nlp-carlo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This project has a large file size*\n",
    "\n",
    "Follow these instructions to use LFS on GitHub\n",
    "- git lfs install\n",
    "- git lfs track 'data/yelp-reviews.csv'\n",
    "- git add .gitattributes\n",
    "- git add data/yelp-reviews.csv\n",
    "- git commit -m 'Track large file with Git LFS'\n",
    "- git push origin main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "# nltk.download('punkt')\n",
    "# nltk.download('stopwords')\n",
    "# nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Load 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>business_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>stars</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gGaF0TtGRx1QOFe0EzuZLg</td>\n",
       "      <td>8W3g9ykr8aGGZMMdVbNPKA</td>\n",
       "      <td>2015-01-01 00:01:02</td>\n",
       "      <td>Lynn and Montecito flowers are simply the best...</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2CqQ3Tnvj7h0rw84WEEjMw</td>\n",
       "      <td>q10HcbeBRtcvsN7WC02fFw</td>\n",
       "      <td>2015-01-01 00:15:12</td>\n",
       "      <td>Love this place! Always loved the food as they...</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lOa1BVmTcbPpu05h3OPAQA</td>\n",
       "      <td>dXOY5UqXhcCQ6f9S-kRc2w</td>\n",
       "      <td>2015-01-01 00:19:36</td>\n",
       "      <td>We stayed here last weekend with our dog. Its ...</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>QFh-h_OZ5Io-mBk32HN28A</td>\n",
       "      <td>8mWVoTXecEkRXTvPONYFuw</td>\n",
       "      <td>2015-01-01 00:20:23</td>\n",
       "      <td>Came here for a quick bite and got the thai sa...</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cAbdvzqtFLaAAMFIyPf2AA</td>\n",
       "      <td>Xr5KEchLr-jvQiAZm1mtNA</td>\n",
       "      <td>2015-01-01 00:47:19</td>\n",
       "      <td>Finally was able to try lunch at Holdren's.  M...</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id               review_id                 date  \\\n",
       "0  gGaF0TtGRx1QOFe0EzuZLg  8W3g9ykr8aGGZMMdVbNPKA  2015-01-01 00:01:02   \n",
       "1  2CqQ3Tnvj7h0rw84WEEjMw  q10HcbeBRtcvsN7WC02fFw  2015-01-01 00:15:12   \n",
       "2  lOa1BVmTcbPpu05h3OPAQA  dXOY5UqXhcCQ6f9S-kRc2w  2015-01-01 00:19:36   \n",
       "3  QFh-h_OZ5Io-mBk32HN28A  8mWVoTXecEkRXTvPONYFuw  2015-01-01 00:20:23   \n",
       "4  cAbdvzqtFLaAAMFIyPf2AA  Xr5KEchLr-jvQiAZm1mtNA  2015-01-01 00:47:19   \n",
       "\n",
       "                                                text  stars language  \n",
       "0  Lynn and Montecito flowers are simply the best...      5       en  \n",
       "1  Love this place! Always loved the food as they...      5       en  \n",
       "2  We stayed here last weekend with our dog. Its ...      5       en  \n",
       "3  Came here for a quick bite and got the thai sa...      2       en  \n",
       "4  Finally was able to try lunch at Holdren's.  M...      2       en  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load data\n",
    "def load_data(file_path):\n",
    "    df = pd.read_csv(file_path)\n",
    "    \n",
    "    return df\n",
    "\n",
    "file_path = 'data/yelp-reviews.csv'\n",
    "df = load_data(file_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **EDA**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values: business_id    0\n",
      "review_id      0\n",
      "date           0\n",
      "text           0\n",
      "stars          0\n",
      "language       0\n",
      "dtype: int64\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150374 entries, 0 to 150373\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count   Dtype \n",
      "---  ------       --------------   ----- \n",
      " 0   business_id  150374 non-null  object\n",
      " 1   review_id    150374 non-null  object\n",
      " 2   date         150374 non-null  object\n",
      " 3   text         150374 non-null  object\n",
      " 4   stars        150374 non-null  int64 \n",
      " 5   language     150374 non-null  object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 6.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# EDA\n",
    "def perform_eda(df):\n",
    "    missing_values = df.isna().sum()\n",
    "    \n",
    "    print(f'Number of missing values: {missing_values}')\n",
    "\n",
    "perform_eda(df)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stars</th>\n",
       "      <td>150374.0</td>\n",
       "      <td>4.006045</td>\n",
       "      <td>1.415824</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min  25%  50%  75%  max\n",
       "stars  150374.0  4.006045  1.415824  1.0  3.0  5.0  5.0  5.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics\n",
    "statistics = df.describe().T\n",
    "print('Summary Statistics')\n",
    "statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data Visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6evSoqlatqj/+8Y+F2uelnDt3To888oh69+6t9evX6/z58/L29lZoaKhee+01rVixQlFRUbmuZSyKe+65R2PHjtWCBQu0cuVKRUVFKSoqStWqVVOVKlV044035rndnDlzLvsnUeai67mOHTumsWPHKjk5Wf369dP06dPz3MbLy0t/+ctfNHXqVP3000/673//q6ioKNedUW+99dbLqik/RRlHHTt2lCT95z//yXUnVkn5Thrs/Kzo0qVLgdc1lQTn++XTTz/Nc/2ECRPc+uX07bff5vpJaWFc7phu0qSJhg8frm+//VbLly93jVfn9YD5vf4//fST4uLici13XrPovLZNkqpUqaK2bdtKUp4/EU1MTNR//vOfAo/z2LFjeX42L1iwQIcPH873cyIkJEQjRozQDz/84HouoqKidNNNN0mSWrRokefjFff1AK51XEMGIF9Vq1aVpDy/bOW0Zs0arV27Vq1bt9bRo0e1ZMkS/fe//9XKlSu1adOmy6pj7969On/+fLG3z+96BOdyf39/+fj45LoT2tXi/BJy5MiRfL9s7Nixw61vTvv3789z+cmTJ1W/fn1VrVpVSUlJha5l586dea43xmjXrl0KDAzUTTfdpMWLF19yn4Vx/vx5ffHFF/riiy9UvXp1tW7dWm3btlXnzp0VFBSkdu3aKTIyUsHBwcrKyir0fitUqKCvv/5aXbp0KbBffoG1JCZId17f5uXlpcaNG6t69eo6ffq0oqKi8uwfGBioyMhI1+3ci1Lv5SrKOHKGWOdEzDkdOXJEp0+fdt3Mx2nGjBkaMGCA/vrXv+r+++/XTz/9pKioKC1fvjzfaySL61Lj2fm+yu8z4HJe/+KO6YEDB+qDDz4oMKzm9/rn9/o5r5F0fqZLUuPGjVWuXDmlp6fr0KFDeW53qePfvXt3vn+w2L17txo0aJDrc+LTTz/VSy+9VOB+r+T7EbgWcYYMQL7q168vSfne8OBixhjdf//9Gj9+vNLT0/Xoo49q7Nix2rhxow4ePHhZd6Zznqkrrvzqv3h5Yc4CXinOL0kFPc+JiYmS8q8zvyDnDLIeHh5XrZbLlZSUpB9//FFDhw5V8+bN9dprr0m6cCbRedOYwho4cKC6dOmi+Ph49ezZUw0aNJC3t7c8PDzk4eHhCksVKlTIc/vLHXvShZvK3HnnnWrVqpX8/f01fPhw3Xjjjfrpp5/yvMvjl19+qaZNm2rt2rW67777VKdOHVWoUEEeHh4qX758gfVerqKMoypVqkhSgX/IyGtdfHy82rRpo2+++UbVqlVTr169NH36dB04cECrV69W69atL+cQ3FxqPDvHspT3eC6J118q/Ji+8847NWLECBljNHDgQDVr1kxVqlRxjdcPPvhAUtHHa16vn/O5+fXXX/Ot++LnJy9F/Zzo2bOnXnrpJaWmpqpv375q3LixKlWq5Dq+OXPmSLqy70fgWkQgA5AnDw8PtWnTRpK0bt26Qm3jcDj0+uuvq1atWgoODtYrr7yin3/+WQ0bNtSXX36prl27XsmS81WrVq1LLr/4i6PzL775hRjnF9GS4rxzX0GTxNapU0dSwV9+S1stThMmTHCNwaLetc5518pevXppzpw5OnLkiNvZiPxus32lZGdn691339X333+vgIAAffzxx27rAwIC1L59e505c0YPPPCAIiMjdeLECZ09e9ZKvQVxfjm++KxLTvmF9l27dunxxx+Xn5+f7r77bg0bNkyxsbFq06aNIiMjC5zCoiguNZ6dY1m6euNZyn9MO8frJ598opEjRyo2NtYtJJfk6+98bnJOu3CxS01cnd9n68XbXvy8Oo/vjTfe0JQpU7R//37XHVGla2t8A1cTgQxAnh599FEFBAQoKytLkZGRRd5+69atmjRpku69