From 212d69b1cab6241c6acf3bce111a694b9f9a1144 Mon Sep 17 00:00:00 2001
From: jcollopy-tulane <jcollopy@tulane.edu>
Date: Mon, 29 Apr 2024 00:28:36 -0500
Subject: [PATCH] Removing More FIles

---
 notebooks/Experiments.ipynb |  107 -
 notebooks/NLP_Project.ipynb | 4566 -----------------------------------
 2 files changed, 4673 deletions(-)
 delete mode 100644 notebooks/Experiments.ipynb
 delete mode 100644 notebooks/NLP_Project.ipynb

diff --git a/notebooks/Experiments.ipynb b/notebooks/Experiments.ipynb
deleted file mode 100644
index 9774ccd..0000000
--- a/notebooks/Experiments.ipynb
+++ /dev/null
@@ -1,107 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Experiments\n",
-    "\n",
-    "Here you can organize all the experiments and exploration as you figure out how to collect and analyze your data and build your NLP tool. The experiments you conduct here will contribute to the report/presentation of your project.\n",
-    "\n",
-    "Once you've finalized everything, you should then transfer the parts that are necessary for your demo to the code in the `nlp` folder."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# configure matplotlib to print pretty figures \n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "from IPython.display import set_matplotlib_formats\n",
-    "set_matplotlib_formats('pdf', 'png')\n",
-    "plt.rcParams['savefig.dpi'] = 75\n",
-    "\n",
-    "plt.rcParams['figure.autolayout'] = False\n",
-    "plt.rcParams['figure.figsize'] = 10, 6\n",
-    "plt.rcParams['axes.labelsize'] = 18\n",
-    "plt.rcParams['axes.titlesize'] = 20\n",
-    "plt.rcParams['font.size'] = 16\n",
-    "plt.rcParams['lines.linewidth'] = 2.0\n",
-    "plt.rcParams['lines.markersize'] = 8\n",
-    "plt.rcParams['legend.fontsize'] = 14\n",
-    "\n",
-    "plt.rcParams['text.usetex'] = True\n",
-    "plt.rcParams['font.family'] = \"serif\"\n",
-    "plt.rcParams['font.serif'] = \"cm\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/pdf": 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\n",
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\n",
-      "text/plain": [
-       "<Figure size 576x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def plot_main_figure():\n",
-    "    plt.figure(figsize=(8,6))\n",
-    "    plt.plot([.1, 1, 5, 10], [.90, .92, .93, .89], 'bo-')\n",
-    "    plt.xlabel('C')\n",
-    "    plt.ylabel('F1')\n",
-    "    plt.savefig('results.pdf')\n",
-    "    plt.show()\n",
-    "    \n",
-    "plot_main_figure()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "df = pd.DataFrame([{'C': .1, 'F1': .90}, {'C': 1, 'F1': .92}, {'C': 5, 'F1': .93}, {'C': 10, 'F1': .89}])\n",
-    "df.to_latex(open('table.tex', 'wt'), 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": "3.8.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/NLP_Project.ipynb b/notebooks/NLP_Project.ipynb
deleted file mode 100644
index 24df0f7..0000000
--- a/notebooks/NLP_Project.ipynb
+++ /dev/null
@@ -1,4566 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cea209e5",
-   "metadata": {},
-   "source": [
-    "# A Text Classification of Milwaukee Bucks's Fan Reactions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35b0438a-f46f-4d1c-8f2b-44930342a42d",
-   "metadata": {},
-   "source": [
-    "## Project Goal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "48ec7eb9-7403-4520-86d8-ffbf337bfb13",
-   "metadata": {},
-   "source": [
-    "The goal of this project is to evaluate the performances of different text classification methods on domain-specific social media data. The data used are comments from Milwaukee Bucks' fans in postgame Reddit threads. Models will be used to predict whether a comment follows a win or a loss. In doing so, one can get a better idea of how B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f02f2231",
-   "metadata": {},
-   "source": [
-    "## Collecting and Preparing the Data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "225cbfe5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importing the Necessary Modules\n",
-    "from tqdm import tqdm\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "# import praw\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.feature_extraction.text import CountVectorizer\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "import re\n",
-    "import matplotlib.pyplot as plt\n",
-    "import nltk\n",
-    "from nltk.corpus import stopwords\n",
-    "from nltk.tokenize import word_tokenize\n",
-    "from nltk.stem import PorterStemmer\n",
-    "from nltk import punkt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "caf7c7d0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Accessing API\n",
-    "user_agent = \"Scraper 1.0 by u/colloj\"\n",
-    "\n",
-    "reddit = praw.Reddit(client_id='-RlNNaCimqKKkJ_aX1FcKg',\n",
-    "                     client_secret='4vSZ7Dm4rtDYXevU8PtIiIK6dtD_Ng',\n",
-    "                     user_agent= user_agent)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "73f585b4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Getting the Data\n",
-    "subreddit_name = 'MkeBucks'\n",
-    "\n",
-    "# Define the string you want to search for in post titles\n",
-    "search_string = '[POSTGAME THREAD] Our Milwaukee Bucks'\n",
-    "start_date = '2023-10-23'\n",
-    "\n",
-    "post_names = []\n",
-    "comments = []\n",
-    "\n",
-    "for submission in reddit.subreddit(subreddit_name).search(search_string, time_filter='year', syntax='lucene'):\n",
-    "    if '[POSTGAME THREAD]' in submission.title:\n",
-    "        print(submission.title)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1a22dbd1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Getting the Data\n",
-    "subreddit_name = 'MkeBucks'\n",
-    "\n",
-    "# Define the string you want to search for in post titles\n",
-    "search_string = '[POSTGAME THREAD] Our Milwaukee Bucks'\n",
-    "start_date = '2023-10-23'\n",
-    "\n",
-    "post_names = []\n",
-    "comments = []\n",
-    "\n",
-    "total_submissions = len(list(reddit.subreddit(subreddit_name).search(search_string, time_filter='year', syntax='lucene')))\n",
-    "\n",
-    "for submission in tqdm(reddit.subreddit(subreddit_name).search(search_string, time_filter='year', syntax='lucene'), total = total_submissions):\n",
-    "    if '[POSTGAME THREAD]' in submission.title:\n",
-    "        submission.comments.replace_more(limit=None)\n",
-    "        for comment in submission.comments.list():\n",
-    "            comments.append(comment.body)\n",
-    "            post_names.append(submission.title)\n",
-    "\n",
-    "post_names, comments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6f756d2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a DataFrame from the lists\n",
-    "df = pd.DataFrame({'Post_Name': post_names, 'Comment': comments})\n",
-    "\n",
-    "# Display the DataFrame\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e9b8311d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_csv(\"bucks.csv\", index = False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cd3135e4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Post_Name</th>\n",
-       "      <th>Comment</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>\\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Took too long to pull Brook and Pat out and gi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>The Bucks, for whatever reason, look like they...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>lmfao if brunson tears this team apart tyrese ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Hear me out... \\n\\nFirst blessoe then jrue, sh...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "\n",
-       "                                             Comment  \n",
-       "0  \\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ...  \n",
-       "1  Took too long to pull Brook and Pat out and gi...  \n",
-       "2  The Bucks, for whatever reason, look like they...  \n",
-       "3  lmfao if brunson tears this team apart tyrese ...  \n",
-       "4  Hear me out... \\n\\nFirst blessoe then jrue, sh...  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(\"bucks.csv\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1f2b3e8c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Post_Name</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>\\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ...</td>\n",
-       "      <td>1/3/2024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Took too long to pull Brook and Pat out and gi...</td>\n",
-       "      <td>1/3/2024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>The Bucks, for whatever reason, look like they...</td>\n",
-       "      <td>1/3/2024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>lmfao if brunson tears this team apart tyrese ...</td>\n",
-       "      <td>1/3/2024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Hear me out... \\n\\nFirst blessoe then jrue, sh...</td>\n",
-       "      <td>1/3/2024</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "\n",
-       "                                             Comment      Date  \n",
-       "0  \\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ...  1/3/2024  \n",
-       "1  Took too long to pull Brook and Pat out and gi...  1/3/2024  \n",
-       "2  The Bucks, for whatever reason, look like they...  1/3/2024  \n",
-       "3  lmfao if brunson tears this team apart tyrese ...  1/3/2024  \n",
-       "4  Hear me out... \\n\\nFirst blessoe then jrue, sh...  1/3/2024  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Getting the Dates\n",
-    "def get_after_last_space(string):\n",
-    "    return string.split(' ')[-1]\n",
-    "df[\"Date\"] = df[\"Post_Name\"].apply(get_after_last_space)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "230d247a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['1/3/2024', '3/4/2024', '2/15/2024', '10/26/2023', '1/31/2024',\n",
-       "       '3/8/2024', '3/10/2024', '11/1/2023', '11/8/2023', '1/6/2024',\n",
-       "       '1/27/2024', '2/03/2024', '11/6/2023', '1/8/2024', '1/11/2024',\n",
-       "       '2/25/2024', '2/23/2024', '11/11/2023', '3/14/2024', '1/14/2024',\n",
-       "       '11/22/2023', '1/4/2024', '11/18/2023', '11/24/2023', '2/04/2024',\n",
-       "       '3/1/2024', '1/1/2024', '3/12/2024', '04/26/2023', '1/24/2024',\n",
-       "       '2/12/2024', '12/11/2023', '10/20/2023', '2/13/2024', '2/8/2024',\n",
-       "       '11/9/2023', '12/23/2023', '3/6/2024', '11/28/2023', '11/30/2023',\n",
-       "       '1/20/2024', '2/27/2024', '12/13/2023', '11/15/2023', '1/13/2024',\n",
-       "       '11/17/2023', '12/2/2023', '12/17/2023', '12/27/2023', '2/06/2024',\n",
-       "       '10/29/2023', '12/29/2023', '1/17/2024', '11/26/2023',\n",
-       "       '12/21/2023', '11/20/2023', '1/22/2024', '1/26/2024', '04/24/2023',\n",
-       "       '12/19/2023', '12/16/2023', '2/9/2024', '2/29/2024', '11/13/2023',\n",
-       "       '04/22/2023', '04/19/2023', '11/3/2023', '04/16/2023', 'round'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[\"Date\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "36c1f8df",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['1/3/2024', '3/4/2024', '2/15/2024', '10/26/2023', '1/31/2024',\n",
-       "       '3/8/2024', '3/10/2024', '11/1/2023', '11/8/2023', '1/6/2024',\n",
-       "       '1/27/2024', '2/03/2024', '11/6/2023', '1/8/2024', '1/11/2024',\n",
-       "       '2/25/2024', '2/23/2024', '11/11/2023', '3/14/2024', '1/14/2024',\n",
-       "       '11/22/2023', '1/4/2024', '11/18/2023', '11/24/2023', '2/04/2024',\n",
-       "       '3/1/2024', '1/1/2024', '3/12/2024', '04/26/2023', '1/24/2024',\n",
-       "       '2/12/2024', '12/11/2023', '10/20/2023', '2/13/2024', '2/8/2024',\n",
-       "       '11/9/2023', '12/23/2023', '3/6/2024', '11/28/2023', '11/30/2023',\n",
-       "       '1/20/2024', '2/27/2024', '12/13/2023', '11/15/2023', '1/13/2024',\n",
-       "       '11/17/2023', '12/2/2023', '12/17/2023', '12/27/2023', '2/06/2024',\n",
-       "       '10/29/2023', '12/29/2023', '1/17/2024', '11/26/2023',\n",
-       "       '12/21/2023', '11/20/2023', '1/22/2024', '1/26/2024', '04/24/2023',\n",
-       "       '12/19/2023', '12/16/2023', '2/9/2024', '2/29/2024', '11/13/2023',\n",
-       "       '04/22/2023', '04/19/2023', '11/3/2023', '04/16/2023'],\n",
-       "      dtype=object)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = df[df[\"Date\"] != \"round\"]\n",
-    "df[\"Date\"].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "150e0ec2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Post_Name</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>\\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Took too long to pull Brook and Pat out and gi...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>The Bucks, for whatever reason, look like they...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>lmfao if brunson tears this team apart tyrese ...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Hear me out... \\n\\nFirst blessoe then jrue, sh...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "\n",
-       "                                             Comment       Date  \n",
-       "0  \\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ... 2024-01-03  \n",
-       "1  Took too long to pull Brook and Pat out and gi... 2024-01-03  \n",
-       "2  The Bucks, for whatever reason, look like they... 2024-01-03  \n",
-       "3  lmfao if brunson tears this team apart tyrese ... 2024-01-03  \n",
-       "4  Hear me out... \\n\\nFirst blessoe then jrue, sh... 2024-01-03  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['Date'] = pd.to_datetime(df['Date'])\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "06d0da79",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Post_Name</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Date</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>\\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Took too long to pull Brook and Pat out and gi...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>The Bucks, for whatever reason, look like they...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>lmfao if brunson tears this team apart tyrese ...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Hear me out... \\n\\nFirst blessoe then jrue, sh...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "\n",
-       "                                             Comment       Date  \n",
-       "0  \\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ... 2024-01-03  \n",
-       "1  Took too long to pull Brook and Pat out and gi... 2024-01-03  \n",
-       "2  The Bucks, for whatever reason, look like they... 2024-01-03  \n",
-       "3  lmfao if brunson tears this team apart tyrese ... 2024-01-03  \n",
-       "4  Hear me out... \\n\\nFirst blessoe then jrue, sh... 2024-01-03  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Getting Data For This Season\n",
-    "df = df[df[\"Date\"] >= \"2023-10-26\"]\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c989b9d3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "64"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Number of Games\n",
-    "len(df[\"Post_Name\"].unique())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "27af1d90",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12266"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Number of Columns\n",
-    "len(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "84123afd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Post_Name</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Result</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>\\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "      <td>Loss</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Took too long to pull Brook and Pat out and gi...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "      <td>Loss</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>The Bucks, for whatever reason, look like they...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "      <td>Loss</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>lmfao if brunson tears this team apart tyrese ...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "      <td>Loss</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...</td>\n",
-       "      <td>Hear me out... \\n\\nFirst blessoe then jrue, sh...</td>\n",
-       "      <td>2024-01-03</td>\n",
-       "      <td>Loss</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (24-10) ...   \n",
-       "\n",
-       "                                             Comment       Date Result  \n",
-       "0  \\n||\\t\\t\\n|:-:|\\t\\t\\n|[](/MIL) **130 -  140** ... 2024-01-03   Loss  \n",
-       "1  Took too long to pull Brook and Pat out and gi... 2024-01-03   Loss  \n",
-       "2  The Bucks, for whatever reason, look like they... 2024-01-03   Loss  \n",
-       "3  lmfao if brunson tears this team apart tyrese ... 2024-01-03   Loss  \n",
-       "4  Hear me out... \\n\\nFirst blessoe then jrue, sh... 2024-01-03   Loss  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create the Result Column\n",
-    "def label_column(string):\n",
-    "    if any(word in string for word in [\"defeat\", \"trounce\", \"beat\"]):\n",
-    "        return \"Win\"\n",
-    "    elif any(word in string for word in [\"fall\", \"sputter\", \"are defeated\"]):\n",
-    "        return \"Loss\"\n",
-    "    else:\n",
-    "        return \"Unknown\"\n",
-    "df[\"Result\"] = df[\"Post_Name\"].apply(label_column)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "d345d448",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Sorting Dataframe By Date\n",
-    "df = df.sort_values(by = \"Date\").reset_index(drop = True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "cca11432",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Post_Name</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Result</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>But have you considered Damian Lillard?</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Help yourself to a Dame flair :)</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
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-       "      <td>Win</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Dame regularly went nuclear with the most medi...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Wait until you see him get hot from 3 and star...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "\n",
-       "                                             Comment       Date Result  \n",
-       "0            But have you considered Damian Lillard? 2023-10-26    Win  \n",
-       "1                   Help yourself to a Dame flair :) 2023-10-26    Win  \n",
-       "2  the most casual 30 point games in the nba, the... 2023-10-26    Win  \n",
-       "3  Dame regularly went nuclear with the most medi... 2023-10-26    Win  \n",
-       "4  Wait until you see him get hot from 3 and star... 2023-10-26    Win  "
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "567ce84e",
-   "metadata": {},
-   "source": [
-    "### Prepare Comments for Modeling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1a615f00",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <td>Win</td>\n",
-       "      <td>but have you considered damian lillard</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Help yourself to a Dame flair :)</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>help yourself to a dame flair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>the most casual 30 point games in the nba, the...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>the most casual 30 point games in the nba ther...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Dame regularly went nuclear with the most medi...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>dame regularly went nuclear with the most medi...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Wait until you see him get hot from 3 and star...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>wait until you see him get hot from 3 and star...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "\n",
-       "                                             Comment       Date Result  \\\n",
-       "0            But have you considered Damian Lillard? 2023-10-26    Win   \n",
-       "1                   Help yourself to a Dame flair :) 2023-10-26    Win   \n",
-       "2  the most casual 30 point games in the nba, the... 2023-10-26    Win   \n",
-       "3  Dame regularly went nuclear with the most medi... 2023-10-26    Win   \n",
-       "4  Wait until you see him get hot from 3 and star... 2023-10-26    Win   \n",
-       "\n",
-       "                                         Comment_Adj  \n",
-       "0             but have you considered damian lillard  \n",
-       "1                     help yourself to a dame flair   \n",
-       "2  the most casual 30 point games in the nba ther...  \n",
-       "3  dame regularly went nuclear with the most medi...  \n",
-       "4  wait until you see him get hot from 3 and star...  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Tokenize, then rejoin\n",
-    "def tokenize(document):\n",
-    "    # YOUR CODE HERE\n",
-    "    document = document.split()\n",
-    "    \n",
-    "    for item in document:\n",
-    "        document = [re.sub(r'^\\W+|\\W+$', \"\", item) for item in document]\n",
-    "            \n",
-    "    document = [item.lower() for item in document]\n",
-    "    \n",
-    "    document = \" \".join(document)\n",
-    "    \n",
-    "    return document\n",
-    "\n",
-    "df[\"Comment_Adj\"] = df[\"Comment\"].apply(tokenize)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "3383201b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>No_Stop</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>But have you considered Damian Lillard?</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>but have you considered damian lillard</td>\n",
-       "      <td>considered damian lillard</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Help yourself to a Dame flair :)</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>help yourself to a dame flair</td>\n",
-       "      <td>help dame flair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>the most casual 30 point games in the nba, the...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>the most casual 30 point games in the nba ther...</td>\n",
-       "      <td>casual 30 point games nba ’ good reason ’ obse...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Dame regularly went nuclear with the most medi...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>dame regularly went nuclear with the most medi...</td>\n",
-       "      <td>dame regularly went nuclear mediocre teams bla...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Wait until you see him get hot from 3 and star...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>wait until you see him get hot from 3 and star...</td>\n",
-       "      <td>wait see get hot 3 start shooting 35-40 ft sho...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "\n",
-       "                                             Comment       Date Result  \\\n",
-       "0            But have you considered Damian Lillard? 2023-10-26    Win   \n",
-       "1                   Help yourself to a Dame flair :) 2023-10-26    Win   \n",
