{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pylab  as plt\n",
    "\n",
    "import re\n",
    "import string\n",
    "\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer    # tfidf\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from joblib import load, dump"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>id</th>\n",
       "      <th>app_id</th>\n",
       "      <th>content</th>\n",
       "      <th>author_id</th>\n",
       "      <th>is_positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>181331361</td>\n",
       "      <td>100</td>\n",
       "      <td>At least its a counter strike -1/100</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>180872601</td>\n",
       "      <td>100</td>\n",
       "      <td>Uh... So far my playthrough has not been great...</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>177836246</td>\n",
       "      <td>100</td>\n",
       "      <td>Better mechanics than cs2</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>177287444</td>\n",
       "      <td>100</td>\n",
       "      <td>buggy mess and NOT fun to play at all</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176678990</td>\n",
       "      <td>100</td>\n",
       "      <td>Whoever came up with this, is gonna fucking ge...</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</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>201139</th>\n",
       "      <td>118775009</td>\n",
       "      <td>570</td>\n",
       "      <td>This trash for sick anime clowns!</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201140</th>\n",
       "      <td>118771828</td>\n",
       "      <td>570</td>\n",
       "      <td>Not noob friendly</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201141</th>\n",
       "      <td>118771331</td>\n",
       "      <td>570</td>\n",
       "      <td>Total trash for anime clowns!</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201142</th>\n",
       "      <td>182234883</td>\n",
       "      <td>730</td>\n",
       "      <td>Unplayable. It keeps trying to update but then...</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201143</th>\n",
       "      <td>177842193</td>\n",
       "      <td>730</td>\n",
       "      <td>Bring back csgo</td>\n",
       "      <td>7.656120e+16</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201144 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  app_id                                            content  \\\n",
       "0       181331361     100               At least its a counter strike -1/100   \n",
       "1       180872601     100  Uh... So far my playthrough has not been great...   \n",
       "2       177836246     100                          Better mechanics than cs2   \n",
       "3       177287444     100              buggy mess and NOT fun to play at all   \n",
       "4       176678990     100  Whoever came up with this, is gonna fucking ge...   \n",
       "...           ...     ...                                                ...   \n",
       "201139  118775009     570                  This trash for sick anime clowns!   \n",
       "201140  118771828     570                                  Not noob friendly   \n",
       "201141  118771331     570                      Total trash for anime clowns!   \n",
       "201142  182234883     730  Unplayable. It keeps trying to update but then...   \n",
       "201143  177842193     730                                    Bring back csgo   \n",
       "\n",
       "           author_id is_positive  \n",
       "0       7.656120e+16    Negative  \n",
       "1       7.656120e+16    Negative  \n",
       "2       7.656120e+16    Negative  \n",
       "3       7.656120e+16    Negative  \n",
       "4       7.656120e+16    Negative  \n",
       "...              ...         ...  \n",
       "201139  7.656120e+16    Negative  \n",
       "201140  7.656120e+16    Negative  \n",
       "201141  7.656120e+16    Negative  \n",
       "201142  7.656120e+16    Negative  \n",
       "201143  7.656120e+16    Negative  \n",
       "\n",
       "[201144 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_df=pd.read_csv('output.csv',encoding='ISO-8859-1')\n",
    "game_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Drop unwanted columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "game_df.drop( [\"id\",\"app_id\",\"author_id\"], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>content</th>\n",
       "      <th>is_positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>At least its a counter strike -1/100</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uh... So far my playthrough has not been great...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Better mechanics than cs2</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>buggy mess and NOT fun to play at all</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Whoever came up with this, is gonna fucking ge...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201139</th>\n",