916NGDFCktSnTx+3Ppd7bU5h5Xf7befyhIQEty8Nzi+a+X3ZaNy4cZ7Li3s8zlt0169fP9+wd8stt7j1vVKc+89vviMPDw/XT+mudC0XO3DggKQLP/srCufcXKtXr861rnr16qpXr95l11YcgwYN0rlz59SrVy+3+Z6cQWTXrl367bffcm1X0LVjV+v95OR8/fO73icwMDDXzxVzysrK0sqVK/Xee+8pKChIv/zyi3x8fPSXv/ylRGvMbzw731c5PwOuhrzGdEHjVSrZawf37dunc+fOqVKlSvkG4Pw+O52aNGmS7x+umjRpIsn9c6Kg4ytfvvwlHw8orQhkAHKpX7++6yL4WbNm6fjx45e1v7Vr10qS6tat67bcOT9NfhOIlpTevXvn+UXeOfdOzsDp/KIUGhqaa5uQkJB8vxQV93hiY2N1+PBhVapUSc8++2yu9QEBAa6ziyV1zVZ+IiMjdf78ed155515Tgb82GOPKTAw0DW5dkko6K/s0oUvas7XYu/evW7rnGcP8puXzvmaXHwmxOmNN95w/QTwatu1a5fmzZun8uXL6+2333Ytd9ab35mJt956K999Xq33k9OSJUskXRgTeZ0l69WrV5H2d/78edfNJ3J+VhSX8/3Sr1+/PNe/8sorbv1KSnHHdEHjtWPHjsWe6D0vZ86c0Zo1ayRJL7zwQq71tWvX1mOPPVbgPgIDA/Xwww/nWv7ggw+qYcOGuT4nCjq+v/71r5c8IweUVgQyAC41atTQyy+/rA0bNqhu3brasWOH+vfvX6htn3jiCQ0dOjTXX1qrV6/u+tKT8+YezuDjvHPYlVKjRg1Nnz7dbTLevn376rHHHtPZs2c1duxYt/6LFi2SdOGM3sWhrHHjxpo5c6br52M5OY+nbdu2Rb573CeffCJJevfdd9W+fXvX8tq1a+urr76St7e31qxZoxUrVhRpv0W1f/9+153VZs2apUaNGrnW3XbbbZo4caKkCxfml9QkyX//+981b948PfTQQ7mC1Q033KCvv/5af/jDH3TmzBnNnTvXbf2lxpDzGrExY8a4nX3s2bOn3nzzzVyT1l5NI0eOlCQ99dRTrjN1O3bsUFJSkgIDAzVkyBBXX29vb40fP1633XZbvvu7Wu8np6VLl2rr1q2qVauWIiIi3M6Gde7cWYMGDcrzBiwffPCBnnnmmVxnz2655Rb9+c9/lpT7s6K4Pv/8c50+fVq33Xabxo4d67o2ycPDQwMGDNBDDz2krKwsjRkzpkQez6m4Y9o5XgcOHOg6myRJLVu21BdffFHi43X06NGSpFdffVWdO3d2La9Ro4b++c9/ytOz4K+J2dnZmjRpkoKCglzLbr75Ztcf9KZMmeL2OeE8vg8++MDtp5L33XefPvnkE6vvR8A26/fep9FoV6855yravXu3iYqKMlFRUWbdunXmwIEDbnPmfP3117kmbXW2vOb9efXVV13bHj161ERHR5tt27a55rE5evSoCQwMdNtPjx49XNts27bNLF++3CxfvtzceuutRvrfXE7Lly8v1DHlNw/Z0KFDTUZGhjl9+rRZt26dOXbsmOtx33zzzTz3GRkZaYwx5uzZsyY2NtZs27bNnD171qxYscLMmTPHGJN7HjIfHx9z6tQpY4wxcXFxJioqyixfvty8/fbbBT53zubcrzHG7Nmzx2zYsMH1/B06dCjXZLHSpefhKs7cYDVr1nRN+JudnW02b95sYmJiXI8VGRmZ57xuxZ2H7D//+Y9r35mZmWbHjh1m7dq15tChQ+bs2bPGGGPS0tJM165dc207dOhQV50bN250jaE6deoY6cKE5c45vxwOh1m/fr3r9Z85c2a+r8el5qK7VLvUxNDOtnLlSmPMhUmLnctefPFF17bHjx8369atMw6Hw5w7d85twuGc+7rU+6kw85AVdRw1b97cJCUlGWMuTCp98WfJhAkTXNtdPCn8d999Z4y58N7as2ePWbt2rdmzZ4+rhmXLluWaF66gdqnX6uGHH3a9j06dOmWio6NNQkKCq4Y+ffrk2ia/uQav9Jj28fEx+/btM8ZcmLds69atJjY21hhjTExMjBk9erQxJvdch5eaA9E5b1jO+cQkmSlTprhq3b9/v1m/fr1JS0szSUlJ5oMPPshzO+f+IiIizMaNG825c+fMtm3bzNatW13zGEZHR+eaMywwMND8+uuvxpgLc6Zt2rTJNV6WLVtmZs+enefzfrmvB432O2jWC6DRaFexOb8gXSw5OdkcOXLEREZGmvfff980bdq0wH3k9QXo+uuvNwMGDDCLFy82hw4dMmlpaebkyZNmw4YNZvDgwaZatWp57uvll182W7ZsMWfOnHHV49xvSQWysLAwExoaahYuXGiSkpLMmTNnzOrVq/Oc0NTZqlSpYkaPHm2OHDliMjIyzP79+837779vvLy8CvxyEBISYhYuXGh+/fVX1xevi7/MXOrLY48ePczKlSuNw+Ew6enpZvfu3WbkyJGmevXqefZ3Kupzc6lWuXJlM3ToULNt2zZz5swZk5KSYqKjo81LL71kypcvX6KP5ePjYx577DEzbdo0s3HjRhMfH2+ysrKMw+EwmzZtMp988kmeYVSSKV++vBk2bJiJjY11Ba+cNYSGhprFixeb5ORkk5KSYjZt2mT69etX4OtxtQLZAw88YIy5EGZq1qzpWv7EE0+YTZs2mYyMDJOUlGSWLl1q7rvvvku+5gW9n65EIJMuTFYdERFhTp48adLS0szWrVvNiy++aCSZEydOGGOM2/s/JCTEfPTRR2bNmjXm+PHjJiMjwxw9etQsX77c9OjRo0hhrLCv1S233GJmz55t4uLiTGZmpklMTDTffvutad26dZ79LzcAXM6Y9vf3N19++aU5ceKE67Nn9OjRxsfHJ9/gdTmBTJLp06eP2bJli0lPTzeJiYnmq6++Mn/4wx/y3e7i5VWrVjVjx441hw4dMhkZGebgwYPmo48+yncC5xtvvNF888035rfffjNpaWlm586d5m9/+5upUKFCvs87gYxW2pvH//8HAABAialevbpOnTql33777YrNmwYApQHXkAEAgBL317/+VVL+dwwEAFxAIAMAAMUSFBSkPn365Jqu4cknn9T7778v6cKNHQAA+eMniwAAoFjCwsK0YsUKnT17VocPH9apU6d0ww03uO6gN2XKFPXt29dylQBwbSOQAQCAYqlVq5beeOMNhYeHuyaCTk5O1ubNmzVt2rRc0xQAAHIjkAEAAACAJVxDBgAAAACWlLddQGlTt25dpaSk2C4DAAAAgGU+Pj46fvx4gX0IZCWobt26iouLs10GAAAAgGtEvXr1CgxlBLIS5DwzVq9ePc6SAQAAAGWYj4+P4uLiLpkLCGRXQEpKCoEMAAAAwCVxUw8AAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAkvK2CwAAAAAghb+3wHYJv0uR7zxku4TLwhkyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWGI1kJUrV07vv/++Dhw4oLS0NO3fv19/+9vf5OHh4dZv2LBhiouLU1pampYvX65mzZq5rffy8tLEiRN18uRJpaam6ocfflC9evXc+vj5+WnWrFlyOBxyOByaNWuWqlWr5tYnMDBQ8+bNU2pqqk6ePKkJEyaoQoUKV+bgAQAAAJR5VgPZ22+/rRdeeEH9+vXTzTffrLfeeksDBgzQyy+/7Orz1ltvqX///urXr59CQ0OVkJCgJUuWqGrVqq4+48ePV5cuXdS9e3e1a9dOVatW1YIFC+Tp+b/Di4iIUHBwsDp16qROnTopODhYs2fPdq339PTUwoULVaVKFbVr107du3dX165dNWbMmKvzZAAAAAAoczwkGVsPPn/+fCUmJurZZ591Lfvmm2+Ulpamp556SpJ0/PhxjR8/XqNGjZJ04WxYYmKi3n77bU2dOlW+vr46efKkevbsqblz50qSAgICdPToUT3wwAOKjIxU06ZNFRsbq1atWmndunWSpFatWmnt2rVq0qSJ9uzZo06dOmnBggUKDAxUfHy8JKlbt2768ssvVbt2baWkpOSq38vLS97e3q5/+/j4KC4uTr6+vnn2BwAAAPIT/t4C2yX8LkW+85DtEvLk4+Oj5OTkS2YDq2fIfvnlF91777268cYbJUktWrRQu3bt9OOPP0qSGjVqpICAAEVGRrq2ycrK0sqVK9W2bVtJUkhIiLy8vNz6xMfHKyYmxtWnTZs2cjgcrjAmSdHR0XI4HG59YmJiXGFMkhYvXqyKFSsqJCQkz/oHDRqk5ORkV4uLiyuJpwUAAABAGVHe5oOPHDlS1apV065du3Tu3DmVK1dOQ4YM0VdffSVJ8vf3lyQlJia6bZeYmKgGDRq4+mRmZsrhcOTq49ze399fJ06cyPX4J06ccOuT83EcDocyMzNdfXIaMWKExo4d6/q38wwZAAAAABSG1UDWrVs39ejRQ0888YR27Nih4OBgjR8/XsePH9esWbNc/Yxx/1Wlh4dHrmU55eyTV//i9LlYVlaWsrKyCqwDAAAAAPJj9SeLn3zyiT7++GN9/fXXiomJ0Zw5czRu3DgNGjRIkpSQkCBJuc5Q1a5d23U2KyEhQd7e3vLz8yuwT506dXI9fq1atdz65HwcPz8/1zVrAAAAAFDSrAayypUr6/z5827Lzp0757o74sGDBxUfH6+OHTu61leoUEFhYWFavXq1JGnjxo3Kyspy6+Pv76+goCBXnzVr1sjPz0+hoaGuPnfccYf8/Pzc+gQFBbmFsvDwcGVkZGjjxo0lfOQAAAAAYPkni/Pnz9eQIUN05MgR7dixQ7fddpv69++vL774wtVn/PjxGjx4sPbu3au9e/dq8ODBSktLU0REhCQpOTlZ06dP15gxY3Tq1CklJSVp9OjR2r59u5YuXSpJ2rVrlxYtWqRp06bp+eeflyRNnTpV8+fP1549eyRJkZGR2rlzp2bPnq0BAwaoevXqGj16tKZNm8YdEwEAAABcEVYD2csvv6z3339fkydPVu3atXX8+HH9/e9/13vvvefqM2rUKFWqVEmTJ0/Wddddp+joaIWHhys1NdXV5/XXX9fZs2c1d+5cVapUScuWLVOvXr3czr49+eSTmjhxoutujPPmzVO/fv1c68+fP68HH3xQkydP1qpVq5Senq6IiAi9+eabV+GZAAAAAFAWWZ2HrLQp7FwDAAAAQE7MQ1Y8zEMGAAAAACgWAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYYj2Q1a1bV7Nnz9avv/6qM2fOaPPmzbr99tvd+gwbNkxxcXFKS0vT8uXL1axZM7f1Xl5emjhxok6ePKnU1FT98MMPqlevnlsfPz8/zZo1Sw6HQw6HQ7NmzVK1atXc+gQGBmrevHlKTU3VyZMnNWHCBFWoUOHKHDgAAACAMs9qIPPz89OqVauUnZ2t+++/X82aNdMbb7whh8Ph6vPWW2+pf//+6tevn0JDQ5WQkKAlS5aoatWqrj7jx49Xly5d1L17d7Vr105Vq1bVggUL5On5v8OLiIhQcHCwOnXqpE6dOik4OFizZ892rff09NTChQtVpUoVtWvXTt27d1fXrl01ZsyYq/JcAAAAACh7PCQZWw8+YsQI/fGPf9Rdd92Vb5/jx49r/PjxGjVqlKQLZ8MSExP19ttva+rUqfL19dXJkyfVs2dPzZ07V5IUEBCgo0eP6oEHHlBkZKSaNm2q2NhYtWrVSuvWrZMktWrVSmvXrlWTJk20Z88ederUSQsWLFBgYKDi4+MlSd26ddOXX36p2rVrKyUl5ZLH4+Pjo+TkZPn6+haqPwAAAOAU/t4C2yX8LkW+85DtEvJU2Gxg9QzZI488og0bNmju3LlKTEzUpk2b9Oyzz7rWN2rUSAEBAYqMjHQty8rK0sqVK9W2bVtJUkhIiLy8vNz6xMfHKyYmxtWnTZs2cjgcrjAmSdHR0XI4HG59YmJiXGFMkhYvXqyKFSsqJCQkz/q9vLzk4+Pj1gAAAACgsKwGshtuuEF9+/bV3r17dd9992nKlCmaOHGievbsKUny9/eXJCUmJrptl5iY6Frn7++vzMxMt5855tXnxIkTuR7/xIkTbn1yPo7D4VBmZqarT06DBg1ScnKyq8XFxRXxGQAAAABQllkNZJ6entq0aZOGDBmiLVu2aOrUqZo2bZr69u3r1s8Y919Venh45FqWU84+efUvTp+LjRgxQr6+vq6W80YiAAAAAFAQq4EsPj5eO3fudFsWGxur+vXrS5ISEhIkKdcZqtq1a7vOZiUkJMjb21t+fn4F9qlTp06ux69Vq5Zbn5yP4+fn57pmLS9ZWVlKSUlxawAAAABQWFYD2apVq9SkSRO3ZTfddJMOHz4sSTp48KDi4+PVsWNH1/oKFSooLCxMq1evliRt3LhRWVlZbn38/f0VFBTk6rNmzRr5+fkpNDTU1eeOO+6Qn5+fW5+goCC3UBYeHq6MjAxt3LixhI8cAAAAAKTyNh983LhxWr16tQYNGqS5c+fqjjvu0HPPPafnnnvO1Wf8+PEaPHiw9u7dq71792rw4MFKS0tTRESEJCk5OVnTp0/XmDFjdOrUKSUlJWn06NHavn27li5dKknatWuXFi1apGnTpun555+XJE2dOlXz58/Xnj17JEmRkZHauXOnZs+erQEDBqh69eoaPXq0pk2bxpkvAAAAAFeE1UC2YcMGdenSRSNGjNA777yjgwcP6rXXXnOFLUkaNWqUKlWqpMmTJ+u6665TdHS0wsPDlZqa6urz+uuv6+zZs5o7d64qVaqkZcuWqVevXjp//ryrz5NPPqmJEye67sY4b9489evXz7X+/PnzevDBBzV58mStWrVK6enpioiI0JtvvnkVngkAAAAAZZHVechKG+YhAwAAQHExD1nxMA8ZAAAAAKBYCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYEmxAtn+/ftVvXr1XMurVaum/fv3X3ZRAAAAAFAWFCuQNWzYUOXKlcu13NvbW/Xq1bvsogAAAACgLChflM4PP/yw67/vu+8+nT592vXvcuXK6d5779WhQ4dKrDgAAAAAKM2KFMi+//57SZIxRjNnznRbl52drUOHDumNN94oseIAAAAAoDQrUiBz/kzxwIEDCg0N1alTp65IUQAAAABQFhQpkDndcMMNJV0HAAAAAJQ5xQpkktS+fXvde++9ql27tjw93e8N0rt378suDAAAAABKu2IFsnfeeUfvvPOONmzYoPj4eBljSrouAAAAACj1ihXIXnjhBfXq1Utz5swp6XoAAAAAoMwo1jxkXl5eWr16dUnXAgAAAABlSrEC2T/+8Q898cQTJV0LAAAAAJQpxfrJYsWKFfXcc8+pQ4cO2rZtm7Kzs93WMxcZAAAAAFxasQJZixYttGXLFklSUFCQ2zpu8AEAAAAAhVOsQNa+ffuSrgMAAAAAypxiXUMGAAAAALh8xTpD9vPPPxf408R777232AUBAAAAQFlRrEDmvH7MqUKFCgoODlZQUJBmzpxZEnUBAAAAQKlXrEDWv3//PJcPGzZMVatWvayCAAAAAKCsKNFryObMmaNnnnmmJHcJAAAAAKVWiQayNm3aKCMjoyR3CQAAAAClVrF+svjtt9+6/dvDw0MBAQFq2bKl3n///RIpDAAAAABKu2IFstOnT7v9+/z589q9e7feeecdLVmypEQKAwAAAIDSrliBjOvEAAAAAODyFSuQOd1+++26+eabZYzRzp07c90OHwAAAACQv2IFslq1aumrr77S3XffLYfDIQ8PD1WrVk3Lly9X9+7d9euvv5Z0nQAAAABQ6hTrLouTJk2Sr6+vbrnlFtWoUUPVq1dXUFCQfH19NXHixJKuEQAAAABKpWKdIevUqZM6dOigXbt2uZbFxsbqpZdeUmRkZIkVBwAAAAClWbHOkHl6eio7OzvX8uzsbHl6lujUZgAAAABQahUrPf3888+aMGGCAgICXMvq1q2rcePGadmyZSVWHAAAAACUZsUKZP369ZOPj48OHTqkffv2ae/evTp48KB8fHz08ssvl3SNAAAAAFAqFesasmPHjikkJEQdOnRQ06ZN5eHhoZ07d3J2DAAAAACKoEhnyO655x7t2LFDPj4+kqSlS5fq008/1aRJk7R+/XrFxMSoXbt2V6RQAAAAAChtihTIXnvtNU2bNk0pKSm51iUnJ+vvf/+7+vfvX2LFAQAAAEBpVqRAduutt+qnn37Kd31kZKRCQkIuuygAAAAAKAuKFMjq1KmT5+3unc6ePatatWpddlEAAAAAUBYUKZDFxcWpefPm+a5v0aKF4uPjL7soAAAAACgLihTIfvzxR7333nvy9vbOta5ixYp69913tWDBghIrDgAAAABKsyLd9v6DDz7QY489pj179ujTTz/V7t27ZYzRzTffrJdeeknlypXThx9+eKVqBQAAAIBSpUiB7MSJE2rbtq0+//xzjRgxQh4eHpIkY4wWL16sF198USdOnLgihQIAAABAaVPkiaGPHDmiBx98UH5+fmrcuLE8PDy0d+9eORyOK1AeAAAAAJReRQ5kTg6HQxs2bCjJWgAAAACgTCnSTT2upIEDB8oYo3HjxrktHzZsmOLi4pSWlqbly5erWbNmbuu9vLw0ceJEnTx5Uqmpqfrhhx9Ur149tz5+fn6aNWuWHA6HHA6HZs2apWrVqrn1CQwM1Lx585SamqqTJ09qwoQJqlChwpU5WAAAAADQNRLIWrZsqeeee05bt251W/7WW2+pf//+6tevn0JDQ5WQkKAlS5aoatWqrj7jx49Xly5d1L17d7Vr105Vq1bVggUL5On5v0OLiIhQcHCwOnXqpE6dOik4OFizZ892rff09NTChQtVpUoVtWvXTt27d1fXrl01ZsyYK3/wAAAAAMos64GsSpUq+uc//6k+ffrot99+c1v32muv6cMPP9R3332nHTt26Omnn1blypX1xBNPSJJ8fX3Vu3dvvfHGG1q2bJm2bNmiHj16qHnz5urQoYMkqWnTprr//vv17LPPau3atVq7dq369Omjhx9+WDfddJMkKTw8XM2aNVOPHj20ZcsWLVu2TG+88Yb69OkjHx+fq/uEAAAAACgzrAeyzz77TAsXLtSyZcvcljdq1EgBAQGKjIx0LcvKytLKlSvVtm1bSVJISIi8vLzc+sTHxysmJsbVp02bNnI4HFq3bp2rT3R0tBwOh1ufmJgYt0mtFy9erIoVKyokJCTf2r28vOTj4+PWAAAAAKCwin1Tj5LQrVs33X777QoNDc21zt/fX5KUmJjotjwxMVENGjRw9cnMzMx1h8fExETX9v7+/nneiv/EiRNufXI+jsPhUGZmpqtPXgYNGqThw4cXfJAAAAAAkA9rZ8iuv/56TZgwQT169FBmZma+/Ywxbv/28PDItSynnH3y6l+cPjmNGDFCvr6+rpbzZiIAAAAAUBBrgSwkJER16tTRxo0blZ2drezsbN1999165ZVXlJ2d7TpjlfMMVe3atV3rEhIS5O3tLT8/vwL71KlTJ9fj16pVy61Pzsfx8/OTl5dXrjNnF8vKylJKSopbAwAAAIDCshbIli1bpqCgIAUHB7va+vXr9c9//lPBwcE6cOCA4uPj1bFjR9c2FSpUUFhYmFavXi1J2rhxo7Kystz6+Pv7KygoyNVnzZo18vPzc/tZ5B133CE/Pz+3PkFBQW6hLDw8XBkZGdq4ceMVfR4AAAAAlF3WriFLTU3Vjh073JadOXNGp06dci0fP368Bg8erL1792rv3r0aPHiw0tLSFBERIUlKTk7W9OnTNWbMGJ06dUpJSUkaPXq0tm/frqVLl0qSdu3apUWLFmnatGl6/vnnJUlTp07V/PnztWfPHklSZGSkdu7cqdmzZ2vAgAGqXr26Ro8erWnTpnHWCwAAAMAVY/WmHpcyatQoVapUSZMnT9Z1112n6OhohYeHKzU11dXn9ddf19mzZzV37lxVqlRJy5YtU69evXT+/HlXnyeffFITJ0503Y1x3rx56tevn2v9+fPn9eCDD2ry5MlatWqV0tPTFRERoTfffPPqHSwAAACAMsdDUsF3yECh+fj4KDk5Wb6+vpxZAwAAQJGEv7fAdgm/S5HvPGS7hDwVNhtYn4cMAAAAAMoqAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCkvO0CAAAArmXh7y2wXcLvUuQ7D9kuAfhd4AwZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYInVQDZw4ECtW7dOycnJSkxM1HfffaebbropV79hw4YpLi5OaWlpWr58uZo1a+a23svLSxMnTtTJkyeVmpqqH374QfXq1XPr4+fnp1mzZsnhcMjhcGjWrFmqVq2aW5/AwEDNmzdPqampOnnypCZMmKAKFSqU/IEDAAAAgCwHsrCwMH322Wdq3bq1OnbsqPLlyysyMlKVK1d29XnrrbfUv39/9evXT6GhoUpISNCSJUtUtWpVV5/x48erS5cu6t69u9q1a6eqVatqwYIF8vT83+FFREQoODhYnTp1UqdOnRQcHKzZs2e71nt6emrhwoWqUqWK2rVrp+7du6tr164aM2bM1XkyAAAAAJQ5HpKM7SKcatasqZMnT+quu+5SVFSUJOn48eMaP368Ro0aJenC2bDExES9/fbbmjp1qnx9fXXy5En17NlTc+fOlSQFBATo6NGjeuCBBxQZGammTZsqNjZWrVq10rp16yRJrVq10tq1a9WkSRPt2bNHnTp10oIFCxQYGKj4+HhJUrdu3fTll1+qdu3aSklJuWT9Pj4+Sk5Olq+vb6H6AwCAa1/4ewtsl/C7FPnOQ7ZL+N1hrBXPtTrWCpsNrqlryJw/IUxKSpIkNWrUSAEBAYqMjHT1ycrK0sqVK9W2bVtJUkhIiLy8vNz6xMfHKyYmxtWnTZs2cjgcrjAmSdHR0XI4HG59YmJiXGFMkhYvXqyKFSsqJCQkz3q9vLzk4+Pj1gAAAACgsK6pQDZ27FhFRUVpx44dkiR/f39JUmJiolu/xMRE1zp/f39lZmbK4XAU2OfEiRO5Hu/EiRNufXI+jsPhUGZmpqtPToMGDVJycrKrxcXFFfGIAQAAAJRl10wg+/TTT9WiRQv95S9/ybXOGPdfVXp4eORallPOPnn1L06fi40YMUK+vr6ulvNGIgAAAABQkGsikE2cOFGPPPKI7rnnHrezTAkJCZKU6wxV7dq1XWezEhIS5O3tLT8/vwL71KlTJ9fj1qpVy61Pzsfx8/NzXbOWl6ysLKWkpLg1AAAAACgs64Fs0qRJeuyxx9S+fXsdOnTIbd3BgwcVHx+vjh07upZVqFBBYWFhWr16tSRp48aNysrKcuvj7++voKAgV581a9bIz89PoaGhrj533HGH/Pz83PoEBQW5hbLw8HBlZGRo48aNJX7cAAAAAFDe5oN/9tlneuKJJ9S5c2elpKS4zmKdPn1aGRkZki7c0n7w4MHau3ev9u7dq8GDBystLU0RERGSpOTkZE2fPl1jxozRqVOnlJSUpNGjR2v79u1aunSpJGnXrl1atGiRpk2bpueff16SNHXqVM2fP1979uyRJEVGRmrnzp2aPXu2BgwYoOrVq2v06NGaNm0aZ74AAAAAXBFWA9mLL74oSVq5cqXb8l69emnmzJmSpFGjRqlSpUqaPHmyrrvuOkVHRys8PFypqamu/q+//rrOnj2ruXPnqlKlSlq2bJl69eql8+fPu/o8+eSTmjhxoutujPPmzVO/fv1c68+fP68HH3xQkydP1qpVq5Senq6IiAi9+eabV+z4AQAAAJRt19Q8ZL93zEMGAEDpw9xQxXOtzg11LWOsFc+1OtZ+l/OQAQAAAEBZYvUni7h6+ItL8Vyrf3EBAABA6cAZMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYUt52AQAAFEf4ewtsl/C7FPnOQ7ZLAABchDNkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYwjxkAEoUc0MVD3NDAQBQNnGGDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAAAAYAmBDAAAAAAsIZABAAAAgCUEMgAAAACwhEAGAAAAAJYQyAAAAADAEgIZAAAAAFhCIAMAAAAASwhkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIcujbt68OHDig9PR0bdiwQe3atbNdEgAAAIBSikB2kT//+c8aP368PvzwQ912222KiorSokWLFBgYaLs0AAAAAKUQgewi/fv31/Tp0zV9+nTt2rVLr7/+uo4ePaq+ffvaLg0AAABAKVTedgHXigoVKigkJEQff/yx2/LIyEi1bds2z228vLzk7e3t+rePj4/b/15LKnvxUhfHtfhaXusYa8XDWCs6xlrxMNaKjrFWPIy1omOsFc+1OtYKWxev+v+rWbOmypcvr8TERLfliYmJ8vf3z3ObQYMGafjw4bmWx8XFXYkSYcPbybYrQFnBWMPVwljD1cJYw9VyjY81Hx8fpaSk5LueQJaDMcbt3x4eHrmWOY0YMUJjx451W1a9enUlJSVdsfpKGx8fH8XFxalevXoFDlTgcjHWcLUw1nC1MNZwtTDWis/Hx0fHjx8vsA+B7P/9+uuvOnv2bK6zYbVr18511swpKytLWVlZbssYpMWTkpLCc4ergrGGq4WxhquFsYarhbFWdIV5vripx//Lzs7Wxo0b1bFjR7flHTt21OrVqy1VBQAAAKA04wzZRcaOHavZs2drw4YNWrNmjZ577jnVr19fU6ZMsV0aAAAAgFKIQHaRuXPnqkaNGnrnnXcUEBCgmJgYPfDAAzpy5Ijt0kqtzMxMDR8+XJmZmbZLQSnHWMPVwljD1cJYw9XCWLuyPCTlfccKAAAAAMAVxTVkAAAAAGAJgQwAAAAALCGQAQAAAIAlBDIAAAAAsIRABivuvPNOzZs3T3FxcTLGqHPnzrZLQik1cOBArVu3TsnJyUpMTNR3332nm266yXZZKIVeeOEFbd26VadPn9bp06e1evVqderUyXZZKOUGDhwoY4zGjRtnuxSUQsOGDZMxxq3Fx8fbLqvUIZDBiipVqmjr1q3q16+f7VJQyoWFhemzzz5T69at1bFjR5UvX16RkZGqXLmy7dJQyhw7dkwDBw5Uy5Yt1bJlS/3888/64Ycf1KxZM9uloZRq2bKlnnvuOW3dutV2KSjFYmJi5O/v72rNmze3XVKpZGg0m80YYzp37my9DlrZaDVr1jTGGHPnnXdar4VW+tupU6fMM888Y70OWulrVapUMbt37zb33nuvWb58uRk3bpz1mmilrw0bNsxs3rzZeh2lvXGGDECZUq1aNUlSUlKS5UpQmnl6eqpbt26qUqWK1qxZY7sclEKfffaZFi5cqGXLltkuBaXcjTfeqLi4OB04cED/+te/1KhRI9sllTrlbRcAAFfT2LFjFRUVpR07dtguBaVQUFCQ1qxZo4oVKyo1NVVdunRRbGys7bJQynTr1k233367QkNDbZeCUi46OlpPPfWU9uzZozp16mjo0KFavXq1brnlFv6wWYIIZADKjE8//VQtWrRQu3btbJeCUmr37t0KDg6Wn5+funbtqpkzZyosLIxQhhJz/fXXa8KECQoPD1dmZqbtclDK/fTTT67/jomJ0Zo1a7R//349/fTT3EimhFn/3SStbDeuIaNdjTZx4kRz5MgR07BhQ+u10MpOW7JkiZkyZYr1Omilp3Xu3NkYY0x2drarGWPMuXPnTHZ2tvH09LReI610t8jISDN58mTrdZSmxhkyAKXepEmT1KVLF9199906dOiQ7XJQhnh4eMjb29t2GShFli1bpqCgILdlM2bM0K5duzRy5EidP3/eUmUoC7y8vHTzzTcrKirKdimlCoEMVlSpUkWNGzd2/btRo0a69dZblZSUpKNHj1qsDKXNZ599pieeeEKdO3dWSkqK6tSpI0k6ffq0MjIyLFeH0uTDDz/UokWLdPToUfn4+Kh79+66++67mYsMJSo1NTXXNbBnzpzRqVOnuDYWJe6TTz7R/PnzdeTIEdWuXVtDhw6Vr6+vZs6cabu0Usf6aTpa2WthYWEmLzNmzLBeG610tfw8/fTT1mujla72j3/8wxw8eNBkZGSYxMREs2TJEtOhQwfrddFKf+O297Qr1f71r3+ZuLg4k5mZaY4dO2a++eYbc/PNN1uvq7Q1j///DwAAAADAVcY8ZAAAAABgCYEMAAAAACwhkAEAAACAJQQyAAAAALCEQAYAAAAAlhDIAAAAAMASAhkAAAAAWEIgAwAAAABLCGQAAFwBDRo0kDFGt956q+1SAADXMAIZAKBMmzFjhowxMsYoOztbhw8f1uTJk+Xn51ekfXz33Xduy44ePSp/f3/FxMSUcMUAgNKEQAYAKPMWLVokf39/NWzYUM8++6wefvhhTZ48+bL2ef78eSUmJurcuXMlVCUAoDQikAEAyrzMzEwlJiYqLi5OS5Ys0ddff63w8HBJkqenp/7xj3/owIEDSktL065du/TKK6+4th02bJh69eqlRx991HWmLSwsLNdPFsPCwmSMUfv27bV+/XqdOXNGq1at0k033eRWy5AhQ5SYmKjk5GRNmzZNI0aM0ObNm6/ekwEAuKoIZAAAXKRRo0bq1KmTsrOzJV0IZMeOHdOf//xnNWvWTO+9954++ugjPf7445Kk0aNH6+uvv3adZfP399fq1avz3f+HH36oN954Qy1bttTZs2f1xRdfuNY98cQTGjJkiN5++22FhIToyJEj6tu375U9YACAdYZGo9FotLLaZsyYYbKzs01KSopJS0szTq+99lq+23z66afm3//+t9s+vvvuO7c+DRo0MMYYc+uttxpJJiwszBhjTPv27V197r//fmOMMd7e3kaSWbNmjZk0aZLbfqKioszmzZutP080Go1GuzKNM2QAgDJv+fLlCg4OVqtWrTRx4kT99NNPmjRpkmv9888/r/Xr1+vEiRNKSUlRnz59VL9+/WI91rZt21z/HR8fL0mqXbu2JKlJkyZat26dW/+c/wYAlC4EMgBAmXfmzBnt379f27dv16uvvipvb28NGzZMkvT4449r3Lhx+uKLLxQeHq7g4GDNmDFDXl5exXos508hJckYI+nCzyJzLnPy8PAo1uMAAH4fCGQAAOTw7rvv6s0331RAQIDuvPNOrV69Wp9//rm2bNmi/fv36w9/+INb/6ysLJUrV+6yH3f37t2644473Ja1bNnysvcLALh2EcgAAMhh5cqV2rFjhwYPHqx9+/apZcuWCg8P14033qj33ntPoaGhbv0PHTqkFi1a6KabblKNGjVUvnz5Yj3upEmT1Lt3bz311FNq3LixhgwZohYtWuQ6awYAKD0IZAAA5GHs2LHq06ePvv/+e/3nP//R119/rejoaNWoUSPXHGXTpk3T7t27tWHDBv3666/64x//WKzHjIiI0IgRIzR69Ght2rRJjRo10pdffqmMjIySOCQAwDXIQxfu7gEAAK5BkZGRSkhI0FNPPWW7FADAFVC831QAAIASV6lSJb3wwgtavHixzp07p7/85S/q2LGjOnToYLs0AMAVwhkyAACuERUrVtT8+fN1++23y9vbW7t379YHH3yg7777znZpAIArhEAGAAAAAJZwUw8AAAAAsIRABgAAAACWEMgAAAAAwBICGQAAAABYQiADAAAAAEsIZAAAAABgCYEMAAAAACwhkAEAAACAJf8Hv1SpByfB6mcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dark mode\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "df['stars'].value_counts().sort_index().plot(kind='bar', color='steelblue')\n",
    "\n",
    "plt.title('Distribution of Star Ratings for Sandbar', fontsize=16)\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data Preprocessing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>business_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>stars</th>\n",
       "      <th>language</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gGaF0TtGRx1QOFe0EzuZLg</td>\n",
       "      <td>8W3g9ykr8aGGZMMdVbNPKA</td>\n",
       "      <td>2015-01-01 00:01:02</td>\n",
       "      <td>lynn and montecito flowers are simply the best...</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2CqQ3Tnvj7h0rw84WEEjMw</td>\n",
       "      <td>q10HcbeBRtcvsN7WC02fFw</td>\n",
       "      <td>2015-01-01 00:15:12</td>\n",
       "      <td>love this place always loved the food as they ...</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lOa1BVmTcbPpu05h3OPAQA</td>\n",
       "      <td>dXOY5UqXhcCQ6f9S-kRc2w</td>\n",
       "      <td>2015-01-01 00:19:36</td>\n",
       "      <td>we stayed here last weekend with our dog its a...</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>QFh-h_OZ5Io-mBk32HN28A</td>\n",
       "      <td>8mWVoTXecEkRXTvPONYFuw</td>\n",
       "      <td>2015-01-01 00:20:23</td>\n",
       "      <td>came here for a quick bite and got the thai sa...</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cAbdvzqtFLaAAMFIyPf2AA</td>\n",
       "      <td>Xr5KEchLr-jvQiAZm1mtNA</td>\n",
       "      <td>2015-01-01 00:47:19</td>\n",
       "      <td>finally was able to try lunch at holdrens  my ...</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id               review_id                 date  \\\n",
       "0  gGaF0TtGRx1QOFe0EzuZLg  8W3g9ykr8aGGZMMdVbNPKA  2015-01-01 00:01:02   \n",
       "1  2CqQ3Tnvj7h0rw84WEEjMw  q10HcbeBRtcvsN7WC02fFw  2015-01-01 00:15:12   \n",
       "2  lOa1BVmTcbPpu05h3OPAQA  dXOY5UqXhcCQ6f9S-kRc2w  2015-01-01 00:19:36   \n",
       "3  QFh-h_OZ5Io-mBk32HN28A  8mWVoTXecEkRXTvPONYFuw  2015-01-01 00:20:23   \n",
       "4  cAbdvzqtFLaAAMFIyPf2AA  Xr5KEchLr-jvQiAZm1mtNA  2015-01-01 00:47:19   \n",
       "\n",
       "                                                text  stars language  \n",
       "0  lynn and montecito flowers are simply the best...      5       en  \n",
       "1  love this place always loved the food as they ...      5       en  \n",
       "2  we stayed here last weekend with our dog its a...      5       en  \n",
       "3  came here for a quick bite and got the thai sa...      2       en  \n",
       "4  finally was able to try lunch at holdrens  my ...      2       en  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Clean text data\n",
    "def clean_data(df, text_column):\n",
    "    \n",
    "    # Remove missing values\n",
    "    df = df.dropna(subset=[text_column, 'stars'])\n",
    "    \n",
    "    # Normalize text data\n",
    "    df[text_column] = df[text_column].str.lower()\n",
    "    \n",
    "    # Remove punctuation and special characters\n",
    "    df[text_column] = df[text_column].apply(lambda x: re.sub(r'[^A-Za-z\\s]', '', x))\n",
    "    \n",
    "    return df\n",
    "\n",
    "df = clean_data(df, text_column='text')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values in text column: 0\n"
     ]
    }
   ],
   "source": [
    "# Check for NaN/missing values in the text column\n",
    "missing_text = df['text'].isna().sum()\n",
    "print(f'Number of missing values in text column: {missing_text}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Feature Engineering** (Tokenization and Lemmatization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>text</th>\n",
       "      <th>tokens</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lynn and montecito flowers are simply the best...</td>\n",
       "      <td>lynn montecito flowers simply best awesome cho...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>love this place always loved the food as they ...</td>\n",
       "      <td>love place always loved food truly due killer ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>we stayed here last weekend with our dog its a...</td>\n",
       "      <td>stayed last weekend dog motel style inside fou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>came here for a quick bite and got the thai sa...</td>\n",
       "      <td>came quick bite got thai salad bowl white bean...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>finally was able to try lunch at holdrens  my ...</td>\n",