-       "2  the most casual 30 point games in the nba, the... 2023-10-26    Win   \n",
-       "3  Dame regularly went nuclear with the most medi... 2023-10-26    Win   \n",
-       "4  Wait until you see him get hot from 3 and star... 2023-10-26    Win   \n",
-       "\n",
-       "                                         Comment_Adj  \\\n",
-       "0             but have you considered damian lillard   \n",
-       "1                     help yourself to a dame flair    \n",
-       "2  the most casual 30 point games in the nba ther...   \n",
-       "3  dame regularly went nuclear with the most medi...   \n",
-       "4  wait until you see him get hot from 3 and star...   \n",
-       "\n",
-       "                                             No_Stop  \n",
-       "0                          considered damian lillard  \n",
-       "1                                    help dame flair  \n",
-       "2  casual 30 point games nba ’ good reason ’ obse...  \n",
-       "3  dame regularly went nuclear mediocre teams bla...  \n",
-       "4  wait see get hot 3 start shooting 35-40 ft sho...  "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Remove Stopwords from Comments\n",
-    "def remove_stopwords(text):\n",
-    "    words = word_tokenize(text)\n",
-    "    stop_words = set(stopwords.words('english'))\n",
-    "    filtered_words = [word for word in words if word.lower() not in stop_words]\n",
-    "    return ' '.join(filtered_words)\n",
-    "\n",
-    "df[\"No_Stop\"] = df[\"Comment_Adj\"].apply(remove_stopwords)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "12835b11",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Post_Name</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Result</th>\n",
-       "      <th>Comment_Adj</th>\n",
-       "      <th>No_Stop</th>\n",
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-       "      <th>0</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>But have you considered Damian Lillard?</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Help yourself to a Dame flair :)</td>\n",
-       "      <td>2023-10-26</td>\n",
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-       "      <td>help yourself to a dame flair</td>\n",
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-       "      <td>help dame flair</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>the most casual 30 point games in the nba, the...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
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-       "      <td>casual 30 point game nba ’ good reason ’ obses...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Dame regularly went nuclear with the most medi...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>dame regularly went nuclear with the most medi...</td>\n",
-       "      <td>dame regularly went nuclear mediocre teams bla...</td>\n",
-       "      <td>dame regularli went nuclear mediocr team blaze...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Wait until you see him get hot from 3 and star...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>wait until you see him get hot from 3 and star...</td>\n",
-       "      <td>wait see get hot 3 start shooting 35-40 ft sho...</td>\n",
-       "      <td>wait see get hot 3 start shoot 35-40 ft shot l...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "\n",
-       "                                             Comment       Date Result  \\\n",
-       "0            But have you considered Damian Lillard? 2023-10-26    Win   \n",
-       "1                   Help yourself to a Dame flair :) 2023-10-26    Win   \n",
-       "2  the most casual 30 point games in the nba, the... 2023-10-26    Win   \n",
-       "3  Dame regularly went nuclear with the most medi... 2023-10-26    Win   \n",
-       "4  Wait until you see him get hot from 3 and star... 2023-10-26    Win   \n",
-       "\n",
-       "                                         Comment_Adj  \\\n",
-       "0             but have you considered damian lillard   \n",
-       "1                     help yourself to a dame flair    \n",
-       "2  the most casual 30 point games in the nba ther...   \n",
-       "3  dame regularly went nuclear with the most medi...   \n",
-       "4  wait until you see him get hot from 3 and star...   \n",
-       "\n",
-       "                                             No_Stop  \\\n",
-       "0                          considered damian lillard   \n",
-       "1                                    help dame flair   \n",
-       "2  casual 30 point games nba ’ good reason ’ obse...   \n",
-       "3  dame regularly went nuclear mediocre teams bla...   \n",
-       "4  wait see get hot 3 start shooting 35-40 ft sho...   \n",
-       "\n",
-       "                                             Stemmed  \n",
-       "0                              consid damian lillard  \n",
-       "1                                    help dame flair  \n",
-       "2  casual 30 point game nba ’ good reason ’ obses...  \n",
-       "3  dame regularli went nuclear mediocr team blaze...  \n",
-       "4  wait see get hot 3 start shoot 35-40 ft shot l...  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Perform Stemming on Comments\n",
-    "stemmer = PorterStemmer()\n",
-    "def stem_text(text):\n",
-    "    words = word_tokenize(text)\n",
-    "    stemmed_words = [stemmer.stem(word) for word in words]\n",
-    "    return ' '.join(stemmed_words)\n",
-    "\n",
-    "# Apply stemming function to text_column\n",
-    "df['Stemmed'] = df['No_Stop'].apply(stem_text)\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "f110e6e5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\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>Post_Name</th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Result</th>\n",
-       "      <th>Comment_Adj</th>\n",
-       "      <th>No_Stop</th>\n",
-       "      <th>Stemmed</th>\n",
-       "      <th>Result_Bin</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>But have you considered Damian Lillard?</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>but have you considered damian lillard</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Help yourself to a Dame flair :)</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>help yourself to a dame flair</td>\n",
-       "      <td>help dame flair</td>\n",
-       "      <td>help dame flair</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>the most casual 30 point games in the nba, the...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>the most casual 30 point games in the nba ther...</td>\n",
-       "      <td>casual 30 point games nba ’ good reason ’ obse...</td>\n",
-       "      <td>casual 30 point game nba ’ good reason ’ obses...</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Dame regularly went nuclear with the most medi...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>dame regularly went nuclear with the most medi...</td>\n",
-       "      <td>dame regularly went nuclear mediocre teams bla...</td>\n",
-       "      <td>dame regularli went nuclear mediocr team blaze...</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...</td>\n",
-       "      <td>Wait until you see him get hot from 3 and star...</td>\n",
-       "      <td>2023-10-26</td>\n",
-       "      <td>Win</td>\n",
-       "      <td>wait until you see him get hot from 3 and star...</td>\n",
-       "      <td>wait see get hot 3 start shooting 35-40 ft sho...</td>\n",
-       "      <td>wait see get hot 3 start shoot 35-40 ft shot l...</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                           Post_Name  \\\n",
-       "0  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "1  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "2  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "3  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "4  [POSTGAME THREAD] Our Milwaukee Bucks (1-0) de...   \n",
-       "\n",
-       "                                             Comment       Date Result  \\\n",
-       "0            But have you considered Damian Lillard? 2023-10-26    Win   \n",
-       "1                   Help yourself to a Dame flair :) 2023-10-26    Win   \n",
-       "2  the most casual 30 point games in the nba, the... 2023-10-26    Win   \n",
-       "3  Dame regularly went nuclear with the most medi... 2023-10-26    Win   \n",
-       "4  Wait until you see him get hot from 3 and star... 2023-10-26    Win   \n",
-       "\n",
-       "                                         Comment_Adj  \\\n",
-       "0             but have you considered damian lillard   \n",
-       "1                     help yourself to a dame flair    \n",
-       "2  the most casual 30 point games in the nba ther...   \n",
-       "3  dame regularly went nuclear with the most medi...   \n",
-       "4  wait until you see him get hot from 3 and star...   \n",
-       "\n",
-       "                                             No_Stop  \\\n",
-       "0                          considered damian lillard   \n",
-       "1                                    help dame flair   \n",
-       "2  casual 30 point games nba ’ good reason ’ obse...   \n",
-       "3  dame regularly went nuclear mediocre teams bla...   \n",
-       "4  wait see get hot 3 start shooting 35-40 ft sho...   \n",
-       "\n",
-       "                                             Stemmed  Result_Bin  \n",
-       "0                              consid damian lillard           1  \n",
-       "1                                    help dame flair           1  \n",
-       "2  casual 30 point game nba ’ good reason ’ obses...           1  \n",
-       "3  dame regularli went nuclear mediocr team blaze...           1  \n",
-       "4  wait see get hot 3 start shoot 35-40 ft shot l...           1  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Win_Loss Function\n",
-    "\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "\n",
-    "label_encoder = LabelEncoder()\n",
-    "df['Result_Bin'] = label_encoder.fit_transform(df['Result'])\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9cc5e5d7",
-   "metadata": {},
-   "source": [
-    "## Naive Bayes Model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "ef60ffda",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import f1_score\n",
-    "from sklearn.naive_bayes import BernoulliNB\n",
-    "\n",
-    "X = df[\"Stemmed\"]\n",
-    "y = df[\"Result_Bin\"]\n",
-    "\n",
-    "# Split the data into training and testing sets\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "e1a484f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "BNB = make_pipeline(CountVectorizer(), BernoulliNB())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "22cacef4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "F1 Score: 0.6985769728331178\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Train and test the model\n",
-    "\n",
-    "BNB.fit(X_train, y_train)\n",
-    "\n",
-    "# Predict on the test set\n",
-    "y_pred = BNB.predict(X_test)\n",
-    "\n",
-    "# Calculate accuracy\n",
-    "f1 = f1_score(y_test, y_pred)\n",
-    "print(\"F1 Score:\", f1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "402343ac-4814-47b6-a1c2-4d78fc7cfb0f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Print the Confusion Matrix\n",
-    "\n",
-    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
-    "\n",
-    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
-    "\n",
-    "cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels = [0,1])\n",
-    "cm_display.plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "33c83ddc-0b43-4810-bdb0-2df5f7179531",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "77.98"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "58.65"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "63.27"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Print TPR, FPR, Precision, Recall\n",
-    "\n",
-    "TN = conf_matrix[0][0]\n",
-    "FN = conf_matrix[1][0]\n",
-    "TP = conf_matrix[1][1]\n",
-    "FP = conf_matrix[0][1]\n",
-    "\n",
-    "tpr = round(100 *(TP / (TP + FN)), 2)\n",
-    "display(tpr)\n",
-    "fpr = round(100* (FP / (FP + TN)), 2)\n",
-    "display(fpr)\n",
-    "precision = round(100*(TP / (TP + FP)),2)\n",
-    "display(precision)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "c0ffc52d-2096-4f31-b011-0dd332e0226d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Stemmed</th>\n",
-       "      <th>Actual_Label</th>\n",
-       "      <th>Pred_Label</th>\n",
-       "      <th>predict_proba</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3116</th>\n",
-       "      <td>lmao thank</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.981377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3721</th>\n",
-       "      <td>fkl</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.969936</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2943</th>\n",
-       "      <td>defens adjust frustrat celtic</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.916624</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8446</th>\n",
-       "      <td>well odd dame least hit first increas miss sec...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.574565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5282</th>\n",
-       "      <td>heat fan twice bet gianni come piss call giann...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.175973</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4863</th>\n",
-       "      <td>spur</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.986595</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4519</th>\n",
-       "      <td>fuck go</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.957687</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4303</th>\n",
-       "      <td>con-naughton</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.987552</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11002</th>\n",
-       "      <td>yeah wonder would close danilo better switch o...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.424276</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2377</th>\n",
-       "      <td>nevah lost season tourney</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.863553</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2454 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                 Stemmed  Actual_Label  \\\n",
-       "3116                                          lmao thank             1   \n",
-       "3721                                                 fkl             1   \n",
-       "2943                       defens adjust frustrat celtic             0   \n",
-       "8446   well odd dame least hit first increas miss sec...             0   \n",
-       "5282   heat fan twice bet gianni come piss call giann...             0   \n",
-       "...                                                  ...           ...   \n",
-       "4863                                                spur             1   \n",
-       "4519                                             fuck go             1   \n",
-       "4303                                        con-naughton             1   \n",
-       "11002  yeah wonder would close danilo better switch o...             1   \n",
-       "2377                           nevah lost season tourney             1   \n",
-       "\n",
-       "       Pred_Label  predict_proba  \n",
-       "3116            1       0.981377  \n",
-       "3721            1       0.969936  \n",
-       "2943            1       0.916624  \n",
-       "8446            1       0.574565  \n",
-       "5282            0       0.175973  \n",
-       "...           ...            ...  \n",
-       "4863            1       0.986595  \n",
-       "4519            1       0.957687  \n",
-       "4303            1       0.987552  \n",
-       "11002           0       0.424276  \n",
-       "2377            1       0.863553  \n",
-       "\n",
-       "[2454 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_test = pd.DataFrame(X_test)\n",
-    "df_test[\"Actual_Label\"] = y_test\n",
-    "df_test[\"Pred_Label\"] = y_pred\n",
-    "probs = BNB.predict_proba(X_test)\n",
-    "df_test['predict_proba'] = [probs[i][1] for i in range(len(probs))]\n",
-    "df_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "115d96b4-a282-4037-8556-10dbc6a8348c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Stemmed</th>\n",
-       "      <th>Actual_Label</th>\n",
-       "      <th>Pred_Label</th>\n",
-       "      <th>predict_proba</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Lmao thanks</td>\n",
-       "      <td>lmao thank</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.981377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>FKL</td>\n",
-       "      <td>fkl</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.969936</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>What were the defensive adjustments that frust...</td>\n",
-       "      <td>defens adjust frustrat celtic</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.916624</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Well the odds of Dame at least hitting the fir...</td>\n",
-       "      <td>well odd dame least hit first increas miss sec...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.574565</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>And the Heat fan who twice now has bet on Gian...</td>\n",
-       "      <td>heat fan twice bet gianni come piss call giann...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.175973</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>5460</th>\n",
-       "      <td>or the spurs</td>\n",
-       "      <td>spur</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.986595</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5461</th>\n",
-       "      <td>What the fuck is going on out there?</td>\n",
-       "      <td>fuck go</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.957687</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5462</th>\n",
-       "      <td>Con-naughton</td>\n",
-       "      <td>con-naughton</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.987552</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5463</th>\n",
-       "      <td>Yeah, I was wondering if they would close it o...</td>\n",
-       "      <td>yeah wonder would close danilo better switch o...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.424276</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5464</th>\n",
-       "      <td>**NEVAH LOST**\\n\\nin the in season tourney*</td>\n",
-       "      <td>nevah lost season tourney</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.863553</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5465 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                Comment  \\\n",
-       "0                                           Lmao thanks   \n",
-       "1                                                   FKL   \n",
-       "2     What were the defensive adjustments that frust...   \n",
-       "3     Well the odds of Dame at least hitting the fir...   \n",
-       "4     And the Heat fan who twice now has bet on Gian...   \n",
-       "...                                                 ...   \n",
-       "5460                                       or the spurs   \n",
-       "5461               What the fuck is going on out there?   \n",
-       "5462                                       Con-naughton   \n",
-       "5463  Yeah, I was wondering if they would close it o...   \n",
-       "5464        **NEVAH LOST**\\n\\nin the in season tourney*   \n",
-       "\n",
-       "                                                Stemmed  Actual_Label  \\\n",
-       "0                                            lmao thank             1   \n",
-       "1                                                   fkl             1   \n",
-       "2                         defens adjust frustrat celtic             0   \n",
-       "3     well odd dame least hit first increas miss sec...             0   \n",
-       "4     heat fan twice bet gianni come piss call giann...             0   \n",
-       "...                                                 ...           ...   \n",
-       "5460                                               spur             1   \n",
-       "5461                                            fuck go             1   \n",
-       "5462                                       con-naughton             1   \n",
-       "5463  yeah wonder would close danilo better switch o...             1   \n",
-       "5464                          nevah lost season tourney             1   \n",
-       "\n",
-       "      Pred_Label  predict_proba  \n",
-       "0              1       0.981377  \n",
-       "1              1       0.969936  \n",
-       "2              1       0.916624  \n",
-       "3              1       0.574565  \n",
-       "4              0       0.175973  \n",
-       "...          ...            ...  \n",
-       "5460           1       0.986595  \n",
-       "5461           1       0.957687  \n",
-       "5462           1       0.987552  \n",
-       "5463           0       0.424276  \n",
-       "5464           1       0.863553  \n",
-       "\n",
-       "[5465 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_full = pd.merge(df_test, df, on = \"Stemmed\", how = \"left\")\n",
-    "test_full = test_full[[\"Comment\", \"Stemmed\", \"Actual_Label\", \"Pred_Label\", \"predict_proba\"]]\n",
-    "test_full.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "45f27716-d6a0-4236-a567-6a447467d57d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
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-       "      <th></th>\n",
-       "      <th>Comment</th>\n",
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-       "      <th>predict_proba</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2268</th>\n",
-       "      <td>Real question, will Thanasis hop on the Pat Be...</td>\n",
-       "      <td>real question thanasi hop pat bev podcast pat ...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.998701</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>753</th>\n",
-       "      <td>![img](emote|t5_2t10o|24578)</td>\n",
-       "      <td>img ] ( emote|t5_2t10o|24578</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.998215</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>754</th>\n",
-       "      <td>![img](emote|t5_2t10o|24578)</td>\n",
-       "      <td>img ] ( emote|t5_2t10o|24578</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.998215</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>636</th>\n",
-       "      <td>Glad I spent $ on courtside for that lol oops!...</td>\n",
-       "      <td>glad spent courtsid lol oop 7.5 min bonu attac...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.997157</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5118</th>\n",
-       "      <td>Love when MJ is a Bucks hater lol you know it ...</td>\n",
-       "      <td>love mj buck hater lol know come heart</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.995318</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                Comment  \\\n",
-       "2268  Real question, will Thanasis hop on the Pat Be...   \n",
