"did not\", tweet)\n",
    "    tweet = re.sub(r\"ain't\", \"am not\", tweet)\n",
    "    tweet = re.sub(r\"you'll\", \"you will\", tweet)\n",
    "    tweet = re.sub(r\"I've\", \"I have\", tweet)\n",
    "    tweet = re.sub(r\"Don't\", \"do not\", tweet)\n",
    "    tweet = re.sub(r\"I'll\", \"I will\", tweet)\n",
    "    tweet = re.sub(r\"I'd\", \"I would\", tweet)\n",
    "    tweet = re.sub(r\"Let's\", \"Let us\", tweet)\n",
    "    tweet = re.sub(r\"you'd\", \"You would\", tweet)\n",
    "    tweet = re.sub(r\"It's\", \"It is\", tweet)\n",
    "    tweet = re.sub(r\"Ain't\", \"am not\", tweet)\n",
    "    tweet = re.sub(r\"Haven't\", \"Have not\", tweet)\n",
    "    tweet = re.sub(r\"Could've\", \"Could have\", tweet)\n",
    "    tweet = re.sub(r\"youve\", \"you have\", tweet)  \n",
    "    tweet = re.sub(r\"donå«t\", \"do not\", tweet) \n",
    "    return tweet\n",
    "\n",
    "# Remove emoji\n",
    "def remove_emoji(tweet):\n",
    "    emoji_pattern = re.compile(\"[\"\n",
    "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                           u\"\\U00002702-\\U000027B0\"\n",
    "                           u\"\\U000024C2-\\U0001F251\"\n",
    "                           \"]+\", flags=re.UNICODE)\n",
    "    return emoji_pattern.sub(r'', tweet)\n",
    "\n",
    "\n",
    "# Applying the cleaning function to both test and training datasets\n",
    "df_train['text'] = df_train['text'].apply(lambda x: clean_text(x))\n",
    "df_test['text'] = df_test['text'].apply(lambda x: clean_text(x))\n",
    "df_train['text'] = df_train['text'].apply(lambda x: remove_emoji(x))\n",
    "df_test['text'] = df_test['text'].apply(lambda x: remove_emoji(x))\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "76005bad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:52.837312Z",
     "iopub.status.busy": "2023-08-12T22:10:52.836890Z",
     "iopub.status.idle": "2023-08-12T22:10:52.852439Z",
     "shell.execute_reply": "2023-08-12T22:10:52.851353Z"
    },
    "papermill": {
     "duration": 0.035432,
     "end_time": "2023-08-12T22:10:52.854581",
     "exception": false,
     "start_time": "2023-08-12T22:10:52.819149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>just happened a terrible car crash</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>heard about earthquake is different cities sta...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>there is a forest fire at spot pond geese are ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>apocalypse lighting spokane wildfires</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>typhoon soudelor kills  in china and taiwan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>10861</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>earthquake safety los angeles  safety fastene...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259</th>\n",
       "      <td>10865</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>storm in ri worse than last hurricane my  hard...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>10868</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>green line derailment in chicago</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3261</th>\n",
       "      <td>10874</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>meg issues hazardous weather outlook hwo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3262</th>\n",
       "      <td>10875</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>cityofcalgary has activated its municipal emer...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3263 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id     keyword     location  \\\n",
       "0         0  no_keyword  no_location   \n",
       "1         2  no_keyword  no_location   \n",
       "2         3  no_keyword  no_location   \n",
       "3         9  no_keyword  no_location   \n",
       "4        11  no_keyword  no_location   \n",
       "...     ...         ...          ...   \n",
       "3258  10861  no_keyword  no_location   \n",
       "3259  10865  no_keyword  no_location   \n",
       "3260  10868  no_keyword  no_location   \n",
       "3261  10874  no_keyword  no_location   \n",
       "3262  10875  no_keyword  no_location   \n",
       "\n",
       "                                                   text  target  \n",
       "0                    just happened a terrible car crash       0  \n",
       "1     heard about earthquake is different cities sta...       0  \n",
       "2     there is a forest fire at spot pond geese are ...       0  \n",
       "3                 apocalypse lighting spokane wildfires       0  \n",
       "4           typhoon soudelor kills  in china and taiwan       0  \n",
       "...                                                 ...     ...  \n",
       "3258  earthquake safety los angeles  safety fastene...       0  \n",
       "3259  storm in ri worse than last hurricane my  hard...       0  \n",
       "3260                  green line derailment in chicago        0  \n",
       "3261          meg issues hazardous weather outlook hwo        0  \n",
       "3262  cityofcalgary has activated its municipal emer...       0  \n",
       "\n",
       "[3263 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c402283",
   "metadata": {
    "papermill": {
     "duration": 0.019408,
     "end_time": "2023-08-12T22:10:52.890293",
     "exception": false,
     "start_time": "2023-08-12T22:10:52.870885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Tokenization:**\n",
    "* Whitespace Tokenization: This is the simplest form of tokenization, where the text is split based on whitespace characters (space, tab, newline)\n",
    "* Word Tokenization (using NLTK): The NLTK library in Python provides a word tokenizer that can handle more complex tokenization cases, such as handling punctuation, contractions, and hyphenated words\n",
    "* Subword Tokenization (using BPE or SentencePiece): Subword tokenization is useful for languages with complex word formation or for handling out-of-vocabulary (OOV) words. Byte Pair Encoding (BPE) and SentencePiece are popular subword tokenization algorithms.\n",
    "\n",
    "**Remove stop words**\n",
    "* Using NLTK (Natural Language Toolkit)\n",
    "* Using spaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4bf966b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:10:52.925263Z",
     "iopub.status.busy": "2023-08-12T22:10:52.924848Z",
     "iopub.status.idle": "2023-08-12T22:11:21.690869Z",
     "shell.execute_reply": "2023-08-12T22:11:21.689485Z"
    },
    "papermill": {
     "duration": 28.78708,
     "end_time": "2023-08-12T22:11:21.693775",
     "exception": false,
     "start_time": "2023-08-12T22:10:52.906695",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /usr/share/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>deed reason earthquak may allah forgiv us</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>forest fire near la rong sask canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>resid ask shelter place notifi offic evacu she...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>peopl receiv wildfir evacu order california</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>got sent photo rubi alaska smoke wildfir pour ...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>10869</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>two giant crane hold bridg collaps nearbi home</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>10870</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>ariahrari thetawniest control wild fire califo...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>10871</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>volcano hawaii</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>10872</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>polic investig ebik collid car littl portug eb...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>10873</td>\n",
       "      <td>no_keyword</td>\n",
       "      <td>no_location</td>\n",
       "      <td>latest home raze northern california wildfir a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id     keyword     location  \\\n",
       "0         1  no_keyword  no_location   \n",
       "1         4  no_keyword  no_location   \n",
       "2         5  no_keyword  no_location   \n",
       "3         6  no_keyword  no_location   \n",
       "4         7  no_keyword  no_location   \n",
       "...     ...         ...          ...   \n",
       "7608  10869  no_keyword  no_location   \n",
       "7609  10870  no_keyword  no_location   \n",
       "7610  10871  no_keyword  no_location   \n",
       "7611  10872  no_keyword  no_location   \n",
       "7612  10873  no_keyword  no_location   \n",
       "\n",
       "                                                   text  target  \n",
       "0             deed reason earthquak may allah forgiv us       1  \n",
       "1                  forest fire near la rong sask canada       1  \n",
       "2     resid ask shelter place notifi offic evacu she...       1  \n",
       "3           peopl receiv wildfir evacu order california       1  \n",
       "4     got sent photo rubi alaska smoke wildfir pour ...       1  \n",
       "...                                                 ...     ...  \n",
       "7608     two giant crane hold bridg collaps nearbi home       1  \n",
       "7609  ariahrari thetawniest control wild fire califo...       1  \n",
       "7610                                     volcano hawaii       1  \n",
       "7611  polic investig ebik collid car littl portug eb...       1  \n",
       "7612  latest home raze northern california wildfir a...       1  \n",
       "\n",
       "[7613 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import nltk\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "nltk.download('punkt')\n",
    "from nltk.corpus import stopwords\n",
    "from pandas.core import apply\n",
    "\n",
    "def preprocessing (text):\n",
    "    \n",
    "    # Tokenize the text\n",
    "    words = word_tokenize(text)\n",
    "    \n",
    "    # Remove stopwords\n",
    "    words = [w for w in words if w not in stopwords.words('english')]\n",
    "    \n",
    "    # Apply stemming to each word\n",
    "    stemmer = PorterStemmer()\n",
    "    stemmed_words = [stemmer.stem(w) for w in words]\n",
    "    \n",
    "    # Join stemmed words back into a single string\n",
    "    processed_text = ' '.join(stemmed_words)\n",
    "    \n",
    "    return processed_text\n",
    "\n",
    "df_train['text'] = df_train['text'].apply(lambda x: preprocessing(x))\n",
    "df_test['text'] = df_test['text'].apply(lambda x: preprocessing(x))\n",
    "df_train\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99cc7327",
   "metadata": {
    "papermill": {
     "duration": 0.016401,
     "end_time": "2023-08-12T22:11:21.727030",
     "exception": false,
     "start_time": "2023-08-12T22:11:21.710629",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **📌Feature Extractions**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d2365f",
   "metadata": {
    "papermill": {
     "duration": 0.01651,
     "end_time": "2023-08-12T22:11:21.760402",
     "exception": false,
     "start_time": "2023-08-12T22:11:21.743892",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    " Transform the processed text data into numerical features that can be used by ML model\n",
    " Some common techniques for text feature extraction:\n",
    "*  **Bag of Words (BoW)**: In this approach, a document is represented as a vector where each element corresponds to the count of a particular word in the document. Stop words (common words like \"the,\" \"and,\" \"is\") are often removed, and the resulting vector captures the frequency of words in the document. The order of words is not considered in this method.\n",
    "* **Term Frequency-Inverse Document Frequency (TF-IDF)**: This method considers the importance of words in a document relative to their frequency in the entire corpus. Words that appear frequently in a specific document but infrequently across the corpus are assigned higher weights. This helps in capturing the significance of words within a document.\n",
    "* **Word Embeddings**: Word embeddings are dense vector representations of words that capture semantic relationships between words. Techniques like Word2Vec, GloVe, and FastText learn embeddings by considering the context of words in a large corpus. These embeddings can be used as features for downstream tasks.\n",
    "* **Word n-grams**: Instead of representing single words, n-grams capture sequences of n words. This helps in preserving some contextual information. For example, bigrams (2-word sequences) or trigrams (3-word sequences) can be used.\n",
    "* **Pretrained Language Models**: Advanced models like GPT, BERT, and their variants learn contextualized representations of words and sentences. These models can be fine-tuned for specific tasks or used to extract features that capture rich semantic information.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "caac1c63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:11:21.796594Z",
     "iopub.status.busy": "2023-08-12T22:11:21.795707Z",
     "iopub.status.idle": "2023-08-12T22:11:22.541233Z",
     "shell.execute_reply": "2023-08-12T22:11:22.540040Z"
    },
    "papermill": {
     "duration": 0.766575,
     "end_time": "2023-08-12T22:11:22.543875",
     "exception": false,