       "      <td>finally able try lunch holdrens husband ordere...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  lynn and montecito flowers are simply the best...   \n",
       "1  love this place always loved the food as they ...   \n",
       "2  we stayed here last weekend with our dog its a...   \n",
       "3  came here for a quick bite and got the thai sa...   \n",
       "4  finally was able to try lunch at holdrens  my ...   \n",
       "\n",
       "                                              tokens  \n",
       "0  lynn montecito flowers simply best awesome cho...  \n",
       "1  love place always loved food truly due killer ...  \n",
       "2  stayed last weekend dog motel style inside fou...  \n",
       "3  came quick bite got thai salad bowl white bean...  \n",
       "4  finally able try lunch holdrens husband ordere...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "# nltk.download('punkt_tab') if not downloaded\n",
    "\n",
    "# Initialize the stopwords object\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# Preprocess text => tokens, remove stopwords and join tokens\n",
    "def preprocess_text_to_tokens(text):\n",
    "    \n",
    "    # Tokeinize text and filter stopwords\n",
    "    filtered_tokens = [\n",
    "        word for word in word_tokenize(text.lower()) if word not in stop_words\n",
    "    ]\n",
    "    \n",
    "    # Join the tokens into a string\n",
    "    return ' '.join(filtered_tokens)\n",
    "\n",
    "# Create a tokens column and apply preprocessing to the text column and store the results in a tokens column\n",
    "df['tokens'] = df['text'].apply(preprocess_text_to_tokens)\n",
    "\n",
    "# Check transformations\n",
    "df[['text', 'tokens']].head() # return only the text and tokens columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>text</th>\n",
       "      <th>lemmas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lynn and montecito flowers are simply the best...</td>\n",
       "      <td>lynn montecito flower simply best awesome choi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>love this place always loved the food as they ...</td>\n",
       "      <td>love place always loved food truly due killer ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>we stayed here last weekend with our dog its a...</td>\n",
       "      <td>stayed last weekend dog motel style inside fou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>came here for a quick bite and got the thai sa...</td>\n",
       "      <td>came quick bite got thai salad bowl white bean...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>finally was able to try lunch at holdrens  my ...</td>\n",
       "      <td>finally able try lunch holdrens husband ordere...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0  lynn and montecito flowers are simply the best...   \n",
       "1  love this place always loved the food as they ...   \n",
       "2  we stayed here last weekend with our dog its a...   \n",
       "3  came here for a quick bite and got the thai sa...   \n",
       "4  finally was able to try lunch at holdrens  my ...   \n",
       "\n",
       "                                              lemmas  \n",
       "0  lynn montecito flower simply best awesome choi...  \n",
       "1  love place always loved food truly due killer ...  \n",
       "2  stayed last weekend dog motel style inside fou...  \n",
       "3  came quick bite got thai salad bowl white bean...  \n",
       "4  finally able try lunch holdrens husband ordere...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "# Initialize the lemmatizer and stopwords object\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# Preprocess text => lemmas, remove stopwords and join lemmas\n",
    "def preprocess_text_to_lemmas(text):\n",
    "    \n",
    "    # Lemmatize text and filter stopwords\n",
    "    lemmas = [\n",
    "        lemmatizer.lemmatize(word) for word in word_tokenize(text.lower()) if word not in stop_words\n",
    "    ]\n",
    "    \n",
    "    # Join the lemmas into a string\n",
    "    return ' '.join(lemmas)\n",
    "\n",
    "# Create a lemmas column and apply preprocessing to the text column and store the results in a lemmas column\n",
    "df['lemmas'] = df['text'].apply(preprocess_text_to_lemmas)\n",
    "\n",
    "# Check transformations\n",
    "df[['text', 'lemmas']].head() # return only the text and lemmas columns"
   ]
  },
  {
   "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>business_id</th>\n",
       "      <th>review_id</th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>stars</th>\n",
       "      <th>language</th>\n",
       "      <th>tokens</th>\n",
       "      <th>lemmas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gGaF0TtGRx1QOFe0EzuZLg</td>\n",
       "      <td>8W3g9ykr8aGGZMMdVbNPKA</td>\n",
       "      <td>2015-01-01 00:01:02</td>\n",
       "      <td>lynn and montecito flowers are simply the best...</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>lynn montecito flowers simply best awesome cho...</td>\n",
       "      <td>lynn montecito flower simply best awesome choi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2CqQ3Tnvj7h0rw84WEEjMw</td>\n",
       "      <td>q10HcbeBRtcvsN7WC02fFw</td>\n",
       "      <td>2015-01-01 00:15:12</td>\n",
       "      <td>love this place always loved the food as they ...</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>love place always loved food truly due killer ...</td>\n",
       "      <td>love place always loved food truly due killer ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lOa1BVmTcbPpu05h3OPAQA</td>\n",
       "      <td>dXOY5UqXhcCQ6f9S-kRc2w</td>\n",
       "      <td>2015-01-01 00:19:36</td>\n",
       "      <td>we stayed here last weekend with our dog its a...</td>\n",
       "      <td>5</td>\n",
       "      <td>en</td>\n",
       "      <td>stayed last weekend dog motel style inside fou...</td>\n",
       "      <td>stayed last weekend dog motel style inside fou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>QFh-h_OZ5Io-mBk32HN28A</td>\n",
       "      <td>8mWVoTXecEkRXTvPONYFuw</td>\n",
       "      <td>2015-01-01 00:20:23</td>\n",
       "      <td>came here for a quick bite and got the thai sa...</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>came quick bite got thai salad bowl white bean...</td>\n",
       "      <td>came quick bite got thai salad bowl white bean...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cAbdvzqtFLaAAMFIyPf2AA</td>\n",
       "      <td>Xr5KEchLr-jvQiAZm1mtNA</td>\n",
       "      <td>2015-01-01 00:47:19</td>\n",
       "      <td>finally was able to try lunch at holdrens  my ...</td>\n",
       "      <td>2</td>\n",
       "      <td>en</td>\n",
