Sentiment Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Once again Mr. Costner has dragged out a movie...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This is an example of why the majority of acti...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>First of all I hate those moronic rappers, who...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not even the Beatles could write songs everyon...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brass pictures (movies is not a fitting word f...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Text  Sentiment\n",
       "0  Once again Mr. Costner has dragged out a movie...          0\n",
       "1  This is an example of why the majority of acti...          0\n",
       "2  First of all I hate those moronic rappers, who...          0\n",
       "3  Not even the Beatles could write songs everyon...          0\n",
       "4  Brass pictures (movies is not a fitting word f...          0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/reviews.csv', encoding=\"ISO-8859-1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Text       50000 non-null  object\n",
      " 1   Sentiment  50000 non-null  int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 781.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sentiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25000</td>\n",
       "      <td>24697</td>\n",
       "      <td>When i got this movie free from my job, along ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25000</td>\n",
       "      <td>24884</td>\n",
       "      <td>Loved today's show!!! It was a variety and not...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Text                                                            \\\n",
       "           count unique                                                top   \n",
       "Sentiment                                                                    \n",
       "0          25000  24697  When i got this movie free from my job, along ...   \n",
       "1          25000  24884  Loved today's show!!! It was a variety and not...   \n",
       "\n",
       "                \n",
       "          freq  \n",
       "Sentiment       \n",
       "0            3  \n",
       "1            5  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Sentiment').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">Text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sentiment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24697</td>\n",
       "      <td>24697</td>\n",
       "      <td>Once again Mr. Costner has dragged out a movie...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24884</td>\n",
       "      <td>24884</td>\n",
       "      <td>I went and saw this movie last night after bei...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Text                                                            \\\n",
       "           count unique                                                top   \n",
       "Sentiment                                                                    \n",
       "0          24697  24697  Once again Mr. Costner has dragged out a movie...   \n",
       "1          24884  24884  I went and saw this movie last night after bei...   \n",
       "\n",
       "                \n",
       "          freq  \n",
       "Sentiment       \n",
       "0            1  \n",
       "1            1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates()\n",