-       "753                        ![img](emote|t5_2t10o|24578)   \n",
-       "754                        ![img](emote|t5_2t10o|24578)   \n",
-       "636   Glad I spent $ on courtside for that lol oops!...   \n",
-       "5118  Love when MJ is a Bucks hater lol you know it ...   \n",
-       "\n",
-       "                                                Stemmed  Actual_Label  \\\n",
-       "2268  real question thanasi hop pat bev podcast pat ...             0   \n",
-       "753                        img ] ( emote|t5_2t10o|24578             0   \n",
-       "754                        img ] ( emote|t5_2t10o|24578             0   \n",
-       "636   glad spent courtsid lol oop 7.5 min bonu attac...             0   \n",
-       "5118             love mj buck hater lol know come heart             0   \n",
-       "\n",
-       "      Pred_Label  predict_proba  \n",
-       "2268           1       0.998701  \n",
-       "753            1       0.998215  \n",
-       "754            1       0.998215  \n",
-       "636            1       0.997157  \n",
-       "5118           1       0.995318  "
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fp_df = test_full[(test_full[\"Actual_Label\"] == 0) & (test_full[\"Pred_Label\"] == 1)].sort_values(by = \"predict_proba\", ascending = False)\n",
-    "fp_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "0b2259e5-ea83-44dc-b375-5adcb864c0eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Stemmed</th>\n",
-       "      <th>Actual_Label</th>\n",
-       "      <th>Pred_Label</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2197</th>\n",
-       "      <td>That series is by far my biggest what-if of th...</td>\n",
-       "      <td>seri far biggest what-if bud era think say cle...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.207599e-16</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3972</th>\n",
-       "      <td>I think there are a few really big takeaways f...</td>\n",
-       "      <td>think realli big takeaway game someth mention ...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.577316e-13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2426</th>\n",
-       "      <td>Khris they say it was sore Achilles. Not sure ...</td>\n",
-       "      <td>khri say sore achil sure mean 's precautionari...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.021511e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4674</th>\n",
-       "      <td>Dame man. Never stops being insane how he can ...</td>\n",
-       "      <td>dame man never stop insan fuckin turn aim bot ...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.235395e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2252</th>\n",
-       "      <td>This is sheer delusion, I’m sorry. \\n\\nThe 20t...</td>\n",
-       "      <td>sheer delus ’ sorri 20th rank defens nba net g...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.694379e-09</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                Comment  \\\n",
-       "2197  That series is by far my biggest what-if of th...   \n",
-       "3972  I think there are a few really big takeaways f...   \n",
-       "2426  Khris they say it was sore Achilles. Not sure ...   \n",
-       "4674  Dame man. Never stops being insane how he can ...   \n",
-       "2252  This is sheer delusion, I’m sorry. \\n\\nThe 20t...   \n",
-       "\n",
-       "                                                Stemmed  Actual_Label  \\\n",
-       "2197  seri far biggest what-if bud era think say cle...             1   \n",
-       "3972  think realli big takeaway game someth mention ...             1   \n",
-       "2426  khri say sore achil sure mean 's precautionari...             1   \n",
-       "4674  dame man never stop insan fuckin turn aim bot ...             1   \n",
-       "2252  sheer delus ’ sorri 20th rank defens nba net g...             1   \n",
-       "\n",
-       "      Pred_Label  predict_proba  \n",
-       "2197           0   8.207599e-16  \n",
-       "3972           0   1.577316e-13  \n",
-       "2426           0   1.021511e-09  \n",
-       "4674           0   1.235395e-09  \n",
-       "2252           0   1.694379e-09  "
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fn_df = test_full[(test_full[\"Actual_Label\"] == 1) & (test_full[\"Pred_Label\"] == 0)].sort_values(by = \"predict_proba\")\n",
-    "fn_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "33668bd6",
-   "metadata": {},
-   "source": [
-    "## Logistic Regression Model "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "5cdb826e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vec = CountVectorizer()\n",
-    "X_train_1 = vec.fit_transform(X_train)\n",
-    "X_test_1 = vec.transform(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "f2d2185b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "F1 Score: 0.6874343717185859\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.metrics import classification_report\n",
-    "\n",
-    "lr = LogisticRegression(max_iter = 1000)\n",
-    "lr.fit(X_train_1, y_train)\n",
-    "y_pred_lr = lr.predict(X_test_1)\n",
-    "\n",
-    "f1 = f1_score(y_test, y_pred_lr)\n",
-    "print(\"F1 Score:\", f1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "e762b3e7-4189-45b5-bd70-03c58185cb19",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "conf_matrix = confusion_matrix(y_test, y_pred_lr)\n",
-    "\n",
-    "cm_display = ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels = [0,1])\n",
-    "cm_display.plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "id": "48a393b8-92e3-4e7b-afd4-b92b45633b68",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.7090252707581227"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.4583723105706268"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.6671195652173914"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# TPR, FPR, Precision\n",
-    "\n",
-    "TN = conf_matrix[0][0]\n",
-    "FN = conf_matrix[1][0]\n",
-    "TP = conf_matrix[1][1]\n",
-    "FP = conf_matrix[0][1]\n",
-    "\n",
-    "tpr = TP / (TP + FN)\n",
-    "display(tpr)\n",
-    "fpr = FP / (FP + TN)\n",
-    "display(fpr)\n",
-    "precision = TP / (TP + FP)\n",
-    "display(precision)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "c7c37d42-8599-401d-995c-99460aade108",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Stemmed</th>\n",
-       "      <th>Actual_Label</th>\n",
-       "      <th>Pred_Label</th>\n",
-       "      <th>predict_proba</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>3116</th>\n",
-       "      <td>lmao thank</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.724096</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3721</th>\n",
-       "      <td>fkl</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.606038</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2943</th>\n",
-       "      <td>defens adjust frustrat celtic</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.709577</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8446</th>\n",
-       "      <td>well odd dame least hit first increas miss sec...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.264181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5282</th>\n",
-       "      <td>heat fan twice bet gianni come piss call giann...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.191379</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4863</th>\n",
-       "      <td>spur</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.805416</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4519</th>\n",
-       "      <td>fuck go</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.596010</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4303</th>\n",
-       "      <td>con-naughton</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.759353</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11002</th>\n",
-       "      <td>yeah wonder would close danilo better switch o...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.969756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2377</th>\n",
-       "      <td>nevah lost season tourney</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.388863</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2454 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                 Stemmed  Actual_Label  \\\n",
-       "3116                                          lmao thank             1   \n",
-       "3721                                                 fkl             1   \n",
-       "2943                       defens adjust frustrat celtic             0   \n",
-       "8446   well odd dame least hit first increas miss sec...             0   \n",
-       "5282   heat fan twice bet gianni come piss call giann...             0   \n",
-       "...                                                  ...           ...   \n",
-       "4863                                                spur             1   \n",
-       "4519                                             fuck go             1   \n",
-       "4303                                        con-naughton             1   \n",
-       "11002  yeah wonder would close danilo better switch o...             1   \n",
-       "2377                           nevah lost season tourney             1   \n",
-       "\n",
-       "       Pred_Label  predict_proba  \n",
-       "3116            1       0.724096  \n",
-       "3721            1       0.606038  \n",
-       "2943            1       0.709577  \n",
-       "8446            0       0.264181  \n",
-       "5282            0       0.191379  \n",
-       "...           ...            ...  \n",
-       "4863            1       0.805416  \n",
-       "4519            1       0.596010  \n",
-       "4303            1       0.759353  \n",
-       "11002           1       0.969756  \n",
-       "2377            0       0.388863  \n",
-       "\n",
-       "[2454 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# \"Most Wrong\" Negative Comments\n",
-    "df_test = pd.DataFrame(X_test)\n",
-    "df_test[\"Actual_Label\"] = y_test\n",
-    "df_test[\"Pred_Label\"] = y_pred_lr\n",
-    "probs = lr.predict_proba(X_test_1)\n",
-    "df_test['predict_proba'] = [probs[i][1] for i in range(len(probs))]\n",
-    "df_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "4f81d8af-9ddb-49ff-9629-d217d4e69267",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>Comment</th>\n",
-       "      <th>Stemmed</th>\n",
-       "      <th>Actual_Label</th>\n",
-       "      <th>Pred_Label</th>\n",
-       "      <th>predict_proba</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Lmao thanks</td>\n",
-       "      <td>lmao thank</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.724096</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>FKL</td>\n",
-       "      <td>fkl</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.606038</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>What were the defensive adjustments that frust...</td>\n",
-       "      <td>defens adjust frustrat celtic</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.709577</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Well the odds of Dame at least hitting the fir...</td>\n",
-       "      <td>well odd dame least hit first increas miss sec...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.264181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>And the Heat fan who twice now has bet on Gian...</td>\n",
-       "      <td>heat fan twice bet gianni come piss call giann...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.191379</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                             Comment  \\\n",
-       "0                                        Lmao thanks   \n",
-       "1                                                FKL   \n",
-       "2  What were the defensive adjustments that frust...   \n",
-       "3  Well the odds of Dame at least hitting the fir...   \n",
-       "4  And the Heat fan who twice now has bet on Gian...   \n",
-       "\n",
-       "                                             Stemmed  Actual_Label  \\\n",
-       "0                                         lmao thank             1   \n",
-       "1                                                fkl             1   \n",
-       "2                      defens adjust frustrat celtic             0   \n",
-       "3  well odd dame least hit first increas miss sec...             0   \n",
-       "4  heat fan twice bet gianni come piss call giann...             0   \n",
-       "\n",
-       "   Pred_Label  predict_proba  \n",
-       "0           1       0.724096  \n",
-       "1           1       0.606038  \n",
-       "2           1       0.709577  \n",
-       "3           0       0.264181  \n",
-       "4           0       0.191379  "
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "test_full = pd.merge(df_test, df, on = \"Stemmed\", how = \"left\")\n",
-    "test_full = test_full[[\"Comment\", \"Stemmed\", \"Actual_Label\", \"Pred_Label\", \"predict_proba\"]]\n",
-    "test_full.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "3fdfacd5-4a0e-488a-bd40-02ebfc5cc020",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Stemmed</th>\n",
-       "      <th>Actual_Label</th>\n",
-       "      <th>Pred_Label</th>\n",
-       "      <th>predict_proba</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>243</th>\n",
-       "      <td>I will be messaging you in 5 months on [**2024...</td>\n",
-       "      <td>messag 5 month 2024-04-02 02:29:31 utc * * ] (...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.999998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4375</th>\n",
-       "      <td>I love Dame, but he needs to stop differing an...</td>\n",
-       "      <td>love dame need stop differ put dick tabl run t...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.993571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>Im convinced marjon and ajax are riding the be...</td>\n",
-       "      <td>im convinc marjon ajax ride bench potenti gift...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.993130</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2959</th>\n",
-       "      <td>I can’t complain . Let him play and get confid...</td>\n",
-       "      <td>’ complain let play get confid bench underperf...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.989628</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4468</th>\n",
-       "      <td>No one wanted to win on the court more than Da...</td>\n",
-       "      <td>one want win court dame last 5 minut superb fu...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.987161</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                Comment  \\\n",
-       "243   I will be messaging you in 5 months on [**2024...   \n",
-       "4375  I love Dame, but he needs to stop differing an...   \n",
-       "999   Im convinced marjon and ajax are riding the be...   \n",
-       "2959  I can’t complain . Let him play and get confid...   \n",
-       "4468  No one wanted to win on the court more than Da...   \n",
-       "\n",
-       "                                                Stemmed  Actual_Label  \\\n",
-       "243   messag 5 month 2024-04-02 02:29:31 utc * * ] (...             0   \n",
-       "4375  love dame need stop differ put dick tabl run t...             0   \n",
-       "999   im convinc marjon ajax ride bench potenti gift...             0   \n",
-       "2959  ’ complain let play get confid bench underperf...             0   \n",
-       "4468  one want win court dame last 5 minut superb fu...             0   \n",
-       "\n",
-       "      Pred_Label  predict_proba  \n",
-       "243            1       0.999998  \n",
-       "4375           1       0.993571  \n",
-       "999            1       0.993130  \n",
-       "2959           1       0.989628  \n",
-       "4468           1       0.987161  "
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fp_df = test_full[(test_full[\"Actual_Label\"] == 0) & (test_full[\"Pred_Label\"] == 1)].sort_values(by = \"predict_proba\", ascending = False)\n",
-    "fp_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "316b8303-1a11-4fce-9bd7-c3256bbb587f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Comment</th>\n",
-       "      <th>Stemmed</th>\n",
-       "      <th>Actual_Label</th>\n",
-       "      <th>Pred_Label</th>\n",
-       "      <th>predict_proba</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2197</th>\n",
-       "      <td>That series is by far my biggest what-if of th...</td>\n",
-       "      <td>seri far biggest what-if bud era think say cle...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.558619e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2826</th>\n",
-       "      <td>I would gladly trade all the downers here. One...</td>\n",
-       "      <td>would gladli trade downer one bad game request...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.647902e-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2427</th>\n",
-       "      <td>There were also a few fast breaks that Dame an...</td>\n",
-       "      <td>also fast break dame khri led bounc ball backw...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.137630e-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2252</th>\n",
-       "      <td>This is sheer delusion, I’m sorry. \\n\\nThe 20t...</td>\n",
-       "      <td>sheer delus ’ sorri 20th rank defens nba net g...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>5.256516e-03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3112</th>\n",
-       "      <td>just literally could never break away from the...</td>\n",
-       "      <td>liter could never break away also sad brook co...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.319801e-02</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                Comment  \\\n",
-       "2197  That series is by far my biggest what-if of th...   \n",
-       "2826  I would gladly trade all the downers here. One...   \n",
-       "2427  There were also a few fast breaks that Dame an...   \n",
-       "2252  This is sheer delusion, I’m sorry. \\n\\nThe 20t...   \n",
-       "3112  just literally could never break away from the...   \n",
-       "\n",
-       "                                                Stemmed  Actual_Label  \\\n",
-       "2197  seri far biggest what-if bud era think say cle...             1   \n",
-       "2826  would gladli trade downer one bad game request...             1   \n",
-       "2427  also fast break dame khri led bounc ball backw...             1   \n",
-       "2252  sheer delus ’ sorri 20th rank defens nba net g...             1   \n",
-       "3112  liter could never break away also sad brook co...             1   \n",
-       "\n",
-       "      Pred_Label  predict_proba  \n",
-       "2197           0   7.558619e-07  \n",
-       "2826           0   2.647902e-03  \n",
-       "2427           0   3.137630e-03  \n",
-       "2252           0   5.256516e-03  \n",
-       "3112           0   1.319801e-02  "
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fn_df = test_full[(test_full[\"Actual_Label\"] == 1) & (test_full[\"Pred_Label\"] == 0)].sort_values(by = \"predict_proba\")\n",
-    "fn_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd2bae4a",
-   "metadata": {},
-   "source": [
-    "## Convolutional Neural Network"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4f013b2-58d2-49dd-a8f8-36e7e38353ee",
-   "metadata": {},
-   "source": [
-    "#### Hyperparameter Tuning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "49f7eced-c24c-45ae-bf50-60780de52b1e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Importing the Necessary Libraries\n",
-    "import tensorflow as tf\n",
-    "import keras as keras\n",
-    "from tensorflow.keras.models import Sequential\n",
-    "from tensorflow.keras.layers import Embedding, Flatten, Dense\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
-    "from tensorflow.keras.optimizers import Adam, RMSprop\n",
-    "from keras.preprocessing.text import Tokenizer\n",
-    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn.metrics import classification_report\n",
-    "from tensorflow.keras import layers\n",
-    "from keras.layers import Conv1D, MaxPooling1D, GlobalMaxPooling1D, Dropout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "3a11da52-56a0-497d-8d6d-9b3db6fa0ad4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Tokenizing the Dataset\n",
-    "tokenizer = Tokenizer()\n",
-    "\n",
-    "tokenizer.fit_on_texts(X_train)\n",
-    "tokenizer.fit_on_texts(X_test)\n",
-    "X_train_2 = tokenizer.texts_to_sequences(X_train)\n",
-    "X_test_2 = tokenizer.texts_to_sequences(X_test)\n",
-    "\n",
-    "vocab_size = len(tokenizer.word_index) + 1\n",
-    "\n",
-    "maxlen = 200\n",
-    "X_train_2 = pad_sequences(X_train_2, padding='post', maxlen=maxlen)\n",
-    "X_test_2 = pad_sequences(X_test_2, padding='post', maxlen=maxlen)\n",
-    "label_encoder = LabelEncoder()\n",
-    "y_train = label_encoder.fit_transform(y_train)\n",
-    "y_test = label_encoder.fit_transform(y_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8536619d-3d59-48dc-bcba-73467de6eca1",
-   "metadata": {},
-   "source": [
-    "### Overfitted Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "id": "16a693c8-19ea-4e7a-9798-f895aa6a1ccc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Accuracy: 0.9749\n",
-      "Testing Accuracy:  0.6214\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Building the initial model\n",
-    "def CNN_model(embedding = 200, filter = 16, kernel = 4, num_1 = 40, lr = 0.01, dropout_rate = 0.5):\n",
-    "    model = Sequential()\n",
-    "    model.add(layers.Embedding(input_dim=vocab_size, \n",
-    "                               output_dim=embedding, \n",
-    "                               input_length=maxlen))\n",
-    "    model.add(Conv1D(filters = filter, kernel_size = kernel, activation = \"relu\"))\n",
-    "    model.add(MaxPooling1D(pool_size = 2))\n",
-    "    model.add(layers.Flatten())\n",
-    "    model.add(layers.Dropout(dropout_rate))\n",
-    "    model.add(layers.Dense(num_1, activation='relu'))\n",
-    "    model.add(layers.Dense(1, activation='sigmoid'))\n",
-    "    model.compile(optimizer= Adam(learning_rate = lr),\n",
-    "                  loss='binary_crossentropy',\n",
-    "                  metrics=['accuracy'])\n",
-    "    return model\n",
-    "\n",
-    "model = CNN_model()\n",
-    "\n",
-    "history = model.fit(X_train_2, y_train,\n",
-    "                    epochs=30,\n",
-    "                    verbose=False,\n",
-    "                    validation_data=(X_test_2, y_test),\n",
-    "                    batch_size=1000)\n",
-    "loss, accuracy = model.evaluate(X_train_2, y_train, verbose=False)\n",
-    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
-    "loss, accuracy = model.evaluate(X_test_2, y_test, verbose=False)\n",