       "      <td>finally able try lunch holdrens husband ordere...</td>\n",
       "      <td>finally able try lunch holdrens husband ordere...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              business_id               review_id                 date  \\\n",
       "0  gGaF0TtGRx1QOFe0EzuZLg  8W3g9ykr8aGGZMMdVbNPKA  2015-01-01 00:01:02   \n",
       "1  2CqQ3Tnvj7h0rw84WEEjMw  q10HcbeBRtcvsN7WC02fFw  2015-01-01 00:15:12   \n",
       "2  lOa1BVmTcbPpu05h3OPAQA  dXOY5UqXhcCQ6f9S-kRc2w  2015-01-01 00:19:36   \n",
       "3  QFh-h_OZ5Io-mBk32HN28A  8mWVoTXecEkRXTvPONYFuw  2015-01-01 00:20:23   \n",
       "4  cAbdvzqtFLaAAMFIyPf2AA  Xr5KEchLr-jvQiAZm1mtNA  2015-01-01 00:47:19   \n",
       "\n",
       "                                                text  stars language  \\\n",
       "0  lynn and montecito flowers are simply the best...      5       en   \n",
       "1  love this place always loved the food as they ...      5       en   \n",
       "2  we stayed here last weekend with our dog its a...      5       en   \n",
       "3  came here for a quick bite and got the thai sa...      2       en   \n",
       "4  finally was able to try lunch at holdrens  my ...      2       en   \n",
       "\n",
       "                                              tokens  \\\n",
       "0  lynn montecito flowers simply best awesome cho...   \n",
       "1  love place always loved food truly due killer ...   \n",
       "2  stayed last weekend dog motel style inside fou...   \n",
       "3  came quick bite got thai salad bowl white bean...   \n",
       "4  finally able try lunch holdrens husband ordere...   \n",
       "\n",
       "                                              lemmas  \n",
       "0  lynn montecito flower simply best awesome choi...  \n",
       "1  love place always loved food truly due killer ...  \n",
       "2  stayed last weekend dog motel style inside fou...  \n",
       "3  came quick bite got thai salad bowl white bean...  \n",
       "4  finally able try lunch holdrens husband ordere...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display transformed dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Time Series Preprocessing**\n",
    "- Create a resampled dataset for time series analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# # Convert review dates to datetime objects\n",
    "# def convert_to_datetime(df, date_column):\n",
    "#     df[date_column] = pd.to_datetime(df[date_column], errors='coerce') # Convert to datetime, handle errors\n",
    "    \n",
    "#     return df\n",
    "\n",
    "# # Set the date as the index\n",
    "# def set_date_as_index(df, date_column):\n",
    "#     df.set_index(date_column, inplace=True)\n",
    "    \n",
    "#     return df\n",
    "\n",
    "# # Resample the data by a specific time interval\n",
    "# def resample_data(df, interval='ME'): # 'M' stands for months, 'D' stands for daily, 'W' for weekly\n",
    "#     df_resampled = df.resample(interval).mean()\n",
    "    \n",
    "#     return df_resampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               stars\n",
      "date                \n",
      "2015-01-31  3.936967\n",
      "2015-02-28  3.932598\n",
      "2015-03-31  3.923698\n",
      "2015-04-30  3.962856\n",
      "2015-05-31  3.933677\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Convert date into datetime object\n",
    "def convert_to_datetime(df, date_column):\n",
    "    df[date_column] = pd.to_datetime(df[date_column], errors='coerce')\n",
    "    return df\n",
    "\n",
    "# Convert date to datetime\n",
    "df = convert_to_datetime(df, date_column='date')\n",
    "\n",
    "# Set date as index\n",
    "df.set_index('date', inplace=True)\n",
    "\n",
    "# Resample numeric data\n",
    "def resample_numeric_data(df, interval='M'):\n",
    "    # Select only numeric columns for resampling\n",
    "    numeric_df = df.select_dtypes(include='number')\n",
    "    df_resampled = numeric_df.resample(interval).mean()\n",
    "    \n",
    "    return df_resampled\n",
    "\n",
    "# Resample numeric data only\n",
    "df_resampled = resample_numeric_data(df, interval='M')\n",
    "print(df_resampled.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed dataframed saved to data/reviews_processed.csv\n"
     ]
    }
   ],
   "source": [
    "# Save the dataframe\n",
    "output_path = 'data/reviews_processed.csv'\n",
    "df_resampled.to_csv(output_path)\n",
    "print(f'Processed dataframed saved to {output_path}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of missing values: date     0\n",
      "stars    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Load cleaned dataframe\n",
    "df_resampled = pd.read_csv('data/reviews_processed.csv', keep_default_na=False)\n",
    "\n",
    "# Check for missing values\n",
    "missing_values = df_resampled.isna().sum()\n",
    "print(f'Number of missing values: {missing_values}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Time Series Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Annotating the hightest and lowest points\n",
    "max_value = df_resampled['stars'].max()\n",
    "min_value = df_resampled['stars'].min()\n",
    "max_date = df_resampled['stars'].idxmax()\n",
    "min_date = df_resampled['stars'].idxmin()\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(df_resampled.index, df_resampled['stars'], color='skyblue')\n",
    "plt.title('Average Star Ratings Over Time', fontsize=16)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Average Star Rating')\n",
    "\n",
    "plt.annotate(f'Max {max_value:.2f}',\n",
    "             xy=(max_date, max_value),\n",
    "             xytext=(max_date, max_value + 0.05),\n",
    "             arrowprops=dict(facecolor='green', shrink=0.05))\n",
    "\n",
    "plt.annotate(f'Min {min_value:.2f}',\n",
    "             xy=(min_date, min_value),\n",
    "             xytext=(min_date, min_value + - 0.05),\n",
    "             arrowprops=dict(facecolor='red', shrink=0.05))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate 3 month-moving average\n",
    "df_resampled['3-month-MA'] = df_resampled['stars'].rolling(window=3).mean()\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(df_resampled.index, df_resampled['stars'], label='Monthly Average', color='skyblue')\n",
    "plt.plot(df_resampled.index, df_resampled['3-month-MA'], label='3-Month Moving Average', color='violet')\n",
    "plt.title('Average Star Ratings Over Time with 3 Month Moving Average', fontsize=16)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Average Star Rating')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Deep Learning**\n",
    "- Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "# # Create sequences from the time series data\n",
    "# def create_sequences(data, sequence_length):\n",
    "    \n",