    "df.groupby('Sentiment').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer(\n",
    "    ngram_range=(1, 2), stop_words='english',\n",
    "    min_df=20\n",
    ")\n",
    "x = vectorizer.fit_transform(df['Text'])\n",
    "y = df['Sentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(\n",
    "    x, y, test_size=0.5, random_state=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(\n",
    "    max_iter=1000, random_state=0\n",
    ")\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay as cmd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2d52a789c10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jPT0d6enpmD9/fpWx+Pv748svvxQDKAD47rvvUFBQgCeffBIA8MYbb2DTpk2IiopCUlISXnnlFTzzzDM4dOhQjZ9BSUkJcnNzDTYygn/8kSMIgAJVS1YlRQr8sMsJQyYbTm5u3q4IQatT8OWHWkz26IHnuneDa/MSNHIuhZnZ3X4mvnwD7b3z4d6xEONezMDT869j13ptvVwSUU1++MoRx/drcPWCGsf2NcLiKR5o6l6Cx57IqdLWUqXHoHG38N02w2zQuOeyYG1Xge3/4/eXTEeDmCz9n//8B127dsXSpUuxceNGg2Pvvvsu/Pz8xMnKHh4eWLt2LQYMGICoqChcuXIF+/btw4kTJ9CjRw8AwEcffQQPD8MJtH+f7Ozu7o5ly5Zh5syZ+OCDD2BpaQmNRgOFQgGttuZ/AHx9fWFjY4OdO3ciICAAALB161aMGTMG9vb2KCgoQEREBPbv3w8fHx8AQKtWrZCQkIANGzZgwIAB1fYbHh6ON998s06fGdWskXNlJuh2lhKOrnezQjl/KqFxrjqJ+cjXjigtMsPA//5R5Vj///yJ/v/5E7ezLKCy1kOhAL6K1sKleUmN79+2ez4K8yxwO8sCjap5P6IH4VamEpnXLeHmXvW7+viobKjUeny/w9Fgf5c+uWjfrQBfXfzZYH/k7mTs3+WIVXPd63XMVHd6GOFZYyY+WbpBBEIAsGLFCjzxxBOYN2+ewf5Tp07h4sWLiIuLE/cJggC9Xo+UlBRcuHABFhYW6N69u3i8TZs2cHAwnFB44MABhIWF4dy5c8jNzUV5eTmKi4tRUFAAG5vapYCVSiUmTpyIuLg4BAQEoKCgAF988QW2bt0KADh37hyKi4sxdOhQg/NKS0vRrVu3GvtduHAh5s6dK77Ozc2FTqer1ZioKtfmJXBwKcUvP9ijVadCAJWTSJOO2uHZ11OrtN+3zRk9h96GxqnmoOVOQLNvW2MoVXp0fbzmrF3KWWtYqvSwsa+QeCVE98+uUTmcm5TiVmbVcrDvU3/g6D4Ncm4ZHota2hyx79793jq5liEs7neEzW6F86dZKmuIBCOsGhMYCDUM/fv3h6+vL15//XVMnTpV3K/X6/Hiiy8iKCioyjnNmzfH+fPnq+1PEO6WLq5evYqRI0dixowZWLZsGRwdHZGQkIDAwECUldU8mbA6/v7+GDBgADIzM7F3715YWVlhxIgR4lgB4Ouvv0bTpk0NzlOpVFX6+vuxex2nqooKzJBxxUp8fTNVhZQka9g2Kodz01KMDryJz9a5oYl7CZq4F2NHpBtUaj36jze8z0p6igrnjtnhjY8vVPs+32xyQbse+bCy0eOXH+wR+7YOAQvTYKOp/LE4sbcRsjOVaOedD0srPc4etkfcymYY6p8JpYorx8h4rKwr4NbybnZHqytBqw6FyLttgbzb5njmlXT89G0j3MpUwrVZKaaGXEdOtgUOxzcy6KdJi2J06pWPxVPaVHmPrBuWBq+LCytnT6RfVeGPDMsq7enh49PnpWswgRBQWSLq1q0b2rZtK+7r3r07kpKS0KZN1f9oAaB9+/YoLy/H6dOn4e3tDQC4ePEibt++LbY5efIkysvLsWrVKpj9tQTo008/NejH0tISFRX//hd8nz59oNPpsH37dnz77beYOHEiLC0r/4Ho0KEDVCoVrl27VmMZjIzj0i82WDzJU3y96c0WAIBBE7MQtDoF/5mVjtJiM3y4qAXycyzg0TUfS+POQ21rOHH0++3OcNSWGqww+7vfE23xyapmKC40Q7PWxZj5zhUM/O/dYMrcQkD8xy7Y9FZzCPrKbNTkedcxcurNerhqMmVtOxdi5ad3A/YXl1bOqdz7/5wQ+XpzuLcvwpAn/4SNfQVuZSrx6xE7hM1uhaICw6Xxvk/9iT8zlPj5h6r3DiIyRQ0qEOrcuTP8/f0RGRkp7nv11VfRu3dvzJ49G9OmTYONjQ2Sk5Oxd+9eREZGon379hgyZAimT5+OqKgoKJVKzJs3D2q1WrwXUevWrVFeXo7IyEiMGTMGP/30E9avX2/w3i1btkR+fj6+//57dOnSBdbW1tUum1coFPDz88P69etx4cIFHDhwQDxmZ2eH+fPn45VXXoFer0e/fv2Qm5uLw4cPw9bWFlOmTKmnT870dOqTh51px2s8rlAAk+ddx+R51+/ZzzOvpeGZ16pO0r/j5ferv3HdHd0H5Yg3YCSqT78etcPw5t41Hl8UUPXGotWJWdkUMSub/ntDADfTVPd8T3r4eGdp6Rrc1S9btsygrNW5c2ccOnQIv//+Ox5//HF069YNixcvRpMmTcQ2H3/8MVxdXdG/f3/85z//wbRp02BnZwcrq8rSSdeuXREREYEVK1agU6dOiIuLq7Jcv0+fPpgxYwaeeuopODs7Y+XKlTWO0d/fH+fOnUPTpk3Rt2/fKuNfsmQJwsPD4enpCV9fX3z11Vdwd+ckQyIiMq47pTGpmylTCH+POh4RaWlp0Ol02LdvHwYPHvywh1Nnubm50Gg0uP5bM9jbNbhYlcgoJrTs87CHQFQvyoUyHCjfgZycHNjb108J8s7vxLg9z0NpI23+VllBKb4Y9n/1Ot6GrEGVxu7X/v37kZ+fDy8vL6SnpyMkJAQtW7ZE//79H/bQiIiI6g2fNSbdIxEIlZWV4fXXX8fly5dhZ2eHPn36IC4uDkpl9XcRJiIiehRw1Zh0j0Qg5OvrC19f34c9DCIiIpKZRyIQIiIiMkXMCEnHQIiIiEimGAhJxyVJREREZLKYESIiIpIpZoSkYyBEREQkUwKkL39/5G4mWEcMhIiIiGSKGSHpOEeIiIiITBYzQkRERDLFjJB0DISIiIhkioGQdCyNERERUa2Ul5fjjTfegLu7O9RqNVq1aoW33noLer1ebCMIAkJDQ+Hm5ga1Wo2BAwciKSnJoJ+SkhLMmTMHjRs3ho2NDcaOHYu0tDSDNtnZ2QgICIBGo4FGo0FAQABu375t9GtiIERERCRTdzJCUrfaWrFiBdavX49169YhOTkZK1euxLvvvovIyEixzcqVKxEREYF169bhxIkT0Gq1GDp0KPLy8sQ2wcHB2LlzJ7Zt24aEhATk5+dj9OjRqKioENv4+fkhMTER8fHxiI+PR2JiIgICAozzwf0NS2NEREQyJQgKCBJLW3fOz83NNdivUqmgUqkM9h05cgTjxo3DqFGjAAAtW7bEJ598gpMnT/7Vl4A1a9Zg0aJFmDBhAgAgNjYWrq6u2Lp1K1588UXk5ORg48aN2Lx5M4YMGQIA2LJlC3Q6Hfbt2wdfX18kJycjPj4eR48eRa9evQAA0dHR8PHxwfnz59GuXTtJ1/x3zAgRERERdDqdWIbSaDQIDw+v0qZfv374/vvvceHCBQDAL7/8goSEBIwcORIAkJKSgoyMDAwbNkw8R6VSYcCAATh8+DAA4NSpUygrKzNo4+bmhk6dOoltjhw5Ao1GIwZBANC7d29oNBqxjbEwI0RERCRTeigk31Dxzvmpqamwt7cX9/8zGwQAr776KnJyctC+fXuYm5ujoqICy5cvx9NPPw0AyMjIAAC4uroanOfq6oqrV6+KbSwtLeHg4FClzZ3zMzIy4OLiUuX9XVxcxDbGwkCIiIhIpoy5asze3t4gEKrO9u3bsWXLFmzduhUdO3ZEYmIigoOD4ebmhilTpojtFArDMQmCUGXfP/2zTXXta9NPXTEQIiIiolpZsGABXnvtNUyePBkA4OXlhatXryI8PBxTpkyBVqsFUJnRadKkiXheZmammCXSarUoLS1Fdna2QVYoMzMTffr0EdvcvHmzyvtnZWVVyTZJxTlCREREMnVnsrTUrbYKCwthZmYYOpibm4vL593d3aHVarF3717xeGlpKQ4dOiQGOd7e3lAqlQZt0tPTcfbsWbGNj48PcnJycPz4cbHNsWPHkJOTI7YxFmaEiIiIZOpB31BxzJgxWL58OZo3b46OHTvi9OnTiIiIwPPPPw+gspwVHByMsLAweHh4wMPDA2FhYbC2toafnx8AQKPRIDAwEPPmzYOTkxMcHR0xf/58eHl5iavIPD09MXz4cEybNg0bNmwAAEyfPh2jR4826ooxgIEQERGRbBlz+XxtREZGYvHixZg1axYyMzPh5uaGF198EUuWLBHbhISEoKioCLNmzUJ2djZ69eqFPXv2wM7OTmyzevVqWFhYYNKkSSgqKsLgwYMRExMDc3NzsU1cXByCgoLE1WVjx47FunXrJF1rdRSCIAhG75Ukyc3NhUajwfXfmsHejtVLejRNaGnc9DZRQ1EulOFA+Q7k5OT86+Tj+3Xnd8J7xyuwsKm6uqsuygtKcOrJ1fU63oaMGSEiIiKZEoxQGpOaUZI7BkJEREQyJQCQWtcx9bIQ6y5ERERkspgRIiIikik9FFAY6c7SpoqBEBERkUw96FVjjyKWxoiIiMhkMSNEREQkU3pBAcUDvKHio4iBEBERkUwJghFWjZn4sjGWxoiIiMhkMSNEREQkU5wsLR0DISIiIpliICQdAyEiIiKZ4mRp6ThHiIiIiEwWM0JEREQyxVVj0jEQIiIikqnKQEjqHCEjDUamWBojIiIik8WMEBERkUxx1Zh0DISIiIhkSvhrk9qHKWNpjIiIiEwWM0JEREQyxdKYdAyEiIiI5Iq1MckYCBEREcmVETJCMPGMEOcIERERkcliRoiIiEimeGdp6RgIERERyRQnS0vH0hgRERGZLGaEiIiI5EpQSJ/sbOIZIQZCREREMsU5QtKxNEZEREQmixkhIiIiueINFSVjIERERCRTXDUmXa0CobVr19a6w6CgoPseDBEREdGDVKtAaPXq1bXqTKFQMBAiIiJ6kEy8tCVVrQKhlJSU+h4HERER1RFLY9Ld96qx0tJSnD9/HuXl5cYcDxEREdWWYKTNhNU5ECosLERgYCCsra3RsWNHXLt2DUDl3KB33nnH6AMkIiIiqi91DoQWLlyIX375BQcPHoSVlZW4f8iQIdi+fbtRB0dERET3ojDSZrrqvHx+165d2L59O3r37g2F4u6H16FDB1y6dMmogyMiIqJ74H2EJKtzRigrKwsuLi5V9hcUFBgERkREREQNXZ0DoZ49e+Lrr78WX98JfqKjo+Hj42O8kREREdG9cbK0ZHUujYWHh2P48OE4d+4cysvL8f777yMpKQlHjhzBoUOH6mOMREREVB0+fV6yOmeE+vTpg59++gmFhYVo3bo19uzZA1dXVxw5cgTe3t71MUYiIiKienFfzxrz8vJCbGysscdCREREdSAIlZvUPkzZfQVCFRUV2LlzJ5KTk6FQKODp6Ylx48bBwoLPcCUiInpguGpMsjpHLmfPnsW4ceOQkZGBdu3aAQAuXLgAZ2dnfPnll/Dy8jL6IImIiIjqQ53nCL3wwgvo2LEj0tLS8PPPP+Pnn39GamoqOnfujOnTp9fHGImIiKg6dyZLS91MWJ0zQr/88gtOnjwJBwcHcZ+DgwOWL1+Onj17GnVwREREVDOFULlJ7cOU1Tkj1K5dO9y8ebPK/szMTLRp08YogyIiIqJa4H2EJKtVIJSbmytuYWFhCAoKwmeffYa0tDSkpaXhs88+Q3BwMFasWFHf4yUiIiIymlqVxho1amTw+AxBEDBp0iRxn/DX2rsxY8agoqKiHoZJREREVfCGipLVKhA6cOBAfY+DiIiI6orL5yWrVSA0YMCA+h4HERER0QN333dALCwsxLVr11BaWmqwv3PnzpIHRURERLXAjJBkdQ6EsrKy8Nxzz+Hbb7+t9jjnCBERET0gDIQkq/Py+eDgYGRnZ+Po0aNQq9WIj49HbGwsPDw88OWXX9bHGImIiIjqRZ0zQvv378cXX3yBnj17wszMDC1atMDQoUNhb2+P8PBwjBo1qj7GSURERP/EVWOS1TkjVFBQABcXFwCAo6MjsrKyAFQ+kf7nn3827uiIiIioRnfuLC11M2X3dWfp8+fPAwC6du2KDRs24Pr161i/fj2aNGli9AESERER1Zc6l8aCg4ORnp4OAFi6dCl8fX0RFxcHS0tLxMTEGHt8REREVBNOlpaszoGQv7+/+L+7deuGK1eu4LfffkPz5s3RuHFjow6OiIiIqD7d932E7rC2tkb37t2NMRYiIiKqAwWM8PR5o4xEvmoVCM2dO7fWHUZERNz3YIiIiIgepFoFQqdPn65VZ39/MCtJ59/eGxYK5cMeBlG9+O7GyYc9BKJ6kZunh0PbB/RmXD4vGR+6SkREJFecLC1ZnZfPExERET0qJE+WJiIiooeEGSHJGAgRERHJlDHuDM07SxMRERHV0vXr1/HMM8/AyckJ1tbW6Nq1K06dOiUeFwQBoaGhcHNzg1qtxsCBA5GUlGTQR0lJCebMmYPGjRvDxsYGY8eORVpamkGb7OxsBAQEQKPRQKPRICAgALdv3zb69TAQIiIikivBSFstZWdno2/fvlAqlfj2229x7tw5rFq1Co0aNRLbrFy5EhEREVi3bh1OnDgBrVaLoUOHIi8vT2wTHByMnTt3Ytu2bUhISEB+fj5Gjx6NiooKsY2fnx8SExMRHx+P+Ph4JCYmIiAg4D4+pHu7r9LY5s2bsX79eqSkpODIkSNo0aIF1qxZA3d3d4wbN87YYyQiIqLqGHGOUG5ursFulUoFlUplsG/FihXQ6XTYtGmTuK9ly5Z3uxIErFmzBosWLcKECRMAALGxsXB1dcXWrVvx4osvIicnBxs3bsTmzZsxZMgQAMCWLVug0+mwb98++Pr6Ijk5GfHx8Th69Ch69eoFAIiOjoaPjw/Onz+Pdu3aSbzou+qcEYqKisLcuXMxcuRI3L59W4zeGjVqhDVr1hhtYERERPTg6HQ6sQyl0WgQHh5epc2XX36JHj16YOLEiXBxcUG3bt0QHR0tHk9JSUFGRgaGDRsm7lOpVBgwYAAOHz4MADh16hTKysoM2ri5uaFTp05imyNHjkCj0YhBEAD07t0bGo1GbGMsdQ6EIiMjER0djUWLFsHc3Fzc36NHD5w5c8aogyMiIqKa3ZksLXUDgNTUVOTk5IjbwoULq7zf5cuXERUVBQ8PD3z33XeYMWMGgoKC8PHHHwMAMjIyAACurq4G57m6uorHMjIyYGlpCQcHh3u2cXFxqfL+Li4uYhtjqXNpLCUlBd26dauyX6VSoaCgwCiDIiIiolow4p2l7e3tYW9vf8+mer0ePXr0QFhYGIDKh68nJSUhKioKzz77rNjun0+aEAThX58+8c821bWvTT91VeeMkLu7OxITE6vs//bbb9GhQwdjjImIiIhq4wFPlm7SpEmV33pPT09cu3YNAKDVagGgStYmMzNTzBJptVqUlpYiOzv7nm1u3rxZ5f2zsrKqZJukqnMgtGDBAsyePRvbt2+HIAg4fvw4li9fjtdffx0LFiww6uCIiIio4ejbty/Onz9vsO/ChQto0aIFgMpkiVarxd69e8XjpaWlOHToEPr06QMA8Pb2hlKpNGiTnp6Os2fPim18fHyQk5OD48ePi22OHTuGnJwcsY2x1Lk09txzz6G8vBwhISEoLCyEn58fmjZtivfffx+TJ0826uCIiIioZg/6hoqvvPIK+vTpg7CwMEyaNAnHjx/Hhx9+iA8//LCyL4UCwcHBCAsLg4eHBzw8PBAWFgZra2v4+fkBADQaDQIDAzFv3jw4OTnB0dER8+fPh5eXl7iKzNPTE8OHD8e0adOwYcMGAMD06dMxevRoo64YA+5z+fy0adMwbdo0/PHHH9Dr9dVOaCIiIqJ69oAfsdGzZ0/s3LkTCxcuxFtvvQV3d3esWbMG/v7+YpuQkBAUFRVh1qxZyM7ORq9evbBnzx7Y2dmJbVavXg0LCwtMmjQJRUVFGDx4MGJiYgwWYcXFxSEoKEhcXTZ27FisW7dO4sVWpRAEwcRvrt3w5ObmQqPRYCDGwUKhfNjDIaoX391IfNhDIKoXuXl6OLS9jJycnH+dfHzf7/HX70SrJWEws7KS1Je+uBiX33q9XsfbkNU5I+Tu7n7PGduXL1+WNCAiIiKqJSOUxvjQ1ToKDg42eF1WVobTp08jPj6ek6WJiIgeJD59XrI6B0Ivv/xytfv/97//4eTJk5IHRERERPSgGO2hqyNGjMCOHTuM1R0RERH9mwd8H6FH0X2tGqvOZ599BkdHR2N1R0RERP/iQS+ffxTVORDq1q2bwWRpQRCQkZGBrKwsfPDBB0YdHBEREVF9qnMgNH78eIPXZmZmcHZ2xsCBA9G+fXtjjYuIiIio3tUpECovL0fLli3h6+srPk+EiIiIHhKuGpOsTpOlLSwsMHPmTJSUlNTXeIiIiKiW7swRkrqZsjqvGuvVqxdOnz5dH2MhIiIieqDqPEdo1qxZmDdvHtLS0uDt7Q0bGxuD4507dzba4IiIiOhfmHhGR6paB0LPP/881qxZg6eeegoAEBQUJB5TKBQQBAEKhQIVFRXGHyURERFVxTlCktU6EIqNjcU777yDlJSU+hwPERER0QNT60DozkPqW7RoUW+DISIiotrjDRWlq9McoXs9dZ6IiIgeMJbGJKtTINS2bdt/DYZu3bolaUBERERED0qdAqE333wTGo2mvsZCREREdcDSmHR1CoQmT54MFxeX+hoLERER1QVLY5LV+oaKnB9EREREj5o6rxojIiKiBoIZIclqHQjp9fr6HAcRERHVEecISVfnR2wQERFRA8GMkGR1fugqERER0aOCGSEiIiK5YkZIMgZCREREMsU5QtKxNEZEREQmixkhIiIiuWJpTDIGQkRERDLF0ph0LI0RERGRyWJGiIiISK5YGpOMgRAREZFcMRCSjKUxIiIiMlnMCBEREcmU4q9Nah+mjIEQERGRXLE0JhkDISIiIpni8nnpOEeIiIiITBYzQkRERHLF0phkDISIiIjkzMQDGalYGiMiIiKTxYwQERGRTHGytHQMhIiIiOSKc4QkY2mMiIiITBYzQkRERDLF0ph0DISIiIjkiqUxyVgaIyIiIpPFjBAREZFMsTQmHQMhIiIiuWJpTDIGQkRERHLFQEgyzhEiIiIik8WMEBERkUxxjpB0DISIiIjkiqUxyVgaIyIiIpPFjBAREZFMKQQBCkFaSkfq+XLHQIiIiEiuWBqTjKUxIiIiMlnMCBEREckUV41Jx0CIiIhIrlgak4ylMSIiIjJZzAgRERHJFEtj0jEQIiIikiuWxiRjIERERCRTzAhJxzlCREREZLKYESIiIpIrlsYkYyBEREQkY6Ze2pKKpTEiIiIyWcwIERERyZUgVG5S+zBhzAgRERHJ1J1VY1K3+xUeHg6FQoHg4GBxnyAICA0NhZubG9RqNQYOHIikpCSD80pKSjBnzhw0btwYNjY2GDt2LNLS0gzaZGdnIyAgABqNBhqNBgEBAbh9+/b9D7YGDISIiIiozk6cOIEPP/wQnTt3Nti/cuVKREREYN26dThx4gS0Wi2GDh2KvLw8sU1wcDB27tyJbdu2ISEhAfn5+Rg9ejQqKirENn5+fkhMTER8fDzi4+ORmJiIgIAAo18HAyEiIiK5Eoy01VF+fj78/f0RHR0NBweHu8MRBKxZswaLFi3ChAkT0KlTJ8TGxqKwsBBbt24FAOTk5GDjxo1YtWoVhgwZgm7dumHLli04c+YM9u3bBwBITk5GfHw8PvroI/j4+MDHxwfR0dHYvXs3zp8/fz+fVI0YCBEREcmUQm+cDQByc3MNtpKSkhrfd/bs2Rg1ahSGDBlisD8lJQUZGRkYNmyYuE+lUmHAgAE4fPgwAODUqVMoKyszaOPm5oZOnTqJbY4cOQKNRoNevXqJbXr37g2NRiO2MRYGQkRERASdTifOx9FoNAgPD6+23bZt23Dq1Klqj2dkZAAAXF1dDfa7urqKxzIyMmBpaWmQSaqujYuLS5X+XVxcxDbGwlVj9Ejo1CsfE2dlwcOrEE7acoQ+3xJH4jXVtg1akYpRAbewfokbdn7kLO5f+dlFdOlTYND24BeNED6zhcG+xwbnwv+Vm3D3LEJxkRnOHLXFshdaGv