       "      <td>This trash for sick anime clowns!</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201140</th>\n",
       "      <td>Not noob friendly</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201141</th>\n",
       "      <td>Total trash for anime clowns!</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201142</th>\n",
       "      <td>Unplayable. It keeps trying to update but then...</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201143</th>\n",
       "      <td>Bring back csgo</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  content is_positive\n",
       "0                    At least its a counter strike -1/100    Negative\n",
       "1       Uh... So far my playthrough has not been great...    Negative\n",
       "2                               Better mechanics than cs2    Negative\n",
       "3                   buggy mess and NOT fun to play at all    Negative\n",
       "4       Whoever came up with this, is gonna fucking ge...    Negative\n",
       "...                                                   ...         ...\n",
       "201139                  This trash for sick anime clowns!    Negative\n",
       "201140                                  Not noob friendly    Negative\n",
       "201141                      Total trash for anime clowns!    Negative\n",
       "201142  Unplayable. It keeps trying to update but then...    Negative\n",
       "201143                                    Bring back csgo    Negative\n",
       "\n",
       "[201144 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "content        427\n",
       "is_positive      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Drop null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "content        0\n",
       "is_positive    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_df=game_df.dropna()\n",
    "game_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_5644\\320265834.py:1: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax=sns.countplot(x=game_df.is_positive,palette=\"viridis\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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4okaPHq1ixYrJ3t5e/fr1y/XZXXnxZ/3GG2/oiy++0HvvvZfj4a7ZPb7wwgvq0qVLrus/8sgjN7W9zMxMPfHEEzp9+rReffVVVahQQW5ubjp27Ji6du16y880c3Z2VuvWrfXtt9/qww8/VGJiotavX6933nknx/4MGjRI4eHhuc6TWwi8GUlJSWrYsKE8PDw0atQoBQcHy8XFRVu3btWrr756S/uXlZWl0NBQTZgwIdfxa91L16BBA/3+++/67rvvtGLFCn388ceaOHGiZsyYoe7du990L7izCFW4Zz355JOaNWuW4uPjVbt27WvWBgQEKCsrSwcPHrS5mTsxMVFJSUkKCAgwlxUtWlRJSUk266enp+d6puBG3UyYCwgI0I8//qizZ8/anK3KvixxZa9WCwgI0P79+3Msz23bbm5uat++vdq3b6/09HS1adNGb7/9toYMGXLVj9PfbKh97rnn9MEHHyglJUUxMTEKDAxUrVq1zPHg4GBJUsmSJW8pLH711Vdq3LixPvnkE5vlSUlJ5tm7m5F9fA4ePGhe+pEuf8Lr8OHDqly58k3PGRwcrBdeeEEzZ85UzZo1bca8vb1VpEgRZWZmXnf/AwICtGfPHhmGYfPn8N9PL+7cuVMHDhzQnDlz1LlzZ3N5bp9Gu9k/z/bt22vOnDlauXKl9u7dK8MwbEJy9tk8R0dHS8J/bn766Sf9+++/+uabb9SgQQNz+eHDh3OtP378uFJTU23OVh04cECSzA9BBAcHa/v27WrSpMkt/cOtWLFi6tatm7p166Zz586pQYMGGjlyJKEqH+LyH+5ZgwcPlpubm7p3767ExMQc47///rs++OADSVKLFi0kXf4E2pWy/2XZsmVLc1lwcLDWrl1rUzdr1qyrnqm6EW5ubjmC2tW0aNFCmZmZmjp1qs3yiRMnys7OTs2bN7/lPm5k2xs3blR8fLy5LDU1VbNmzVJgYKBCQkIkSf/++6/Nek5OTgoJCZFhGMrIyLjq/DdzHKTLv4TT0tI0Z84cxcbG6rnnnrMZDw8Pl4eHh955551ct3vloy1y4+DgkOMs08KFC2/5vp3q1avL29tbM2bMUHp6url89uzZN7Xf/zV06FBlZGRo7NixNssdHBzUtm1bff3117metb1y/8PDw3Xs2DGbRxNcvHhRH330UY45Jduzb4ZhmO+lK2UHjRvdt7CwMBUrVkwxMTGKiYlRjRo1FBQUZI6XLFlSjRo10syZM696ufN25bZ/6enp+vDDD3Otv3TpkmbOnGlTO3PmTHl7e6tatWqSLof/Y8eO5TiW0uWHoKampl61n/++l9zd3VW2bNnrPooBeYMzVbhnBQcHa968eWrfvr0qVqxo80T1X375RQsXLjSfK1W5cmV16dJFs2bNMk//b9y4UXPmzFHr1q3VuHFjc97u3bvrpZdeUtu2bfXEE09o+/btWr58+S2duchWrVo1TZ8+XW+99ZbKli2rkiVL2pzJuFKrVq3UuHFjvfHGGzpy5IgqV66sFStW6LvvvlO/fv3MszN3wmuvvab//e9/at68ufr27atixYppzpw5Onz4sL7++mvzpu6mTZvK19dXdevWlY+Pj/bu3aupU6eqZcuW1/zwwM0cB0mqWrWqypYtqzfeeENpaWk2ZzWky5eBp0+frk6dOqlq1aqKiIiQt7e3jh49qqVLl6pu3bo5wumVnnzySY0aNUrdunVTnTp1tHPnTs2dO/eW7n+SLp9heeutt9SrVy89/vjjat++vQ4fPqzPPvvslueU/v+zVXPmzMkx9u6772r16tWqWbOmevTooZCQEJ0+fVpbt27Vjz/+qNOnT0u6/AiEqVOnqkOHDnr55ZdVqlQpzZ071zyrmH2GpUKFCgoODtagQYN07NgxeXh46Ouvv871HsHsUNG3b1+Fh4fLwcFBERER1zw+bdq00fz585Wamqr3338/R820adNUr149hYaGqkePHnrwwQeVmJio+Ph4/f333zmeIZabzZs366233sqxvFGjRqpTp46KFi2qLl26qG/fvrKzs9MXX3xx1Uu4fn5+eu+993TkyBE99NBDiomJ0bZt2zRr1izzYbmdOnXSggUL9NJLL2n16tWqW7euMjMztW/fPi1YsEDLly9X9erVc50/JCREjRo1UrVq1VSsWDFt3rxZX331FV+1k1/lwScOgbvqwIEDRo8ePYzAwEDDycnJKFKkiFG3bl1jypQpNh+PzsjIMN58800jKCjIcHR0NPz9/Y0hQ4bY1BiGYWRmZhqvvvqqUaJECaNw4cJGeHi4cejQoas+UmHTpk026//38QCGcfkj/i1btjSKFClyQx+tP3v2rNG/f3/Dz8/PcHR0NMqVK2eMGzfO5tENhnHzj1S4kdrff//dePbZZw0vLy/DxcXFqFGjhrFkyRKbmpkzZxoNGjQwihcvbjg7OxvBwcHGK6+8YiQnJ5s1uT1S4WrHIbdjlu2NN94wJBlly5a9as+rV682wsPDDU9PT8PFxcUIDg42unbtamzevPma+3rx4kVj4MCBRqlSpQxXV1ejbt26Rnx8vNGwYUObP6PcHmVgGJcfvyDJ+Oyzz2yWf/jhh0ZQUJDh7OxsVK9e3Vi7dm2OOa/mykcqXOngwYOGg4NDrn0kJiYaUVFRhr+/v+Ho6Gj4+voaTZo0MWbNmmVT98cffxgtW7Y0XF1dDW9vb2PgwIHG119/bUgyfv31V7Nuz549RlhYmOHu7m6UKFHC6NGjh7F9+/Yc+3rp0iWjT58+hre3t2FnZ2fzeAVd5dEkcXFxhiTDzs7O+Ouvv3I9Br///rvRuXNnw9fX13