    "#     sequences = []\n",
    "#     targets = []\n",
    "    \n",
    "#     # Loop\n",
    "#     for start_index in range(len(data) - sequence_length):\n",
    "#         end_index = start_index + sequence_length\n",
    "#         sequence = data[start_index:end_index]\n",
    "#         target = data[end_index]\n",
    "        \n",
    "#         sequences.append(sequence)\n",
    "#         targets.append(target)\n",
    "    \n",
    "#     return np.array(sequences), np.array(targets)\n",
    "\n",
    "# # Sequence length 3 for quarterly, 6 is semi-annual, 12 is for annual\n",
    "# sequence_length = 3 # for 3 months or quarterly\n",
    "# X, y = create_sequences(df_resampled['stars'].values, sequence_length)\n",
    "\n",
    "# # Split the data into training and testing sets\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LSTM Model (Long Short Term Memory) Deep Learning Model\n",
    "\n",
    "common errors:\n",
    "- ModuleNotFoundError: No module named 'tensorflow'\n",
    "- `pip install tensorflow`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tensorflow as tf\n",
    "# from tensorflow.keras.models import Sequential\n",
    "# from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
    "\n",
    "# def build_lstm_model(input_shape, units_first_layer, units_second_layer, dropout_first, dropout_second):\n",
    "    \n",
    "#     # Initialize model\n",
    "#     model = Sequential()\n",
    "    \n",
    "#     # Add first layer with dropout\n",
    "#     model.add(LSTM(units=units_first_layer, return_sequences=True, input_shape=input_shape))\n",
    "#     model.add(Dropout(dropout_first))\n",
    "    \n",
    "#     # Add second layer with dropout\n",
    "#     model.add(LSTM(units=units_second_layer, return_sequences=False))\n",
    "#     model.add(Dropout(dropout_second))\n",
    "    \n",
    "#     # Add output layer\n",
    "#     model.add(Dense(units=1))\n",
    "    \n",
    "#     return model\n",
    "\n",
    "# # Compile the model\n",
    "# def compile_model(model, optimizer, loss):\n",
    "#     model.compile(optimizer=optimizer, loss=loss)\n",
    "    \n",
    "#     return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Define input shape\n",
    "# input_shape = (X_train.shape[1], 1)\n",
    "\n",
    "# # Build the model\n",
    "# model = build_lstm_model(input_shape, units_first_layer=100, units_second_layer=50, dropout_first=0.5, dropout_second=0.2)\n",
    "\n",
    "# # Compile the model\n",
    "# model = compile_model(model, optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from tqdm.keras import TqdmCallback\n",
    "\n",
    "# # Train the model\n",
    "# history = model.fit(\n",
    "#     X_train,\n",
    "#     y_train,\n",
    "#     epochs=100,\n",
    "#     batch_size=32,\n",
    "#     validation_split=0.2,\n",
    "#     callbacks=[TqdmCallback(verbose=1)]\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "# def evaluate_model(model, X_test, y_test):\n",
    "    \n",
    "#     test_loss = model.evaluate(X_test, y_test)\n",
    "#     print(f'Test Loss: {test_loss}')\n",
    "    \n",
    "#     return test_loss\n",
    "\n",
    "# def calculate_metrics(y_test, predictions):\n",
    "#     mse = mean_squared_error(y_test, predictions)\n",
    "#     mae = mean_absolute_error(y_test, predictions)\n",
    "    \n",
    "#     print(f'Mean Squared Error: {mse}')\n",
    "#     print(f'Mean Absolute Error: {mae}')\n",
    "    \n",
    "#     return mse, mae\n",
    "\n",
    "# def predict_and_evaluate(model, X_test, y_test):\n",
    "    \n",
    "#     # Evaluate model\n",
    "#     test_loss = evaluate_model(model, X_test, y_test)\n",
    "#     predictions = model.predict(X_test)\n",
    "    \n",
    "#     # Calculate metrics\n",
    "#     mse, mae = calculate_metrics(y_test, predictions)\n",
    "    \n",
    "#     return predictions, mse, mae\n",
    "\n",
    "# predictions, mse, mae = predict_and_evaluate(model, X_test, y_test)\n",
    "\n",
    "# import matplotlib.pyplot as plt\n",
    "# from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "# def evaluate_model(model, X_test, y_test):\n",
    "#     test_loss = model.evaluate(X_test, y_test)\n",
    "#     print(f'Test Loss: {test_loss}')\n",
    "    \n",
    "#     return test_loss\n",
    "\n",
    "# def calculate_metrics(y_test, predictions):\n",
    "#     mse = mean_squared_error(y_test, predictions)\n",
    "#     mae = mean_absolute_error(y_test, predictions)\n",
    "    \n",
    "#     print(f'Mean Squared Error: {mse}')\n",
    "#     print(f'Mean Absolute Error: {mae}')\n",
    "    \n",
    "#     return mse, mae\n",
    "\n",
    "# def predict_and_evaluate(model, X_test, y_test):\n",
    "    \n",
    "#     # Evaluate model\n",
    "#     test_loss = evaluate_model(model, X_test, y_test)\n",
    "#     predictions = model.predict(X_test)\n",
    "    \n",
    "#     # Calculate metrics\n",
    "#     mse, mae = calculate_metrics(y_test, predictions)\n",
    "    \n",
    "#     return predictions, mse, mae\n",
    "\n",
    "# predictions, mse, mae = predict_and_evaluate(model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# def plot_training_and_predictions(history, y_test, predictions):\n",
    "    \n",
    "#     fig, axes = plt.subplots(1, 2, figsize=(18, 6))\n",
    "    \n",
    "#     # Training and validation loss\n",
    "#     axes[0].plot(history.history['loss'], label='Training Loss')\n",
    "#     axes[0].plot(history.history['val_loss'], label='Validation Loss')\n",
    "#     axes[0].set_title('Model Loss')\n",
    "#     axes[0].set_xlabel('Loss')\n",
    "#     axes[0].set_xlabel('Epoch')\n",
    "#     axes[0].legend()\n",
    "    \n",
    "#     # Prediction VS Actual Values\n",
    "#     axes[1].plot(y_test, label='Actual Values')\n",
    "#     axes[1].plot(predictions, label='Predicted Values')\n",
    "#     axes[1].set_title('Model Predictions VS Actual Values')\n",
    "#     axes[1].set_xlabel('Time')\n",
    "#     axes[1].set_xlabel('Star Rating')\n",
    "#     axes[1].legend()\n",
    "    \n",
    "#     plt.tight_layout()\n",
    "#     plt.show()\n",
    "    \n",
    "# print('LSTM Model Performance')\n",
    "# plot_training_and_predictions(history, y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save model\n",
    "# model.save('models/lstm_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **VADER**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "import nltk\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "# nltk.download('vader_lexicon') # if not already downloaded\n",
    "\n",
    "# Initialize the VADER object\n",
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "# Apply VADER to the text data\n",
    "def apply_vader(text):\n",
    "    \n",
    "    return analyzer.polarity_scores(text)\n",
    "\n",
