Final Model.ipynb

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  {
   "cell_type": "markdown",
   "id": "25be9c9e",
   "metadata": {
    "id": "25be9c9e"
   },
   "source": [
    "# Importing Libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "f4075ed9",
   "metadata": {
    "id": "f4075ed9"
   },
   "outputs": [],
   "source": [
    "import bs4\n",
    "import math\n",
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "import timeit\n",
    "import re\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "import nltk\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer \n",
    "import spacy\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "import plotly.express as px\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import lightgbm as ltb\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import category_encoders as ce\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import f1_score\n",
    "from gensim.models import KeyedVectors\n",
    "from gensim import models\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec29166",
   "metadata": {
    "id": "dec29166"
   },
   "source": [
    "# Data Scrapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "ad0ad641",
   "metadata": {
    "id": "ad0ad641"
   },
   "outputs": [],
   "source": [
    "def amazon_data(webpage, page_number, pages_to_extract):\n",
    "    \n",
    "    \"\"\"\n",
    "    Given a URL,page number and number of pages to extract; this function extracts review, date, summary\n",
    "    and creates a dataframe\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    webpage = webpage[:-1]\n",
    "    amazon_review = []\n",
    "    amazon_date = []\n",
    "    amazon_summary = []\n",
    "\n",
    "    def scrape_data_amazon(webpage, page_number, pages_to_extract):\n",
    "        next_page = webpage + str(page_number)\n",
    "        response= requests.get(str(next_page))\n",
    "        soup = BeautifulSoup(response.content,\"html.parser\")\n",
    "        soup_review = soup.findAll(\"div\",{\"class\":\"a-row a-spacing-small review-data\"})\n",
    "        soup_summary = soup.findAll(\"a\",{\"class\":\"a-size-base a-link-normal review-title a-color-base review-title-content a-text-bold\"})\n",
    "        soup_date = soup.findAll(\"span\",{\"class\":\"a-size-base a-color-secondary review-date\"})[2:] # 10 reviews\n",
    "        for x in range(len(soup_review)):\n",
    "            amazon_review.append(soup_review[x].text.strip())\n",
    "            amazon_date.append(soup_date[x].text.strip())\n",
    "            amazon_summary.append(soup_summary[x].text.strip())\n",
    "\n",
    "   #Generating the next page url\n",
    "        if page_number < pages_to_extract:\n",
    "            page_number = page_number + 1\n",
    "            #print(page_number)\n",
    "            scrape_data_amazon(webpage, page_number,pages_to_extract)\n",
    "            \n",
    "    scrape_data_amazon(webpage, page_number, pages_to_extract)\n",
    "    data_amazon = {'Date':amazon_date, 'Review': amazon_review, 'Summary':amazon_summary}\n",
    "    df_amazon = pd.DataFrame(data_amazon, columns = ['Date','Review','Summary'])\n",
    "    \n",
    "    def get_date_amazon(text):\n",
    "        return ' '.join(text.split()[-3:])\n",
    "    \n",
    "    df_amazon[\"Date\"] = [get_date_amazon(x) for x in df_amazon[\"Date\"].values]\n",
    "    df_amazon.dropna(inplace = True)\n",
    "    \n",
    "    return df_amazon\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "59afd200",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "59afd200",
    "outputId": "1635dd9b-7d9c-4ca3-f8f0-e263aaa21452"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time : 160.23851951000006\n"
     ]
    }
   ],
   "source": [
    "url = 'https://www.amazon.in/New-Apple-iPhone-11-64GB/product-reviews/B08L8DV7BX/ref=cm_cr_getr_d_paging_btm_prev_1?ie=UTF8&reviewerType=all_reviews&pageNumber=1'\n",
    "page = 1\n",
    "extract = 300\n",
    "\n",
    "starttime = timeit.default_timer()\n",
    "df = amazon_data(url,page,extract)\n",
    "print(\"Execution time :\", timeit.default_timer() - starttime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "5b7531b4",
   "metadata": {
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    },
    "id": "5b7531b4",
    "outputId": "b4d56769-0934-444e-a7b2-09333d562a99"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Review</th>\n",
       "      <th>Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12 August 2022</td>\n",
       "      <td>Superb</td>\n",
       "      <td>Name is enough</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14 August 2022</td>\n",
       "      <td>superb</td>\n",
       "      <td>love it</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13 August 2022</td>\n",
       "      <td>Ok</td>\n",
       "      <td>Ok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9 August 2022</td>\n",
       "      <td>Very nice</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13 August 2022</td>\n",
       "      <td>Nice</td>\n",
       "      <td>Nothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2375</th>\n",
       "      <td>24 November 2020</td>\n",
       "      <td>All are good but charger and earpod are missing</td>\n",
       "      <td>Excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2376</th>\n",
       "      <td>11 July 2021</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>Excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2377</th>\n",
       "      <td>23 December 2020</td>\n",
       "      <td>Happy with my busying decision. No issues with...</td>\n",
       "      <td>Purchased it on discount and very satisfied wi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2378</th>\n",
       "      <td>16 December 2020</td>\n",
       "      <td>Extremely happy with IPhone 11. Bought it at 5...</td>\n",
       "      <td>Upgraded from IPhone 6 to IPhone 11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2379</th>\n",
       "      <td>22 November 2020</td>\n",
       "      <td>Great Touch. Wish Camera could be better !!</td>\n",
       "      <td>IPhone 11 Review</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>2380 rows × 3 columns</p>\n",
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      "text/plain": [
       "                  Date                                             Review  \\\n",
       "0       12 August 2022                                             Superb   \n",
       "1       14 August 2022                                             superb   \n",
       "2       13 August 2022                                                 Ok   \n",
       "3        9 August 2022                                          Very nice   \n",
       "4       13 August 2022                                               Nice   \n",
       "...                ...                                                ...   \n",
       "2375  24 November 2020    All are good but charger and earpod are missing   \n",
       "2376      11 July 2021                                          Excellent   \n",
       "2377  23 December 2020  Happy with my busying decision. No issues with...   \n",
       "2378  16 December 2020  Extremely happy with IPhone 11. Bought it at 5...   \n",
       "2379  22 November 2020        Great Touch. Wish Camera could be better !!   \n",
       "\n",
       "                                                Summary  \n",
       "0                                        Name is enough  \n",
       "1                                               love it  \n",
       "2                                                    Ok  \n",
       "3                                                  Good  \n",
       "4                                               Nothing  \n",
       "...                                                 ...  \n",
       "2375                                          Excellent  \n",
       "2376                                          Excellent  \n",
       "2377  Purchased it on discount and very satisfied wi...  \n",
       "2378                Upgraded from IPhone 6 to IPhone 11  \n",
       "2379                                   IPhone 11 Review  \n",
       "\n",
       "[2380 rows x 3 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = df\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d92aa4fb",
   "metadata": {
    "id": "d92aa4fb"
   },
   "source": [
    "# Splitting Data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "4beba80b",
   "metadata": {
    "id": "4beba80b"
   },
   "outputs": [],
   "source": [
    "x = raw_data.drop([\"Summary\"],axis = 1) # dropping the summary column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "c213c82f",
   "metadata": {
    "id": "c213c82f"
   },
   "outputs": [],
   "source": [
    "x_train,x_test = train_test_split(x,test_size = 0.2,random_state = 0) # splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "883f04a8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "883f04a8",
    "outputId": "dc272f47-e513-4c54-b7bd-0d63e670f5bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1904, 2), (476, 2))"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape,x_test.shape # size of train and test set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a20e047",
   "metadata": {
    "id": "3a20e047"
   },
   "source": [
    "# Sentence Tokenization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "93591e00",
   "metadata": {
    "id": "93591e00"
   },
   "outputs": [],
   "source": [
    "def split_review(text):\n",
    "    \"\"\"\n",
    "    This function splits the review into multiple sentences based on the following conjunctions\n",
    "    \n",
    "    \"\"\"\n",
    "    delimiters = \".\", \"but\", \"and\", \"also\"\n",
    "    regex_pattern = '|'.join(map(re.escape, delimiters)) # applying the above delimiters\n",
    "    splitted = re.split(regex_pattern, text) # splitting the review \n",
    "    return splitted # this returns a list of multiple reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c257c91",
   "metadata": {
    "id": "9c257c91"
   },
   "source": [
    "# Data Cleaning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "3ET42wlTXEOo",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3ET42wlTXEOo",
    "outputId": "e5c483d6-e180-4980-aa57-8ea67020d502"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "1f176ffc",
   "metadata": {
    "id": "1f176ffc"
   },
   "outputs": [],
   "source": [
    "lemma = WordNetLemmatizer() # applying lemmatization to return dictionary form of words\n",
    "\n",
    "all_stopwords = stopwords.words('english') # this consists all the stopwords, which will be removed later.\n",
    "\n",
    "# Removing the following words from list containing stopwords\n",
    "all_stopwords.remove('not')\n",
    "all_stopwords.remove('but')\n",
    "all_stopwords.remove('because')\n",
    "all_stopwords.remove('against')\n",
    "all_stopwords.remove('between')\n",
    "all_stopwords.remove('up')\n",
    "all_stopwords.remove('down')\n",
    "all_stopwords.remove('in')\n",
    "all_stopwords.remove('out')\n",
    "all_stopwords.remove('once')\n",
    "all_stopwords.remove('before')\n",
    "all_stopwords.remove('after')\n",
    "all_stopwords.remove('few')\n",
    "all_stopwords.remove('more')\n",
    "all_stopwords.remove('most')\n",
    "all_stopwords.remove('no')\n",
    "all_stopwords.remove('nor')\n",
    "all_stopwords.remove('same')\n",
    "all_stopwords.remove('some')\n",
    "\n",
    "def clean_aspect_spacy(reviews):\n",
    "    \"\"\"\n",
    "    This function removes punctuations, stopwords and other non alpha numeric characters.\n",
    "    We expand the contractions and replace some words by an empty string\n",
    "    \n",
    "    \"\"\"\n",
    "    statement = reviews.lower().strip()\n",
    "    statement = statement.replace(\"won't\", \"will not\").replace(\"cannot\", \"can not\").replace(\"can't\", \"can not\")\\\n",