B0dDRKly5tPPnkk8ZXX3113eMn6aqv0aNHG4ZhGOvXrzdq1apluLq6Gn5+fsbgwYON5cuX5/h/MPs9s3nzZqN27dqGi4uLERAQYEydOjXHdtPT04333nvPqFSpkuHs7GwULVrUqFatmvHmm2/avC/++/fIW2+9ZdSoUcPw8vIyXF1djQoVKhhvv/22+bgG5C92hpEP75YFAOQLkyZNUv/+/fX333+rdOnSed0OkK8RqgAAki7f33PlM5ouXryoRx99VJmZmebN1wCujnuqAACSpDZt2uiBBx5QlSpVlJycrC+//FL79u3T3Llz87o1oEAgVAEAJF3+BODHH3+suXPnKjMzUyEhIZo/f36ODwAAyB2X/wAAACzAc6oAAAAsQKgCAACwAPdU3UVZWVk6fvy4ihQpclvfMQcAAO4ewzB09uxZ+fn5mQ85zg2h6i46fvz4Nb84EwAA5F9//fWXypQpc9VxQtVdlP31HH/99Zc8PDzyuBsAAHAjUlJS5O/vf82v2ZIIVXdV9iU/Dw8PQhUAAAXM9W7d4UZ1AAAACxCqAAAALECoAgDcc9auXatWrVrJz89PdnZ2WrRokc24YRgaPny4SpUqJVdXV4WFhengwYPm+JEjRxQZGamgoCC5uroqODhYI0aMUHp6ulmzf/9+NW7cWD4+PnJxcdGDDz6ooUOHKiMjw6xp1KiR7Ozscrxatmxp1uQ2bmdnp3Hjxt25A4Q7gnuqAAD3nNTUVFWuXFkvvvii2rRpk2N87Nixmjx5subMmaOgoCANGzZM4eHh2rNnj1xcXLRv3z5lZWVp5syZKlu2rHbt2qUePXooNTVV77//viTJ0dFRnTt3VtWqVeXl5aXt27erR48eysrK0jvvvCNJ+uabb2yC2L///qvKlSurXbt25rITJ07Y9PbDDz8oMjJSbdu2vROHBncQX1NzF6WkpMjT01PJycncqA4Ad4mdnZ2+/fZbtW7dWtLls1R+fn4aOHCgBg0aJElKTk6Wj4+PZs+erYiIiFznGTdunKZPn64//vjjqtsaMGCANm3apHXr1uU6PmnSJA0fPlwnTpyQm5tbrjWtW7fW2bNntXLlypvYS9xJN/r7m8t/AID7yuHDh5WQkKCwsDBzmaenp2rWrKn4+PirrpecnKxixYpddfzQoUOKjY1Vw4YNr1rzySefKCIi4qqBKjExUUuXLlVkZOQN7AnyG0IVAOC+kpCQIEny8fGxWe7j42OO/dehQ4c0ZcoU9erVK8dYnTp15OLionLlyql+/foaNWpUrnNs3LhRu3btUvfu3a/a25w5c1SkSJFcL1ki/yNUAQBwDceOHVOzZs3Url079ejRI8d4TEyMtm7dqnnz5mnp0qXmPVf/9cknnyg0NFQ1atS46rY+/fRTdezYUS4uLpb1j7uHG9UBAPcVX19fSZcvtZUqVcpcnpiYqCpVqtjUHj9+XI0bN1adOnU0a9asXOfL/vqxkJAQZWZmqmfPnho4cKAcHBzMmtTUVM2fP/+qZ7Ekad26ddq/f79iYmJuddeQxzhTBQC4rwQFBcnX19fmRvCUlBRt2LBBtWvXNpcdO3ZMjRo1UrVq1fTZZ59d84t0s2VlZSkjI0NZWVk2yxcuXKi0tDS98MILV133k08+UbVq1VS5cuVb2CvkB5ypAgDcc86dO6dDhw6ZPx8+fFjbtm1TsWLF9MADD6hfv3566623VK5cOfORCn5+fuYnBLMDVUBAgN5//32dOnXKnCv7TNfcuXPl6Oio0NBQOTs7a/PmzRoyZIjat28vR0dHm34++eQTtW7dWsWLF8+135SUFC1cuFDjx4+3+EjgbiJUAQDuOZs3b1bjxo3NnwcMGCBJ6tKli2bPnq3BgwcrNTVVPXv2VFJSkurVq6fY2FjzXqa4uDgdOnRIhw4dUpkyZWzmzn4SUaFChfTee+/pwIEDMgxDAQEBio6OVv/+/W3q9+/fr59//lkrVqy4ar/z58+XYRjq0KGDJfuPvMFzqu4inlMFAEDBw3OqAAAA7iJCFQAAgAW4pwoACpCm84fkdQtAvrMiYkxetyCJM1WAjbNnz6pfv34KCAiQq6ur6tSpo02bNpnj586dU3R0tMqUKSNXV1eFhIRoxowZNnP06tVLwcHBcnV1lbe3t55++mnt27cv1+39+++/KlOmjOzs7JSUlGQz9tNPP6lq1apydnZW2bJlNXv2bKt3FwBgIUIVcIXu3bsrLi5OX3zxhXbu3KmmTZsqLCxMx44dk3T5E0SxsbH68ssvtXfvXvXr10/R0dFavHixOUf2M2327t2r5cuXyzAMNW3aVJmZmTm2FxkZqUceeSTH8sOHD6tly5Zq3Lixtm3bpn79+ql79+5avnz5ndt5AMBt4dN/dxGf/svfLly4oCJFiui7775Ty5YtzeXVqlVT8+bN9dZbb+nhhx9W+/btNWzYsFzHc7Njxw5VrlxZhw4dUnBwsLl8+vTpiomJ0fDhw9WkSROdOXNGXl5ekqRXX31VS5cu1a5du8z6iIgIJSUlKTY21uI9R0HC5T8gpzt9+a9AfPpv7dq1atWqlfz8/GRnZ6dFixbZjBuGoeHDh6tUqVJydXVVWFiYDh48aFNz+vRpdezYUR4eHvLy8lJkZKTOnTtnU7Njxw7Vr19fLi4u8vf319ixY3P0snDhQlWoUEEuLi4KDQ3VsmXLbroXFGyXLl1SZmZmju/ccnV11c8//yzp8henLl68WMeOHZNhGFq9erUOHDigpk2b5jpnamqqPvvsMwUFBZlfZSFJe/bs0ahRo/T555/n+pTm+Ph4hYWF2SwLDw9XfHz87e4mAOAOydNQlZqaqsqVK2vatGm5jo8dO1aTJ0/WjBkztGHDBrm5uSk8PFwXL140azp27Kjdu3crLi5OS5Ys0dq1a9WzZ09zPCUlRU2bNlVAQIC2bNmicePGaeTIkTbf4fTLL7+oQ4cOioyM1G+//abWrVurdevWNmcJbqQXFGxFihRR7dq1NXr0aB0/flyZmZn68ssvFR8frxMnTkiSpkyZopCQEJUpU0ZOTk5q1qyZpk2bpgYNGtjM9eGHH8rd3V3u7u764YcfFBcXJycnJ0lSWlqaOnTooHHjxumBBx7ItZeEhAT5+PjYLPPx8VFKSoouXLhwB/YeAHC78jRUZV8yeeaZZ3KMGYahSZMmaejQoXr66af1yCOP6PPPP9fx48fNM1p79+5VbGysPv74Y9WsWVP16tXTlClTNH/+fB0/flzS5a8RSE9P16effqpKlSopIiJCffv21YQJE8xtffDBB2rWrJleeeUVVaxYUaNHj1bVqlU1derUG+4F94YvvvhChmGodOnScnZ21uTJk9WhQwfzbNKUKVP066+/avHixdqyZYvGjx+vqKgo/fjjjzbzdOzYUb/99pvWrFmjhx56SM8995wZwIcMGaKKFSte8zvAAAAFT769Uf3w4cNKSEiwuQTi6empmjVrmpdA4uPj5eXlperVq5s1YWFhsre314YNG8yaBg0amGcJpMuXUfbv368zZ86YNde61HIjveQmLS1NKSkpNi/kb8HBwVqzZo3OnTunv/76Sxs3blRGRoYefPBBXbhwQa+//romTJigVq1a6ZFHHlF0dLTat2+v999/32YeT09PlStXTg0aNNBXX32lffv26dtvv5UkrVq1SgsXLlShQoVUqFAhNWnSRJJUokQJjRgxQtLl7xZLTEy0mTMxMVEeHh5ydXW9C0cCAHCz8u1zqhISEiQp10sg2WMJCQkqWbKkzXihQoVUrFgxm5qgoKAcc2SPFS1a9KqXWq6c43q95GbMmDF68803r7+zyHfc3Nzk5uamM2fOaPny5Ro7dqwyMjKUkZGR4x4oBweHHN9IfyXDMGQYhtLS0iRJX3/9tc0lvE2bNunFF1/UunXrzBvZa9euneO+vri4ONWuXduqXQQAWCzfhqp7wZAhQ8wv8ZQu39915c3KyH+yH4FQvnx5HTp0SK+88ooqVKigbt26ydHRUQ0bNtQrr7wiV1dXBQQEaM2aNfr888/Ny8l//PGHYmJi1LRpU3l7e+vvv//Wu+++K1dXV7Vo0UKSbD4BKEn//POPJKlixYrmp/9eeuklTZ06VYMHD9aLL76oVatWacGCBVq6dOndOxgAgJuSby//+fr6SlKul0Cyx3x9fXXy5Emb8UuXLun06dM2NbnNceU2rlZz5fj1esmNs7OzPDw8bF7I35KTkxUVFaUKFSqoc+fOqlevnpYvXy5HR0dJl79J/rHHHlPHjh0VEhKid999V2+//bZeeuklSZKLi4vWrVunFi1aqGzZsmrfvr2KFCmiX375JcdZ1WsJCgrS0qVLFRcXp8qVK2v8+PH6+OOPFR4efkf2GwBw+/LtmaqgoCD5+vpq5cqVqlKliqTLZ3o2bNig3r17S7p8iSQpKUlbtmxRtWrVJF2+XyUrK0s1a9Y0a9544w1lZGSYvxjj4uJUvnx5FS1a1KxZuXKl+vXrZ27/ykstN9IL7g3PPfecnnvuuauO+/r66rPPPrvquJ+fX47LdtfTqFEj5fa4uEaNGum33367qbkAAHknT89UnTt3Ttu2bdO2bdskXb4hfNu2bTp69Kjs7OzUr18/vfXWW1q8eLF27typzp07y8/PT61bt5Z0+XJJs2bN1KNHD23cuFHr169XdHS0IiIi5OfnJ0l6/vnn5eTkpMjISO3evVsxMTH64IMPbC7Lvfzyy4qNjdX48eO1b98+jRw5Ups3b1Z0dLQk3VAvAADg/panZ6o2b96sxo0bmz9nB50uXbpo9uzZGjx4sFJTU9WzZ08lJSWpXr16io2NtXk449y5cxUdHa0mTZrI3t5ebdu21eTJk81xT09PrVixQlFRUapWrZpKlCih4cOH2zzLqk6dOpo3b56GDh2q119/XeXKldOiRYv08MMPmzU30gsAALh/8TU1d9Hd+pqa+r1G37G5gYJq3cxh1y8qAPiaGiAnvqYGAADgHkKoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAskK9DVWZmpoYNG6agoCC5uroqODhYo0ePlmEYZo1hGBo+fLhKlSolV1dXhYWF6eDBgzbznD59Wh07dpSHh4e8vLwUGRmpc+fO2dTs2LFD9evXl4uLi/z9/TV27Ngc/SxcuFAVKlSQi4uLQkNDtWzZsjuz4wAAoMDJ16Hqvffe0/Tp0zV16lTt3btX7733nsaOHaspU6aYNWPHjtXkyZM1Y8YMbdiwQW5ubgoPD9fFixfNmo4dO2r37t2Ki4vTkiVLtHbtWvXs2dMcT0lJUdOmTRUQEKAtW7Zo3LhxGjlypGbNmmXW/PLLL+rQoYMiIyP122+/qXXr1mrdurV27dp1dw4GAADI1+yMK0/75DNPPvmkfHx89Mknn5jL2rZtK1dXV3355ZcyDEN+fn4aOHCgBg0aJElKTk6Wj4+PZs+erYiICO3du1chISHatGmTqlevLkmKjY1VixYt9Pfff8vPz0/Tp0/XG2+8oYSEBDk5OUmSXnvtNS1atEj79u2TJLVv316pqalasmSJ2UutWrVUpUoVzZgx44b2JyUlRZ6enkpOTpaHh4clxyg39XuNvmNzAwXVupnD8roFSzSdPySvWwDynRURY+7o/Df6+ztfn6mqU6eOVq5cqQMHDkiStm/frp9//lnNmzeXJB0+fFgJCQkKCwsz1/H09FTNmjUVHx8vSYqPj5eXl5cZqCQpLCxM9vb22rBhg1nToEEDM1BJUnh4uPbv368zZ86YNVduJ7smezsAAOD+ViivG7iW1157TSkpKapQoYIcHByUmZmpt99+Wx07dpQkJSQkSJJ8fHxs1vPx8THHEhISVLJkSZvxQoUKqVixYjY1QUFBOebIHitatKgSEhKuuZ3cpKWlKS0tzfw5JSXlhvcdAAAULPn6TNWCBQs0d+5czZs3T1u3btWcOXP0/vvva86cOXnd2g0ZM2aMPD09zZe/v39etwQAAO6QfB2qXnnlFb322muKiIhQaGioOnXqpP79+2vMmMvXTn19fSVJiYmJNuslJiaaY76+vjp58qTN+KVLl3T69GmbmtzmuHIbV6vJHs/NkCFDlJycbL7++uuvm9p/AABQcOTrUHX+/HnZ29u26ODgoKysLElSUFCQfH19tXLlSnM8JSVFGzZsUO3atSVJtWvXVlJSkrZs2WLWrFq1SllZWapZs6ZZs3btWmVkZJg1cXFxKl++vIoWLWrWXLmd7Jrs7eTG2dlZHh4eNi8AAHBvytehqlWrVnr77be1dOlSHTlyRN9++60mTJigZ555RpJkZ2enfv366a233tLixYu1c+dOde7cWX5+fmrdurUkqWLFimrWrJl69OihjRs3av369YqOjlZERIT8/PwkSc8//7ycnJwUGRmp3bt3KyYmRh988IEGDBhg9vLyyy8rNjZW48eP1759+zRy5Eht3rxZ0dHRd/24AACA/Cdf36g+ZcoUDRs2TP/3f/+nkydPys/PT7169dLw4cPNmsGDBys1NVU9e/ZUUlKS6tWrp9jYWLm4uJg1c+fOVXR0tJo0aSJ7e3u1bdtWkydPNsc9PT21YsUKRUVFqVq1aipRooSGDx9u8yyrOnXqaN68eRo6dKhef/11lStXTosWLdLDDz98dw4GAADI1/L1c6ruNTynCsg7PKcKuHfxnCoAAIB7CKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAC+T5UHTt2TC+88IKKFy8uV1dXhYaGavPmzea4YRgaPny4SpUqJVdXV4WFhengwYM2c5w+fVodO3aUh4eHvLy8FBkZqXPnztnU7NixQ/Xr15eLi4v8/f01duzYHL0sXLhQFSpUkIuLi0JDQ7Vs2bI7s9MAAKDAydeh6syZM6pbt64cHR31ww8/aM+ePRo/fryKFi1q1owdO1aTJ0/WjBkztGHDBrm5uSk8PFwXL140azp27Kjdu3crLi5OS5Ys0dq1a9WzZ09zPCUlRU2bNlVAQIC2bNmicePGaeTIkZo1a5ZZ88svv6hDhw6KjIzUb7/9ptatW6t169batWvX3TkYAAAgX7MzDMPI6yau5rXXXtP69eu1bt26XMcNw5Cfn58GDhyoQYMGSZKSk5Pl4+Oj2bNnKyIiQnv37lVISIg2bdqk6tWrS5JiY2PVokUL/f333/Lz89P06dP1xhtvKCEhQU5OTua2Fy1apH379kmS2rdvr9TUVC1ZssTcfq1atVSlShXNmDHjhvYnJSVFnp6eSk5OloeHxy0fl+up32v0HZsbKKjWzRyW1y1Youn8IXndApDvrIgYc0fnv9Hf3/n6TNXixYtVvXp1tWvXTiVLltSjjz6qjz76yBw/fPiwEhISFBYWZi7z9PRUzZo1FR8fL0mKj4+Xl5eXGagkKSwsTPb29tqwYYNZ06BBAzNQSVJ4eLj279+vM2fOmDVXbie7Jns7uUlLS1NKSorNCwAA3Jvydaj6448/NH36dJUrV07Lly9X79691bdvX82ZM0eSlJCQIEny8fGxWc/Hx8ccS0hIUMmSJW3GCxUqpGLFitnU5DbHldu4Wk32eG7GjBkjT09P8+Xv739T+w8AAAqOfB2qsrKyVLVqVb3zzjt69NFH1bNnT/Xo0eOGL7fltSFDhig5Odl8/fXXX3ndEgAAuEPydagqVaqUQkJCbJZVrFhRR48elST5+vpKkhITE21qEhMTzTFfX1+dPHnSZvzSpUs6ffq0TU1uc1y5javVZI/nxtnZWR4eHjYvAABwb8rXoapu3brav3+/zbIDBw4oICBAkhQUFCRfX1+tXLnSHE9JSdGGDRtUu3ZtSVLt2rWVlJSkLVu2mDWrVq1SVlaWatasadasXbtWGRkZZk1cXJzKly9vftKwdu3aNtvJrsneDgAAuL/dUqh68MEH9e+//+ZYnpSUpAcffPC2m8rWv39//frrr3rnnXd06NAhzZs3T7NmzVJUVJQkyc7OTv369dNbb72lxYsXa+fOnercubP8/PzUunVrSZfPbDVr1kw9evTQxo0btX79ekVHRysiIkJ+fn6SpOeff15OTk6KjIzU7t27FRMTow8++EADBgwwe3n55ZcVGxur8ePHa9++fRo5cqQ2b96s6Ohoy/YXAAAUXIVuZaUjR44oMzMzx/K0tDQdO3bstpvK9thjj+nbb7/VkCFDNGrUKAUFBWnSpEnq2LGjWTN48GClpqaqZ8+eSkpKUr169RQbGysXFxezZu7cuYqOjlaTJk1kb2+vtm3bavLkyea4p6enVqxYoaioKFWrVk0lSpTQ8OHDbZ5lVadOHc2bN09Dhw7V66+/rnLlymnRokV6+OGHLdtfAABQcN3Uc6oWL14sSWrdurXmzJkjT09PcywzM1MrV65UXFxcjkt2uIznVAF5h+dUAfeu/PKcqps6U5V9Sc3Ozk5dunSxGXN0dFRgYKDGjx9/890CAAAUcDcVqrKysiRdvkF806ZNKlGixB1pCgAAoKC5pXuqDh8+bHUfAAAABdothSpJWrlypVauXKmTJ0+aZ7Cyffrpp7fdGAAAQEFyS6HqzTff1KhRo1S9enWVKlVKdnZ2VvcFAABQoNxSqJoxY4Zmz56tTp06Wd0PAABAgXRLD/9MT09XnTp1rO4FAACgwLqlUNW9e3fNmzfP6l4AAAAKrFu6/Hfx4kXNmjVLP/74ox555BE5OjrajE+YMMGS5gAAAAqKWwpVO3bsUJUqVSRJu3btshnjpnUAAHA/uqVQtXr1aqv7AAAAKNBu6Z4qAAAA2LqlM1WNGze+5mW+VatW3XJDAAAABdEthars+6myZWRkaNu2bdq1a1eOL1oGAAC4H9xSqJo4cWKuy0eOHKlz587dVkMAAAAFkaX3VL3wwgt87x8AALgvWRqq4uPj5eLiYuWUAAAABcItXf5r06aNzc+GYejEiRPavHmzhg0bZkljAAAABckthSpPT0+bn+3t7VW+fHmNGjVKTZs2taQxAACAguSWQtVnn31mdR8AAAAF2i2FqmxbtmzR3r17JUmVKlXSo48+aklTAAAABc0thaqTJ08qIiJCP/30k7y8vCRJSUlJaty4sebPny9vb28rewQAAMj3bunTf3369NHZs2e1e/dunT59WqdPn9auXbuUkpKivn37Wt0jAABAvndLZ6piY2P1448/qmLFiuaykJAQTZs2jRvVAQDAfemWzlRlZWXJ0dExx3JHR0dlZWXddlMAAAAFzS2Fqscff1wvv/yyjh8/bi47duyY+vfvryZNmljWHAAAQEFxS6Fq6tSpSklJUWBgoIKDgxUcHKygoCClpKRoypQpVvcIAACQ793SPVX+/v7aunWrfvzxR+3bt0+SVLFiRYWFhVnaHAAAQEFxU2eqVq1apZCQEKWkpMjOzk5PPPGE+vTpoz59+uixxx5TpUqVtG7dujvVKwAAQL51U6Fq0qRJ6tGjhzw8PHKMeXp6qlevXpowYYJlzQEAABQUNxWqtm/frmbNml11vGnTptqyZcttNwUAAFDQ3FSoSkxMzPVRCtkKFSqkU6dO3XZTAAAABc1NharSpUtr165dVx3fsWOHSpUqddtNAQAAFDQ3FapatGihYcOG6eLFiznGLly4oBEjRujJJ5+0rDkAAICC4qYeqTB06FB98803euihhxQdHa3y5ctLkvbt26dp06YpMzNTb7zxxh1pFAAAID+7qVDl4+OjX375Rb1799aQIUNkGIYkyc7OTuHh4Zo2bZp8fHzuSKMAAAD52U0//DMgIEDLli3TmTNndOjQIRmGoXLlyqlo0aJ3oj8AAIAC4ZaeqC5JRYsW1WOPPWZlLwAAAAXWLX33HwAAAGwRqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAgUqVL377ruys7NTv379zGUXL15UVFSUihcvLnd3d7Vt21aJiYk26x09elQtW7ZU4cKFVbJkSb3yyiu6dOmSTc1PP/2kqlWrytnZWWXLltXs2bNzbH/atGkKDAyUi4uLatasqY0bN96J3QQAAAVQgQlVmzZt0syZM/XII4/YLO/fv7++//57LVy4UGvWrNHx48fVpk0bczwzM1MtW7ZUenq6fvnlF82ZM0ezZ8/W8OHDzZrDhw+rZcuWaty4sbZt26Z+/fqpe/fuWr58uVkTExOjAQMGaMSIEdq6dasqV66s8PBwnTx58s7vPAAAyPcKRKg6d+6cOnbsqI8++khFixY1lycnJ+uTTz7RhAkT9Pjjj6tatWr67LPP9Msvv+jXX3+VJK1YsUJ79uzRl19+qSpVqqh58+YaPXq0pk2bpvT0dEnSjBkzFBQUpPHjx6tixYqKjo7Ws88+q4kTJ5rbmjBhgnr06KFu3bopJCREM2bMUOHChfXpp5/e3YMBAADypQIRqqKiotSyZUuFhYXZLN+yZYsyMjJslleoUEEPPPCA4uPjJUnx8fEKDQ2Vj4+PWRMeHq6UlBTt3r3brPnv3OHh4eYc6enp2rJli02Nvb29wsLCzJrcpKWlKSUlxeYFAADuTYXyuoHrmT9/vrZu3apNmzblGEtISJCTk5O8vLxslvv4+CghIcGsuTJQZY9nj12rJiUlRRcuXNCZM2eUmZmZa82+ffuu2vuYMWP05ptv3tiOAgCAAi1fn6n666+/9PLLL2vu3LlycXHJ63Zu2pAhQ5ScnGy+/vrrr7xuCQAA3CH5OlRt2bJFJ0+eVNWqVVWoUCEVKlRIa9as0eTJk1WoUCH5+PgoPT1dSUlJNuslJibK19dXkuTr65vj04DZP1+vxsPDQ66uripRooQcHBxyrcmeIzfOzs7y8PCweQEAgHtTvg5VTZo00c6dO7Vt2zbzVb16dXXs2NH8b0dHR61cudJcZ//+/Tp69Khq164tSapdu7Z27txp8ym9uLg4eXh4KCQkxKy5co7smuw5nJycVK1aNZuarKwsrVy50qwBAAD3t3x9T1WRIkX08MMP2yxzc3NT8eLFzeWRkZEaMGCAihUrJg8PD/Xp00e1a9dWrVq1JElNmzZVSEiIOnXqpLFjxyohIUFDhw5VVFSUnJ2dJUkvvfSSpk6dqsGDB+vFF1/UqlWrtGDBAi1dutTc7oABA9SlSxdVr15dNWrU0KRJk5Samqpu3brdpaMBAADys3wdqm7ExIkTZW9vr7Zt2yotLU3h4eH68MMPzXEHBwctWbJEvXv3Vu3ateXm5qYuXbpo1KhRZk1QUJCWLl2q/v3764MPPlCZMmX08ccfKzw83Kxp3769Tp06peHDhyshIUFVqlRRbGxsjpvXAQDA/cnOMAwjr5u4X6SkpMjT01PJycl39P6q+r1G37G5gYJq3cxhed2CJZrOH5LXLQD5zoqIMXd0/hv9/Z2v76kCAAAoKAhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWyNehasyYMXrsscdUpEgRlSxZUq1bt9b+/fttai5evKioqCgVL15c7u7uatu2rRITE21qjh49qpYtW6pw4cIqWbKkXnnlFV26dMmm5qefflLVqlXl7OyssmXLavbs2Tn6mTZtmgIDA+Xi4qKaNWtq48aNlu8zAAAomPJ1qFqzZo2ioqL066+/Ki4uThkZGWratKlSU1PNmv79++v777/XwoULtWbNGh0/flxt2rQxxzMzM9WyZUulp6frl19+0Zw5czR79mwNHz7crDl8+LBatmypxo0ba9u2berXr5+6d++u5cuXmzUxMTEaMGCARowYoa1bt6py5coKDw/XyZMn787BAAAA+ZqdYRhGXjdxo06dOqWSJUtqzZo1atCggZKTk+Xt7a158+bp2WeflSTt27dPFStWVHx8vGrVqqUffvhBTz75pI4fPy4fHx9J0owZM/Tqq6/q1KlTcnJy0quvvqqlS5dq165d5rYiIiKUlJSk2NhYSVLNmjX12GOPaerUqZKkrKws+fv7q0+fPnrttdduqP+UlBR5enoqOTlZHh4eVh4aG/V7jb5jcwMF1bqZw/K6BUs0nT8kr1sA8p0VEWPu6Pw3+vs7X5+p+q/k5GRJUrFixSRJW7ZsUUZGhsLCwsyaChUq6IEHHlB8fLwkKT4+XqGhoWagkqTw8HClpKRo9+7dZs2Vc2TXZM+Rnp6uLVu22NTY29srLCzMrMlNWlqaUlJSbF4AAODeVGBCVVZWlvr166e6devq4YcfliQlJCTIyclJXl5eNrU+Pj5KSEgwa64MVNnj2WPXqklJSdGFCxf0zz//KDMzM9ea7DlyM2bMGHl6epovf3//m99xAABQIBSYUBUVFaVdu3Zp/vz5ed3KDRsyZIiSk5PN119//ZXXLQEAgDukUF43cCOio6O1ZMkSrV27VmXKlDGX+/r6Kj09XUlJSTZnqxITE+Xr62vW/PdTetmfDryy5r+fGExMTJSHh4dcXV3l4OAgBweHXGuy58iNs7OznJ2db36HAQBAgZOvz1QZhqHo6Gh9++23WrVqlYKCgmzGq1WrJkdHR61cudJctn//fh09elS1a9eWJNWuXVs7d+60+ZReXFycPDw8FBISYtZcOUd2TfYcTk5Oqlatmk1NVlaWVq5cadYAAID7W74+UxUVFaV58+bpu+++U5EiRcz7lzw9PeXq6ipPT09FRkZqwIABKlasmDw8PNSnTx/Vrl1btWrVkiQ1bdpUISEh6tSpk8aOHauEhAQNHTpUUVFR5lmkl156SVOnTtXgwYP14osvatWqVVqwYIGWLl1q9jJgwAB16dJF1atXV40aNTRp0iSlpqaqW7dud//AAACAfCdfh6rp06dLkho1amSz/LPPPlPXrl0lSRMnTpS9vb3atm2rtLQ0hYeH68MPPzRrHRwctGTJEvXu3Vu1a9eWm5ubunTpolGjRpk1QUFBWrp0qfr3768PPvhAZcqU0ccff6zw8HCzpn379jp16pSGDx+uhIQEValSRbGxsTluXgcAAPenAvWcqoKO51QBeYfnVAH3Lp5TBQAAcA8hVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBU3aRp06YpMDBQLi4uqlmzpjZu3JjXLQEAgHyAUHUTYmJiNGDAAI0YMUJbt25V5cqVFR4erpMnT+Z1awAAII8Rqm7ChAkT1KNHD3Xr1k0hISGaMWOGChcurE8//TSvWwMAAHmMUHWD0tPTtWXLFoWFhZnL7O3tFRYWpvj4+DzsDAAA5AeF8rqBguKff/5RZmamfHx8bJb7+Pho3759ua6TlpamtLQ08+fk5GRJUkpKyp1rVNKl9It3dH6gILrT77u75dL5tOsXAfeZO/3+zp7fMIxr1hGq7qAxY8bozTffzLHc398/D7oB7m+es9/J6xYA3CGekRPvynbOnj0rT0/Pq44Tqm5QiRIl5ODgoMTERJvliYmJ8vX1zXWdIUOGaMCAAebPWVlZOn36tIoXLy47O7s72i/yXkpKivz9/fXXX3/Jw8Mjr9sBYCHe3/cXwzB09uxZ+fn5XbOOUHWDnJycVK1aNa1cuVKtW7eWdDkkrVy5UtHR0bmu4+zsLGdnZ5tlXl5ed7hT5DceHh78pQvco3h/3z+udYYqG6HqJgwYMEBdunRR9erVVaNGDU2aNEmpqanq1q1bXrcGAADyGKHqJrRv316nTp3S8OHDlZCQoCpVqig2NjbHzesAAOD+Q6i6SdHR0Ve93AdcydnZWSNGjMhxCRhAwcf7G7mxM673+UAAAABcFw//BAAAsAChCgAAwAKEKgAAAAsQqoB8IjAwUJMmTcrrNgBcw08//SQ7OzslJSVds4738/2JUIX7QteuXWVnZ6d3333XZvmiRYvu+tPtZ8+enetDYDdt2qSePXve1V6Ae1X2e97Ozk5OTk4qW7asRo0apUuXLt3WvHXq1NGJEyfMB0HyfsaVCFW4b7i4uOi9997TmTNn8rqVXHl7e6tw4cJ53QZwz2jWrJlOnDihgwcPauDAgRo5cqTGjRt3W3M6OTnJ19f3uv8Y4/18fyJU4b4RFhYmX19fjRkz5qo1P//8s+rXry9XV1f5+/urb9++Sk1NNcdPnDihli1bytXVVUFBQZo3b16O0/wTJkxQaGio3Nzc5O/vr//7v//TuXPnJF2+dNCtWzclJyeb/4oeOXKkJNvLBc8//7zat29v01tGRoZKlCihzz//XNLlr0kaM2aMgoKC5OrqqsqVK+urr76y4EgB9wZnZ2f5+voqICBAvXv3VlhYmBYvXqwzZ86oc+fOKlq0qAoXLqzmzZvr4MGD5np//vmnWrVqpaJFi8rNzU2VKlXSsmXLJNle/uP9jP8iVOG+4eDgoHfeeUdTpkzR33//nWP8999/V7NmzdS2bVvt2LFDMTEx+vnnn20e9tq5c2cdP35cP/30k77++mvNmjVLJ0+etJnH3t5ekydP1u7duzVnzhytWrVKgwcPlnT50sGkSZPk4eGhEydO6MSJExo0aFCOXjp27Kjvv//eDGOStHz5cp0/f17PPPOMJGnMmDH6/PPPNWPGDO3evVv9+/fXCy+8oDVr1lhyvIB7jaurq9LT09W1a1dt3rxZixcvVnx8vAzDUIsWLZSRkSFJioqKUlpamtauXaudO3fqvffek7u7e475eD8jBwO4D3Tp0sV4+umnDcMwjFq1ahkvvviiYRiG8e233xrZb4PIyEijZ8+eNuutW7fOsLe3Ny5cuGDs3bvXkGRs2rTJHD948KAhyZg4ceJVt71w4UKjePHi5s+fffaZ4enpmaMuICDAnCcjI8MoUaKE8fnnn5vjHTp0MNq3b28YhmFcvHjRKFy4sPHLL7/YzBEZGWl06NDh2gcDuA9c+Z7Pysoy4uLiDGdnZ6N169aGJGP9+vVm7T///GO4uroaCxYsMAzDMEJDQ42RI0fmOu/q1asNScaZM2cMw+D9DFt8TQ3uO++9954ef/zxHP+i3L59u3bs2KG5c+eaywzDUFZWlg4fPqwDBw6oUKFCqlq1qjletmxZFS1a1GaeH3/8UWPGjNG+ffuUkpKiS5cu6eLFizp//vwN32NRqFAhPffcc5o7d646deqk1NRUfffdd5o/f74k6dChQzp//ryeeOIJm/XS09P16KOP3tTxAO5VS5Yskbu7uzIyMpSVlaXnn39ebdq00ZIlS1SzZk2zrnjx4ipfvrz27t0rSerbt6969+6tFStWKCwsTG3bttUjjzxyy33wfr5/EKpw32nQoIHCw8M1ZMgQde3a1Vx+7tw59erVS3379s2xzgMPPKADBw5cd+4jR47oySefVO/evfX222+rWLFi+vnnnxUZGan09PSbunG1Y8eOatiwoU6ePKm4uDi5urqqWbNmZq+StHTpUpUuXdpmPb6LDLiscePGmj59upycnOTn56dChQpp8eLF112ve/fuCg8P19KlS7VixQqNGTNG48ePV58+fW65F97P9wdCFe5L7777rqpUqaLy5cuby6pWrao9e/aobNmyua5Tvnx5Xbp0Sb/99puqVasm6fK/MK/8NOGWLVuUlZWl8ePHy97+8i2LCxYssJnHyclJmZmZ1+2xTp068vf3V0xMjH744Qe1a9dOjo6OkqSQkBA5Ozvr6NGjatiw4c3tPHCfcHNzy/F+rlixoi5duqQNGzaoTp06kqR///1X+/fvV0hIiFnn7++vl156SS+99JKGDBmijz76KNdQxfsZVyJU4b4UGhqqjh07avLkyeayV199VbVq1VJ0dLS6d+8uNzc37dmzR3FxcZo6daoqVKigsLAw9ezZU9OnT5ejo6MGDhwoV1dX8+PVZcuWVUZGhqZMmaJWrVpp/fr1mjFjhs22AwMDde7cOa1cuVKVK1dW4cKFr3oG6/nnn9eMGTN04MABrV692lxepEgRDRo0SP3791dWVpbq1aun5ORkrV+/Xh4eHurSpcsdOGpAwVeuXDk9/fTT6tGjh2bOnKkiRYrotddeU+nSpfX0009Lkvr166fmzZvroYce0pkzZ7R69WpVrFgx1/l4P8NGXt/UBdwNV960mu3w4cOGk5OTceXbYOPGjcYTTzxhuLu7G25ubsYjjzxivP322+b48ePHjebNmxvOzs5GQECAMW/ePKNkyZLGjBkzzJoJEyYYpUqVMlxdXY3w8HDj888/t7mx1TAM46WXXjKKFy9uSDJGjBhhGIbtja3Z9uzZY0gyAgICjKysLJuxrKwsY9KkSUb58uUNR0dHw9vb2wgPDzfWrFlzewcLuAfk9p7Pdvr0aaNTp06Gp6en+T49cOCAOR4dHW0EBwcbzs7Ohre3t9GpUyfjn3/+MQwj543qhsH7Gf8/O8MwjDzMdECB9vfff8vf318//vijmjRpktftAADyEKEKuAmrVq3SuXPnFBoaqhMnTmjw4ME6duyYDhw4YN4fAQC4P3FPFXATMjIy9Prrr+uPP/5QkSJFVKdOHc2dO5dABQDgTBUAAIAV+JoaAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgC4gxo1aqR+/fpds2b27Nny8vK6K/0AuHMIVQAKvK5du6p169a3tO6RI0dkZ2dnvooXL66mTZvqt99+s6S3b775RqNHjzZ/DgwM1KRJk2xq2rdvf0Nf2A0gfyNUAYCkH3/8USdOnNDy5ct17tw5NW/eXElJSbc9b7FixVSkSJFr1ri6uqpkyZK3vS0AeYtQBeCe89VXXyk0NFSurq4qXry4wsLClJqaes11ihcvLl9fX1WvXl3vv/++EhMTtWHDBknS119/rUqVKsnZ2VmBgYEaP368zboffvihypUrJxcXF/n4+OjZZ581x668/NeoUSP9+eef6t+/v3lmTLK9/HfgwAHZ2dlp3759NtuYOHGigoODzZ937dql5s2by93dXT4+PurUqZP++eefWzpeAKxBqAJwTzlx4oQ6dOigF198UXv37tVPP/2kNm3a6Gaec+zq6ipJSk9P15YtW/Tcc88pIiJCO3fu1MiRIzVs2DDNnj1bkrR582b17dtXo0aN0v79+xUbG6sGDRrkOu8333yjMmXKaNSoUTpx4oROnDiRo+ahhx5S9erVNXfuXJvlc+fO1fPPPy9JSkpK0uOPP65HH31UmzdvVmxsrBITE/Xcc8/d8D4CsB5fUwPgnnLixAldunRJbdq0UUBAgCQpNDT0htdPSkrS6NGj5e7urho1amjAgAFq0qSJhg0bJuly6NmzZ4/GjRunrl276ujRo3Jzc9OTTz6pIkWKKCAgQI8++miucxcrVkwODg4qUqSIfH19r9pDx44dNXXqVPNerAMHDmjLli368ssvJUlTp07Vo48+qnfeecdc59NPP5W/v78OHDighx566Ib3F4B1OFMF4J5SuXJlNWnSRKGhoWrXrp0++ugjnTlz5rrr1alTR+7u7ipatKi2b9+umJgY+fj4aO/evapbt65Nbd26dXXw4EFlZmbqiSeeUEBAgB588EF16tRJc+fO1fnz529rHyIiInTkyBH9+uuvki6fpapataoqVKggSdq+fbtWr14td3d385U99vvvv9/WtgHcOkIVgHuKg4OD4uLi9MMPPygkJERTpkxR+fLldfjw4WuuFxMTo+3bt+vMmTP6/fff1aJFixvaXpEiRbR161b973//U6lSpTR8+HBVrlz5tm5y9/X11eOPP6558+ZJkubNm6eOHTua4+fOnVOrVq20bds2m9fBgweveukRwJ1HqAJwz7Gzs1PdunX15ptv6rfffpOTk5O+/fbba67j7++v4ODgHM+LqlixotavX2+zbP369XrooYfk4OAgSSpUqJDCwsI0duxY7dixQ0eOHNGqVaty3Y6Tk5MyMzOvuw8dO3ZUTEyM4uPj9ccffygiIsIcq1q1qnbv3q3AwECVLVvW5uXm5nbduQHcGYQqAPeUDRs26J133tHmzZt19OhRffPNNzp16pQqVqx4S/MNHDhQK1eu1OjRo3XgwAHNmTNHU6dO1aBBgyRJS5Ys0eTJk7Vt2zb9+eef+vzzz5WVlaXy5cvnOl9gYKDWrl2rY8eOXfPTem3atNHZs2fVu3dvNW7cWH5+fuZYVFSUTp8+rQ4dOmjTpk36/ffftXz5cnXr1u2GAhuAO4NQBeCe4uHhobVr16pFixZ66KGHNHToUI0fP17Nmze/pfmqVq2qBQsWaP78+Xr44Yc1fPhwjRo1Sl27dpUkeXl56ZtvvtHjjz+uihUrasaMGfrf//6nSpUq5TrfqFGjdOTIEQUHB8vb2/uq2y1SpIhatWql7du321z6kyQ/Pz+tX79emZmZatq0qUJDQ9WvXz95eXnJ3p6/1oG8YmfczOeMAQAAkCv+SQMAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFjg/wO99jzl2lXTAgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.countplot(x=game_df.is_positive,palette=\"viridis\")\n",
    "# Add counts on top of each bar\n",
    "for p in ax.patches:\n",
    "    count = int(p.get_height())  # Get the height of the bar (count)\n",
    "    ax.annotate(f'{count}', (p.get_x() + p.get_width() / 2, p.get_height()), \n",
    "                ha='center', va='bottom', fontsize=10, color='black')\n",
    "\n",
    "# Customize the plot\n",
    "plt.title('Count of Positive and Negative Labels')\n",
    "plt.xlabel('Is Positive')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 preprocesing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. lowercase all the words\n",
    "2. Remove numbers\n",
    "3. Remove punctuation\n",
    "4. Remove stopwords\n",
    "5. Stemming/Lemmatization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre processing function\n",
    "def preprocess_text(text):\n",
    "    # Convert text to lowercase\n",
    "    text = text.lower()\n",
    "    \n",
    "    # Remove numbers\n",
    "    text = re.sub(r'\\d+', '', text)\n",
    "    \n",
    "    # Remove punctuation\n",
    "    text = re.sub(r'[^\\w\\s]', '', text)\n",
    "    \n",
    "    # Tokenize text\n",
    "    tokens = word_tokenize(text)\n",
    "    \n",
    "    # Remove stopwords\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    filtered_tokens = [word for word in tokens if word not in stop_words]\n",
    "    \n",
    "    # Lemmatization\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    lemmatized_tokens = [lemmatizer.lemmatize(word) for word in filtered_tokens]\n",