-    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "id": "a0fd2b0c-55a8-48d1-a865-afd992f4bb4c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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tErNUtmzZfDvX119/reuvv14Oh0PffPONevXqlW/nvpLD4VBiYqJ8POGTBQAA3I3dbgKB7AQBzu/PnjUjCnKTsyN+bnb2v3TJNAk8flw6ccKEHydOmG3Tpoxr8fVNGy6kFzak10n/yp/t8ePJ4UDKwODAAXN/RsqVSw4JnF9r1pSqVTOvD0COcKWAXHfHHXeoTJkymjlzpl5++eWk26OjozVv3jyNGzdO//77rwYPHqyIiAidPXtWNWrU0KhRo/TAAw9k+LxXTmf4+++/NXDgQG3YsEHVq1fXO++8k+Yxzz33nBYsWKCjR4+qbNmy6tWrl0aPHq0iRYpo5syZGjt2rCQzfUGSZsyYoX79+qWZzvDnn39q6NChWrdunQIDA9W9e3e9/fbbKvrfH7x+/frp/PnzatWqlcLDwxUXF6f7779fkydPVpEs/jhNmzZNvXv3lsPh0LRp09KECH/99ZdGjBihiIgIORwONWjQQDNnzlSNGjUkmWAmPDxce/fuVYkSJdS9e3e9//77OnjwoKpVq6bNmzerQYMGkqTz58+rePHi+vXXX9WuXTutWLFC7du3108//aQXXnhB27Zt05IlS1S5cmU9/fTTWr9+vS5duqQ6depo/Pjx6tixY1JdsbGxeumll/TFF1/o1KlTqly5sp5//nkNGDBA1157rR577DE988wzScdv375d9evX199//51UOwAAbicmxgyBj4w0W1b7zjDA2Zk9s4vZzISGSqVKma1kyeT9K78vWdJccGe2dF9ef/KfmCidPm0u6p0rEKS3f+aMWRbv4EGzZSY4OG24UKSI9PffJjD4++/Mpx8EB6cNCWrVkq691v0a9QEejhDBwzhXGLFCYGD2/ib5+PioT58+mjlzpkaPHp10gf7ll18qLi5OvXr1UnR0tBo1aqTnnntOISEhWrRokR566CFVr15dN910U5bnsNvtuueee1SqVCmtX79eUVFR6fZKCA4O1syZM1W+fHn9+eefevjhhxUcHKwRI0aoZ8+e2r59u3766aekqRehoaFpniM6Olq33367mjVrpo0bN+rUqVMaNGiQBg8erJkzZyYd9+uvv6pcuXL69ddftXfvXvXs2VMNGjTQww8/nOHr2Ldvn9atW6dvvvlGDodDTz31lPbv36/q1atLko4dO6Y2bdqoXbt2+uWXXxQSEqI1a9Yo4b/hi1OmTNHTTz+tN954Q506dVJkZKTWrFmT5c/vSiNGjNDEiRNVvXp1FStWTEePHlXnzp312muvyd/fX7NmzVLXrl21e/duVf6vK22fPn20bt06vfvuu7rhhht04MABnTlzRjabTQMGDNCMGTNShQjTp09X69atCRAAAHknISH5Ij+ji/6sQoG4ONfrCA3NOghIeV+JEp71ibi3t1lRoGxZKbPRpbGx0smTmQcNx49LFy6Ybfdus2XEx0eqUSN1SODcd65wACDPESJ4mOjorEd75ZWLF83KJtkxYMAAvfXWW0mfdEvmIvKee+5R8eLFVbx48VQXmEOGDNFPP/2kL7/8MlshwrJly7Rz504dPHhQFStWlCSNGzdOnTp1SnXciy++mLRftWpVDR8+XPPmzdOIESMUEBCgokWLysfHJ9PpC5999pkuX76s2bNnJ/VkeP/999W1a1dNmDBB11xzjSSpePHiev/99+Xt7a3atWurS5cuWr58eaYhwvTp09WpU6ek/gu33367pk+frtdee02S9H//938KDQ3V3Llzk0Y01KxZM+nxr732moYPH66hQ4cm3dakSZMsf35XeuWVV3TLLbckfV+yZEndcMMNqc6zYMECLVy4UIMHD9aePXs0f/58/fzzz0mjE5zBhyT1799fo0eP1oYNG9S0aVPFx8fr008/1VtvvZXj2gAASGK3m+Hze/ea7e+/k/cPHJCionLnPDabWYouNFQqViz11yv3nYGA82uJEmYIPyQ/P6lKFbNl5sKF5GAhZbgQG2tGEjgDg6pVPStsAQooQgTkidq1a6tFixaaPn262rdvr3379ikiIkJLly6VJCUmJuqNN97QvHnzdOzYMcXGxio2NjbbjRN37typypUrJwUIktS8efM0x3311VeaPHmy9u7dq4sXLyohIUEhISE5ei07d+7UDTfckKq2li1bym63a/fu3UkhQt26deXt7Z10TLly5fTnn39m+LyJiYmaNWtWqmkYvXv31rBhwzR27Fh5e3try5Ytat26dbpTIk6dOqXjx4+rQ4cOOXo96WncuHGq7y9duqSxY8fqhx9+0PHjx5WQkKDLly/r8OHDkqQtW7bI29tbbdu2Tff5ypUrpy5dumj69Olq2rSpfvjhB8XExOi+++5zuVYAQAFnt5sl9pzhQMrAYN8+6fLlrJ8jMDDji/7s7AcH00k+PwUHm5CgVi2rKwGQDYQIHiYw0IwIsOrcOTFw4EANHjxY//d//6cZM2aoSpUqSRe84eHhmjRpkiZPnqx69eopKChITz31lOKyOYTQkU4DItsVQ9jWr1+v+++/X2PHjtVtt92W9Il+eHh4jl6Hw+FI89zpnfPKC32bzSZ7JvMilyxZomPHjqlnz56pbk9MTNTSpUvVqVOnTFdqyGoVB6//3vyk/FnFx8ene+yV4c2zzz6rJUuWaOLEiQoLC1NAQIDuvffepP8+2VlBYtCgQXrooYc0adIkzZgxQz179lQgXY4BAJKZU3/4cNqgYO9eExTExmb8WG9v84l0WJj5lDoszGw1apiRAKGhfFoNAHmIEMHD2GzZn1JgtR49emjo0KH6/PPPNWvWLD388MNJF90RERHq1q2bevfuLcn0OPj7779Vp06dbD33ddddp8OHD+v48eMqX768JLN8ZEpr1qxRlSpV9MILLyTddujQoVTH+Pr6KjExMctzzZo1S5cuXUq62F6zZo28vLxSTS3IqWnTpun+++9PVZ8kvfHGG5o2bZo6deqk+vXra9asWYqPj08TUgQHB6tq1apavnx50pSRlJyrWZw4cUINGzaUpDRLWWYkIiJC/fr109133y1Junjxog6maIhUr1492e12rVy5MlWzxZQ6d+6soKAgTZkyRT/++KNWrVqVrXMDAAqIhATp0KG00w727jXL8WUQbEtKXnrPGRCkDAuqVCEkAAALESIgzxQtWlQ9e/bUqFGjFBkZqX79+iXdFxYWpq+//lpr165V8eLF9fbbb+vkyZPZDhE6duyoWrVqqU+fPgoPD1dUVFSai/GwsDAdPnxYc+fOVZMmTbRo0SItWLAg1TFVq1bVgQMHtGXLFlWsWFHBwcHy8/NLdUyvXr308ssvq2/fvhozZoxOnz6tIUOG6KGHHkqaypBTp0+f1vfff6+FCxfq+uuvT3Vf37591aVLF50+fVqDBw/We++9p/vvv18jR45UaGio1q9fr6ZNm6pWrVoaM2aMHnvsMZUpU0adOnXShQsXtGbNGg0ZMkQBAQFq1qyZ3njjDVWtWlVnzpxJ1SMiM2FhYfrmm2/UtWtX2Ww2vfTSS6lGVVStWlV9+/bVgAEDkhorHjp0SKdOnVKPHj0kSd7e3urXr59GjhypsLCwdKebAAA8mMNhuu8fOJD+dvCgCRIy4utrRg84w4GUgUGlSiZIAAC4Hf51Rp4aOHCgpk2bpltvvTWpq78kvfTSSzpw4IBuu+02BQYG6pFHHtFdd92lyMjIbD2vl5eXFixYoIEDB6pp06aqWrWq3n33Xd1+++1Jx3Tr1k3Dhg3T4MGDFRsbqy5duuill17SmDFjko7p3r27vvnmG7Vv317nz59PWuIxpcDAQC1ZskRDhw5VkyZNUi3xeLWcTRrT62fQvn17BQcHa86cOXr66af1yy+/6Nlnn1Xbtm3l7e2tBg0aqGXLlpJM4BATE6NJkybpmWeeUalSpXTvvfcmPdf06dM1YMAANW7cWLVq1dKbb76pW2+9Ncv6Jk2apAEDBqhFixYqVaqUnnvuOUVd0axqypQpGjVqlJ544gn9+++/qly5skaNGpXqmIEDB2rcuHEaMGDA1fyYAABWi4pKGwyk/P7Spcwf7++fHBSkHE0QFiZVrGimJgAAPIrNkd7kcuSbqKgohYaGKjIyMk3Dv5iYGB04cEDVqlWTv7+/RRUCV2/NmjVq166djh49mumoDX7XAcAiMTFmykFGownOns36OcqXl6pVS71VrWpCg/LlaVAIAG4os+vQrDASAUCui42N1ZEjR/TSSy+pR48eVz3tAwDgosRE6ciRjEOCEyeyfo6SJdOGBM6goEoVM9oAAFBoECIAyHVffPGFBg4cqAYNGmjOnDlWlwMABZ/dbkKBv/4y244d5uvOnWa0QWaKFs04JKhWzSy/BwDAfwgRAOS6fv36pektAQDIBXa76UvgDAuc265d0uXL6T/G1zc5EEgvJChZ0iz/BABANhAiAAAAuJuUYYFzVIFzZEFGYYGfn1SnjlS3rnTddeZr3bomKKCBIQAglxAiAAAAWMVuN40NU44q2LHDhAXR0ek/xs9Pql07OSRwhgbVqxMWAADyHCGCB2ABDRR0/I4DKNAcDiky0jQx3L8/dWCQnbAg5aiCunUJCwAAliJEcGNFihSRJEVHRysgIMDiaoC8E/3fG2jn7zwAeASHQ/r3XxMOHD9uvqbcUt6WWXNDX9/kkQUpA4Pq1SUf3qoBANwLf5ncmLe3t4oVK6ZTp05JkgIDA2Wj8REKEIfDoejoaJ06dUrFihWTN5+sAXAHiYnS6dPphwEpbzt5UoqPz/7zFismVaqUegpC3bpSjRqEBQAAj8FfLDdXtmxZSUoKEoCCqFixYkm/6wCQZ6KjpX/+Sd5OnkwbEBw/Lp06ZYKE7CpVSipXLvVWvnza2xhVCAAoAAgR3JzNZlO5cuVUpkwZxefk0w7AQxQpUoQRCACu3sWLyYFAyoAgve3ixew/r80mXXNN1uFA2bJmOgIAAIUEIYKH8Pb25kILAFDwORxSVFTWgYBzy6gpYUb8/U04cM01JgDIKCAoU4YpBgAApIO/jgAAwBpnzkiLF5tt//7kYCA2NmfPExiYHAyk3MqWTXtbcLAZZQAAAK4KIQIAAMg/u3dL338vLVworVkj2e3pHxccnH4wkN5WtGj+vgYAAAoxQgQAAJB3EhKkdetMaLBwobRnT+r7GzSQunaVGjdOHQzQhBAAALdEiAAAAHLXhQvS0qUmNFi0SPr33+T7ihSR2reX7rzThAeVK1tXJwAAyDFCBAAA4LojR5KnKfz6qxQXl3xfiRJS584mOLjtNikkxLo6AQCASwgRAABAzjkc0ubNydMUNm9OfX9YmNStmwkOWrRgpQMAAAoI/qIDAIDsiYkxowwWLjSjDo4dS77PZjNhwZ13mq1WLVZBAACgACJEAAAAGTt92izBuHChtGSJdOlS8n1BQWZ6wp13mukKpUtbVycAAMgXhAgAACCZw2GWYXSONli7NvUyjOXLJ482aN9e8ve3rlYAAJDvCBEAAHA3Z89KP/0krV5tphDY7am3xMS0t+X09oyOvXgx9TQFSWrYMDk4aNiQaQoAABRihAgAAFjN4ZD++sssh/jDD2k//c9vRYpIN99sQoM77mAZRgAAkIQQAQAAK8TESCtWmNDghx+kQ4dS33/99abfQKlSkpeX5O1tvl655cbtKW/z9jbnZhlGAACQDkIEAADyy7FjpknhDz9Iy5ZJ0dHJ9/n5SR06SF26mK1KFevqBAAAyAAhAgAAecVulzZuTJ6msHlz6vsrVDDTBbp0MdMHgoKsqRMAACCbCBEAAMhNUVHS0qUmOFi8WDp1Kvk+m0266SYTGtxxh3TDDTQpBAAAHoUQAQAAV/39txlpsGiRtGqVFB+ffF9IiOlt0KWL1KmTVKaMdXUCAAC4iBABAICcioszyy86g4M9e1LfX7Nm8jSFVq0kX19r6gQAAMhlhAgAAGTHqVPSjz+a4GDpUjNtwcnHR2rbNjk4uPZa6+oEAADIQ4QIAABkJD5e+v576aOPpJ9/lhyO5PvKlJE6dzbBwS23sCQiAAAoFAgRAAC40qFD0scfS9OnSydOJN9+443JTREbN5a8vKyrEQAAwAKECAAASFJCgulv8NFH0k8/JY86uOYaacAAadAgqXp1a2sEAACwGCECAKBwO3JE+uQTado06dix5Ns7dJAefVTq1o3GiAAAAP8hRAAAFD6JiWa0wUcfmdEHdru5vVQpqX9/6ZFHpLAwa2sEAABwQ4QIAIDC4/hxM+Lgk0+kw4eTb2/Xzow6uPtuyc/PsvIAAADcHSECAKBgs9vNkowffWRWWkhMNLeXKCH162dGHdSqZWmJAAAAnoIQAQBQMJ08aVZX+Phj6eDB5NtbtZIee0zq3l3y97esPAAAAE9EiAAAKDjsdumXX8yog2+/NSsuSFKxYlKfPmbUQd26VlYIAADg0QgRAACe79QpaeZMaepUad++5NubNze9Du67TwoMtKw8AACAgsLL6gJyw+HDh/XMM8+oTp06CgoKUokSJdS0aVNNnDhR0dHRuXKObdu26ZFHHlHNmjUVFBSkkJAQ1a1bVyNGjNDhlM25AAD5w+GQfv1Vuv9+qWJF6bnnTIAQEiL973/Stm3S2rVS374ECAAAALnE5nA4HFYX4YpFixapV69eioyMTPf+WrVqafHixapevfpVn+Pll1/Wq6++qox+VCEhIZo5c6buvvvuHD93VFSUQkNDFRkZqZCQkKuuEQAKjX//TR51sGdP8u1NmphRB/ffLwUFWVYeAACAu3PlOtSjQ4StW7eqRYsWio6OVtGiRTVy5Ei1b99ely9f1ty5c/Xxxx9LkmrXrq2NGzeqaNGiOT7HG2+8oZEjR0qSypUrp2effVYtWrSQJK1du1ZvvvmmTp48KT8/P/3yyy9J92UXIQIAZIPDIa1eLX34ofTVV1JcnLm9aFGpVy8THjRsaG2NAAAAHqLQhgjt27fXihUr5OPjo1WrVql58+ap7n/rrbc0YsQISdLYsWM1evToHD3/sWPHVKNGDcXGxqp8+fLauHGjypcvn+aYpk2b6vjx47rhhhv0xx9/yMsr+7NECBEAIBPnzkmzZ5tGiTt3Jt/esKFZYeGBB6TgYOvqAwAA8ECuXId6bE+EjRs3asWKFZKkgQMHpgkQJGn48OGqU6eOJGny5MmKj4/P0Tnmzp2r2NhYSSaEuDJAkKQKFSpo7NixkszIiB9//DFH5wAAXMHhSO5lUL689NRTJkAIDJQGDZI2bpT++MOstECAAAAAkK88NkT49ttvk/b79++f7jFeXl7q06ePJOncuXNJoUN2bdy4MWm/U6dOGR53++23J+1/9dVXOToHAOA/589L778v1a8vtWxpRiDExEg33CB98IF04oT08cdS48ZWVwoAAFBoeewSjxEREZKkoKAgNWrUKMPj2rZtm7S/evVq3XLLLdk+x9mzZ5P2r7nmmgyPS3nfypUrs/38AFDoORzShg1musLcudLly+b2gACpZ0/T6+CmmySbzdo6AQAAIMmDQ4Sd/82NDQsLk49Pxi+jdu3aaR6TXUEpuntHRkaqZMmS6R6XcmWIgwcPKjo6WoEsJwYAGYuKkj7/3IQHW7Yk3163rgkOHnpIKlbMquoAAACQAY+czhATE6MzZ85IkipWrJjpscWLF08KA44cOZKj8zj7KUiZjzBYtWpV0r7D4dDRo0dzdB4AKDQ2bTK9DMqXlx5/3AQIfn4mNFi9WvrzT2nIEAIEAAAAN+WRIcKFCxeS9rOzbKMzRLh48WKOztOtW7ek/VdeeUUxMTFpjomJidErr7ySYX1Xio2NVVRUVKoNAAq0ixeTexk0bmz2L12SateWJk2Sjh83/Q9atmTaAgAAgJvzyBAh5cW8r69vlsf7+flJki4759pm00033aQ777xTkll5oW3btlq+fLmio6MVHR2t5cuXq23bttq6dWuqOjI7z/jx4xUaGpq0VapUKUc1AYDH2LLFjDYoX96MPti0SfL1lR58UFq5Utqxw6y8UKKE1ZUCAAAgmzyyJ4K/v3/SflxcXJbHO5dpDAgIyPG5Zs2apU6dOmn9+vXasGGDOnbsmOaYJk2a6Prrr9eMGTMkScGZLDk2cuRIPf3000nfR0VFESQAKDguXZLmzTO9DjZsSL792mtNkNCvn1SqlGXlAQAAwDUeORIh5UV6dqYoXLp0SVL2pj5cqVixYlq5cqUmTZqk6667LtV9ZcuW1UsvvaSIiIhU0xKKFy+e4fP5+fkpJCQk1QYAHm/7dtPLoEIFaeBAEyAUKSL16CEtXy7t3i098wwBAgAAgIfz2JEIpUqV0pkzZ7JsYnju3LmkEOFqP/H39fXVU089paeeekqRkZE6deqUihYtqrJly8r23/zdbdu2STIBR1bNHgGgQEhIkObPl/7v/6S1a5Nvr17djDro318qU8a6+gAAAJDrPDJEkMzKCREREdq7d68SEhIyXOZx165dqR7jKmcvg5T++ecf7d27V5KZ2uDl5ZEDPAAge2JjpVmzpAkTpP37zW0+PlK3bmZ5xg4dJP4dBAAAKJA89l1eq1atJJmpCps2bcrwuJRLM7Zs2TJPavniiy/kcDgkST169MiTcwCA5S5dkiZPlmrUMGHB/v1mesIrr0iHD0tffSXdcgsBAgAAQAHmse/07rrrrqR9Z0PDK9ntds2ePVuS6W3Qvn37XK8jKipKEyZMSDrHgw8+mOvnAABLRUZK48ZJVatKw4ZJx46Z3geTJ0uHDkkvvSSVK2d1lQAAAMgHHhsiNG3aVK1bt5YkTZs2TevWrUtzTHh4uHbu3ClJGjp0qIoUKZLq/pkzZ8pms8lms2nMmDHpnufEiROKj49P974LFy6oe/fuOnnypCRp4sSJma7MAAAe5fRp6YUXpMqVzdczZ0y/g6lTpX37pKFDpcBAq6sEAABAPvLYngiS9M4776hly5a6fPmybr31Vo0aNUrt27fX5cuXNXfuXE2dOlWSVLNmTQ0fPvyqzvHZZ59p4sSJ6tu3r9q2baty5copKipK69ev1wcffKDDhw9Lkvr376+BAwfm2msDAMscPSqFh5uwIDra3HbdddKoUVLPnqb/AQAAAAolj34n2LBhQ82bN0+9e/dWVFSURo0aleaYmjVratGiRS6NEPjnn3/05ptv6s0330xzn4+Pj4YPH65x48Zd9fMDgFvYt880S5w5U3KOwGrc2IxCuPNOeh0AAADAs0MESeratau2bdumd955R4sWLdLRo0fl6+ursLAw3XfffRo8eLACXRhue8899ygmJka//PKL9u3bp1OnTsnPz08VK1bUrbfeqoEDB6pu3bq5+IoAIJ/99Zc0frz0xReS3W5ua9PGhAe33CL9t5QtAAAAYHM4lxWAJaKiohQaGqrIyEiFhIRYXQ6AwuT3303DxAULkm+7/XYTHvy3Ag4AAAAKHleuQz1+JAIAIIdWrZJef11autR8b7NJ99xjeh7ceKO1tQEAAMCtESIAQGHgcEhLlpjwYPVqc5u3t/Tgg9Lzz5vGiQAAAEAWCBEAoCCz2810hXHjpD/+MLf5+kr9+0sjRpglGwEAAIBsIkQAgIIoIcE0Shw/Xtq509wWGCg99pj09NNShQrW1gcAAACPRIgAAAVJTIw0a5ZZqvHAAXNbaKg0ZIg0dKhUqpS19QEAAMCjESIAQEGQkCB98IH0xhvSiRPmttKlpWHDpCeeMEECAAAA4CJCBADwdDt3Sv36SRs2mO8rVpSefVYaNMhMYQAAAAByCSECAHiqxEQpPFwaPVqKjTWjDcaPlwYMkPz8rK4OAAAABRAhAgB4ol27zOiD334z33fqJE2dakYhAAAAAHnEy+oCAAA5kJgovfWW1KCBCRBCQqRp06RFiwgQAAAAkOcYiQAAnmLXLql/f2n9evP9bbdJH38sVapkbV0AAAAoNBiJAADuLjFRmjjRjD5Yv96MPvjkE+nHHwkQAAAAkK8YiQAA7mz3bjP6YN068/2tt5oAgfAAAAAAFmAkAgC4I+fKCw0amAAhONhMXfjpJwIEAAAAWIaRCADgbvbsMaMP1q41399yixl9ULmytXUBAACg0GMkAgC4i8REadIk6YYbTIAQHGyWbVyyhAABAAAAboGRCADgDv7+24w+WLPGfN+xo1m6kfAAAAAAboSRCABgJbtdmjzZjD5Ys0YqWlT66CNp6VICBAAAALgdRiIAgFX27jWjD1avNt936GBGH1SpYm1dAAAAQAYYiQAA+c1ul955R6pf3wQIRYtKU6ZIP/9MgAAAAAC3xkgEAMhPe/dKAwZIERHm+5tvNqMPqla1tCwAAAAgOxiJAAD5wW6X3n3XjD6IiJCCgqQPPjCjDwgQAAAA4CEYiQAAeW3fPjP6YNUq83379mb0QbVq1tYFAAAA5BAjEQAgr9jt0nvvmdEHq1aZ0Qf/93/SsmUECAAAAPBIjEQAgLywf78ZfbBypfm+XTtp+nTCAwAAAHg0QgQAyG2bNpnQ4OJFKTBQevNN6fHHJS8GfwEAAMCzESIAQG6KjJR69DABQosW0pw5UvXqVlcFAAAA5ApCBADILQ6HNGiQmcpQtaq0aJFUrJjVVQEAAAC5hrG1AJBbpkyRvvpK8vGR5s4lQAAAAECBQ4gAALlh82Zp2DCzP2GCdNNN1tYDAAAA5AFCBABwVVSU6YMQFyd17ZocJgAAAAAFDCECALjC4ZAefVTau1eqVEmaOVOy2ayuCgAAAMgThAgA4IqPPzb9D7y9zdcSJayuCAAAAMgzhAgAcLW2bZOGDjX748aZJR0BAACAAowQAQCuxsWLpg9CTIzUqZP0zDNWVwQAAADkOUIEAMgph0N6/HFp926pQgVp9mzJi39OAQAAUPDxrhcAcmrmTOnTT00fhC++kEqVsroiAAAAIF8QIgBATvz1l/S//5n9V16RWre2th4AAAAgHxEiAEB2Xbpk+iBcvizdeqv0/PNWVwQAAADkK0IEAMiuIUOkHTukcuWkOXPogwAAAIBCh3fAAJAdc+ZIM2aY4ODzz6UyZayuCAAAAMh3hAgAkJVdu8xqDJL08stSu3aWlgMAAABYhRABADJz+bLpg3DpknTzzdILL1hdEQAAAGAZQgQAyMzQodKff5rpC599ZpZ1BAAAAAopQgQAyMgXX0gffyzZbCZAKFvW6ooAAAAASxEiAEB69uyRHnnE7L/wgtSxo7X1AAAAAG6AEAEArhQTY/ogXLwotWljmikCAAAAIEQAgDSeflraulUqVcos5+jjY3VFAAAAgFsgRACAlL78UpoyxezPmSNVqGBtPQAAAIAbIUQAAKd9+6RBg8z+889Lt99ubT0AAACAmyFEAABJio2VevaUoqKkli2lV1+1uiIAAADA7RAiAIAkjRghbdoklShhlnakDwIAAACQBiECACxYIL37rtmfPVuqVMnaegAAAAA3RYgAoHA7eFAaMMDsP/OM1KWLpeUAAAAA7owQAUDhFRdn+iCcPy81ayaNG2d1RQAAAIBbI0QAUHiNHClt2CAVLy7NnSsVKWJ1RQAAAIBbI0QAUDh9/7309ttmf8YMqUoVa+sBAAAAPAAhAoDC5/BhqW9fs//UU1K3bpaWAwAAAHgKQgQAhUt8vHT//dK5c1LjxtKECVZXBAAAAHgMQgQAhcuLL0rr1kkhIdK8eZKvr9UVAQAAAB6DEAFA4fHjj9Kbb5r96dOl6tWtrQcAAADwMIQIAAqHY8ekPn3M/v/+J3Xvbm09AAAAgAciRABQ8CUkSA88IJ05IzVsKE2caHVFAAAAgEciRABQ8I0ZI0VESMHB0vz5kr+/1RUBAAAAHokQAUDBtmSJNG6c2f/4YykszNp6AAAAAA9GiACg4Nq3zyzn6HBIjz4q9expdUUAAACARyNEAFAwXbwo3XWXdP681KyZ9M47VlcEAAAAeDxCBAAFj8Mh9e8vbd8ulS0rff215OdndVUAAACAxyNEAFDwvPGG9NVXUpEiJkAoX97qigAAAIACgRABQMHy44/SCy+Y/fffl1q0sLYeAAAAoAAhRABQcPz9t/TAA8mNFB95xOqKAAAAgAKFEAFAwXDhgmmkGBlpRh+8+67VFQEAAAAFDiECAM9nt0t9+0o7dpj+B199Jfn6Wl0VAAAAUOAQIgDwfOPGSQsWmODg66+lcuWsrggAAAAokAgRAHi2H36QRo82+x98IDVrZm09AAAAQAFGiADAc+3eLfXqZRopPvGENHCg1RUBAAAABRohAgDPFBVlGilGRUmtWkmTJlldEQAAAFDgESIA8Dx2u9Snj7Rrl1ShAo0UAQAAgHxCiADA87z6qvTdd5Kfn2moeM01VlcEAAAAFAqECAA8y8KF0pgxZv/DD6UmTSwtBwAAAChMCBEAeI5du6Tevc3+4MFSv36WlgMAAAAUNoQIADxDZKTUrZt04YLUpo309ttWVwQAAAAUOoQIANyf3W5GIOzZI1WqJH35pVSkiNVVAQAAAIUOIQIA9zd2rPTDD6aR4jffSGXKWF0RAAAAUCgRIgBwbwsWSK+8YvanTpUaN7a2HgAAAKAQI0QA4L527JD69DH7Q4cm7wMAAACwBCECAPd0/rx0113SxYtS+/bSW29ZXREAAABQ6BEiAHA/iYlSr17S339LlStL8+bRSBEAAABwA4QIANzPyy9LixdL/v6mJ0Lp0lZXBAAAAECECADczddfS6+/bvY/+US68UZr6wEAAACQhBABgPvYvl3q29fsP/20mdIAAAAAwG0QIgBwD+fOmUaKly5JHTpIEyZYXREAAACAKxAiALBeYqL04IPSvn1S1arS3LmSj4/VVQEAAAC4AiECAOu9+KL0009SQID07bdSqVJWVwQAAAAgHYQIAKw1f770xhtmf/p06YYbrK0HAAAAQIYIEQBYZ9s2qX9/s//ss9L991tbDwAAAIBMESIAsMbZs6aRYnS0dMst0vjxVlcEAAAAIAuECADyX0KCGXVw4IBUvbpppOjtbXVVAAAAALJAiAAg/40aJf38sxQYaBoplihhdUUAAAAAsoEQAUD+mjtXeustsz9jhlSvnrX1AAAAAMg2QgQA+WfrVmnAALP//PNSjx7W1gMAAAAgRwpEiHD48GE988wzqlOnjoKCglSiRAk1bdpUEydOVHR0dK6cY8eOHRoyZIjq1aunkJAQ+fr6qnTp0mrfvr0mTZqkCxcu5Mp5gAIrMdEECJcvS7ffLr32mtUVAQAAAMghm8PhcFhdhCsWLVqkXr16KTIyMt37a9WqpcWLF6t69epXfY7w8HA9//zzSkhIyPCYKlWqaOHChapfv36OnjsqKkqhoaGKjIxUSEjIVdcIuL0PP5Qef1wKDZX27JHKlLG6IgAAAKBQcuU61KNHImzdulU9evRQZGSkihYtqtdff11r167V8uXL9fDDD0uSdu/erS5duujixYtXdY758+frmWeeUUJCgnx9fTVs2DAtWrRIv/32mz7//HO1atVKknTo0CHdfvvtGYYZQKF25oxppihJr75KgAAAAAB4KI8eidC+fXutWLFCPj4+WrVqlZo3b57q/rfeeksjRoyQJI0dO1ajR4/O8Tnq1aun7du3S5J++OEHdenSJc0x3bt31zfffCPJjFp4+umns/38jERAofDoo9LUqVL9+tKmTZKPj9UVAQAAAIVWoRyJsHHjRq1YsUKSNHDgwDQBgiQNHz5cderUkSRNnjxZ8fHxOTpHVFRUUoBw4403phsgSNLLL7+ctL927docnQMo8H7/Xfr4Y7P//vsECAAAAIAH89gQ4dtvv03a79+/f7rHeHl5qU+fPpKkc+fOJYUO2RUXF5e0n1lPhRo1aiTtx8bG5ugcQIFmt0uDB0sOh9Srl9S6tdUVAQAAAHCBx4YIERERkqSgoCA1atQow+Patm2btL969eocnaNUqVIqUaKEJGn//v0ZHrdv376k/Zo1a+boHECBNnOm9NtvUtGi0ltvWV0NAAAAABd5bIiwc+dOSVJYWJh8MhkeXbt27TSPyYlHHnlEkvTHH3/oxx9/TPeYV199VZLk7e2tQYMG5fgcQIF07pz03HNmf8wYqVw5S8sBAAAA4DqPnJwcExOjM2fOSJIqVqyY6bHFixdXUFCQLl26pCNHjuT4XC+88IJ+//13LVu2THfffbcGDx6sDh06qFSpUtq/f7+mTJmilStXytvbW++++25SD4aMxMbGppryEBUVleOaAI8werRZlaFOHenJJ62uBgAAAEAu8MgQ4cKFC0n7RYsWzfJ4Z4hwNcs8Fi1aVD/++KNmzpypN954Q+Hh4QoPD091zD333KMRI0bopptuyvL5xo8fr7Fjx+a4DsCjbNkiffCB2X//falIEUvLAQAAAJA7PHI6Q0xMTNK+r69vlsf7+flJki5fvnxV5/v999/1xRdfZNgXYdmyZZo1a1a2RhWMHDlSkZGRSdvVjI4A3JrDYZop2u1Sjx7SzTdbXREAAACAXOKRIYK/v3/SfsoVFDLinD4QEBCQ43N99dVXateunX755RfVq1dPCxYs0L///qu4uDjt27dP48aNU3x8vKZMmaIWLVro5MmTmT6fn5+fQkJCUm1AgfLZZ9KaNVJgoDRxotXVAAAAAMhFHhkiBAcHJ+1nZ4rCpUuXJGVv6kNK//zzj/r166fY2FjVrVtXa9eu1V133aUSJUqoSJEiql69ukaOHKnvv/9eNptNf/31l4YMGZKzFwMUJFFR0rPPmv2XXpIqVbK2HgAAAAC5yiNDBH9/f5UqVUqSdPTo0UyPPXfuXFKIUCmHFzRz585NeuyoUaMUFBSU7nEdOnRQhw4dJEnffPONzp07l6PzAAXG2LHSyZPStddKw4ZZXQ0AAACAXOaRIYKkpFUQ9u7dq4SEhAyP27VrV5rHZFfKJSFvvPHGTI9t1KiRJMlut2vPnj05Og9QIPz1l/TOO2b/3Xel/3qRAAAAACg4PDZEaNWqlSQzVWHTpk0ZHrdy5cqk/ZYtW+boHD4+yYtXZBZUSFJ8fHy6jwMKBYdDGjJESkyU7rpLuv12qysCAAAAkAc8NkS46667kvZnzJiR7jF2u12zZ8+WJBUrVkzt27fP0TmqVauWtB8REZHpsatWrZIk2Ww2Va1aNUfnATze/PnSr79K/v7SpElWVwMAAAAgj3hsiNC0aVO1bt1akjRt2jStW7cuzTHh4eFJUxKGDh2qIlesVT9z5kzZbDbZbDaNGTMmzeO7dOkim80mSXr99dd17NixdGuZOnWqfv/9d0lSs2bNVLJkyat+XYDHuXhRGj7c7I8cKRGiAQAAAAWWR4+7f+edd9SyZUtdvnxZt956q0aNGqX27dvr8uXLmjt3rqZOnSpJqlmzpoY7L3JyoHbt2urfv7+mT5+uY8eOqWHDhnrqqafUunVrBQcH68iRI5o7d64+//xzSZK3t7fGjRuXq68RcHuvvy4dOyZVq5a8MgMAAACAAsmjQ4SGDRtq3rx56t27t6KiojRq1Kg0x9SsWVOLFi1KtSxkTnzwwQe6dOmS5s2bp9OnT+uFF15I97igoCBNnTpV7dq1u6rzAB5p924pPNzsv/OOFBBgbT0AAAAA8pTHTmdw6tq1q7Zt26Zhw4apZs2aCgwMVLFixdS4cWNNmDBBmzdvVlhY2FU/v5+fn+bOnatffvlFffr0Uc2aNRUUFCQfHx+VKFFCzZs310svvaRdu3bpwQcfzMVXBrg5h0N68kkpPl7q3Fm64w6rKwIAAACQx2wOh8NhdRGFWVRUlEJDQxUZGamQkBCrywGyb8EC6Z57JF9fs7yjC2EdAAAAgPzjynWox49EAGCB6Ghp2DCz/+yzBAgAAABAIUGIACDnJkyQDh2SKlUyKzIAAAAAKBQIEQDkzL59JkSQpEmTpKAga+sBAAAAkG8IEQDkzLBhUmys1LGj6YkAAAAAoNAgRACQfYsWSd9/L/n4SO+9J9lsVlcEAAAAIB8RIgDInpgYaehQsz9smFS7trX1AAAAAMh3hAgAsmfiRNMPoXx56aWXrK4GAAAAgAUIEQBk7dAhadw4sz9xohQcbG09AAAAACxBiAAga8OHS5cvS23bSvffb3U1AAAAACxCiAAgcz//LH39teTtTTNFAAAAoJAjRACQsbg4acgQsz94sFSvnrX1AAAAALAUIQKAjE2eLO3eLZUpI40da3U1AAAAACxGiAAgfceOSa+8YvbffFMKDbW2HgAAAACWI0QAkL5nnpEuXZJatJAeesjqagAAAAC4AUIEAGmtWCHNnWuaKL7/vuTFPxUAAAAACBEAXCk+PrmZ4mOPSQ0bWlsPAAAAALdBiAAgtf/7P2n7dqlkSem116yuBgAAAIAbIUQAkOzkSenll83++PFSiRLW1gMAAADArbgUItSpU0dvvfWW/vnnn9yqB4CVnntOioqSmjSRBg60uhoAAAAAbsalEGH37t16/vnnValSJd11111auHChEhMTc6s2APlpzRpp9myaKQIAAADIkEtXCQ0bNpTD4VBCQoK+//573X333apQoYJGjBihnTt35laNAPJaYqI0eLDZHzhQatrU2noAAAAAuCWXQoRNmzZp69atGjp0qEqVKiWHw6FTp04pPDxc119/vVq0aKFp06bp4sWLuVUvgLzw0UfSli1SsWLSuHFWVwMAAADATdkcDocjN57IORphxowZ+umnn5SQkCCbzSZJCgwM1H333af+/furdevWuXG6AiMqKkqhoaGKjIxUSEiI1eWgMDp9WqpZUzp/3qzM8MQTVlcEAAAAIA+5ch2aayFCSv/8849mzZqlWbNmJU1rcAYKNWrU0MCBA9WnTx+VK1cut0/tcQgRYLmHH5Y++URq0ED6/XfJ29vqigAAAADkIbcLEVL67bffNG3aNM2fP19RUVHmpDabvL29ddttt2ngwIHq2rWrvAvphQshAiy1YYPUrJnkcEirV0stW1pdEQAAAIA85sp1aJ63X7/ppps0depUffbZZypbtmzSiISEhAQtXrxY3bt3V5UqVTR58mQlJCTkdTkAnOx200zR4ZD69CFAAAAAAJClPA0RDh06pFdeeUXVq1fXnXfeqX/++UcOh0Pe3t7q1KmTKlWqJIfDoePHj2v48OFq1qyZzp07l5clAXCaPl3auFEKCZEmTLC6GgAAAAAeINdDhJiYGH322Wfq2LGjatSoobFjx+rgwYNyOBwKCwvTG2+8oaNHj2rRokU6ePCglixZoo4dO8rhcGjz5s0aO3ZsbpcE4EoxMdLLL5v9MWOksmUtLQcAAACAZ8i1ngjr16/XjBkzNG/ePF24cEGS5HA45O/vr+7du+vhhx9WmzZtMnz80KFD9d5776lq1arav39/bpTkEeiJAEt88IH0v/9JFStKe/dKfn5WVwQAAAAgn7hyHerjyolPnDihOXPmaMaMGdqzZ48kExxI0g033KBBgwapd+/eCg0NzfK5+vXrp/fee09HjhxxpSQAWYmLk954w+w//zwBAgAAAIBscylEqFy5sux2e1JwEBISovvvv1+DBg1S48aNc/RczvTDbre7UhKArMyeLR05IpUrJw0caHU1AAAAADyISyFCYmKiJKl58+Z6+OGH1aNHDwUGBl7Vc11zzTWaMWOGK+UAyEp8vDRunNl/9lnJ39/aegAAAAB4FJdChGHDhmnQoEGqU6eOy4UULVpUffv2dfl5AGTi88+lAwekMmWkRx+1uhoAAAAAHsalECE8PDy36gCQ1xITk0chDB8uXeWoIQAAAACFV64v8QjATc2fL+3ZI5UoIT3+uNXVAAAAAPBALoUIZ86c0YABAzRgwAAdP348y+OPHTumAQMGaODAgYqMjHTl1ABywm6XXnvN7A8bJgUHW1sPAAAAAI/kUojw1VdfaebMmdqyZYvKly+f5fEVKlTQ1q1bNXPmTH3zzTeunBpATnzzjbRjhxQaKg0ZYnU1AAAAADyUSyHCd999J5vNpnvvvTfbj+nRo4ccDgchApBf7Hbp1VfN/tChJkgAAAAAgKvgUoiwd+9eSVLTpk2z/ZjGjRtLkv7++29XTg0gu77/Xtq2TSpa1IQIAAAAAHCVXAoRnH0QSpcune3HlCpVSpLpjwAgjzkcyaMQhgwxTRUBAAAA4Cq5FCL4+flJUo6aJEZFRUmSbDabK6cGkB0//SRt2mSWcxw2zOpqAAAAAHg4l0KEihUrSpLWrVuX7cesWbNGkmmyCCAPORzSK6+Y/ccfl3IwYggAAAAA0uNSiNCuXTs5HA699957SSMMMhMVFaX3339fNptN7dq1c+XUALKyfLm0fr3k7y8984zV1QAAAAAoAFwKER599FF5eXnpxIkT6tKli06ePJnhsSdPnlSXLl10/Phx2Ww2Pfroo66cGkBWnL0QHnlEKlvW2loAAAAAFAg+rjy4bt26evLJJzV58mStXbtW1157rXr06KE2bdqoXLlystlsOn78uFatWqX58+crOjpaNptN//vf/9SgQYNcegkA0li5Ulq1SvL1lUaMsLoaAAAAAAWEzeFwOFx5ArvdrocfflgzZswwT5hBw0TnaQYNGqSPPvqIxor/iYqKUmhoqCIjIxUSEmJ1OSgoOnY00xkee0yaMsXqagAAAAC4EVeuQ12aziBJXl5emjZtmhYsWKBmzZpJMoFByk2SWrZsqYULF2rq1KkECEBeWrfOBAg+PtLzz1tdDQAAAIACxKXpDCl169ZN3bp109mzZ7VlyxadOXNGklSqVCk1bNhQxYsXz61TAciMsxdC375SlSrW1gIAAACgQHF5OgNcw3QG5KqNG6WmTSVvb2n3bqlGDasrAgAAAOBmLJ3OAMCNvPaa+frggwQIAAAAAHIdIQJQUGzdKi1cKNls0gsvWF0NAAAAgAIo13oiXLhwQcuWLdPWrVt15swZXb58WZnNlLDZbJo2bVpunR6AcxRCz55SrVrW1gIAAACgQMqVJR5fffVVhYeH69KlS9l6jMPhkM1mU2JioiunLhDoiYBc8ddf0vXXm/0//0zeBwAAAIAruHId6vJIhH79+umzzz6Tw+GQt7e3SpYsqVOnTslms6lixYo6d+6cLl68KMmMPihVqpQCAwNdPS2AlF5/3Xzt3p0AAQAAAECecaknwpIlS/Tpp59KMmHCqVOntGzZsqT7Dx06pKioKO3atUtDhw6Vl5eXihcvrh9//FEHDhxwrXIAxu7d0rx5Zv/FF62tBQAAAECB5lKIMGPGDElS3bp1NX36dBUvXlw2my3NcTVr1tSkSZO0YMEC7du3T507d1ZkZKQrpwbgNG6cZLdLXbtKDRpYXQ0AAACAAsylEGH9+vWy2Wz63//+l63j77jjDvXt21eHDh3Su+++68qpAUjS/v3SZ5+Z/ZdesrYWAAAAAAWeSyHCqVOnJJmRBk7e3t5J+7GxsWkec++998rhcGjBggWunBqAJI0fLyUmSrffLjVpYnU1AAAAAAo4l0IEpxIlSiTtBwcHJ+07Q4aUypQpI0k6ePBgbpwaKLwOHZJmzTL7jEIAAAAAkA9cChGuueYaSdLZs2dT3ebr6ytJ2rZtW5rHHD58WJIUExPjyqkBTJggxcdLN98stWhhdTUAAAAACgGXQoR69epJknbs2JF0m4+Pjxo2bCgpufFiSh999JEkqUqVKq6cGijcjh2Tpk0z+6NHW1sLAAAAgELDpRChXbt2cjgcqZZ1lKTevXsn9T3o06ePFi1apC+//FJdu3bVkiVLZLPZ1K1bN5cKBwq1t96S4uKk1q2ltm2trgYAAABAIWFzOByOq33wgQMHVKNGDfn5+engwYNJ0xsSEhLUrFkz/fHHH2mWfHQ4HKpSpYr++OMPFS9e3LXqC4CoqCiFhoYqMjJSISEhVpcDT3DypFStmhQTIy1dKt1yi9UVAQAAAPAgrlyHujQSoVq1atq/f7+2b9+e6sQ+Pj76+eef1atXL/n4+MjhcMiZVXTp0kUREREECMDVCg83AcJNN0kdO1pdDQAAAIBCxKWRCNlx4cIF/f3330pISFBYWFiqlRzASATk0OnTUtWqUnS0tGiR1Lmz1RUBAAAA8DCuXIf65FFNSYKDg3XjjTfm9WmAwmHSJBMgNGokdepkdTUAAAAAChmXpjN4eXnJx8dHb775Zm7VAyAjZ89K779v9l98Ubqi3wgAAAAA5DWXQgRfX185HA61bt06t+oBkJF335UuXJDq15fuvNPqagAAAAAUQi6FCOXLl5ckeXt750oxADIQGSm9847Zf/FFycul/3UBAAAA4Kq4dCXSpk0bSdIff/yRK8UAyMD770vnz0t16kjdu1tdDQAAAIBCyqUQYciQIfL29tbEiRMVFRWVWzUBSOniRdNQUZJeeIFRCAAAAAAs49LVSKNGjfTee+/p0KFDatu2rdauXZtbdQFwmjJF+vdf6dprpZ49ra4GAAAAQCHm0hKPAwYMkCTVqlVLW7duVevWrVWpUiXVr19fxYsXz7RXgs1m07Rp01w5PVDwRUdLEyea/VGjJJ88X5UVAAAAADJkczgcjqt9sJeXl2wplplzPpUti6XnHA6HbDabEhMTr/bUBUZUVJRCQ0MVGRmpkJAQq8uBu5k8WRo2TKpaVdqzRypSxOqKAAAAAHg4V65DXfpYs3LlylkGBgCuUkyM9OabZn/UKAIEAAAAAJZzKUQ4ePBgLpUBII1p06QTJ6RKlaS+fa2uBgAAAABca6wIII/ExUkTJpj9556TfH2trQcAAAAARIgAuKdZs6QjR6Ry5aSBA62uBgAAAAAkESIA7ic+Xho/3uyPGCH5+1tbDwAAAAD8x6WeCIcPH3bp5JUrV3bp8UCB9Nln0oEDUpky0iOPWF0NAAAAACRxKUSoVq3aVT/WZrMpISHBldMDBU9iojRunNkfPlwKDLS2HgAAAABIwaUQweFw5FYdACRp3jzp77+lkiWlJ56wuhoAAAAASMWlEGHGjBlZHnPp0iXt3r1bX3/9tY4fP64WLVro4YcfduW0QMFkt0uvvWb2hw2Tiha1th4AAAAAuILNkU/DCeLj4/Xkk09q6tSpGj58uN588838OK3bi4qKUmhoqCIjIxUSEmJ1ObDSl19KPXpIxYpJBw9KoaFWVwQAAACgAHLlOjTfVmcoUqSIpkyZojZt2ig8PFxLlizJr1MD7i/lKIQnnyRAAAAAAOCW8n2Jx8cff1wOh0Pvvfdefp8acF8LF0rbtknBwdLQoVZXAwAAAADpyvcQ4dprr5Uk/f777/l9asB9vfGG+Tp4sFSihLW1AAAAAEAG8j1EiIyMTPUVKPQOHpR++03y8mIUAgAAAAC3lu8hwqxZsyRJ5cqVy+9TA+7p++/N11atpGuusbYWAAAAAMhEvoUIf//9tx577DHNmjVLNptNnTt3zq9TA+7tu+/M1zvvtLYOAAAAAMiCjysPrl69epbH2O12nT9/XhcuXEi6rUyZMnrhhRdcOTVQMJw/L61cafYJEQAAAAC4OZdChIMHD+b4Mc2aNdOMGTOYzgBI0o8/SgkJUp060n9NRwEAAADAXbkUIvTt2zfLY7y8vBQcHKxq1aqpbdu2atCggSunBAqWhQvN127drK0DAAAAALLBpRBhxowZuVUHUPjExUmLF5t9pjIAAAAA8AD5vjoDgP+sWiVFRUllykg33WR1NQAAAACQJUIEwCrOVRm6dpW8+F8RAAAAgPtzaTpDYmKi1qxZI0m64YYbFBoamunx58+f17Zt2yRJbdq0ceXUgGdzOOiHAAAAAMDjuBQiLFu2TJ06dVLJkiV1+PDhLI/39fVV9+7ddfbsWS1btkzt27d35fSA59q6VTp8WAoIkDp0sLoaAAAAAMgWl8ZQz58/X5J03333KSAgIMvjAwMD1bNnTzkcDs2bN8+VUwOezTkK4dZbpcBAa2sBAAAAgGxyKUTYtGmTbDabbr755mw/xjn6YMOGDa6cGvBszn4ITGUAAAAA4EFcChGOHj0qSapWrVq2H1O1alVJ0rFjx1w5dSqHDx/WM888ozp16igoKEglSpRQ06ZNNXHiREVHR1/1865YsUI2my1HW7t27XLtdaGAOnJE+uMPyWaTunSxuhoAAAAAyDaXeiI4L9AdDke2H+M8NioqypVTJ1m0aJF69eqlyMjIVHVt3LhRGzdu1CeffKLFixerevXquXK+rNSqVStfzgMP9v335muLFmZ5RwAAAADwEC6FCKVLl9bRo0e1a9cuNW7cOFuP2bVrlySpZMmSrpxakrR161b16NFD0dHRKlq0qEaOHKn27dvr8uXLmjt3rj7++GPt3r1bXbp00caNG1W0aNEcPX+TJk30559/Znnc4MGDtXLlSklS3759r+q1oBBxTmW4805r6wAAAACAHHIpRGjSpImOHDmi2bNnq3fv3tl6zMyZM2Wz2XTjjTe6cmpJ0lNPPaXo6Gj5+Pho6dKlat68edJ9N998s6699lqNGDFCu3bt0ttvv63Ro0fn6PmDgoJ0/fXXZ3rM+fPntX79eklSWFiYWrRokfMXgsIjKkr69VezTz8EAAAAAB7GpZ4I9957ryRp+fLlCg8Pz/L48PBw/fLLL5LMig6u2Lhxo1asWCFJGjhwYKoAwWn48OGqU6eOJGny5MmKj4936ZzpmTdvnmJjYyVJDz30UK4/PwqYn36S4uOlmjUlpr4AAAAA8DAuhQg9e/bUDTfcIIfDoREjRqh79+6KiIhIdbGekJCgiIgI3XPPPRoxYoRsNpuuv/76bI9cyMi3336btN+/f/90j/Hy8lKfPn0kSefOnUsKHXLT7NmzJUk2m40QAVlzLu3IKAQAAAAAHsil6Qw2m00LFixQy5YtdeLECX377bf69ttvVaRIEZUoUUI2m03//vtvUqjgcDhUvnx5fffdd7LZbC4VHhERIclMOWjUqFGGx7Vt2zZpf/Xq1brllltcOm9K+/bt09q1ayVJrVu3ztEqFSiE4uOlRYvMPv0QAAAAAHggl0YiSGbJxs2bN6vbf5+sOhwOxcXF6eTJkzpx4oTi4uKSVmS455579McffyQt8+iKnTt3SjJ9CHx8Ms5CateuneYxucU5CkGioSKyYfVq6fx5qVQpKZ3pNwAAAADg7lwaieBUpkwZLViwQLt379bixYu1efNmnTlzRpJUqlQp3XjjjerSpYuuvfba3DidYmJikp6/YsWKmR5bvHhxBQUF6dKlSzpy5EiunN/p008/lSQFBAQk9YcAMuRcleGOOyRvb2trAQAAAICrkCshglOtWrVUKx+axV24cCFpPzvLNjpDhIsXL+ZaDREREdq/f78k6e6771ZISEi2HhcbG5vUiFGSoqKicq0muDGHg34IAAAAADyey9MZrBATE5O07+vrm+Xxfn5+kqTLly/nWg1z5sxJ2nc2b8yO8ePHKzQ0NGmrVKlSrtUEN7Z9u3TggOTvL+ViXw4AAAAAyE8eGSL4+/sn7cfFxWV5vPOT/4CAgFw5f2xsrL788ktJUvny5dWxY8dsP3bkyJGKjIxM2nJ7igXclHMUQseOUlCQtbUAAAAAwFVyKUTYvHmzvL29FRAQoGPHjmV5/LFjx+Tv7y8fHx/t2LHjqs8bHByctJ+dKQqXLl2SlL2pD9nx3Xff6fz585KkXr16yTsH89v9/PwUEhKSakMh4OyHwKoMAAAAADyYSyHCvHnz5HA4dMcdd6hChQpZHl+hQgXdeeedstvtmjt37lWf19/fX6VKlZIkHT16NNNjz507lxQi5NbUgZSrMuRkKgMKqePHpY0bzX7XrtbWAgAAAAAucClEWLFihWw2mzp16pTtx3Tp0kWStGzZMldOrTp16kiS9u7dq4SEhAyP27VrV5rHuOLUqVNasmSJJOnGG2/U9ddf7/JzooD7/nvz9aabpLJlra