     "start_time": "2023-08-12T22:11:21.777300",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7613, 13448)\n",
      "(3263, 13448)\n",
      "[[0 0 0 ... 0 0 0]]\n",
      "(7613, 58170)\n",
      "(3263, 58170)\n",
      "[[0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "#BOW\n",
    "#There is a lot of parameters for preprocessing\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "bow = CountVectorizer()\n",
    "bow_train = bow.fit_transform(df_train['text'])\n",
    "bow_test = bow.transform(df_test[\"text\"])\n",
    "#print((bow.vocabulary_))\n",
    "#print(bow.get_feature_names_out())\n",
    "print(bow_train.shape)\n",
    "print(bow_test.shape)\n",
    "# Keeping only non-zero elements to preserve space \n",
    "print(bow_train[0].todense())\n",
    "\n",
    "#TF-IDF\n",
    "tfidf = TfidfVectorizer(ngram_range=(1, 2))\n",
    "train_tfidf = tfidf.fit_transform(df_train['text'])\n",
    "test_tfidf = tfidf.transform(df_test[\"text\"])\n",
    "#print((tfidf.vocabulary_))\n",
    "#print(bow.get_feature_names_out())\n",
    "print(train_tfidf.shape)\n",
    "print(test_tfidf.shape)\n",
    "print(train_tfidf[0].todense())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4fa3d1f",
   "metadata": {
    "papermill": {
     "duration": 0.017401,
     "end_time": "2023-08-12T22:11:22.578535",
     "exception": false,
     "start_time": "2023-08-12T22:11:22.561134",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **🔎Model selection**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b3b03f9",
   "metadata": {
    "papermill": {
     "duration": 0.016757,
     "end_time": "2023-08-12T22:11:22.612699",
     "exception": false,
     "start_time": "2023-08-12T22:11:22.595942",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "🔴 **Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "874b2590",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:11:22.649065Z",
     "iopub.status.busy": "2023-08-12T22:11:22.648615Z",
     "iopub.status.idle": "2023-08-12T22:11:25.412933Z",
     "shell.execute_reply": "2023-08-12T22:11:25.412127Z"
    },
    "papermill": {
     "duration": 2.785169,
     "end_time": "2023-08-12T22:11:25.415295",
     "exception": false,
     "start_time": "2023-08-12T22:11:22.630126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Mean f1: 0.598398051191276\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression with BOW\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import model_selection\n",
    "#function for plotting\n",
    "def plot_learning_evolution(x,y,title):\n",
    "    plt.plot(x, y, marker='o')\n",
    "    plt.xlabel('Number of Cross-Validation Folds (cv)')\n",
    "    plt.ylabel('F1 Score')\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    \n",
    "clf = LogisticRegression()\n",
    "# Perform 5-fold cross-validation and use accuracy as the scoring metric\n",
    "scores_bow = model_selection.cross_val_score(clf, bow_train , df_train[\"target\"], cv=5, scoring=\"f1\")\n",
    "cv_values = range(1, len(scores_bow) + 1)  # x-axis: cv\n",
    "score_values = scores_bow  # y-axis: scores\n",
    "plot_learning_evolution(cv_values,score_values,'Logistic Regression with BOW')\n",
    "print(\"\\033[1m Mean f1:\", scores_bow.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "060c1b06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:11:25.453591Z",
     "iopub.status.busy": "2023-08-12T22:11:25.452808Z",
     "iopub.status.idle": "2023-08-12T22:11:31.651237Z",
     "shell.execute_reply": "2023-08-12T22:11:31.650117Z"
    },
    "papermill": {
     "duration": 6.221513,
     "end_time": "2023-08-12T22:11:31.654925",
     "exception": false,
     "start_time": "2023-08-12T22:11:25.433412",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Mean f1: 0.5090994027800585\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression with TF-IDF\n",
    "clf_tfidf = LogisticRegression()\n",
    "scores_tfidf = model_selection.cross_val_score(clf_tfidf, train_tfidf, df_train[\"target\"], cv=5, scoring=\"f1\")\n",
    "plot_learning_evolution(cv_values,scores_tfidf,'Logistic Regression with TF-IDF')\n",
    "print(\"\\033[1m Mean f1:\", scores_tfidf.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73dd9f31",
   "metadata": {
    "papermill": {
     "duration": 0.018532,
     "end_time": "2023-08-12T22:11:31.692314",
     "exception": false,
     "start_time": "2023-08-12T22:11:31.673782",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "🔴 **Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6f6c2507",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:11:31.732368Z",
     "iopub.status.busy": "2023-08-12T22:11:31.731680Z",
     "iopub.status.idle": "2023-08-12T22:11:32.043326Z",
     "shell.execute_reply": "2023-08-12T22:11:32.042220Z"
    },
    "papermill": {
     "duration": 0.335893,
     "end_time": "2023-08-12T22:11:32.047170",
     "exception": false,
     "start_time": "2023-08-12T22:11:31.711277",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Mean f1: 0.6632844204006008\n"
     ]
    }
   ],
   "source": [
    "#Naive Bayes with BOW\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "clf_NB = MultinomialNB()\n",
    "scores_NB = model_selection.cross_val_score(clf_NB, bow_train , df_train[\"target\"], cv=5, scoring=\"f1\")\n",
    "plot_learning_evolution(cv_values,scores_NB,'Naive Bayes with BOW')\n",
    "print(\"\\033[1m Mean f1:\", scores_NB.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1ab8cd",
   "metadata": {
    "papermill": {
     "duration": 0.019197,
     "end_time": "2023-08-12T22:11:32.086303",
     "exception": false,
     "start_time": "2023-08-12T22:11:32.067106",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f36671d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:11:32.127124Z",
     "iopub.status.busy": "2023-08-12T22:11:32.126728Z",
     "iopub.status.idle": "2023-08-12T22:11:32.443753Z",
     "shell.execute_reply": "2023-08-12T22:11:32.442520Z"
    },
    "papermill": {
     "duration": 0.341817,
     "end_time": "2023-08-12T22:11:32.447624",
     "exception": false,
     "start_time": "2023-08-12T22:11:32.105807",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAHFCAYAAADmGm0KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB1UUlEQVR4nO3deVhU1f8H8PewzbAjiKyKiCKyuACJ4FoiLmVquZu5tVhmmqlp5s8lyzSzrG9qFm7lVgq5k7hruIsruIICAiKggCLbzPn9gUwODAgIDgPv1/PM8zT3nnvv584dmw/3nHs+EiGEABERERGp0NF0AEREREQ1EZMkIiIiIjWYJBERERGpwSSJiIiISA0mSURERERqMEkiIiIiUoNJEhEREZEaTJKIiIiI1GCSRERERKQGkySiGmL16tWQSCSQyWS4fft2ifVdunSBp6dnpfY9cuRING7c+DkjrLjZs2dDIpEoXzo6OrCzs0OvXr3w77//vvB4aoJbt25BIpFg9erVymURERGYPXs2Hjx4UKJ948aN8dprr1X4OCNHjlT57Et7jRw5EkDh96u0NpcuXSrXOS1atEi57ODBgyr7MDAwgLW1Ndq3b48ZM2ao/Y4X/RtQ95o8eXKFPwOi56Wn6QCISFVubi6++OIL/P7771W2z5kzZ2LChAlVtr+KCgsLg7m5ORQKBeLi4rBw4UJ06dIFJ06cgLe3t8bi0gQ7OzscO3YMLi4uymURERGYM2cORo4cCQsLiyo5zsyZMzF27Fjl+7Nnz2LcuHH4+uuv8fLLLyuXW1tbK/+7SZMmWLduXYl9PR1rRRUdTy6XIy0tDSdOnMDKlSvx/fff49dff8WwYcNKbLNq1Sq4ubmpLLO3t690DESVxSSJqIbp0aMH1q9fj8mTJ6NVq1ZVss/n+ZGrCj4+Pqhfvz4AICAgAG3btoWLiws2b95c55IkqVSKdu3aVftxXFxcVK57Tk4OAKBZs2alHt/Q0LDKYyt+vNdffx2ffvopAgMDMXLkSLRs2RJeXl4q23h6esLX17dK4yCqDHa3EdUwU6dOhZWVFT777LNntv3555/RqVMnNGjQAMbGxvDy8sLChQuRn5+v0q54d1ubNm3QsWPHEvuTy+VwcHDAG2+8oVyWl5eHefPmwc3NDVKpFNbW1hg1ahTu3btX6XM0NzcHAOjr6yuX5eTk4NNPP0Xr1q1hbm4OS0tL+Pv7Y+vWrSrbdu3aFW5ubihem1sIgaZNm+LVV1+tcOz79+9Hly5dYGVlBUNDQzRq1AhvvvkmsrOzSz2HKVOmwNzcHHK5XLls/PjxkEgk+Pbbb5XL0tLSoKOjg59++glAye622bNnY8qUKQAAZ2dnZffSwYMHVY4XFhYGb29vGBoaws3NDStXriw1tprO0tISv/zyCwoKCvD9999rOhyiUjFJIqphTE1N8cUXX+Cff/7B/v37y2x78+ZNDB06FL///jt27NiBMWPG4Ntvv8X7779f5najRo3C0aNHcf36dZXle/bsQWJiIkaNGgUAUCgU6NOnD7755hsMHToUO3fuxDfffIPw8HB06dIFjx8/Ltc5yeVyFBQUIC8vDzdu3MC4ceMglUrRv39/ZZvc3Fykp6dj8uTJ+Pvvv7FhwwZ06NABb7zxBtauXatsN2HCBFy9ehX79u1TOcbu3btx8+ZNjBs3rkKx37p1C6+++ioMDAywcuVKhIWF4ZtvvoGxsTHy8vJKPafAwEBkZmbi5MmTymV79+6FoaEhwsPDlcv27dsHIQQCAwPV7uedd97B+PHjAQAhISE4duwYjh07pnKH7fz58/j000/xySefYOvWrWjZsiXGjBmDw4cPP/Ozr6iCggKVl0KhqPJjAMBLL70EOzs7tedQ9H15+kWkEYKIaoRVq1YJAOLUqVMiNzdXNGnSRPj6+gqFQiGEEKJz587Cw8Oj1O3lcrnIz88Xa9euFbq6uiI9PV25bsSIEcLJyUn5PjU1VRgYGIjPP/9cZR8DBw4UNjY2Ij8/XwghxIYNGwQAsWXLFpV2p06dEgDE0qVLyzynWbNmCQAlXmZmZiIkJKTMbQsKCkR+fr4YM2aMaNOmjcp5NmnSRPTp00elfc+ePYWLi4vy8ypv7Js3bxYAxLlz58qMp7hHjx4JAwMDMXfuXCGEEAkJCQKA+Oyzz4ShoaHIyckRQgjx7rvvCnt7e+V2sbGxAoBYtWqVctm3334rAIjY2NgSx3FychIymUzcvn1buezx48fC0tJSvP/+++WO98CBAwKA+Ouvv9Su79y5s9prNWzYsGfuu+icvv3223IfTwgh/Pz8hKGhofJ90b8Bda+i7yTRi8Q7SUQ1kIGBAebNm4fTp0/jzz//LLVdZGQkXn/9dVhZWUFXVxf6+vp4++23IZfLce3atVK3s7KyQu/evbFmzRrlnYL79+9j69atePvtt6GnVzhccceOHbCwsEDv3r1V/qpv3bo1bG1tS3QJlWbv3r04deoUTp48iR07diAwMBCDBw9GaGioSru//voL7du3h4mJCfT09KCvr4/g4GBER0cr2+jo6OCjjz7Cjh07EBcXB6DwjlpYWBg+/PBDSCSSCsXeunVrGBgY4L333sOaNWsQExNTrnMyMjKCv78/9u7dCwAIDw+HhYUFpkyZgry8PBw9elR57qXdRSqv1q1bo1GjRsr3MpkMrq6uap8Qex4uLi44deqUyuvLL79Uri9+d0cU6/KsqNK2X7t2bYk4ir6TRC8SkySiGmrw4MHw9vbGjBkzSowxAoC4uDh07NgRd+7cwZIlS3DkyBGcOnUKP//8MwA8syts9OjRuHPnjrJraMOGDcjNzVU+Eg4Ad+/exYMHD2BgYAB9fX2VV3JyMlJTU8t1Lq1atYKvry9eeuklvPrqq/jrr7/QtGlTZdcYUNjVNHDgQDg4OOCPP/7AsWPHcOrUKYwePVo56Pjp2A0NDbF8+XIAhWOzDA0NMXr06ArH7uLigr1796JBgwYYN26ccsDzkiVLnnlegYGBOH78OB49eoS9e/filVdegZWVFXx8fLB3717ExsYiNjb2uZMkKyurEsukUmm5uzvLSyaTwdfXV+Xl7OysXF/8c1yzZs1zHS8uLk7tU2stWrQoEQeRJjA1J6qhJBIJFixYgG7dumHFihUl1v/999949OgRQkJC4OTkpFx+7ty5cu2/e/fusLe3x6pVq9C9e3esWrUKfn5+cHd3V7apX78+rKysEBYWpnYfpqamFTupJ3R0dODh4YG//voLKSkpaNCgAf744w84Oztj06ZNyrtBQOFYpeLMzc0xYsQI/Pbbb5g8eTJWrVqFoUOHqjw+X5HYO3bsiI4dO0Iul+P06dP46aefMHHiRNjY2GDw4MGlnkfXrl0xc+ZMHD58GPv27cOsWbOUy/fs2aNMMLp27Vqhz6emOnXqlMr7pxOoijp58iSSk5MxZsyY5w2LqNowSSKqwQIDA9GtWzfMnTsXDRs2VFlXlEhIpVLlMiEEfv3113LtW1dXF8OHD8cPP/yAI0eO4PTp0/jll19U2rz22mvYuHEj5HI5/Pz8nvNs/iOXy3Hx4kVIpVKYmZkBgHLCwacTpOTk5BJPtxX5+OOPsXTpUvTv3x8PHjzARx999Nyx6+rqws/PD25ubli3bh3Onj1bZpLUtm1bmJmZ4YcffkBycjK6desGoPC6LViwAH/++Sfc3d2fOcdP0TWs6jtDVa2q7uikp6dj7Nix0NfXxyeffFIl+ySqDkySiGq4BQsWwMfHBykpKfDw8FAu79atGwwMDDBkyBBMnToVOTk5WLZsGe7fv1/ufY8ePRoLFizA0KFDYWhoiEGDBqmsHzx4MNatW4devXphwoQJaNu2LfT19ZGQkIADBw6gT58+6Nev3zOPc+bMGeVj/3fv3sXKlStx5coVfPLJJ5DJZAAKk5qQkBB8+OGH6N+/P+Lj4/Hll1/Czs6uxFN4AODq6ooePXpg9+7d6NChQ4k5pcob+/Lly7F//368+uqraNSoEXJycpSP1z+rm0xXVxedO3fG9u3b4ezsrJyXqH379pBKpdi3bx8+/vjjZ34+RfMELVmyBCNGjIC+vj6aN29e6Tt1Ncn169dx/PhxKBQK5WSSwcHByMzMxNq1a1W+00Q1DZMkohquTZs2GDJkCNavX6+y3M3NDVu2bMEXX3yBN954A1ZWVhg6dCgmTZqEnj17lmvfrq6uCAgIQEREBIYNG6ZMZIro6upi27ZtWLJkCX7//XfMnz8fenp6cHR0ROfOnUtMAliaHj16KP/b0tISzZo1w8qVKzFixAjl8lGjRiElJQXLly/HypUr0aRJE0ybNg0JCQmYM2eO2v0OGjQIu3fvLnEXqSKxt27dGnv27MGsWbOQnJwMExMTeHp6Ytu2bQgKCnrmuQUGBmL79u0qCZVUKkWHDh0QHh5ervFIXbp0wfTp07FmzRr8+uuvUCgUOHDgALp06fLMbWu6zz//HACgp6cHc3NzuLq6YvTo0XjvvfdUuomJaiKJeN7HE4iINOTNN9/E8ePHcevWLZWJKYmIqgLvJBGRVsnNzcXZs2dx8uRJhIaGYvHixUyQiKha8E4SEWmVW7duwdnZGWZmZhg6dCj+97//QVdXV9NhEVEtxCSJiIiISA1OJklERESkBpMkIiIiIjWYJBERERGpwafbKkmhUCAxMRGmpqYqMwQTERFRzSWEQFZWFuzt7aGjU/a9IiZJlZSYmFiiTAQRERFph/j4eDg6OpbZhklSJRWVC4iPj1fWniIiIqKaLTMzEw0bNixX2R8mSZVU1MVmZmbGJImIiEjLlGeoDAduExEREanBJImIiIhIDSZJRERERGowSSIiIiJSg0kSERERkRpMkoiIiIjUYJJEREREpAaTJCIiIiI1mCQRERERqcEZt4mIiKhGkSsETsamIyUrBw1MZWjrbAldnRdfTJ5JEhEREdUYYZeSMGd7FJIycpTL7MxlmNXbHT087V5oLOxuIyIiohoh7FISPvjjrEqCBADJGTn44I+zCLuU9ELjYZJEREREGidXCMzZHgWhZl3RsjnboyBXqGtRPZgkERERkcadjE0vcQfpaQJAUkYOTsamv7CYmCQRERGRxqVklZ4gVaZdVWCSRERERBrXwFRWpe2qApMkIiIi0ri2zpawMy89AZKg8Cm3ts6WLywmJklERESkcbo6Eszq7a52XdEMSbN6u7/Q+ZI4TxIRERHVCE2sTdQut9XQPElMkoiIiKhGCDl7BwAQ2KIBxnRowhm3iYiIiBQKga3nCpOkN70d4e9ipeGIOCaJiIiIaoDjMWlIysiBmUwPr7RooOlwADBJIiIiohogJLLwLtKrLe0h1dPVcDSFmCQRERGRRj3Ok2P3xcK6bG94O2g4mv8wSSIiIiKN2hOVjEd5cjS0NISvUz1Nh6PEJImIiIg0KvRJV1u/1g6QSF78U2yl0XiStHTpUjg7O0Mmk8HHxwdHjhwpte3IkSMhkUhKvDw8PJRtfv31V3Ts2BH16tVDvXr1EBgYiJMnT6rsZ/bs2SX2YWtrW23nSEREROrdy8rFkeupAIB+3o4ajkaVRpOkTZs2YeLEiZgxYwYiIyPRsWNH9OzZE3FxcWrbL1myBElJScpXfHw8LC0tMWDAAGWbgwcPYsiQIThw4ACOHTuGRo0aISgoCHfu3FHZl4eHh8q+Ll68WK3nSkRERCVtO58IuUKgdUMLONc31nQ4KjSaJC1evBhjxozBO++8gxYtWuCHH35Aw4YNsWzZMrXtzc3NYWtrq3ydPn0a9+/fx6hRo5Rt1q1bhw8//BCtW7eGm5sbfv31VygUCuzbt09lX3p6eir7sra2rtZzJSIiopJCIxMA1KwB20U0liTl5eXhzJkzCAoKUlkeFBSEiIiIcu0jODgYgYGBcHJyKrVNdnY28vPzYWmpWhDv+vXrsLe3h7OzMwYPHoyYmJiKnwQRERFV2vW7Wbh0JxN6OhK81tJe0+GUoLEZt1NTUyGXy2FjY6Oy3MbGBsnJyc/cPikpCbt378b69evLbDdt2jQ4ODggMDBQuczPzw9r166Fq6sr7t69i3nz5iEgIACXL1+GlZX6GT5zc3ORm5urfJ+ZmfnMGImIiKh0RXMjdWneAJbGBhqOpiSND9wuPopdCFGuke2rV6+GhYUF+vbtW2qbhQsXYsOGDQgJCYFMJlMu79mzJ9588014eXkhMDAQO3fuBACsWbOm1H3Nnz8f5ubmylfDhg2fGSMRERGpp1AIbH2SJNXErjZAg0lS/fr1oaurW+KuUUpKSom7S8UJIbBy5UoMHz4cBgbqM89Fixbh66+/xp49e9CyZcsy92dsbAwvLy9cv3691DbTp09HRkaG8hUfH1/mPomIiKh0x2PTkJiRA1OZHl5xqxllSIrTWJJkYGAAHx8fhIeHqywPDw9HQEBAmdseOnQIN27cwJgxY9Su//bbb/Hll18iLCwMvr6+z4wlNzcX0dHRsLOzK7WNVCqFmZmZyouIiIgqJ/Rs4V2k11raQaZfM8qQFKexMUkAMGnSJAwfPhy+vr7w9/fHihUrEBcXh7FjxwIovHtz584drF27VmW74OBg+Pn5wdPTs8Q+Fy5ciJkzZ2L9+vVo3Lix8k6ViYkJTExMAACTJ09G79690ahRI6SkpGDevHnIzMzEiBEjqvmMiYiI6HGeHLsvFf4+92tTs+ZGeppGk6RBgwYhLS0Nc+fORVJSEjw9PbFr1y7l02pJSUkl5kzKyMjAli1bsGTJErX7XLp0KfLy8tC/f3+V5bNmzcLs2bMBAAkJCRgyZAhSU1NhbW2Ndu3a4fjx42U+JUdERERVIzz6Lh7mFsCxXs0qQ1KcRAghNB2ENsrMzIS5uTkyMjLY9UZERFQBo1adxIGr9zD+lab4NKj5Cz12RX6/Nf50GxEREdUd97JycbioDEmbmvlUWxEmSURERPTCbH9ShqRVQws0sTbRdDhlYpJEREREL0xo0dxINfwuEsAkiYiIiF6QGylZuHgnA3o6EvRuVfPKkBTHJImIiIheiJCzRWVIrGtkGZLimCQRERFRtVMoBLaeSwRQs+dGehqTJCIiIqp2J2LTcefBY5jK9NC1Rc0sQ1IckyQiIiKqdqGRCQCAV71qbhmS4pgkERERUbXKyZdj98WiMiQ1/6m2IkySiIiIqFqFR91FVm4BHCwM8VJjS02HU25MkoiIiKhaFc2N1K+NA3R0JBqOpvyYJBEREVG1SX2Yi0PX7gEA+nlrT1cbwCSJiIiIqpGyDImjOVxqeBmS4pgkERERUbV5uqtN2zBJIiIiompxI+UhLiRoTxmS4pgkERERUbUomhups6s1rEykGo6m4pgkERERUZVTKAT+jnxShkTLBmwXYZJEREREVe7krSdlSKR6CGxho+lwKoVJEhEREVW50LOFA7Z7aVEZkuKYJBEREVGVysmXY9fFJADa29UGMEkiIiKiKrY3+r8yJG21qAxJcUySiIiIqEoVdbX1bWOvVWVIimOSRERERFUm7ekyJG0cNRzN82GSRERERFVm+/lEFCgEWjqao2kD7SpDUhyTJCIiIqoy2lyGpDgmSURERFQlbt57iPMJGdDV0jIkxTFJIiIioipRNGC7s6s16mthGZLimCQRERHRc1MoRK3qagOYJBEREVEVOPWkDImJVA/d3LWzDElxTJKIiIjouRXdRerpaau1ZUiKY5JEREREzyUnX46dtaAMSXFMkoiIiOi57ItOQVZOAezNZWjnbKXpcKoMkyQiIiJ6LqGRCQCAPm0ctLoMSXFMkoiIiKjS0h7m4uDVwjIkb9SSp9qKMEkiIiKiSttxIQkFCgFPBzM0szHVdDhVikkSERERVVqIcm4k7S5mqw6TJCIiIqqUm/ce4nz8A+jqSPB6LShDUhyTJCIiIqqUv5/cRerYrD6sTbW/DElxGk+Sli5dCmdnZ8hkMvj4+ODIkSOlth05ciQkEkmJl4eHh0q7LVu2wN3dHVKpFO7u7ggNDX2u4xIREZGq2liGpDiNJkmbNm3CxIkTMWPGDERGRqJjx47o2bMn4uLi1LZfsmQJkpKSlK/4+HhYWlpiwIAByjbHjh3DoEGDMHz4cJw/fx7Dhw/HwIEDceLEiUofl4iIiFSdvn0fCfcLy5AEudtqOpxqIRFCCE0d3M/PD97e3li2bJlyWYsWLdC3b1/Mnz//mdv//fffeOONNxAbGwsnJycAwKBBg5CZmYndu3cr2/Xo0QP16tXDhg0bquS4AJCZmQlzc3NkZGTAzMysXNsQERHVFtNDLmDDyXj093HEogGtNB1OuVXk91tjd5Ly8vJw5swZBAUFqSwPCgpCREREufYRHByMwMBAZYIEFN5JKr7P7t27K/dZFcclIiKqy3Ly5dhxobAMSW2bG+lpepo6cGpqKuRyOWxsVCsF29jYIDk5+ZnbJyUlYffu3Vi/fr3K8uTk5DL3Wdnj5ubmIjc3V/k+MzPzmTESERHVRvuvFJYhsTOXoV2T2lOGpDiND9yWSFSnLxdClFimzurVq2FhYYG+fftWap8VPe78+fNhbm6ufDVs2PCZMRIREdVGIWcLB2z3aV27ypAUp7EkqX79+tDV1S1x9yYlJaXEXZ7ihBBYuXIlhg8fDgMDA5V1tra2Ze6zssedPn06MjIylK/4+PhnniMREVFtk/4oDwevpgAA3vCuvV1tgAaTJAMDA/j4+CA8PFxleXh4OAICAsrc9tChQ7hx4wbGjBlTYp2/v3+Jfe7Zs0e5z8oeVyqVwszMTOVFRERU1+y4kIgChYCHvRlca1kZkuI0NiYJACZNmoThw4fD19cX/v7+WLFiBeLi4jB27FgAhXdv7ty5g7Vr16psFxwcDD8/P3h6epbY54QJE9CpUycsWLAAffr0wdatW7F3714cPXq03MclIiIi9Yq62mrr3EhP02iSNGjQIKSlpWHu3LlISkqCp6cndu3apXxaLSkpqcTcRRkZGdiyZQuWLFmidp8BAQHYuHEjvvjiC8ycORMuLi7YtGkT/Pz8yn1cIiIiKinm3kOci38AHQnweuvaV4akOI3Ok6TNOE8SERHVNYv3XMWP+2+gs6s11oxuq+lwKkUr5kkiIiIi7SGEQOi5wq622j5guwiTJCIiInqm07fvIz79MYwNdGttGZLimCQRERHRMxUN2O7haQdDA10NR/NiMEkiIiKiMuXky7HzQiKAutPVBjBJIiIiomc4cCUFmTkFsDWr3WVIimOSRERERGUKiXxShqSNPXRrcRmS4pgkERERUanuP12GpI2jhqN5sZgkERERUal2XEhEvlzA3c4MzW1rdxmS4pgkERERUamKutrq0oDtIkySiIiISK3Y1EeIjHtShqRV7S9DUhyTJCIiIlIr9MldpA7NrNHATKbhaF48JklERERUghACfxd1tbWpe11tAJMkIiIiUuPM7fuIS8+GkYEugjxsNB2ORjBJIiIiohKKBmz38LSFkYGehqPRDCZJREREpCK3QI6dF5IA1L25kZ7GJImIiIhUHLiSgozH+bAxk8Lfpe6UISmOSRIRERGpKHqqrW9rhzpVhqQ4JklERESk9CA7D/uvFJYh6VcHJ5B8GpMkIiIiUtpxIQn5coEWdmZwszXTdDgaxSSJiIiIlELr+NxIT2OSRERERACA22mPcOb2fehIgD6t614ZkuKYJBERERGA/+4itW9av06WISmOSRIRERFBCPFfV1sdH7BdhEkSERER4WzcA9xOKyxD0t3DVtPh1AhMkoiIiAihkQkAgB4edbcMSXFMkoiIiOq4vAIFdjwpQ1LX50Z6GpMkIiKiOu7A1RQ8yC4sQxLgUl/T4dQYTJKIiIjquNCzhQO2+9TxMiTFMUkiIiKqwzKy8/8rQ8IJJFUwSSIiIqrDdlxMRJ5cATdbU7Swq9tlSIpjkkRERFSHFXW1cW6kkpgkERER1VFxadk4rSxDwiSpOCZJREREddTTZUhsWIakBCZJREREdVBhGZLCCSQ5YFs9JklERER1UGT8A9xKy4ahPsuQlIZJEhERUR1UNGC7h6ctjKUsQ6IOkyQiIqI6Jq9Age0XEgGwq60sTJKIiIjqmINPypA0MJWifVOWISmNxpOkpUuXwtnZGTKZDD4+Pjhy5EiZ7XNzczFjxgw4OTlBKpXCxcUFK1euVK7v0qULJBJJiderr76qbDN79uwS621t2R9LRER1Q9FTbX1a27MMSRk02gm5adMmTJw4EUuXLkX79u3xyy+/oGfPnoiKikKjRo3UbjNw4EDcvXsXwcHBaNq0KVJSUlBQUKBcHxISgry8POX7tLQ0tGrVCgMGDFDZj4eHB/bu3at8r6urW8VnR0REVPNkZOdjX3RRGRJHDUdTs2k0SVq8eDHGjBmDd955BwDwww8/4J9//sGyZcswf/78Eu3DwsJw6NAhxMTEwNLSEgDQuHFjlTZFy4ts3LgRRkZGJZIkPT093j0iIqI6Z+fFJGUZEnd7liEpi8a62/Ly8nDmzBkEBQWpLA8KCkJERITabbZt2wZfX18sXLgQDg4OcHV1xeTJk/H48eNSjxMcHIzBgwfD2NhYZfn169dhb28PZ2dnDB48GDExMWXGm5ubi8zMTJUXERGRtuHcSOWnsTtJqampkMvlsLGxUVluY2OD5ORktdvExMTg6NGjkMlkCA0NRWpqKj788EOkp6erjEsqcvLkSVy6dAnBwcEqy/38/LB27Vq4urri7t27mDdvHgICAnD58mVYWVmpPfb8+fMxZ86cSp4tERGR5sWnZ+PUrfuQsAxJuWh84LZEojpgTAhRYlkRhUIBiUSCdevWoW3btujVqxcWL16M1atXq72bFBwcDE9PT7Rt21Zlec+ePfHmm2/Cy8sLgYGB2LlzJwBgzZo1pcY5ffp0ZGRkKF/x8fEVPVUiIiKNUpYhcakPW3OWIXkWjSVJ9evXh66ubom7RikpKSXuLhWxs7ODg4MDzM3NlctatGgBIQQSEhJU2mZnZ2Pjxo3K8U5lMTY2hpeXF65fv15qG6lUCjMzM5UXERGRtigsQ1KYJLGrrXw0liQZGBjAx8cH4eHhKsvDw8MREBCgdpv27dsjMTERDx8+VC67du0adHR04OioOkL/zz//RG5uLt56661nxpKbm4vo6GjY2dlV4kyIiIhqvnPxDxCb+giG+rro4ckHl8pDo91tkyZNwm+//YaVK1ciOjoan3zyCeLi4jB27FgAhV1cb7/9trL90KFDYWVlhVGjRiEqKgqHDx/GlClTMHr0aBgaGqrsOzg4GH379lU7xmjy5Mk4dOgQYmNjceLECfTv3x+ZmZkYMWJE9Z4wERGRhhTdReruYcMyJOWk0U9p0KBBSEtLw9y5c5GUlARPT0/s2rULTk5OAICkpCTExcUp25uYmCA8PBzjx4+Hr68vrKysMHDgQMybN09lv9euXcPRo0exZ88etcdNSEjAkCFDkJqaCmtra7Rr1w7Hjx9XHpeIiKg2yStQYPv5J2VIvDk3UnlJhBBC00Foo8zMTJibmyMjI4Pjk4iIqEYLj7qLd9eehrWpFMemvQI9XY0/t6UxFfn9rrufEhERUR1RNDdSn1b2dTpBqih+UkRERLVYxuN87C0qQ+LNp9oqgkkSERFRLbbrYhLyChRobmMKdzsOD6kIJklERES1WOjZJ3MjeTuUOlkzqcckiYiIqJaKT8/GyVvpT8qQ2Gs6HK3DJImIiKiW+vvJ3EgBLlawMzd8RmsqjkkSERFRLaRahoRzI1UGkyQiIqJa6HxCBmJSH0Gmr8MyJJXEJImIiKgWCj1bODdSdw9bmLAMSaUwSSIiIqpl8uUKbL+QBADo14ZzI1UWkyQiIqJa5tDVe0h/lIf6JlJ0aFpf0+FoLSZJREREtUzRgO0+rVmG5HnwkyMiIqpFMh7nIzz6LgB2tT0vJklERES1yO4nZUhcbUzgYc8yJM+DSRIREVEtEvLU3EgsQ/J8mCQRERHVEvHp2TgZW1iGpG8bliF5XkySiIiIaomt5wrvIvk3YRmSqsAkiYiIqBYQQjzV1cYB21WhUklSQUEB9u7di19++QVZWVkAgMTERDx8+LBKgyMiIqLyuZCQgZh7hWVIenrZaTqcWqHC85Tfvn0bPXr0QFxcHHJzc9GtWzeYmppi4cKFyMnJwfLly6sjTiIiIipD0dxIQe4sQ1JVKnwnacKECfD19cX9+/dhaPhff2e/fv2wb9++Kg2OiIiIni1frsD284kAgH7e7GqrKhVONY8ePYp///0XBgYGKsudnJxw586dKguMiIiIyufwtXtIe1KGpCPLkFSZCt9JUigUkMvlJZYnJCTA1NS0SoIiIiKi8isasP16K5YhqUoV/iS7deuGH374QfleIpHg4cOHmDVrFnr16lWVsREREdEzZObkIzyqsAzJG+xqq1IV7m5bvHgxXnnlFbi7uyMnJwdDhw7F9evXUb9+fWzYsKE6YiQiIqJSFJUhadaAZUiqWoWTJAcHB5w7dw4bN27EmTNnoFAoMGbMGAwbNkxlIDcRERFVv5CzT+ZG8nZgGZIqVqEkKT8/H82bN8eOHTswatQojBo1qrriIiIiomdIuJ+NE0VlSFqzq62qVWhMkr6+PnJzc5mpEhER1QBbzxU+9t/O2Qr2FuzNqWoVHrg9fvx4LFiwAAUFBdURDxEREZWDEAIhZxMAcG6k6lLhMUknTpzAvn37sGfPHnh5ecHY2FhlfUhISJUFR0REROpdvJOBm/ceQaqng56etpoOp1aqcJJkYWGBN998szpiISIionIqGrAd5GELU5m+hqOpnSqcJK1atao64iAiIqJyeroMyRtt2NVWXSpdAe/evXu4evUqJBIJXF1dYW1tXZVxERERUSmOXC8qQ2KAjs1YhqS6VHjg9qNHjzB69GjY2dmhU6dO6NixI+zt7TFmzBhkZ2dXR4xERET0lKKutt4sQ1KtKvzJTpo0CYcOHcL27dvx4MEDPHjwAFu3bsWhQ4fw6aefVkeMRERE9IRKGZI2jhqOpnarcHfbli1bsHnzZnTp0kW5rFevXjA0NMTAgQOxbNmyqoyPiIiInhJ2MRm5BQo0bWACTweWIalOFb6TlJ2dDRsbmxLLGzRowO42IiKiahYS+WRupDYsQ1LdKpwk+fv7Y9asWcjJyVEue/z4MebMmQN/f/8qDY6IiIj+c+fBYxyPSQcA9OVTbdWuwknSkiVLEBERAUdHR3Tt2hWBgYFo2LAhIiIisGTJkgoHsHTpUjg7O0Mmk8HHxwdHjhwps31ubi5mzJgBJycnSKVSuLi4YOXKlcr1q1evhkQiKfF6OqmrzHGJiIg07e/IwgHb7ZpYwoFlSKpdhcckeXp64vr16/jjjz9w5coVCCEwePBgDBs2DIaGFbtgmzZtwsSJE7F06VK0b98ev/zyC3r27ImoqCg0atRI7TYDBw7E3bt3ERwcjKZNmyIlJaVEiRQzMzNcvXpVZZlMJnuu4xIREWmSEAKhT5IkDth+MSRCCKGpg/v5+cHb21tlsHeLFi3Qt29fzJ8/v0T7sLAwDB48GDExMbC0tFS7z9WrV2PixIl48OBBlR1XnczMTJibmyMjIwNmZhw4R0RE1etiQgZ6/+8opHo6OP1FIGfZrqSK/H5XuLtt/vz5Kt1bRVauXIkFCxaUez95eXk4c+YMgoKCVJYHBQUhIiJC7Tbbtm2Dr68vFi5cCAcHB7i6umLy5Ml4/PixSruHDx/CyckJjo6OeO211xAZGflcxwUKu/kyMzNVXkRERC9K0YDtbu42TJBekAonSb/88gvc3NxKLPfw8MDy5cvLvZ/U1FTI5fIST8rZ2NggOTlZ7TYxMTE4evQoLl26hNDQUPzwww/YvHkzxo0bp2zj5uaG1atXY9u2bdiwYQNkMhnat2+P69evV/q4QGFyaG5urnw1bNiw3OdKRET0PAqeLkPizQHbL0qFk6Tk5GTY2dmVWG5tbY2kpKQKB1D88UUhRKmPNCoUCkgkEqxbtw5t27ZFr169sHjxYqxevVp5N6ldu3Z466230KpVK3Ts2BF//vknXF1d8dNPP1X6uAAwffp0ZGRkKF/x8fEVPlciIqLKOHI9FakP82BlbICOzVgG7EWpcJLUsGFD/PvvvyWW//vvv7C3ty/3furXrw9dXd0Sd29SUlLUzsMEAHZ2dnBwcIC5ublyWYsWLSCEQEJCgtptdHR08NJLLynvJFXmuAAglUphZmam8iIiInoRQiL/K0OizzIkL0yFP+l33nkHEydOxKpVq3D79m3cvn0bK1euxCeffIJ333233PsxMDCAj48PwsPDVZaHh4cjICBA7Tbt27dHYmIiHj58qFx27do16OjowNFR/Uh/IQTOnTunvPtVmeMSERFpSlZOPvZcLvzDnl1tL5ioIIVCIaZOnSpkMpnQ0dEROjo6wsjISMyZM6eiuxIbN24U+vr6Ijg4WERFRYmJEycKY2NjcevWLSGEENOmTRPDhw9Xts/KyhKOjo6if//+4vLly+LQoUOiWbNm4p133lG2mT17tggLCxM3b94UkZGRYtSoUUJPT0+cOHGi3Mctj4yMDAFAZGRkVPi8iYiIymvTqTjh9NkO8cqiA0KhUGg6HK1Xkd/vCs+TJJFIsGDBAsycORPR0dEwNDREs2bNIJVKK5ygDRo0CGlpaZg7dy6SkpLg6emJXbt2wcnJCQCQlJSEuLg4ZXsTExOEh4dj/Pjx8PX1hZWVFQYOHIh58+Yp2zx48ADvvfcekpOTYW5ujjZt2uDw4cNo27ZtuY9LRERUU4SefTI3krcjy5C8YM89T9Lt27fx6NEjuLm5QUen7vSTcp4kIiKqbokPHqP9gv0QAjj62ctwrGek6ZC0XrXMk7RmzRr88MMPKsvee+89NGnSBF5eXvD09OQTX0RERFXo73N3IATg52zJBEkDyp0kLV++XOWpsrCwMKxatQpr167FqVOnYGFhgTlz5lRLkERERHWNEOKprjYO2NaEco9JunbtGnx9fZXvt27ditdffx3Dhg0DAHz99dcYNWpU1UdIRERUB11OzMT1lIeQ6umgp1fJ+Qmp+pX7TtLjx49V+u4iIiLQqVMn5fsmTZqUOWM1ERERlV/Ik7tIge42MGMZEo0od5Lk5OSEM2fOACgs7XH58mV06NBBub7oaTIiIiJ6PgVyBbYVlSFpw642TSl3d9vbb7+NcePG4fLly9i/fz/c3Nzg4+OjXB8REQFPT89qCZKIiKguOXIjFakPc2FlbIBOrixDoinlTpI+++wzZGdnIyQkBLa2tvjrr79U1v/7778YMmRIlQdIRERU1xQN2GYZEs167nmS6irOk0RERNXhYW4BfOeFIydfga3j2qNVQwtNh1SrVMs8SURERFT9dl9MQk6+Ak2sjdHSkWN9NYlJEhERUQ0SGvlkbqQ2DixDomFMkoiIiGqIpIzHOBaTBgDo05pPtWkakyQiIqIa4u/IRAgBtHW2RENLliHRNCZJRERENYAQAqGRCQA4N1JNUWVJUnx8PEaPHl1VuyMiIqpTLidm4trdhzBgGZIao8qSpPT0dKxZs6aqdkdERFSnFA3Y7tbCBuaGLENSE5R7Mslt27aVuT4mJua5gyEiIqqLCuQKbD1XWIakL7vaaoxyJ0l9+/aFRCJBWXNP8lFFIiKiijv6pAxJPSN9dGYZkhqj3N1tdnZ22LJlCxQKhdrX2bNnqzNOIiKiWquoq613K3sY6PGZqpqi3FfCx8enzEToWXeZiIiIqKSHuQX453IyAKAfu9pqlHJ3t02ZMgWPHj0qdX3Tpk1x4MCBKgmKiIiorgi7lIycfAWc6xujNeu01SjlTpI6duxY5npjY2N07tz5uQMiIiKqS4rmRurHMiQ1Trm722JiYtidRkREVIWSMh4j4mZhGRJ2tdU85U6SmjVrhnv37infDxo0CHfv3q2WoIiIiOqCrecKy5C81Lgey5DUQOVOkorfRdq1a1eZY5SIiIiodEIIhJ4tfKqtXxtHDUdD6vA5QyIiIg2ISsrE1btZMNDVwassQ1IjlTtJkkgkJQaUcYAZERFR5RTdReraogHMjViGpCYq99NtQgiMHDkSUqkUAJCTk4OxY8fC2NhYpV1ISEjVRkhERFTLFMgV2Hq+sAwJB2zXXOVOkkaMGKHy/q233qryYIiIiOqCf2+m4V5WYRmSLs0baDocKkW5k6RVq1ZVZxxERER1RujZwrmRXmvJMiQ1Ga8MERHRC/QotwD/XC6cQqefN7vaajImSURERC9Q2KVkPM6Xw7m+MdqwDEmNxiSJiIjoBQqNLHyqrW9rliGp6co9JomIqC6QKwROxqYjJSsHDUxlaOtsCV0d/pBR1UjOyMG/N1MB8Kk2bcAkiYjoibBLSZizPQpJGTnKZXbmMszq7Y4enpzsj57f1nN3IATg61QPjaxYhqSmY3cbEREKE6QP/jirkiABhX/5f/DHWYRdStJQZFSbFHW1ccC2dmCSRER1nlwhMGd7FISadUXL5myPglyhrgVR+UQlZuJKcmEZkte87DUdDpUDkyQiqvNOxqaXuIP0NAEgKSMHJ2PTX1xQVOuERhbOjfSKG8uQaAsmSURU5yVnlp4gPS0lq3ztiIqTKwS2nntShoRdbVpD40nS0qVL4ezsDJlMBh8fHxw5cqTM9rm5uZgxYwacnJwglUrh4uKClStXKtf/+uuv6NixI+rVq4d69eohMDAQJ0+eVNnH7NmzlQV7i162trbVcn5EVLNF3EjF4j1Xy9W2gamsmqOh2urfG6lIycqFhZE+XmYZEq2h0afbNm3ahIkTJ2Lp0qVo3749fvnlF/Ts2RNRUVFo1KiR2m0GDhyIu3fvIjg4GE2bNkVKSgoKCgqU6w8ePIghQ4YgICAAMpkMCxcuRFBQEC5fvgwHh/+ydw8PD+zdu1f5XldXt/pOlIhqnNtpj/DVzmjsiSqc+VgCqB2TVESqpwMvB/MXEhvVPkUDtl9raccyJFpEIoTQ2EhEPz8/eHt7Y9myZcplLVq0QN++fTF//vwS7cPCwjB48GDExMTA0tKyXMeQy+WoV68e/ve//+Htt98GUHgn6e+//8a5c+cqHXtmZibMzc2RkZEBMzOzSu+HiF6srJx8/O/ADaw6egt5cgV0dSQY5tcILR3MMWXzBQClJ0u+TvUQPPIlmBtyPAmV36PcAvjO24vH+XJs+SAAPk71NB1SnVaR32+NpbN5eXk4c+YMgoKCVJYHBQUhIiJC7Tbbtm2Dr68vFi5cCAcHB7i6umLy5Ml4/PhxqcfJzs5Gfn5+iaTq+vXrsLe3h7OzszLxKktubi4yMzNVXkSkPeQKgU2n4vDyokP45VAM8uQKdGxWH7sndMTcPp7o79sQy97yhq25apeanbkMk4NcYSbTw+nb9zHol2NIKecYJiIA+OdyYRmSxlZG8G5koelwqAI01t2WmpoKuVwOGxsbleU2NjZITk5Wu01MTAyOHj0KmUyG0NBQpKam4sMPP0R6errKuKSnTZs2DQ4ODggMDFQu8/Pzw9q1a+Hq6oq7d+9i3rx5CAgIwOXLl2FlZaV2P/Pnz8ecOXMqebZEpEknY9MxZ/tlXE4s/OPGub4xZvRqga4tGqiUhejhaYdu7rZqZ9zu2sIGb688iSvJWXhzeQR+H+2HxvWNNXVKpEWUZUjasAyJttH4jNvFvzBCiFK/RAqFAhKJBOvWrYO5eeHYgMWLF6N///74+eefYWhoqNJ+4cKF2LBhAw4ePAiZ7L+/Dnv27Kn8by8vL/j7+8PFxQVr1qzBpEmT1B57+vTpKusyMzPRsGHDip0sEb1Q8enZ+Gb3Fey8WDgRpKlUDx93bYYRAY1LHReiqyOBv0vJP5Za2Jlhy9gADF95ArfTstF/+TGsGf0SPOw5TolKdzczB//eYBkSbaWx7rb69etDV1e3xF2jlJSUEneXitjZ2cHBwUGZIAGFY5iEEEhISFBpu2jRInz99dfYs2cPWrZsWWYsxsbG8PLywvXr10ttI5VKYWZmpvIioprpUW4BFv1zFV0XH8LOi0nQkQBD2jbCgSld8G6nJpUeONvIygh/jfVHCzszpD7MxeBfjuNETFoVR0+1ydZzd6AQgI9TPThZ8c6jttFYkmRgYAAfHx+Eh4erLA8PD0dAQIDabdq3b4/ExEQ8fPhQuezatWvQ0dGBo6Ojctm3336LL7/8EmFhYfD19X1mLLm5uYiOjoadHWszEWkzhUJgy5kEvPLdQfzvwA3kFSjQrokldozviPlveKG+ifS5j9HAVIZN77dDW2dLZOUW4O2VJxH+5Ak5ouJCzj4pQ8K7SFpJo88hTpo0Cb/99htWrlyJ6OhofPLJJ4iLi8PYsWMBFHZxFT2RBgBDhw6FlZUVRo0ahaioKBw+fBhTpkzB6NGjlV1tCxcuxBdffIGVK1eicePGSE5ORnJyskpiNXnyZBw6dAixsbE4ceIE+vfvj8zMTIwYMeLFfgBEVGXO3L6Pfssi8Olf53E3MxeNLI2w/C0fbHi3Hdztq/bOr5lMH2tHt0VgCxvkFigw9o8z+Ot0fJUeg7RfdNJTZUha8o9wbaTRMUmDBg1CWloa5s6di6SkJHh6emLXrl1wcnICACQlJSEuLk7Z3sTEBOHh4Rg/fjx8fX1hZWWFgQMHYt68eco2S5cuRV5eHvr3769yrFmzZmH27NkAgISEBAwZMgSpqamwtrZGu3btcPz4ceVxiUh7JD54jAVhV5SzGRsb6GLcK00xur0zZPrVN/+ZTF8Xy9/yxrSQi9h8JgFTNl/A/ew8vNfJpdqOSdqlaMD2y27WsDAy0HA0VBkanSdJm3GeJCLNepwnxy+Hb2L5oZvIyVdAIgH6eztiSvfmaGD24mbGFkJg/u4rWHG4cBqR9zs3wbQebnyKqY6TKwQCvtmHu5m5WP6WD3p4sqpDTVGR32+NP91GRFQRQghsO5+IBbuvIPFJUVpfp3qY1dsDXo4v/kkziUSCz3u1gKWxAb7ZfQW/HIrB/Ud5+LqfF/R0ObNyXRVxMxV3M3NhbqiPl92sNR0OVRKTJCLSGufjH2DujiicuX0fAOBgYYhpPd3wWks7jd+5GdvZBZZGBpgWcgF/nk7Ag+x8/DikTbV2+VHNFXr2vzIkUj1+B7QVkyQiqvHuZuZgYdhVbDlbONWHob4uPujigvc6NalRScjAlxrC3Egf4zdEYk/UXYxYeRK/jvCFmYxlTOqS7LwChF0unN7mDW8+1abNeC+YiGqsnHw5fj5wAy8vOqhMkN5o44ADk7vg467NalSCVKS7hy3WjGoLE6keTsSmY8iK47iXlavpsOgF+udyMrLz5HCyMoJ3I9Zp02ZMkoioxhFCYNfFJAQuPoRv/7mK7Dw52jSyQOiHAVg8qHWJ+mo1jb+LFTa+1w5Wxga4nJiJAcsjEJ+eremw6AUpmhupb2uWIdF2TJKIqEa5dCcDg1Ycx4frziLh/mPYmsnww6DW2DI2AG206K9yTwdzbP4gAI71DHErLRtvLovA1eQsTYdF1SyFZUhqFSZJRFQj3MvKxbQtF9D7f0dxMjYdUj0dfNy1GfZP7oy+bRygo6N9f5E71zfGlg8C0NzGFClZuRiwPAJnbqdrOiyqRlvPJUIhAO9GFiyAXAswSSIijcotkOOXQzfx8qKD2HgqHkIUPhG079POmNTNFUYG2v18iY1ZYRkTH6d6yMwpwLDfTuDAlRRNh0XVJOTJBJL9vB2f0ZK0AZMkItIIIQT+uZyMoO8PY/7uK3iYWwAvB3P8NdYf/xvqDcd6RpoOscpYGBngjzF+6NLcGjn5Cry79jT+fvJjSrXHleRMRCdlQl9Xgte8WIakNtDuP9GISCtdSc7Elzui8O+NNACAtakUU7s3x5vejlrZrVYehga6+PVtX0z56zz+PpeIiZvO4X52Hka1d9Z0aFRFiuZGerl5A9QzZhmS2oBJEhG9MOmP8rA4/CrWn4iDQgAGejp4p4MzPny5KUyktf9/R/q6Olg8sDUsjAywOuIW5myPQvqjPEzq5sqnoLScXCHw97nCJIlzI9Uetf//SkSkcflyBdYeu40le68hM6cAANDT0xaf92qBhpa1p1utPHR0JJjV2x1Wxgb4Lvwaftp/A2mP8vBlH0/o1tK7aHXBsZtpT5UhaaDpcKiKMEkiomp14EoKvtwZhZh7jwAALezM8H+vucPfxUrDkWmORCLB+K7NUM/YADO3XsL6E3F4kJ2H7we1ZgkLLRUSWTjZ6assQ1KrMEkiompxIyULX+6IxqFr9wAAVsYGmNy9OQb6NuQdkyfeaueEekYGmLgpErsuJiPz8WksH+5TJ7oea5PsvAKEXXpShoRzI9Uq/JdIRFXqQXYefth7Hb8fvw25QkBfV4JR7Z3x0StNWcNMjVdb2sHcUB/v/X4aR2+kYtivx7FqVFtYcuCv1thz+S6y8+RoZGkEHyftmfCUno1TABBRlSiQK7Am4ha6LDqI1RG3IFcIBLawwZ5POuPzXi2YIJWhQ7P62PBuO9Qz0sf5hAz0Xx6BOw8eazosKqeiuZH6tmEZktqGSRIRPbcj1++h55IjmLXtMh5k58PVxgS/j2mL30b4wpmzDpdLq4YW+GtsAOzMZYi59wj9l0XgRgrLmNR0KZk5OHq9sEuZZUhqHyZJRFRpsamP8M6aUxgefBLXUx6inpE+vuzjgV0fd0THZtaaDk/rNG1ggi0fBMDF2hhJGTkYsPwYzsU/0HRYVIZt5wvLkLRpZME/CGohJklEVGEZj/Px1c4oBH1/CHujU6CnI8Go9o1xcPLLGO7fGHq6/F9LZdlbGOKvsQFo1dAC97PzMfTX4zjy5E4F1TwhTyaQ5IDt2on/JyOicpMrBNaduI1XFh3Er0dikS8X6NLcGmETO2FWbw+YG3HcUVWwNDbA+nf80LFZfWTnyTF69SnsuJCo6bComKvJWYgqKkPS0l7T4VA14NNtRFQuETdTMXd7FK4kF46TcbE2xhevuePl5pw4rzoYS/Xw2whfTPrzPHZeSML4DZG4n52P4e2cNB0aPVE0N1IXliGptZgkEVGZ4tKy8dWuKPxz+S4AwEymh4mBrhju