    "    \n",
    "    # Join tokens back into a string\n",
    "    preprocessed_text = ' '.join(lemmatized_tokens)\n",
    "    \n",
    "    return preprocessed_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_5644\\1109731849.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  game_df['clean_content']=game_df.content.apply(preprocess_text)\n"
     ]
    }
   ],
   "source": [
    "game_df['clean_content']=game_df.content.apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>content</th>\n",
       "      <th>is_positive</th>\n",
       "      <th>clean_content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>At least its a counter strike -1/100</td>\n",
       "      <td>Negative</td>\n",
       "      <td>least counter strike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uh... So far my playthrough has not been great...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>uh far playthrough great glitched texture cont...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Better mechanics than cs2</td>\n",
       "      <td>Negative</td>\n",
       "      <td>better mechanic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>buggy mess and NOT fun to play at all</td>\n",
       "      <td>Negative</td>\n",
       "      <td>buggy mess fun play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Whoever came up with this, is gonna fucking ge...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>whoever came gon na fucking get negative revie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201139</th>\n",
       "      <td>This trash for sick anime clowns!</td>\n",
       "      <td>Negative</td>\n",
       "      <td>trash sick anime clown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201140</th>\n",
       "      <td>Not noob friendly</td>\n",
       "      <td>Negative</td>\n",
       "      <td>noob friendly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201141</th>\n",
       "      <td>Total trash for anime clowns!</td>\n",
       "      <td>Negative</td>\n",
       "      <td>total trash anime clown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201142</th>\n",
       "      <td>Unplayable. It keeps trying to update but then...</td>\n",
       "      <td>Negative</td>\n",
       "      <td>unplayable keep trying update immediately stop...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201143</th>\n",
       "      <td>Bring back csgo</td>\n",
       "      <td>Negative</td>\n",
       "      <td>bring back csgo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200717 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  content is_positive  \\\n",
       "0                    At least its a counter strike -1/100    Negative   \n",
       "1       Uh... So far my playthrough has not been great...    Negative   \n",
       "2                               Better mechanics than cs2    Negative   \n",
       "3                   buggy mess and NOT fun to play at all    Negative   \n",
       "4       Whoever came up with this, is gonna fucking ge...    Negative   \n",
       "...                                                   ...         ...   \n",
       "201139                  This trash for sick anime clowns!    Negative   \n",
       "201140                                  Not noob friendly    Negative   \n",
       "201141                      Total trash for anime clowns!    Negative   \n",
       "201142  Unplayable. It keeps trying to update but then...    Negative   \n",
       "201143                                    Bring back csgo    Negative   \n",
       "\n",
       "                                            clean_content  \n",
       "0                                    least counter strike  \n",
       "1       uh far playthrough great glitched texture cont...  \n",
       "2                                       better mechanic c  \n",
       "3                                     buggy mess fun play  \n",
       "4       whoever came gon na fucking get negative revie...  \n",
       "...                                                   ...  \n",
       "201139                             trash sick anime clown  \n",
       "201140                                      noob friendly  \n",
       "201141                            total trash anime clown  \n",
       "201142  unplayable keep trying update immediately stop...  \n",
       "201143                                    bring back csgo  \n",
       "\n",
       "[200717 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_5644\\1073721943.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  game_df['is_positive']=np.where(game_df.is_positive=='Negative',0,1)\n"
     ]
    }
   ],
   "source": [
    "game_df['is_positive']=np.where(game_df.is_positive=='Negative',0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>content</th>\n",
       "      <th>is_positive</th>\n",
       "      <th>clean_content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>At least its a counter strike -1/100</td>\n",