0FAAAAAFzgUojgnM9/3XXXZfsxztUbshpBkJVWrVpJMlMVNm3alOFxK1euTNpv2bKlS+eUpM8//zwptGAUArKFVRkAAAAAFBAuhQj//vuvpNSNDrPiXCnh1KlTrpxad911V9L+jBkz0j3GbrcnTT0oVqyY2rdv79I5peSpDD4+PnrwwQddfj4UcBcvSsuXm336IQAAAADwcC6FCMWLF5ckHT58ONuPcY5AcLWhYNOmTdW6dWtJ0rRp07Ru3bo0x4SHh2vnzp2SpKFDh6pIkSKp7p85c6ZsNptsNpvGjBmT5Tn/+usvbd68WZLUqVMnlS5d2qXXgEJg6VIpNlaqUUPKwYgdAAAAAHBHLoUIzmkMC53DtbNhwYIFkpKnNbjinXfeUUBAgBISEnTrrbdq/PjxWr9+vX799Vc9+uijGjFihCSpZs2aGj58uMvnmzVrVtJ+3759XX4+FALOVRm6dZNsNmtrAQAAAAAXuRQidO7cWQ6HQ7Nnz1ZERESWx69atUpz5syRzWbTHXfc4cqpJUkNGzbUvHnzFBISoosXL2rUqFFq3ry5br75Zk2dOlWSCRAWLVqUalnIq2G32/X5559LMiMwcqN+FHAJCdIPP5h9pjIAAAAAKABcChEeffRRlSpVSomJiercubPee+89xcTEpDkuJiZG7777rrp06aLExEQVL15cjz/+uCunTtK1a1dt27ZNw4YNU82aNRUYGKhixYqpcePGmjBhgjZv3qywsDCXz7N8+XIdO3ZMktSzZ8+k3g5Ahtaulc6elUqUkHKhqScAAAAAWM3mcDgcrjzBsmXL1LlzZyUmJkqSAgMD1bhxY5UrV042m03Hjx/X77//rujoaDkcDhUpUkQ//PCDbrnlllx5AZ4uKipKoaGhioyMdLlPBNzM8OHS229LDz0k/deQEwAAAACs5sp1qI+rJ+/YsaOWLFmi3r1768SJE7p06ZJWrVqV6hhnTlGhQgXNmTNH7dq1c/W0gHtzOFL3QwAAAACAAsDlEEGS2rdvr3379mn27NlatGiRNm/erDNnzkiSSpUqpRtvvFFdu3ZV7969mQaAwmHnTmnfPsnXV7r1VqurAQAAAIBckSshgiT5+/vrkUce0SOPPJLlsRs3btTs2bP13nvv5dbpAffiXLGkQwfJxaaeAAAAAOAuXGqsmBNHjx7V+PHjdd1116lZs2b64IMP8uvUQP5zTmVgVQYAAAAABUiujURIz6VLl/TVV19p9uzZWrlyZVJvBIfDIZvNlpenBqxz8qT0229mv2tXa2sBAAAAgFyU6yGCw+HQsmXLNHv2bH377beKjo5Oul2SKlasqHvuuUfdu3fP7VMD7uGHH0xjxcaNpQoVrK4GAAAAAHJNroUIf/31l2bNmqXPP/9cJ06ckJQcHFSuXFn33Xef7r33Xt100025dUrAPTn7IbAqAwAAAIACxqUQ4fTp0/rss880e/Zsbd26VVJycFCsWDGdP39eNptNb775pnr06OF6tYC7u3RJ+vlns08/BAAAAAAFTI5DhNjYWH333XeaPXu2li5dqsTExKTgwM/PT507d1bv3r3VuXNnBQQE5HrBgFtbtkyKiZGqVpXq1bO6GgAAAADIVdkOEVavXq3Zs2fryy+/VFRUlKTkBomtW7dW79691aNHD4WGhuZZsYDbS7kqA81DAQAAABQw2Q4R2rRpI5vNljTq4LrrrlOvXr3Uq1cvVa5cOc8KBDxGYqJpqijRDwEAAABAgZTj6QzBwcF655131K9fvzwoB/Bg69dLp09LxYpJrVtbXQ0AAAAA5DqvnBzscDh08eJFDRw4UPXr19ebb76po0eP5lVtgGdxrsrQubNUpIi1tQAAAABAHsh2iBAREaFBgwYpNDRUDodD27dv18iRI1W1alW1b99e06dPT+qVABRKKfshAAAAAEABlO0QoWXLlpo6dapOnjypefPmqXPnzvL29pbdbteqVav08MMPq2zZsrrvvvu0cOFCJSQk5GXdgHvZvdtsRYpInTpZXQ0AAAAA5IkcTWeQJF9fX91333364YcfdOzYMYWHh6t+/fpyOByKiYnRN998o7vvvlvXXHNNXtQLuCfnVIb27aWQEGtrAQAAAIA8kuMQIaXSpUtr2LBh2rx5s7Zt26ann35aZcuWlcPh0Llz52T7b4m7p59+WkOHDlVERESuFA24HaYyAAAAACgEbA7nmo25xG63a+nSpZo9e7a+++47Xb582Zzov0ChTJkyuvvuu9W9e3d16NAhN0/tkaKiohQaGqrIyEiF8Am2Zzp9WrrmGsnhkA4flipVsroiAAAAAMiQK9ehuR4ipHThwgXNmzdPc+bM0erVq+U8lc1mk81mo2+CCBEKhBkzpAEDpIYNpT/+sLoaAAAAAMiUK9ehLk1nyEpwcLAGDRqklStXat++fRozZozCwsLkcDiUh9kFkL+c/RC6dbO2DgAAAADIY3kaIqRUtWpVjR49Wnv27FFERIQefvjh/Do1kHcuX5aWLjX79EMAAAAAUMD5WHHSli1bqmXLllacGshdy5dL0dGmD0KDBlZXAwAAAAB5Kt9GIgAFUspVGf5rHgoAAAAABRUhAnC17Hbp++/NPv0QAAAAABQChAjA1dqwQfrnHykkRGrb1upqAAAAACDPESIAV8u5KkOnTpKvr7W1AAAAAEA+IEQArlbKfggAAAAAUAgQIgBXY+9eaccOycfHjEQAAAAAgEKAEAG4Gs6pDG3aSMWLW1sLAAAAAOQTQgTgajhDBFZlAAAAAFCIECIAOfXvv1JEhNmnHwIAAACAQoQQAcipxYslu12qX1+qWtXqagAAAAAg3xAiADnlXJWBqQwAAAAAChlCBCAnYmKkn34y+0xlAAAAAFDIECIAOfHrr9KlS1L58lKjRlZXAwAAAAD5ihAByAnnqgx33inZbNbWAgAAAAD5jBAByC67naUdAQAAABRqhAhAdm3aJB0/LhUtKrVvb3U1AAAAAJDvCBGA7HKOQrj9dsnPz9paAAAAAMAChAhAdjmXdmRVBgAAAACFFCECkB0HDkh//il5e0tdulhdDQAAAABYghAByA7nVIZWraQSJaytBQAAAAAsQogAZAerMgAAAAAAIQKQpXPnpJUrzT79EAAAAAAUYoQIQFZ+/FFKTJTq1pVq1LC6GgAAAACwDCECkBVWZQAAAAAASYQIQObi4sxIBIl+CAAAAAAKPUIEIDMrVkgXLkhly0pNmlhdDQAAAABYihAByIxzVYauXSUv/ncBAAAAULhxVQRkxOFIDhHohwAAAAAAhAhAhrZskY4ckQIDpQ4drK4GAAAAACxHiABkxLkqw223SQEB1tYCAAAAAG6AEAHICFMZAAAAACAVQgQgPYcPS5s3m2aKXbpYXQ0AAAAAuAVCBCA9339vvrZoIZUubW0tAAAAAOAmCBGA9Dj7IXTrZm0dAAAAAOBGCBGAK0VGSitWmH36IQAAAABAEkIE4Eo//STFx0u1a0s1a1pdDQAAAAC4DUIE4ErOqQyMQgAAAACAVAgRgJRiY6XFi80+/RAAAAAAIBVCBCCl+fNNT4QKFaSbbrK6GgAAAABwK4QIQErvvWe+Pv645O1tbS0AAAAA4GYIEQCn336TNm6U/PykRx6xuhoAAAAAcDuECICTcxTC/fdLpUtbWwsAAAAAuCFCBECSTp40/RAkacgQa2sBAAAAADdFiABI0tSpUny81Ly51KiR1dUAAAAAgFsiRADi4qQPPzT7jEIAAAAAgAwRIgDffCOdOCGVLSt17251NQAAAADgtnysLgCwnLOh4mOPSb6+1taCXBMfbxbb+PVX6c8/pVtukfr2lXz4Vw8AAAC4ajaHw+GwuojCLCoqSqGhoYqMjFRISIjV5RQ+f/xheiAUKSIdPmxGI8AjJSZKW7aY0OCXX6RVq6RLl1IfU6uW9Prr0j33SDabJWUCAAAAlnPlOpTP5FC4OUch3HcfAYKHcTikv/5KDg1WrJDOn099TMmSUvv2UliY9PHH0u7d0r33Sk2aSOPHSx06WFE5gMLIbpfeekvatk3q1Enq3FkqUcLqqgAAyDlGIliMkQgWOn1aqlRJio2V1q2TmjWzuiJkwuGQ9u5NDg1+/VU6dSr1MSEhUtu20s03m/CgXj3J67/OL1FRUni42ZwjFDp2lMaNM6ECAOSVy5elfv2SVxKWJG9vqU0bqVs3s1WtalV1AIDCyJXrUEIEixEiWGjcOOmFF6TGjaUNGxjf7oYOH04ODX75RTp6NPX9AQFS69bJocGNN2bd8+DUKTOlYcoU0zdBMv00X3tNql07b14HgMLr1CkTEqxfb2bO9e8vrV0rbd+e+rj69aU77zTHNmrEnyQAQN4iRPBghAgWSUiQqlUzV6WzZkl9+lhdEST980/q0GDfvtT3+/pKzZubwODmm6WmTSU/v6s718GD0pgx0uzZZpSDl5d5c//yy2aACgC4audOqUsX6cABqXhxsxhQu3bmvv37pe++M1tEhJnu4FShQnKg0L49PX8BALmPEMGDESJY5KuvTB+E0qWlI0eu/ko0F8THmzeTFy+ai9fSpQvPJ1Bnz0orVyZPT/jrr9T3e3ubqQbO0KBFCykwMHdr2L5devFF80ZeMr8KgwdLI0eangoAcDWWLzejnCIjpRo1pEWLTHPX9Pz7r7R4sfl36KefUjeFDQ42PRS6dTN9FIoVy5fyAQAFHCGCByNEsEjbtqZ9/wsvmHHs+eDff01jv127Un/dt88MjHAKCJAqVzZblSrJm/P7ChXMkFhPYrebwODUKROYrFhhQoPNm80oACebTbrhBhMY3HyzmaqQX/9brFsnPf+8+bWQzHmffVZ66impaNH8qQFAwTBtmlk1OCFBatlS+vZbqVSp7D02JsYEq999Jy1cKJ08mXyfj4/589WtmxmpUKVKnpQPACgECBE8GCGCBbZtM1eq3t5mTHvFirn21PHxZtjqlUHBrl0mRMhIUJD5tCnlm8WMeHlJ5cunDRdSfp/XF70Ohxk5ceqU2U6fTt5PbztzxizBmJ46dZJDg7Ztrf303+GQliwxoxC2bDG3lSkjvfSS9MgjDCkGkDm73WTTb7xhvn/wQRMo+Ptf/fNt3JgcKFw5WqtBg+RAoWHDwjOKzV3ExUk7dpi/E+XLW10NAOQMIYIHI0SwwMMPS598YqYzpGyVnQNnz6Y/qmDv3tSjCq5UubIZzlq7duqvFSqYN3+xsaZNw6FDpqngoUPJ2+HDZouLy7q+EiXSBgwp99ObMhEba8KArAIB5xYTk/OfW4kSZiXNFi1MaNCunVSuXM6fJ6/Z7eZX48UXk/syVKsmvfKK9MADJn8CgJQuX5b69pW+/NJ8P3q06buSmxf2e/eaMOG776TVq1P3UahUKbmPQtu2hJ554fhxM2rNuW3aZP52SuZvbPPmyVuDBp7/3+DiRTNicONG6fffTRj20EPmbzeBFeD5CBE8GCFCPjt71ow8uHzZjFtv3TrDQxMSMh5VcOZMxqcIDDTBwJVhwbXXmhEHrrDbTfPB9AIG535kZNbP4+9v3vCUKGFGSJw6lb3HXSkw0HwCk52tVCnPm4YRH28+RRw7NnmUSL16ZmGPLl14EwXA+Ocfc/H+22/m37lPPsn7fr1nzpg+C999Z0ZQRUcn3xcamtxHoVMn8z1yJi7OjEhLGRocPpz2uNBQ6cKF1IGOZP7ONmqUOlhwx9DcKSbGDNTcuDE5NNi5M+3rkqSwMGnQILNs6TXX5HupAHIJIYIHI0TIZ2+9JY0YYaYzbN6c5iowMdEMRV240Hzi41wCMD0VK6YdUVC7thlV4OWVx68jE5GRqUOFK/dPnEjdhyAlb+/shwKlS7seiniK6Gjp3XelCROk8+fNbS1bmiHLrVpZWhoAi+3YYULFgwfNCgwLFpiRAPnp8mXTyPG776TvvzehhlORImb017XXpp3+5ok9dvLKiRNpRxlcOeLOy0u6/vrUwcC115pP7DdsSH7s+vXmM4srVamSdrSCFT//hAQzNcYZFmzcKP35Z/rveSpWNCthN25s+lB/9pl5vZLp0XHnnWaA5y23MEoP8DSECB6MECEfJSaa+PzgQfMx0cCBaQ559llp4sTk7wMCkkcVpAwLatb03GZ7zikThw9L586ZEQLOYKBYMWsDEHd37pz05pvSO++YN+2SuXgYN86s8V4YHTxoQqlq1ayuBMh/y5aZFRiiosyfl0WLzN8HK9nt5oLWuXzkzp0ZH5uyx05G09889W9dZuLj044yOHQo7XElSkjNmiVf9DdtavoXZcXhkPbsSf3827enDfD9/c3FecpgoWzZXHmJSex2U0vKwGDz5vSnJJYqZVZEcm6NG6et5+JFM93v449NWOJUubI0YIDZWCY5Z6Kjpa1bzX8rHx+zFSmSdj+j23jfhqtFiODBCBHy0XffSXfdZd4VHD1qEoIUZswwf/wk6b33TLpesSL/OCOt48elV181b6ISE82AlgcfND0Tqle3urq8FRVlOscvXWo2Z8+Ixo2l3r2l++9neCsKh08+kR5/3Hyq26qVGYGQ3RUY8pPzYvbK0WmHDyfP589M8eLphwvO/TJl3H9q18mTqS/of/897UW0zZZ2lEHNmrn32qKi0o5WOHcu7XHVqqWuoX797I9WcDhMsOsMCzZuNCMqLlxIe2xIiPl32xkWNGli/pvm5PX++af5/2DOnOTX4uUl3X67GZ3QpQsjXdLjcJipI0uXmqlIERHZ63eVEZste2HDlbf5+pr/9t26md+1gjaSJDJS+vFH6YcfzNTdEiXMv2dZfb3aRrieiBDBgxEi5KOOHc14z+eeS26d/Z+ICKlDB/PpxOjRZg48kJW//za/L3Pnmu99fKRHHzUNGXP70ySrJCaaN6TO0GDdutQrbfj4mDdEztu8vc2w1t69TWZXWKa8oPCw280KLm++ab5/8EFp+nTJz8/aunLKbjeNdDPqr+McrZYVP7/0w4WKFc2b8exczOTmJ6vx8eZT3ZShwcGDaY8rXjztKIP8fBvmHCGQss6//ko7WiEgwFzgpwwWypQx9x0/njow+P339FeCCgiQbrwxdWAQFpZ7H5LExEhff22C9ZUrk28vW1bq398M/KxRI3fO5alOnpR+/tn8Hf3559RTjiTTL6NoUfP7m5CQ/DXlfnx8xtNRXVW6tHTHHSZQuOUW0/PKEx05ktx8dsWKzKclZyQgIG24kJ0AIjTU84IYQgQPRoiQT3bskOrWNX8x9+9Ptbj2gQPmzcOZM2bBhrlzGX2AnNm8WRo1SvrpJ/N9YKD01FNmZEu1ap73+3T4cPInJMuXp72QuPZa6dZbzdaunXkDOX+++TRqw4bk44KCpLvvNt28b77ZXCAAniw62jRM/Ppr8/3LL5vN3T+Jv1pRUckrA6UXNhw/njcXNdn5ZDW9+xMSzKfjzulmKZ+vbt20owzc7d/myMi0oxWcfXhSql7d/Lt7/Hja+4oUMW2fUgYGderk37+/e/aYhsQzZ5qmzU4dOphmjHff7XmB29WIiTErqDgD+K1bU98fFCS1b5/8tzS7o17s9rTBQnphQ3Zui4oytS1enPr3LCDABAnduplgwRlauSPnqA7n9K0//kh9f+3a5nXUqmVe49mz5j3N2bOp98+dM1t6jURzolix1OHC+++bc7srQgQPRoiQT554Qpoyxfz1+uabpJujokzDqb/+Ml2UV63y3PQV1luxwnxCmXKeaGCgdN11Zphsyq18efe58Lh40dTufLOze3fq+0NDzRvA224zbywy63+wZ49pvPXppyavcypb1iyP2bs369nDM508ad6MbthgLtSmTTMBWWEWFycdO5b+SIbjx83FihWfrBYrlnaUgSeuUGG3m3+PU45W2LEj+Wfm5WXCkZSBQb167nGRHhdnmnx+/LH5u+KsuWRJE8Q9/LAJNwoKh8P8t1myxLzelSvTTplp1Cg5NGje3D3+O0nm/8NVq5IvxFOuQmKzmffJ3bqZzeqeL5KpNyIiud6U/UxcqdduN1N/0gsYMgsfzp6VLl1K/zl37HDv33NCBA9GiJAPIiNNC+pLl8xk7vbtJZnh1926mUZY5cqZoYAVKlhcKzyew2GG0k2YYBLxjOYch4amDRbq1jVDCvOa3W5GTzhDgzVrUg/58/Iyb8Cdb3aaNMn5p1gOhwlTPv1Umjcv9RDbOnVMmPDgg1LVqrnykoA89ddfZn73oUPmE6YFC6Q2bayuqmDIzier2f2U1W43oW2tWu43yiC3REaaaQt+fiaQ9YQpYwcPmik/06eb0MmpZUsTJtx3n2d+gHP6tGmu6vxbeuXIkPLlzd/Q224zQXx+/H13VXY/2e/WTbrppvz7/ywqyoz2/O479xw5EReXfuhw993u3ZyWEMGDESLkg8mTpWHDzFXatm1JH4E+84wUHm7mbK5aZS6UgNyUkGA+jd++PXn76y/zyVLKvgIplSmTfrjg6j8Px44lv9FZtsxM30mpalXzRufWW83Ug2LFXDtfSnFx5tOZTz81bwBSBiutW5tA4b77zNA/wN38/LN0773JKzAsXmym9ADImYQEcyH4ySem2Z3z72BoqNSrl5nu0LChtTVmJi5OWrs2ebrflRfYAQFmeVdnAH/ddZ4/6i5lj4FffzX/DZ3KlJG6djUX7x07pulX7rJjx1KfO2XzyYLSw8FqhAgejBAhj9nt5iOJvXulDz80Xe9k0nDnCo/z5kk9elhYIwqd2Fgz7P/KcGH//oyH9laubMKElOFCnToZ/9GOjjbhmDM4+Ouv1PcHB5uwwPlmp0aN/HmzExlpZhR9+ql5U+B8vb6+5pPe3r3NV3cZ5onCbepUMxsuMdEEXgsWmOHYAFxz/Ljpm/DJJ6Y3lVOjRmZ0wgMP5G+jy/Q4HCb0d/4dXbEi7bD1G25I/jvaqlXB7uzvXO3AORogKir5vsBA8zNwjga4mpVqHA7zfsg5CuL331PfX7Nm8iiIZs08r4mhOyJE8GCECHls8WJzRVKsmFnWMShIq1aZxDQ+3jTEGjPG6iIB49Ils6b7leHC0aPpH2+zmU9GneFC3brmU4OlS818wZSf+NtsZrSN881Os2bWL7119Kj0xRemIeOffybfXqyYCfZ69zbDXQvqsGS4L7tdev556a23zPe9e5uLHcItIHfZ7Wam6SefmIDZObUuMNAsGdy9e+5/wp2VU6eSpymk7A8gmU/fnX9Hb7ml4KzElFNxcabvw3ffmdECR44k3+flZf52Oy/4w8Iyfp6EBNOA0hkcpAyUbDbzXsX5PLVr593rKawIETwYIUIe69TJjJ17+mkpPFz795sGS//+ay5SvviCCxS4v/PnTZiQMlzYvj3tlIQrVaqUPEWhQwczl9tdbdtmRid89lnqeaVVqphhrr17u3dzIhQc0dGmYaKzB++YMWYpV08flgy4uzNnpNmzTTPGXbusrsbw8zOjkJzBQb16vG+8ksNh+iw5g4ArV6K47rrkIKBJE/Nv7JIl5thFi0z/ACd/f/NBX7duZqrENdfk72spbAgRPBghQh7avdvEljabtHevokpVV/PmplNq48YmQWUOFTzZqVOpQ4UdO8yn+M43O7Vqed6FT2Ki+X/z00+lr74ynZKdbrzRhAn332+aoWb2HJcvp95iYnL2fXq3eXubN0CtW5vuz8HBef/zKMhiY81w1YgI03Xe3z/1dJ3q1fN/uOrJk9Kdd5pGu76+Zupbr175WwNQ2DkcpuHvJ59ImzblzQoemQkISA4O2rThvWJOHTqU3Mtg5cq0fRQiI1OPlCxZMrm/wa23ekaz0IKCEMGDESLkoSeflN57T7rjDiV++73uvNPMbihf3izRxUoMgHu7fNksEfbpp2YepvONiJeX1KCBGQab3sV+yjcsecXLyzQAa9PGvNls1cozOm9b6cIFExZERJjtt9/SLoGWkr+/+QTryl4glSrlTTi2fbuZ/Xb4sHlTu2CB+W8LALg6584l91H48cfkDwZq1EgendCiRc5XgELuIETwYIQIeeTCBZMSXLggLVmi4Utu1dtvm3R51SozEgGA5zhzxjRB/fRTs3Rkdvn6mv/v/f3NV+d2Nd9HR5tPxyIiUs/bdKpTx1x0tm5twoXKlXPv9XuiM2fMXNdVq8zPbPPmtKuSlC5tflatWpm50M4+IDt2mEAoPcHByT1AUoYLZcpcfbiwdKlZISQqyqy8sHhx5vN4AQA5ExtrwuNSpczfS08bKVkQESJ4MEKEPPL++9KQIVKtWpo2fIcGPWImsLESA+D59u41F5spL/TTu+j398+74fBHj5oLY+cF8pWrX0gmRHCOVGjdOnl2VUF15EjyzyMiwgQBV6paNTlkad3adNtO72eSmGiCmiubjO7alfFIk1Kl0oYLdetmvXToRx9J//ufOWebNqYXAiswAAAKOkIED0aIkAfsdjMGdvdurXzya90y5R7Fx5vmWC+/bHVxAAqif/81n7o7g4U//kj/U/dWrZIvom+4wXOHcDqXPnMGBqtWmXmwV7ruutRBSqVKrp03Lk76+++0TUb37ct43nSFCqlDheuvN3UFBEgjRkjh4ea4hx4yDd1YgQEAUBgQIngwQoQ8sHSpdNtt2h9UT039tujfs17q2dOsxFCQPwUE4D4uXjTTLpyfzK9fn3b+f9GiZhks5wV206buu8Z4YqLpuJ1ypMHp06mP8fY2zS+dIUnLlle3VvjViI42oxSuDBdSLjuWks1mQp1Tp8z3r7wivfgifyMAAIUHIYIHI0TIA127KvKHVWpRYpd2nC2nxo3NG9/8XmcYAJxiY02Xceen9mvWmA7VKfn6miDB+cl9ixaSVX8WYmLMCgXOwGDNmtQrZUgm8LjppuR6mzc3wYg7iYw00yquDBec4YGvrzRjhvTgg9bWCQBAfiNE8GCECLls/34l1qiprlqoH9VZ5cubN8Lly1tdGAAkS0w0F7MpP9k/eTL1MV5eZuh9fi8lGRsr/fln6iW4JBNoOKdjtG5tGtR66tD/06dNuFCjhlSxotXVAACQ/wgRPBghQi4bPlxPv11Bk/S0AgLMG/NGjawuCgAy53CYhpEpewzs329tTddck3q1iXr18q5RJQAAyF+uXId6aEsnIB2XLumTKfGapKclSbNmESAA8Aw2m1la8NprpQEDzG3HjpllEePj87+W664ztdAjAAAAXIkQAQXGipeW6/HLps322DF23Xefl8UVAcDVq1DBbAAAAO6kQFxlHT58WM8884zq1KmjoKAglShRQk2bNtXEiRMVHR2dq+datmyZ+vXrp7CwMAUFBSk0NFQ1a9bUvffeqylTpujixYu5ej5kz769DnV/t40SVET3N9ytl0YXiF9tAAAAAHArHt8TYdGiRerVq5cir2xz/Z9atWpp8eLFql69ukvnOXfunPr376/vvvsu0+M2b96sBg0aZPt56YngushIqfkNl7TzUJCaeP2ulceuVUDZUKvLAgAAAAC3VGh7ImzdulU9evRQdHS0ihYtqpEjR6p9+/a6fPmy5s6dq48//li7d+9Wly5dtHHjRhW9yrWnIiMjdcstt2jTpk2SpC5duuj+++9XWFiYEhMTdejQIW3cuFFfffVVbr48ZENCgnT//dLOQ0GqoKP6tvfXCig73uqyAAAAAKBA8uiRCO3bt9eKFSvk4+OjVatWqXnz5qnuf+uttzRixAhJ0tixYzV69OirOk+fPn00Z84c+fj46NNPP1XPnj3TPc7hcCgxMVE+PtnPZhiJ4Jphw6TJk6UARWu1WunGvz41HcEAAAAAAOly5TrUYyeOb9y4UStWrJAkDRw4ME2AIEnDhw9XnTp1JEmTJ09W/FW0uF69erXmzJkjSXrxxRczDBAkyWaz5ShAgGs+/tgECJI0W310Y4cSBAgAAAAAkIc8NkT49ttvk/b79++f7jFeXl7q06ePJNPTwBk65MT7778vSSpatKiGDx+e48cjb6xYIT3xhNl/JWCc7tXX0pAhltYEAAAAAAWdx4YIERERkqSgoCA1atQow+Patm2btL969eocnSMuLi6pkWKnTp2SeiokJCTo0KFDOnz4sOLi4nJaOly0d6/Uvft//RCa7teLl1+QqlaV7rjD6tIAAAAAoEDz2BBh586dkqSwsLBMpxDUrl07zWOya+vWrYqJiZEkNW/eXCdPnlT//v1VrFgxVa1aVVWqVFFoaKg6d+6stWvXXsWrQE5FRkpdu0pnz0pNmzo0/fIDsklmWIK3t9XlAQAAAECB5pEhQkxMjM6cOSNJqlixYqbHFi9eXEFBQZKkI0eO5Og8O3bsSHXOevXqaebMmbp06VKq23/88Ue1bt1ak50T9JEnEhKknj2lXbukihWlb5//TQF/bpACAqSBA60uDwAAAAAKPI8MES5cuJC0n51lG50hwsWLF3N0nrNnzybtjx07VmfOnNEdd9yh33//XTExMfrnn3/0wQcfKCQkRHa7XU8//bR+/PHHTJ8zNjZWUVFRqTZkzzPPSEuWSIGB0sKFUrkv3jZ39OollShhbXEAAAAAUAh4ZIjgnGIgSb6+vlke7+fnJ0m6fPlyjs6TcsRBbGysunbtqu+++06NGjWSn5+fypQpo8cff1yLFi2Sl5eXHA6HRowYocxWzRw/frxCQ0OTtkqVKuWopsJq6lTpnXfM/pw5UsPSR6VvvjE30FARAAAAAPKFR4YI/v7+SfvZaWwYGxsrSQoICLjq80jSW2+9JS+vtD+yVq1a6Z577pEkbd++Xdu3b8/wOUeOHKnIyMikLadTLAqjX3+V/vc/s//aa9I990j68EMpMVFq00aqX9/S+gAAAACgsPDIECE4ODhpPztTFJwjCrIz9SGj81SrVk21atXK8NjbbrstaX/jxo0ZHufn56eQkJBUGzKWciWGBx+URo2SFBNjhiZIjEIAAAAAgHzkkSGCv7+/SpUqJUk6evRopseeO3cuKUTI6dSBlMdn1cAx5bGnTp3K0XmQvk2bpC5dpHPnpJtukj75RLLZJM2fL50+bbor3nWX1WUCAAAAQKHhkSGCJNWpU0eStHfvXiUkJGR43K5du9I8Jrvq1q2btJ+YmJjpsSnvz2zJSWTt2DGpb1+pcWNpz57/VmL41izCIIdDeu89c+Djj0v8rAEAAAAg33hsiNCqVStJZqrCpk2bMjxu5cqVSfstW7bM0TmqVKmiypUrS5L27duX6bEp769QoUKOzgPj0iVp7FipZk1p9mxz20MPSevXS2XL/nfQb79Jv/8u+flJDz9sWa0AAAAAUBh5bIhwV4ph7DNmzEj3GLvdrtn/XY0WK1ZM7du3z/F5unfvLkn6559/tHbt2gyP+8a5UoCk1q1b5/g8hZndbkKDWrWkMWOk6GipVStpwwZze6pMxjkK4f77pdKlrSgXAAAAAAotjw0RmjZtmnSxPm3aNK1bty7NMeHh4dq5c6ckaejQoSpSpEiq+2fOnCmbzSabzaYxY8ake56nnnoqaZWGJ598MtWyj06ffvqpVqxYIUnq0qVLlv0TkCwiQmra1ExfOHZMqlZN+vJLadUqqUmTKw4+edLcKdFQEQAAAAAs4LEhgiS98847CggIUEJCgm699VaNHz9e69ev16+//qpHH31UI0aMkCTVrFlTw4cPv6pzVK5cWa+88ookadOmTWratKlmzZqlTZs26ZdfftHgwYPVr18/SVJISIgmTZqUK6+toNu/X7r3XrNC46ZNUnCwNGGCtGOHud1mS+dBH30kxcdLzZtLjRrle80AAAAAUNh5dFe6hg0bat68eerdu7eioqI0atSoNMfUrFlTixYtSrVcY049++yzOnv2rCZMmKAdO3YkhQYplSlTRt9++62uvfbaqz5PYRAZKb32mvTuu1JcnOTlJT3yiOmFUKZMJg+Mi5M+/NDsP/lkvtQKAAAAAEjNo0ciSFLXrl21bds2DRs2TDVr1lRgYKCKFSumxo0ba8KECdq8ebPCwsJcPs/48eO1Zs0aPfTQQ6patar8/PwUGhqqJk2a6NVXX9WePXvUvHnzXHhFBVNCgjRlihQWJk2caDKBW2+Vtm41t2caIEjS11+b6Qzlykn/9akAAAAAAOQvm8PhcFhdRGEWFRWl0NBQRUZGKiQkxOpy8sRPP0nDh5upCpJUp44UHi7dfnsG0xbS06KFtG6dGbIwenSe1QoAAAAABZ0r16EePxIB7mvHDqlTJ7Pt2CGVLCm9/74ZfdCpUw4ChE2bTIBQpIiZ+wAAAAAAsIRH90SAezp92izV+NFHUmKiufZ/8knpxRelYsWu4gmdyzr26CGVLZuLlQIAAAAAcoIQAbkmNtZc77/2mmmgKEn33GNWXbjqthSnTklffGH2WdYRAAAAACxFiACXORzSN99II0aYpRslqWFDadIkqW1bF5/8449NF8YmTaSbbnK5VgAAAADA1aMnAlyyaZPUrp10770mQChXTpoxQ/r991wIEOLjzdINEqMQAAAAAMANMBIBV+XYMWnUKGn2bPN9QID07LNmK1o0l06ycqU5UalSph8CAAAAAMBShAjIkUuXpIkTpTfflKKjzW29e0vjxkmVKuXyyZYuNV+7dJH8/HL5yQEAAAAAOUWIgGyx26VPPzWjD44dM7e1bGn6HjRpkkcnXbbMfL3lljw6AQAAAAAgJ+iJgGx56SWpb18TIFStKs2fL0VE5GGAcPq0tHmz2e/YMY9OAgAAAADICUIEZMvDD0tlypjlGnfulO67T7LZ8vCEy5ebr/XrS9dck4cnAgAAAABkF9MZkC1Vq0qHD+dja4KffzZfmcoAAAAAAG6DkQjItnwLEBwOQgQAAAAAcEOECHA/e/ZIR45Ivr5S69ZWVwMAAAAA+A8hAtyPcxRCq1ZSYKC1tQAAAAAAkhAiwP0wlQEAAAAA3BIhAtxLfLz0669mnxABAAAAANwKIQLcy4YN0oULUsmSUsOGVlcDAAAAAEiBEAHuxTmVoUMHyYtfTwAAAABwJ1ylwb04Q4SOHa2tAwAAAACQBiEC3EdkpPTbb2affggAAAAA4HYIEeA+VqyQEhOlsDCpalWrqwEAAAAAXIEQAe5j2TLzlVEIAAAAAOCWCBHgPpz9EAgRAAAAAMAtESLAPRw5Iu3ebVZkaN/e6moAAAAAAOkgRIB7cI5CaNpUKlbM0lIAAAAAAOkjRIB7YCoDAAAAALg9QgRYz26nqSIAAAAAeABCBFhv61bpzBmpaFGpWTOrqwEAAAAAZIAQAdZzTmVo104qUsTSUgAAAAAAGSNEgPXohwAAAAAAHoEQAda6fFmKiDD7hAgAAAAA4NYIEWCt1aul2FipQgWpdm2rqwEAAAAAZIIQAdZyTmXo2FGy2aytBQAAAACQKUIEWIt+CAAAAADgMQgRYJ1Tp6QtW8x+x46WlgIAAAAAyBohAqzzyy/ma/360jXXWFsLAAAAACBLhAiwDlMZAAAAAMCjECLAGg4HIQIAAAAAeBhCBFhjzx7pyBHJ11dq3drqagAAAAAA2UCIAGs4RyG0aiUFBlpbCwAAAAAgWwgRYA2mMgAAAACAxyFEQP6Lj5d+/dXsEyIAAAAAgMcgRED+27BBunBBKllSatjQ6moAAAAAANlEiID855zK0KGD5MWvIAAAAAB4Cq7gkP/ohwAAAAAAHokQAfkrMlL67TezT4gAAAAAAB6FEAH5a8UKKTFRCguTqlSxuhoAAAAAQA4QIiB/MZUBAAAAADwWIQLyFyECAAAAAHgsQgTkn8OHpT17zIoM7dtbXQ0AAAAAIIcIEZB/li0zX5s2lYoVs7QUAAAAAEDOESIg/zCVAQAAAAA8GiEC8ofdnjwSgRABAAAAADwSIQLyx9at0pkzUtGiUrNmVlcDAAAAALgKhAjIH86pDO3aSUWKWFoKAAAAAODqECIgf9APAQAAAAA8HiEC8t7ly1JEhNknRAAAAAAAj0WIgLy3erUUGytVqCDVrm11NQAAAACAq0SIgLyXciqDzWZtLQAAAACAq0aIgLxHPwQAAAAAKBAIEZC3Tp2Stmwx+x06WFoKAAAAAMA1hAjIW8v/v717j6qqzP84/jmKgIB3QkX4iTeURlOWV1JT8jKZd01rphJK0yy7zFg5Oq2sNVOZK2/LaRmaac7UmDlaKlZKSzAnL5ja1HhLwQs6qRijKEgc2L8/+LF/B7lt4Fy4vF9rncXD2c9+znfXs77Cl2c/+6uCr3fdJbVs6dlYAAAAAABVQhEBrsWtDAAAAABQa1BEgOsYBkUEAAAAAKhFKCLAdU6elNLSJG9vaeBAT0cDAAAAAKgiighwncJVCAMGSH5+no0FAAAAAFBlFBHgOtzKAAAAAAC1CkUEuEZurrRrV0GbIgIAAAAA1AoUEeAaBw5ImZlSixZSZKSnowEAAAAAOAFFBLhG4a0MQ4ZI9ZhmAAAAAFAb8NsdXIP9EAAAAACg1qGIAOe7dk3av7+gTREBAAAAAGoNighwvsREKS9P6tRJatvW09EAAAAAAJyEIgKcj1sZAAAAAKBWoogA5yssIgwd6tk4AAAAAABORREBznXunHTyZMETGaKjPR0NAAAAAMCJKCLAuQpXIfTpIzVt6tFQAAAAAADORREBzsV+CAAAAABQa1FEgPPk50tffVXQpogAAAAAALUORQQ4z3ffSenpUkCA1K+fp6MBAAAAADgZRQQ4T+GtDIMHSw0aeDQUAAAAAIDzUUSA87AfAgAAAADUahQR4BzZ2dLXXxe0KSIAAAAAQK1EEQHOsWePlJMjtWkjdeni6WgAAAAAAC5AEQHO4Xgrg83m2VgAAAAAAC5BEQHOwX4IAAAAAFDrUURA1V2+LB05UtAeOtSjoQAAAAAAXIciAqruq68KvnbvLgUFeTYWAAAAAIDLUERA1RXeysAqBAAAAACo1SgioGoMg/0QAAAAAKCOoIiAqjlxQkpLk7y9pYEDPR0NAAAAAMCFKCKgagpXIQwYIPn5eTYWAAAAAIBLUURA1SQkFHzlVgYAAAAAqPUoIqDycnOlXbsK2hQRAAAAAKDWo4iAyjtwQMrMlFq0kCIjPR0NAAAAAMDFKCKg8gr3QxgyRKrHVAIAAACA2o7f/FB5PNoRAAAAAOoUigionGvXpP37C9oUEQAAAACgTqCIgMpJTJTy8qROnaS2bT0dDQAAAADADSgioHK4lQEAAAAA6pxaUUQ4d+6cXnjhBUVERMjf31/NmzdXnz599PbbbysrK6tKY7/66quy2WyWXomJic65oJqAIgIAAAAA1Dleng6gquLj4/Xwww/r2rVr5ntZWVlKTk5WcnKy3nvvPW3fvl3t27f3YJS1zLlz0smTBU9kiI72dDQAAAAAADep0UWE7777TpMnT1ZWVpYCAgI0d+5cRUdHKzs7W+vXr9eqVat04sQJjRw5UsnJyQoICKjS533//fdlHm/Xrl2Vxq8xClch9OkjNWni2VgAAAAAAG5To4sIzz//vLKysuTl5aUdO3YoKirKPHbvvfeqU6dOeumll3T8+HEtXrxYr7zySpU+r2vXrlUNuXbgVgYAAAAAqJNq7J4IycnJ5h4EU6dOLVJAKDR79mxFRERIkpYuXarc3Fx3hlg75edLX31V0KaIAAAAAAB1So0tInz66adm+7HHHiuxT7169TRlyhRJUkZGRt3a+NBVjhyR0tOlgACpXz9PRwMAAAAAcKMaW0T4+uuvJUn+/v7q2bNnqf0GDRpktvfs2ePyuGq9hISCr4MHSw0aeDQUAAAAAIB71dgiwrFjxyRJHTt2lJdX6Vs7dOnSpdg5lTVs2DC1aNFC3t7eCgoK0uDBg7VgwQJlZGRUadwahf0QAAAAAKDOqpFFhFu3bik9PV2SFBISUmbfZs2ayd/fX5J0/vz5Kn1uQkKCfv75Z+Xm5urKlStKSkrS3Llz1b59e3322WdVGrtGyM6W/m8FCEUEAAAAAKh7auTTGTIzM822lcc2+vv76+bNm7px40alPq9bt24aN26c+vTpo+DgYOXm5urEiRP68MMPtWPHDv33v//VxIkTtXXrVo0YMaLMsXJycpSTk2N+f/369UrF5BF79kg5OVKbNpLDCg8AAAAAQN1QI4sIt27dMtve3t7l9vfx8ZEkZWdnV/iznn/+eb366qvF3u/bt6+mTJmiuLg4Pfnkk8rLy9O0adN06tQpNWzYsNTx3nzzTb322msVjqNacLyVwWbzbCwAAAAAALerkbcz+Pr6mu1ffvml3P6Ff/kv65f70jRt2rTM4zNmzNC0adMkSRcvXtSmTZvK7D937lxdu3bNfFX1Fgu3Yj8EAAAAAKjTamQRoVGjRmbbyi0KN2/elGTt1ofKmDFjhtlOSkoqs6+Pj48aN25c5FUjXL5c8HhHSRo61KOhAAAAAAA8o0YWEXx9fRUYGChJSktLK7NvRkaGWUQIDQ11STx33nmn2b5w4YJLPsPjvvqq4Gv37lJQkGdjAQAAAAB4RI0sIkhSRESEJOnUqVOy2+2l9jt+/Hixc5zNMAyXjFutNGok3XOPdP/9no4EAAAAAOAhNbaIMGDAAEkFtyp8++23pfZzvL2gf//+Lonl6NGjZjs4ONgln+Fxo0ZJSUnSG294OhIAAAAAgIfU2CLCuHHjzPaaNWtK7JOfn69169ZJKtggMTo62iWxxMXFme1Bgwa55DMAAAAAAPC0GltE6NOnjwYOHChJWr16tfbu3Vusz6JFi3Ts2DFJ0nPPPacGDRoUOb527VrZbDbZbLYSH+P4/fff69SpU2XGERcXp9WrV0uSWrVqpfHjx1fmcgAAAAAAqPa8PB1AVSxbtkz9+/dXdna2hg8frnnz5ik6OlrZ2dlav369Vq5cKUkKDw/X7NmzKzz+t99+q2nTpik6OlojRoxQt27d1KJFC9ntdh0/flx/+9vftPP/HntYv359xcXFyd/f36nXCAAAAABAdVGjiwiRkZH6+OOP9cgjj+j69euaN29esT7h4eGKj48v8ljIisjLy1NCQoISEhJK7dOiRQutXr1aY8aMqdRnAAAAAABQE9ToIoIkjR49Wv/617+0bNkyxcfHKy0tTd7e3urYsaMmTZqkWbNmyc/Pr1Jj33///eatEocPH9alS5d09epVGYah5s2bq3v37rrvvvsUGxurxo0bO/nKAAAAAACoXmxGnXg+YfV1/fp1NWnSRNeuXaMQAQAAAABwuar8HlpjN1YEAAAAAADuRREBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABYQhEBAAAAAABY4uXpAOo6wzAkSdevX/dwJAAAAACAuqDw98/C30crgiKCh2VmZkqSQkNDPRwJAAAAAKAuyczMVJMmTSp0js2oTOkBTpOfn6+LFy+qUaNGstlsng6nTNevX1doaKjOnz+vxo0bezoc1EDMITgD8wjOwDyCMzCP4AzMI1RVZeaQYRjKzMxUcHCw6tWr2C4HrETwsHr16ikkJMTTYVRI48aNSXCoEuYQnIF5BGdgHsEZmEdwBuYRqqqic6iiKxAKsbEiAAAAAACwhCICAAAAAACwhCICLPPx8dH8+fPl4+Pj6VBQQzGH4AzMIzgD8wjOwDyCMzCPUFXunkNsrAgAAAAAACxhJQIAAAAAALCEIgIAAAAAALCEIgIAAAAAALCEIgIAAAAAALCEIgLKde7cOb3wwguKiIiQv7+/mjdvrj59+ujtt99WVlaWp8NDNWaz2Sy9Bg8e7OlQ4SGXL1/Wtm3b9Morr2jEiBEKDAw050VsbGyFx/viiy80YcIEhYSEyMfHRyEhIZowYYK++OIL5wePasEZc2jt2rWW89XatWtdej3wjEOHDumNN97QiBEjFBoaKh8fHwUEBCg8PFyxsbH6+uuvKzQeuahucsY8Ih/VbdevX9f69es1e/ZsDRo0SB07dlSTJk3k7e2toKAgDR48WAsXLtTVq1ctjeeyXGQAZdi2bZvRpEkTQ1KJr86dOxunT5/2dJiopkqbN7e/Bg0a5OlQ4SFlzYuYmBjL4+Tn5xvTp08vc7zp06cb+fn5rrsYeIQz5tCaNWss56s1a9a49Hrgfvfcc4+l//ePPvqokZOTU+ZY5KK6y1nziHxUt+3cudPS//vAwEDjiy++KHUcV+cir/KKDKi7vvvuO02ePFlZWVkKCAjQ3LlzFR0drezsbK1fv16rVq3SiRMnNHLkSCUnJysgIMDTIaOamjlzpp566qlSj/v7+7sxGlRXoaGhioiI0I4dOyp87ssvv6yVK1dKkiIjI/XSSy+pQ4cOOn36tBYuXKjDhw9r5cqVuuOOO/TnP//Z2aGjmqjKHCr05ZdfKjg4uNTjISEhlR4b1dOFCxckScHBwZo0aZIGDhyo//mf/1FeXp727t2rRYsW6cKFC/rrX/8qu92ujz76qNSxyEV1lzPnUSHyUd0UGhqq6Oho9ezZU6GhoWrdurXy8/OVlpamjRs3atOmTUpPT9eYMWOUnJysu+66q9gYLs9Fla2SoPYbPHiwIcnw8vIyvvnmm2LHFy5caFayXnvtNQ9EiOqucH7Mnz/f06GgmnrllVeMrVu3Gj/99JNhGIaRmppa4b8i//jjj4aXl5chyejVq5eRlZVV5PjNmzeNXr16mfns1KlTzr4MeJAz5pDjX/5SU1NdFyyqpZEjRxoff/yxYbfbSzx+5coVIzw83Jwju3fvLrEfuahuc9Y8Ih/VbaXNH0ebN28258iECROKHXdHLmJPBJQoOTlZiYmJkqSpU6cqKiqqWJ/Zs2crIiJCkrR06VLl5ua6M0QAtcBrr72mUaNGqWXLlpUeY8mSJbLb7ZKk5cuXq2HDhkWO+/n5afny5ZIku92upUuXVvqzUP04Yw6hbtu2bZsmT56s+vXrl3g8MDBQixYtMr/fuHFjif3IRXWbs+YR6rbS5o+jcePGqUuXLpKk3bt3FzvujlxEEQEl+vTTT832Y489VmKfevXqacqUKZKkjIwMs+gAAO5iGIY+++wzSVKXLl3Ur1+/Evv169dPnTt3llSQ3wzDcFuMAGo+xw2AT58+Xew4uQhWlDePAKsKbwW+detWkffdlYsoIqBEhbvH+vv7q2fPnqX2GzRokNnes2ePy+MCAEepqanmfaiO+agkhcfT0tJ05swZV4cGoBb55ZdfzHa9esV/fCYXwYry5hFgxbFjx3TkyBFJMlckFHJXLmL2okTHjh2TJHXs2FFeXqXvv+k4cQvPAW73ySefqHPnzmrYsKEaNWqkTp06KSYmRrt27fJ0aKjhHPPO7f+Q3o58BStiY2PVsmVLeXt7KzAwUP369dPLL79s/lCGuikpKclsl5RryEWworx5dDvyEQplZWXpxx9/1OLFixUdHa28vDxJ0nPPPVekn7tyEUUEFHPr1i2lp6dLKn/X12bNmpnLac6fP+/y2FAzHT16VCdPntStW7d048YNnTp1SuvWrdO9996r8ePH69q1a54OETWUY94pL1+FhoaWeB7gKCkpSZcvX1Zubq6uXr2q/fv36/XXX1fHjh0VFxfn6fDgAfn5+VqwYIH5/eTJk4v1IRehPFbm0e3IR3Xb2rVrZbPZZLPZ5O/vr/DwcM2ePVuXLl2SJL3wwgt6+OGHi5zjrlzEIx5RTGZmptm28thGf39/3bx5Uzdu3HBlWKiB/Pz8NGbMGA0ZMkRdunRRQECArly5oqSkJL377ru6evWqPv30U40dO1Y7d+5UgwYNPB0yapiK5CvHR4mSr3C79u3ba8KECYqKijJ/sEpJSdE//vEPbdy4Ubdu3dKTTz4pm82m6dOnezhauNOSJUt04MABSdL48ePVq1evYn3IRSiPlXlUiHyEsvTo0UPvvvuu+vbtW+yYu3IRRQQU47hBh7e3d7n9fXx8JEnZ2dkuiwk104ULF9S0adNi7w8bNkzPPPOMRowYocOHDyspKUkrVqzQs88+6/4gUaNVJF8V5iqJfIWixo8fr5iYGNlstiLv9+7dWw8++KC2bdumCRMmKDc3V7/73e80ZswYtWrVykPRwp2SkpL0hz/8QZIUFBSkFStWlNiPXISyWJ1HEvkI/2/cuHFmsSk7O1unT5/Whg0btHnzZj388MNaunSpRo0aVeQcd+UibmdAMb6+vmbbcQOY0uTk5EhSsceHACUVEAq1bNlSGzduNBNc4aNmgIqoSL4qzFUS+QpFNWnSpNgP7I5GjRql+fPnSyq4L3X16tXuCg0e9O9//1vjx4+X3W6Xj4+PNmzYUOqjRMlFKE1F5pFEPsL/a9q0qbp27aquXbuqd+/eeuihh7Rp0yatW7dOKSkpGjt2rNauXVvkHHflIooIKKZRo0Zm28rSlps3b0qydusD4Kh9+/YaNmyYJOnUqVO6ePGihyNCTVORfFWYqyTyFSruiSeeMH+wd9wcDbVTamqqhg8froyMDNWvX19///vfy9zpnFyEklR0HllFPqrbHn30UU2aNEn5+fmaNWuWMjIyzGPuykUUEVCMr6+vAgMDJRU88qMsGRkZ5gR03JwDsOrOO+802+w2jIpy3DSovHzluGkQ+QoVFRQUZP7bSK6q3S5evKihQ4fq4sWLstlsev/99zV+/PgyzyEX4XaVmUdWkY8wduxYSQWFgM8//9x83125iCICShQRESGp4K/Ddru91H7Hjx8vdg5QEYZheDoE1GCORSjHfFQS8hWqinxV+6Wnp2vYsGFKSUmRVHCr3ZQpU8o9j1wER5WdRxVBPqrb7rjjDrN99uxZs+2uXEQRASUaMGCApILq1rfffltqP8clVP3793d5XKh9jh49araDg4M9GAlqonbt2pnzprwlnbt375YktWnTRmFhYa4ODbXM5cuXdfXqVUnkqtrq2rVr+vWvf23+u7RgwQI9/fTTls4lF6FQVeaRVeQjOK5AcbwVwV25iCICSjRu3DizvWbNmhL75Ofna926dZIKNv6Ijo52R2ioRVJSUrRz505JBfsjtGnTxsMRoaax2Wzmkr7jx49r3759Jfbbt2+fWXEfO3ZsmZtWASVZuXKl+Zc/Z9zTjOolKytLI0eO1KFDhyRJf/zjHzVnzhzL55OLIFV9HllFPsInn3xitrt162a23ZaLDKAUAwcONCQZXl5exjfffFPs+MKFCw1JhiRj/vz57g8Q1dqWLVuM3NzcUo//9NNPRmRkpDmHFi1a5MboUF2lpqaacyImJsbSOSdOnDC8vLwMSUavXr2MrKysIsezsrKMXr16mfns5MmTLogc1UVF51Bqaqpx6NChMvts3brV8Pb2NiQZvr6+RlpampOiRXWQk5NjDB8+3Jw3zz33XKXGIRfVbc6YR+QjrFmzxsjOzi6zz+LFi815FhYWVuznbXfkIpthcEMNSnb48GH1799f2dnZCggI0Lx58xQdHa3s7GytX79eK1eulCSFh4fr4MGDRXYDBcLCwpSbm6uJEycqKipKYWFhatiwodLT05WYmKh3333XXIo3YMAAJSQkFHleLeqGPXv26NSpU+b36enpevHFFyUV3CI1bdq0Iv1jY2NLHGfu3LlasGCBJCkyMlJz5sxRhw4ddPr0ab311ls6fPiw2e+NN95wwZXAU6o6hxITExUdHa2oqCiNHj1aPXr0UFBQkAzDUEpKijZu3KiNGzeaf/X7y1/+4vSlyfCsiRMnatOmTZKke++9V0uXLi3zr3Le3t4KDw8v8Ri5qO5yxjwiHyEsLEyZmZmaOHGiBgwYoA4dOiggIECZmZn6/vvv9eGHH+qf//ynpII5FB8fr6FDhxYbx+W5qMJlB9QpW7ZsMRo3bmxWu25/hYeHGz/++KOnw0Q11LZt21LnjeNr4sSJRkZGhqfDhYfExMRYmieFr9Lk5eUZjz/+eJnnTp061cjLy3Pj1cEdqjqHdu3aZek8Pz8/Iy4uzgNXCFeryPyRZLRt27bUschFdZcz5hH5CFZ/fg4JCTF27NhR6jiuzkWsREC5zp49q2XLlik+Pl5paWny9vZWx44dNWnSJM2aNUt+fn6eDhHVUFJSkpKSkrR3716lpKQoPT1d169fV0BAgEJDQ3X33XcrJiZGUVFRng4VHhQbG6sPPvjAcv/y/snavn27Vq5cqeTkZKWnpyswMFC9e/fWjBkzNGLEiKqGi2qoqnMoMzNTW7Zs0d69e3Xw4EH95z//UXp6uux2u5o1a6Zf/epXGjJkiKZNm6agoCBnh49qoKL3Ardt21Znzpwpsw+5qO5xxjwiH+H06dNKSEjQrl27dOzYMV26dElXr16Vr6+vWrZsqR49emjUqFGaPHmypd/BXJWLKCIAAAAAAABLeDoDAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAACABWfOnJHNZpPNZtPatWs9HQ4AAB5BEQEAAJQpMTHR/OXZ6uv555/3dNgAAMAFKCIAAAAAAABLvDwdAAAAqDlmzpypp556qtx+gYGBbogGAAC4G0UEAABgWVBQkLp27erpMAAAgIdwOwMAAAAAALCEIgIAAHC5sLAw2Ww2xcbGSpKSk5P1m9/8RqGhofL19VVoaKhiY2N17NgxS+Nt3bpVDzzwgEJCQuTj46MWLVooKipKCxYs0I0bNyyN8cMPP+iZZ55Rt27d1KxZM/n5+aljx4667777tGLFCl25cqXcMXbu3KnRo0erVatW8vHxUbt27TRz5kylpaVZigEAgJrGZhiG4ekgAABA9ZWYmKjo6GhJ0vz58/Xqq69WeIywsDCdPXtWMTExuueeezRjxgzZ7fZi/Xx8fPTBBx/owQcfLHGcW7du6be//a02b95c6mcFBwcrPj5ePXr0KPF4Xl6eXnzxRS1btkz5+fmljhMTE1PkUY5nzpxRu3btJElr1qzR8ePH9dZbb5V47h133KGkpCRFRESUOj4AADURKxEAAIDbHDlyRE8++aSCgoK0fPly7d+/X0lJSZozZ458fHyUk5OjRx55RAcOHCjx/JiYGLOA0L17d61bt07Jycn68ssv9dhjj8lms+nixYsaMmSILly4UOIY06dP15IlS5Sfn6/WrVvr9ddf165du3To0CF9+eWX+tOf/qTu3buXeR2rVq3SW2+9pUGDBumjjz7SwYMHlZCQoClTpkiSrly5oscff7wK/6UAAKieWIkAAADK5LgSwerTGTp37qwGDRqY3xeuRJCktm3bat++fWrVqlWRc3bt2qXhw4fLbrerV69eSk5OLnI8Pj5eo0aNkiQNGTJE27dvl7e3d5E+q1at0vTp0yVJkydP1scff1zk+GeffaZx48ZJkqKiorR9+3Y1bdq0xGtIS0tTSEiI+b3jSgRJeuKJJxQXFyebzVbkvCeeeELvvfeeJOnQoUOKjIwscXwAAGoiiggAAKBMjkUEq1JTUxUWFmZ+71hE2LhxoyZOnFjieU899ZRWrFghSTpw4IB69+5tHrv//vv1+eefq0GDBjp9+rRCQ0NLHGPYsGFKSEiQl5eXzp07p9atW5vHoqKitG/fPvn5+enkyZNq06aN5WtyLCK0bt1aqamp8vHxKdbvxIkT6tKliyRp2bJlevbZZy1/BgAA1R23MwAAALdp1qyZxo4dW+pxx1sAEhISzLbdbldSUpKkgiJBaQUEqWAlQOE5iYmJ5vtXr17V/v37JRWsUqhIAeF2DzzwQIkFBKlgFUZAQIAkKSUlpdKfAQBAdUQRAQAAWDZ//nwZhlHuy3EVgqPIyEh5eXmVOn6PHj3MWxR++OEH8/2UlBRlZWVJkvr27VtmjI7HHcc4cuSIChdg3nPPPWVfaDkKVxqUplmzZpKkzMzMKn0OAADVDUUEAADgNkFBQWUe9/LyUvPmzSVJP//8s/m+Y7tly5ZljuG414Ljeenp6Wbb8RaHyvDz8yvzeL16BT9i5eXlVelzAACobigiAAAAt7l9E8KSlLddk5UxnBEHAAAojiICAABwm0uXLpV53G63KyMjQ5LMFQm3t3/66acyx3A87nheYGCg2b548aK1gAEAQBEUEQAAgNscOXJEdru91OPfffedfvnlF0lS165dzffbt29v3kJQuDliaQ4cOGC2HceIjIw0VyDs3r274sEDAACKCAAAwH1+/vlnbd26tdTj77//vtkeOnSo2fby8tKgQYMkSTt37tT58+dLHeO9996TJNWvX1+DBw8232/evLnuvvtuSdKGDRtYjQAAQCVQRAAAAG71+9//vsTbGpKSkrRy5UpJUs+ePdW7d+8ix59++mlJUm5urh5//HFzxYKj999/Xzt27JAkTZw4sdgGinPmzJEkZWVladKkSbp27VqpcaalpVXgqgAAqBtKf8YSAADAbS5fvlzksYmladiwoTp06FDs/e7du+vo0aPq2bOn5s6dqz59+ignJ0fbt2/XkiVLZLfb5eXlpXfeeafYuSNHjtSkSZP0ySefKCEhQX379tXs2bMVERGhjIwMrV+/3lzJ0Lx5cy1evLjYGKNHj9bUqVO1evVqffPNN7rzzjs1a9Ys9e/fX40bN1Z6eroOHjyoDRs26K677tLatWsr/h8JAIBajCICAACwbMWKFVqxYkW5/bp3764jR44Ue79Hjx6aNWuWZs6cqVmzZhU77u3trQ8++EB9+/Ytcdx169bJbrdr8+bNOnLkiB599NFifYKDgxUfH682bdqUOEZcXJwaNmyod955RxcvXtS8efNK7HfXXXeVcYUAANRN3M4AAADcatq0afr66681efJkBQcHy9vbW23atNGUKVN0+PBhPfTQQ6We6+vrq02bNmnLli2aMGGCeX6zZs3Ut29fvfnmmzpx4oR69OhR6hj169fX8uXLdfDgQU2fPl3h4eHy9/eXn5+fOnXqpPvvv1+rVq3SkiVLXHD1AADUbDajvIcxAwAAVFFYWJjOnj2rmJgYbhEAAKAGYyUCAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAAAAAACwhCICAAAAAACwhKczAAAAAAAAS1iJAAAAAAAALKGIAAAAAAAALKGIAAAAAAAALKGIAAAAAAAALKGIAAAAAAAALKGIAAAAAAAALKGIAAAAAAAALKGIAAAAAAAALKGIAAAAAAAALPlfs11/atq4kcwAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot Accuracy Over Number of Epochs\n",
-    "\n",
-    "fig, ax = plt.subplots(1, 1, figsize=(12,8))\n",
-    "ax.plot(history.history['accuracy'], 'r', label='Training Accuracy')\n",
-    "ax.plot(history.history['val_accuracy'], 'b' ,label='Validation Accuracy')\n",
-    "ax.set_xlabel(r'Epoch', fontsize=20)\n",
-    "ax.set_ylabel(r'Accuracy', fontsize=20)\n",
-    "ax.legend()\n",
-    "ax.tick_params(labelsize=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "f9d8fc87-af42-4ab2-afdd-680ebdf27216",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 108 candidates, totalling 324 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/hs/br_4rpdj68nc3sfdpgv0xgn80000gn/T/ipykernel_74732/2346769631.py:29: DeprecationWarning: KerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
-      "  model = KerasClassifier(build_fn=CNN_model, verbose=0)\n",
-      "2024-04-18 08:48:32.894289: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 08:48:32.894792: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 08:48:32.901589: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 08:48:32.902734: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 08:48:32.903000: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 08:48:32.904463: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 08:48:32.904648: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 08:48:32.907500: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "[CV] END dropout_rate=0.4, filter=24, kernel=4, lr=0.01, num_1=60; total time=  23.7s\n",
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-      "[CV] END dropout_rate=0.4, filter=24, kernel=4, lr=0.01, num_1=100; total time=  23.4s\n",
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-      "[CV] END dropout_rate=0.4, filter=24, kernel=5, lr=0.001, num_1=80; total time=  28.3s\n",
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-      "103/103 [==============================] - 3s 21ms/step\n",
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-      "[CV] END dropout_rate=0.4, filter=36, kernel=5, lr=0.001, num_1=100; total time=  38.6s\n",
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-      "103/103 [==============================] - 3s 27ms/step\n",
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-      "103/103 [==============================] - 3s 20ms/step\n",
-      "[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.01, num_1=80; total time=  39.8s\n",
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-      "103/103 [==============================] - 2s 17ms/step\n",
-      "103/103 [==============================] - 2s 24ms/step\n",
-      "[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=60; total time=  30.4s\n",
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-      "103/103 [==============================] - 3s 23ms/step\n",
-      "103/103 [==============================] - 2s 22ms/step\n",
-      "[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=80; total time=  30.9s\n",
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-      " 95/103 [==========================>...] - ETA: 0s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.01, num_1=100; total time=  31.0s\n",
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-      "103/103 [==============================] - 2s 21ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=60; total time=  25.6s\n",
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-      "103/103 [==============================] - 3s 29ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=60; total time=  26.7s\n",
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-      "103/103 [==============================] - 3s 25ms/step\n",
-      " 95/103 [==========================>...] - ETA: 0s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=100; total time=  34.0s\n",
-      "  1/103 [..............................] - ETA: 14s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=80; total time=  34.4s\n",
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-      " 36/103 [=========>....................] - ETA: 1s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=100; total time=  33.6s\n",
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-      "103/103 [==============================] - 2s 13ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=80; total time=  25.0s\n",
-      " 64/103 [=================>............] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=80; total time=  26.2s\n",
-      "103/103 [==============================] - 3s 30ms/step\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=100; total time=  28.2s\n",
-      " 20/103 [====>.........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=100; total time=  28.2s\n",
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-      "103/103 [==============================] - 3s 27ms/step\n",
-      " 97/103 [===========================>..] - ETA: 0s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=100; total time=  27.2s\n",
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-      "103/103 [==============================] - 2s 21ms/step\n",
-      "103/103 [==============================] - 2s 18ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=60; total time=  26.9s\n",
-      "  8/103 [=>............................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=60; total time=  26.7s\n",
-      "103/103 [==============================] - 2s 15ms/step\n",
-      "103/103 [==============================] - 2s 16ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=80; total time=  25.6s\n",
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-      "103/103 [==============================] - 3s 28ms/step\n",
-      "103/103 [==============================] - 3s 27ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=80; total time=  28.8s\n",
-      " 43/103 [===========>..................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=100; total time=  28.1s\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
-      " 59/103 [================>.............] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=100; total time=  28.2s\n",
-      "103/103 [==============================] - 3s 25ms/step\n",
-      " 47/103 [============>.................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=60; total time=  30.4s\n",
-      " 52/103 [==============>...............] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=100; total time=  29.2s\n",
-      " 35/103 [=========>....................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=60; total time=  30.0s\n",
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-      "103/103 [==============================] - 2s 16ms/step\n",
-      "103/103 [==============================] - 2s 14ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=60; total time=  30.2s\n",
-      " 27/103 [======>.......................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=80; total time=  30.5s\n",
-      "103/103 [==============================] - 3s 22ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=80; total time=  31.2s\n",
-      "  1/103 [..............................] - ETA: 39s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=80; total time=  31.8s\n",
-      "103/103 [==============================] - 3s 22ms/step\n",
-      "103/103 [==============================] - 3s 23ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=100; total time=  31.3s\n",
-      " 28/103 [=======>......................] - ETA: 0s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=60; total time=  29.5s\n",
-      " 33/103 [========>.....................] - ETA: 0s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=100; total time=  31.3s\n",
-      "  6/103 [>.............................] - ETA: 0s [CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=100; total time=  30.3s\n",
-      "103/103 [==============================] - 2s 14ms/step\n",
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-      "103/103 [==============================] - 2s 15ms/step\n",
-      "103/103 [==============================] - 2s 15ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=60; total time=  29.8s\n",
-      " 15/103 [===>..........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=60; total time=  28.5s\n",
-      "103/103 [==============================] - 3s 25ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=80; total time=  28.1s\n",
-      "  7/103 [=>............................] - ETA: 0s [CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=80; total time=  27.2s\n",
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-      " 18/103 [====>.........................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=100; total time=  27.5s\n",
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-      "103/103 [==============================] - 3s 22ms/step\n",
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-      "103/103 [==============================] - 2s 18ms/step\n",
-      "103/103 [==============================] - 2s 18ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=60; total time=  26.7s\n",
-      "  7/103 [=>............................] - ETA: 4s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=60; total time=  27.2s\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=80; total time=  26.5s\n",
-      " 12/103 [==>...........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=60; total time=  26.5s\n",
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-      " 31/103 [========>.....................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=100; total time=  25.9s\n",
-      "  4/103 [>.............................] - ETA: 4s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=100; total time=  25.8s\n",
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-      "103/103 [==============================] - 3s 23ms/step\n",
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-      "103/103 [==============================] - 2s 18ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=60; total time=  27.4s\n",
-      "  4/103 [>.............................] - ETA: 8s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=100; total time=  28.3s\n",
-      "103/103 [==============================] - 4s 35ms/step\n",
-      "103/103 [==============================] - 3s 33ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=60; total time=  28.5s\n",
-      "  5/103 [>.............................] - ETA: 3s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=80; total time=  27.9s\n",
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-      " 21/103 [=====>........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=100; total time=  27.9s\n",
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-      " 43/103 [===========>..................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=80; total time=  28.6s\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
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-      "103/103 [==============================] - 2s 19ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=100; total time=  30.5s\n",
-      "  9/103 [=>............................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=100; total time=  30.8s\n",
-      "103/103 [==============================] - 3s 30ms/step\n",
-      "103/103 [==============================] - 3s 31ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=60; total time=  30.8s\n",
-      "  5/103 [>.............................] - ETA: 2s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=60; total time=  30.6s\n",
-      " 16/103 [===>..........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=60; total time=  29.6s\n",
-      " 22/103 [=====>........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=80; total time=  30.5s\n",
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-      "103/103 [==============================] - 3s 22ms/step\n",
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-      "103/103 [==============================] - 2s 20ms/step\n",
-      "103/103 [==============================] - 2s 20ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=100; total time=  30.3s\n",
-      " 25/103 [======>.......................] - ETA: 2s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=100; total time=  30.6s\n",
-      "103/103 [==============================] - 4s 37ms/step\n",
-      "103/103 [==============================] - 4s 38ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=100; total time=  30.6s\n",
-      "  1/103 [..............................] - ETA: 20s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=60; total time=  29.7s\n",
-      "103/103 [==============================] - 3s 32ms/step\n",
-      "103/103 [==============================] - 4s 33ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=60; total time=  30.7s\n",
-      " 11/103 [==>...........................] - ETA: 1s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=80; total time=  30.1s\n",
-      " 14/103 [===>..........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=60; total time=  30.0s\n",
-      " 11/103 [==>...........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=80; total time=  30.1s\n",
-      "103/103 [==============================] - 2s 21ms/step\n",
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-      "103/103 [==============================] - 2s 20ms/step\n",
-      "103/103 [==============================] - 2s 20ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=100; total time=  31.4s\n",
-      " 15/103 [===>..........................] - ETA: 3s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=80; total time=  30.7s\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=100; total time=  33.4s\n",
-      "  7/103 [=>............................] - ETA: 4s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=100; total time=  33.0s\n",
-      "103/103 [==============================] - 4s 30ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=60; total time=  35.7s\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      " 18/103 [====>.........................] - ETA: 1s[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=60; total time=  35.5s\n",
-      " 22/103 [=====>........................] - ETA: 1s[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=60; total time=  35.6s\n",
-      " 31/103 [========>.....................] - ETA: 1s[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=80; total time=  35.5s\n",
-      "103/103 [==============================] - 2s 20ms/step\n",
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-      "103/103 [==============================] - 2s 19ms/step\n",
-      "103/103 [==============================] - 2s 19ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=80; total time=  30.1s\n",
-      " 15/103 [===>..........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=80; total time=  31.5s\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=100; total time=  30.9s\n",
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-      "103/103 [==============================] - 4s 34ms/step\n",
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-      "[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.001, num_1=80; total time=  29.6s\n",
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-      "103/103 [==============================] - 4s 37ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.001, num_1=80; total time=  29.9s\n",
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-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.01, num_1=60; total time=  34.5s\n",
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-      "103/103 [==============================] - 5s 40ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.01, num_1=80; total time=  38.8s\n",
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-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.001, num_1=60; total time=  37.4s\n",
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-      "103/103 [==============================] - 2s 19ms/step\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.001, num_1=80; total time=  44.4s\n",
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-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.01, num_1=60; total time=  27.9s\n",
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-      "103/103 [==============================] - 1s 9ms/step\n",
-      "103/103 [==============================] - 1s 11ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.01, num_1=80; total time=  32.7s\n",
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-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.001, num_1=60; total time=  29.0s\n",
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-      "103/103 [==============================] - 1s 8ms/step\n",
-      "103/103 [==============================] - 1s 8ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.001, num_1=60; total time=  24.5s\n",
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-      "103/103 [==============================] - 1s 11ms/step\n",
-      "103/103 [==============================] - 1s 10ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.01, num_1=60; total time=  25.3s\n",
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-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.001, num_1=60; total time=  27.3s\n",
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-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.001, num_1=100; total time=  33.4s\n",
-      "  4/103 [>.............................] - ETA: 1s [CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.01, num_1=60; total time=  34.0s\n",
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-      "103/103 [==============================] - 2s 19ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.001, num_1=80; total time=  36.4s\n",
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-      "103/103 [==============================] - 3s 28ms/step\n",
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-      "103/103 [==============================] - 3s 28ms/step\n",
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-      "103/103 [==============================] - 2s 13ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.01, num_1=60; total time=  34.5s\n",
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-      "103/103 [==============================] - 3s 27ms/step\n",
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-      "103/103 [==============================] - 2s 13ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.01, num_1=100; total time=  33.5s\n",
-      "  5/103 [>.............................] - ETA: 1s [CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.001, num_1=60; total time=  33.1s\n",
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-      "103/103 [==============================] - 2s 14ms/step\n",
-      "103/103 [==============================] - 1s 10ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.001, num_1=80; total time=  34.3s\n",
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-      " 11/103 [==>...........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.001, num_1=80; total time=  34.6s\n",
-      " 19/103 [====>.........................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.001, num_1=100; total time=  33.9s\n",
-      "  3/103 [..............................] - ETA: 2s [CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.001, num_1=100; total time=  34.3s\n",
-      " 30/103 [=======>......................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.001, num_1=100; total time=  33.9s\n",
-      " 64/103 [=================>............] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=60; total time=  34.7s\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