    "                           .replace(\"n't\", \" not\").replace(\"what's\", \"what is\").replace(\"it's\", \"it is\")\\\n",
    "                           .replace(\"'ve\", \" have\").replace(\"i'm\", \"i am\").replace(\"'re\", \" are\")\\\n",
    "                           .replace(\"he's\", \"he is\").replace(\"she's\", \"she is\").replace(\"*****\",\" \")\\\n",
    "                           .replace(\"%\", \" percent \").replace(\"₹\", \" rupee \").replace(\"$\", \" dollar \")\\\n",
    "                           .replace(\"€\", \" euro \").replace(\"'ll\", \" will\").replace(\"doesn't\", \"does not\")\n",
    "    \n",
    "    statement = re.sub('[^a-zA-Z]',' ',statement) # replacing whatever isn't letters by an empty string\n",
    "    statement = statement.split() # forming list of words in a given review\n",
    "    final_statement = [lemma.lemmatize(word) for word in statement if not word in set(all_stopwords)]\n",
    "    final_statement_ = ' '.join(final_statement) # joining the words and forming the review again without stopwords\n",
    "    return final_statement_  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f723e4c7",
   "metadata": {
    "id": "f723e4c7"
   },
   "source": [
    "# Form Dataframe again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "nbhud99jXQBl",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nbhud99jXQBl",
    "outputId": "1490c4e7-b5c0-44f7-c7da-8a7fcd28e7da"
   },
   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "eb7f1e6f",
   "metadata": {
    "id": "eb7f1e6f"
   },
   "outputs": [],
   "source": [
    "def get_splitted_reviews(df):\n",
    "    \"\"\"\n",
    "    This function applies the above defined splitting function and forms a dataframe again\n",
    "    \n",
    "    \"\"\"\n",
    "    reviews= [] # this will contain our reviews\n",
    "    dates = [] # this will contain our dates\n",
    "\n",
    "    for i,j in enumerate(df[\"Review\"].values): # for each review\n",
    "        review_split = split_review(j) # apply the splitting the function\n",
    "        review_split_ = [x for x in review_split if len(x.split()) >= 3] # review containing less than 3 words are removed\n",
    "        duplicate_dates = [str(df[\"Date\"].values[i]) for h in range(len(review_split_))] # repeat the dates for splitted reviews\n",
    "        reviews.extend(review_split_) # add reviews to list\n",
    "        dates.extend(duplicate_dates) # add dates to list\n",
    "        \n",
    "    reviews_ = [clean_aspect_spacy(text) for text in reviews] # applying the cleaning function \n",
    "        \n",
    "    data = pd.DataFrame({\"Date\": dates,\"Review\": reviews_}) # create new dataframe\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "7de8b357",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7de8b357",
    "outputId": "415bae31-004f-4683-97cd-543275429cbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time difference is : 0.6005813249998937\n"
     ]
    }
   ],
   "source": [
    "# Train set:\n",
    "\n",
    "starttime = timeit.default_timer()\n",
    "train_data = get_splitted_reviews(x_train) \n",
    "print(\"The time difference is :\", timeit.default_timer() - starttime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "b6846de2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b6846de2",
    "outputId": "f2a489f3-10ab-408c-d506-9cd456db45f1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time difference is : 0.13655680600004416\n"
     ]
    }
   ],
   "source": [
    "# Test set:\n",
    "\n",
    "starttime = timeit.default_timer()\n",
    "test_data = get_splitted_reviews(x_test)\n",
    "print(\"The time difference is :\", timeit.default_timer() - starttime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "3a5fccdb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "3a5fccdb",
    "outputId": "48ef9cbf-ead1-4241-cb8b-822f48ca65ec"
   },
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>30 August 2021</td>\n",
       "      <td>good performance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20 November 2020</td>\n",
       "      <td>apple apple product nice</td>\n",
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       "      <th>2</th>\n",
       "      <td>20 November 2020</td>\n",
       "      <td>easy use</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20 November 2020</td>\n",
       "      <td>bundal watt charger u buy new fast charger enj...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20 November 2020</td>\n",
       "      <td>face id wonderful</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>7196</th>\n",
       "      <td>17 October 2020</td>\n",
       "      <td>sound quality one power rich in heaven</td>\n",
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       "      <td>17 October 2020</td>\n",
       "      <td>actually express inner feeling smooth well good</td>\n",
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       "      <th>7198</th>\n",
       "      <td>14 November 2021</td>\n",
       "      <td>great product came expected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7199</th>\n",
       "      <td>14 November 2021</td>\n",
       "      <td>yes not fake believe rumour</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7200</th>\n",
       "      <td>17 July 2021</td>\n",
       "      <td>nice product delivery getting late appario ret...</td>\n",
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      "text/plain": [
       "                  Date                                             Review\n",
       "0       30 August 2021                                   good performance\n",
       "1     20 November 2020                           apple apple product nice\n",
       "2     20 November 2020                                           easy use\n",
       "3     20 November 2020  bundal watt charger u buy new fast charger enj...\n",
       "4     20 November 2020                                  face id wonderful\n",
       "...                ...                                                ...\n",
       "7196   17 October 2020             sound quality one power rich in heaven\n",
       "7197   17 October 2020    actually express inner feeling smooth well good\n",
       "7198  14 November 2021                        great product came expected\n",
       "7199  14 November 2021                        yes not fake believe rumour\n",
       "7200      17 July 2021  nice product delivery getting late appario ret...\n",
       "\n",
       "[7201 rows x 2 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
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   "id": "adc52975",
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    },
    "id": "adc52975",
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   },
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    {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 September 2022</td>\n",
       "      <td>costly thing provided</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 September 2022</td>\n",
       "      <td>also provide charging adapter phone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1 September 2022</td>\n",
       "      <td>time go slow feel slow esp after release new p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10 January 2021</td>\n",
       "      <td>product good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10 January 2021</td>\n",
       "      <td>battery get zero within hour after six month c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>1630</th>\n",
       "      <td>6 August 2021</td>\n",
       "      <td>wonderful iphone specially dy not want yo carr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1631</th>\n",
       "      <td>6 August 2021</td>\n",
       "      <td>due hard fan iphone</td>\n",
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       "    <tr>\n",
       "      <th>1632</th>\n",
       "      <td>17 November 2021</td>\n",
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       "      <td>10 October 2021</td>\n",
       "      <td>experienced heating issue in iphone otherwise ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>10 October 2021</td>\n",
       "      <td>good camera</td>\n",
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       "<p>1635 rows × 2 columns</p>\n",
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       "\n",
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       "                                                     [key], {});\n",
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       "\n",
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       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "                  Date                                             Review\n",
       "0     1 September 2022                              costly thing provided\n",
       "1     1 September 2022                also provide charging adapter phone\n",
       "2     1 September 2022  time go slow feel slow esp after release new p...\n",
       "3      10 January 2021                                       product good\n",
       "4      10 January 2021  battery get zero within hour after six month c...\n",
       "...                ...                                                ...\n",
       "1630     6 August 2021  wonderful iphone specially dy not want yo carr...\n",
       "1631     6 August 2021                                due hard fan iphone\n",
       "1632  17 November 2021                         nice mobile genuine seller\n",
       "1633   10 October 2021  experienced heating issue in iphone otherwise ...\n",
       "1634   10 October 2021                                        good camera\n",
       "\n",
       "[1635 rows x 2 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce93d338",
   "metadata": {
    "id": "ce93d338"
   },
   "source": [
    "# Aspect Extraction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "3c0c33f2",
   "metadata": {
    "id": "3c0c33f2"
   },
   "outputs": [],
   "source": [
    "def apply_extraction(row,nlp):\n",
    "    \"\"\"\n",
    "    This function extracts aspect and its corresponding description from the review by \n",
    "    applying 7 different rules of pos tagging\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    prod_pronouns = ['it','this','they','these']\n",
    "    review_body = row['Review']\n",
    "    doc=nlp(review_body)\n",
    "    \n",
    "    rule1_pairs = []\n",
    "    rule2_pairs = []\n",
    "    rule3_pairs = []\n",
    "    rule4_pairs = []\n",
    "    rule5_pairs = []\n",
    "    rule6_pairs = []\n",
    "    rule7_pairs = []\n",
    "    \n",
    "    for token in doc:\n",
    "        A = \"999999\"\n",
    "        M = \"999999\"\n",
    "        if token.dep_ == \"amod\" and not token.is_stop:\n",
    "            M = token.text\n",
    "            A = token.head.text\n",
    "\n",
    "            # add adverbial modifier of adjective (e.g. 'most comfortable headphones')\n",
    "            M_children = token.children\n",
    "            for child_m in M_children:\n",
    "                if(child_m.dep_ == \"advmod\"):\n",
    "                    M_hash = child_m.text\n",
    "                    M = M_hash + \" \" + M\n",
    "                    break\n",
    "\n",
    "            # negation in adjective, the \"no\" keyword is a 'det' of the noun (e.g. no interesting characters)\n",
    "            A_children = token.head.children\n",
    "            for child_a in A_children:\n",
    "                if(child_a.dep_ == \"det\" and child_a.text == 'no'):\n",
    "                    neg_prefix = 'not'\n",
    "                    M = neg_prefix + \" \" + M\n",
    "                    break\n",
    "\n",
    "        if(A != \"999999\" and M != \"999999\"):\n",
    "            if A in prod_pronouns :\n",
    "                A = \"product\"\n",
    "            dict1 = {\"noun\" : A, \"adj\" : M, \"rule\" : 1}\n",