    "# Create vader_scores and vader_compound columns\n",
    "df['vader_scores'] = df['text'].apply(apply_vader)\n",
    "df['vader_compound'] = df['vader_scores'].apply(lambda score_dict: score_dict['compound'])\n",
    "\n",
    "# Convert compound score to sentiment labels\n",
    "def vader_sentiment_label(compound_score):\n",
    "    if compound_score >= 0.05:\n",
    "        return 'Positive'\n",
    "    elif compound_score <= -0.05:\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Neutral'\n",
    "    \n",
    "df['vader_sentiment'] = df['vader_compound'].apply(vader_sentiment_label)\n",
    "\n",
    "# Map stars to true_label for evaluation\n",
    "def map_stars_to_sentiment(stars):\n",
    "    if stars >= 4:\n",
    "        return 'Positive'\n",
    "    elif stars < 3:\n",
    "        return 'Negative'\n",
    "    else:\n",
    "        return 'Neutral'\n",
    "    \n",
    "df['true_label'] = df['stars'].apply(map_stars_to_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "labels = ['Negative', 'Neutral', 'Positive']\n",
    "\n",
    "# Create confusion matrix\n",
    "cm = confusion_matrix(df['true_label'], df['vader_sentiment'], labels=labels)\n",
    "\n",
    "# Plot confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=labels)\n",
    "\n",
    "disp.plot(cmap='Purples')\n",
    "plt.title('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "# Vectorize text\n",
    "def vectorize_text(text_data):\n",
    "    vectorizer = TfidfVectorizer()\n",
    "    X = vectorizer.fit_transform(text_data)\n",
    "    \n",
    "    return X, vectorizer\n",
    "\n",
    "# Create binary target based on star rating\n",
    "def prepare_target_variable(stars, threshold=4):\n",
    "    return stars >= threshold\n",
    "\n",
    "# Classifier model\n",
    "def train_naive_bayes(X_train, y_train):\n",
    "    nb_classifier = MultinomialNB()\n",
    "    nb_classifier.fit(X_train, y_train)\n",
    "    \n",
    "    return nb_classifier\n",
    "\n",
    "# Evaluate model\n",
    "def evaluate_model_nb(model, X_test, y_test):\n",
    "    test_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, test_pred)\n",
    "    \n",
    "    return accuracy, test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes Accuracy with TF-IDF (Multi-Class): 0.81\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Vectorize text\n",
    "X, vectorizer = vectorize_text(text_data=df['lemmas'])\n",
    "y = df['true_label']\n",
    "\n",
    "# Split the dataset into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "\n",
    "# Train model\n",
    "nb_classifier = train_naive_bayes(X_train, y_train)\n",
    "accuracy, test_pred = evaluate_model_nb(nb_classifier, X_test, y_test)\n",
    "\n",
    "print(f'Naive Bayes Accuracy with TF-IDF (Multi-Class): {accuracy:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.93      0.39      0.55      5540\n",
      "     Neutral       0.04      0.00      0.00      2271\n",
      "    Positive       0.80      1.00      0.89     22264\n",
      "\n",
      "    accuracy                           0.81     30075\n",
      "   macro avg       0.59      0.46      0.48     30075\n",
      "weighted avg       0.77      0.81      0.76     30075\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "# Classification report\n",
    "report = classification_report(y_test, test_pred, labels=labels)\n",
    "print(report)\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, test_pred, labels=labels)\n",
    "\n",
    "# Plot confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=labels)\n",
    "disp.plot(cmap='Purples')\n",
    "plt.title('Sentiment Analysis with Naive Bayes and TF-IDF (Multi-Class)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "# Calculate learning curves\n",
    "train_sizes, train_scores, test_scores = learning_curve(nb_classifier, X, y,\n",
    "                                                        cv=5,\n",
    "                                                        n_jobs=-1,\n",
    "                                                        train_sizes=np.linspace(0.1, 1.0, 10))\n",
    "\n",
    "# Plot the curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, np.mean(train_scores, axis=1), label='Training score', color='blue')\n",
    "plt.plot(train_sizes, np.mean(test_scores, axis=1), label='Testing score', color='orange')\n",
    "plt.title('Naive Bayes Learning Curve')\n",
    "plt.xlabel('Training Set Size')\n",
    "plt.ylabel('Score')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model and vectorizer saved successfully\n"
     ]
    }
   ],
   "source": [
    "# import joblib\n",
    "# import os\n",
    "\n",
    "# # Create a directory for models if it doesn't exist already\n",
    "# os.makedirs('models', exist_ok=True)\n",
    "\n",
    "# # Save the model and vectorizer\n",
    "# joblib.dump(nb_classifier, 'models/naive-bayes_model.pkl')\n",
    "# joblib.dump(vectorizer, 'models/vectorizer.pkl')\n",
    "# print('Model and vectorizer saved in the models directory')\n",
    "\n",
    "import joblib\n",
    "import os\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "# Assume you have X and y already from the preprocessing steps\n",
    "vectorizer = TfidfVectorizer()\n",
    "X = vectorizer.fit_transform(df['lemmas'])\n",
    "y = df['true_label']\n",
    "\n",
    "nb_classifier = MultinomialNB()\n",
    "nb_classifier.fit(X, y)\n",
    "\n",
    "# Create a directory for models if it doesn't exist already\n",
    "os.makedirs('models', exist_ok=True)\n",
    "\n",
    "# Save the model and vectorizer\n",
    "joblib.dump(nb_classifier, 'models/naive-bayes_model.pkl')\n",
    "joblib.dump(vectorizer, 'models/vectorizer.pkl')\n",
    "print('Model and vectorizer saved successfully')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded Naive Bayes Model: <class 'sklearn.naive_bayes.MultinomialNB'> and vectorizer: <class 'sklearn.feature_extraction.text.TfidfVectorizer'>\n"
     ]
    }
   ],
   "source": [
    "# Load model and vectorizer\n",
    "loaded_nb_classifier = joblib.load('models/naive-bayes_model.pkl')\n",
    "loaded_vectorizer = joblib.load('models/vectorizer.pkl')\n",
    "print(f'Loaded Naive Bayes Model: {type(loaded_nb_classifier)} and vectorizer: {type(loaded_vectorizer)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Statement: Positive\n"
     ]
    }
   ],
   "source": [
    "# Test with sample input\n",
    "sample_text = ['This app is awesome BRO!']\n",
    "sample_vectorized = loaded_vectorizer.transform(sample_text) # transform the text into a matrix of token counts\n",
    "prediction = loaded_nb_classifier.predict(sample_vectorized)\n",
    "\n",
    "print(f'Predicted Statement: {prediction[0]}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ds-gg",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}