2ayHSdOWqD//eBC34/Y41bN5VYujEFfUbkiMcFAdiySotv4pyQn2OO9t0KMTssDS3bFYttblyxRPRbbkg6bouyUgW8B+Vi9tvX4eBcbvBex/bZI261K1KS1bBS6+HVOx9LNl4xaLNnuyM+/9AZaZdVsLWvQL9Rt/FS2PV6/Qyolox4Q8XU1FTY29uLu1UqVZWmqampePnll7Fnzx5YWVnV2KVCoTB8C0Gosq/KMP7Rprr2temnrkw2ELpy5Qrc3d1x+vRpdO3atcZ2AwcORNeuXbFmzZoHNjaqOytrPS4nWWHPNgcs2Xi1xnY+w3PQvnsh/kiv/qv/zRZHfPyuVnxdUmyYNO038jaC303Dpne0SPypORQKAS3bF/+zGyJJigvN0KpjEYZNvoVlL7hXOf7p/1zw+YfOmLfmGpq1KsHWNa5YOLk1Nv6YDGtbPYoLzfD6063RqkMRVvy/iwCA2JVNsGSKO97f/TvM/vpa//i1BmsW6PDca+no2jcfggBc+c3wx23HBmfs2OCMF964gfbdC1FaYoaMq5b1/hlQ7RjzWWP29vYGgVB1Tp06hczMTHh7e4v7Kioq8MMPP2DdunXi/J2MjAw0adJEbJOZmSlmibRaLUpLS5GdnW2QFcrMzESfPn3ENjdv3qzy/llZWVWyTVI1+NLY1KlToVAooFAooFQq0apVK8yfPx8FBQX/fvI96HQ6pKeno1OnTgCAgwcPQqFQVFma9/nnn2PZsmWS3ovq38kD9ohd2QQ/fduoxjZO2jLMfvs6VsxugfLy6v+iKCkyQ3aWUtwK88zFY2bmAma8dQPRbzfB15sb4/plFdIuWSHh65rfk+h+9HwiD1NfzUC/kTlVjgkCsOsjZ0wOuol+I3PQsn0x5r9/DSVFZjiws/JHJem4DW6mWmLemmtw9yyGu2cx5q2+hguJNkhMsAUAVJQD65c0xbQ3bmD0s3+iWesS6NqU4PHRd98z77Y5Ylc0wYL3r+GJCbfh1rIULdsVo/ew3AfzQdC/u3MfIalbLQ0ePBhnzpxBYmKiuPXo0QP+/v5ITExEq1atoNVqsXfvXvGc0tJSHDp0SAxyvL29oVQqDdqkp6fj7NmzYhsfHx/k5OTg+PHjYptjx44hJydHbGMsssgIDR8+HJs2bUJZWRl+/PFHvPDCCygoKEBUVNR992lubg6tVvuv7RwdHe/7PajhUCgEhKy9hs+inHH1Qs3p3EETsvHEk9m4nWWBEwfssWWVK4oKKoMhD68iOLuVQdAr8L895+HgXI7LSWpEv+V2zz6JjCnjmiVuZSrhPeDuUmRLlQCv3vk4d9IGowL+RFmpAlAASkvhb230MDMTkHTcFt375+P3M9b4I90SCjNg1tC2yM5SolXHIkxbckMssf38gx30AvBHhhIv9G+PogIzePYowPQlN+DStOyBXzs9fHZ2dmIC4Q4bGxs4OTmJ+4ODgxEWFgYPDw94eHggLCwM1tbW8PPzAwBoNBoEBgZi3rx5cHJygqOjI+bPnw8vLy9x8rWnpyeGDx+OadOmYcOGDQCA6dOnY/To0WjXrp1Rr6nBZ4SAyjqlVquFTqeDn58f/P39sWvXLpSUlCAoKAguLi6wsrJCv379cOLECfG87Oxs+Pv7w9nZGWq1Gh4eHti0aROAytKYQqFAYmIirly5gkGDBgEAHBwcoFAoMHXqVACVpbE7N4pauHAhevfuXWV8nTt3xtKlS8XXmzZtgqenJ6ysrNC+fXt88MEH97y+kpKSKrP1ybgmzc5ERQWwa2PjGtsc+NwB78xqgQVPtkbcGlf0G5ljMFdC26JyBcUz8zLwyRpXLHnWHfk55nj384uwa1ReQ69ExnUrs/LvVwdnw0DEwbkM2X8da+9dACtrPTYud0NxoQLFhWaIXuYGvV4hnn+nvLVllRZPB9/EWx9fhq2mAgsmtEFutrnYRtAD29a6YsZb1/HGh1eQl22BhZNbVwZb9NA97BsqVickJATBwcGYNWsWevTogevXr2PPnj2ws7MT26xevRrjx4/HpEmT0LdvX1hbW+Orr76CufndLHxcXBy8vLwwbNgwDBs2DJ07d8bmzZuNO1jIJCP0T2q1GmVlZQgJCcGOHTsQGxuLFi1aYOXKlfD19cXFixfh6OiIxYsX49y5c/j222/RuHFjXLx4EUVFRVX60+l02LFjB5588kmcP38e9vb2UKvVVdr5+/vjnXfewaVLl9C6dWsAQFJSEs6cOYPPPvsMABAdHY2lS5di3bp16NatG06fPo1p06bBxsYGU6ZMqfZ6wsPD8eabbxrxE6K/a+NViPEv/IHZvm0B1PyP97dbncT/ffW8Gtcvq/C/735HG69CXDxjLc6r+OR9VyR80wgAsOoVHbacOofHR+fgmy1O1fRKVE/+8VUWBIW4r5FTBd7YcAWRC5vhi42NoTADBo3PRhuvQpj99Tuj/2vJ9NMv38TjoyrLYfNWX8Mz3h3x4+5GGBXwJ/QCUF5mhlnLrsN7YGUGamHUFTzdpRN+OWyLHgPzQA9ZA3j6/MGDBw1eKxQKhIaGIjQ0tMZzrKysEBkZicjIyBrbODo6YsuWLdIGVwuyC4SOHz+OrVu3YtCgQYiKikJMTAxGjBgBoDII2bt3LzZu3IgFCxbg2rVr6NatG3r06AEAaNmyZbV9mpubiyUwFxcXNGrUqNp2nTp1QufOnbF161YsXrwYQGXE2rNnT7Rt2xYAsGzZMqxatQoTJkwAALi7u+PcuXPYsGFDjYHQwoULMXfuXPF1bm4udDpd3T4YqpFXrwI0alyOLSfOifvMLYBpS29g/LQsTOnVodrzLp5Ro6xUgabuJbj41+odALj2+92VFGWlZsi4qoJL09L6vQiivzi6VGYfszOVcHK9m4m8/YeFwYow74F5iDmSjJw/zWFuAdhqKjC5S0dodZWZTce/zm3ucXeyv6VKgLZFCTKvKw3eq3nbu20aOVXA3rFcbEMkd7Ioje3evRu2trawsrKCj48P+vfvjzlz5qCsrAx9+/YV2ymVSjz22GNITk4GAMycORPbtm1D165dERISYpSbMPn7+yMuLg5A5TK+Tz75BP7+/gAqZ7OnpqYiMDAQtra24vb222/j0qVLNfapUqnE2fq1mbVPdbNvhwNmDG6LmUPvbn+kW+CzKGcs8mtV43kt2hVDaSngz78CoN9/VaO0WIFmre/eZMzcQoCrrhQ307iKhh4MbfNSOLqU4ecf7pYZykoVOHPUFh16VF1EonGqgK2mAokJtrj9h4U40dmjcyGUKj3SLt0N7MvLgJuplnBtVll269izsr+/t8nNNkfuLQu4co5Qg9AQS2NyI4uM0J3sj1KphJubG5RKJX755RcA975XwYgRI3D16lV8/fXX2LdvHwYPHozZs2fjvffeu++x+Pn54bXXXsPPP/+MoqIipKamYvLkyQAA/V+55ujoaIO7YQIwqHuS8VlZV8DN/W5WRqsrRauORci7bY6s65bIyzb8qpeXK5CdqUTapcpJzk1alOCJCdk4/r09cm9ZoHnbYkxfegO/