7wR9dqtVK6meLn4c3AYWhvpYdyIOM/++hPSHefi4a1M+RaVhcoXA1sjCu3vsaqu9mCQRkVoPcwvwv/03sPJoLPLkCuhIgGF+Tvikmyvn8HmBdHUkmNfXE1YmUvy47zq+33sN97Pz8H+vudfaYsDa4HhMGpIzc2Am08MrLXg3tbZikkREKhQKgc1nErDwn6tIfZgLAOjQtD5mvuaO5ramGo6ubpJIJJjUzRWWRvqYvT0KqyNu4X52Hr7t3woGerybpwlFA7ZfbWnPMiS1GJMkIlI6dSsdc7ZfxqU7mQCAxlZGmPGqOwJbNGD3Tg0wsr0z6hkb4NM/z2PruUQ8yM7Hsre8YWTA/5W/SI/z5Ai7lAQAeMObXW21Gf9lERES7mdj/u4r2Hmh8H/8plI9jO/aFCMCGvOv5BqmT2sHmBnq44M/zuDQtXt467cTWDnyJVgYsQv0RdkTlYxHeXI0tDSEL8uQ1Gq8T0tUh2XnFeC7PVfR9btD2HkhCRIJMKRtQxyY0gXvdXJhglRDvdy8Ada90w7mhvo4G/cAA385huSMHE2HVWcUdbX1a80yJLUdkySiOkihEAg5m4CXFx3ET/tvILdAAT9nS+wY3wHz32iJ+iZSTYdIz+DjVA9/vu8PGzMprt19iDeXRSDm3kNNh1XrpWTlKCf37OftqOFoqLoxSSKqY87G3ccbyyIw6c/zuJuZC8d6hlg2zBsb32sHD3tzTYdHFdDc1hSbxwbAub4x7jx4jAHLj+FiQoamw6rVtp0rLEPSuiHLkNQFTJKI6oikjMeYuDESbyyNwLn4BzAy0MWU7s2xd1Jn9PSyY7eBlmpoaYS/xvrD08EMaY/yMOTX44i4marpsGqt0MgnZUg4YLtOYJJEVMs9zpNjyd7reGXRIfx9rnDyu/4+jjg4uQvGvdwUMn2OO9J29U2k2PBuO7RrYomHuQUYufIUwi4lazqsWufa3SxcTsyEng7LkNQVTJKIaikhBLadT0TX7w7i+73X8DhfDl+netj2UXssGtAKDcxkmg6RqpCpTB+rR7VFdw8b5MkV+HDdGWw8GafpsGqVogHbXZo34ISqdQSnACCqhS4kPMDc7VE4ffs+AMDeXIZpvVqgd0t2q9VmMn1d/DzUGzNCL2HT6XhMC7mI+9n5GNu5Ca/7c1IoBLaeY1dbXcMkiagWScnMwcJ/rmLL2QQIARjq62JsZxe816kJDA3YrVYX6Onq4Js3vWBpYoBlB29iQdgVpD/KxfSeLVjG5Dkcj0lDUkYOTGV6eMWNZUjqCiZJRLVATr4cwUdjsfTADTzKkwMA+ra2x2c93WBnbqjh6OhFk0gk+KyHGyyNDPDVrmj8eiQW6Y/y8c2bXixKXEkhTwZsv9bSjuP46hAmSURaTAiBsEvJ+Hp3NOLTHwMAWjW0wKze7vBuxJmA67p3OzVBPWMDfLblAracTcCD7Dz8PMybP/IV9DhPjt0XC2ej79eGcyPVJUySiLTU5cQMzN0ehROx6QAAGzMppvV0Q59WDuxWIaX+Po6wMNTHuPVnse9KCt4OPolfR/jC3FBf06FpjaIyJI71WIakruF9VyItk/owF9NDLuC1n47iRGw6pHo6+PiVpjgwuQv6tXFkgkQlBLrbYO3otjCV6uHkrXQMXnEcKVksY1JeRXMj9WvDP0DqGo0nSUuXLoWzszNkMhl8fHxw5MiRMtvn5uZixowZcHJyglQqhYuLC1auXKnSZsuWLXB3d4dUKoW7uztCQ0Of+7hEmpZXoMCKwzfx8rcHseFkPIQAXm1ph32fdsakoOasBE9l8mtihU3v+6O+iRTRSZnov+wYbqc90nRYNd69rFwcuV44OWe/Nnyqra7RaJK0adMmTJw4ETNmzEBkZCQ6duyInj17Ii6u9Lk9Bg4ciH379iE4OBhXr17Fhg0b4Obmplx/7NgxDBo0CMOHD8f58+cxfPhwDBw4ECdOnHiu4xJpihAC4VF3EfT9IXy96wqycgvg6WCGP9/3x89DveFYz0jTIZKWcLc3w5YP/NHI0ghx6dl4c9kxRCVmajqsGm3b+UTIFQKtGlqgibWJpsOhF0wihBCaOrifnx+8vb2xbNky5bIWLVqgb9++mD9/fon2YWFhGDx4MGJiYmBpaal2n4MGDUJmZiZ2796tXNajRw/Uq1cPGzZsqNRx1cnMzIS5uTkyMjJgZmZWrm2IKupqcha+3BGFozcK/5KtbyLF1O7N0d+H3WpUeSmZOXh75UlcSc6CqUwPwSNeQltn9f9Prete++kILt3JxJzXPTAioLGmw6EqUJHfb43dScrLy8OZM2cQFBSksjwoKAgRERFqt9m2bRt8fX2xcOFCODg4wNXVFZMnT8bjx4+VbY4dO1Zin927d1fuszLHBQq7+TIzM1VeRNUl/VEeZv59CT2XHMbRG6kw0NXBB11ccHBKFwx8qSETJHouDcxk2PS+P15qXA9ZOQUYHnwCe6PuajqsGuf63SxculNYhqR3K5YhqYs0NoghNTUVcrkcNjY2KsttbGyQnKy+5lBMTAyOHj0KmUyG0NBQpKam4sMPP0R6erpyXFJycnKZ+6zMcQFg/vz5mDNnToXPk6gi8uUK/H7sNn7Yew2ZOQUAgB4etvi8Vws0smK3GlUdc0N9rB3th4+ePPX2/h9nsPDNlnjTh4+4FymaG6lLc2uWIamjND5wu/hU+UKIUqfPVygUkEgkWLduHdq2bYtevXph8eLFWL16tcrdpPLssyLHBYDp06cjIyND+YqPjy/X+RGV14GrKejxw2HM3RGFzJwCuNmaYv27flg+3IcJElULQwNdLB/ugze8HSBXCHz613n8diRG02HVCAqFwFblU21MHOsqjd1Jql+/PnR1dUvcvUlJSSlxl6eInZ0dHBwcYG5urlzWokULCCGQkJCAZs2awdbWtsx9Vua4ACCVSiGVSit0jkTlcSPlIebtjMLBq/cAAJbGBpgc1ByDXmoIXXarUTXT19XBov6tYGlkgN+OxmLezmikPcrD1O7N63S9t+OxaUh8UoakawuWIamrNHYnycDAAD4+PggPD1dZHh4ejoCAALXbtG/fHomJiXj48KFy2bVr16CjowNHx8JM39/fv8Q+9+zZo9xnZY5LVB0ysvMxZ/tl9PjhMA5evQc9HQne6eCMA5O7YKhfIyZI9MLo6Egw49UWmNqjOQBg2cGbmB5yEQVyhYYj05zQs4V3kV71YhmSukyjE6tMmjQJw4cPh6+vL/z9/bFixQrExcVh7NixAAq7uO7cuYO1a9cCAIYOHYovv/wSo0aNwpw5c5CamoopU6Zg9OjRMDQsrE81YcIEdOrUCQsWLECfPn2wdetW7N27F0ePHi33cYmqU4FcgfUn47A4/BoeZOcDAAJbNMDnvVrwEWPSGIlEgg+7NIWlkQE+D72IjaficT87D0sGt6lzScLjPDl2XyrsbeDcSHWbRpOkQYMGIS0tDXPnzkVSUhI8PT2xa9cuODk5AQCSkpJU5i4yMTFBeHg4xo8fD19fX1hZWWHgwIGYN2+esk1AQAA2btyIL774AjNnzoSLiws2bdoEPz+/ch+XqLLkCoGTselIycpBA1MZ2jpbqtwROno9FXN3XMa1u4V3Q5s1MMHM19zRydVaUyETqRjcthEsjPTx8YZz+OfyXYxadQor3vaBqazulDHZG30XD3ML4GBhiJcac2qEukyj8yRpM86TRMWFXUrCnO1RSMr4r9yDnbkMs3q7o7mtGb7aGY290YWPWVsY6WNSN1cMbdsIeqzKTjVQxI1UvLv2NB7lyeHpYIbVo9qivkndGJc5evUp7L+Sgo9eborJ3ZtrOhyqYhX5/WaSVElMkuhpYZeS8MEfZ1H8H5MEgACgqwPIFYCujgTD2zlhYmAzWBjxkWKq2S4mZGDkqpNIe5QH5/rG+H1M21o/w3vqw1z4fb0PcoXAvk87w4Vd4LWOVkwmSVRbyBUCc7ZHlUiQACiXyRVAp2b18c/Ejpj9ugcTJNIKXo7m+GusPxwsDBGb+ghvLovAtbtZmg6rWm0vKkPiaM4EiZgkET2vk7HpKl1spfmgS1M0bWD6AiIiqjpNrE2w5YMANGtggruZuRiw/BjO3L6v6bCqTahybiQO2CYmSUTPLSXr2QlSRdoR1TS25jL8NdYfbRpZIONxPt767QQOXk3RdFhV7kbKQ1xIyGAZElJikkT0nEyl5XtItIGprJojIao+FkYGWPeOHzq7WuNxvhzvrDmNrefuaDqsKhUamQAA6OxqDas6MkidysYkiaiS5AqBTafiMGXzhTLbSVD4lBurrJO2MzLQw69v++L1VvYoUAhM3HQOayJuaTqsKqFQCPwdmQgA6OfNrjYqpNF5koi01fGYNHy5IwqXEzMBAA1MpUjJylU+zVakaIakWb3dOYM21QoGejr4YVBr1DPSx5pjtzFr22WkPcrDJ4HNtLqMyclb6bjz4DFMpXoIbFF6iSqqW5gkEVVAfHo2vt4VrZyN11Smhwldm+Ft/8bYf+VuiXmSbJ/Mk9TD005TIRNVOR0dCWa/7gFLYym+33sNP+67jvRHuZjzuqfW/jFQVIakF8uQ0FOYJBGVw8PcAvx84AaCj8QiT66AjgQY0rYRJnVzVY5d6OFph27utmXOuE1UW0gkEkwIbAZLEwP839ZL+ON4HB5k52PxwNYw0NOukRw5+XLsupgEgF1tpIpJElEZFAqBzWcSsPCfq0h9mAsAaN/UCjNfc4ebbclJyHR1JPB3sXrRYRJpzPB2TrAw1MekP89hx4UkZDzOx/K3fGBczgcaaoK90XeR9aQMSVuWIaGnaM+3mOgFOxmbjjnbLyvHHTW2MsKMV90R2KKBVo+9IKpqvVvZw9xQH2P/OIMj11Mx9LcTWD3yJdQz1o5JU4u62vq2sYcO7/zSU7TrnijRCxCfno1x685i4C/HcDkxE6ZSPczo1QL/fNIJ3dxtmCARqdHJ1Rrr3vGDhZE+zsc/wIBfjiHxwWNNh/VMaQ9zcejaPQBAvzaOGo6GahomSURPPMwtwMKwK+i6+BB2XkyCjgQY6tcIB6Z0wbudmkCqx8GcRGVp06ge/nrfH3bmMtxIeYj+yyJwI+WhpsMq0/bziShQCLR0NEfTBixDQqqYJFGdp1AI/Hk6Hi8vOoilB28ir0AB/yZW2PlxR3zdz6vOVD4nqgrNbEyx+YMANLE2RmJGDgYsj8D5+AeaDqtULENCZWGSRHXaydh0vP7zUUzdfAH3snLhZGWEFcN9sP5dP7SwK7s6NBGp52BhiL/e90dLR3Pcz87HkF+P4+j1VE2HVcLNew9xPiEDuixDQqVgkkR1Unx6NsatLxx3dOlOJkykepje0w17PumEIA9bjjsiek5WJlKsf7cd2je1QnaeHKNXn1I+Zl9TFA3Y7uxqzTvGpBaTJKpTHuUWYNE/VwvHHV1IguTJfEcHJnfB+51dOO6IqAqZSPWwcuRL6OVlizy5AuPWn8W6E7c1HRaAwm52drXRs3AKAKoTFAqBkMg7WBh2BSlZhfMdtWtiif97zQPu9uxWI6ouUj1d/DTEGxZGl7D+RBxmhF7C/Ud5GPdyU43esT31VBmSbu4sQ0LqMUmiWu/0rXTM3RGFCwkZAIBGlkb4vFcLdPfg4/xEL4KujgRf9fWElbEBftp/A4v2XEPaozzMfNVdY/MSFd1F6ullyzIkVComSVRrJdzPxje7r2DHhcJxECZSPXz0SlOMat+Y3WpEL5hEIsGnQc1Rz8gAc3dEYdW/t/AgOx8L+7eEvu6LHfmRky/HzqIyJJwbicrAJIlqnUe5BVh+6CZWHI5BboECEgkwyLchPg1qDmtTDs4k0qTRHZxRz1gfU/66gNDIO3iQnYelw3xgaPDi/nDZF52CrJzCMiR+zixDQqVjkkS1RtFAzIX/XMHdzMJxR37Olvi/3u7wsDfXcHREVKRfG0dYGBrgg3VncODqPbwVfAIrR7wEcyP9F3L80MgEAECf1ixDQmXj021UK5y5nY5+S//Fp3+dx93MXDS0NMTyt7yx8b12TJCIaqCX3RrgjzF+MJPp4czt+xj4yzHczcyp9uOmPczFwauFZUje8OZTbVQ2Jkmk1e48eIzxGyLx5rJjOJ+QAWMDXXzWww3hn3RGD087DswmqsF8G1viz7H+aGAqxdW7WXhzWQRiUx9V6zF3XEhCgULAy8EcTRuYVuuxSPsxSSKtlJ1XgMV7ruKVRQex/XyictzRgSld8EEXFz6tQqQl3GzNsOWDADhZGSHh/mMMWB6BS3cyqu14IZwbiSqASRJpFYVCIORsAl5edBA/7r+B3AIF2jpbYvtHHbCgf0s0MJVpOkQiqqCGlkbYPDYA7nZmSH2YhyErjuN4TFqVH+fmvYc4H/8AujoSvN6aZUjo2ZgkkdY4c/s++i2LwKQ/C8cdOdYzxLJh3tj0Xjt4OnDcEZE2szaVYuP77eDnbIms3AK8vfIk/rmcXKXH+PvJXaROzeqzDAmVC5MkqvESHzzGhI2ReHNZYTVxYwNdTO3RHHsndUZPL447IqotzGT6WDO6Lbq52yCvQIEP/jiDP0/HV8m+VcqQeHNuJCofTgFANVZ2XgGWH4rBisM3kZNfON/RAB9HTA5qjgZm7FYjqo1k+rpYNswb00Mu4q8zCZi6+QLuP8rD+51dnmu/p2/fR8L9xzCR6iGIZUionJgkUY2jUAhsO5+Ib3ZfQfKTR4LbNrbEzNfc4eXIbjWi2k5PVwcL+7eEpbEBfjkcg/m7ryDtUR6m93Sr9J3jormRenqyDAmVH5MkqlHOxt3H3O1ROBf/AADgYGGIz3u1QC8vW3arEdUhEokE03u1gKWxAebvvoIVh2OQ/igP37zhBb0KljHJyZcryxP149xIVAFMkqhGSHzwGAvDruDvc4kAACMDXYx7uSnGdHDmX31Eddj7nV1Qz9gA07ZcwOYzCXiQnY//DW1Tof8v7L9SWIbE3lyGds5W1Rgt1TZMkkijHufJ8cvhm1h+6L9xR/29HTGlO8cdEVGhgb4NYWGoj482RGJv9F28vfIkfhvhCzNZ+cqYhJwtHLDdp40Dy5BQhfDpNtIIIQS2nruDV747iB/2XkdOvgIvNa6HbeM64NsBrZggEZGKIA9brB3dFqZSPZyMTcfgX47jXlbuM7dLf5SHg1dTAABvcAJJqiAmSfTCRcbdxxvLIjBh4zkkZeTAwcIQ/xvaBn++78+B2URUqnZNrLDhvXaob2KAqKRM9F8egfj07DK32XEhEQUKAU8HMzSzYRkSqhgmSfTCJGfk4JNN59BvaQQi4x7AyEAXk4Ncse/TznitpT0HZhPRM3k6mGPz2AA41jPE7bRsvLksAleSM0ttX9TV1q8N50aiimOSRNXucZ4cS/Zex8uLDionc3vT2xEHJnfBR68048BsIqqQxvWNseWDADS3MUVKVi4GLj+GU7fSS7SLufcQ54rKkLRiGRKqOCZJVG2Kxh11/e4gvt97DY/z5fB1qodtH7XHdwNbwYbjjoiokmzMZPjzfX/4OtVDZk4B3vrtBPZfuQsAkCsEjt1Mw4LdVwAAHZpawdqUZUio4jSeJC1duhTOzs6QyWTw8fHBkSNHSm178OBBSCSSEq8rV64o23Tp0kVtm1dffVXZZvbs2SXW29raVut51jXn4h/gzSfjjhKfjDv6aUgb/DXWHy0dLTQdHhHVAuZG+vh9jB9ebm6N3AIF3l17BrO3XUKHBfsx5Nfj+CeqMGk6H5+BsEtJGo6WtJFGpwDYtGkTJk6ciKVLl6J9+/b45Zdf0LNnT0RFRaFRo0albnf16lWYmZkp31tbWyv/OyQkBHl5ecr3aWlpaNWqFQYMGKCyDw8PD+zdu1f5XleXXT5VITkjBwvDriDkSbeaob4uPuzignc7NWG3GhFVOUMDXax42xdTN19AaOQdrI64XaJNxuN8fPDHWSx7yxs9PO00ECVpK40mSYsXL8aYMWPwzjvvAAB++OEH/PPPP1i2bBnmz59f6nYNGjSAhYWF2nWWlpYq7zdu3AgjI6MSSZKenh7vHlWhnHw5VhyOwbKDN/E4Xw4AeMPbAVO7u8HWnN1qRFR99HV1sPDNlvjncjKy8+Ql1gsAEgBztkehm7stdDlXEpWTxrrb8vLycObMGQQFBaksDwoKQkRERJnbtmnTBnZ2dujatSsOHDhQZtvg4GAMHjwYxsbGKsuvX78Oe3t7ODs7Y/DgwYiJiSlzP7m5ucjMzFR5UeG4o23nE/HKooNYHF447sjHqR62jmuPxQNbM0Eiohfi9O37ahOkIgJAUkYOTsaWHOBNVBqN3UlKTU2FXC6HjY1qNWYbGxskJyer3cbOzg4rVqyAj48PcnNz8fvvv6Nr1644ePAgOnXqVKL9yZMncenSJQQHB6ss9/Pzw9q1a+Hq6oq7d+9i3rx5CAgIwOXLl2FlpX7K+vnz52POnDmVPNva6Xz8A8zdEYUzt+8DAOzNZZjWqwV6t7Tj4/xE9EKlZOVUaTsioAaUJSn+YyqEKPUHtnnz5mjevLnyvb+/P+Lj47Fo0SK1SVJwcDA8PT3Rtm1bleU9e/ZU/reXlxf8/f3h4uKCNWvWYNKkSWqPPX36dJV1mZmZaNiw4bNPsBa6m5mDhWFXseVsYVVtQ31dfNDFBe92bAJDA447IqIXr4Fp+e5al7cdEaDBJKl+/frQ1dUtcdcoJSWlxN2lsrRr1w5//PFHieXZ2dnYuHEj5s6d+8x9GBsbw8vLC9evXy+1jVQqhVRatx8hzcmX47cjMVh68KbytvYbbRwwpUdz2Jkbajg6IqrL2jpbws5chuSMHAg16yUAbM1laOtsqWYtkXoaG5NkYGAAHx8fhIeHqywPDw9HQEBAufcTGRkJO7uSTyv8+eefyM3NxVtvvfXMfeTm5iI6Olrtfqjw7t6OC4no+t0hLNpzDdl5cng3ssDf49pj8aDWTJCISON0dSSY1dsdQGFC9LSi97N6u3PQNlWIRrvbJk2ahOHDh8PX1xf+/v5YsWIF4uLiMHbsWACFXVx37tzB2rVrARQ+/da4cWN4eHggLy8Pf/zxB7Zs2YItW7aU2HdwcDD69u2rdozR5MmT0bt3bzRq1AgpKSmYN28eMjMzMWLEiOo9YS10MSEDc3dcxqlbheOO7MxlmNbTDa+3YhkRIqpZenjaYdlb3pizPQpJGf+NPbI1l2FWb3c+/k8VptEkadCgQUhLS8PcuXORlJQET09P7Nq1C05OTgCApKQkxMXFKdvn5eVh8uTJuHPnDgwNDeHh4YGdO3eiV69eKvu9du0ajh49ij179qg9bkJCAoYMGYLU1FRYW1ujXbt2OH78uPK4VDju6Nt/CscdCQHI9HXwQeemeK8Txx0RUc3Vw9MO3dxtcTI2HSlZOWhgWtjFxjtIVBkSIYS67lt6hszMTJibmyMjI0NlYkttl5MvR/DRWPx84IZy3FG/Ng6YynFHRERUC1Tk91vjT7dRzSCEwK6Lyfh6VzTuPHgMAGjd0AL/19sd3o3qaTg6IiKiF49JEuHSnQzM3R6Fk0+qaNua/TfuSIe3qImIqI5iklSHpWTl4Nuwq9j81Lij9zu54P3OTWBkwK8GERHVbfwlrIOKxh0tPXADj56MO+rT2h6f9XCDvQXHHREREQFMkuoUIQR2Xyocd5Rwv3DcUauGFvi/19zh48RxR0RERE9jklRHXLqTgbk7opTFHW3NZPisZ3P0aeXAcUdERERqMEmq5VKycrDon6v460zhuCOpng7e7+yCsRx3REREVCb+StZSOflyrPr3Fn4+cAMPcwsAAK+3ssdnPd3gwHFHREREz8QkqZYRQiDsUjK+3h2N+PQn444czfF/vd3h48TCjkREROXFJKkWuXQnA1/uiMKJJ+OObMyk+KyHG/q25rgjIiKiimKSVAvcy8rFd3uuYtPp+P/GHXVqgvc7u8BYyktMRERUGfwF1WK5BYXjjv63/79xR71b2WMaxx0RERE9NyZJWkgIgX8u38XXu6IRl54NAGjpaI7/e80dvo057oiIiKgqMEnSMlGJmZi74zKOxxSOO2pgKsXUHm54ow3HHREREVUlJkk1jFwhcDI2HSlZOWhgKkNbZ0vo6kiQ+rBw3NHGU4Xjjgz0dPBexyb4oAvHHREREVUH/rrWIGGXkjBnexSSMnKUy2zNpAhwqY89UXeV445ea2mHaT3d4FjPSFOhEhER1XpMkmqIsEtJ+OCPsxDFlidn5iIk8g4AwMuhcL6jlzjuiIiIqNoxSaoB5AqBOdujSiRIT7Mw1EfIBwHQ19N5YXERERHVZfzFrQFOxqardLGp8+BxPk7fvv+CIiIiIiImSTVASlbZCVJF2xEREdHzY5JUAzQwlVVpOyIiInp+TJJqgLbOlrAzl6G0WY4kAOzMC6cDICIioheDSVINoKsjwaze7gBQIlEqej+rtzt0OVkkERHRC8MkqYbo4WmHZW95w9ZctUvN1lyGZW95o4ennYYiIyIiqps4BUAN0sPTDt3cbdXOuE1EREQvFpOkGkZXRwJ/FytNh0FERFTnsbuNiIiISA0mSURERERqMEkiIiIiUoNJEhEREZEaTJKIiIiI1GCSRERERKQGkyQiIiIiNZgkEREREanBJImIiIhIDc64XUlCCABAZmamhiMhIiKi8ir63S76HS8Lk6RKysrKAgA0bNhQw5EQERFRRWVlZcHc3LzMNhJRnlSKSlAoFEhMTISpqSkkkqotQJuZmYmGDRsiPj4eZmZmVbrvmoDnp/1q+znW9vMDav858vy0X3WdoxACWVlZsLe3h45O2aOOeCepknR0dODo6FitxzAzM6u1X36A51cb1PZzrO3nB9T+c+T5ab/qOMdn3UEqwoHbRERERGowSSIiIiJSg0lSDSSVSjFr1ixIpVJNh1IteH7ar7afY20/P6D2nyPPT/vVhHPkwG0iIiIiNXgniYiIiEgNJklEREREajBJIiIiIlKDSRIRERGRGkySXrDDhw+jd+/esLe3h0Qiwd9///3MbQ4dOgQfHx/IZDI0adIEy5cvr/5An0NFz/HgwYOQSCQlXleuXHkxAVfA/Pnz8dJLL8HU1BQNGjRA3759cfXq1Wdup03XsDLnqE3XcNmyZWjZsqVygjp/f3/s3r27zG206foBFT9Hbbp+6syfPx8SiQQTJ04ss522Xcci5Tk/bbuGs2fPLhGrra1tmdto4voxSXrBHj16hFatWuF///tfudrHxsaiV69e6NixIyIjI/H555/j448/xpYtW6o50sqr6DkWuXr1KpKSkpSvZs2aVVOElXfo0CGMGzcOx48fR3h4OAoKChAUFIRHjx6Vuo22XcPKnGMRbbiGjo6O+Oabb3D69GmcPn0ar7zyCvr06YPLly+rba9t1w+o+DkW0YbrV9ypU6ewYsUKtGzZssx22ngdgfKfXxFtuoYeHh4qsV68eLHUthq7foI0BoAIDQ0ts83UqVOFm5ubyrL3339ftGvXrhojqzrlOccDBw4IAOL+/fsvJKaqlJKSIgCIQ4cOldpG269hec5Rm6+hEELUq1dP/Pbbb2rXafv1K1LWOWrr9cvKyhLNmjUT4eHhonPnzmLChAmlttXG61iR89O2azhr1izRqlWrcrfX1PXjnaQa7tixYwgKClJZ1r17d5w+fRr5+fkaiqp6tGnTBnZ2dujatSsOHDig6XDKJSMjAwBgaWlZahttv4blOcci2nYN5XI5Nm7ciEePHsHf319tG22/fuU5xyLadv3GjRuHV199FYGBgc9sq43XsSLnV0SbruH169dhb28PZ2dnDB48GDExMaW21dT1Y4HbGi45ORk2NjYqy2xsbFBQUIDU1FTY2dlpKLKqY2dnhxUrVsDHxwe5ubn4/fff0bVrVxw8eBCdOnXSdHilEkJg0qRJ6NChAzw9PUttp83XsLznqG3X8OLFi/D390dOTg5MTEwQGhoKd3d3tW219fpV5By17foBwMaNG3H27FmcOnWqXO217TpW9Py07Rr6+flh7dq1cHV1xd27dzFv3jwEBATg8uXLsLKyKtFeU9ePSZIWkEgkKu/Fk0nSiy/XVs2bN0fz5s2V7/39/REfH49FixbVyH/cRT766CNcuHABR48efWZbbb2G5T1HbbuGzZs3x7lz5/DgwQNs2bIFI0aMwKFDh0pNIrTx+lXkHLXt+sXHx2PChAnYs2cPZDJZubfTlutYmfPTtmvYs2dP5X97eXnB398fLi4uWLNmDSZNmqR2G01cP3a31XC2trZITk5WWZaSkgI9PT212XZt0a5dO1y/fl3TYZRq/Pjx2LZtGw4cOABHR8cy22rrNazIOapTk6+hgYEBmjZtCl9fX8yfPx+tWrXCkiVL1LbV1utXkXNUpyZfvzNnziAlJQU+Pj7Q09ODnp4eDh06hB9//BF6enqQy+UlttGm61iZ81OnJl/D4oyNjeHl5VVqvJq6fryTVMP5+/tj+/btKsv27NkDX19f6Ovrayiq6hcZGVnjbn8DhX+5jB8/HqGhoTh48CCcnZ2fuY22XcPKnKM6NfUaqiOEQG5urtp12nb9SlPWOapTk69f165dSzwJNWrUKLi5ueGzzz6Drq5uiW206TpW5vzUqcnXsLjc3FxER0ejY8eOatdr7PpV67BwKiErK0tERkaKyMhIAUAsXrxYREZGitu3bwshhJg2bZoYPny4sn1MTIwwMjISn3zyiYiKihLBwcFCX19fbN68WVOn8EwVPcfvv/9ehIaGimvXrolLly6JadOmCQBiy5YtmjqFUn3wwQfC3NxcHDx4UCQlJSlf2dnZyjbafg0rc47adA2nT58uDh8+LGJjY8WFCxfE559/LnR0dMSePXuEENp//YSo+Dlq0/UrTfGnv2rDdXzas85P267hp59+Kg4ePChiYmLE8ePHxWuvvSZMTU3FrVu3hBA15/oxSXrBih7TLP4aMWKEEEKIESNGiM6dO6tsc/DgQdGmTRthYGAgGjduLJYtW/biA6+Aip7jggULhIuLi5DJZKJevXqiQ4cOYufOnZoJ/hnUnRcAsWrVKmUbbb+GlTlHbbqGo0ePFk5OTsLAwEBYW1uLrl27KpMHIbT/+glR8XPUputXmuJJRG24jk971vlp2zUcNGiQsLOzE/r6+sLe3l688cYb4vLly8r1NeX6SYR4MvKJiIiIiJQ4cJuIiIhIDSZJRERERGowSSIiIiJSg0kSERERkRpMkoiIiIjUYJJEREREpAaTJCIiIiI1mCQRERERqcEkiagGunXrFiQSCc6dO6fpUJSuXLmCdu3aQSaToXXr1poOR+O6dOmCiRMnKt83btwYP/zwQ5nbSCQS/P3338997KraT3WaPXv2M78nI0eORN++fZ/rODNnzsR77733XPt42ksvvYSQkJAq2x9pNyZJRGqMHDkSEokE33zzjcryv//+GxKJRENRadasWbNgbGyMq1evYt++faW2S05Oxvjx49GkSRNIpVI0bNgQvXv3LnObF6l3794IDAxUu+7YsWOQSCQ4e/Zshfd76tSpKv2xBkpPNJKSktCzZ88qPVZxq1evhkQiKfH67bffqvW4FXH37l0sWbIEn3/+eZXtc+bMmZg2bRoUCkWV7ZO0F5MkolLIZDIsWLAA9+/f13QoVSYvL6/S2968eRMdOnSAk5MTrKys1La5desWfHx8sH//fixcuBAXL15EWFgYXn75ZYwbN67Ufefn51c6rooaM2YM9u/fj9u3b5dYt3LlSrRu3Rre3t4V3q+1tTWMjIyqIsRnsrW1hVQqrfbjmJmZISkpSeU1bNiwaj9ueQUHB8Pf3x+NGzeusn2++uqryMjIwD///FNl+yTtxSSJqBSBgYGwtbXF/PnzS22j7i/9H374QeV/2kVdCl9//TVsbGxgYWGBOXPmoKCgAFOmTIGlpSUcHR2xcuXKEvu/cuUKAgICIJPJ4OHhgYMHD6qsj4qKQq9evWBiYgIbGxsMHz4cqampyvVdunTBRx99hEmTJqF+/fro1q2b2vNQKBSYO3cuHB0dIZVK0bp1a4SFhSnXSyQSnDlzBnPnzoVEIsHs2bPV7ufDDz+ERCLByZMn0b9/f7i6usLDwwOTJk3C8ePHVfa3fPly9OnTB8bGxpg3bx4AYNmyZXBxcYGBgQGaN2+O33//vcTn3ahRI0ilUtjb2+Pjjz9Wrlu6dCmaNWsGmUwGGxsb9O/fX22Mr732Gho0aIDVq1erLM/OzsamTZswZswYpKWlYciQIXB0dISRkRG8vLywYcMGtfsrUry77fr16+jUqRNkMhnc3d0RHh5eYpvPPvsMrq6uMDIyQpMmTTBz5kxlwrh69WrMmTMH58+fV97FKYq5eHfbxYsX8corr8DQ0BBWVlZ477338PDhQ+X6ou/gokWLYGdnBysrK4wbN+6ZyalEIoGtra3Ky9DQEAAQFxeHPn36wMTEBGZmZhg4cCDu3r1b6r7kcjkmTZoECwsLWFlZYerUqSheOnTz5s3w8vJSnkdgYCAePXpU6j43btyI119/XWWZQqHAggUL0LRpU0ilUjRq1AhfffUVAMDf3x/Tpk1TaX/v3j3o6+vjwIEDAABdXV306tXrmdeb6ohqL6FLpIVGjBgh+vTpI0JCQoRMJhPx8fFCCCFCQ0PF0/9sZs2aJVq1aqWy7ffffy+cnJxU9mVqairGjRsnrly5IoKDgwUA0b17d/HVV1+Ja9euiS+//FLo6+uLuLg4IYQQsbGxAoBwdHQUmzdvFlFRUeKdd94RpqamIjU1VQghRGJioqhfv76YPn26iI6OFmfPnhXdunUTL7/8svLYnTt3FiYmJmLKlCniypUrIjo6Wu35Ll68WJiZmYkNGzaIK1euiKlTpwp9fX1x7do1IYQQSUlJwsPDQ3z66aciKSlJZGVlldhHWlqakEgk4uuvv37m5wtANGjQQAQHB4ubN2+KW7duiZCQEKGvry9+/vlncfXqVfHdd98JXV1dsX//fiGEEH/99ZcwMzMTu3btErdv3xYnTpwQK1asEEIIcerUKaGrqyvWr18vbt26Jc6ePSuWLFlS6vGnTJkiGjduLBQKhXLZ6tWrhVQqFenp6SIhIUF8++23IjIyUty8eVP8+OOPQldXVxw/flzls326KruTk5P4/vvvhRBCyOVy4enpKbp06SIiIyPFoUOHRJs2bQQAERoaqtzmyy+/FP/++6+IjY0V27ZtEzY2NmLBggVCCCGys7PFp59+Kjw8PERSUpJISkoS2dnZys+vaD+PHj1SVlG/ePGi2Ldvn3B2dhYjRoxQHmfEiBHCzMxMjB07VkRHR4vt27cLIyMj5eenzqpVq4S5ubnadQqFQrRp00Z06NBBnD59Whw/flx4e3urVG0v/m9jwYIFwtzcXPl9HjNmjDA1NRV9+vQRQhR+n/X09MTixYtFbGysuHDhgvj555/VfteEECI9PV1IJBKVayKEEFOnThX16tUTq1evFjdu3BBHjhwRv/76qxBCiJ9++kk0atRI5br/9NNPwsHBQcjlcuWypUuXisaNG5f62VDdwSSJSI2iJEkIIdq1aydGjx4thKh8kuTk5KTyP+HmzZuLjh07Kt8XFBQIY2NjsWHDBiHEf0nSN998o2yTn58vHB0dlT+iM2fOFEFBQSrHjo+PFwDE1atXhRCFP+StW7d+5vna29uLr776SmXZSy+9JD788EPl+1atWolZs2aVuo8TJ04IACIkJOSZxwMgJk6cqLIsICBAvPvuuyrLBgwYIHr16iWEEOK7774Trq6uIi8vr8T+tmzZIszMzERmZuYzjy2EENHR0QKAMgETQohOnTqJIUOGlLpNr169xKeffqp8X1aS9M8//whdXV1lci2EELt37y6RJBW3cOFC4ePjo3yv7vslhGqStGLFClGvXj3x8OFD5fqdO3cKHR0dkZycLIT47ztYUFCgbDNgwAAxaNCgUmNZtWqVACCMjY2VLxsbGyGEEHv27BG6urrKpF4IIS5fviwAiJMnT6qN3c7OTu33uejf2ZkzZwQAcevWrVJjelpkZKQAoBJDZmamkEqlyqSouJSUFKGnpycOHz6sXObv7y+mTJmi0m7r1q1CR0dH5d8s1U3sbiN6hgULFmDNmjWIioqq9D48PDygo/PfPzcbGxt4eXkp3+vq6sLKygopKSkq2/n7+yv/W09PD76+voiOjgYAnDlzBgcOHICJiYny5ebmBqBw/FARX1/fMmPLzMxEYmIi2rdvr7K8ffv2ymOVh3jSdVLege3F44qOji4zhgEDBuDx48do0qQJ3n33XYSGhqKgoAAA0K1bNzg5OaFJkyYYPnw41q1bh+zsbADAunXrVD6jI0eOwM3NDQEBAcouzps3b+LIkSMYPXo0gMKuoa+++gotW7aElZUVTExMsGfPHsTFxZXr3KKjo9GoUSM4Ojoqlz19LYts3rwZHTp0gK2tLUxMTDBz5sxyH+PpY7Vq1QrGxsYqn5tCocDVq1eVyzw8PKCrq6t8b2dnV+L7VpypqSnOnTunfEVERCiP2bBhQzRs2FDZ1t3dHRYWFmq/MxkZGUhKSlL7fS7SqlUrdO3aFV5eXhgwYAB+/fXXMscDPn78GEDh2MGnP4vc3Fx07dpV7TbW1tbo1q0b1q1bBwCIjY3FsWPHSoyzMjQ0hEKhQG5ubqnHp7qBSRLRM3Tq1Andu3dX+wSNjo5OiXEV6sZ56Ovrq7yXSCRql5XniZqiJEShUKB3794qP2Lnzp1TjoUp8vSPZ3n2W0QIUaEn+Zo1awaJRFLuxEpdXGXF0LBhQ1y9ehU///wzDA0N8eGHH6JTp07Iz8+Hqakpzp49iw0bNsDOzg7/93//h1atWuHBgwd4/fXXVT6foh/mMWPGYMuWLcjMzMSqVavg5OSk/HH97rvv8P3332Pq1KnYv38/zp07h+7du5d74Hvx74S6czt+/DgGDx6Mnj17YseOHYiMjMSMGTMqPLi+rOv09PLKfN90dHTQtGlT5atJkyZlHrOi35mn6erqIjw8HLt374a7uzt++uknNG/eHLGxsWrb169fHwBUEqmi8VJlGTZsGDZv3oz8/HysX78eHh4eaNWqlUqb9PR0GBkZlWt/VLsxSSIqh2+++Qbbt29X/iVdxNraGsnJySo/ilU5t9HTg50LCgpw5swZ5d0ib29vXL58GY0bN1b5IWvatGm5EyOg8Akme3t7HD16VGV5REQEWrRoUe79WFpaonv37vj555/VDrZ98OBBmdu3aNHimTEYGhri9ddfx48//oiDBw/i2LFjuHjxIoDCOxOBgYFYuHAhLly4gFu3bmH//v0wNTVV+WyKfvgGDhwIXV1drF+/HmvWrMGoUaOUP/BHjhxBnz598NZbb6FVq1Zo0qQJrl+/Xu7Pwt3dHXFxcUhMTFQuO3bsmEqbf//9F05OTpgxYwZ8fX3RrFmzEk/cGRgYQC6XP/NY586dU/nM//33X+jo6MDV1bXcMVdE0fnFx8crl0VFRSEjI0Ptd8bc3Bx2dnZqv89Pk0gkaN++PebMmYPIyEgYGBggNDRUbQwuLi4wMzNTucPbrFkzGBoaljndRN++fZGTk4OwsDCsX78eb731Vok2ly5dqtQTjlT7MEkiKgcvLy8MGzYMP/30k8ryLl264N69e1i4cCFu3ryJn3/+Gbt3766y4/78888IDQ3FlStXMG7cONy/f1/ZJTRu3Dikp6djyJAhOHnyJGJiYrBnzx6MHj36mT+sxU2ZMgULFizApk2bcPXqVUybNg3nzp3DhAkTKrSfpUuXQi6Xo23bttiyZQuuX7+O6Oho/Pjjj2q7m4rHsHr1aixfvhzXr1/H4sWLERISgsmTJwMofNorODgYly5dQkxMDH7//XcYGhrCyckJO3bswI8//ohz587h9u3bWLt2LRQKBZo3b17q8UxMTDBo0CB8/vnnSExMxMiRI5XrmjZtivDwcERERCA6Ohrvv/8+kpOTy/05BAYGonnz5nj77bdx/vx5HDlyBDNmzFBp07RpU8TFxWHjxo24efMmfvzxxxIJQePGjREbG4tz584hNTVVbffPsGHDIJPJMGLECFy6dAkHDhzA+PHjMXz4cNjY2JQ75ooIDAxEy5YtMWzYMJw9exYnT57E22+/jc6dO5favTthwgR88803yu/zhx9+qJI4nzhxAl9//TVOnz6NuLg4hISE4N69e6Um6jo6OggMDFRJrGUyGT777DNMnToVa9euxc2bN3H8+HEEBwcr2xgbG6NPnz6YOXMmoqOjMXTo0BL7PnLkCIKCgir56VCtorHRUEQ12NMDt4vcunVLSKVSUfyfzbJly0TDhg2FsbGxePvtt8VXX31VYuB28X0VH/QrhOrA36KB2+vXrxd+fn7CwMBAtGjRQuzbt09lm2vXrol+/foJCwsLYWhoKNzc3MTEiROVT++oO446crlczJkzRzg4OAh9fX3RqlUrsXv3bpU2zxq4XSQxMVGMGzdOODk5CQMDA+Hg4CBef/11ceDAAWUblDKAeenSpaJJkyZCX19fuLq6irVr1yrXhYaGCj8/P2FmZiaMjY1Fu3btxN69e4UQQhw5ckR07txZ1KtXTxgaGoqWLVuKTZs2PTPWiIgIAaDEAPi0tDTRp08fYWJiIho0aCC++OIL8fbbb6tcx7IGbgshxNWrV0WHDh2EgYGBcHV1FWFhYSXOe8qUKcLKykqYmJiIQYMGie+//17libKcnBzx5ptvCgsLCwFArFq1Su3nd+HCBfHyyy8LmUwmLC0txbvvvqvyVJi67+CECRNUnkYrrqyn24QQ4vbt2+L1118XxsbGwtTUVAwYMEA5UFyIkgO38/PzxYQJE4SZmZmwsLAQkyZNUvlMo6KiRPfu3YW1tbWQSqXC1dVV/PTTT6UeXwghwsLCSjyZJpfLxbx584STk5PQ19cXjRo1KvHE5c6dOwUA0alTpxL7TEhIEPr6+iqD7qnukgihpvOciIiohhNCoF27dpg4cSKGDBlSJfucMmUKMjIysGLFiirZH2k3drcREZFWkkgkWLFihfIpx6rQoEEDfPnll1W2P9JuvJNEREREpAbvJBERERGpwSSJiIiISA0mSURERERqMEkiIiIiUoNJEhEREZEaTJKIiIiI1GCSRERERKQGkyQiIiIiNZgkEREREanx/9qIALPz9Y7YAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Mean f1: 0.618621086640702\n"
     ]
    }
   ],
   "source": [
    "# Fitting a simple Naive Bayes with TF-IDF\n",
    "clf_NB = MultinomialNB()\n",
    "scores_NB_tfidf = model_selection.cross_val_score(clf_NB, train_tfidf , df_train[\"target\"], cv=5, scoring=\"f1\")\n",
    "plot_learning_evolution(cv_values,scores_NB_tfidf,'Naive Bayes with TF-IDF')\n",
    "print(\"\\033[1m Mean f1:\", scores_NB_tfidf.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d7de999",
   "metadata": {
    "papermill": {
     "duration": 0.020403,
     "end_time": "2023-08-12T22:11:32.488596",
     "exception": false,
     "start_time": "2023-08-12T22:11:32.468193",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "🔴 **SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2a68657",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:11:32.531608Z",
     "iopub.status.busy": "2023-08-12T22:11:32.531239Z",
     "iopub.status.idle": "2023-08-12T22:11:56.159738Z",
     "shell.execute_reply": "2023-08-12T22:11:56.158645Z"
    },
    "papermill": {
     "duration": 23.654675,
     "end_time": "2023-08-12T22:11:56.163819",
     "exception": false,
     "start_time": "2023-08-12T22:11:32.509144",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAHFCAYAAADmGm0KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB4BElEQVR4nO3deVhUZfsH8O/MsAy7ssgu4oIL4AYq4Fqa+1K95pa7tlmmmVa+1c8ly7Qy01dNTVyy1EotE1PR3BVxATdQSVBQQAQUkB3m+f2BMzkyICBwGPh+rmuuyznnmXPuwwG5OfezyIQQAkRERESkRS51AEREREQ1EZMkIiIiIh2YJBERERHpwCSJiIiISAcmSUREREQ6MEkiIiIi0oFJEhEREZEOTJKIiIiIdGCSRERERKQDkyQiKrPTp0/jpZdeQsOGDWFsbAx7e3v4+/vj/fffBwDcu3cPRkZGGDFiRInHSE9Ph6mpKQYPHgwA2LBhA2QyGWQyGQ4fPlysvRACTZs2hUwmQ48eParisnDz5k3IZDJs2LBBs+3kyZOYO3cuHjx4UKx9o0aNMHDgwAqfT3296peZmRlatmyJefPmITMzs1j7zMxMfPnll2jXrh3Mzc1hZmaGtm3b4osvvijW3svLCy1btix2jJ07d0Imk8Hf37/Yvh9//BEymQy7du2q8DUR1UZMkoioTIKCghAQEID09HQsXrwY+/fvx3fffYfOnTtj27ZtAAA7OzsMHjwYv//+O+7fv6/zOFu3bkV2djYmTZqktd3CwgLr1q0r1v7IkSO4ceMGLCwsKv+iHnF0dMSpU6cwYMAAzbaTJ09i3rx5OpOkyjB06FCcOnUKp06dwh9//IGhQ4di/vz5GDt2rFa7u3fvws/PD/Pnz0efPn2wc+dO/P777+jXrx8WLFgAPz8/3L17V9P+ueeew9WrV5GYmKh1nMOHD8PMzAxnz55FRkZGsX1yuRzdunWrkmsl0luCiKgMunXrJpo0aSLy8/OL7SssLNT8e8+ePQKAWL58uc7jdOrUSdjb22uOs379egFATJ48WZiYmIi0tDSt9qNHjxb+/v7C09NTdO/evfIu6Cm++uorAUDExMQU2+fm5iYGDBhQ4WMDEG+//Xax7WPGjBFyuVxkZ2drtvXu3VsYGBiIY8eOFWt/7NgxYWBgIPr06aPZtn37dgFAbNmyRatt69atxbRp04ShoaHYs2eP1r7GjRsLHx+fCl8PUW3FJ0lEVCYpKSmwtbWFgYFBsX1y+b//lfTp0wcuLi5Yv359sXaRkZE4ffo0xo4dW+w4I0eOBABs2bJFsy0tLQ3bt2/HxIkTyxTjrFmzYGVlhcLCQs22qVOnQiaT4auvvtK6FrlcjuXLlwMoXm6bO3cuZs2aBQBwd3cvsRy4d+9etG/fHiYmJmjRogUCAwPLFGdJrKysIJPJoFAoAABnz57F/v37MWnSJHTp0qVY+y5dumDixInYt28fzp07BwDo0aNHsVhTUlJw6dIlDBgwAD4+Pjh06JBmX1xcHKKjo/Hcc889U+xEtRGTJCIqE39/f5w+fRrvvvsuTp8+jfz8fJ3t5HI5xo8fj/Pnz+PChQta+9SJk66kx9LSEkOHDtVKNLZs2QK5XI7hw4eXKcZevXohPT0doaGhmm0HDhyAiYkJgoODNdsOHjwIIQR69eql8ziTJ0/G1KlTAQA7duzQlMXat2+vaXPhwgW8//77eO+99/DHH3+gdevWmDRpEo4ePVqmWIUQKCgoQEFBAR48eIA//vgDGzduxIgRI2BoaAgAmphffPHFEo+j3qdua21tjdatW2slQkeOHIFCoUBAQAC6d++ulUCp2zFJItJB6kdZRKQfkpOTRZcuXQQAAUAYGhqKgIAAsXDhQpGRkaHVNjo6WshkMvHuu+9qtuXn5wsHBwfRuXNnrbbqctuZM2fEoUOHBABx+fJlIYQQHTp0EOPHjxdCiDKV2zIzM4WRkZGYP3++EEKI27dvCwDiww8/FCYmJiInJ0cIIcRrr70mnJycNJ+LiYkRAMT69es1255WblMqleLWrVuabdnZ2cLa2lq88cYbpcYohNB8DZ989evXTzx8+FDT7s033xQAxNWrV0s8VmRkpAAg3nrrLc226dOnCwAiPj5eCCHE1KlThZ+fnxCiqByqUCg0Zc0JEyYIhUIh0tPTnxo3UV3DJ0lEVCY2NjY4duwYzpw5gy+//BJDhgzB9evXMXv2bHh7eyM5OVnT1t3dHc899xx++ukn5OXlAQD++usvJCYmllo66969O5o0aYLAwEBcunQJZ86cKXOpDQBMTU3h7++PAwcOACh6ulKvXj3MmjULeXl5OH78OICip0slPUUqq7Zt26Jhw4aa90qlEh4eHrh161aZPj9s2DCcOXMGZ86cwdGjR7Fs2TKcPXsWffv2RW5ubpnjEEIAKBoxp6Z+KqR+YnT48GHNyEB12U79xOvw4cPw9fWt0o7xRPqKSRIRlYuvry8+/PBD/Prrr4iPj8d7772HmzdvYvHixVrtJk2ahJSUFM2w8vXr18Pc3BzDhg0r8dgymQwTJkzA5s2b8f3338PDwwNdu3YtV3y9evVCSEgIMjMzceDAATz//POwsbGBj48PDhw4gJiYGMTExDxzkmRjY1Nsm7GxMbKzs8v0eTs7O/j6+sLX1xddu3bF1KlTsWzZMhw/flzTN0qdhMXExJR4nJs3bwIAXF1dNdu6d+8OuVyOQ4cOISUlBZcvX0b37t0BFI0ibNeuHQ4fPozY2FjExMSw1EZUAiZJRFRhhoaGmDNnDgDg8uXLWvtefvll1K9fH4GBgbh37x52796N4cOHw9zcvNRjjh8/HsnJyfj+++8xYcKEcsfUs2dP5OXl4ejRozh48CBeeOEFzfbg4GBN352ePXuW+9hVrXXr1gCg6culjv33338v8TPqfeq2QFEHcHUipB7e37lzZ83+7t2749ChQ+yPRPQUTJKIqEwSEhJ0bo+MjAQAODk5aW1XKpUYNWoU9u/fj0WLFiE/P79MpTNnZ2fMmjULgwYNwrhx48odZ8eOHWFpaYmlS5ciMTFRkzz06tULYWFh+OWXX9CqVati8T7J2NgYAMr8ZKgyhIeHAwAaNGgAoOipXe/evbFu3TqcOHGiWPvjx48jMDAQffv2hY+Pj9a+5557DlFRUfj555/h4+OjVU7r3r07wsPD8fvvv8PQ0FArgSKifxUfy0tEpIN6aP+gQYPQokULqFQqhIeH45tvvoG5uTmmTZtW7DOTJk3CihUrsGTJErRo0QIBAQFlOteXX35Z4TgVCgW6d++OP//8E+7u7mjSpAkAoHPnzjA2NsbBgwfx7rvvPvU43t7eAIDvvvsO48aNg6GhIZo3b15pfXfu3r2LkJAQAEBOTg7Cw8OxYMEC1KtXT+sJ2qZNm9CrVy/07t0b7777ruYJ2N9//43vvvsOLVq00JopXO25557D119/jZ07d2LmzJla+9QlzD/++AMBAQEwMzOrlGsiqm34JImIyuSTTz5B/fr18e2332Lw4MHo168fli1bhl69eiE0NFSTVDyuXbt2aNeuHYQQ5eqA/azU/Y0e73dkbGys6bRclv5IPXr0wOzZs/Hnn3+iS5cu6NChg2Yuosrw22+/wd/fH/7+/ujduzeWL1+OwYMH4+zZs3Bzc9O0s7e3R0hICD755BPs3bsXQ4YMwZAhQxAUFIT//ve/CAkJgb29fbHjd+3aFQYGBhBCaPojqdWrVw+tW7eGEKLKlnohqg1kQj00goiIiIg0+CSJiIiISAcmSUREREQ6MEkiIiIi0oFJEhEREZEOTJKIiIiIdGCSRERERKQDJ5OsIJVKhfj4eFhYWGgtLElEREQ1lxACGRkZcHJyglxe+rMiJkkVFB8fr7WgJBEREemPuLg4uLi4lNqGSVIFqZcmiIuLg6WlpcTREBERUVmkp6fD1dW1TEsMMUmqIHWJzdLSkkkSERGRnilLVxl23CYiIiLSgUkSERERkQ5MkoiIiIh0YJJEREREpAOTJCIiIiIdmCQRERER6cAkiYiIiEgHJklEREREOjBJIiIiItKBM24TERFRjVKoEgiNSUVSRg4aWCjR0d0aCnn1LybPJImIiIhqjL2XEzDvzwgkpOVotjlaKTFnUCv09XKs1lhYbiMiIqIaYe/lBLy1+bxWggQAiWk5eGvzeey9nFCt8TBJIiIiIskVqgTm/RkBoWOfetu8PyNQqNLVomowSSIiIiLJhcakFnuC9DgBICEtB6ExqdUWE5MkIiIiklxSRskJUkXaVQYmSURERCS5BhbKSm1XGZgkERERkeQ6ulvD0arkBEiGolFuHd2tqy0mJklEREQkOYVchjmDWuncp54hac6gVtU6XxKTJCIiIqoR/JvYwkBHEuRgpcSq0e2rfZ4kTiZJRERENcKBiLsoUAk0tTPDZy96c8ZtIiIiIgAIulQ0WeTANk7wb2IjcTQstxEREVENkJaVj2NR9wAAA7yrt6xWEiZJREREJLn9EYnILxRobm+BZvYWUocDgEkSERER1QDqUlv/GvIUCWCSRERERBJLy8rH8ahkAMCA1g4SR/MvJklEREQkqX0RiShQCbRwsEDTBjWj1AYwSSIiIiKJBV0sKrXVlA7bakySiIiISDL3M/Nw4p+iUlv/1kySiIiIiAAUjWorUAm0dLREEztzqcPRwiSJiIiIJLP7UaltYA17igQwSSIiIiKJpGbm4eSNFAA1a+i/GpMkIiIiksS+K4koVAl4OlnC3dZM6nCKYZJEREREklCPaquJT5EAJklEREQkgZSHuTgVXVRqq2lD/9WYJBEREVG123flLgpVAl7OlmhUA0ttAJMkIiIikkDQpXgAwABvJ4kjKRmTJCIiIqpWyQ9zcepGzS61AUySiIiIqJrtvZwIlQBau1ihoY2p1OGUSPIkaeXKlXB3d4dSqYSPjw+OHTtWavvc3Fx8/PHHcHNzg7GxMZo0aYLAwEDN/h49ekAmkxV7DRgwQNNm7ty5xfY7ONScVYeJiIhqs5q6VtuTDKQ8+bZt2zB9+nSsXLkSnTt3xurVq9GvXz9ERESgYcOGOj8zbNgw3L17F+vWrUPTpk2RlJSEgoICzf4dO3YgLy9P8z4lJQVt2rTBK6+8onUcT09PHDhwQPNeoVBU8tURERHRk5IycnA6puZOIPk4SZOkJUuWYNKkSZg8eTIAYOnSpdi3bx9WrVqFhQsXFmu/d+9eHDlyBNHR0bC2tgYANGrUSKuNerva1q1bYWpqWixJMjAw4NMjIiKiarbvUamtjWs9uFrX3FIbIGG5LS8vD+fOnUPv3r21tvfu3RsnT57U+Zldu3bB19cXixcvhrOzMzw8PDBz5kxkZ2eXeJ5169ZhxIgRMDPTHl4YFRUFJycnuLu7Y8SIEYiOjn72iyIiIqJS7daU2mr+gwrJniQlJyejsLAQ9vb2Wtvt7e2RmJio8zPR0dE4fvw4lEoldu7cieTkZEyZMgWpqala/ZLUQkNDcfnyZaxbt05re6dOnbBp0yZ4eHjg7t27WLBgAQICAnDlyhXY2NjoPHdubi5yc3M179PT08t7yURERHVaUkYOQm+mAqj5pTagBnTclslkWu+FEMW2qalUKshkMvz000/o2LEj+vfvjyVLlmDDhg06nyatW7cOXl5e6Nixo9b2fv364T//+Q+8vb3Rq1cvBAUFAQA2btxYYpwLFy6ElZWV5uXq6lreSyUiIqrT9l5OhBBAW9d6cKlfs0ttgIRJkq2tLRQKRbGnRklJScWeLqk5OjrC2dkZVlZWmm0tW7aEEAK3b9/WapuVlYWtW7dq+juVxszMDN7e3oiKiiqxzezZs5GWlqZ5xcXFPfW4RERE9C91qW1g65r/FAmQMEkyMjKCj48PgoODtbYHBwcjICBA52c6d+6M+Ph4PHz4ULPt+vXrkMvlcHFx0Wr7yy+/IDc3F6NHj35qLLm5uYiMjISjY8k3zdjYGJaWllovIiIiKpu76Tk486jU1k8PSm2AxOW2GTNm4IcffkBgYCAiIyPx3nvvITY2Fm+++SaAoqc3Y8eO1bQfNWoUbGxsMGHCBERERODo0aOYNWsWJk6cCBMTE61jr1u3Di+++KLOPkYzZ87EkSNHEBMTg9OnT2Po0KFIT0/HuHHjqvaCiYiI6qi/LiVACKB9w3pwrmfy9A/UAJJOATB8+HCkpKRg/vz5SEhIgJeXF/bs2QM3NzcAQEJCAmJjYzXtzc3NERwcjKlTp8LX1xc2NjYYNmwYFixYoHXc69ev4/jx49i/f7/O896+fRsjR45EcnIy7Ozs4Ofnh5CQEM15iYiIqHIFXXo0qq11zV2r7UkyIYSQOgh9lJ6eDisrK6SlpbH0RkREVIrEtBz4LTwIADg1+3k4Wkn3JKk8v78lH91GREREtdueR0+RfN3qS5oglReTJCIiIqpS6lKbPsyN9DgmSURERFRlEtKyce7WfQBMkoiIiIg09lwqmg+xQ6P6cLBSShxN+TBJIiIioioTdDEeADBAz54iAUySiIiIqIrceZCN87EPIJPpzwSSj2OSRERERFXir0cdtjs0soa9pX6V2gAmSURERFRF9G2tticxSSIiIqJKF5eahfC4olJbXy8HqcOpECZJREREVOn+ulz0FKmTuzUaWOhfqQ1gkkRERERVIOjR0H99HNWmxiSJiIiIKlVcahYuxD2AXAb00dNSG8AkiYiIiCqZeq22Tu42eltqA5gkERERUSVTr9U2QE9HtakxSSIiIqJKE5uShYu30yDX41FtakySiIiIqNKonyL5N7GBrbmxxNE8GyZJREREVGmCLqnXanOSOJJnxySJiIiIKsXN5ExcvpMOhVyGPp72UofzzJgkERERUaXQlNoa28BGz0ttAJMkIiIiqiR7asmoNjUmSURERPTMYpIzcSVeXWrT71FtakySiIiI6JmpnyIFNLGBtZmRxNFUDiZJRERE9Mx2XyxKkgbWklIbwCSJiIiIntGNew8RmZAOA7kMvVvVjlIbwCSJiIiIntGeR0+ROje1Rf1aUmoDmCQRERHRM6ota7U9iUkSERERVdg/SRm4mpgBQ4UMfWpRqQ1gkkRERETPIOhiIoCiUpuVqaHE0VQuJklERERUYZoJJL1rV6kNYJJEREREFRR1NwPX7haV2mrTqDY1JklERERUIeoO212b2dW6UhvAJImIiIgqKOhi7S21AUySiIiIqAKu381AVNJDGCnk6NXKXupwqgSTJCIiIio39TIk3TxsYWVS+0ptAJMkIiIiKichBIIuxgOofRNIPo5JEhEREZXLtbsZuHEvE0YGcvRqWTtLbQCTJCIiIiondYftbs3sYKGsnaU2gEkSERERlYMQQjP0f2AtLrUBTJKIiIioHK4mZiD6UamtZ8sGUodTpZgkERERUZmpS209PGp3qQ2oAUnSypUr4e7uDqVSCR8fHxw7dqzU9rm5ufj444/h5uYGY2NjNGnSBIGBgZr9GzZsgEwmK/bKycl5pvMSERHVdY+X2mrzqDY1AylPvm3bNkyfPh0rV65E586dsXr1avTr1w8RERFo2LChzs8MGzYMd+/exbp169C0aVMkJSWhoKBAq42lpSWuXbumtU2pVD7TeYmIiOq6iIR0xCRnwthAjp61eFSbmkwIIaQ6eadOndC+fXusWrVKs61ly5Z48cUXsXDhwmLt9+7dixEjRiA6OhrW1tY6j7lhwwZMnz4dDx48qLTz6pKeng4rKyukpaXB0tKyTJ8hIiLSZ4v3XsXKwzfQ19MB34/xkTqcCinP72/Jym15eXk4d+4cevfurbW9d+/eOHnypM7P7Nq1C76+vli8eDGcnZ3h4eGBmTNnIjs7W6vdw4cP4ebmBhcXFwwcOBBhYWHPdF6gqMyXnp6u9SIiIqor6lqpDZCw3JacnIzCwkLY22s/rrO3t0diYqLOz0RHR+P48eNQKpXYuXMnkpOTMWXKFKSmpmr6JbVo0QIbNmyAt7c30tPT8d1336Fz5864cOECmjVrVqHzAsDChQsxb968Z7xqIiIi/XQlPh23UrKgNJTj+Ra1e1SbmuQdt2UymdZ7IUSxbWoqlQoymQw//fQTOnbsiP79+2PJkiXYsGGD5mmSn58fRo8ejTZt2qBr16745Zdf4OHhgeXLl1f4vAAwe/ZspKWlaV5xcXEVuVwiIiK9pH6K9FzzBjAzlrRLc7WR7CptbW2hUCiKPb1JSkoq9pRHzdHREc7OzrCystJsa9myJYQQuH37Npo1a1bsM3K5HB06dEBUVFSFzwsAxsbGMDY2LvP1ERER1RZFa7XVrVIbIOGTJCMjI/j4+CA4OFhre3BwMAICAnR+pnPnzoiPj8fDhw81265fvw65XA4XFxednxFCIDw8HI6OjhU+LxERUV12+U46YlPrVqkNkLjcNmPGDPzwww8IDAxEZGQk3nvvPcTGxuLNN98EUFTiGjt2rKb9qFGjYGNjgwkTJiAiIgJHjx7FrFmzMHHiRJiYmAAA5s2bh3379iE6Ohrh4eGYNGkSwsPDNccsy3mJiIjoX7svxQMAerawh6lR3Si1ARLPkzR8+HCkpKRg/vz5SEhIgJeXF/bs2QM3NzcAQEJCAmJjYzXtzc3NERwcjKlTp8LX1xc2NjYYNmwYFixYoGnz4MEDvP7660hMTISVlRXatWuHo0ePomPHjmU+LxERERWpq6U2QOJ5kvQZ50kiIqK64ELcAwxZcQImhgqc//QFmBgppA7pmejFPElERERU86lHtfVs2UDvE6TyYpJEREREOj1eahtYx0ptAJMkIiIiKkF43APceZANUyMFejSvO6Pa1JgkERERkU57NKU2eygN61apDWCSRERERDpojWrzrnulNoBJEhEREekQFvcA8Wk5MDNSoEdzO6nDkQSTJCIiIipG/RSpV6u6WWoDmCQRERHRE1QqoemPVFdLbQCTJCIiInpCWNx9JKTlwNzYAN086mapDWCSRERERE/Y/ajU9kIdLrUBTJKIiIjoMSy1/YtJEhEREWmci72Pu+m5sDA2QFcPW6nDkRSTJCIiItIIeqzUZmxQd0ttAJMkIiIiekSr1FYH12p7EpMkIiIiAgCcvXUfSRm5sFAaoEuzul1qA5gkERER0SNBF+MBAL1bOdT5UhvAJImIiIgAFKoE9lxOBAAMZKkNAJMkIiIiAnDmZiruZeTCUmmAzk1ZagOYJBERERH+HdXWx9MBRgZMDwAmSURERHVeoUrgr8sc1fYkJklERER13OmYFCQ/zIOViSFLbY9hkkRERFTHqedG6uNpD0MFUwM1fiWIiIjqsIJCFfY+GtU2oLWTxNHULEySiIiI6rDQmFQkP8xDPVNDBDSxkTqcGoVJEhERUR22+1Gpra+nA0ttT+BXg4iIqI7SLrVxVNuTmCQRERHVUSHRqUjNzEN9U0P4N2ap7UlMkoiIiOqooEtFa7X19XKEAUttxfArQkREVAflP1Zq41ptujFJIiIiqoNO3UjB/ax8WJsZoZO7tdTh1EhMkoiIiOog9QSSfb0cWGorAb8qREREdUx+oQp7rzwqtXmz1FYSJklERER1zMkbKXiQlQ9bcyN0ZKmtREySiIiI6pigi+pRbSy1lYZfGSIiojokr0CFfVfuAgAGeHOtttIwSSIiIqpDTtxIRlp2PmzNjVlqewomSURERHVI0MWiUW39vR2gkMskjqZmY5JERERUR+QVqLD/0ai2ARzV9lRMkoiIiOqIE/8kIz2nAHYWxvBtxFLb00ieJK1cuRLu7u5QKpXw8fHBsWPHSm2fm5uLjz/+GG5ubjA2NkaTJk0QGBio2b927Vp07doV9evXR/369dGrVy+EhoZqHWPu3LmQyWRaLwcHhyq5PiIioppit7rU5sVSW1kYSHnybdu2Yfr06Vi5ciU6d+6M1atXo1+/foiIiEDDhg11fmbYsGG4e/cu1q1bh6ZNmyIpKQkFBQWa/YcPH8bIkSMREBAApVKJxYsXo3fv3rhy5QqcnZ017Tw9PXHgwAHNe4VCUXUXSkREJLHcgkLsj3hUamvNUW1lIRNCCKlO3qlTJ7Rv3x6rVq3SbGvZsiVefPFFLFy4sFj7vXv3YsSIEYiOjoa1ddkeExYWFqJ+/fr43//+h7FjxwIoepL0+++/Izw8vMKxp6enw8rKCmlpabC0tKzwcYiIiKrDwci7mLTxLBpYGCNkdk/I6+iTpPL8/pas3JaXl4dz586hd+/eWtt79+6NkydP6vzMrl274Ovri8WLF8PZ2RkeHh6YOXMmsrOzSzxPVlYW8vPziyVVUVFRcHJygru7uybxIiIiqq3+HdXmWGcTpPKSrNyWnJyMwsJC2Nvba223t7dHYmKizs9ER0fj+PHjUCqV2LlzJ5KTkzFlyhSkpqZq9Ut63EcffQRnZ2f06tVLs61Tp07YtGkTPDw8cPfuXSxYsAABAQG4cuUKbGxsdB4nNzcXubm5mvfp6enlvWQiIiJJ5OQXIjiiaALJga05qq2sJO2TBAAymXY2K4Qotk1NpVJBJpPhp59+gpWVFQBgyZIlGDp0KFasWAETExOt9osXL8aWLVtw+PBhKJVKzfZ+/fpp/u3t7Q1/f380adIEGzduxIwZM3See+HChZg3b16FrpGIiEhKx6KSkZFbAAdLJdo3rC91OHpDsnKbra0tFApFsadGSUlJxZ4uqTk6OsLZ2VmTIAFFfZiEELh9+7ZW26+//hpffPEF9u/fj9atW5cai5mZGby9vREVFVVim9mzZyMtLU3ziouLe9olEhER1QjqtdpYaisfyZIkIyMj+Pj4IDg4WGt7cHAwAgICdH6mc+fOiI+Px8OHDzXbrl+/DrlcDhcXF822r776Cp999hn27t0LX1/fp8aSm5uLyMhIODqW/AjS2NgYlpaWWi8iIqKaLie/EAcikwAAA1hqKxdJ50maMWMGfvjhBwQGBiIyMhLvvfceYmNj8eabbwIoenqjHpEGAKNGjYKNjQ0mTJiAiIgIHD16FLNmzcLEiRM1pbbFixfjk08+QWBgIBo1aoTExEQkJiZqJVYzZ87EkSNHEBMTg9OnT2Po0KFIT0/HuHHjqvcLQEREVMWOXr+Hh7kFcLRSop1rPanD0SuS9kkaPnw4UlJSMH/+fCQkJMDLywt79uyBm5sbACAhIQGxsbGa9ubm5ggODsbUqVPh6+sLGxsbDBs2DAsWLNC0WblyJfLy8jB06FCtc82ZMwdz584FANy+fRsjR45EcnIy7Ozs4Ofnh5CQEM15iYiIaougSxzVVlGSzpOkzzhPEhER1XQ5+YXw+SwYmXmF2DElgJ22oSfzJBEREVHVOnztHjLzCuFcz4SltgpgkkRERFRL/Vtqcyhxeh0qGZMkIiKiWig7rxAHI4smkORabRVToSSpoKAABw4cwOrVq5GRkQEAxYbmExERkXQOX0tC1qNSWxsXq6d/gIop9+i2W7duoW/fvoiNjUVubi5eeOEFWFhYYPHixcjJycH3339fFXESERFROex+VGob2NqRpbYKKveTpGnTpsHX1xf379/XWgbkpZdewsGDBys1OCIiIiq/7LxC/M0JJJ9ZuZ8kHT9+HCdOnICRkZHWdjc3N9y5c6fSAiMiIqKKOXQtCdn5hXCpbwJvZ5baKqrcT5JUKhUKCwuLbb99+zYsLCwqJSgiIiKquKCLRaW2ASy1PZNyJ0kvvPACli5dqnkvk8nw8OFDzJkzB/3796/M2IiIiKicsvIKcPBq0ai2gd4c1fYsyl1uW7JkCZ5//nm0atUKOTk5GDVqFKKiomBra4stW7ZURYxERERURn9fTUJOvgoNrU3h5cwVIZ5FuZMkZ2dnhIeHY+vWrTh37hxUKhUmTZqEV199VasjNxEREVU/ltoqT7mSpPz8fDRv3hy7d+/GhAkTMGHChKqKi4iIiMopM7cAf199NKrNm6PanlW5+iQZGhoiNzeXmSkREVENdPBqEnILVGhkYwpPJ5banlW5O25PnToVixYtQkFBQVXEQ0RERBUUdDEeAEttlaXcfZJOnz6NgwcPYv/+/fD29oaZmZnW/h07dlRacERERFQ2D3MLcOjaPQDAAI5qqxTlTpLq1auH//znP1URCxEREVXQwci7yCtQwd3WDC0dOW9hZSh3krR+/fqqiIOIiIiegWZUmzdLbZWl3EmS2r1793Dt2jXIZDJ4eHjAzs6uMuMiIiKiMsrIycfh649KbVyrrdKUu+N2ZmYmJk6cCEdHR3Tr1g1du3aFk5MTJk2ahKysrKqIkYiIiEpxMDIJeQUqNLYzQwsHltoqS7mTpBkzZuDIkSP4888/8eDBAzx48AB//PEHjhw5gvfff78qYiQiIqJS7H5UahvIUlulKne5bfv27fjtt9/Qo0cPzbb+/fvDxMQEw4YNw6pVqyozPiIiIipFek4+jmpKbRzVVpnK/SQpKysL9vb2xbY3aNCA5TYiIqJqdiDiLvIKVWjawBwe9uZSh1OrlDtJ8vf3x5w5c5CTk6PZlp2djXnz5sHf379SgyMiIqLScVRb1Sl3ue27775D37594eLigjZt2kAmkyE8PBxKpRL79u2rihiJiIhIh7TsfByLSgbAUW1VodxJkpeXF6KiorB582ZcvXoVQgiMGDECr776KkxMTKoiRiIiItJBXWpr1sAcHvYc1VbZKjRPkomJCV577bXKjoWIiIjKIejSo1IbnyJViXL3SVq4cCECAwOLbQ8MDMSiRYsqJSgiIiIqXVpWPo5FqddqY5JUFcqdJK1evRotWrQott3T0xPff/99pQRFREREpdsfkYj8QoHm9hZoxlJblSh3kpSYmAhHx+IZq52dHRISEiolKCIiIiodS21Vr9xJkqurK06cOFFs+4kTJ+DkxEmsiIiIqtqDrDwcfzSqrT9LbVWm3B23J0+ejOnTpyM/Px/PP/88AODgwYP44IMPuCwJERFRNdh/5S4KVAItHCzQtAEnkKwq5U6SPvjgA6SmpmLKlCnIy8sDACiVSnz44YeYPXt2pQdIRERE2nY/KrUNZKmtSsmEEKIiH3z48CEiIyNhYmKCZs2awdjYuLJjq9HS09NhZWWFtLQ0WFpaSh0OERHVEfcz89Dh8wMoUAn8/X53NLbjk6TyKM/v73L3SVIzNzdHhw4dYGFhgRs3bkClUlX0UERERFRG+yMSUaASaOloyQSpipU5Sdq4cSOWLl2qte31119H48aN4e3tDS8vL8TFxVV2fERERPSY3RdZaqsuZU6Svv/+e1hZWWne7927F+vXr8emTZtw5swZ1KtXD/PmzauSIImIiAhIzczDyRspADiqrTqUueP29evX4evrq3n/xx9/YPDgwXj11VcBAF988QUmTJhQ+RESERERAGDflUQUqgQ8nSzhbmsmdTi1XpmfJGVnZ2t1cDp58iS6deumed+4cWMkJiZWbnRERESkEXSRE0hWpzInSW5ubjh37hwAIDk5GVeuXEGXLl00+xMTE7XKcURERFR5Uh7m4uSNogkkuVZb9ShzuW3s2LF4++23ceXKFfz9999o0aIFfHx8NPtPnjwJLy+vKgmSiIiortt7JREqAXg7W8HNhqW26lDmJ0kffvghJk+ejB07dkCpVOLXX3/V2n/ixAmMHDmy3AGsXLkS7u7uUCqV8PHxwbFjx0ptn5ubi48//hhubm4wNjZGkyZNEBgYqNVm+/btaNWqFYyNjdGqVSvs3Lnzmc9LREQkJZbaJCAktHXrVmFoaCjWrl0rIiIixLRp04SZmZm4detWiZ8ZPHiw6NSpkwgODhYxMTHi9OnT4sSJE5r9J0+eFAqFQnzxxRciMjJSfPHFF8LAwECEhIQ803mflJaWJgCItLS0il08ERFRGd3LyBHuH+0Wbh/uFrEpmVKHo9fK8/u7wjNuV4ZOnTqhffv2WLVqlWZby5Yt8eKLL2LhwoXF2u/duxcjRoxAdHQ0rK2tdR5z+PDhSE9Px19//aXZ1rdvX9SvXx9btmyp0Hl14YzbRERUXTaH3MInv19Gaxcr7Hqny9M/QCWqlhm3n1VeXh7OnTuH3r17a23v3bs3Tp48qfMzu3btgq+vLxYvXgxnZ2d4eHhg5syZyM7O1rQ5depUsWP26dNHc8yKnBcoKvOlp6drvYiIiKqDptTGDtvVqtwL3FaW5ORkFBYWwt7eXmu7vb19iVMJREdH4/jx41Aqldi5cyeSk5MxZcoUpKamavolJSYmlnrMipwXABYuXMjJMomIqNolZeTgdAwnkJSCZE+S1GQymdZ7IUSxbWoqlQoymQw//fQTOnbsiP79+2PJkiXYsGGD1tOkshyzPOcFgNmzZyMtLU3z4hIsRERUHfZdLhrV1sa1HlytTaUOp06R7EmSra0tFApFsac3SUlJxZ7yqDk6OsLZ2VlrPqaWLVtCCIHbt2+jWbNmcHBwKPWYFTkvABgbG8PY2Lhc10hERPSsNGu18SlStau0J0lxcXGYOHFimdsbGRnBx8cHwcHBWtuDg4MREBCg8zOdO3dGfHw8Hj58qNl2/fp1yOVyuLi4AAD8/f2LHXP//v2aY1bkvERERFJISs9B6M1UAEA/bweJo6l7Ki1JSk1NxcaNG8v1mRkzZuCHH35AYGAgIiMj8d577yE2NhZvvvkmgKIS19ixYzXtR40aBRsbG0yYMAERERE4evQoZs2ahYkTJ8LExAQAMG3aNOzfvx+LFi3C1atXsWjRIhw4cADTp08v83mJiIhqgr8uJ0IIoF3DenCpz1JbdStzuW3Xrl2l7o+Oji73yYcPH46UlBTMnz8fCQkJ8PLywp49e+Dm5gYASEhIQGxsrKa9ubk5goODMXXqVPj6+sLGxgbDhg3DggULNG0CAgKwdetWfPLJJ/j000/RpEkTbNu2DZ06dSrzeYmIiGoCjmqTVpnnSZLL5ZDJZCituUwmQ2FhYaUFV5NxniQiIqpKd9Nz4LfwIIQATn70PJzqmUgdUq1QJfMkOTo6Yvv27VCpVDpf58+ff+bAiYiIqMhflxIgBNC+YT0mSBIpc5Lk4+NTaiL0tKdMREREVHZBl9RrtTlJHEndVeY+SbNmzUJmZmaJ+5s2bYpDhw5VSlBERER1WWJaDs7cvA8A6M9RbZIpc5LUtWvXUvebmZmhe/fuzxwQERFRXbfn0VMkX7f6cLRiqU0qZS63RUdHs5xGRERUDf4ttXFUm5TKnCQ1a9YM9+7d07wfPnw47t69WyVBERER1VXxD7Jx7tZ9yGRAPy8mSVIqc5L05FOkPXv2lNpHiYiIiMpPXWrr4GYNByulxNHUbZIvcEtERET/Yqmt5ihzkiSTySCTyYptIyIiospx50E2wmIfPCq1cVSb1Mo8uk0IgfHjx8PY2BgAkJOTgzfffBNmZmZa7Xbs2FG5ERIREdURf6lLbY2s0cCSpTaplTlJGjdunNb70aNHV3owREREddnuR2u1DWSprUYoc5K0fv36qoyDiIioTotLzUJ4XFGprS9LbTUCO24TERHVAH9dLnqK1MndGg0sWGqrCZgkERER1QBBF7lWW03DJImIiEhicalZuHA7DXIZ0NeTpbaagkkSERGRxNRzI/k1toGdhbHE0ZAakyQiIiKJ7eEEkjUSkyQiIiIJxaZk4eKjUlsfltpqFCZJREREElKX2vyb2MDWnKW2moRJEhERkYSCLsUDAAZ4c1RbTVPmySSJiOqCQpVAaEwqkjJy0MBCiY7u1lDIuU4lVY2byZm4fCcdCrkMfTztpQ6HnsAkiYjokb2XEzDvzwgkpOVotjlaKTFnUCv09WKHWqp86lJbQBMb2LDUVuOw3EZEhKIE6a3N57USJABITMvBW5vPY++j2ZCJKpNmAklvJuE1EZMkIqrzClUC8/6MgNCxT71t3p8RKFTpakFUMdH3HiIiQV1q46i2mohJEhHVeaExqcWeID1OAEhIy0FoTGr1BUW1nnpupM5NbVHfzEjiaEgXJklEVOclZZScIFWkHVFZBF1KBAAMZKmtxmKSRER1XllXXOfK7FRZbtx7iMiEdBjIZejNUW01FpMkIqrzMrLzn9rG1twIHd2tqyEaqgv2XPy31FbPlKW2mopJEhHVaX9dSsCUn89r3pc0I1JugQpxqVnVExTVekFcq00vMEkiojrrj/A7eGdLGApUAkPaOmHFqHZwsNIuqTlYGsOlngkycgowNjCU/ZLomf2TlIGriRkwVMjQpxVHtdVknEySiOqkX8/G4YPtFyEEMNTHBYv+0xoKuQx9vRyLzbidkpmL/6w6idjULIwPPINtb/jBQmko9SWQngq6WNRhu0tTW1iZ8vuoJuOTJCKqc34+HYtZvxUlSCM7NsTiRwkSACjkMvg3scGQts7wb2IDhVyGBhZK/DixE2zNjRCRkI7XN51DbkGhxFdB+kqzVltrrtVW0zFJIqI6ZcOJGPx35yUAwPiARvjiJS/Iy7A2WyNbM2yY0BFmRgqcik7Be9vCObkkldv1uxm4fvchDBUyvNCKo9pqOiZJRFRnrDl6A3P/jAAAvN6tMeYMagWZrOyL13o5W2HNWF8YKmTYcykRc3ddgRBMlKjs1MuQdGtmBysTltpqOiZJRFQnLD8YhS/2XAUATH2+KWb3a1GuBEmtc1NbfDu8LWQy4MeQW1j+9z+VHSrVYns4qk2vMEkiolpNCIFv9l/DN8HXAQDvv+CB93s3r1CCpDawtRPmDvIEACwJvo6fT8dWSqxUu12/m4GopIcwUsjRi6U2vcAkiYhqLSEEvvzrquZpz+x+LTC1Z7NKOfa4gEaY+nxTAMAnv1/C3suJlXJcqr12q0ttHraw5OhIvcAkiYhqJSEE5v0ZgdVHowEAcwa1whvdm1TqOWa84IERHVyhEsC7W8MQEp1Sqcen2kMIgaCL6lFtLLXpCyZJRFTrqFQCH/9+GRtO3gQAfP6SFyZ0dq/088hkMix40QsvtLJHXoEKr208i4j49Eo/D+m/a3czcONeJowM5OjVkqU2fSF5krRy5Uq4u7tDqVTCx8cHx44dK7Ht4cOHIZPJir2uXr2qadOjRw+dbQYMGKBpM3fu3GL7HRw46ylRbVCoEvhg+0X8fDoWMhnw1dDWeLWTW5Wdz0Ahx/KR7dCxkTUycgswbn0oly+hYtSj2rp72HEiUj0iaZK0bds2TJ8+HR9//DHCwsLQtWtX9OvXD7GxpXeCvHbtGhISEjSvZs3+7WOwY8cOrX2XL1+GQqHAK6+8onUMT09PrXaXLl2qkmskoupTUKjCjF/C8du521DIZVg6vC1e8XWt8vMqDRVYO84XLRwscC8jF2MDQ5HyMLfKz0v6oajUVpQkDWSpTa9ImiQtWbIEkyZNwuTJk9GyZUssXboUrq6uWLVqVamfa9CgARwcHDQvhUKh2Wdtba21Lzg4GKampsWSJAMDA612dnZ2VXKNRFQ98gpUmLolDH+Ex8NALsPyke0wpK1ztZ3fysQQGyd2hHM9E8QkZ2LChjN4mFtQbeenmisyIQPRyUWltp4stekVyZKkvLw8nDt3Dr1799ba3rt3b5w8ebLUz7Zr1w6Ojo7o2bMnDh06VGrbdevWYcSIETAzM9PaHhUVBScnJ7i7u2PEiBGIjo4u9Ti5ublIT0/XehFRzZBbUIgpP53DX5cTYaSQY9VoH/T3rv6/2O0tldg0qSPqmxri4u00vLX5HPIKVNUeB9Us6mVInmtuB3NjLpmqTyRLkpKTk1FYWAh7e+2s2t7eHomJuofSOjo6Ys2aNdi+fTt27NiB5s2bo2fPnjh69KjO9qGhobh8+TImT56stb1Tp07YtGkT9u3bh7Vr1yIxMREBAQFISSl5ZMrChQthZWWlebm6Vs0j/EKVwKkbKfgj/A5O3UjhsgdET5GTX4jXN53DgcgkGBvIsWasj6TLPTSxM8f6CR1hYqjAsahkzPz1AlT8Oa6zhBDYc6nodxrXatM/kqe0T07oJoQocZK35s2bo3nz5pr3/v7+iIuLw9dff41u3boVa79u3Tp4eXmhY8eOWtv79eun+be3tzf8/f3RpEkTbNy4ETNmzNB57tmzZ2vtS09Pr/REae/lBMz7MwIJaTmabY5WSswZ1Ap9vVjHJnpSVl4BJm88i5M3UqA0lGPduA7o3NRW6rDQ1rUevh/jg0kbzmDXhXjYmBvh/waWbwkUqh0iEtIRk5wJYwM5erZoIHU4VE6SPUmytbWFQqEo9tQoKSmp2NOl0vj5+SEqKqrY9qysLGzdurXYUyRdzMzM4O3trfM4asbGxrC0tNR6Vaa9lxPw1ubzWgkSACSm5eCtzeex93JCpZ6PSN89zC3A+MAzOHkjBWZGCmyc0LFGJEhq3T3s8PUrbQAA60/cxPdHSi/pU+2k7rD9XPMGMGOpTe9IliQZGRnBx8cHwcHBWtuDg4MREBBQ5uOEhYXB0bH4U5ZffvkFubm5GD169FOPkZubi8jISJ3HqQ6FqqJJ73Q9kFdvm/dnBEtvRI+kZedjzLrTCL2ZCgulAX6c3AmdGttIHVYxL7ZzxicDWgIAFu29il/OxkkcEVUnIQSCuFabXpM0rZ0xYwbGjBkDX19f+Pv7Y82aNYiNjcWbb74JoKjEdefOHWzatAkAsHTpUjRq1Aienp7Iy8vD5s2bsX37dmzfvr3YsdetW4cXX3wRNjbF/+OcOXMmBg0ahIYNGyIpKQkLFixAeno6xo0bV7UXXILQmNRiT5AeJwAkpOUgNCYV/k1q3i8Cour0ICsPY9aF4tKdNFiZGGLzpE7wdrGSOqwSTe7aGPce5mL1kWjM3nEJ1qZGXLerjrgSn45bKVlQGsrxPEtteknSJGn48OFISUnB/PnzkZCQAC8vL+zZswdubkUTvyUkJGjNmZSXl4eZM2fizp07MDExgaenJ4KCgtC/f3+t416/fh3Hjx/H/v37dZ739u3bGDlyJJKTk2FnZwc/Pz+EhIRozlvdkjJKTpAq0o6otkp5mItXfziNq4kZsDYzwuZJndDKqXJL31Xho74tkJyRh+3nb+Ptn8/j59c6wcfNWuqwqIqp12p7vgVLbfpKJoRgDacC0tPTYWVlhbS0tGfun3TqRgpGrg15arstr/nxSRLVWUnpOXj1h9OISnoIW3Nj/PxaJ3jYW0gdVpnlF6rwxo/n8PfVJFiZGOLXN/31Kn4qHyEEun11CHGp2Vgxqj3LbTVIeX5/S74sCQEd3a3haKVEaeNe6psaoqM7//KkuikhLRsj1oQgKukhHCyV+OUNP71LMAwVcqwY1R7tGtZDWnY+xq4LxZ0H2VKHRVXk0p00xKVmw8RQgedacLJifcUkqQZQyGWYM6gVAJSYKN3PyseivVeRX8iJ6ahuuX0/C8NXhyA6ORPO9Uzwyxv+aGxnLnVYFWJipEDguA5o2sAciek5GLvuNO5n5kkdFlUBdYft51s2gKkRS236iklSDdHXyxGrRreHg5VSa7ujlVIzt8aao9EYtTYEiaV08iaqTW6lZGL46hDEpmahobUptr3hh4Y2plKH9Uzqmxlh08SOcLRS4sa9ouVLsvK4fEltorVWmwQzv1PlYZ+kCqrMPkmPK1QJhMakIikjBw0slOjobg2FXIa9lxMw69eLyMgtgI2ZEZaOaIuuzfgIl2qvG/ceYtTaENxNz0VjWzP8/JpfsT8i9FnU3QwM/f4U0rLz8VxzO6wZ6wtDBf9urQ0uxD3AkBUnYGKowPlPX4CJkeLpH6Jqwz5Jekwhl8G/iQ2GtHWGfxMbKORFBbi+Xo74c2oXtHK0REpmHsYGhmLpgeucO4lqpWuJGRi+uihBatbAHFvfqF0JEgA0s7dA4PgOUBrKcejaPXy4/SL4N2vtoC619WzZgAmSnmOSpEca2Zphx5QAjOzoCiGApQeiMH59KJIf5kodGlGluRKfhpFrQ5D8MBctHS2x9XU/NLCoXQmSmo9bfawY1R4KuQw7zt/Bl39dlTokekZapTaOaNN7TJL0jNJQgYUvt8aSYW00C2gOWHYMZ26mSh0a0TO7ePsBRq09jdTMPLR2scKW1zrBxtxY6rCqVM+W9vjyZW8AwOqj0Vh7lMuX6LPwuAe48yAbpkYK9GjOCST1HZMkPfVyexf88U5nNLEzw930XIxYE4I1R2/wcT3prXO37uPVtaeRlp2P9g3rYfPkTqhnaiR1WNXiFV9XfNSvBQDg8z2R2Bl2W+KIqKLUT5F6tbSH0pClNn3HJEmPedhbYNc7XTCkrRMKVQJf7LmK1388h7SsfKlDIyqX09EpGLPuNDJyC9DR3RqbJnWCpdJQ6rCq1RvdGmNSF3cAwKxfL+LwtSSJI6LyUqkE9nCttlqFSZKeMzM2wNLhbbHgRS8YKeQIjriLgf87hku306QOjahMjkclY9z6UGTlFaJzUxtsmNAB5nVwCQeZTIaP+7fEkLZOKFAJvLX5PMJi70sdFpVDWNwDxKflwMxIge4eHH1cGzBJqgVkMhlG+7lh+1sBcLU2QVxqNv6z6iR+DLnF8hvVaIeuJmHixjPIyVehR3M7rBvXoU5PvCeXy/DV0Dbo2swW2fmFmLjhDP5Jeih1WFRG6qdIL7Riqa22YJJUi3i7WGH3O13xQit75BWq8OnvlzFtazgyczlRHdU8+68k4vUfzyKvQIUXWtlj9Rgf/mIBYGQgx/ejfdDGxQr3s/IxLjCUE8jqAe1Sm5PE0VBlYZJUy1iZGmLNGB983L8lFHIZdl2Ix+D/Hcf1uxlSh0akEXQxAVN+Oo/8QoEB3o5Y+Wp7GBswQVIzMzZA4PgOaGxrhjsPsjEuMJR9DWu4sLj7SEjLgbmxAbo2s5U6HKokTJJqIZlMhte6Nca21/3gYFm09MGQ/53AjvMcMUPS+z3sDqZuOY8ClcCLbZ3w3Yi2nGlaBxtzY2yc2BENLIxx7W4GJm86g5z8QqnDohLsvshSW23E/5lqMd9G1gh6t4umf8OMXy7go+0X+R8tSeaXM3F475dwqATwio8LvhnWFgZMkErkam2KjRM7wkJpgDM37+Odn8NQwEWuaxytUhvXaqtV+L9TLWdjbowNEzrivV4ekMmArWfi8PLKk7iZnCl1aFTHbA65hQ+2X4QQwKudGmLRf1prlt2hkrV0tMQPY31hZCDHgci7+HjnZQ7IqGHOxd7H3fRcWBgboKsHS221CZOkOkAhl2Far2b4cWIn2JgZISIhHQOXH8dfj/7yIapqgcdj8MnvlwEAEzo3woIXvSBnglRmnRrbYPnIdpDLgG1n4/DN/utSh0SPUU8g+YKnPfvW1TJMkuqQLs1sEfRuV3RoVB8Pcwvw1k/nMf/PCOQV8PE9VZ3vj9zA/N0RAIA3ujfG/w1sBZmMCVJ59fF0wOcvFS1f8r9D/2DDiRiJIyIAKHys1Ma12mofJkl1jIOVEj+/5oc3ujUGAASeiMHwNadw50G2xJFRbbTsYJRm0dZ3ezbDR31bMEF6BiM7NsT7L3gAAObtjsCfF+IljojO3kxFUkYuLJQG6NKUE0jWNkyS6iBDhRyz+7fE2rG+sFQaICz2AQYsO4ZDXAaBKokQAl/vu4YlwUVloVl9mmPGCx5MkCrBO883xVh/NwgBzPglHMejkqUOqU5TP0Xq4+kAIwP+Sq1teEfrsBda2SPo3a7wdrbCg6x8TFh/Bl/vu4ZCFTuFUsUJIfDFnkj879A/AICP+7fE2881lTiq2kMmk2HOIE8MaO2I/EKBN348y2WIJFKoEthzOREAR7XVVkyS6jhXa1P89pY/xvi5ASjq6zD6h9NIyuAMv1R+KpXA3F1XsPZYUX+ZeYM98dqj0i5VHoVchiXD2iCgiQ0y8woxfn0oYjhitdqduZmKexm5sFQaoHNTjmqrjZgkEYwNFPjsRS8sG9kOpkYKnIpOwYBlxxESnSJ1aKRHVCqBj3+/hI2nbkEmA754yRvjAhpJHVatZWygwOoxPvB0skRKZh7GBp5GUjr/uKlO6lFtLLXVXryrpDG4jRN2vdMFHvbmuJeRi1FrQ7Di0D9QsfxGT1GoEpj120VsCY2DXAZ8NbQNRnVqKHVYtZ6F0hAbJnSEm40p4lKzMW79GaTncPmS6lCoEvjrsnqtNpbaaismSaSlaQNz/P52Z7zc3hkqAXy17xombTyD+5l5UodGNVR+oQrTt4Vj+/nbUMhlWDqiHYb6uEgdVp1hZ2GMTRM7wtbcGJEJ6Xh901nOql8NTsekIPlhHqxMDFlqq8WYJFExpkYG+OaVNlj0H28YG8hx6No9DFx+HOFxD6QOjWqYvAIVpv4chj8vxMNQIcOKUe0wuA1XQK9ubjZm2DChA8yNDRASnYr3toVzAEYVU5fa+no6cO3BWox3lnSSyWQY3qEhdk7pjEY2prjzIBuvfH8SG07EcEkEAgDk5Bfirc3nsPdKIowUcnw/2gd9vVh2kIqXsxXWjPGBkUKOvy4n4v/+4PIlVaWgUIW96lFtLLXVakySqFStnCyxa2oX9PNyQH6hwNw/I/DOz2HIYL+HOi0nvxCvbTqLg1eTYGwgx9pxvujZ0l7qsOq8gKa2+HZ4W8hkwE+nY/HdwSipQ6qVTsekIiUzD/VNDeHfxEbqcKgKMUmip7JUGmLlq+0xZ1ArGMhlCLqUgMH/O4GI+HSpQyMJZOUVYML6MzgWlQwTQwXWj++A7h6cabimGNDaEfMHewIAlh6IwuaQWxJHVPsEPZpAsq8XS221He8ulYlMJsOEzu745U1/OFkpEZOciZdWnsAvZ+KkDo2qUUZOPsYFhuJUdArMjQ2waVJHBLDTao0zxr8R3u3ZDADw6R+XuZh1JXq81NafE0jWekySqFzaN6yPoHe7okdzO+QWqPDB9ouY+esFZOdxNE1tl5adjzHrQnHm5n1YKA3w46SO6NDIWuqwqATv9WqGkR0bQghg2tZwnLrBec8qQ0h0KlLVpbbGLLXVdkySqNzqmxkhcFwHzOrTHHIZ8Nu523hxxQncuPdQ6tCoitzPzMOrP4QgPO4B6pkaYstrfmjXsL7UYVEpZDIZFrzohT6e9sgrVOH1TWdxJZ7LlzyroEtFiwr39XKEAUtttR7vMFWIXC7D2881xebJnWBrboxrdzMwePlxrkpeCyU/zMXItSG4fCcdNmZG2PKaH7ycraQOi8pAIZfhuxHt0NHdGhm5BRi//gxiU7KkDktv5T9WahvIUW11ApMkeiYBTWyxZ1oX+DW2RmZeIaZuCcP//XEZuQUsv9UGSek5GLEmBFcTM2BnYYytr/uhpaOl1GFROSgNFVg71hctHCxwLyMXYwNPI/lhrtRh6aVTN1JwPysfNmZG6OTOUnNdwCSJnlkDCyU2T+qEt59rAgDYdOoWXvn+FOJS+RerPktIy8bwNSH4J+khHK2U+OUNfzSzt5A6LKoAKxNDbJzYES71TXAzJQsT1p/Bw9wCqcPSO5oJJL0cWGqrI3iXqVIYKOSY1acF1o/vgHqmhrh4Ow0Dlh3DgYi7UodGFRCXmoVhq08hJjkTLvVN8Msb/nC3NZM6LHoG9pZKbJrYEdZmRrh0Jw1v/ngOeQUqqcPSG/mFKuy9wgkk6xomSVSpnmvRAEHvdkVb13pIzynA5E1nsfCvSBQU8j9jfXEzORPDV59CXGo23GxMse0Nf7ham0odFlWCxnbmWD++A0yNFDj+TzLe//UCF7AuoxP/JCMtOx+25kbo5M5RbXUFkySqdM71ip48TOjcCACw+kg0Rq09jbvpOdIGRk/1T9JDDFt9CvFpOWhiZ4Zf3vCHcz0TqcOiStTGtR5Wj/GBoUKGPy/EY/7uCC5fUgZ7Hs011c/LEQq5TOJoqLowSaIqYWQgx5xBnlj5anuYGxsg9GYq+n93DMejkqUOjUpwLTEDI9acQlJGLprbW2Dr6/6wt1RKHRZVga7N7PD1K20AABtO3sTKwzckjqhmyytQYd+Voq4DnECybmGSRFWqv7cj/pzaBS0dLZGSmYcxgafx3YEoPuKvYS7fScOINaeQ/DAPrRwtseV1P9hZGEsdFlWhIW2d8X8DWwEAvtp3DdvOxEocUc114oa61GaMjhzVVqdIniStXLkS7u7uUCqV8PHxwbFjx0pse/jwYchksmKvq1evatps2LBBZ5ucHO1ST3nOS8/G3dYMO6cEYEQHVwgBfHvgOsatD0UKhyHXCBfiHmDU2hDcz8pHGxcrbHnND9ZmRlKHRdVgYhd3vNWjaFTq7B2XEMyBFjqpR7X193Zgqa2OkTRJ2rZtG6ZPn46PP/4YYWFh6Nq1K/r164fY2NL/orl27RoSEhI0r2bNmmntt7S01NqfkJAApfLfskFFz0sVpzRU4Mv/tMbXr7SB0lCOY1HJGLDsOM7dSpU6tDrt7M1UvPrDaaTnFMDHrT42T+4EK1NDqcOiavRBn+YY5usClQDe+fk8ztzkz+Tjikptj0a1sdRW50iaJC1ZsgSTJk3C5MmT0bJlSyxduhSurq5YtWpVqZ9r0KABHBwcNC+FQqG1XyaTae13cHColPPSsxvq44I/3u6CxnZmSEzPwfDVIfjhWDQ7jkrg1I0UjA0MxcPcAvg1tsamiR1hoWSCVNfIZDJ88ZI3erZogNwCFSZtOINriRlSh1VjHP/nHjJyCtDAwhi+XKuwzpEsScrLy8O5c+fQu3dvre29e/fGyZMnS/1su3bt4OjoiJ49e+LQoUPF9j98+BBubm5wcXHBwIEDERYW9sznzc3NRXp6utaLKqa5gwV2vdMFg9o4oUAlsCAoEm/8eA5p2flSh1ZnHIu6hwkbQpGVV4iuzWyxfnxHmBkbSB0WScRAIcf/RrWHj1t9pOcUYGzgady+z8lgAWC3ptTGUW11kWRJUnJyMgoLC2Fvb6+13d7eHomJiTo/4+joiDVr1mD79u3YsWMHmjdvjp49e+Lo0aOaNi1atMCGDRuwa9cubNmyBUqlEp07d0ZUVFSFzwsACxcuhJWVlebl6upa0UsnAObGBlg2oi0+G+IJI4Uc+yPuYtDy47h8hwtwVrVDV5MwaeNZ5OSr8FxzO6wd6wsTI8XTP0i1momRAuvG+cLD3hx303MxNjAUqZl5UoclqdyCQgQ/GtXGCSTrJsk7bstk2pm5EKLYNrXmzZvjtddeQ/v27eHv74+VK1diwIAB+PrrrzVt/Pz8MHr0aLRp0wZdu3bFL7/8Ag8PDyxfvrzC5wWA2bNnIy0tTfOKi4sr76XSE2QyGcb4N8Jvb/nDpb4JYlOz8PKqk/jp9C2W36rIviuJeP3Hs8grUKF3K3usHuMLpSETJCpSz9QIGyd2hJOVEtH3MjFhwxlk5dXd5UuORyUjI7cA9pbG8GlYX+pwSAKSJUm2trZQKBTFnt4kJSUVe8pTGj8/P81TIl3kcjk6dOigaVPR8xobG8PS0lLrRZWjtUs9BE3til4t7ZFXoMLHOy/jvW3hyOTaUpVq98V4TPnpPPILBQa0dsSKV9vDyEDyv5OohnG0MsGmSR1Rz9QQF+Ie4K3N55FfR2fMD3qs1CZnqa1Okux/SCMjI/j4+CA4OFhre3BwMAICAsp8nLCwMDg6lvwYVAiB8PBwTZvKOi9VLitTQ6wd64PZ/VpAIZfh9/B4DFlxAlF32YG0MuwMu413t4ShUCXwcjtnfDe8LQy5QCeVoGkDCwSO7wCloRxHrt/DB79drHNzm+XkF2qmROCotrpL0p6aM2bMwJgxY+Dr6wt/f3+sWbMGsbGxePPNNwEUlbju3LmDTZs2AQCWLl2KRo0awdPTE3l5edi8eTO2b9+O7du3a445b948+Pn5oVmzZkhPT8eyZcsQHh6OFStWlPm8JA2ZTIY3ujdBu4b1MXXLefyT9BCD/3cCX7zshZfauUgdnt7adiYWH+24BCGA4b6u+OJlb3ZApadq37A+Vr3qg8mbzmJn2B3YWRjjv/1bSh1WtTn2qNTmYKlEe5ba6ixJk6Thw4cjJSUF8+fPR0JCAry8vLBnzx64ubkBABISErTmLsrLy8PMmTNx584dmJiYwNPTE0FBQejfv7+mzYMHD/D6668jMTERVlZWaNeuHY4ePYqOHTuW+bwkrY7u1gh6tyumbQ3DiX9S8N62CwiNuY85g1qx/0w5/XjqJj794woAYIyfG+YN9mTZgMrsuRYNsPg/rfH+rxew5mg0bM2N8Hq3JlKHVS2CLsYDYKmtrpMJ9pCtkPT0dFhZWSEtLY39k6pIoUpg2cEoLPs7CkIAnk6WWPlqe7jZmEkdml744Vg0FgRFAgAmdXHHJwNaljo4gagka47ewBd7ilY2+OaVNviPT+1+spuTXwifz4KRmVeI7W8FwMeNT5Jqk/L8/manBKqxFHIZ3nvBAxsndIS1mRGuxKdj4PLj2Hu55KkaqMjKw/9oEqS3ejRhgkTP5PVuTfBaV3cAwAfbL+LQ1SSJI6paR67fQ2ZeIZyslGjnWk/qcEhCTJKoxuvmYYegd7vA160+MnIK8Obmc/hsd0SdHXFTGiEElh64jsV7rwEApvdqhg/6NGeCRM9sdr+WeKmdMwpVAlN+Oo/zsfelDqnKcFQbqTFJIr3gaGWCLa/74fVujQEA647HYPjqU4h/kC1xZDWHEAJf7buGpQeKprv4oG9zTO/lwQSJKoVcLsPioa3R3cMO2fmFmLjhDP5Jqn2jT3PyC3EgkhNIUhEmSaQ3DBVy/Ld/S6we4wMLpQHOxz7AgGXHcPha7X70XxZCCHweFImVh28AAD4Z0BJTejSVOCqqbQwVcqwa3R5tXOvhQVY+xq4LRUJa7fpD5fC1e8jKK4RzPRO0ZamtzmOSRHqnj6cDgqZ2hZezJe5n5WPChjNYsv8aCuvYPC5qKpXA//1xBT8cjwEAfDbEE5O7NpY4KqqtTI0MsH58BzS2M0N8Wg7GrgvFg6zas3xJ0KWiUtuA1o58CktMkkg/NbQxxW9vBuDVTg0hBLDs738wZt1p3MvIlTq0aqVSCfx35yX8GHILMhnw5cveGOPfSOqwqJazNjPCpokdYW9pjKikh5i88Syy8wqlDuuZZecV4uCjUlt/TiBJYJJEekxpqMDnL3njuxFtYWqkwMkbKRiw7BhOR6dIHVq1KChUYeavF7D1TBzksqKh2SM6NpQ6LKojXOqbYtPETrBUGuDsrfuYuuU8CvR8MMXha0maUlsbFyupw6EagEkS6b0hbZ2x653OaNbAHEkZuRj1w2msOnyjVi+jkF+owvRt4dgRdgcKuQzfjWiHl9vX7rlrqOZp7mCBdeM7wNhAjgORSfjvzkt6vTj17keltoEstdEjTJKoVmjawAJ/vNMZLz8aorxo71W8tulsreoroZZXoMI7P5/H7osJMFTIsGJUewxq4yR1WFRHdWhkjf+Nag+5DPjl7G18te+a1CFVSFZeAf6OLBoEwlFtpMYkiWoNUyMDfDOsDRa+7A0jAzkOXk3CgGXHcSHugdShVZqc/EK8ufkc9l25CyMDOVaP8UFfLwepw6I67oVW9lj4sjcAYOXhGwh8NIhAnxy6eg/Z+YVwtTaBtzNLbVSESRLVKjKZDCM7NsSOtwLgZmOKOw+yMfT7k9h48qZelwGAok6lr206i7+vJkFpKMcPY33xfAt7qcMiAgAM79AQs/o0BwDM3x2BXRfiJY6ofIIuFcU7wNuJpTbSYJJEtZKXsxX+nNoFfTztkV8oMGfXFbyzJQwPcwukDq1CMnMLMGFDKI5FJcPUSIH14zuim4ed1GERaZnSownGBzQCALz/SziORd2TNqAyysorwN+PlloZyFIbPYZJEtValkpDfD/aB58ObAUDuQxBFxMwePlxRCakSx1auWTk5GNcYChColNhbmyATRM7wr+JjdRhERUjk8nwfwNbYWBrR+QXCrzx4zm9KHf/fTUJOfkquNmYwtOJC5bTv5gkUa0mk8kwqYs7tr3hD0crJaKTM/HiihP45Wyc1KGVSVpWPkavC8XZW/dhqTTA5smd4NvIWuqwiEokl8vwzbA26NLUFll5hZiw4Qyi7z2UOqxSqddqG+DNUW2kjUkS1Qk+bvUR9G5XdPewQ26BCh/8dhGzfr1QoyfAS83Mw6gfQnAh7gHqmxri59f8uEwC6QVjAwW+H+MDb2crpGbmYWxgKJLSc6QOS6fM3H9LbZxAkp7EJInqDGszI6wf3wEze3tALgN+PXcbL608USP/yr2XkYtRa0NwJT4dtuZG2PK6H7w44ob0iLmxAdZP6IBGNqa4fT8bYwNDkZ6TL3VYxRy8moTcAhUasdRGOjBJojpFLpfhneebYfOkTrA1N8LVxAwMWn4cuy/WnJE4d9NzMGLNKVxNzEADC2Nsfd0fLRz4nzfpH1tzY/w4qRPsLIxxNTEDr208i5z8mvX0NujRzz7XaiNdmCRRnRTQ1BZ73u2Kju7WyMwrxDs/h2HurivIK5B2WYX4B9kYvvoUbtzLhJOVEr+84Y+mDcwljYnoWbham2LDhA6wMDbA6ZhUTNsaVmMWo36YW4BD14pG4A3w5oSsVByTJKqzGlgq8fPkTnirRxMAwIaTN/HK6lO4fT9LknjiUrMwbPUp3EzJgqu1Cba94Y9GtmaSxEJUmTydrLBmrC+MFHLsu3IXn/x+uUbMW3Yw8i7yClRobGuGlo4WUodDNRCTJKrTDBRyfNi3BQLH+8LKxBAX4h5gwLLj+Pvq3WqNIyY5E8NWn8Lt+9lwtzXDttf94WptWq0xEFUl/yY2+G5EW8hkwJbQWCw9ECV1SNitHtXGUhuVgEkSEYDnW9gj6N0uaONaD2nZ+Zi44SwW7b1aLaua/5OUgeGrTyEhLQdNG5hj2+t+cKpnUuXnJapu/bwd8dkQLwDAdwej8GPILcliycjJx5Hrj0ptnECSSsAkiegRl/qm+PUNf82MwasO38CoH05X6dDlq4npGL46BEkZuWjhYIGtr/uhgaWyys5HJLXRfm6Y3qsZAOD//riMPZcSJInjYGQS8gpUaGJnhub2LLWRbkySiB5jZCDH3MGe+N+odjA3NkBoTCr6LzuGk/8kV/q5Lt9Jw4g1IUjJzIOXsyW2vOYHW3PjSj8PUU0zrWczvNqpIYQApm8Nx8kblf/z9TT/ltq4VhuVjEkSkQ4DWzth1zud0cLBAskP8zB63WksPxgFVSWNygmLvY9Ra0PwICsfbVzr4afJfqhvZlQpxyaq6WQyGeYP8UI/LwfkFarw+qZzuHwnrdrOn56Tj6PqUhsnkKRSMEkiKkFjO3PsnNIZw3xdoBLAN8HXMWHDGaRm5j3Tcc/cTMWYdaFIzymAr1t9bJ7UEVYmhpUUNZF+UMhl+HZ4W/g1tsbD3AKMX38Gt1Iyq+XcByLuIq9QhaYNzOFhzyk2qGRMkohKYWKkwOKhbfDV0NZQGspx5Po9DFh2DOdu3a/Q8U7eSMa4wFA8zC2Af2MbbJzYERZKJkhUNykNFVgz1hctHS2R/DAXYwNDcS8jt8rPy7XaqKyYJBGVwSu+rvj97c5obGuGhLQcDF99Cj8ciy7XXC9Hr9/DhPVnkJVXiK7NbBE4vgPMjA2qMGqims9SaYiNEzrA1doEt1KyMH59KDKqcPmStOx8HI3iqDYqGyZJRGXUwsESf7zTGQNaO6JAJbAgKBJvbT5fpvWoDkbexeSNZ5FboELPFg2wdqwvTIwU1RA1Uc3XwFKJTRM7wcbMCFfi0/Hm5nPILaia5UuCI+4iv1DAw94cHhzVRk/BJImoHCyUhvjfyHaYP8QThgoZ9l5JxKDlxzWdTgtVAqdupOCP8Ds4dSMFhSqBvZcT8Obmc8grVKGvpwNWjfaB0pAJEtHj3G3NsGFCR5gZKXDinxTM+OVCpQ2UeJxmrTYuQ0JlwGf9ROUkk8kw1r8RWrvUw9s/ncetlCy8vOokhrZ3wd/XkpCY9u+8SvVMDJGekw+VAAa1ccKSYW1gqODfJkS6eLtYYfUYX0zYEIqgiwmwNTPC3MGeldZvKC0rH8cfTecxoLVDpRyTajf+b01UQW1d6yHo3S7o2aIB8gpU+Dk0VitBAoAH2UUJkp+7DZYOb8sEiegpujSzxZJhRcuXbDx1CysO/VNpx94fkYj8QoEWDhZo2oClNno6/o9N9AzqmRrh+9E+sHhKB+xbqdUztJmoNhjUxglzBrYCAHy9/zq2hsZWynGDHs3u3Z9zI1EZMUkiekZnb91HRm5BqW0S0nIQGpNaTRER6b/xnd3x9nNNAAD/3XkJ+68kPtPxHmTl4XhUUamNSRKVFZMkomeUlFG2td3K2o6Iiszs3RzDfV2hEsDULWHP9IfG/it3UaBSl9o4gSSVDZMkomfUwKJsC9KWtR0RFZHJZPj8JS/0ammP3AIVJm08g6uJ6RU61u5HpbaBnBuJyoFJEtEz6uhuDUcrJUoafyMD4GilREd36+oMi6hWMFDI8b9R7dChUX1k5BRg7LpQxKVmlesY9zPzcOIfltqo/JgkET0jhVyGOYOKOpk+mSip388Z1AoKOZc/IKoIpaECP4ztgOb2FkjKyMW4wNByraG470oiClUCrRwt0diOpTYqOyZJRJWgr5cjVo1uDwcr7ZKag5USq0a3R18v/vVK9CysTA2xcWJHONczQXRyJiZsOIPMpwyYUFOPauMyJFReMlGexadIIz09HVZWVkhLS4OlpaXU4VANUagSCI1JRVJGDhpYFJXY+ASJqPL8k/QQr3x/Evez8tHNww4/jPWFkUHJf++nZuahw+cHUKgSODyzBxrZmlVjtFQTlef3t+RPklauXAl3d3colUr4+Pjg2LFjJbY9fPgwZDJZsdfVq1c1bdauXYuuXbuifv36qF+/Pnr16oXQ0FCt48ydO7fYMRwcOPsqPTuFXAb/JjYY0tYZ/k1smCARVbKmDcwROL4DTAwVOHr9Hj74rfTlS9SlNi9nSyZIVG6SJknbtm3D9OnT8fHHHyMsLAxdu3ZFv379EBtb+sRh165dQ0JCgubVrFkzzb7Dhw9j5MiROHToEE6dOoWGDRuid+/euHPnjtYxPD09tY5x6dKlKrlGIiKqXO0a1seq0e1hIJfh9/B4fL4nEiUVRYIucgJJqjhJk6QlS5Zg0qRJmDx5Mlq2bImlS5fC1dUVq1atKvVzDRo0gIODg+alUPy7WOhPP/2EKVOmoG3btmjRogXWrl0LlUqFgwcPah3DwMBA6xh2dnZVco1ERFT5ejRvgK9eaQ0AWHc8BmuORhdrk/IwFydvPFqrjUkSVYBkSVJeXh7OnTuH3r17a23v3bs3Tp48Wepn27VrB0dHR/Ts2ROHDh0qtW1WVhby8/Nhba09/DoqKgpOTk5wd3fHiBEjEB1d/Afscbm5uUhPT9d6ERGRdF5q54JPBrQEACz86yp+O3dba//eK4lQCcDb2QpuNiy1UflJliQlJyejsLAQ9vb2Wtvt7e2RmKh7+nlHR0esWbMG27dvx44dO9C8eXP07NkTR48eLfE8H330EZydndGrVy/Ntk6dOmHTpk3Yt28f1q5di8TERAQEBCAlJaXE4yxcuBBWVlaal6urazmvmIiIKtvkro3xRrfGAIAPt1/E31fvolAlcOpGCjacuAkA6OfNPqdUMZKNbouPj4ezszNOnjwJf39/zfbPP/8cP/74o1Zn7NIMGjQIMpkMu3btKrZv8eLF+PLLL3H48GG0bt26xGNkZmaiSZMm+OCDDzBjxgydbXJzc5Gbm6t5n56eDldXV45uIyKSmEolMPO3C9hx/g4MFTJYKg2R8tg8SnYWxvhsiCen4iAAejK6zdbWFgqFothTo6SkpGJPl0rj5+eHqKioYtu//vprfPHFF9i/f3+pCRIAmJmZwdvbW+dx1IyNjWFpaan1IiIi6cnlMiz6T2t4OVkiv1BoJUgAkJyRi7c2n8feywkSRUj6SrIkycjICD4+PggODtbaHhwcjICAgDIfJywsDI6O2n8dfPXVV/jss8+wd+9e+Pr6PvUYubm5iIyMLHYcIiLSD3KZDPce5urcpy6XzPszAoWlTBdA9CQDKU8+Y8YMjBkzBr6+vvD398eaNWsQGxuLN998EwAwe/Zs3LlzB5s2bQIALF26FI0aNYKnpyfy8vKwefNmbN++Hdu3b9ccc/Hixfj000/x888/o1GjRponVebm5jA3L5qOfubMmRg0aBAaNmyIpKQkLFiwAOnp6Rg3blw1fwWIiKgyhMak4m667iQJKEqUEtJyEBqTCv8mNtUXGOk1SZOk4cOHIyUlBfPnz0dCQgK8vLywZ88euLm5AQASEhK05kzKy8vDzJkzcefOHZiYmMDT0xNBQUHo37+/ps3KlSuRl5eHoUOHap1rzpw5mDt3LgDg9u3bGDlyJJKTk2FnZwc/Pz+EhIRozktERPolKSOnUtsRAVyWpMK4LAkRUc1x6kYKRq4NeWq7La/58UlSHacXHbeJiIgqS0d3azhaKVHSQkAyAI5WRespEpUVkyQiItJ7CrkMcwa1AoBiiZL6/ZxBrbieIpULkyQiIqoV+no5YtXo9nCwUmptd7BSYtXo9pwnicpN0o7bRERElamvlyNeaOWA0JhUJGXkoIFFUYmNT5CoIpgkERFRraKQy9g5myoFy21EREREOjBJIiIiItKBSRIRERGRDkySiIiIiHRgkkRERESkA5MkIiIiIh2YJBERERHpwCSJiIiISAcmSUREREQ6cMbtChJCAADS09MljoSIiIjKSv17W/17vDRMkiooIyMDAODq6ipxJERERFReGRkZsLKyKrWNTJQllaJiVCoV4uPjYWFhAZmschdOTE9Ph6urK+Li4mBpaVmpx64JeH36r7ZfY22/PqD2XyOvT/9V1TUKIZCRkQEnJyfI5aX3OuKTpAqSy+VwcXGp0nNYWlrW2m9+gNdXG9T2a6zt1wfU/mvk9em/qrjGpz1BUmPHbSIiIiIdmCQRERER6cAkqQYyNjbGnDlzYGxsLHUoVYLXp/9q+zXW9usDav818vr0X024RnbcJiIiItKBT5KIiIiIdGCSRERERKQDkyQiIiIiHZgkEREREenAJKmaHT16FIMGDYKTkxNkMhl+//33p37myJEj8PHxgVKpROPGjfH9999XfaDPoLzXePjwYchksmKvq1evVk/A5bBw4UJ06NABFhYWaNCgAV588UVcu3btqZ/Tp3tYkWvUp3u4atUqtG7dWjNBnb+/P/76669SP6NP9w8o/zXq0/3TZeHChZDJZJg+fXqp7fTtPqqV5fr07R7OnTu3WKwODg6lfkaK+8ckqZplZmaiTZs2+N///lem9jExMejfvz+6du2KsLAw/Pe//8W7776L7du3V3GkFVfea1S7du0aEhISNK9mzZpVUYQVd+TIEbz99tsICQlBcHAwCgoK0Lt3b2RmZpb4GX27hxW5RjV9uIcuLi748ssvcfbsWZw9exbPP/88hgwZgitXruhsr2/3Dyj/Narpw/170pkzZ7BmzRq0bt261Hb6eB+Bsl+fmj7dQ09PT61YL126VGJbye6fIMkAEDt37iy1zQcffCBatGihte2NN94Qfn5+VRhZ5SnLNR46dEgAEPfv36+WmCpTUlKSACCOHDlSYht9v4dluUZ9vodCCFG/fn3xww8/6Nyn7/dPrbRr1Nf7l5GRIZo1ayaCg4NF9+7dxbRp00psq4/3sTzXp2/3cM6cOaJNmzZlbi/V/eOTpBru1KlT6N27t9a2Pn364OzZs8jPz5coqqrRrl07ODo6omfPnjh06JDU4ZRJWloaAMDa2rrENvp+D8tyjWr6dg8LCwuxdetWZGZmwt/fX2cbfb9/ZblGNX27f2+//TYGDBiAXr16PbWtPt7H8lyfmj7dw6ioKDg5OcHd3R0jRoxAdHR0iW2lun9c4LaGS0xMhL29vdY2e3t7FBQUIDk5GY6OjhJFVnkcHR2xZs0a+Pj4IDc3Fz/++CN69uyJw4cPo1u3blKHVyIhBGbMmIEuXbrAy8urxHb6fA/Leo36dg8vXboEf39/5OTkwNzcHDt37kSrVq10ttXX+1eea9S3+wcAW7duxfnz53HmzJkytde3+1je69O3e9ipUyds2rQJHh4euHv3LhYsWICAgABcuXIFNjY2xdpLdf+YJOkBmUym9V48miT9ye36qnnz5mjevLnmvb+/P+Li4vD111/XyB9utXfeeQcXL17E8ePHn9pWX+9hWa9R3+5h8+bNER4ejgcPHmD79u0YN24cjhw5UmISoY/3rzzXqG/3Ly4uDtOmTcP+/fuhVCrL/Dl9uY8VuT59u4f9+vXT/Nvb2xv+/v5o0qQJNm7ciBkzZuj8jBT3j+W2Gs7BwQGJiYla25KSkmBgYKAz264t/Pz8EBUVJXUYJZo6dSp27dqFQ4cOwcXFpdS2+noPy3ONutTke2hkZISmTZvC19cXCxcuRJs2bfDdd9/pbKuv968816hLTb5/586dQ1JSEnx8fGBgYAADAwMcOXIEy5Ytg4GBAQoLC4t9Rp/uY0WuT5eafA+fZGZmBm9v7xLjler+8UlSDefv748///xTa9v+/fvh6+sLQ0NDiaKqemFhYTXu8TdQ9JfL1KlTsXPnThw+fBju7u5P/Yy+3cOKXKMuNfUe6iKEQG5urs59+nb/SlLaNepSk+9fz549i42EmjBhAlq0aIEPP/wQCoWi2Gf06T5W5Pp0qcn38Em5ubmIjIxE165dde6X7P5VabdwKiYjI0OEhYWJsLAwAUAsWbJEhIWFiVu3bgkhhPjoo4/EmDFjNO2jo6OFqampeO+990RERIRYt26dMDQ0FL/99ptUl/BU5b3Gb7/9VuzcuVNcv35dXL58WXz00UcCgNi+fbtUl1Cit956S1hZWYnDhw+LhIQEzSsrK0vTRt/vYUWuUZ/u4ezZs8XRo0dFTEyMuHjxovjvf/8r5HK52L9/vxBC/++fEOW/Rn26fyV5cvRXbbiPj3va9enbPXz//ffF4cOHRXR0tAgJCREDBw4UFhYW4ubNm0KImnP/mCRVM/UwzSdf48aNE0IIMW7cONG9e3etzxw+fFi0a9dOGBkZiUaNGolVq1ZVf+DlUN5rXLRokWjSpIlQKpWifv36okuXLiIoKEia4J9C13UBEOvXr9e00fd7WJFr1Kd7OHHiROHm5iaMjIyEnZ2d6NmzpyZ5EEL/758Q5b9Gfbp/JXkyiagN9/FxT7s+fbuHw4cPF46OjsLQ0FA4OTmJl19+WVy5ckWzv6bcP5kQj3o+EREREZEGO24TERER6cAkiYiIiEgHJklEREREOjBJIiIiItKBSRIRERGRDkySiIiIiHRgkkRERESkA5MkIiIiIh2YJBHVQDdv3oRMJkN4eLjUoWhcvXoVfn5+UCqVaNu2rdThSK5Hjx6YPn265n2jRo2wdOnSUj8jk8nw+++/P/O5K+s4VWnu3LlP/T4ZP348XnzxxWc6z6efforXX3/9mY7xuA4dOmDHjh2VdjzSb0ySiHQYP348ZDIZvvzyS63tv//+O2QymURRSWvOnDkwMzPDtWvXcPDgwRLbJSYmYurUqWjcuDGMjY3h6uqKQYMGlfqZ6jRo0CD06tVL575Tp05BJpPh/Pnz5T7umTNnKvWXNVByopGQkIB+/fpV6rmetGHDBshksmKvH374oUrPWx53797Fd999h//+97+VdsxPP/0UH330EVQqVaUdk/QXkySiEiiVSixatAj379+XOpRKk5eXV+HP3rhxA126dIGbmxtsbGx0trl58yZ8fHzw999/Y/Hixbh06RL27t2L5557Dm+//XaJx87Pz69wXOU1adIk/P3337h161axfYGBgWjbti3at29f7uPa2dnB1NS0MkJ8KgcHBxgbG1f5eSwtLZGQkKD1evXVV6v8vGW1bt06+Pv7o1GjRpV2zAEDBiAtLQ379u2rtGOS/mKSRFSCXr16wcHBAQsXLiyxja6/9JcuXar1n7a6pPDFF1/A3t4e9erVw7x581BQUIBZs2bB2toaLi4uCAwMLHb8q1evIiAgAEqlEp6enjh8+LDW/oiICPTv3x/m5uawt7fHmDFjkJycrNnfo0cPvPPOO5gxYwZsbW3xwgsv6LwOlUqF+fPnw8XFBcbGxmjbti327t2r2S+TyXDu3DnMnz8fMpkMc+fO1XmcKVOmQCaTITQ0FEOHDoWHhwc8PT0xY8YMhISEaB3v+++/x5AhQ2BmZoYFCxYAAFatWoUmTZrAyMgIzZs3x48//ljs692wYUMYGxvDyckJ7777rmbfypUr0axZMyiVStjb22Po0KE6Yxw4cCAaNGiADRs2aG3PysrCtm3bMGnSJKSkpGDkyJFwcXGBqakpvL29sWXLFp3HU3uy3BYVFYVu3bpBqVSiVatWCA4OLvaZDz/8EB4eHjA1NUXjxo3x6aefahLGDRs2YN68ebhw4YLmKY465ifLbZcuXcLzzz8PExMT2NjY4PXXX8fDhw81+9Xfg19//TUcHR1hY2ODt99++6nJqUwmg4ODg9bLxMQEABAbG4shQ4bA3NwclpaWGDZsGO7evVvisQoLCzFjxgzUq1cPNjY2+OCDD/Dk0qG//fYbvL29NdfRq1cvZGZmlnjMrVu3YvDgwVrbVCoVFi1ahKZNm8LY2BgNGzbE559/DgDw9/fHRx99pNX+3r17MDQ0xKFDhwAACoUC/fv3f+r9pjqiypfQJdJD48aNE0OGDBE7duwQSqVSxMXFCSGE2Llzp3j8x2bOnDmiTZs2Wp/99ttvhZubm9axLCwsxNtvvy2uXr0q1q1bJwCIPn36iM8//1xcv35dfPbZZ8LQ0FDExsYKIYSIiYkRAISLi4v47bffREREhJg8ebKwsLAQycnJQggh4uPjha2trZg9e7aIjIwU58+fFy+88IJ47rnnNOfu3r27MDc3F7NmzRJXr14VkZGROq93yZIlwtLSUmzZskVcvXpVfPDBB8LQ0FBcv35dCCFEQkKC8PT0FO+//75ISEgQGRkZxY6RkpIiZDKZ+OKLL5769QUgGjRoINatWydu3Lghbt68KXbs2CEMDQ3FihUrxLVr18Q333wjFAqF+Pvvv4UQQvz666/C0tJS7NmzR9y6dUucPn1arFmzRgghxJkzZ4RCoRA///yzuHnzpjh//rz47rvvSjz/rFmzRKNGjYRKpdJs27BhgzA2Nhapqani9u3b4quvvhJhYWHixo0bYtmyZUKhUIiQkBCtr+3jq7K7ubmJb7/9VgghRGFhofDy8hI9evQQYWFh4siRI6Jdu3YCgNi5c6fmM5999pk4ceKEiImJEbt27RL29vZi0aJFQgghsrKyxPvvvy88PT1FQkKCSEhIEFlZWZqvn/o4mZmZmlXUL126JA4ePCjc3d3FuHHjNOcZN26csLS0FG+++aaIjIwUf/75pzA1NdV8/XRZv369sLKy0rlPpVKJdu3aiS5duoizZ8+KkJAQ0b59e61V25/82Vi0aJGwsrLSfD9PmjRJWFhYiCFDhgghir6fDQwMxJIlS0RMTIy4ePGiWLFihc7vNSGESE1NFTKZTOueCCHEBx98IOrXry82bNgg/vnnH3Hs2DGxdu1aIYQQy5cvFw0bNtS678uXLxfOzs6isLBQs23lypWiUaNGJX5tqO5gkkSkgzpJEkIIPz8/MXHiRCFExZMkNzc3rf+EmzdvLrp27ap5X1BQIMzMzMSWLVuEEP8mSV9++aWmTX5+vnBxcdH8Ev30009F7969tc4dFxcnAIhr164JIYp+kbdt2/ap1+vk5CQ+//xzrW0dOnQQU6ZM0bxv06aNmDNnTonHOH36tAAgduzY8dTzARDTp0/X2hYQECBee+01rW2vvPKK6N+/vxBCiG+++UZ4eHiIvLy8Ysfbvn27sLS0FOnp6U89txBCREZGCgCaBEwIIbp16yZGjhxZ4mf69+8v3n//fc370pKkffv2CYVCoUmuhRDir7/+KpYkPWnx4sXCx8dH817X95cQ2knSmjVrRP369cXDhw81+4OCgoRcLheJiYlCiH+/BwsKCjRtXnnlFTF8+PASY1m/fr0AIMzMzDQve3t7IYQQ+/fvFwqFQpPUCyHElStXBAARGhqqM3ZHR0ed38/qn7Nz584JAOLmzZslxvS4sLAwAUArhvT0dGFsbKxJip6UlJQkDAwMxNGjRzXb/P39xaxZs7Ta/fHHH0Iul2v9zFLdxHIb0VMsWrQIGzduRERERIWP4enpCbn83x83e3t7eHt7a94rFArY2NggKSlJ63P+/v6afxsYGMDX1xeRkZEAgHPnzuHQoUMwNzfXvFq0aAGgqP+Qmq+vb6mxpaenIz4+Hp07d9ba3rlzZ825ykI8Kp2UtWP7k3FFRkaWGsMrr7yC7OxsNG7cGK+99hp27tyJgoICAMALL7wANzc3NG7cGGPGjMFPP/2ErKwsAMBPP/2k9TU6duwYWrRogYCAAE2J88aNGzh27BgmTpwIoKg09Pnnn6N169awsbGBubk59u/fj9jY2DJdW2RkJBo2bAgXFxfNtsfvpdpvv/2GLl26wMHBAebm5vj000/LfI7Hz9WmTRuYmZlpfd1UKhWuXbum2ebp6QmFQqF57+joWOz77UkWFhYIDw/XvE6ePKk5p6urK1xdXTVtW7VqhXr16un8nklLS0NCQoLO72e1Nm3aoGfPnvD29sYrr7yCtWvXltofMDs7G0BR38HHvxa5ubno2bOnzs/Y2dnhhRdewE8//QQAiImJwalTp4r1szIxMYFKpUJubm6J56e6gUkS0VN069YNffr00TmCRi6XF+tXoaufh6GhodZ7mUymc1tZRtSokxCVSoVBgwZp/RILDw/X9IVRe/yXZ1mOqyaEKNdIvmbNmkEmk5U5sdIVV2kxuLq64tq1a1ixYgVMTEwwZcoUdOvWDfn5+bCwsMD58+exZcsWODo64v/+7//Qpk0bPHjwAIMHD9b6+qh/MU+aNAnbt29Heno61q9fDzc3N80v12+++QbffvstPvjgA/z9998IDw9Hnz59ytzx/cnvCV3XFhISghEjRqBfv37YvXs3wsLC8PHHH5e7c31p9+nx7RX5fpPL5WjatKnm1bhx41LPWd7vmccpFAoEBwfjr7/+QqtWrbB8+XI0b94cMTExOtvb2toCgFYipe4vVZpXX30Vv/32G/Lz8/Hzzz/D09MTbdq00WqTmpoKU1PTMh2PajcmSURl8OWXX+LPP//U/CWtZmdnh8TERK1fipU5t9HjnZ0LCgpw7tw5zdOi9u3b48qVK2jUqJHWL7KmTZuWOTECikYwOTk54fjx41rbT548iZYtW5b5ONbW1ujTpw9WrFihs7PtgwcPSv18y5YtnxqDiYkJBg8ejGXLluHw4cM4deoULl26BKDoyUSvXr2wePFiXLx4ETdv3sTff/8NCwsLra+N+hffsGHDoFAo8PPPP2Pjxo2YMGGC5hf8sWPHMGTIEIwePRpt2rRB48aNERUVVeavRatWrRAbG4v4+HjNtlOnTmm1OXHiBNzc3PDxxx/D19cXzZo1KzbizsjICIWFhU89V3h4uNbX/MSJE5DL5fDw8ChzzOWhvr64uDjNtoiICKSlpen8nrGysoKjo6PO7+fHyWQydO7cGfPmzUNYWBiMjIywc+dOnTE0adIElpaWWk94mzVrBhMTk1Knm3jxxReRk5ODvXv34ueff8bo0aOLtbl8+XKFRjhS7cMkiagMvL298eqrr2L58uVa23v06IF79+5h8eLFuHHjBlasWIG//vqr0s67YsUK7Ny5E1evXsXbb7+N+/fva0pCb7/9NlJTUzFy5EiEhoYiOjoa+/fvx8SJE5/6i/VJs2bNwqJFi7Bt2zZcu3YNH330EcLDwzFt2rRyHWflypUoLCxEx44dsX37dkRFRSEyMhLLli3TWW56MoYNGzbg+++/R1RUFJYsWYIdO3Zg5syZAIpGe61btw6XL19GdHQ0fvzxR5iYmMDNzQ27d+/GsmXLEB4ejlu3bmHTpk1QqVRo3rx5ieczNzfH8OHD8d///hfx8fEYP368Zl/Tpk0RHByMkydPIjIyEm+88QYSExPL/HXo1asXmjdvjrFjx+LChQs4duwYPv74Y602TZs2RWxsLLZu3YobN25g2bJlxRKCRo0aISYmBuHh4UhOTtZZ/nn11VehVCoxbtw4XL58GYcOHcLUqVMxZswY2Nvblznm8ujVqxdat26NV199FefPn0doaCjGjh2L7t27l1jenTZtGr788kvN9/OUKVO0EufTp0/jiy++wNmzZxEbG4sdO3bg3r17JSbqcrkcvXr10kqslUolPvzwQ3zwwQfYtGkTbty4gZCQEKxbt07TxszMDEOGDMGnn36KyMhIjBo1qtixjx07ht69e1fwq0O1imS9oYhqsMc7bqvdvHlTGBsbiyd/bFatWiVcXV2FmZmZGDt2rPj888+Lddx+8lhPdvoVQrvjr7rj9s8//yw6deokjIyMRMuWLcXBgwe1PnP9+nXx0ksviXr16gkTExPRokULMX36dM3oHV3n0aWwsFDMmzdPODs7C0NDQ9GmTRvx119/abV5Wsdttfj4ePH2228LNzc3YWRkJJydncXgwYPFoUOHNG1QQgfmlStXisaNGwtDQ0Ph4eEhNm3apNm3c+dO0alTJ2FpaSnMzMyEn5+fOHDggBBCiGPHjonu3buL+vXrCxMTE9G6dWuxbdu2p8Z68uRJAaBYB/iUlBQxZMgQYW5uLho0aCA++eQTMXbsWK37WFrHbSGEuHbtmujSpYswMjISHh4eYu/evcWue9asWcLGxkaYm5uL4cOHi2+//VZrRFlOTo74z3/+I+rVqycAiPXr1+v8+l28eFE899xzQqlUCmtra/Haa69pjQrT9T04bdo0rdFoTyptdJsQQty6dUsMHjxYmJmZCQsLC/HKK69oOooLUbzjdn5+vpg2bZqwtLQU9erVEzNmzND6mkZERIg+ffoIOzs7YWxsLDw8PMTy5ctLPL8QQuzdu7fYyLTCwkKxYMEC4ebmJgwNDUXDhg2LjbgMCgoSAES3bt2KHfP27dvC0NBQq9M91V0yIXQUz4mIiGo4IQT8/Pwwffp0jBw5slKOOWvWLKSlpWHNmjWVcjzSbyy3ERGRXpLJZFizZo1mlGNlaNCgAT777LNKOx7pNz5JIiIiItKBT5KIiIiIdGCSRERERKQDkyQiIiIiHZgkEREREenAJImIiIhIByZJRERERDowSSIiIiLSgUkSERERkQ5MkoiIiIh0+H+KBP1uKalw1QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Mean f1: 0.5797950727730947\n"
     ]
    }
   ],
   "source": [
    "#SVM with BOW\n",
    "from sklearn.svm import SVC\n",
    "sv=SVC()\n",
    "scores_sv = model_selection.cross_val_score(sv, bow_train , df_train[\"target\"], cv=5, scoring=\"f1\")\n",
    "plot_learning_evolution(cv_values,scores_sv,'SVM with BOW')\n",
    "print(\"\\033[1m Mean f1:\", scores_sv.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "16ef7750",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:11:56.208096Z",
     "iopub.status.busy": "2023-08-12T22:11:56.207657Z",
     "iopub.status.idle": "2023-08-12T22:12:28.773691Z",
     "shell.execute_reply": "2023-08-12T22:12:28.772553Z"
    },
    "papermill": {
     "duration": 32.613749,
     "end_time": "2023-08-12T22:12:28.798842",
     "exception": false,
     "start_time": "2023-08-12T22:11:56.185093",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m Mean f1: 0.45709979728628786\n"
     ]
    }
   ],
   "source": [
    "#SVM with TF-IDF\n",
    "from sklearn.svm import SVC\n",
    "sv=SVC()\n",
    "scores_sv_tfidf = model_selection.cross_val_score(sv, train_tfidf , df_train[\"target\"], cv=5, scoring=\"f1\")\n",
    "plot_learning_evolution(cv_values,scores_sv_tfidf,'SVM with TF-IDF')\n",
    "print(\"\\033[1m Mean f1:\", scores_sv_tfidf.mean())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f679b5",
   "metadata": {
    "papermill": {
     "duration": 0.021527,
     "end_time": "2023-08-12T22:12:28.842183",
     "exception": false,
     "start_time": "2023-08-12T22:12:28.820656",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **🎯Model Training & Model Evaluation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f557a3",
   "metadata": {
    "papermill": {
     "duration": 0.021475,
     "end_time": "2023-08-12T22:12:28.885469",
     "exception": false,
     "start_time": "2023-08-12T22:12:28.863994",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we have seen in the graphs above, our dataset works better with the BOW method. \n",
    "Therefore, we train our models with the set of vectors obtained from this method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37ac16fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:12:28.931319Z",
     "iopub.status.busy": "2023-08-12T22:12:28.930940Z",
     "iopub.status.idle": "2023-08-12T22:12:33.556092Z",
     "shell.execute_reply": "2023-08-12T22:12:33.554820Z"
    },
    "papermill": {
     "duration": 4.651682,
     "end_time": "2023-08-12T22:12:33.558981",
     "exception": false,
     "start_time": "2023-08-12T22:12:28.907299",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.87      0.83      1446\n",
      "           1       0.80      0.69      0.74      1067\n",
      "\n",
      "    accuracy                           0.80      2513\n",
      "   macro avg       0.80      0.78      0.79      2513\n",
      "weighted avg       0.80      0.80      0.79      2513\n",
      "\n",
      "  ==================================================\n",
      "MultinomialNB()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.85      0.83      1446\n",
      "           1       0.79      0.74      0.76      1067\n",
      "\n",
      "    accuracy                           0.80      2513\n",
      "   macro avg       0.80      0.79      0.80      2513\n",
      "weighted avg       0.80      0.80      0.80      2513\n",
      "\n",
      "  ==================================================\n",
      "SVC()\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.92      0.84      1446\n",
      "           1       0.86      0.64      0.74      1067\n",
      "\n",
      "    accuracy                           0.80      2513\n",
      "   macro avg       0.82      0.78      0.79      2513\n",
      "weighted avg       0.81      0.80      0.80      2513\n",
      "\n",
      "  ==================================================\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(bow_train, df_train['target'], test_size=0.33, random_state=42)\n",
    "from sklearn.metrics import classification_report\n",
    "models=[clf,clf_NB,sv]\n",
    "\n",
    "for model in models:\n",
    "    \n",
    "    model.fit(x_train,y_train)\n",
    "    y_pred=model.predict(x_test)\n",
    "    print(model)\n",
    "    print(classification_report(y_test,y_pred))\n",
    "    print(\"  ==================================================\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05d90eaf",
   "metadata": {
    "papermill": {
     "duration": 0.022307,
     "end_time": "2023-08-12T22:12:33.604179",
     "exception": false,
     "start_time": "2023-08-12T22:12:33.581872",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Submission**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8f0ea22a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:12:33.650416Z",
     "iopub.status.busy": "2023-08-12T22:12:33.649743Z",
     "iopub.status.idle": "2023-08-12T22:12:35.183962Z",
     "shell.execute_reply": "2023-08-12T22:12:35.182711Z"
    },
    "papermill": {
     "duration": 1.560541,
     "end_time": "2023-08-12T22:12:35.186688",
     "exception": false,
     "start_time": "2023-08-12T22:12:33.626147",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  target\n",
       "0   0       1\n",
       "1   2       1\n",
       "2   3       1\n",
       "3   9       0\n",
       "4  11       1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we found that SVM is better than other\n",
    "submission[\"target\"] = sv.predict(bow_test)\n",
    "submission.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0444df03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-12T22:12:35.236008Z",
     "iopub.status.busy": "2023-08-12T22:12:35.235591Z",
     "iopub.status.idle": "2023-08-12T22:12:35.251571Z",
     "shell.execute_reply": "2023-08-12T22:12:35.250420Z"
    },
    "papermill": {
     "duration": 0.042641,
     "end_time": "2023-08-12T22:12:35.253975",
     "exception": false,
     "start_time": "2023-08-12T22:12:35.211334",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission.to_csv(\"submission.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 120.475835,
   "end_time": "2023-08-12T22:12:36.399861",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-08-12T22:10:35.924026",
   "version": "2.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}