-      " 80/103 [======================>.......] - ETA: 0s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=60; total time=  34.5s\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
-      "103/103 [==============================] - 3s 25ms/step\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
-      "103/103 [==============================] - 3s 22ms/step\n",
-      "103/103 [==============================] - 2s 17ms/step\n",
-      "103/103 [==============================] - 2s 14ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=80; total time=  37.0s\n",
-      " 16/103 [===>..........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=100; total time=  37.2s\n",
-      " 23/103 [=====>........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=60; total time=  37.0s\n",
-      " 44/103 [===========>..................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=100; total time=  37.1s\n",
-      "  4/103 [>.............................] - ETA: 11s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=80; total time=  37.1s\n",
-      " 11/103 [==>...........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=80; total time=  37.9s\n",
-      " 19/103 [====>.........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=100; total time=  37.1s\n",
-      " 46/103 [============>.................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=60; total time=  36.9s\n",
-      "103/103 [==============================] - 5s 38ms/step\n",
-      "103/103 [==============================] - 5s 40ms/step\n",
-      "103/103 [==============================] - 5s 35ms/step\n",
-      "103/103 [==============================] - 4s 31ms/step\n",
-      "103/103 [==============================] - 4s 29ms/step\n",
-      "103/103 [==============================] - 3s 27ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "103/103 [==============================] - 2s 18ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=60; total time=  40.3s\n",
-      " 28/103 [=======>......................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=60; total time=  41.3s\n",
-      " 35/103 [=========>....................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=80; total time=  40.6s\n",
-      " 54/103 [==============>...............] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=80; total time=  41.2s\n",
-      " 41/103 [==========>...................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=80; total time=  41.5s\n",
-      " 43/103 [===========>..................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=100; total time=  40.6s\n",
-      " 31/103 [========>.....................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=100; total time=  40.9s\n",
-      " 36/103 [=========>....................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=100; total time=  40.8s\n",
-      "103/103 [==============================] - 4s 33ms/step\n",
-      "103/103 [==============================] - 3s 31ms/step\n",
-      "103/103 [==============================] - 4s 29ms/step\n",
-      "103/103 [==============================] - 3s 27ms/step\n",
-      "103/103 [==============================] - 3s 25ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=60; total time=  39.2s\n",
-      "  7/103 [=>............................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=60; total time=  39.0s\n",
-      " 12/103 [==>...........................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=80; total time=  39.0s\n",
-      " 22/103 [=====>........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=60; total time=  39.1s\n",
-      " 29/103 [=======>......................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=100; total time=  39.1s\n",
-      " 24/103 [=====>........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=80; total time=  39.3s\n",
-      " 50/103 [=============>................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=80; total time=  39.2s\n",
-      " 38/103 [==========>...................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=100; total time=  39.1s\n",
-      "103/103 [==============================] - 4s 35ms/step\n",
-      "103/103 [==============================] - 4s 36ms/step\n",
-      "103/103 [==============================] - 4s 35ms/step\n",
-      "103/103 [==============================] - 4s 33ms/step\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "103/103 [==============================] - 3s 25ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.001, num_1=60; total time=  43.5s\n",
-      " 10/103 [=>............................] - ETA: 0s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=100; total time=  44.6s\n",
-      "103/103 [==============================] - 1s 8ms/step\n",
-      "103/103 [==============================] - 1s 8ms/step\n",
-      "Best score is 0.62 using {'dropout_rate': 0.4, 'filter': 24, 'kernel': 6, 'lr': 0.001, 'num_1': 100}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Performing GridSearch\n",
-    "\n",
-    "from sklearn.model_selection import GridSearchCV\n",
-    "\n",
-    "def CNN_model(embedding = 200, filter = 16, kernel = 4, num_1 = 40, lr = 0.01, dropout_rate = 0.5):\n",
-    "    model = Sequential()\n",
-    "    model.add(layers.Embedding(input_dim=vocab_size, \n",
-    "                               output_dim=embedding, \n",
-    "                               input_length=maxlen))\n",
-    "    model.add(Conv1D(filters = filter, kernel_size = kernel, activation='relu'))\n",
-    "    model.add(MaxPooling1D(pool_size=2))\n",
-    "    model.add(layers.Flatten())\n",
-    "    model.add(layers.Dropout(dropout_rate))\n",
-    "    model.add(layers.Dense(num_1, activation='relu'))\n",
-    "    model.add(layers.Dense(1, activation='sigmoid'))\n",
-    "    model.compile(optimizer= Adam(learning_rate = lr),\n",
-    "                  loss='binary_crossentropy',\n",
-    "                  metrics=['accuracy'])\n",
-    "    return model\n",
-    "\n",
-    "model = CNN_model()\n",
-    "\n",
-    "param_grid = {\n",
-    "    'filter': [24, 36],\n",
-    "    'kernel': [4,5,6],\n",
-    "    'num_1': [60, 80, 100],\n",
-    "    'lr': [0.01, 0.001],\n",
-    "    'dropout_rate': [0.4, 0.5, 0.6]\n",
-    "}\n",
-    "\n",
-    "model = KerasClassifier(build_fn=CNN_model, verbose=0)\n",
-    "\n",
-    "# Perform GridSearchCV\n",
-    "search = GridSearchCV(estimator=model, param_grid=param_grid, cv=3, n_jobs=-1, scoring='accuracy', verbose = 2)\n",
-    "search_results = search.fit(X_train_2, y_train)\n",
-    "\n",
-    "# Get the best score and best parameters\n",
-    "best_score = search_results.best_score_\n",
-    "best_params = search_results.best_params_\n",
-    "\n",
-    "print(\"Best score is {:.2f} using {}\".format(best_score, best_params))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "5aee8737-1e38-4da8-848e-794b81e3ba30",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Accuracy: 0.9899\n",
-      "Testing Accuracy:  0.6092\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Evaluate performance of new model (why is test accuracy lower?)\n",
-    "\n",
-    "best_model = CNN_model(num_1 = 100, lr = 0.001,\n",
-    "                      kernel = 6, filter = 24, dropout_rate = 0.4)\n",
-    "\n",
-    "history = best_model.fit(X_train_2, y_train,\n",
-    "                    epochs=30,\n",
-    "                    verbose=False,\n",
-    "                    validation_data=(X_test_2, y_test),\n",
-    "                    batch_size=1000)\n",
-    "\n",
-    "# Evaluate the model on the test data\n",
-    "\n",
-    "loss, accuracy = best_model.evaluate(X_train_2, y_train, verbose=False)\n",
-    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
-    "loss, accuracy = best_model.evaluate(X_test_2, y_test, verbose=False)\n",
-    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "613d5614-5f99-4109-b326-5ebaca0150b3",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1, 1, figsize=(12,8))\n",
-    "ax.plot(history.history['accuracy'], 'r', label='Training Accuracy')\n",
-    "ax.plot(history.history['val_accuracy'], 'b' ,label='Validation Accuracy')\n",
-    "ax.set_xlabel(r'Epoch', fontsize=20)\n",
-    "ax.set_ylabel(r'Accuracy', fontsize=20)\n",
-    "ax.legend()\n",
-    "ax.tick_params(labelsize=20)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cedcc4aa-db06-4802-a1f5-d922fb1f4842",
-   "metadata": {},
-   "source": [
-    "### Adjusted Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "id": "15b96a69-7dee-4d64-a0db-7e47d6113858",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Accuracy: 0.7781\n",
-      "Testing Accuracy:  0.6333\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Building a model that avoids overfitting\n",
-    "\n",
-    "from keras.regularizers import l2\n",
-    "from keras.callbacks import EarlyStopping\n",
-    "\n",
-    "def CNN_model_adj(embedding=200, filter=16, kernel=4, num_1=40, lr=0.01, dropout_rate=0.5):\n",
-    "    model = Sequential()\n",
-    "    model.add(layers.Embedding(input_dim=vocab_size, \n",
-    "                               output_dim=embedding, \n",
-    "                               input_length=maxlen))\n",
-    "    model.add(Conv1D(filters=filter, kernel_size=kernel, activation=\"relu\"))\n",
-    "    model.add(MaxPooling1D(pool_size=2))\n",
-    "    model.add(layers.Flatten())\n",
-    "    model.add(layers.Dropout(dropout_rate))\n",
-    "    model.add(layers.Dense(num_1, activation='relu', kernel_regularizer=l2(0.001)))\n",
-    "    model.add(layers.Dense(1, activation='sigmoid'))\n",
-    "    model.compile(optimizer=Adam(learning_rate=lr),\n",
-    "                  loss='binary_crossentropy',\n",
-    "                  metrics=['accuracy'])\n",
-    "    return model\n",
-    "\n",
-    "# Define early stopping callback\n",
-    "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
-    "\n",
-    "model_adj = CNN_model_adj()\n",
-    "\n",
-    "history_adj = model_adj.fit(X_train_2, y_train,\n",
-    "                    epochs=30,\n",
-    "                    verbose=False,\n",
-    "                    validation_data=(X_test_2, y_test),\n",
-    "                    batch_size=1000,\n",
-    "                    callbacks=[early_stopping])  \n",
-    "\n",
-    "loss, accuracy = model_adj.evaluate(X_train_2, y_train, verbose=False)\n",
-    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
-    "loss, accuracy = model_adj.evaluate(X_test_2, y_test, verbose=False)\n",
-    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "83f033b0-5a36-426d-9a61-5ca8f60c7252",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Accuracy over epochs\n",
-    "\n",
-    "fig, ax = plt.subplots(1, 1, figsize=(12,8))\n",
-    "ax.plot(history_adj.history['accuracy'], 'r', label='Training Accuracy')\n",
-    "ax.plot(history_adj.history['val_accuracy'], 'b' ,label='Validation Accuracy')\n",
-    "ax.set_xlabel(r'Epoch', fontsize=20)\n",
-    "ax.set_ylabel(r'Accuracy', fontsize=20)\n",
-    "ax.legend()\n",
-    "ax.tick_params(labelsize=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "id": "ebaca738-d225-4ee6-915a-208d325010ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 3 folds for each of 108 candidates, totalling 324 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/hs/br_4rpdj68nc3sfdpgv0xgn80000gn/T/ipykernel_78730/4022294683.py:28: DeprecationWarning: KerasClassifier is deprecated, use Sci-Keras (https://github.com/adriangb/scikeras) instead. See https://www.adriangb.com/scikeras/stable/migration.html for help migrating.\n",
-      "  model_adj = KerasClassifier(build_fn=CNN_model_adj, verbose=0)\n",
-      "2024-04-18 13:49:05.642775: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 13:49:05.642798: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 13:49:05.642788: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 13:49:05.645046: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 13:49:05.645439: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 13:49:05.652489: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 13:49:05.654238: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
-      "2024-04-18 13:49:05.654947: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "[CV] END dropout_rate=0.4, filter=24, kernel=4, lr=0.01, num_1=80; total time=  23.9s\n",
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-      "103/103 [==============================] - 4s 37ms/step\n",
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-      "[CV] END dropout_rate=0.4, filter=24, kernel=5, lr=0.01, num_1=100; total time=  48.0s\n",
-      "103/103 [==============================] - 4s 37ms/step\n",
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-      "103/103 [==============================] - 4s 35ms/step\n",
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-      "103/103 [==============================] - 2s 15ms/step\n",
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-      " 57/103 [===============>..............] - ETA: 1s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.01, num_1=80; total time=  50.9s\n",
-      "103/103 [==============================] - 4s 38ms/step\n",
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-      "103/103 [==============================] - 3s 24ms/step\n",
-      "[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=60; total time=  45.5s\n",
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-      " 52/103 [==============>...............] - ETA: 1s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=80; total time=  45.1s\n",
-      "103/103 [==============================] - 2s 18ms/step\n",
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-      "103/103 [==============================] - 3s 25ms/step\n",
-      "[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=60; total time=  44.2s\n",
-      "  1/103 [..............................] - ETA: 49s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.01, num_1=100; total time=  48.2s\n",
-      " 11/103 [==>...........................] - ETA: 4s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=60; total time=  45.8s\n",
-      "103/103 [==============================] - 4s 37ms/step\n",
-      "103/103 [==============================] - 4s 38ms/step\n",
-      "103/103 [==============================] - 4s 37ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=60; total time=  29.4s\n",
-      " 36/103 [=========>....................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=60; total time=  30.3s\n",
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-      " 34/103 [========>.....................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=80; total time=  29.7s\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
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-      "103/103 [==============================] - 3s 24ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=100; total time=  38.0s\n",
-      " 14/103 [===>..........................] - ETA: 3s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=100; total time=  38.4s\n",
-      " 24/103 [=====>........................] - ETA: 2s[CV] END dropout_rate=0.4, filter=36, kernel=6, lr=0.001, num_1=100; total time=  38.2s\n",
-      "103/103 [==============================] - 3s 27ms/step\n",
-      "103/103 [==============================] - 3s 27ms/step\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=80; total time=  30.3s\n",
-      "  6/103 [>.............................] - ETA: 1s [CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=80; total time=  29.3s\n",
-      "  1/103 [..............................] - ETA: 11s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=100; total time=  29.9s\n",
-      "  8/103 [=>............................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=100; total time=  30.3s\n",
-      " 16/103 [===>..........................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.01, num_1=100; total time=  29.7s\n",
-      "103/103 [==============================] - 2s 17ms/step\n",
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-      "103/103 [==============================] - 2s 17ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=60; total time=  29.4s\n",
-      "103/103 [==============================] - 4s 34ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=60; total time=  30.2s\n",
-      " 19/103 [====>.........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=60; total time=  29.9s\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=80; total time=  31.4s\n",
-      " 11/103 [==>...........................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=80; total time=  30.0s\n",
-      " 13/103 [==>...........................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=100; total time=  29.9s\n",
-      " 16/103 [===>..........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=100; total time=  29.8s\n",
-      " 12/103 [==>...........................] - ETA: 3s[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=80; total time=  30.7s\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
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-      "103/103 [==============================] - 3s 26ms/step\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=4, lr=0.001, num_1=100; total time=  29.4s\n",
-      "103/103 [==============================] - 2s 18ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=60; total time=  33.9s\n",
-      "  1/103 [..............................] - ETA: 9s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=60; total time=  33.0s\n",
-      "103/103 [==============================] - 2s 15ms/step\n",
-      "103/103 [==============================] - 2s 16ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=60; total time=  38.6s\n",
-      " 27/103 [======>.......................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=100; total time=  38.7s\n",
-      " 42/103 [===========>..................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=80; total time=  38.8s\n",
-      "  1/103 [..............................] - ETA: 21s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=80; total time=  38.5s\n",
-      " 48/103 [============>.................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=80; total time=  38.7s\n",
-      "103/103 [==============================] - 3s 25ms/step\n",
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-      "103/103 [==============================] - 3s 22ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=100; total time=  36.7s\n",
-      "103/103 [==============================] - 2s 14ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=60; total time=  33.4s\n",
-      " 15/103 [===>..........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.01, num_1=100; total time=  33.8s\n",
-      "103/103 [==============================] - 3s 30ms/step\n",
-      "103/103 [==============================] - 3s 30ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=60; total time=  35.0s\n",
-      " 63/103 [=================>............] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=80; total time=  35.6s\n",
-      " 11/103 [==>...........................] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=60; total time=  35.8s\n",
-      " 15/103 [===>..........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=80; total time=  35.7s\n",
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-      "103/103 [==============================] - 3s 28ms/step\n",
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-      "103/103 [==============================] - 2s 21ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=5, lr=0.001, num_1=100; total time=  34.3s\n",
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-      "103/103 [==============================] - 1s 12ms/step\n",
-      "103/103 [==============================] - 2s 21ms/step\n",
-      "103/103 [==============================] - 2s 22ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=60; total time=  36.0s\n",
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-      "  1/103 [..............................] - ETA: 17s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=80; total time=  36.8s\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
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-      "103/103 [==============================] - 2s 21ms/step\n",
-      "103/103 [==============================] - 2s 21ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=80; total time=  36.1s\n",
-      " 52/103 [==============>...............] - ETA: 0s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=100; total time=  34.5s\n",
-      " 17/103 [===>..........................] - ETA: 0s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=100; total time=  34.5s\n",
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-      "103/103 [==============================] - 2s 21ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=80; total time=  33.6s\n",
-      " 13/103 [==>...........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=60; total time=  33.9s\n",
-      " 14/103 [===>..........................] - ETA: 3s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=60; total time=  33.6s\n",
-      " 53/103 [==============>...............] - ETA: 1s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.01, num_1=100; total time=  33.9s\n",
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-      "103/103 [==============================] - 3s 28ms/step\n",
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-      "103/103 [==============================] - 3s 23ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=80; total time=  33.4s\n",
-      "  1/103 [..............................] - ETA: 12s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=100; total time=  32.3s\n",
-      "  1/103 [..............................] - ETA: 14s[CV] END dropout_rate=0.5, filter=24, kernel=6, lr=0.001, num_1=80; total time=  33.1s\n",
-      "103/103 [==============================] - 2s 17ms/step\n",
-      "103/103 [==============================] - 2s 18ms/step\n",
-      "103/103 [==============================] - 2s 17ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=60; total time=  34.8s\n",
-      "  5/103 [>.............................] - ETA: 1s  [CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=60; total time=  34.3s\n",
-      " 12/103 [==>...........................] - ETA: 5s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=60; total time=  35.0s\n",
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-      "103/103 [==============================] - 5s 38ms/step\n",
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-      "103/103 [==============================] - 2s 22ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=80; total time=  35.9s\n",
-      " 35/103 [=========>....................] - ETA: 0s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=80; total time=  37.0s\n",
-      "103/103 [==============================] - 2s 14ms/step\n",
-      "103/103 [==============================] - 2s 16ms/step\n",
-      "103/103 [==============================] - 2s 19ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=100; total time= 1.0min\n",
-      " 18/103 [====>.........................] - ETA: 2s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=100; total time= 1.0min\n",
-      " 40/103 [==========>...................] - ETA: 1s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.01, num_1=100; total time= 1.0min\n",
-      "103/103 [==============================] - 3s 27ms/step\n",
-      " 88/103 [========================>.....] - ETA: 0s[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=60; total time= 1.0min\n",
-      "103/103 [==============================] - 3s 27ms/step\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
-      "103/103 [==============================] - 2s 20ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=60; total time= 1.0min\n",
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-      "103/103 [==============================] - 2s 17ms/step\n",
-      "103/103 [==============================] - 2s 15ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=80; total time= 1.0min\n",
-      "103/103 [==============================] - 2s 13ms/step\n",
-      "103/103 [==============================] - 1s 12ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=100; total time=  42.3s\n",
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-      "103/103 [==============================] - 4s 39ms/step\n",
-      "103/103 [==============================] - 4s 37ms/step\n",
-      "103/103 [==============================] - 4s 35ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=4, lr=0.001, num_1=100; total time=  43.1s\n",
-      " 63/103 [=================>............] - ETA: 0s[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=60; total time=  43.6s\n",
-      " 20/103 [====>.........................] - ETA: 1s[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=60; total time=  44.8s\n",
-      "103/103 [==============================] - 2s 23ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=60; total time=  43.9s\n",
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-      "103/103 [==============================] - 2s 22ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=80; total time=  41.6s\n",
-      " 62/103 [=================>............] - ETA: 1s[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=80; total time=  42.7s\n",
-      "103/103 [==============================] - 4s 42ms/step\n",
-      " 43/103 [===========>..................] - ETA: 2s[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.01, num_1=100; total time=  43.1s\n",
-      "103/103 [==============================] - 4s 35ms/step\n",
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-      " 36/103 [=========>....................] - ETA: 1s[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.001, num_1=60; total time=  43.4s\n",
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-      "103/103 [==============================] - 3s 26ms/step\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=5, lr=0.001, num_1=80; total time=  39.2s\n",
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-      "  1/103 [..............................] - ETA: 19s[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.01, num_1=60; total time=  41.7s\n",
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-      "103/103 [==============================] - 4s 34ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.01, num_1=60; total time=  46.6s\n",
-      "103/103 [==============================] - 8s 76ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.01, num_1=80; total time=  47.6s\n",
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-      "103/103 [==============================] - 5s 41ms/step\n",
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-      "103/103 [==============================] - 4s 36ms/step\n",
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-      "103/103 [==============================] - 5s 45ms/step\n",
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-      "103/103 [==============================] - 4s 35ms/step\n",
-      "103/103 [==============================] - 4s 31ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.001, num_1=60; total time=  50.2s\n",
-      "103/103 [==============================] - 4s 36ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.001, num_1=60; total time=  49.8s\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.001, num_1=80; total time=  49.6s\n",
-      "103/103 [==============================] - 3s 30ms/step\n",
-      "[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.001, num_1=80; total time=  52.9s\n",
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-      "103/103 [==============================] - 2s 20ms/step\n",
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-      " 61/103 [================>.............] - ETA: 0s[CV] END dropout_rate=0.5, filter=36, kernel=6, lr=0.001, num_1=100; total time=  53.3s\n",
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-      "103/103 [==============================] - 2s 22ms/step\n",
-      "103/103 [==============================] - 2s 20ms/step\n",
-      "103/103 [==============================] - 2s 20ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.01, num_1=60; total time=  33.7s\n",
-      "103/103 [==============================] - 4s 37ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.01, num_1=60; total time=  34.5s\n",
-      "103/103 [==============================] - 4s 34ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.01, num_1=60; total time=  35.9s\n",
-      "103/103 [==============================] - 3s 25ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.01, num_1=80; total time=  34.3s\n",
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-      "103/103 [==============================] - 2s 23ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.01, num_1=80; total time=  34.9s\n",
-      " 25/103 [======>.......................] - ETA: 1s[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.01, num_1=100; total time=  33.3s\n",
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-      "103/103 [==============================] - 2s 16ms/step\n",
-      "103/103 [==============================] - 2s 15ms/step\n",
-      "103/103 [==============================] - 2s 16ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.01, num_1=100; total time=  33.1s\n",
-      "103/103 [==============================] - 3s 32ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.001, num_1=60; total time=  35.1s\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.001, num_1=60; total time=  34.8s\n",
-      "103/103 [==============================] - 5s 47ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.001, num_1=60; total time=  35.8s\n",
-      "  1/103 [..............................] - ETA: 2:08[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.001, num_1=80; total time=  35.1s\n",
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-      " 19/103 [====>.........................] - ETA: 2s[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.001, num_1=100; total time=  35.8s\n",
-      "103/103 [==============================] - 4s 29ms/step\n",
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-      "103/103 [==============================] - 3s 28ms/step\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.001, num_1=100; total time=  30.0s\n",
-      "103/103 [==============================] - 4s 39ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=4, lr=0.001, num_1=100; total time=  30.9s\n",
-      "103/103 [==============================] - 4s 39ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.01, num_1=60; total time=  40.4s\n",
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-      "103/103 [==============================] - 4s 39ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.01, num_1=80; total time=  39.4s\n",
-      "  1/103 [..............................] - ETA: 15s[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.01, num_1=60; total time=  40.5s\n",
-      "  6/103 [>.............................] - ETA: 3s[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.01, num_1=60; total time=  40.5s\n",
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-      "103/103 [==============================] - 3s 23ms/step\n",
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-      "103/103 [==============================] - 3s 23ms/step\n",
-      "103/103 [==============================] - 2s 19ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.01, num_1=100; total time=  38.6s\n",
-      "103/103 [==============================] - 4s 41ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.01, num_1=100; total time=  39.2s\n",
-      "103/103 [==============================] - 4s 38ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.01, num_1=100; total time=  44.2s\n",
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-      "103/103 [==============================] - 4s 37ms/step\n",
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-      "103/103 [==============================] - 4s 35ms/step\n",
-      " 27/103 [======>.......................] - ETA: 2s[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.001, num_1=60; total time=  43.3s\n",
-      " 11/103 [==>...........................] - ETA: 1s[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.001, num_1=80; total time=  40.9s\n",
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-      "103/103 [==============================] - 2s 20ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.001, num_1=80; total time=  35.6s\n",
-      "103/103 [==============================] - 5s 42ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.001, num_1=100; total time=  36.9s\n",
-      "103/103 [==============================] - 4s 39ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.001, num_1=100; total time=  38.5s\n",
-      " 27/103 [======>.......................] - ETA: 2s[CV] END dropout_rate=0.6, filter=24, kernel=5, lr=0.001, num_1=100; total time=  39.1s\n",
-      " 13/103 [==>...........................] - ETA: 4s[CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.01, num_1=60; total time=  40.6s\n",
-      " 63/103 [=================>............] - ETA: 1s[CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.01, num_1=60; total time=  40.7s\n",
-      "103/103 [==============================] - 3s 31ms/step\n",
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-      "103/103 [==============================] - 3s 31ms/step\n",
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-      "103/103 [==============================] - 2s 21ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.01, num_1=80; total time=  40.3s\n",
-      "103/103 [==============================] - 5s 45ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.01, num_1=80; total time=  38.5s\n",
-      "103/103 [==============================] - 4s 39ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.001, num_1=60; total time=  39.4s\n",
-      "  5/103 [>.............................] - ETA: 2s [CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.001, num_1=60; total time=  39.2s\n",
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-      " 11/103 [==>...........................] - ETA: 1s[CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.01, num_1=100; total time=  39.7s\n",
-      " 27/103 [======>.......................] - ETA: 2s[CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.01, num_1=100; total time=  38.6s\n",
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-      "103/103 [==============================] - 3s 30ms/step\n",
-      "103/103 [==============================] - 3s 23ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=24, kernel=6, lr=0.001, num_1=80; total time=  38.3s\n",
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-      "103/103 [==============================] - 1s 9ms/step\n",
-      "103/103 [==============================] - 1s 10ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.01, num_1=60; total time=  41.7s\n",
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-      "103/103 [==============================] - 4s 40ms/step\n",
-      " 11/103 [==>...........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.01, num_1=60; total time=  41.9s\n",
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-      "103/103 [==============================] - 4s 32ms/step\n",
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-      "103/103 [==============================] - 3s 27ms/step\n",
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-      "103/103 [==============================] - 2s 17ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.01, num_1=80; total time=  32.5s\n",
-      " 19/103 [====>.........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.01, num_1=100; total time=  33.5s\n",
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-      "103/103 [==============================] - 4s 32ms/step\n",
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-      " 13/103 [==>...........................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.01, num_1=100; total time=  34.7s\n",
-      "103/103 [==============================] - 3s 28ms/step\n",
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-      "103/103 [==============================] - 3s 23ms/step\n",
-      "103/103 [==============================] - 2s 19ms/step..] - ETA: \n",
-      "103/103 [==============================] - 2s 18ms/step\n",
-      "103/103 [==============================] - 2s 18ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.001, num_1=60; total time=  31.2s\n",
-      "  1/103 [..............................] - ETA: 24s[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.001, num_1=60; total time=  31.6s\n",
-      "103/103 [==============================] - 1s 13ms/step\n",
-      "103/103 [==============================] - 2s 14ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=4, lr=0.001, num_1=80; total time=  38.8s\n",
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-      "103/103 [==============================] - 4s 40ms/step\n",
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-      "103/103 [==============================] - 4s 34ms/step\n",
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-      "103/103 [==============================] - 4s 33ms/step\n",
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-      " 95/103 [==========================>...] - ETA: 0s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=60; total time=  39.6s\n",
-      "  1/103 [..............................] - ETA: 11s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=60; total time=  39.6s\n",
-      "103/103 [==============================] - 2s 18ms/step\n",
-      "103/103 [==============================] - 2s 19ms/step\n",
-      "103/103 [==============================] - 2s 21ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=60; total time=  44.1s\n",
-      " 43/103 [===========>..................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=80; total time=  44.9s\n",
-      " 47/103 [============>.................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=80; total time=  44.3s\n",
-      " 55/103 [===============>..............] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=100; total time=  44.3s\n",
-      "103/103 [==============================] - 5s 42ms/step\n",
-      " 44/103 [===========>..................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=80; total time=  44.7s\n",
-      "103/103 [==============================] - 4s 32ms/step\n",
-      " 49/103 [=============>................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=100; total time=  44.8s\n",
-      "103/103 [==============================] - 4s 33ms/step\n",
-      "103/103 [==============================] - 3s 30ms/step\n",
-      " 27/103 [======>.......................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=60; total time=  44.7s\n",
-      "103/103 [==============================] - 3s 23ms/step\n",
-      " 52/103 [==============>...............] - ETA: 0s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.01, num_1=100; total time=  45.1s\n",
-      "103/103 [==============================] - 2s 19ms/step\n",
-      "103/103 [==============================] - 2s 17ms/step\n",
-      "103/103 [==============================] - 2s 20ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=60; total time=  49.2s\n",
-      "103/103 [==============================] - 5s 45ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=80; total time=  48.5s\n",
-      "  1/103 [..............................] - ETA: 36s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=80; total time=  49.2s\n",
-      "  5/103 [>.............................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=60; total time=  49.4s\n",
-      " 44/103 [===========>..................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=80; total time=  49.1s\n",
-      "103/103 [==============================] - 4s 37ms/step\n",
-      "103/103 [==============================] - 4s 38ms/step\n",
-      "103/103 [==============================] - 4s 40ms/step\n",
-      " 67/103 [==================>...........] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=100; total time=  49.5s\n",
-      "103/103 [==============================] - 4s 38ms/step\n",
-      " 92/103 [=========================>....] - ETA: 0s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=100; total time=  48.0s\n",
-      "103/103 [==============================] - 3s 26ms/step\n",
-      " 30/103 [=======>......................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=5, lr=0.001, num_1=100; total time=  49.1s\n",
-      "103/103 [==============================] - 3s 23ms/step\n",
-      "103/103 [==============================] - 3s 24ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=60; total time=  40.0s\n",
-      "103/103 [==============================] - 6s 52ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=60; total time=  42.7s\n",
-      "  4/103 [>.............................] - ETA: 1s [CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=80; total time=  42.4s\n",
-      " 13/103 [==>...........................] - ETA: 3s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=60; total time=  43.1s\n",
-      " 20/103 [====>.........................] - ETA: 2s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=80; total time=  43.3s\n",
-      "103/103 [==============================] - 3s 30ms/step\n",
-      "103/103 [==============================] - 3s 31ms/step\n",
-      " 99/103 [===========================>..] - ETA: 0s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=80; total time=  44.1s\n",
-      "103/103 [==============================] - 3s 29ms/step\n",
-      "103/103 [==============================] - 3s 27ms/step\n",
-      " 31/103 [========>.....................] - ETA: 1s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=100; total time=  45.8s\n",
-      "  1/103 [..............................] - ETA: 11s[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=100; total time=  45.0s\n",
-      "103/103 [==============================] - 2s 18ms/step\n",
-      "103/103 [==============================] - 2s 15ms/step\n",
-      "103/103 [==============================] - 2s 15ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.01, num_1=100; total time=  49.5s\n",
-      "103/103 [==============================] - 1s 11ms/step\n",
-      "[CV] END dropout_rate=0.6, filter=36, kernel=6, lr=0.001, num_1=60; total time=  51.3s\n",
-      "103/103 [==============================] - 1s 5ms/step\n",
-      "Best score is 0.63 using {'dropout_rate': 0.5, 'filter': 36, 'kernel': 5, 'lr': 0.001, 'num_1': 80}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Gridsearch\n",
-    "\n",
-    "def CNN_model_adj(embedding=200, filter=16, kernel=4, num_1=40, lr=0.01, dropout_rate=0.5):\n",
-    "    model = Sequential()\n",
-    "    model.add(layers.Embedding(input_dim=vocab_size, \n",
-    "                               output_dim=embedding, \n",
-    "                               input_length=maxlen))\n",
-    "    model.add(Conv1D(filters=filter, kernel_size=kernel, activation=\"relu\"))\n",
-    "    model.add(MaxPooling1D(pool_size=2))\n",
-    "    model.add(layers.Flatten())\n",
-    "    model.add(layers.Dropout(dropout_rate))\n",
-    "    model.add(layers.Dense(num_1, activation='relu', kernel_regularizer=l2(0.001)))\n",
-    "    model.add(layers.Dense(1, activation='sigmoid'))\n",
-    "    model.compile(optimizer=Adam(learning_rate=lr),\n",
-    "                  loss='binary_crossentropy',\n",
-    "                  metrics=['accuracy'])\n",
-    "    return model\n",
-    "\n",
-    "# Define early stopping callback\n",
-    "early_stopping = EarlyStopping(monitor='val_loss', patience=3, restore_best_weights=True)\n",
-    "\n",
-    "param_grid = {\n",
-    "    'filter': [24, 36],\n",
-    "    'kernel': [4,5,6],\n",
-    "    'num_1': [60, 80, 100],\n",
-    "    'lr': [0.01, 0.001],\n",
-    "    'dropout_rate': [0.4, 0.5, 0.6]\n",
-    "}\n",
-    "\n",
-    "model_adj = KerasClassifier(build_fn=CNN_model_adj, verbose=0)\n",
-    "\n",
-    "# Perform GridSearchCV\n",
-    "search = GridSearchCV(estimator=model_adj, param_grid=param_grid, cv=3, n_jobs=-1, scoring='accuracy', verbose = 2)\n",
-    "search_results = search.fit(X_train_2, y_train)\n",
-    "\n",
-    "# Get the best score and best parameters\n",
-    "best_score = search_results.best_score_\n",
-    "best_params = search_results.best_params_\n",
-    "\n",
-    "print(\"Best score is {:.2f} using {}\".format(best_score, best_params))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "b4ce72e6-4bfc-4d66-9dde-346282b85a6d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training Accuracy: 0.9886\n",
-      "Testing Accuracy:  0.6002\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Why is accuracy lower?\n",
-    "\n",
-    "best_model_adj = CNN_model_adj(num_1 = 80, lr = 0.001,\n",
-    "                      kernel = 5, filter = 36, dropout_rate = 0.5)\n",
-    "\n",
-    "history = best_model_adj.fit(X_train_2, y_train,\n",
-    "                    epochs=30,\n",
-    "                    verbose=False,\n",
-    "                    validation_data=(X_test_2, y_test),\n",
-    "                    batch_size=1000)\n",
-    "\n",
-    "# Evaluate the model on the test data\n",
-    "loss, accuracy = best_model_adj.evaluate(X_train_2, y_train, verbose=False)\n",
-    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
-    "loss, accuracy = best_model_adj.evaluate(X_test_2, y_test, verbose=False)\n",
-    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "4dfadce6-864f-4661-b362-2c60075737f9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1, 1, figsize=(12,8))\n",
-    "ax.plot(history.history['accuracy'], 'r', label='Training Accuracy')\n",
-    "ax.plot(history.history['val_accuracy'], 'b' ,label='Validation Accuracy')\n",
-    "ax.set_xlabel(r'Epoch', fontsize=20)\n",
-    "ax.set_ylabel(r'Accuracy', fontsize=20)\n",
-    "ax.legend()\n",
-    "ax.tick_params(labelsize=20)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python (testenv)",
-   "language": "python",
-   "name": "testenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}