n1Hj3AkbAEBhvjm+3uyEgHk3kXXDEplpSvx3ZhYA4Mfd1d+ziOh+FBWY4UbK3eAjI9USl86qYdeoHC7NyjD+hSxsi3RF01YlaOpegk/WukKl1mPQf7LFc77b5ojmHsXQOJUj+ZQNopY0xX+mZ0HXpjKQt7HTY1TAn9i8SgtntzK4NCvFZ1GVN7B7fPRtAECz1iXw8c1B1JKmeHllKmzs9Pi/sCZo1qYYXfqyLNYg8OnzkskiELKxsUGbNm0M9rVp0waWlpZISEgQZ6KXlZXh5MmTBk/BdXZ2xtSpUzF16lQ8/vjjWLBgQbWBkKVl5V/0f3/gW3WaNWuG/v37Iy4uDkVFRRgyZIh4TwNXV1c0bdoUly9fFrNE9GC07VKEd3fczbrNePMGAGDPdgeseqX5v55fXqZA1375GB/4B6xs9PjjhhLHvrdHXIQr9Pq7wXb0MjdUVCgQsvYaLK30OH/aGq9ObI38HFn8p0QyceEXa4T89+6/eRtCmwIAhk66hflrrmHS7EyUFpth3cJmyPvrhorhn1yCta1ePCftkgqbwpsg77Y5XHWleDroJiZMzzJ4n2mLr8PcXMDKoOYoLTZDu26FWPH/LsGu0d1/BxesvYoNS5tiybOtoDADOvfOx/K4y7BgZYweEbL919vGxgYzZ87EggUL4OjoiObNm2PlypUoLCxEYGAgAGDJkiXw9vZGx44dUVJSgt27d8PT07Pa/lq0aAGFQoHdu3dj5MiRUKvVsLW1rbatv78/QkNDUVpaitWrVxscCw0NRVBQEOzt7TFixAiUlJTg5MmTyM7ONpgHRMb16xFb+Lp1qXX7f84LyrphiQVPtqmh9V0V5QpEv+WG6Lfc6jxGotrq0icf391IrPG4QgEEzM9AwPyaHzUQuCgdgYvS7/k+Fkpg+tIbmL70Ro1tbOz0mBuRirkRqf86bnrwjHlDRVMlizlCNXnnnXfw5JNPIiAgAN27d8fFixfx3XffiXeqtLS0xMKFC9G5c2f0798f5ubm2LZtW7V9NW3aFG+++SZee+01uLq64qWXXqrxfSdOnIg///wThYWFGD9+vMGxF154AR999BFiYmLg5eWFAQMGICYmBu7uVe8OS0REJMlDevr8o0QhCCZeHGyAcnNzodFoMBDjYKFg/pkeTffKeBDJWW6eHg5tLyMnJ6feFr/c+Z3wGf4WLJTSbuhaXlaMI/FL6nW8DZlsS2NERESmjqUx6RgIERERyZVeqNyk9mHCGAgRERHJVQO4s7TcyXqyNBEREZEUzAgRERHJlAJGmCNklJHIFwMhIiIiueKdpSVjaYyIiIhMFjNCREREMsXl89IxECIiIpIrrhqTjKUxIiIiMlnMCBEREcmUQhCgkDjZWer5csdAiIiISK70f21S+zBhLI0RERGRyWJGiIiISKZYGpOOgRAREZFccdWYZAyEiIiI5Ip3lpaMc4SIiIjIZDEjREREJFO8s7R0DISIiIjkiqUxyVgaIyIiIpPFjBAREZFMKfSVm9Q+TBkDISIiIrliaUwylsaIiIjIZDEjREREJFe8oaJkDISIiIhkio/YkI6lMSIiIjJZzAgRERHJFSdLS8ZAiIiISK4EAFKXv5t2HMRAiIiISK44R0g6zhEiIiIik8WMEBERkVwJMMIcIaOMRLYYCBEREckVJ0tLxtIYERERmSxmhIiIiORKD0BhhD5MGAMhIiIimeKqMelYGiMiIiKTxYwQERGRXHGytGQMhIiIiOSKgZBkLI0RERGRyWJGiIiISK6YEZKMgRAREZFccfm8ZAyEiIiIZIrL56XjHCEiIiIyWcwIERERyRXnCEnGQIiIiEiu9AKgkBjI6E07EGJpjIiIiEwWM0JERERyxdKYZAyEiIiIZMsIgRBMOxBiaYyIiIhMFjNCREREcsXSmGQMhIiIiORKL0ByaYurxoiIiIhMEzNCREREciXoKzepfZgwBkJERERyxTlCkjEQIiIikivOEZKMc4SIiIjIZDEjREREJFcsjUnGQIiIiEiuBBghEDLKSGSLpTEiIiIyWcwIERERyRVLY5IxI0RERCRXer1xtloKDw9Hz549YWdnBxcXF4wfPx7nz583aCMIAkJDQ+Hm5ga1Wo2BAwciKSnJoE1JSQnmzJmDxo0bw8bGBmPHjkVaWppBm+zsbAQEBECj0UCj0SAgIAC3b9++74+qJgyEiIiIqFYOHTqE2bNn4+jRo9i7dy/Ky8sxbNgwFBQUiG1WrlyJiIgIrFu3DidOnIBWq8XQoUORl5cntgkODsbOnTuxbds2JCQkID8/H6NHj0ZFRYXYxs/PD4mJiYiPj0d8fDwSExMREBBg9GtSCIKJ58QaoNzcXGg0GgzEOFgolA97OET14rsbiQ97CET1IjdPD4e2l5GTkwN7e/v6eY+/fieGOAfCwsxSUl/l+lLsy9p4X+PNysqCi4sLDh06hP79+0MQBLi5uSE4OBivvvoqgMrsj6urK1asWIEXX3wROTk5cHZ2xubNm/HUU08BAG7cuAGdTodvvvkGvr6+SE5ORocOHXD06FH06tULAHD06FH4+Pjgt99+Q7t27SRd898xI0RERCRXd+YISd1QGVz9fSspKfnXt8/JyQEAODo6AgBSUlKQkZGBYcOGiW1UKhUGDBiAw4cPAwBOnTqFsrIygzZubm7o1KmT2ObIkSPQaDRiEAQAvXv3hkajEdsYCwMhIiIigk6nE+fjaDQahIeH37O9IAiYO3cu+vXrh06dOgEAMjIyAACurq4GbV1dXcVjGRkZsLS0hIODwz3buLi4VHlPFxcXsY2xcNUYERGRXBnxERupqakGpTGVSnXP01566SX8+uuvSEhIqHJMoVAYvBYEocq+f/pnm+ra16afumJGiIiISKYEQW+UDQDs7e0NtnsFQnPmzMGXX36JAwcOoFmzZuJ+rVYLAFWyNpmZmWKWSKvVorS0FNnZ2fdsc/PmzSrvm5WVVSXbJBUDISIiIrkShMqMjpStDmumBEHASy+9hM8//xz79++Hu7u7wXF3d3dotVrs3btX3FdaWopDhw6hT58+AABvb28olUqDNunp6Th79qzYxsfHBzk5OTh+/LjY5tixY8jJyRHbGAtLY0RERFQrs2fPxtatW/HFF1/Azs5OzPxoNBqo1WooFAoEBwcjLCwMHh4e8PDwQFhYGKytreHn5ye2DQwMxLx58+Dk5ARHR0fMnz8fXl5eGDJkCADA09MTw4cPx7Rp07BhwwYAwPTp0zF69GijrhgDGAgRERHJl2CEOUJ1yAhFRUUBAAYOHGiwf9OmTZg6dSoAICQkBEVFRZg1axays7PRq1cv7NmzB3Z2dmL71atXw8LCApMmTUJRUREGDx6MmJgYmJubi23i4uIQFBQkri4bO3Ys1q1bd58XWTPeR6gB4n2EyBTwPkL0qHqQ9xEabOcPC4XE+wgJpfg+L65ex9uQcY4QERERmSyWxoiIiOTqAZfGHkUMhIiIiGRK0OshKGr/0NRq+xCknS93LI0RERGRyWJGiIiISK5YGpOMgRAREZFc6QVAwUBICpbGiIiIyGQxI0RERCRXggBA4mRnE88IMRAiIiKSKUEvQJBYGjP1+yozECIiIpIrQQ/pGSEunyciIiIyScwIERERyRRLY9IxECIiIpIrlsYkYyDUAN2JzstRJvk+WUQNVW6eaf/jS4+u3PzK7/aDyLQY43eiHGXGGYxMMRBqgPLy8gAACfjmIY+EqP44tH3YIyCqX3l5edBoNPXSt6WlJbRaLRIyjPM7odVqYWlpaZS+5EYhmHpxsAHS6/W4ceMG7OzsoFAoHvZwHnm5ubnQ6XRITU2Fvb39wx4OkdHxO/5gCYKAvLw8uLm5wcys/tYkFRcXo7S01Ch9WVpawsrKyih9yQ0zQg2QmZkZmjVr9rCHYXLs7e35I0GPNH7HH5z6ygT9nZWVlckGL8bE5fNERERkshgIERERkcliIEQmT6VSYenSpVCpVA97KET1gt9xoppxsjQRERGZLGaEiIiIyGQxECIiIiKTxUCIiIiITBYDIaI6atmyJdasWfOwh0H0r65cuQKFQoHExMR7ths4cCCCg4MfyJiIGhoGQtSgTJ06FQqFAu+8847B/l27dj3wu2zHxMSgUaNGVfafOHEC06dPf6BjoUfbne+9QqGAUqlEq1atMH/+fBQUFEjqV6fTIT09HZ06dQIAHDx4EAqFArdv3zZo9/nnn2PZsmWS3otIrhgIUYNjZWWFFStWIDs7+2EPpVrOzs6wtrZ+2MOgR8zw4cORnp6Oy5cv4+2338YHH3yA+fPnS+rT3NwcWq0WFhb3foiAo6Mj7OzsJL0XkVwxEKIGZ8iQIdBqtQgPD6+xzeHDh9G/f3+o1WrodDoEBQUZ/PWcnp6OUaNGQa1Ww93dHVu3bq1S0oqIiICXlxdsbGyg0+kwa9Ys5OfnA6j8y/m5555DTk6O+Jd6aGgoAMPS2NNPP43JkycbjK2srAyNGzfGpk2bAFQ+d2jlypVo1aoV1Go1unTpgs8++8wInxQ9SlQqFbRaLXQ6Hfz8/ODv749du3ahpKQEQUFBcHFxgZWVFfr164cTJ06I52VnZ8Pf3x/Ozs5Qq9Xw8PAQv3t/L41duXIFgwYNAgA4ODhAoVBg6tSpAAxLYwsXLkTv3r2rjK9z585YunSp+HrTpk3w9PSElZUV2rdvjw8++KCePhmi+sVAiBocc3NzhIWFITIyEmlpaVWOnzlzBr6+vpgwYQJ+/fVXbN++HQkJCXjppZfENs8++yxu3LiBgwcPYseOHfjwww+RmZlp0I+ZmRnWrl2Ls2fPIjY2Fvv370dISAgAoE+fPlizZg3s7e2Rnp6O9PT0av869/f3x5dffikGUADw3XffoaCgAE8++SQA4I033sCmTZsQFRWFpKQkvPLKK3jmmWdw6NAho3xe9GhSq9UoKytDSEgIduzYgdjYWPz8889o06YNfH19cevWLQDA4sWLce7cOXz77bdITk5GVFQUGjduXKU/nU6HHTt2AADOnz+P9PR0vP/++1Xa+fv749ixY7h06ZK4LykpCWfOnIG/vz8AIDo6GosWLcLy5cuRnJyMsLAwLF68GLGxsfXxURDVL4GoAZkyZYowbtw4QRAEoXfv3sLzzz8vCIIg7Ny5U7jzdQ0ICBCmT59ucN6PP/4omJmZCUVFRUJycrIAQDhx4oR4/PfffxcACKtXr67xvT/99FPByclJfL1p0yZBo9FUadeiRQuxn9LSUqFx48bCxx9/LB5/+umnhYkTJwqCIAj5+fmClZWVcPjwYYM+AgMDhaeffvreHwaZjL9/7wVBEI4dOyY4OTkJ//3vfwWlUinExcWJx0pLSwU3Nzdh5cqVgiAIwpgxY4Tnnnuu2n5TUlIEAMLp06cFQRCEAwcOCACE7Oxsg3YDBgwQXn75ZfF1586dhbfeekt8vXDhQqFnz57ia51OJ2zdutWgj2XLlgk+Pj51uWyiBoEZIWqwVqxYgdjYWJw7d85g/6lTpxATEwNbW1tx8/X1hV6vR0pKCs6fPw8LCwt0795dPKdNmzZwcHAw6OfAgQMYOnQomjZtCjs7Ozz77LP4888/6zRBValUYuLEiYiLiwMAFBQU4IsvvhD/cj537hyKi4sxdOhQg/F+/PHHBn9xE+3evRu2trawsrKCj48P+vfvjzlz5qCsrAx9+/YV2ymVSjz22GNITk4GAMycORPbtm1D165dERISgsOHD0sei7+/v/idFgQBn3zyifidzsrKQmpqKgIDAw2+02+//Ta/0yRL955BR/QQ9e/fH76+vnj99dfFuQwAoNfr8eKLLyIoKKjKOc2bN8f58+er7U/429Nkrl69ipEjR2LGjBlYtmwZHB0dkZCQgMDAQJSVldVpnP7+/hgwYAAyMzOxd+9eWFlZYcSIEeJYAeDrr79G06ZNDc7jc5/o7wYNGoSoqCgolUq4ublBqVTil19+AYAqKyYFQRD3jRgxAlevXsXXX3+Nffv2YfDgwZg9ezbee++9+x6Ln58fXnvtNfz8888oKipCamqqOBfuznc6OjoavXr1MjjP3Nz8vt+T6GFhIEQNWnh4OLp164a2bduK+7p3746kpCS0adOm2nPat2+P8vJynD59Gt7e3gCAixcvGiwZPnnyJMrLy7Fq1SqYmVUmRj/99FODfiwtLVFRUfGvY+zTpw90Oh22b9+Ob7/9FhMnToSlpSUAoEOHDlCpVLh27RoGDBhQp2sn02JjY1PlO92mTRtYWloiISEBfn5+ACon4588edLgvj/Ozs6YOnUqpk6discffxwLFiyoNhC68738t+91s2bN0L9/f8TFxaGoqAhDhgyBq6srAMDV1RVNmzbF5cuXxSwRkZwxEKIGrXPnzvD390dkZKS479VXX0Xv3r0xe/ZsTJs2DTY2NkhOTsbevXsRGRmJ9u3bY8iQIZg+fbr4F/a8efOgVqvFv6Jbt26N8vJyREZGYsyYMfjpp5+wfv16g/du2bIl8vPz8f3336NLly6wtraudtm8QqGAn58f1q9fjwsXLuDAgQPiMTs7O8yfPx+vvPIK9Ho9+vXrh9zcXBw+fBi2traYMmVKPX1y9CiwsbHBzJkzsWDBAjg6OqJ58+ZYuXIlCgsLERgYCABYsmQJvL290bFjR5SUlGD37t3w9PSstr8WLVpAoVBg9+7dGDlyJNRqNWxtbatt6+/vj9DQUJSWlmL16tUGx0JDQxEUFAR7e3uMGDECJSUlOHnyJLKzszF37lzjfghE9e0hz1EiMvDPSaOCIAhXrlwRVCqV8Pev6/Hjx4WhQ4cKtra2go2NjdC5c2dh+fLl4vEbN24II0aMEFQqldCiRQth69atgouLi7B+/XqxTUREhNCkSRNBrVYLvr6+wscff1xlIumMGTMEJycnAYCwdOlSQRAMJ0vfkZSUJAAQWrRoIej1eoNjer1eeP/994V27doJSqVScHZ2Fnx9fYVDhw5J+7DokVHd9/6OoqIiYc6cOULjxo0FlUol9O3bVzh+/Lh4fNmyZYKnp6egVqsFR0dHYdy4ccLly5cFQag6WVoQBOGtt94StFqtoFAohClTpgiCUHWytCAIQnZ2tqBSqQRra2shLy+vyrji4uKErl27CpaWloKDg4PQv39/4fPPP5f0ORA9DApB+NvECaJHVFpaGnQ6nTiHgoiICAAYCNEjaf/+/cjPz4eXlxfS09MREhKC69ev48KFC1AqlQ97eERE1EBwjhA9ksrKyvD666/j8uXLsLOzQ58+fRAXF8cgiIiIDDAjRERERCaLN1QkIiIik8VAiIiIiEwWAyEiIiIyWQyEiIiIyGQxECIiIiKTxUCIiKoVGhqKrl27iq+nTp2K8ePHP/BxXLlyBQqFAomJiTW2admyJdasWVPrPmNiYtCoUSPJY1MoFNi1a5fkfojo4WEgRCQjU6dOhUKhgEKhgFKpRKtWrTB//nwUFBTU+3u///77iImJqVXb2gQvREQNAW+oSCQzw4cPx6ZNm1BWVoYff/wRL7zwAgoKChAVFVWlbVlZmdFuIqnRaIzSDxFRQ8KMEJHMqFQqaLVa6HQ6+Pn5wd/fXyzP3Cln/d///R9atWoFlUoFQRCQk5OD6dOnw8XFBfb29njiiSfwyy+/GPT7zjvvwNXVFXZ2dggMDERxcbHB8X+WxvR6PVasWIE2bdpApVKhefPmWL58OQDA3d0dANCtWzcoFAoMHDhQPG/Tpk3w9PSElZUV2rdvjw8++MDgfY4fP45u3brBysoKPXr0wOnTp+v8GUVERMDLyws2NjbQ6XSYNWsW8vPzq7TbtWsX2rZtCysrKwwdOhSpqakGx7/66it4e3vDysoKrVq1wptvvony8vI6j4eIGi4GQkQyp1arUVZWJr6+ePEiPv30U+zYsUMsTY0aNQoZGRn45ptvcOrUKXTv3h2DBw/GrVu3AACffvopli5diuXLl+PkyZNo0qRJlQDlnxYuXIgVK1Zg8eLFOHfuHLZu3QpXV1cAlcEMAOzbtw/p6en4/PPPAQDR0dFYtGgRli9fjuTkZISFhWHx4sWIjY0FABQUFGD06NFo164dTp06hdDQUMyfP7/On4mZmRnWrl2Ls2fPIjY2Fvv370dISIhBm8LCQixfvhyxsbH46aefkJubi8mTJ4vHv/vuOzzzzDMICgrCuXPnsGHDBsTExIjBHhE9Ih7eg++JqK6mTJkijBs3Tnx97NgxwcnJSZg0aZIgCIKwdOlSQalUCpmZmWKb77//XrC3txeKi4sN+mrdurWwYcMGQRAEwcfHR5gxY4bB8V69egldunSp9r1zc3MFlUolREdHVzvOlJQUAYBw+vRpg/06nU7YunWrwb5ly5YJPj4+giAIwoYNGwRHR0ehoKBAPB4VFVVtX3/XokULYfXq1TUe//TTTwUnJyfx9aZNmwQAwtGjR8V9ycnJAgDh2LFjgiAIwuOPPy6EhYUZ9LN582ahSZMm4msAws6dO2t8XyJq+DhHiEhmdu/eDVtbW5SXl6OsrAzjxo1DZGSkeLxFixZwdnYWX586dQr5+flwcnIy6KeoqAiXLl0CACQnJ2PGjBkGx318fHDgwIFqx5CcnIySkhIMHjy41uPOyspCamoqAgMDMW3aNHF/eXm5OP8oOTkZXbp0gbW1tcE46urAgQMICwvDuXPnkJubi/LychQXF6OgoAA2NjYAAAsLC/To0UM8p3379mjUqBGSk5Px2GOP4dSpUzhx4oRBBqiiogLFxcUoLCw0GCMRyRcDISKZGTRoEKKioqBUKuHm5lZlMvSdH/o79Ho9mjRpgoMHD1bp636XkKvV6jqfo9frAVSWx3r16mVwzNzcHAAgGOEZ0FevXsXIkSMxY8YMLFu2DI6OjkhISEBgYKBBCRGoXP7+T3f26fV6vPnmm5gwYUKVNlZWVpLHSUQNAwMhIpmxsbFBmzZtat2+e/fuyMjIgIWFBVq2bFltG09PTxw9ehTPPvusuO/o0aM19unh4QG1Wo3vv/8eL7zwQpXjlpaWACozKHe4urqiadOmuHz5Mvz9/avtt0OHDti8eTOKiorEYOte46jOyZMnUV5ejlWrVsHMrHIa5KefflqlXXl5OU6ePInHHnsMAHD+/Hncvn0b7du3B1D5uZ0/f75OnzURyQ8DIaJH3JAhQ+Dj44Px48djxYoVaNeuHW7cuIFvvvkG48ePR48ePfDyyy9jypQp6NGjB/r164e4uDgkJSWhVatW1fZpZWWFV199FSEhIbC0tETfvn2RlZWFpKQkBAYGwsXFBWq1GvHx8WjWrBmsrKyg0WgQGhqKoKAg2NvbY8SIESgpKcHJkyeRnZ2NuXPnws/PD4sWLUJgYCDeeOMNXLlyBe+9916drrd169YoLy9HZGQkxowZg59++gnr16+v0k6pVGLOnDlYu3YtlEolXnrpJfTu3VsMjJYsWYLRo0dDp9Nh4sSJMDMzw6+//oozZ87g7bffrvv/EUTUIHHVGNEjTqFQ4JtvvkH//v3x/PPPo23btpg8eTKuXLkirvJ66qmnsGTJErz66qvw9vbG1atXMXPmzHv2u3jxYsybNw9LliyBp6cnnnrqKWRmZgKonH+zdu1abNiwAW5ubhg3bhwA4IUXXsBHH32EmJgYeHl5YcCAAYiJiRGX29va2uKrr77CuXPn0K1bNyxatAgrVqyo0/V27doVERERWLFiBTp16oS4uDiEh4dXaWdtbY1XX30Vfn5+8PHxgVqtxrZt28Tjvr6+2L17N/bu3YuePXuid+/eiIiIQIsWLeo0HiJq2BSCMYryRERERDLEjBARERGZLAZCREREZLIYCBEREZHJYiBEREREJouBEBEREZksBkJERERkshgIERERkcliIEREREQmi4EQERERmSwGQkRERGSyGAgRERGRyfr/xc2MklrYt+QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmd.from_estimator(\n",
    "    model, x_test, y_test, display_labels=['Negative', 'Positive']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09183229467277926"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = 'The long lines and poor customer service really turned me off'\n",
    "model.predict_proba(vectorizer.transform([text]))[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8536148133593483"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = 'The food was great and the service was excellent!'\n",
    "model.predict_proba(vectorizer.transform([text]))[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}