    "            rule1_pairs.append(dict1)\n",
    "\n",
    "        #print(\"--- SPACY : Rule 1 Done ---\")\n",
    "        \n",
    "# -----------------------------------------------------------------------------------------------------------------------------        \n",
    "        # # SECOND RULE OF DEPENDANCY PARSE -\n",
    "        # # M - Sentiment modifier || A - Aspect\n",
    "        # Direct Object - A is a child of something with relationship of nsubj, while\n",
    "        # M is a child of the same something with relationship of dobj\n",
    "        # Assumption - A verb will have only one NSUBJ and DOBJ\n",
    "        children = token.children\n",
    "        A = \"999999\"\n",
    "        M = \"999999\"\n",
    "        add_neg_pfx = False\n",
    "        for child in children :\n",
    "            if(child.dep_ == \"nsubj\" and not child.is_stop):\n",
    "                A = child.text\n",
    "                # check_spelling(child.text)\n",
    "\n",
    "            if((child.dep_ == \"dobj\" and child.pos_ == \"ADJ\") and not child.is_stop):\n",
    "                M = child.text\n",
    "                #check_spelling(child.text)\n",
    "\n",
    "            if(child.dep_ == \"neg\"):\n",
    "                neg_prefix = child.text\n",
    "                add_neg_pfx = True\n",
    "\n",
    "        if (add_neg_pfx and M != \"999999\"):\n",
    "            M = neg_prefix + \" \" + M\n",
    "\n",
    "        if(A != \"999999\" and M != \"999999\"):\n",
    "            if A in prod_pronouns :\n",
    "                A = \"product\"\n",
    "            dict2 = {\"noun\" : A, \"adj\" : M, \"rule\" : 2}\n",
    "            rule2_pairs.append(dict2)\n",
    "\n",
    "        #print(\"--- SPACY : Rule 2 Done ---\")\n",
    "# -----------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "        ## THIRD RULE OF DEPENDANCY PARSE -\n",
    "        ## M - Sentiment modifier || A - Aspect\n",
    "        ## Adjectival Complement - A is a child of something with relationship of nsubj, while\n",
    "        ## M is a child of the same something with relationship of acomp\n",
    "        ## Assumption - A verb will have only one NSUBJ and DOBJ\n",
    "        ## \"The sound of the speakers would be better. The sound of the speakers could be better\" - handled using AUX dependency\n",
    "\n",
    "        children = token.children\n",
    "        A = \"999999\"\n",
    "        M = \"999999\"\n",
    "        add_neg_pfx = False\n",
    "        for child in children :\n",
    "            if(child.dep_ == \"nsubj\" and not child.is_stop):\n",
    "                A = child.text\n",
    "                # check_spelling(child.text)\n",
    "\n",
    "            if(child.dep_ == \"acomp\" and not child.is_stop):\n",
    "                M = child.text\n",
    "\n",
    "            # example - 'this could have been better' -> (this, not better)\n",
    "            if(child.dep_ == \"aux\" and child.tag_ == \"MD\"):\n",
    "                neg_prefix = \"not\"\n",
    "                add_neg_pfx = True\n",
    "\n",
    "            if(child.dep_ == \"neg\"):\n",
    "                neg_prefix = child.text\n",
    "                add_neg_pfx = True\n",
    "\n",
    "        if (add_neg_pfx and M != \"999999\"):\n",
    "            M = neg_prefix + \" \" + M\n",
    "                #check_spelling(child.text)\n",
    "\n",
    "        if(A != \"999999\" and M != \"999999\"):\n",
    "            if A in prod_pronouns :\n",
    "                A = \"product\"\n",
    "            dict3 = {\"noun\" : A, \"adj\" : M, \"rule\" : 3}\n",
    "            rule3_pairs.append(dict3)\n",
    "            #rule3_pairs.append((A, M, sid.polarity_scores(M)['compound'],3))\n",
    "#print(\"--- SPACY : Rule 3 Done ---\")\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    " \n",
    "        ## FOURTH RULE OF DEPENDANCY PARSE -\n",
    "        ## M - Sentiment modifier || A - Aspect\n",
    "\n",
    "        #Adverbial modifier to a passive verb - A is a child of something with relationship of nsubjpass, while\n",
    "        # M is a child of the same something with relationship of advmod\n",
    "\n",
    "        #Assumption - A verb will have only one NSUBJ and DOBJ\n",
    "\n",
    "        children = token.children\n",
    "        A = \"999999\"\n",
    "        M = \"999999\"\n",
    "        add_neg_pfx = False\n",
    "        for child in children :\n",
    "            if((child.dep_ == \"nsubjpass\" or child.dep_ == \"nsubj\") and not child.is_stop):\n",
    "                A = child.text\n",
    "                # check_spelling(child.text)\n",
    "\n",
    "            if(child.dep_ == \"advmod\" and not child.is_stop):\n",
    "                M = child.text\n",
    "                M_children = child.children\n",
    "                for child_m in M_children:\n",
    "                    if(child_m.dep_ == \"advmod\"):\n",
    "                        M_hash = child_m.text\n",
    "                        M = M_hash + \" \" + child.text\n",
    "                        break\n",
    "                #check_spelling(child.text)\n",
    "\n",
    "            if(child.dep_ == \"neg\"):\n",
    "                neg_prefix = child.text\n",
    "                add_neg_pfx = True\n",
    "\n",
    "        if (add_neg_pfx and M != \"999999\"):\n",
    "            M = neg_prefix + \" \" + M\n",
    "\n",
    "        if(A != \"999999\" and M != \"999999\"):\n",
    "            if A in prod_pronouns :\n",
    "                A = \"product\"\n",
    "            dict4 = {\"noun\" : A, \"adj\" : M, \"rule\" : 4}\n",
    "            rule4_pairs.append(dict4)\n",
    "            #rule4_pairs.append((A, M,sid.polarity_scores(M)['compound'],4)) # )\n",
    "\n",
    "#print(\"--- SPACY : Rule 4 Done ---\")\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "        ## FIFTH RULE OF DEPENDANCY PARSE -\n",
    "        ## M - Sentiment modifier || A - Aspect\n",
    "\n",
    "        #Complement of a copular verb - A is a child of M with relationship of nsubj, while\n",
    "        # M has a child with relationship of cop\n",
    "\n",
    "        #Assumption - A verb will have only one NSUBJ and DOBJ\n",
    "\n",
    "        children = token.children\n",
    "        A = \"999999\"\n",
    "        buf_var = \"999999\"\n",
    "        for child in children :\n",
    "            if(child.dep_ == \"nsubj\" and not child.is_stop):\n",
    "                A = child.text\n",
    "                # check_spelling(child.text)\n",
    "\n",
    "            if(child.dep_ == \"cop\" and not child.is_stop):\n",
    "                buf_var = child.text\n",
    "                #check_spelling(child.text)\n",
    "\n",
    "        if(A != \"999999\" and buf_var != \"999999\"):\n",
    "            if A in prod_pronouns :\n",
    "                A = \"product\"\n",
    "            dict5 = {\"noun\" : A, \"adj\" : token.text, \"rule\" : 5}\n",
    "            rule5_pairs.append(dict5)\n",
    "            #rule5_pairs.append((A, token.text,sid.polarity_scores(token.text)['compound'],5))\n",
    "\n",
    "#print(\"--- SPACY : Rule 5 Done ---\")\n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "        ## SIXTH RULE OF DEPENDANCY PARSE -\n",
    "        ## M - Sentiment modifier || A - Aspect\n",
    "        ## Example - \"It ok\", \"ok\" is INTJ (interjections like bravo, great etc)\n",
    "\n",
    "        children = token.children\n",
    "        A = \"999999\"\n",
    "        M = \"999999\"\n",
    "        if(token.pos_ == \"INTJ\" and not token.is_stop):\n",
    "            for child in children :\n",
    "                if(child.dep_ == \"nsubj\" and not child.is_stop):\n",
    "                    A = child.text\n",
    "                    M = token.text\n",
    "                    # check_spelling(child.text)\n",
    "\n",
    "        if(A != \"999999\" and M != \"999999\"):\n",
    "            if A in prod_pronouns :\n",
    "                A = \"product\"\n",
    "            dict6 = {\"noun\" : A, \"adj\" : M, \"rule\" : 6}\n",
    "            rule6_pairs.append(dict6)\n",
    "\n",
    "            #rule6_pairs.append((A, M,sid.polarity_scores(M)['compound'],6))\n",
    "\n",
    "#print(\"--- SPACY : Rule 6 Done ---\")\n",
    "    \n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "        ## SEVENTH RULE OF DEPENDANCY PARSE -\n",
    "    ## M - Sentiment modifier || A - Aspect\n",
    "    ## ATTR - link between a verb like 'be/seem/appear' and its complement\n",
    "    ## Example: 'this is garbage' -> (this, garbage)\n",
    "\n",
    "        children = token.children\n",
    "        A = \"999999\"\n",
    "        M = \"999999\"\n",
    "        add_neg_pfx = False\n",
    "        for child in children :\n",
    "            if(child.dep_ == \"nsubj\" and not child.is_stop):\n",
    "                A = child.text\n",
    "                # check_spelling(child.text)\n",
    "\n",
    "            if((child.dep_ == \"attr\") and not child.is_stop):\n",
    "                M = child.text\n",
    "                #check_spelling(child.text)\n",
    "\n",
    "            if(child.dep_ == \"neg\"):\n",
    "                neg_prefix = child.text\n",
    "                add_neg_pfx = True\n",
    "\n",
    "        if (add_neg_pfx and M != \"999999\"):\n",
    "            M = neg_prefix + \" \" + M\n",
    "\n",
    "        if(A != \"999999\" and M != \"999999\"):\n",
    "            if A in prod_pronouns :\n",
    "                A = \"product\"\n",
    "            dict7 = {\"noun\" : A, \"adj\" : M, \"rule\" : 7}\n",
    "            rule7_pairs.append(dict7)\n",
    "            #rule7_pairs.append((A, M,sid.polarity_scores(M)['compound'],7))\n",
    "\n",
    "\n",
    "\n",
    "#print(\"--- SPACY : All Rules Done ---\")\n",
    "    \n",
    "# ------------------------------------------------------------------------------------------------------------------------------\n",
    "\n",
    "    aspects = []\n",
    "\n",
    "    aspects = rule1_pairs + rule2_pairs + rule3_pairs +rule4_pairs +rule5_pairs + rule6_pairs + rule7_pairs\n",
    "\n",
    "    dic = {\"aspect_pairs\" : aspects}\n",
    "    return dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "d4cfc680",
   "metadata": {
    "id": "d4cfc680"
   },
   "outputs": [],
   "source": [
    "def extract_aspects(reviews,nlp):\n",
    "    \n",
    "    \"\"\"\n",
    "    Applying the aspect extraction function and returning a dictionary \n",
    "    with key = Aspect & value = Description\n",
    "    \n",
    "    \"\"\"\n",
    "    print(\"Entering Apply function!\")\n",
    "    aspect_list = reviews.apply(lambda row: apply_extraction(row,nlp), axis=1) #going through all the rows in the dataframe\n",
    "    return aspect_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "1ab23f45",
   "metadata": {
    "id": "1ab23f45"
   },
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\") # loading the library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "25b94438",
   "metadata": {
    "id": "25b94438"
   },
   "outputs": [],
   "source": [
    "reviews_train = train_data[[\"Review\"]] # extracting aspects from train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "aee9c665",
   "metadata": {
    "id": "aee9c665"
   },
   "outputs": [],
   "source": [
    "reviews_test = test_data[[\"Review\"]] # extracting aspects from test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "22963b0e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "22963b0e",
    "outputId": "5137f109-9fdc-41d4-9c06-c105370e4c16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Apply function!\n",
      "Execution Time : 50.36931649799999\n"
     ]
    }
   ],
   "source": [
    "# Train set\n",
    "starttime = timeit.default_timer()\n",
    "\n",
    "aspect_list_train = extract_aspects(reviews_train,nlp)\n",
    "aspect_list_train = list(aspect_list_train)\n",