       "      <td>0</td>\n",
       "      <td>least counter strike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uh... So far my playthrough has not been great...</td>\n",
       "      <td>0</td>\n",
       "      <td>uh far playthrough great glitched texture cont...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Better mechanics than cs2</td>\n",
       "      <td>0</td>\n",
       "      <td>better mechanic c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>buggy mess and NOT fun to play at all</td>\n",
       "      <td>0</td>\n",
       "      <td>buggy mess fun play</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Whoever came up with this, is gonna fucking ge...</td>\n",
       "      <td>0</td>\n",
       "      <td>whoever came gon na fucking get negative revie...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             content  is_positive  \\\n",
       "0               At least its a counter strike -1/100            0   \n",
       "1  Uh... So far my playthrough has not been great...            0   \n",
       "2                          Better mechanics than cs2            0   \n",
       "3              buggy mess and NOT fun to play at all            0   \n",
       "4  Whoever came up with this, is gonna fucking ge...            0   \n",
       "\n",
       "                                       clean_content  \n",
       "0                               least counter strike  \n",
       "1  uh far playthrough great glitched texture cont...  \n",
       "2                                  better mechanic c  \n",
       "3                                buggy mess fun play  \n",
       "4  whoever came gon na fucking get negative revie...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Train Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(140501, 1)\n",
      "(140501, 1)\n",
      "(60216, 1)\n",
      "(60216, 1)\n"
     ]
    }
   ],
   "source": [
    "X=game_df[['clean_content']]\n",
    "y=game_df[['is_positive']]\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus=[i for i in X_train.clean_content]\n",
    "# corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. TF_IDF construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "TF_IDF=TfidfVectorizer(max_features=50000,ngram_range=(1,3))\n",
    "tfidf_matrix=TF_IDF.fit_transform(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140501, 50000)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "# Apply TruncatedSVD for dimensionality reduction\n",
    "svd = TruncatedSVD(n_components=300, random_state=42)  # n_components = Number of components to retain\n",
    "reduced_matrix = svd.fit_transform(tfidf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\sklearn\\base.py:1474: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF=RandomForestClassifier()\n",
    "RF.fit(reduced_matrix,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Test data convert into vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60216, 50000)\n",
      "(60216, 300)\n"
     ]
    }
   ],
   "source": [
    "test_metrix=TF_IDF.transform(X_test.clean_content)\n",
    "reduced_test_matrix = svd.transform(test_metrix)\n",
    "\n",
    "print(test_metrix.shape)\n",
    "print(reduced_test_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Predictions and clasification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = RF.predict(reduced_test_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.82      0.79     27553\n",
      "           1       0.84      0.79      0.81     32663\n",
      "\n",
      "    accuracy                           0.80     60216\n",
      "   macro avg       0.80      0.81      0.80     60216\n",
      "weighted avg       0.81      0.80      0.81     60216\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predict,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Save Model and Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['random_forest_model.pkl']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Save the TF-IDF Vectorizer\n",
    "joblib.dump(TF_IDF, 'tfidf_vectorizer.pkl')\n",
    "\n",
    "# Save the Dimensionality Reduction Transformer (e.g., SVD)\n",
    "joblib.dump(svd, 'dimensionality_reduction.pkl')\n",
    "\n",
    "# Save the Trained Model\n",
    "joblib.dump(RF, 'random_forest_model.pkl')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Load model and vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load model\n",
    "tf_idf = load('tfidf_vectorizer.pkl')\n",
    "isa_pipeline =load('dimensionality_reduction.pkl')\n",
    "model = load('random_forest_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 11. Sample prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt=\"nice game\"\n",
    "tfidf_matrix = tf_idf.transform([prompt])\n",
    "reduced_matrix=isa_pipeline.transform(tfidf_matrix)\n",
    "model.predict(reduced_matrix)"
   ]
  }
 ],
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