    "\n",
    "print(\"Execution Time :\", timeit.default_timer() - starttime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "78272a44",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "78272a44",
    "outputId": "2113e186-9653-4ca8-97bc-214b652db155"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entering Apply function!\n",
      "Execution Time : 11.463111323999783\n"
     ]
    }
   ],
   "source": [
    "# Test set\n",
    "starttime = timeit.default_timer()\n",
    "\n",
    "aspect_list_test = extract_aspects(reviews_test,nlp)\n",
    "aspect_list_test = list(aspect_list_test)\n",
    "\n",
    "print(\"Execution Time :\", timeit.default_timer() - starttime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "75a31803",
   "metadata": {
    "id": "75a31803"
   },
   "outputs": [],
   "source": [
    "def add_data(data,aspect_list):    \n",
    "    \"\"\"\n",
    "    This function adds aspect and the description to the dataframe such that if one review has multiple aspects,\n",
    "    then the reviews are repeated such that each row consists of single aspect and description\n",
    "    \n",
    "    \"\"\"\n",
    "    rev_ = [] # list containing reviews\n",
    "    dates_ = [] # list containing dates\n",
    "    aspects_ = [] # list containing aspects\n",
    "    description_ = [] # list containing description\n",
    "    \n",
    "    \n",
    "    for i,j in enumerate(aspect_list):\n",
    "        length = len(list(j.values())[0])\n",
    "        if len(list(j.values())[0]) != 0:\n",
    "            rev_a = [data[\"Review\"].values[i] for k in range(len(list(j.values())[0]))]\n",
    "            dates_a = [data[\"Date\"].values[i] for k in range(len(list(j.values())[0]))] \n",
    "            aspects_a = [list(j.values())[0][h][\"noun\"] for h in range(length)]\n",
    "            descrip_a = [list(j.values())[0][h][\"adj\"] for h in range(length)]\n",
    "        \n",
    "            rev_.extend(rev_a)\n",
    "            dates_.extend(dates_a)\n",
    "            aspects_.extend(aspects_a)\n",
    "            description_.extend(descrip_a)\n",
    "        \n",
    "        # if a review doesn't contain any aspect then we add \"neutral\" for both the aspect & description column\n",
    "    \n",
    "        else: \n",
    "            rev_.append(data[\"Review\"].values[i])\n",
    "            dates_.append(data[\"Date\"].values[i])\n",
    "            aspects_.append('neutral')\n",
    "            description_.append('neutral')\n",
    "        \n",
    "    data_ = pd.DataFrame({\"Date\": dates_,\"Review\": rev_,\"Aspect\":aspects_,\"Description\":description_})\n",
    "    return data_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "fb12011f",
   "metadata": {
    "id": "fb12011f"
   },
   "outputs": [],
   "source": [
    "# Train set\n",
    "\n",
    "train_data_with_aspects = add_data(train_data,aspect_list_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "117460f0",
   "metadata": {
    "id": "117460f0"
   },
   "outputs": [],
   "source": [
    "# Test set\n",
    "\n",
    "test_data_with_aspects = add_data(test_data,aspect_list_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "e4a2f900",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "e4a2f900",
    "outputId": "f7f9160a-8920-4bd6-e63b-7ad412b03f63"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-63d83170-0963-4d5e-8343-8b5c62d4264d\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Date</th>\n",
       "      <th>Review</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30 August 2021</td>\n",
       "      <td>good performance</td>\n",
       "      <td>performance</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20 November 2020</td>\n",
       "      <td>apple apple product nice</td>\n",
       "      <td>product</td>\n",
       "      <td>nice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20 November 2020</td>\n",
       "      <td>easy use</td>\n",
       "      <td>use</td>\n",
       "      <td>easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20 November 2020</td>\n",
       "      <td>bundal watt charger u buy new fast charger enj...</td>\n",
       "      <td>charger</td>\n",
       "      <td>new</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20 November 2020</td>\n",
       "      <td>bundal watt charger u buy new fast charger enj...</td>\n",
       "      <td>charger</td>\n",
       "      <td>fast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9175</th>\n",
       "      <td>14 November 2021</td>\n",
       "      <td>yes not fake believe rumour</td>\n",
       "      <td>rumour</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9176</th>\n",
       "      <td>17 July 2021</td>\n",
       "      <td>nice product delivery getting late appario ret...</td>\n",
       "      <td>delivery</td>\n",
       "      <td>nice</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9177</th>\n",
       "      <td>17 July 2021</td>\n",
       "      <td>nice product delivery getting late appario ret...</td>\n",
       "      <td>seller</td>\n",
       "      <td>late</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9178</th>\n",
       "      <td>17 July 2021</td>\n",
       "      <td>nice product delivery getting late appario ret...</td>\n",
       "      <td>seller</td>\n",
       "      <td>retile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9179</th>\n",
       "      <td>17 July 2021</td>\n",
       "      <td>nice product delivery getting late appario ret...</td>\n",
       "      <td>packing</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9180 rows × 4 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-63d83170-0963-4d5e-8343-8b5c62d4264d')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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      "text/plain": [
       "                  Date                                             Review  \\\n",
       "0       30 August 2021                                   good performance   \n",
       "1     20 November 2020                           apple apple product nice   \n",
       "2     20 November 2020                                           easy use   \n",
       "3     20 November 2020  bundal watt charger u buy new fast charger enj...   \n",
       "4     20 November 2020  bundal watt charger u buy new fast charger enj...   \n",
       "...                ...                                                ...   \n",
       "9175  14 November 2021                        yes not fake believe rumour   \n",
       "9176      17 July 2021  nice product delivery getting late appario ret...   \n",
       "9177      17 July 2021  nice product delivery getting late appario ret...   \n",
       "9178      17 July 2021  nice product delivery getting late appario ret...   \n",
       "9179      17 July 2021  nice product delivery getting late appario ret...   \n",
       "\n",
       "           Aspect Description  \n",
       "0     performance        good  \n",
       "1         product        nice  \n",
       "2             use        easy  \n",
       "3         charger         new  \n",
       "4         charger        fast  \n",
       "...           ...         ...  \n",
       "9175       rumour        fake  \n",
       "9176     delivery        nice  \n",
       "9177       seller        late  \n",
       "9178       seller      retile  \n",
       "9179      packing        good  \n",
       "\n",
       "[9180 rows x 4 columns]"
      ]
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       "      <th>2034</th>\n",
       "      <td>17 November 2021</td>\n",
       "      <td>nice mobile genuine seller</td>\n",
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       "      <th>2035</th>\n",
       "      <td>10 October 2021</td>\n",
       "      <td>experienced heating issue in iphone otherwise ...</td>\n",
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       "      <th>2036</th>\n",
       "      <td>10 October 2021</td>\n",
       "      <td>good camera</td>\n",
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       "      <td>good</td>\n",
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       "                  Date                                             Review  \\\n",
       "0     1 September 2022                              costly thing provided   \n",
       "1     1 September 2022                also provide charging adapter phone   \n",
       "2     1 September 2022  time go slow feel slow esp after release new p...   \n",
       "3      10 January 2021                                       product good   \n",
       "4      10 January 2021  battery get zero within hour after six month c...   \n",
       "...                ...                                                ...   \n",
       "2032  17 November 2021                         nice mobile genuine seller   \n",
       "2033  17 November 2021                         nice mobile genuine seller   \n",
       "2034  17 November 2021                         nice mobile genuine seller   \n",
       "2035   10 October 2021  experienced heating issue in iphone otherwise ...   \n",
       "2036   10 October 2021                                        good camera   \n",
       "\n",
       "       Aspect  Description  \n",
       "0       thing       costly  \n",
       "1     neutral      neutral  \n",
       "2       phone          new  \n",
       "3     product         good  \n",
       "4     battery      changed  \n",
       "...       ...          ...  \n",
       "2032   seller         nice  \n",
       "2033  genuine       mobile  \n",
       "2034   seller      genuine  \n",
       "2035    issue  experienced  \n",
       "2036   camera         good  \n",
       "\n",
       "[2037 rows x 4 columns]"
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  {
   "cell_type": "markdown",
   "id": "ae9adbf7",
   "metadata": {
    "id": "ae9adbf7"
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   "source": [
    "# Sentiment / polarity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "a4a442d4",
   "metadata": {
    "id": "a4a442d4"
   },
   "outputs": [],
   "source": [
    "# Vader sentiment analysis:\n",
    "def sentiment_scores(sentence):\n",
    "    senti = SentimentIntensityAnalyzer() \n",
    "    sentiment_dict = senti.polarity_scores(sentence) # this line returns the polarity for sentence\n",
    "  \n",
    "    if sentiment_dict['compound'] >= 0.05 : # if the compound score is >= 0.05 then the review is positive\n",
    "        return (\"Positive\"),sentiment_dict['pos'],sentiment_dict['compound']\n",
    "\n",
    "    elif sentiment_dict['compound'] <= - 0.05 : # if the compound score is <= -0.05 then the review is negative\n",
    "        return (\"Negative\"),sentiment_dict['neg'],sentiment_dict['compound']\n",
    "\n",
    "    else :\n",
    "        return (\"Neutral\"),sentiment_dict['neu'],sentiment_dict['compound'] # if compound score is in between 0.05 and -0.05 then the review is neutral\n",
    "    \n",
    "# Reference---> (https://www.geeksforgeeks.org/python-sentiment-analysis-using-vader/)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "4acc6e25",
   "metadata": {
    "id": "4acc6e25"
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   "source": [
    "def form_final_data(data):\n",
    "    starttime = timeit.default_timer()\n",
    "    \n",
    "    sentiment_ = [] # this list carries the sentiment for the review\n",
    "    #pol_value = [] # this list consists of  the polarity for the review\n",
    "    compound = [] # this consists of score based on which the sentiments are defined\n",
    "\n",
    "    for u in data[\"Review\"].values: # applying the above defined function by iterating through each review\n",
    "        a,b,c = sentiment_scores(u)\n",
    "        sentiment_.append(a)\n",
    "        #pol_value.append(b)\n",
    "        compound.append(c)\n",
    "        \n",
    "    data[\"Sentiment\"] = sentiment_ # adding the sentiment to dataframe\n",
    "    #data[\"Polarity\"] = pol_value\n",
    "    data[\"Score\"] = compound # adding the score to dataframe\n",
    "    \n",
    "    data[\"Date\"] = pd.to_datetime(data['Date']) \n",
    "    data.sort_values(by = 'Date', inplace = True) # sorting the date in ascending order\n",
    "    \n",
    "    data[\"Year\"] = pd.DatetimeIndex(data['Date']).year # extracting year from date\n",
    "    data[\"Month\"] = pd.DatetimeIndex(data['Date']).month # extracting month from date\n",
    "    data[\"Week\"] = pd.DatetimeIndex(data['Date']).week # extracting week from date\n",
    "    \n",
    "    final_dff = data.reset_index().drop([\"index\"], axis=1) # resetting the index\n",
    "    \n",
    "    print(\"The time difference is :\", timeit.default_timer() - starttime)\n",
    "    return final_dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "f8e7755f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f8e7755f",
    "outputId": "a323ce1b-440a-4946-ab44-87399194f66c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time difference is : 116.32936665200032\n"
     ]
    }
   ],
   "source": [
    "final_train = form_final_data(train_data_with_aspects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "d5134db0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d5134db0",
    "outputId": "f0a8b7eb-cda3-4bcd-902a-855156b1501d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time difference is : 25.65609221800014\n"
     ]
    }
   ],
   "source": [
    "final_test = form_final_data(test_data_with_aspects)"
   ]
  },
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>speechless not ask more</td>\n",
       "      <td>neutral</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-09-30</td>\n",
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       "      <td>premium</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td></td>\n",
       "      <td>neutral</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>9</td>\n",
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       "      <td>...</td>\n",
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       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>9179</th>\n",
       "      <td>2022-09-13</td>\n",
       "      <td>not recommend go definitely go eleven worst ba...</td>\n",
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       "      <td>9</td>\n",
       "      <td>37</td>\n",
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      "text/plain": [
       "           Date                                             Review   Aspect  \\\n",
       "0    2019-09-30                        must try slow motion selfie   selfie   \n",
       "1    2019-09-30                          must say really impressed  neutral   \n",
       "2    2019-09-30                            speechless not ask more  neutral   \n",
       "3    2019-09-30                         planning buy premium phone    phone   \n",
       "4    2019-09-30                                                     neutral   \n",
       "...         ...                                                ...      ...   \n",
       "9175 2022-08-17                            medium could not loaded  neutral   \n",
       "9176 2022-08-17                              month since purchased  neutral   \n",
       "9177 2022-08-27                                        bad battery  battery   \n",
       "9178 2022-09-13  not recommend go definitely go eleven worst ba...  battery   \n",
       "9179 2022-09-13  not recommend go definitely go eleven worst ba...   backup   \n",
       "\n",
       "     Description Sentiment   Score  Year  Month  Week  \n",
       "0           slow   Neutral  0.0000  2019      9    40  \n",
       "1        neutral  Positive  0.5256  2019      9    40  \n",
       "2        neutral   Neutral  0.0000  2019      9    40  \n",
       "3        premium   Neutral  0.0000  2019      9    40  \n",
       "4        neutral   Neutral  0.0000  2019      9    40  \n",
       "...          ...       ...     ...   ...    ...   ...  \n",
       "9175     neutral   Neutral  0.0000  2022      8    33  \n",
       "9176     neutral   Neutral  0.0000  2022      8    33  \n",
       "9177         bad  Negative -0.5423  2022      8    34  \n",
       "9178       worst  Negative -0.9112  2022      9    37  \n",
       "9179       worst  Negative -0.9112  2022      9    37  \n",
       "\n",
       "[9180 rows x 9 columns]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
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  {
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       "           Date                                             Review    Aspect  \\\n",
       "0    2019-09-28                                            got guy   neutral   \n",
       "1    2019-09-28                                             matter   neutral   \n",
       "2    2019-09-28                        display could oled in price   neutral   \n",
       "3    2019-09-28                     best processor bionic chipcons  chipcons   \n",
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       "...         ...                                                ...       ...   \n",
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       "2033 2022-07-31   month use battery life percent not good prodecut  prodecut   \n",
       "2034 2022-09-01  time go slow feel slow esp after release new p...     phone   \n",
       "2035 2022-09-01                also provide charging adapter phone   neutral   \n",
       "2036 2022-09-01                              costly thing provided     thing   \n",
       "\n",
       "     Description Sentiment   Score  Year  Month  Week  \n",
       "0        neutral   Neutral  0.0000  2019      9    39  \n",
       "1        neutral   Neutral  0.0258  2019      9    39  \n",
       "2        neutral   Neutral  0.0000  2019      9    39  \n",
       "3         bionic  Positive  0.6369  2019      9    39  \n",
       "4           best  Positive  0.6369  2019      9    39  \n",
       "...          ...       ...     ...   ...    ...   ...  \n",
       "2032     neutral  Positive  0.1779  2022      7    30  \n",
       "2033        good  Negative -0.3412  2022      7    30  \n",
       "2034         new   Neutral  0.0000  2022      9    35  \n",
       "2035     neutral   Neutral  0.0000  2022      9    35  \n",
       "2036      costly  Negative -0.1027  2022      9    35  \n",
       "\n",
       "[2037 rows x 9 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8013e0f",
   "metadata": {
    "id": "a8013e0f"
   },
   "source": [
    "# Basic Feature Extraction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "dd622b10",
   "metadata": {
    "id": "dd622b10"
   },
   "outputs": [],
   "source": [
    "def character_count(text):\n",
    "    \"\"\"\n",
    "    This function returns the number of letters in a review\n",
    "    \n",
    "    \"\"\"\n",
    "    return len(text)\n",
    "\n",
    "def word_count(text):\n",
    "    \"\"\"\n",
    "    This function returns the number of words in a review\n",
    "    \n",
    "    \"\"\"\n",
    "    return len(text.split())\n",
    "\n",
    "def unique_words(text):\n",
    "    \"\"\"\n",
    "    This function returns the number of unique words in a review\n",
    "    \n",
    "    \"\"\"\n",
    "    return len(set(text.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "bcb4f610",
   "metadata": {
    "id": "bcb4f610"
   },
   "outputs": [],
   "source": [
    "# Train set:\n",
    "\n",
    "final_train['char_count'] = final_train[\"Review\"].apply(lambda x:character_count(x))\n",
    "final_train['word_count'] = final_train[\"Review\"].apply(lambda x:word_count(x))\n",
    "final_train['unique_word_count'] = final_train[\"Review\"].apply(lambda x:unique_words(x))\n",
    "final_train['avg_wordlength'] = final_train['char_count']/final_train['word_count']\n",
    "final_train['unique_vs_words'] = final_train['unique_word_count']/final_train['word_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "f675eed5",
   "metadata": {
    "id": "f675eed5"
   },
   "outputs": [],
   "source": [
    "# Test set:\n",
    "\n",
    "final_test['char_count'] = final_test[\"Review\"].apply(lambda x:character_count(x))\n",
    "final_test['word_count'] = final_test[\"Review\"].apply(lambda x:word_count(x))\n",
    "final_test['unique_word_count'] = final_test[\"Review\"].apply(lambda x:unique_words(x))\n",
    "final_test['avg_wordlength'] = final_test['char_count']/final_test['word_count']\n",
    "final_test['unique_vs_words'] = final_test['unique_word_count']/final_test['word_count']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00395a66",
   "metadata": {
    "id": "00395a66"
   },
   "source": [
    "# Data Visualization:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a454ab00",
   "metadata": {
    "id": "a454ab00"
   },
   "source": [
    "# Frequency of top 30 Aspects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "cc9d6151",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "id": "cc9d6151",
    "outputId": "e9b20dd2-ce1a-44e3-cb8d-c7d7b6cdc625"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3dafd75450>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top = final_train[\"Aspect\"].value_counts()[1:30]\n",
    "asp = list(dict(top).keys())\n",
    "count = list(dict(top).values())\n",
    "\n",
    "plt.figure(figsize=(18, 7))\n",
    "sns.barplot(x=count,y=asp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbe1867e",
   "metadata": {
    "id": "fbe1867e"
   },
   "source": [
    "# Overall Sentiment for Unigram & Bigram aspects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "9d336314",
   "metadata": {
    "id": "9d336314"
   },
   "outputs": [],
   "source": [
    "def count_avg(data,group_name,text,text2):\n",
    "    \"\"\"\n",
    "    This function returns the overall sentiment for an aspect by calculating the mean of the column (score)\n",
    "    \n",
    "    \"\"\"\n",
    "    dict_version = {}\n",
    "    form_group = data.groupby(by = (group_name))\n",
    "    for i in list(dict(data[group_name].value_counts()).keys())[1:30]:\n",
    "        get_aspect = form_group.get_group(i)\n",
    "        result = get_aspect[\"Score\"].mean() # calculating the mean of score for a particular aspect\n",
    "        dict_version[i] = result\n",
    "    \n",
    "    plt.figure(figsize=(18, 7))\n",
    "    x_ver = list(dict_version.keys())\n",
    "    y_ver = sorted(list(dict_version.values()))\n",
    "    plt.barh(x_ver,y_ver)\n",
    "    plt.xlabel(text2, fontsize = 16)\n",
    "    plt.ylabel(\"Aspects\", fontsize = 16)\n",
    "    plt.title(text,fontsize = 16)\n",
    "    return plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "9108b3ff",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 465
    },
    "id": "9108b3ff",
    "outputId": "e7a9c6df-591d-47de-d09d-758b71a49adf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_avg(final_train,'Aspect','Overall sentiment of aspects','Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3c42cb",
   "metadata": {
    "id": "3f3c42cb"
   },
   "source": [
    "# Overall Sentiments over a Time Period:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "72b5bcde",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 734
    },
    "id": "72b5bcde",
    "outputId": "414437d1-1a7e-4943-89e6-6110f61f1d82"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 5))\n",
    "sns.lineplot(x=\"Date\", y=\"Score\", data=final_train, hue=\"Sentiment\", palette=\"Dark2\")\n",
    "\n",
    "fig = px.pie(final_train, values= list(dict(final_train[\"Sentiment\"].value_counts()).values()),\n",
    "             names=list(dict(final_train[\"Sentiment\"].value_counts()).keys()), \n",
    "             title='Sentiments across reviews', width = 400, height = 400)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0013dc",
   "metadata": {
    "id": "2c0013dc"
   },
   "source": [
    "# Basic Feature Visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "2d8a33a2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "2d8a33a2",
    "outputId": "49e64e42-296c-4dc1-881b-537aa5d122d8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-37f835d6-72c6-4040-b3ed-3f1b2e762143\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Date</th>\n",
       "      <th>Review</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Description</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Score</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Week</th>\n",
       "      <th>char_count</th>\n",
       "      <th>word_count</th>\n",
       "      <th>unique_word_count</th>\n",
       "      <th>avg_wordlength</th>\n",
       "      <th>unique_vs_words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>must try slow motion selfie</td>\n",
       "      <td>selfie</td>\n",
       "      <td>slow</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5.40</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>must say really impressed</td>\n",
       "      <td>neutral</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.5256</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6.25</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37f835d6-72c6-4040-b3ed-3f1b2e762143')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-37f835d6-72c6-4040-b3ed-3f1b2e762143 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-37f835d6-72c6-4040-b3ed-3f1b2e762143');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "        Date                       Review   Aspect Description Sentiment  \\\n",
       "0 2019-09-30  must try slow motion selfie   selfie        slow   Neutral   \n",
       "1 2019-09-30    must say really impressed  neutral     neutral  Positive   \n",
       "\n",
       "    Score  Year  Month  Week  char_count  word_count  unique_word_count  \\\n",
       "0  0.0000  2019      9    40          27           5                  5   \n",
       "1  0.5256  2019      9    40          25           4                  4   \n",
       "\n",
       "   avg_wordlength  unique_vs_words  \n",
       "0            5.40              1.0  \n",
       "1            6.25              1.0  "
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "9e0dfa63",
   "metadata": {
    "id": "9e0dfa63"
   },
   "outputs": [],
   "source": [
    "def basic_feature_plots(target,feature,data):\n",
    "    \"\"\"\n",
    "    This function helps to plot distributio of featuers and shows the target class\n",
    "    \n",
    "    \"\"\"\n",
    "    plt.figure(figsize=(12, 6))\n",
    "\n",
    "    plt.subplot(1,2,1)\n",
    "    sns.violinplot(x = target, y = feature, data = data)\n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    sns.kdeplot(data[data[target] == \"Positive\"][feature] , label = \"Positive\", color = 'green')\n",
    "    sns.kdeplot(data[data[target] == \"Negative\"][feature] , label = \"Negative\" , color = 'red')\n",
    "    sns.kdeplot(data[data[target] == \"Neutral\"][feature] , label = \"Neutral\" , color = 'blue')\n",
    "    plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "2a1a7e69",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "2a1a7e69",
    "outputId": "492d5f53-8019-4587-d174-e311d8c7a74a"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "basic_feature_plots(\"Sentiment\",\"char_count\",final_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "53d42977",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "53d42977",
    "outputId": "8afa971a-27fd-4c0e-9a19-7d4ac3d8bdac"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CD7Ju3To2btzI5s2bmTt3LgA//OEPqayspK2tjeXLl3PVVVcxNbotaBoocZYxT8vRieSf5uZjq80QVJz3JTpdUkSGNXHiRG688UbuueceysvLe8efe+453n67b/+lo0eP9tsqOx5nn312b9IMcM899/DEE08AsGfPHrZt26bEWURE5Hi0tMCECceOT5wIwxS8RHJWPJXhVLr11ls588wz+exnP9s7FolEeOWVVygrK+t3blFREZFIX0dYe3v7kK87PuYv4HXr1vHcc8/x8ssvM27cOC688MJhr00F9TiLiEjeGa7irB5nkeSrrKzk2muv5cEHH+wdu/TSS7n33nt7j6MtGHPmzGHjxo0AbNy4kffeew+AiooKmob5B9rY2MiUKVMYN24c77zzDq+88koqPsqwlDiLiEjeifY4D6QeZ5HUue222/qtrnHPPfdQU1PD0qVLWbhwIffffz8AV111FfX19SxatIj77ruP+fPnAzB16lRWrFjB4sWL+epXv3rM669cuZLu7m4WLFjA6tWrOffcc9PzwWKoVUNERPJOc3PfToGxKiqgvR26u4MNUUTk+MT2LM+YMYPW1tbe42nTprFmzZpjrikvL+fZZ58d9PV+8pOf9Du+8MILex+Xlpby9NNPD3rdrl27Eoh69FRxFhGRvDNcxRnUriEio6PEWURE8k5z8+CTA6NVaLVriMhoKHEWEZG8o4qziKSCEmcREck7qjjLWOHumQ4hZ43mv50SZxERySuRCLS2quIs+a+srIy6ujolz6Pg7tTV1R2zxvRINKdYRETySnRSvyrOku9mzZrF3r17qa2tzXQoOamsrIxZs2YldI0SZxERySstLcH9YBXnaDIdPUcklxUXF/fbjlpST60aIiKSV6LLyg5WcY4m00qcRWQ0lDiLiEheGa7iHB2L2bNBRCRuSpxFRCSvDFdxLi8HM1WcRWR0lDiLiEheGa7ibBaMq+IsIqOhxFlERPLKcIlzdFwVZxEZDSXOIiKSV4Zr1YiOK3EWkdFIeeJsZpPN7HEze8fMtpjZn5hZpZn9xsy2hfdTUh2HiIiMDfFUnNWqISKjkY6K8z8Dv3b304DTgS3AauB5d58HPB8ei2SEdlwSyS+qOItIqqQ0cTazScAFwIMA7t7p7g3AFcDD4WkPA1emMg4RERk71OMsIqmS6orzXKAW+JGZvWZm/8vMxgMz3H1/eM4BYEaK4xARkTGipQVKSqCwcPDn1aohIqOV6sS5CDgT+L67nwG0MKAtw4PvyQf9rtzMVplZjZnVaB92SRW1aojkl/b2YL3moahVQ0RGK9WJ815gr7u/Gh4/TpBIHzSzEwDC+0ODXezuD7j7MndfVlVVleJQZawzs0yHICJJ0NY2fOKsirOIjFZKE2d3PwDsMbOPhEOXAG8Da4GbwrGbgCdTGYeIiIwd7e1QVjb08+pxFpHRKkrDe/w34BEzKwF2Ap8lSNgfNbPPAe8D16YhDpFhqWVDJD+MlDhPmACtrRCJQIF2MxCRBKQ8cXb3TcCyQZ66JNXvLZIItWqI5Id4WjXcg/OGWnlDRGQw+ltbRETySjytGqB2DRFJnBJnGfOilWa1aojkh3hW1QAlziKSOCXOIiKSV9ra4qs4a2UNEUmUEmcZ81RpFskv8UwOBFWcRSRxSpxFRPKIma00s61mtt3MVg/y/AVmttHMus3s6gHP9ZjZpvC2NmZ8rpm9Gr7mmnCVpKwVz+RAUMVZRBKnxFkkpFU1JNeZWSHwPeATwELg02a2cMBpu4HPAD8Z5CXa3L06vF0eM34ncLe7fxg4Anwu6cEnkSYHikiqKHEWEckfZwPb3X2nu3cCPwWuiD3B3Xe5+xtAJJ4XtOAvyosJdn4FeBi4MnkhJ58mB4pIqihxljFPq2pIHjkJ2BNzvDcci1eZmdWY2StmFk2OpwIN7t490mua2arw+pra2tpEY08aTQ4UkVRJx86BIiKSG05x931mdirwWzN7E2iM92J3fwB4AGDZsmUZ+UvUXa0aIpI6qjiLiOSPfcDsmONZ4Vhc3H1feL8TWAecAdQBk80sWmhJ6DXTrbMzSJ41OVBEUkGJs4hI/lgPzAtXwSgBrgfWjnANAGY2xcxKw8fTgBXA2x70MP0OiK7AcRPwZNIjT5L29uB+uIpzcTGUlKjiLCKJU+IsIpInwj7km4FngC3Ao+7+lpl928wuBzCz5Wa2F7gG+IGZvRVevgCoMbPXCRLlO9z97fC5rwNfNrPtBD3PD6bvUyUmmjgPV3GGYIKgEmcRSZR6nEVE8oi7PwU8NWDsmzGP1xO0Wwy87iVgyRCvuZNgxY6s19YW3A9XcYagXUOtGiKSKFWcRUQkb8TTqgFB4qyKs4gkSomziIjkjWjFWa0aIpIKSpxFRCRvJFJxVquGiCRKibOIiOSNeCcHqlVDREZDibOIiOSNeCcHTpigirOIJE6Js4iI5A1NDhSRVFLiLCIieUOTA0UklZQ4i4hI3tDkQBFJJSXOMuYFOwqLSD5IZHJgVxd0dqY+JhHJH0qcRUQkbyQyORDUriEiiVHiLBIys0yHICLHKZFWDVDiLCKJUeIsIiJ5o60NCguhuHj485Q4i8hoKHEWCanXWST3tbePXG2GvlYNTRAUkUQocRYJqVVDJPe1t0Np6cjnqeIsIqOhxFlERPJGZ6cSZxFJHSXOMuZFK81q1RDJfR0d8SXOatUQkdFQ4iwiInmjsxNKSkY+TxVnERkNJc4iIpI3VHEWkVQqSvUbmNkuoAnoAbrdfZmZVQJrgDnALuBadz+S6lhERCS/qeIsIqmUrorzRe5e7e7LwuPVwPPuPg94PjwWERE5LvFWnMvLwUyJs4gkJlOtGlcAD4ePHwauzFAcIiKSR+KtOJsFVWe1aohIItKRODvwrJltMLNV4dgMd98fPj4AzEhDHCIikufirThDkDir4iwiiUh5jzNwnrvvM7PpwG/M7J3YJ93dzWzQdcDCRHsVwMknn5z6SEVEJKfFW3EGJc4ikriUV5zdfV94fwh4AjgbOGhmJwCE94eGuPYBd1/m7suqqqpSHaqMUVq/WSR/JFJxnjBBrRoikpiUJs5mNt7MKqKPgUuBzcBa4KbwtJuAJ1MZh4iIjA2qOItIKqW6VWMG8ES4M1sR8BN3/7WZrQceNbPPAe8D16Y4DpER9fT0ZDoEETlOiVacm5pSG4+I5JeUJs7uvhM4fZDxOuCSVL63SKLUsiGS+xKtOB84kNp4RCS/aOdAGfPCb0SIRCIZjkREjpdW1RCRVFLiLGOeKs0i+SORirMmB4pIopQ4i4QKCvTPQSTXqeIsIqmkTEEkFG3ZEJHc1NMDkUjiq2qoS0tE4qXEWSRUWFiY6RBE5Dh0dAT3iayqAdDWlpp4RCT/KHEWEckjZrbSzLaa2XYzWz3I8xeY2UYz6zazq2PGq83sZTN7y8zeMLPrYp57yMzeM7NN4a06XZ8nEZ2dwX0iFWdQu4aIxC8dW26LZDW1aEi+MLNC4HvAx4G9wHozW+vub8ecthv4DPCVAZe3Aje6+zYzOxHYYGbPuHtD+PxX3f3x1H6C4zPainNzM0yfnpqYRCS/KHEWEckfZwPbwzX0MbOfAlcAvYmzu+8Kn+vX2evu78Y8/sDMDgFVQAM5QhVnEUk1tWqIiOSPk4A9Mcd7w7GEmNnZQAmwI2b4H8MWjrvNLM6abnolWnFW4iwiiVLiLCIivczsBOB/A59192hV+hvAacByoBL4+hDXrjKzGjOrqa2tTUu8sRKtOMe2aoiIxEOJs4hI/tgHzI45nhWOxcXMJgL/Dvxf7v5KdNzd93ugA/gRQUvIMdz9AXdf5u7LqqqqRvUBjocqziKSakqcRUTyx3pgnpnNNbMS4HpgbTwXhuc/AfzbwEmAYRUaC2bSXglsTmrUSTLaHmdVnEUkXkqcRUTyhLt3AzcDzwBbgEfd/S0z+7aZXQ5gZsvNbC9wDfADM3srvPxa4ALgM4MsO/eImb0JvAlMA/5HGj9W3KKJc6KraqjiLCLx0qoaIiJ5xN2fAp4aMPbNmMfrCVo4Bl73Y+DHQ7zmxUkOMyWirRpaVUNEUkUVZxERyQuJVpzVqiEiiVLiLGOeu2c6BBFJgkQrzsXFwbmqOItIvJQ4i4S0g6BIbku04gxB1VkVZxGJlxJnERHJC4lWnCGYIKiKs4jES4mzSEgtGyK5bbQVZyXOIhIvJc4iIbVqiOS20VSc1aohIolQ4iwiInlhNBVntWqISCKUOIuE1KohkttGW3FW4iwi8VLiLBJSq4ZIbkt0y21Qq4aIJEaJs0hIFWeR3NbRAYWFwS1eatUQkUQocZYxT5Xm7HTgwAGaVQqUBHR2JtbfDKo4i0hilDiLSFa69tpr+fu///tMhyE5pKMjsTYNUMVZRBITd+JsZtfEMyYikiwbN27MdAiSQ0Zbce7q6uuPFhEZTiIV52/EOSYiclzUby6jMZqK8/jxwb2qziISj6KRTjCzTwB/BpxkZvfEPDUR6E5VYCIydnV1dWU6BMlBo6k4T5gQ3Le0wJQpyY9JRPLLiIkz8AFQA1wObIgZbwK+lIqgRGRs64guyCuSgOOpOGuCoIjEY8TE2d1fB143s5+4u8pAkrcikUimQ5BQa2trpkOQHDTaHmdQq4aIxCeRHuezzew3Zvaume00s/fMbGc8F5pZoZm9Zma/Co/nmtmrZrbdzNaYWYI1ApHkU19t9lDFWUZjtKtqgBJnEYlPIonzg8A/AecBy4Fl4X08bgG2xBzfCdzt7h8GjgCfSyAOEclz7e3tmQ5BctDxVJzVqiEi8UgkcW5096fd/ZC710VvI11kZrOATwL/Kzw24GLg8fCUh4ErE4xbJGlUac4+mhwoo6GKs4ikWjyTA6N+Z2b/L/BzoPd7VHcfaaHV7wJfAyrC46lAg7tHV+TYC5yUQBwiKVFQoP2AskV3txbskcR1dkJlZWLXqMdZRBKRSOJ8Tni/LGbMCarHgzKzTwGH3H2DmV2YaHBmtgpYBXDyyScnerlIQrT1dvbQtwAyGsNVnHc37ubLz3yZlR9eyV8uuYGynbth/nzGjw/+3atVQ0TiEXeJzd0vGuQ2ZNIcWgFcbma7gJ8SJNn/DEw2s2jSPgvYN8R7PuDuy9x9WVVVVbyhikiOU/UfzOznZvZJM9N/jDh1dg6dON9fcz8/2/IzPv/k37D5kqVw2mlw6aVMKGwDVHEWkfjEXXE2s28ONu7u3x7qGnf/BuHugmHF+SvufoOZPQZcTZBM3wQ8mUDMIimhinP2KCpK5MuwvPUvwGeBe8KfmT9y960ZjimrdXQMPjnQ3Vnz1hou/dClfOytNpb94Q/0/MV/ovDnT1B+/92Y/Z0qziISl0QqGS0xtx7gE8CcUb7v14Evm9l2gp7nB0f5OiKSh0oTXRohD7n7c+5+A3AmsAt4zsxeMrPPmllxZqPLTkNVnDfs38DOIzu5btF1fOaPLeytgH+//Qa44grszjuoqHCOHk1/vCKSe+Iu67j7/xd7bGZ3Ac8kcP06YF34eCdwdrzXiqSD+mqzR1lZWaZDyApmNhX4S+CvgNeARwiWBL0JuDBzkWWnoSrOj771KMUFxfynkz7G5Jf+hn/+03Je2foYl3/lK/Dkk0ye2kJj44T0BywiOed4eufGEfQni+QFtWpkDyXOYGZPAH8g+Fn75+5+ubuvcff/BijLG8RQFecX97zIn8z+E6ZsegeLRCi86BLWbl1L57nLYf58JrcfoKEh/fGKSO6JO3E2szfN7I3w9hawlWCpOZG8oIpz9igvL890CNngX919obv/T3ffD2BmpQDuvmz4S8emoSrO2+q28ZGpH4E//AEKC5m98lrautvYdPB1uPpqJrXsp+Gw1g4XkZElUnH+FPDn4e1S4ER3vy8lUYmkkSrN2ack0V0s8tP/GGTs5bRHkSPcoavr2IpzQ3sDta21zKucBy+9BNXVnH3aJQC8uPtF+LM/YzJHaNyn2YEiMrJElqN7H5hMkDj/J2BhqoISkbFtLK+qYWYzzewsoNzMzjCzM8PbhQRtGzKIzs7gfmDFeVvdNgDmT50PmzdDdTUnVpzInMlzeGnvS3DOOUwuaaXhsDbdEZGRJbIc3S3A3xDsHPmRJ0cAACAASURBVAjwiJk94O73piQyEZGx6TLgMwRzSP4pZrwJ+LtMBJQLoonzwIrztvogcT7NquDQIVgY1Hw+OvujrNu1Di8sZNIpU2jcXgSRCGgNcREZRiJlnc8B57h7C4CZ3UnwtaESZxGRJHH3h4GHzewqd/9ZpuPJFR0dwf1gFWfDmLM/2OiEBQsA+Oisj/KTN3/C7sbdTF5wAo3bJhKp2UjB2WofF5GhJZI4G8H6zVE94ZiIiCSJmf2lu/8YmGNmXx74vLv/0yCXjXlDVZzfrX+XkyedTOm7O4KBmIozwMt7X2bSGZ8israQ5rW/ZaISZxEZRiLfSf0IeNXMbjez24FX0MYlIiLJNj68nwBUDHIblpmtNLOtZrbdzFYP8vwFZrbRzLrN7OoBz91kZtvC200x42eFKyttN7N7LAtn1A5XcZ4/dT5s2QLjxsHs2QAsmr6IksISXtv/GpNnBav7Nb6wKZ0hi0gOSmQDlH8ys3UEi+8DfNbdX0tJVCIiY5S7/yC8/4dErzWzQuB7wMeBvcB6M1vr7m/HnLaboIf6KwOurQS+BSwDHNgQXnsE+D7BHJdXgaeAlcDTicaXSoNVnN2dbfXb+M+L/zO89x6cempvD3NJYQkLqxby+sHXWT45OL+hZjuzu7qgWBszisjgElnH+Vxgm7vf4+73ADvM7JzUhSYiMnaZ2f9jZhPNrNjMnjezWjP7yxEuOxvY7u473b0T+ClwRewJ7r7L3d8AIgOuvQz4jbvXh8nyb4CVZnYCMNHdX/FgsfN/A65MxmdMpsEqzvVt9TS0N/Dhyg/Drl1wyin9rqmeWc2mA5uYNCk4bmgvhZqa9AQsIjkpkVaN7wOxC102h2MiOU0bn0iWutTdjxKsob8L+DDw1RGuOQnYE3O8NxyLx1DXnhQ+HvE1zWyVmdWYWU1tbW2cb5scg1WcP2j6AIBZE2fB++8fmzjPqOZgy0F6Sg4D0MgkeOGFtMQrIrkpkcTZPCbDcPcIiU0uFBGR+EV/vn4SeMzdGzMZTDzc/QF3X+buy6qqqtL63oOt43yg+QAAJ3kFNDQMWnEG+KBrCwANJy5S4iwiw0okcd5pZn8bfm1YHK7rvDNVgYmkWxbOd5Kx7Vdm9g5wFvC8mVUB7SNcsw+YHXM8KxyLx1DX7gsfj+Y10ybaqhFbcT7YchCAk46Em5vMmdPvmqUzlgLwXlswKbDx1DPgj3+Ebm2GIiKDSyRx/i/ARwl+YO4FzgFWpSIoEZGxzt1XE/zMXebuXUALA/qVB7EemGdmc82sBLgeWBvnWz4DXGpmU8xsCnAp8Iy77weOmtm54WoaNwJPjuIjpdRwFeeq2pZgYEDFeUr5FE6ZdApbW/4DgIYTF0JzM7ymee8iMrhEVtU4RPBDeFBm9g13/59JiUokA9TrLFnoNIL1nGN/Vv/bUCe7e7eZ3UyQBBcCP3T3t8zs20CNu681s+XAE8AU4M/N7B/cfZG715vZ/02QfAN8293rw8dfAB4CyglW08iqFTVg8IrzgeYDlBeVU/5B2G998snHXFc9s5o362ooL4eGylODwRdegOXLUxyxiOSiZPYoXwMocZacpVYNySZm9r+BDwGb6Nt8KrqqxZDc/SmCJeNix74Z83g9/VsvYs/7IfDDQcZrgMUJhJ92g00OPNhykJkTZmL790NhIcyYccx1p884nV+++0tmTIlwpGsCzJsXJM5f+cox54qIJDNxVtYhIpI8y4CFrq9C4jLYcnQHmg8wY8IM2PQBzJzZu4ZzrOqZ1UQ8wviJ7Rw+PA7+9E/hscegpydItkVEYiTS4zwS/XCXnKb8RLLMZmBmpoPIFYNVnA80H2DmhJmwfz+ccMKg10VX1igY30BdHUHi3NgIb7yR4ohFJBclM3FWxVlEJHmmAW+b2TNmtjZ6y3RQ2WqwyYEHmw8yY/yMYRPnOZPnMLF0It2lB/sSZ9CydCIyqGS2ajyWxNcSERnrbs90ALlk4OTArp4uDrce7qs4n3vuoNeZGafPOJ13i9+nue4MmD0b5s4NEudbb01T9CKSK0ZMnM3sXoZpw3D3vw3vv5PEuETSRpMCJRu5+wtmdgowz92fM7NxBCtlyCAGVpxrW2txnBPLqqC2dsiKMwTtGq+wDa933A370z+FX/4S3EE/H0QkRjytGjXABqAMOBPYFt6qgZJhrhPJCdHeZvU4SzYxs78BHgd+EA6dBPwicxFlt4EV54PNweYns9vCgWES59NnnE5X6X66u42jR4HzzoO6Oti6NYURi0guGrHi7O4PA5jZfwXOc/fu8Ph+4A+pDU8k9aIV556enhHOFEmrLwJnA68CuPs2M5ue2ZCyV7TiXFwc3Pdut90S1oeGSZyXzlgK434PwOHDMOm884In/vhHOO20lMQrIrkpkcmBU4CJMccTwjGRvNDV1ZXpEERidbh7Z/Qg3ARFX4sMoaMjqDZHOyuiifP05vA/2cyhFyhZNH0RlAd7vdTVAfPnw7RpQeIsIhIjkcmBdwCvmdnvCFbQuABNXpE8EG3RiEQiGY5EpJ8XzOzvgHIz+zjB7n2/zHBMWauz89jNTwCmtITfJFVVDXntuOJxnDSzjH2EibMZrFgBL76YuoBFJCfFVXE2swJgK3AOwVatPwf+JNrGIZIP1KohWWY1UAu8CXyeYDfAv89oRFmss7P/UnR1rXWUF5VTeqQpGJg6ddjrF54c7CpYVxcOnHcebN8OBw6kIFoRyVVxVZzdPWJm33P3M4AnUxyTSEZ0d3dnOgSRXuHP3V8Av3D32kzHk+2irRpR9W31VJZXwqHDQeNzRcWw11fPnc1vgP2HOoGSIHGGoOp81VUpi1tEcksiPc7Pm9lVprW7JE8pcZZsYIHbzewwwTd9W82s1sy+menYstnAinN9e5g4Hz4c9CuP8Ktr2akfBuvhnd2Hg4Ezz4SyMvU5i0g/iSTOnyfY5KTTzJrC29EUxSWSdmrVkCzxJWAFsNzdK929kqBNboWZfSmzoWWvgRXnuta6/onzCM44cSmUHeG9DxqDgZISOOccJc4i0k/cibO7V7h7gbsXh48r3H3iyFeK5AZVnLPHGF9T+6+AT7v7e9EBd98J/CVwY8aiynIDJwf2tmrEmTifOuVUbHw9ew+29w2uWAGvvQbNzSmIWERyUSIVZ8zscjO7K7x9KlVBiaRTdDWNzs7OEc4USYtidz88cDDscy7OQDw5oaNjQKtGWz1Ty6fGnTgXFhQybmI7hw/H/NF23nnQ0wOvvpqCiEUkF8WdOJvZHcAtwNvh7RYz+58jXFNmZv9hZq+b2Vtm9g/h+Fwze9XMtpvZGjPTDoSSMdGEuSO69ZhIZg33F5z+uhtCbMXZ3fsqznV1cSXOAJWVEZoaYv42Offc4P4//iPJ0YpIrkqk4vxnwMfd/Yfu/kNgJfDJEa7pAC5299MJtuheaWbnAncCd7v7h4EjwOcSD10kOaIJc3t7+whniqTF6WZ2dJBbE7Ak08Flq9jJgW3dbXT0dDC1ZDLU18edOJ8wo5ju5sm923UzZQp86ENQU5OiqEUk1yTUqgFMjnk8aaSTPRBtDisObw5cDDwejj8MXJlgHCJJ09baEty3tWU4Eokay4v3uHuhu08c5Fbh7mrVGELs5MD6tmAXwJndZRCJxJ04nzJzArRO5c1Db/YNLlumxFlEeiWSOH8H2GhmD5nZw8AG4B9HusjMCs1sE3AI+A2wA2hw9+hMrL3ASYmFLZI8LS3B33atra0ZjkRERiu24hxNnGe0FwYDcSbO82ZPhe5xbNj9dt/g8uWwezccOpTMcEUkRyWSOH8K+CFBwvw4wc6Ba0a6yN173L0amAWcDZwW7xua2SozqzGzmtparf8vqdHa2hbeK3EWyVWDVZyrWsNvLuKtOJ8wAYCNO97vG1y2LLhX1VlESCxxfjC8vxz4Z+B7ZnZLvBe7ewPwO+BPgMlmFt21cBawb4hrHnD3Ze6+rKqqKoFQReIXbdGItmyISO6JnRxY1xrsmz2lJVybfYTttqOiv2be3LW/b/CMM4LNU5Q4iwiJreP8O4LWjP8O/CuwDPivw11jZlVmNjl8XA58HNhCkEBfHZ52E9rGWzKkp6eHjs4uQBVnkVwWuxxdtOI8qTnsCIyz4jx9enC/fc9ReiJh0j1xInzkI0qcRQRIbDm654EXgesItoFd7u4jtV2cAPzOzN4A1gO/cfdfAV8Hvmxm24Gp9FWzRdIqdiUNJc4iuSu24hxNnCccDSf8xpk4RyvOXUcns+PIjr4nNEFQREJFI5/S6w3gLGAx0Ag0mNnL7j7kUgTu/gZwxiDjOwn6nUUyqqurK+axlsgVyVUDJweWFpZSXN8IZWUwblxcrxGtONMynTcOvsH8qfOD4zPPhB//GA4cgJkzkx+8iOSMRFo1vuTuFwB/AdQBPwIaUhWYSDpEt9kuNKe7uyfD0UjUGN9yW0Zh4OTAyvJKLLqGc5zLG06aBMXFDq0zePNgzJJ0S8Lls996K8lRi0iuSaRV42YzWwO8BlxBsMLGJ1IVmEg6RBPnkoKg31myQ/T/i0i8+lWc28NdA+PcbjvKDKZPNyZ2f7j/Ws7RxHnz5iRGLCK5KJFWjTLgn4ANMWswi+S0aGXTTBXObBLbQiMykkgEuruPrTgnmjhD0K4R6Tylf+I8fXrwOm++OfSFIjImJNKqcZe7v6qkWfJJdIc697G7U102UsVZEtEZTk9IVuJc0HYCO+p30NIZLlFpFlSdVXEWGfMS3XJbJK/0Js6M7W2es40qzpKIjo7gPnZy4JTyKUHiHOcazlFVVcGqGo7zVm1MT/PixUGPcySSpKhFJBcpcRYJaUJa9lDFWRIxsOLc0N5AZclkaGhIOHGePh2ajpQD9J8guHgxNDfD++8PcaWIjAVKnGVMiybLBepxzioRVfUkAdHEubQUuiPdNHc2M6OnDNxh8uSEXmv6dGhrLaDcp/HGwTf6ntAEQRFBibOMcb2TA1HFWSRXRVs1SkrgaMdRAKZ3FgeDU6Yk9FrRtZznl67oP0Fw0aLgXomzyJimxFnGtGgvbVmR09Wl9oBsoX7z0TOzlWa21cy2m9nqQZ4vNbM14fOvmtmccPwGM9sUc4uYWXX43LrwNaPPTR/4upkUW3FuaA+2F5jaXhgMjjJxPqV4OW8eerPvD+qJE+Hkk7WyhsgYp8Q5zR599FGeeuqpTIchoY6wVDW+yOnu6dFazlmioEA/mkbDzAqB7xGssb8Q+LSZLRxw2ueAI+7+YeBu4E4Ad3/E3avdvRr4K+A9d98Uc90N0efd/VDKP0wCYivOfYlz+MfXKBPnGSzhcOthDrYc7Hty0SLYsuV4wxWRHKbfTml23333cccdd2Q6DAlFE+cJxd7vWDKruLg40yHkqrOB7e6+0907gZ8SbFgV6wrg4fDx48AldmyJ/9PhtTlhsIrzpPawUpxg4lxVFdxP7pkH0L/P+bTTYOtWrawhMoYpcZYxraUlWKe1siz4Rdja2prJcCRUVJTI3kwS4yRgT8zx3nBs0HPCdfkbgYFLT1wH/J8BYz8K2zT++yCJdkYNVnGe2Bp+ezTKxLm042RgwMoap50GbW2wZ88gV4rIWKDEWca05uZmAKrCxDl6LJmlinPmmNk5QKu7x86Cu8HdlwDnh7e/GuLaVWZWY2Y1tbW1aYg2ELscXWN7IwATWsK1wBNcVWP8+ODW0jCemRNm9p8guGBBcK92DZExS4mzjGlNTU0AVJVH+h1LZhUWFmY6hFy1D5gdczwrHBv0HDMrAiYBdTHPX8+AarO77wvvm4CfELSEHMPdH3D3Ze6+rCpauk2DwVo1ypvboagoyIITNH06HDoES2csPbZVA+Cdd443ZBHJUUqc00gTz7JPNFGersQ5q2hy4KitB+aZ2VwzKyFIgtcOOGctcFP4+Grgtx4uHWFmBcC1xPQ3m1mRmU0LHxcDnwKyak22ga0ahlF6tDVo0xhFV8n06VBbC0umL+Ht2rfpjoQr7kybBpWVSpxFxjD9dkojJc7Zp6mpidIiY0ppkDgfPXo0wxEJaDm60Qp7lm8GngG2AI+6+1tm9m0zuzw87UFgqpltB74MxC5ZdwGwx913xoyVAs+Y2RvAJoKK9b+m+KMkZGDFeWLpRKyhIeH+5qhoxXnJ9CV09HSwvX578IRZUHVW4iwyZmkGThqpipZ9jh49yvhiGB+uqqHEOTto58DRc/engKcGjH0z5nE7cM0Q164Dzh0w1gKclfRAk6hfxbmjgcll4Xbbo0ycq6pgwwZYMiPYLfDNg29y2rSwTWPBAvjlL5MRtojkIGVyaaS+zezT3t5OaaFTVui9x5J50Y1pROIRW3FubG8MEucjR4674rxg2kIKrODYPudDh6C+PgmRi0iuUeKcRvr6Ofu0t7dTWhCh0KDAtI5zttD/B0nEwB7nSWWTjjtx7u6G9uYy5k+d339ljegEwa1bjzNqEclFSpxlTOvo6KDYIphBSaGp4pwltJ62JCJ2ObqG9oa+inOCS9FFRXcPjE4QHDRxVp+zyJikxFnGNHfvnXRv4bFknnrNJRHRinN0cuDk0knH1eMcTZyjS9LtPLKTpo5wxZ25c4MMXWs5i4xJSpxlTCsoKCDiQeYccU3gzBYNDQ29j9XvLCMZWHGeERkHPT1JSZyXTA8mCL5V+1YwWFgI8+er4iwyRilLkDGtsLAwJnF2TeDMEvUxE6+OHDmSwUgkF3R2BivFFRRGONpxlJldZcETx7GqBoSJc8zKGr20JJ3ImKXEWca00tJSOt2IOHRFgmPJvLq6vo3sDh8+nMFIJBd0dATV5ubOJhxnWme40uooE+dp04L7Q4dgzuQ5jC8ef2yf886dfT0iIjJmKHFOo+7u7kyHIAOUl5fT0VNAR0/fsWRebW1t72MlzjKSzs7+221P6wh/tY0ycS4pCS49dAgKrIAlM5b0X5JuwYKgFWT79uMNXURyjBLnNGppael9rElo2aG8vJz2Hujosd5jybza2looi3ksMoxoxTmaOFe2hzN+R7mqBvRtuw19K2v0/tzWyhoiY5YS5zSKXZtW1efsUFZWRkd3X+JcVlaW4YgEgiqzT3Sw/v3OIoPp7AwS58aORgAmtoZfIY2y4gx9m6BAkDjXt9Wzv3l/MDB/fnCvxFlkzFHinCGqOGeHsrIyOnucdiXOWaX+SD1e7hSUFWhyoIyoo6N/q0bSE+dwgmBvu8aECTB7thJnkTFIiXMalZSU9D4uLi7OYCQSFW3NaOpSq0Y2aWpqghLwEteazjKiaMU5mjiPb+6EggKoqBj1a1ZV9a84w4CVNRYs0FrOImOQEuc0ik3KtP12doguP9cVVpyLiooyGY4QfBvT0d4BReBFTltbW6ZDkiw3cHJgeUtH0N98HOuyT58OdXXB1ttTx03lxIoTj11Z4513QN8eiowpSpzTKLbiLNkhuuFJd/i7T3/QZF5nZ2fQylQIXuDaBl1GFJ0c2Nge9DiXHG05romBECTO7kHyDENsvd3SAvv2Hdf7iEhuSWnibGazzex3Zva2mb1lZreE45Vm9hsz2xbej74RLYcoKcs+0f8nPZHgWDsHZl5PT9ifWhDcuns0kVaG19EBZWVBxXl88XgKGhqPq78Z+nYPjK6ssXTGUt6ufZuunnAny+jKGmrXEBlTUp0ldAO3uftC4Fzgi2a2EFgNPO/u84Dnw2ORtIsmaaVFQclZq51kH4/oq3AZXnt7X+I8uWwyNDQkLXGO7XPu7OlkW/22YGDBguBeEwRFxpSUJs7uvt/dN4aPm4AtwEnAFcDD4WkPA1emMo5soZU0sk90icDxYeLcoZ3Aso++qJER9CbOHWHifORI8hPngVtvz5gBkyYpcRYZY9L2vbSZzQHOAF4FZrh7uCAmB4AZ6Yojkzo7OzMdggzQ0dGBAeVKnLOX/t6UEUQT58b2xr7E+Th7nKuqgvto4rxg2gIKrbBvSTqzvgmCIjJmpCVxNrMJwM+AW92939pSHpRhB/3VaGarzKzGzGryYfcwtQFkn+bmZsaVGOPCxLm5uTnDEUkkEjacW3CLeCSj8Uj2i23VmFQ2KSmtGpWVwaIc0cS5tKiUj0z7yLETBNXjLDKmpDxxNrNigqT5EXf/eTh80MxOCJ8/ATg02LXu/oC7L3P3ZVXRP/9zWG9CIFnj6NGjVBQ7FcXeeyyZ1fsHZjg5sKurK6PxSPaLTZyrbEIwW/A4E+eCgv5rOcMgK2ssWAD790Nj43G9l4jkjlSvqmHAg8AWd/+nmKfWAjeFj28CnkxlHNmid7UA1O+cLRobGxlf1ENJIRQXBseSWb3tMoXghVqOTkYWmzif2BWul3+crRoQ9DnHftm5ZPoSdjXs4mhH+Ad2dGWNrVuP+71EJDekuuK8Avgr4GIz2xTe/gy4A/i4mW0DPhYe573YBED9ztmh7nAtTZ3wv7eWM6UU6qKLtkrG9LbL7AWKobW1NaPxSPZrb4fSUqexo5Hp3eF6+cdZcYb+225DsCQdwOZDm4OBaOKsPmeRMSOl26S5+x8Zek78Jal872wU2wZw9OhR8qH9JNfVHjpEUY/xflMhU0q6qa0dtGtI0qipqQkAazN8ktPc1Iy7ax10GZR7kDgXlnTRHemmqiP8tZakxHn9+r7j2JU1Pjr7o3DqqVBUpD5nkTFEuz2k0eHDh3sf58Nkx1zX1tZGU0srRQVB20xlaYRDBw9mOCppaAi2TaYAKAtanDRpU4bS3Q2RCFAYbM0+tSP8tZaEVo2BPc6nTDqFipKKvj7n4mKYN0+Js8gYosQ5jQ7GJGUHlaBl3IEDBwAoDv8VTCvvobb2cL9edEm/+vr64EEBUDpgTGSAaAdcpChInCe3h99MJKnifPRo33uYGYunL+5bkg5gyRLYtOm430tEcoMS5zSK7Z9VIpB5+/cHS4kXFwYV5+nlEbp7evp9MyDp1/vvxMDLgv83+vciQ2kL8mUihUEv/KS2cPWiJCXO0H+C4NIZS3nz0Jt9E7zPPhvefx9UDBEZE5Q4p1Hs5hraaCPz9u3bB0BJ+K+gqjzSb1wyo66uLvjJZEC4QIISZxlKtBrcUxi081S0hMsXTpp03K89WOK8ZPoSGtob2NcU/pw455zg/j/+47jfT0SynxLnNCopKel9XFSU0nmZEocPPviAsiKj0ILK0Ywwcf7ggw8yGdaYV19f3/eTqSy402onMpRo4txlwaTScS2dMGFC0H98nAZuuw19K2u8fuD1YOCMM6CwUImzyBihxDmNJsVUQCYnYeKKHJ99+/YxY1xfP/PUsgiFBao4Z1rt4Vo8/GOGYrACU+IsQ+pNnAuCxLm8qT0pbRoweOJcPbMaw9iwf0MwMH48LF6sxFlkjFDinEbToz+FBzyWzPhg3x6ml/Vtg15gUFWuinOmHa47DIXhgYGVm1o1ZEjRxLmTYLnPkubWpKyoAcGqGtA/ca4oreAj0z5CzQc1fYNnnx0kztrYSiTvKXFOIyXO2cPdOXDgUG9fc1RVaRf7lThnTHd3N0cbjvb7yRQpi2j5RhlSNHFup4HSwlIKG44mreJcUQGlpf0TZ4BlJy47NnFuaIDt25PyviKSvZQ4p1FlZWXv46lTp2YwEjly5AidXV1UlfVPnKeVRziwX4lzphw+fDhYrSDmJ5OXO/sP7s9cUJLVoolzG0eYXDYZjhxJWsXZ7NhttwGWnbCM/c37+aAp/Flx9tnB/SuvJOV9RSR7KXFOo/Hjx/c+Li0tzWAkEl3DedqAxLmqLELD0SbaomtcSVr1tskU9o35eOfQgUNaX1sGdUzi3NCQtIozHLvtNgQVZ4ANH4R9zosWBe/5u98l7X1FJDspcU6jSKQvSXP1wmVUb+Jc3j8Zix5rg5rM2LNnT/AgJnGmItg9MLrutgzPzFaa2VYz225mqwd5vtTM1oTPv2pmc8LxOWbWZmabwtv9MdecZWZvhtfcY1m0/3k0cW71eiaVTQoqzilOnKtnVlNgBX0TBAsL4ZJL4De/UZ+zSJ5T4pxGjY2NvY+1hXBmRRPjqQMqztHjQwN/U0pa7NixAyux/q0aE4NEZOfOnRmKKneYWSHwPeATwELg02a2cMBpnwOOuPuHgbuBO2Oe2+Hu1eHtv8SMfx/4G2BeeFuZqs+QqGji3Bw5TGXxRGhqSlqrBgyeOI8vGc+CaQv69zlfeins3QvvvJO09xaR7KPEOY327t3b+7i3siYZcfDgQcqLjXEDltOOJs7RirSk15Z3tuCTPNj8JGoSYLB169ZMhZVLzga2u/tOd+8EfgpcMeCcK4CHw8ePA5cMV0E2sxOAie7+igdflf0bcGXyQx+d2MT5xEjYDpfEinNVVZA4DywkRycI9n57+PGPB/fPPpu09xaR7KPEOY1iK2aqnmXWvn37mFF+bM9sZalTWKAl6TKho6ODHdt3EKns/y0ARWCTjbfeeiszgeWWk4DYv8r3hmODnuPu3UAjEJ2tPNfMXjOzF8zs/Jjz98ZcP9hrAmBmq8ysxsxq0rUSSjRxborUckJnuGNOkls12tth4JeEZ51wFgdbDvZNEJwzB+bNU+IskueUOKfRzp07sbIJWGGxEucM27dnd781nKMKDKaP0yYombBlyxa6u7vxacf2iPZM7WHzW5vp7j72/5kkzX7gZHc/A/gy8BMzm5jIC7j7A+6+zN2XVUUXQU6xaOLc2H2Q6V3hboFJbtWAQVbWCCcIHtOusW4ddHQk7f1FJLsocU6jffs+oLtkIl5WoYpmBnV3d7P/4MF+uwbGmlHWyZ7d76c5Knn99XAL42nHPufTnM6OTt5R/+hI9gGzY45nhWODnmNmRQTNMHXu3uHudQDuvgHYAcwPz581wmtmTDRx7qCBaZ1h4pzkijMc2+d8+szTKbTC/onzypXQ2gq//33S3l9EsosS5zQ6XHcYLx5Hd1G5thDOoPfee4+enggnTxg8cZ49oYf3WzRxpwAAIABJREFUd++mQ1WjtKrZUINNMSgZ5MkweXnttdfSGlMOWg/MM7O5ZlYCXA+sHXDOWuCm8PHVwG/d3c2sKpxciJmdSjAJcKe77weOmtm5YS/0jcCT6fgw8Whvh5IShwJnanvYqp2GxHlc8TgWTV/Ut7IGwMUXQ1kZ/PKXSXt/EckuSpzTqKmpCS8ug6IyGmJW2JD0evfddwGYUzF44jy3ooeengjvvfdeOsMa01pbW9m8eTM904dYq7kUbIrx6quvpjewHBP2LN8MPANsAR5197fM7Ntmdnl42oPAVDPbTtCSEV2y7gLgDTPbRDBp8L+4e3Sv8y8A/wvYTlCJfjotHygO7e1QUhr0xU9uC9t8UtCqMdhCO2edcFb/CYLjxgWTBNeu1bJ0InmqaORTJBna2tpob2vDpwaTVxqO7MPdyaLlUMeMrVu3UlZkzBgXGfT5OROD5O2dd97htNNOS2doY9bGjRvp6e7BZw6dbPTM6GHz5s00NTVRUVGRxuhyi7s/BTw1YOybMY/bgWsGue5nwM+GeM0aYHFyI02O9nYoDhPnidHEOcmrasDgifPyE5fzo00/YlfDLuZOmRsM/vmfBxXnzZthyZKkxSEi2UEV5zSJ9jRHyiqIlFbQ0dFOfX39CFdJKry2cQPzJ3VSMMTfLFVlESrLYNOmTekNbAz7/e9/H6zfPEh/c5Sf6EQiEV5++eX0BSZZr70dikuCSaMVrV1QUgLl5Ul7/bIyqKgYPHH+6OyPAvDSnpf6Bj/1qeBe7RoieUmJc5pEJzVFxlUSGVcJaF3aTDh8+DDv797DwildQ55jBgsmd7BxQ412eEyDjo4Ofv+H/5+9+w6PssoeOP49U1ImBQgJNfRO6B0RC1JUVBBZ0V0UO7a1Lyvrz7WisnYFK9iQDios6CJIFQRCDV16CRAgBEjPlPv7430DIUBIwrSE+3meeWbmnbec6WfunHvvYtzV3UV/IsWAOITffvvNb7FpwS8nB6whxvvZkZFnlGl4+Z+8KlXOHVUDoEWVFkSFRLF0/9IzC6tXhw4ddOKsaeWUTpz9JCkpCbGHocIq4omMA4vlzCgCmt/kdy5rHlP0sGbNY1ycOHlKDxvoB4sXLyYrMwtV5yI/UgTctdysWLECf40RrAW/7Gyw2o3EOSwj26tlGvnON3sggNVipUt8l7NbnMEo11ixAswZSjVNKz904uwnq1evwRlZzWgJsdjwRFRh9Zo1gQ7rspOYmEhkiFywY2C+FjHGF/GqVauKXE+7dNN/mI5EyemRM4qi6hvlGjNnFh4oQrtc5eSA2IwRcEJOZXm1Y2C+CyXOYJRrbDiygVO5p84svPlmo3Pg7Nlej0XTtMDSibMfHDt2jCNHUnBHVTu9zBVVlR07dughz/xIKUXiyhUkVMq9YH1zvsphihqRisSVK/0T3GUqKSmJzZs2427oPnua7QuJNGqdp/8wnaysLJ/HpwW/nBzAnotVrFhPnvJrizNAt1rd8CgPKw4UGPGlTRuIj9eJs6aVQzpx9oPt27cD4Ik80/PJExGLx+1m586dgQrrsrN7925Sj6fRMubC9c0FtayUy7p16/SPGx/6+uuvkTBB1S1+LbmnqYeM9Ax++uknH0amlRXZ2YAth4phFZETJ3ySOMfFGTXOnvMMxNM5vjNWsbJ4b4FJT0SgZ09YsADcRf+7pWla2aITZz/Yu9eYhc4TfuYvRE+48eG+b9++gMR0Ocqvb064SH1zvoQYF3lOJ1u2bPFlWJetdevWsXr1atyN3SUbGLMyUA2+H/+9bnXWyMoC7BlUCKsAaWk+K9Vwu43dFxYdGk2HGh1YsGfB2Tf07GlsoEfn0bRyRSfOfpCSkoLYQsAWdnqZCo08fZvmH2vWrCHOAXHh5x+/ubAmFV1YxNhO8y6lFGPGjEHCBdWg5COXuBPcZKRnMG3aNB9Ep5UlmZngsaVTMbQC+KjFOX8SlAv1Se1RrwcrkleQkZdRYGEP43zePK/Ho2la4OjE2Q9OnjwJ9kLjilpsiC3EuE3zOY/Hw7q1a2hWofhlFxF2RZ0oD2t14ux1a9euJSkpCXeTErY254sBVV0xYeIEMjIyLr6+Vm5lZoLLdorqRBnNwj5MnC9U59yjXg9cHhe/7/v9zMLq1SEhAfTwiZpWrujE2Q8yMjLwWEPOvcEWQmZmpv8Dugzt3LmT9IzMiw5DV1jzSnls3ryJnJwcH0V2eRr3/Tijtbl+6cfJ9iR4yMrM4r96vNzLWmYmuKynqOl2GAt8VKoBRY+sEWINYf7u+WffcN118PvvZg9GTdPKA504+0F6egYei/2c5coaolvL/CS/3KKoiU/Op3klJ06Xmw0bNvgirMvSzp07Wb1qNe4GbrBewo4qAVVh0pRJuFwl+0GklQ9KGTXOedY0quWFGgt92OJ8oco6h91B1/iuzNtVqCyjZ0+j96Ke7VLTyg2dOPvB0WPHUIVLNQC3NYyjx44FIKLLT2JiItUjFDFhJWvhbFLRhVWM7TXvmDFjBmItXW1zYe6GbtJS0/Q03Jep3FxjpItcy3Gq5Zg1P3FxXj9ObCxYrXDo0IXXub7h9aw9vJbkU8lnFl51lTHCxuLFF95Q07QyxaeJs4h8JSJHRGRjgWUxIjJXRLab595vHggi2dnZHD2Sgics+pzbPGEV2Lt3H57zjXGkeU16ejqrV6+iXWzJh5ULs0HzGCeLFi7Q0297QU5ODnN+nYO7phvOU71UYtWMabhnzJjhhZ1pZU1+pVuepBGXbX6dVa7s9eNYrVCjBuzff+F1bm58MwCztxcYu7lCBWNM50WLvB6TpmmB4esW52+A6wstex74TSnVCPjNvF5ubdmyBaUUnohzW0E8kXFkZ2WyZ88e/wd2GVmyZAlut4fOVfNKtX2XKnkcOpzCtm3bvBzZ5WfhwoVkZ2VfUm3zWSzgruMmMTFRj1BzGTrdRSQkk8pZ5msqNvaC61+KWrXgwIEL3948rjl1K9blv38Wqrm/6iqjVCOvdJ8/mqYFF58mzkqpxcDxQov7Ad+al78F+vsyhkBLTEwEsZw1a2A+d3SNM+toPjNnzv+o4oB655lme9y2cPamW9mbbuX1VZGM23ZuSU2HKk6sFpgzZ44/wi23lFJMmz7NmF67iNxG1gmcAE6AZaHFuF7UfuspFEpPw30ZOj2Mtz2TmEwzcY6J8cmx4uOLTpxFhJsb38y8XfPIchYYX/yqq4zOgatW+SQuTdP8KxA1zlWVUvmVYoeBqgGIwW+WLfsDd1RVsIUQsvcPQvaeqcVUoZHgqMTy5csDGGH5tm3bNtauXUePGlnIefKvvelWst0Wst0Wtp6wszf93N5qEXZF1yq5zJ41i/T0dD9EXT6tX7+eP7f9ibtR0dNrywlBnObpqCAnLjIXd4QxDfcPP/6gJ0S5zJxucbZnUSHTZZRG2M/tiO0N+YlzURVbtzS5hRxXDr9s/+XMwu7djXNd56xp5UJAOwcqo2j0gh9DIvKQiKwSkVVHLzTyfBBLTU1l9+5duCvEA2DJTMWSmXrWOs7omqxPStLTOvvIxIkTCbcLPeIv7fHtWzeHnNxcPc1zKXk8HkZ/MhpxCKqO92vFPU09ZGZkMn78eK/vWwteBUs1Ik/l+KxMA4xSjawsY46VC7mm7jVUi6zG+A0FXodxcdCsmU6cNa2cCETinCIi1QHM8wuMjAlKqS+UUh2UUh3ifNBT2tfyp3jOL8k4H3eFmricTj3cmQ/s3buXhQsW0KNGNo7STLJRQK1ID60qO5k6ZbIee7sUfv75Z7Zt3YY7oZQTnlxMDHhqe5g4aeLpKe618u9Mi3MmjlNZPukYmC/eaP8osoOgzWLjzhZ3Mnv7bI5nF6hSvOoqYzxn97nlYpqmlS2BSJxnAkPMy0OActsdPjExEbGH4Ym48Ie5O6oaWCy6ztkHPhk9mlAb3FjHO5MP3FY/mxMnT+lWzRI6cOAAH370IVTBJ63N+VRrhdvi5pVXX8HpLNl43VrZdKbGOYuQE+k+bXHOT5yLqnMGGNxqMHnuPKZumnpm4dVXQ3o6rF/vs/g0TfMPXw9HNxH4A2giIgdE5H7gLaCXiGwHeprXyx2Xy8WSJb/jjK4JUsTDbLXjjqrOwkWL9HBnXpSYmMgfy5fTr04mFUK887g2qOCmW7VcJk+exOHDh72yz/IuNzeXf7/0b/I8ebg7Fl3bfMnCwNXexY7tO/j00099eCAtWOS3OEtoNtbUNL+0OF8scW5brS0JcQmMXTv2zML8Omc9LJ2mlXm+HlXjTqVUdaWUXSkVr5Qaq5RKVUpdp5RqpJTqqZQqPOpGubBgwQIyMtJxxTa86Lquyg04dPAgq3Sva69wuVx8/NGHxDmgT23v1o7f3jAb8bgZPXq0V/dbXn3wwQfs2L4DV0cXOPxwwJrgaehh2rRpzJs37+Lra2VafuJcKToESU31aYtz9epgs8HFKoFEhEc6PELiwURWJq80FsbHQ/36us5Z08oBPXOgD2RnZ/PFF19CRMzpjoFFcVVugIRG8Mmnn+qpg71g0qRJ7Nm7j7saZWD38iu8cpiiX90sFi1apGeru4hZs2Yxe/ZsPE09cOEyf69TrRXEwlsj32L37t3+O7Dmd/mlGlXDQyAjw6eJs9UK9erB9u0XX/eu1ncRGRLJ6MQCP7CvugqWLDGmOtQ0rczSibOXKaV4++13SEk5THbtrpx3DLTCLFaya3dh5w79F/OlSk5O5puvv6ZjlTzaxfmmzrVvnRziIxXvvfO2Hv7sArZv3857778HVUG18HMJkgXcXdw4LU5eeOEF/RyVY/ktzg1t5jSUPizVAGjUqHiJc3RoNENaD2HSxkkcSjdHX73qKkhNhS1bfBqjpmm+pRNnL3K73bz77rvMmzeXvPgOeKKrF3/bmHo4qyYwdepUvvzyS13vXApKKd55+22sysXdTXyXLNkscF/TdFKOHmPMmDE+O05ZlZubyyuvvoLb5sbd2cd1zRcSDq5OLg4cOMAnn3wSgAA0f8jMBLHlUscTZizwYYsznEmci/Px/FSXp3C6nXy88mNjwVVXGee6XEPTyjSdOHvJiRMnGPbPfzJz5kzyqrfGWaN1ifeRV6czzrgmjBs3jpdffkUPe1ZCM2fOZPWaNdzRMINKob794dG4opte8TlMnz6NpKQknx6rrPnqq6/Yt3cfrg4uCA1gIFXA08TDzJkzWblyZQAD0XwlMxMkJJv4XDNx9kOLc2YmFKdvcMOYhtzW/DY+SfyEU7mnjBrnmjV14qxpZZxOnL1g5cqVDLnnXlatWk1u3W44a3csXolGYWIhr96V5NXqyIKFC7j3vvv1+M7FdPjwYT4ZPYoWMS561MzzyzEHNcwmNhzeevMNcnK8M+RdWbdnzx4mT5mMp64Hzp1l3u9UgkKihfc/eF8PUVcOZWWBsmdQLdcs1fBxi3NDs693cco1AIZdMYyTuSf5cvWXxnfCVVcZibP+R1HTyiydOF+CrKws3nnnHZ577jnScjxkNb8ZV9Vml7ZTEZw1WpPdtC+H09J5/PHH+eyzz8jL808yWBZ5PB5GvvUWypXH/c0yS/WbpTTCbPBg03QOJB/kyy+/9M9Bg9yo0aNQVoVqFSSJgRVcrVwkH0jmhx9+CHQ0mpelZ7hRtgyqZJtvej+UakDxE+eONTtybd1reX/5++S584zE+eBB2LnTd0FqmuZTOnEupU2bNnHPvfcyc+Z/cVZrSWZCPzwR3vvQ9kRXIzPhVvJiGzNhwgQeePBBdu3a5bX9lyc//fQTq9es4c6GGcSF+7fHevMYF73ic5g6derpmSIvV0lJSaxcsRJ3E3dgSzQKqw5Uhe/Gfac7CpYzJ045wZ5F5WxzQUyMT49Xpw6EhMDWrcXfZli3YSSnJzNhwwRd56xp5YBOnEvI4/Ewfvx4HnvsMQ6nZZDd7Eby6nQGiw/mEbaFkFe/OzlN+rA3OYUHH3yIGTNm6I6DBezfv5/PPv2ElpX9V6JR2KBG2VSNULz5xojLOjEbM3YMEi6ohsH3+nS3cJN+Kl23OpczJ9NdEJJJxQw3REcbWa0PWa3QogWsW1f8bfo06EOrqq14e9nbeJo2MVrFdeKsaWWWTpxL4OTJk/zzn8/z+eefk1exDpkJ/Us0ckZpuSvWIqNFf3IcVXj33Xd59dVXL+sELZ/b7ebNN0Zg9Th5sFmG30o0CguzwtBm6Rw5coSPP/44MEEE2IYNG1i3dh3uxm7wwW/ISxYDqppiwsQJ+r1TjmRkesCeSXRGns/LNPK1bw+rVxe/TFlEGHbFMDYf3czPO34xZhHUibOmlVk6cS6mdevWcc+997IyMZHculeQ27AH2Ir/f3TI3j+wZKViyUolbPMsQvaWcPIMu4OcJn3Ii+/Ab/Pnc9/997Nt27YS3ovyZcqUKWzctJm7GmcQExbYVs7GFd3cWDuH2bNns3z58oDGEgjffPMNEiao+sHX2pzP09xDRnoGM2bMCHQompdkZAAhmThOZvl8RI187dpBWtrFZxAs6PaE26ldoTYjl440yjV274b9+30XpKZpPqMT54vIzc1l1KhRPPHkk6RmOs0OgM1LPGqGJTMVcTsRtxNr+mEsmaklD0YEZ802ZDe9kUPHTjD04Yf55ptvLsvZBvfs2cOYMV/SIS6PbtVKX6KR7RLCw8MZOHAg4eHhZLtK32w9oH428ZGKkW+9SXp6eqn3U9Zs3bqVxMRE3I281Nrs5KznBG8NhlEZqAoTJk4gN9e7U7FrgZGeboXQk4Sd8O2sgQW1b2+cr15d/G3sVjvPdHmG3/f9zvomFY2FS5Z4PzhN03xOJ84XoJRi0aJF/PVvg5kyZQrOuKZGaYYXOwCWlie6OhktbiWvYl2++uor7h4yhMTExECH5Tcul4s3RrxOmLi4t1nWJZVoZLmEvn378sQTT9C3b1+yLiFxDrHC0ObppKWl8dFHH5U+qDLm+/HfIyGCauCl1mYnZz0nXkucAXdTNydPnOR///uf93YaZETkehHZJiI7ROT589weKiKTzdtXiEhdc3kvEVktIhvM8x4Ftllo7nOdeariv3t0YVnpNgg7ie34Cb8lzi1bgs1WssQZ4IF2DxATHsMraT8a9di6XEPTyqRgrEYMKKUUK1as4Kuvvmbr1i0QEUN2sxvxRNcIdGhns4WR2/BaXJUbcGD/cp599lnatm3LvffeS5s2bQIdnU9NnjyZrdv+5PGWGVQIubRkzWFTzJ49G4DZs2dTxXZp+6sX7eaWutn8NGcOPXr0oGvXrpe0v2CXnJzMksVLcDd1g91LO7Vz1nPi1RE64oAYo9X55ptvxmIpX20HImIFRgO9gANAoojMVEptLrDa/UCaUqqhiNwBjAQGAceAm5VSB0WkBTAHqFlgu78ppVb55Y4Ug9sNedmhhDhysKQcgWr+GTg8LMxodV64sGTbRYRE8FjHx3ht8WtkdLqKyEWLfBKfpmm+Vb6+NS5BTk4Os2bN4t5772PYsGFs3XOA3HpXmh0AgyxpLsBdqTaZLW4jt3Zn1m3axhNPPMHQoQ8zd+7ccjnhw969e/nqq7F0jMujc5VLv3/hNkV2djbTpk0jOzub8EtMnAH61cshPlLxztv/ISMj45L3F8xmzZqFQnmvtRnAzlnPidcScgABT0MPhw4eKq/DB3YCdiildiml8oBJQL9C6/QDvjUvTwOuExFRSq1VSh00l28CwkUkmAYWPMupU8Z5nMMNOTlQtarfjt27N6xYASdOlGy7xzs9TpgtjDk1s40x7VJSfBOgpmk+c1knzkopNm/ezHvvvcetAwbwn//8h10paeTW605my4G4qjQFKQMPkcWKq3pLMlrfTm6drmzZk8xrr73GrQNuY/To0ewsJ4PtK6V49523CRU3Q5peWomGL9kt8GCzdFJTUxk7dmygw/EZpRRzfp2DqqYgPNDRFJ+KV0iIMHfu3ECH4gs1gYK9zg5wdqvxWesopVzASYwK8IJuA9YopQoWg39tlmm8KHL+d5+IPCQiq0Rk1dGjRy/lflzUyZPGec0Qc+x2P7U4g5E4ezwwf37JtqsSUYW7Wt3Fe44kY8HPP3s/OE3TfOqyLNU4cOAAv/32G7/Oncv+ffsQiw1npdo4a12NJ6pa6abLDgYWG65qCbiqNsd6MhnXkS1MnjKVyZMn06BhQ3r36kWPHj2o6seWGW9aunQp69YnMaRJFhVDg3f0BoAGFdxcWyOXn376kQEDBlCrVq1Ah+R1hw4d4tjRY6i2wf1cnMMKnlgPa9auCXQkQUlEEjDKN3oXWPw3pVSyiEQB04G7gO8Kb6uU+gL4AqBDhw4+fWHkJ861bWbnaD9+rnXuDFFR8MsvMGBAybb9e6e/02r1l5ysVokK06fDvff6JkhN03ziskmcU1NTjWT517n8+acxjJsnuhrOelfiiqkPNt8OnO9XIrgrxuOuGE+uMwdb6k52HN7Bzk8/5dNPP6VFi5b07t2La6+9lgoVKgQ62mJxuVx89uknVI9QXFuzbIyIMKB+NktTwvj88894/fURgQ7H6/bt2weAqljGEmeMmA9vPozL5cJmK1cfg8lAwV9p8eay861zQERsQAUgFUBE4oEfgbuVUqf/qlJKJZvn6SIyAaMk5JzE2Z9OJ84Ws2TLjy3OdjvcfDNMnw6jRkFoCQpaWlZtybX1rmVyk0Qe/PVX5ORJKCOfw5qmlfNSDbfbzcKFC3n22WcZMGAAo0aNYltyKrm1O5HV5g6ym91klGOUp6S5MHsYrmoJZCX0I6v17eTFt2fjrgO899579O/fn+HD/8Xy5cuDfjbC33//nX37D3B7g0xsZeRVWyFU0bd2FosXLzmdZJYnZbpjnfmnUpm+D+eXCDQSkXoiEgLcAcwstM5MYIh5eSAwXymlRKQiMBt4Xim1NH9lEbGJSKx52Q7cBGz08f24qGPHjYS5tsoxFvj5n7S77jLGcy5NtcUTnZ/gmwYZiNMJs2Z5PzhN03ymXDW15HO73fz3v/9l/ISJpBw+BGFR5FVvjSu2ASq8UoCCyiM8PJy+ffsye/ZsMtz+nx5ahUXjrNkWZ402WLKOYzu2g2WJa1i69Hdq167D4MF/o0+fPlygfDGgtm/fjlWgbWzZ6vDYPs7J9F3hbN++ndq1awc6HK+qWdMonZVUQcUG9w+vwizHLFStVjUoX+uXQinlEpHHMUbEsAJfKaU2icirwCql1ExgLDBORHYAxzGSa4DHgYbAv0Xk3+ay3kAmMMdMmq3APOBLv92pC9h/5BRQmTquDGMubD9NgJKvZ08jV//mG7j11pJte3Pjm3mmRR2OVjxE3PTp8Le/+SRGTdO8r9wlzikpKbz66mts2JCEJ6oqeY2uw12pTsA7+Ykrj763GGPTAkyZGcBxZEXwRFQmL6IyebU6YEvdxd6UjbzxxhvMnz+f4cOHU6lSgH5gXMDu3bupFqHKTGtzvmoONyLGhC3lTc2aNWnevDlbdm3B1cBVdj5NTgApcMM9N5S7xBlAKfUz8HOhZf8ucDkH+Mt5tnsdeP0Cu23vzRi9IfloBlCZ+NyTUKUK+PnfA5sN7r8f3nzTmAiwXr3ib2u1WHms89+Z0OQ5/j57FpbUVL8n/pqmlU4ZS0OK5nQ6+cewYWzcspXc+leT3ewm3DH1Ap40AyhbCLNnz+ajjz5i9uzZqGApD7FYccU1IiuhP7l1urIicRUvvvgiHo8n0JGdJeXwIcKs7kCHUWIi4LDB4cOHAx2KTzz44IOQCZZEC5SFRudcsP1ho1LlSvTv3z/Q0WiX4HBqNgDx6Uf9Wt9c0KOPGo3do0aVfNv7293P5E4OLHlOGD/e+8FpmuYTgc8oveiPP/5gz+7dZNfrjiuuUXCNjmENOXtsWmuQJM75RHBVSyCnVieSkpLYuDHgJYxn6dmrNztP2lh71JuD+kKdKDfhVg/hVg9NKzqpE+Xd5PyXvWFkOqFXr15e3W+waN++PY888ghyQJAVAl6Y/V1VVCi7eYpT3ut8mAG2RTasOVbeHPEmMTEx3tmvFhBHj+eBNZeY1BSoWXjEPf+oWRMGDoSxY6GkQ7ZXDKvIlTc/RmINyP38EwjyfiaaphnKVeJ8uqOP/gAqPfOxs1qtAQ7kbAMHDqR2rXi+3x5Bnhdz27uaZFMnyk2dKDf/1yGDu5pke23fqTnCjD0OunfvTqdOnby232AzaNAgHnroIawHrNgW2CD90van2iioCFQEzzUe4/qlOgi232xEeCIYOXIkzZs3v/R9agGVmuaCsBPYDx+F+PiAxfHEE8YIH99+e/F1C3u6y9N8295G6OZtsCpoJmXUNK0I5Spx7tixIw0bNiJ8zxJsR7bqBLokPB5sh5II27+Stm3b0qRJk0BHdBa73c6TTz1NSpbwzvoojuUE0b8J57H7lJX/rKuAsth4/PHHAx2OT4kIgwcP5u233ybCHYHtVxuySSAYKmuywfKHBetSK/Vq1WPMl2Po2LFjoKPSvODESYU1LB05dixgLc4AXbpAx47w8cfGpCglUT2qOmF33UOWHU5+ONI3AWqa5lXlKnEODQ1l5Mi3aNUigdDdv+PYNAPb0T/B44X/j8srdx62lC1EbPqR0H0r6dKlM6+99lpQjm3bsWNHhg0bxu6sCIavqMTigyFB99vI5YEfd4XxcmI0OSExvPnWW1SvXj3QYflFp06d+O7b7+hxbQ8smy3Y5trgIIGpfXaDbBNsc2yEpITwwAMP8MXnX1CjRo0ABKP5QvopwRGaZVwJYOIsAk8+Cdu2wa+/lnz7f9zwOhPa2nBM/lFPwa1pZUC5SpwB4uLi+OjDD3nhhReIrxRO6K7FRK6bRMju37Ge2B+wJNoTURlltaOsdtxR1fBEBLAHtduJ9fgeQszHJnTPUupXr8yIESMY+dZbREdHBy62i7jpppv4+ptvadS0BV9sjuCDpEhO5gZH63NypoVXV1Vg+q6IkK0TAAAgAElEQVRwevTsyXfjvr/sWjdjY2N56aWXeO+996hRoQbWpVasi6zm9Bp+oED2GgmzJclCp3adGDduHHfffTchIUHWr0C7JFnpdirYzcQ5gKUaAH/5i9E/8cMPS75t1ciqZD/+MHaXhz1vDPN+cJqmeZUE+8QX+Tp06KBWlbAGTCnF2rVrmTFjBsuW/UFubg5iteOMqo47ugaeqKpGAuunUTfCNhsD3ec0v8kvxzvN48aSeQxregrWUwexph8Cj5twRwRXX9Wdfv360bx58zI1NJfb7Wbq1Kl8+eUX2JSb62tlcUOdHBylaCh/fVUkAP/XoYS9e0ypOcLM3eEsPBhKRGQkz/1jGNdcc02p9lWeuFwuZs2axdivxnLyxEk88R5USwWRF9/WstB4T3quKcF/3ylg3WCFNGjQsAGPPfoYHTp0KGX03iUiq5VSwRGMn5TmM7skQmv8SVNHMut39oBNmyDAdeuvvgovvQRbtkDTpiXbNsuZxbJ2cbTbnUv4wSOER+uOq5oWSEV9Zgff//FeJCK0a9eOdu3akZuby/r161m2bBlLl/1Byr7lxjpWO66IONxRVfFExOGOjAW7I8CRXwKlkLwsLJlHsWQexZaegjXzGMpsaa8ZH0+3PrfRrVs3WrZsGZQlGcVhtVq54447uOKKK/jyyy/5cdEi5iY7uLF2Jr1r5RLmh76NJ3OF/+4J47fkMJRYuaX/LQwZMkSP1mCy2Wz079+f3r17M2nSJCZMnEDenDw89T2o5gpKME1xkU6YCfNhiKsSx9D/G0rPnj3L46yAmsmjPORlRFA1KvClGvmGDoURI4yh6Uo6PJ3D7qDSv14j5q/PMunF27njw3m+CVLTtEtWrluci3L06FE2bNjAhg0bWLd+Pbt27jw97bSEReIMr3w6kfZExIHt0r/lfdLi7MzBmnkUS4aRKNuzU1G5xpeJxWqlUaNGtG7VilatWtGiRYtym9Rt27aNr8aO5Y/ly4kOhVvqZNGjZi4hxUigS9rinOEUZu8N5df9DpxKuOGGGxgyZAjVAjSWbFlx7NgxvvnmG2bNmoWyKdwJblQDdXr664KK1eKcB7JRsOy04IhwcM+Qe7j11lsJDfVWRu49usXZu5JPHSQ+pjL3NZzO2IMPG8NaBME/ZvfcA9OmwYEDULFiCTdWiv2Nq5Jx4igb50/iLy0H+SJETdOK4bJtcS5KXFwcPXr0oEePHgBkZWWxfft2tm3bxtatW9m0eQuHDhT40A+vgMsRizsyDk9EFTwRsX6fqQqPC0vmMSwZR7BmmEly9inAaF2Pj69F887dadq0KU2bNqVhw4ZBmUT4QpMmTRj5n/+wceNGxo4Zw/dr1vC//Q4GNcigS1WnV75TXR6Yuz+Un/Y4yHLBddddx7333kutWrUufeeXgdjYWJ577jkGDhzIBx98wJo1a5C9gqudC0oyUaUC2S9Yk6yQA7cOuJX77rsvqGvzNe/anLwP3DWo5TpmTNkXBEkzGJ0Ev/0WPvkE/vWvEm4sQvWX38U2+G5effNuqr9VkytrX+mTODVNK73LNnEuzOFw0Lp1a1q3bn16WXp6+ulEevPmzWzesoXje3cCBUs8quGOroYnsipYvFwf4HZhyTiM9dRhbOmHsWYeRXmMMb7iqlQloXM7mjVrRrNmzWjcuDEORxkuMfGSFi1a8P4HH7B69WpGj/qY0Rt3MWe/m8GNM2lYoXTjoykFq47ambQzgpRMoVPHjjzy6KM0aNDAy9FfHurWrcv777/P/Pnz+fCjDzkx/wSelh5Uo/O3Pp/FCbJasOy30LhpY5579rmgGzpR871Ne4zRJ2pmJkOLEsx17WNt28Itt8DIkfDQQxAbW7LtbXf8Fdcrr/DvxQfo+v2N/HbPAtrXCLrZzjXtshawxFlErgc+BKzAGKXUW4GK5UKioqLo0KHDWR2Mjh07xsaNG1m/fj1r161j9661qGSF2EJwRtfEVak27oq1S1/a4czGlrYPa9pe7OkHUW4XFouFho0a0bbN1bRu3ZqEhAQqVSpJE93lp3379nw5Zixz5szhi88/4+VEK12q5nFHw2xiw8/++7+o2QJ3n7IyfruDrWk26tauxdsvP0Hnzp19HX65JyJcd911dOrUiTfffJPff/8ddVTh6ewBG+efLfAU2JbZIAPuu/8+Bg8eHHQT9Wj+8ef+4wBUS9thtDgHkTffhJYtjcR52rQS/jFptWJ78SWa3X03g3ZE0/v73iy6ZxEtqrTwWbyappVMQGqcRcQK/An0Ag4AicCdSqnNF9rG1z20Sys9PZ2kpCSWLVvGkt9/50RaGlgsuCrVxVm1udESbf6NeMEaZ6WwnErGnrIF24l9oBSxcVW4+qrudOnShZYtW+rW5EuQlZXFxIkTmTRxAhaPi8danKJN7MWHJfx1fyjf/+kgKiqK+x94kJtuuqnMdqYMZkoppk6dyujRo1FxCnc397k/6dON6bKjQqN49eVXadeuXUBiLS1d4+xd17/0EXNefYLldKbzB381aiSCyAcfwNNPQ//+8NFHUKJqLpcLmjcn1yY0vOcUTotiwZAFNItr5rN4NU07WzDWOHcCdiildgGIyCSgH3DBxDlYRUVF0a1bN7p168azzz7L1q1bmTdvHrN//oXszbtQkVXIqdMFT2SV825vOZlM+P4VkHmc6OgK3PzXv9KjRw8aNmxYpoaHC2YOh4P777+fvn378q/hz/Pe+l38rVEWvWvlnrc00u2BcX+GM+9AGFdc0ZUXXvg/oqKi/B/4ZUJEuP3226lYsSIjRozA+ocV95XuM2Ub2WBbbCMqJIpRH42ibt26gQxXCwLJKbkAVCY16FqcwcjjnU548UVo0ADuugveeQeK9UehzQZvvknowIEkWl6iDZ/R47seLByykCaxuixJ0wItUOM11QT2F7h+wFxWplksFpo3b84TTzzBTz/+wLPPPktMiJvwTTMJ2fvH2St7XITsXET41l+oGhXKCy+8wA8/TGfo0KE0atRIJ80+UK1aNUaN/oQrrujGuD8dfLMtHFehQRsyncaU3vMOhHHHHXcwYsQbOmn2k969e/PMM8/AYZAd5utfgWWNBZvTxocffKiTZg2Aw4eN82ocDsrEWQT+8Q9jNsGhQ+G776BDB9i3r5g7GDAArr6aaiNHs6j/DDzKw7XfXsv21O0+jVvTtIsL6oFOReQhEVklIquOHj0a6HBKJDw8nH79+jFxwngGDBiA/fAmJC8Tj6OyMfnItl+xH9vO3XffzffjvqNPnz56ZjM/cDgcvD5iBHfeeSe/HQhj6s7w07cpBaM3RrL5RAjDhg3j0Ucf1TW0fnbLLbfQqXMnrBuNETM4CHJQGDp0qO6MqQHGGM5pR0IJt2URKVnQsGGgQ7qgOnXg449h0SI4dgxuugkyijPqpYhR73H8OE3e+JwFQxbg8rjo830fUjL0tNyaFkiBSpyTgYJVX/HmsrMopb5QSnVQSnWIi4vzW3De5HA4eOqpp3jkkUew5KbjCY3CnrwGy6mDDB8+nAceeOCyGTIuWFgsFh555BFuvPFG/rcvjORM422w+qidpFQbjz76GDfd5OfZHTXAKNv4++N/R7kUsluw7rASVyWO2267LdChaUEiJSMF96kqVA05CnXrQnj4RbcJtCuugKlTYeNGeOGFYm7Upg0MHw5ff03z35KY/dfZpGSm0HdCXzLySjfLqaZply5QiXMi0EhE6olICHAHMDNAsfjFHXfcQceOHQndt4KQg+vp1asXN9xwQ6DDuqw9/PDDhDscjNsWQa4bvt8eSb26dbj11lsDHdplrU6dOrRu0xrLNgscgf79+uuWf+20XWm7IKM6NThc8rmtA6h3b3j0UaMFuth9Jl96Cbp0gaFD6ZgaypSBU1h3eB23T70dp9vp03g1TTu/gCTOSikX8DgwB9gCTFFKbQpELP4iIvzlL38BjFFMjMtaIFWsWJH7H3iQjcdtfLXFwbFseOrpZ/TIGUGgS+cuYOYFnTp1CmwwWlDZcGQDpNcgPndfmUqcAd54A2Ji4P/+r5gb2O0waRJERUGvXvSlEZ/d9Bm/7PiFv//yd8rKzL+aVp4ErMZZKfWzUqqxUqqBUmpEoOLwp1atWp2+3Lhx4wBGouXr1asXAEsPhxJbOYa2bdsGOCINoHnz5qcv69pmraB1h9Yjp2oS7y57iXN0NDz/PMyZA7//XsyN6tSBefOMThjdu/NAbgLDrxzO56s/551l7/g0Xk3TzhXUnQPLG4fDgd1uJzY2Dou/p+vWzis6Oppa8caALi1atrrI2pq/VK9e/fRl/Q+AVtDqnftQLgd12QMtyt7EII8+CnFx8PrrJdioaVNYvNhoeb7mGl4/2JRBCYMYNm8Y0zZP81msmqadS2dvfvbdd9/x6aefBDoMrYCWrYxp1gtOt64FVlxcHF26duG+++4LdChaEPEoD5v/zAKgruyDMviedTiMyVHmzIHVq0uwYdOmsGIFdOuG5e4hfL+sGt1rdOWuH+9i4Z6FvgpX07RCAjJzYGkE68yBWtl36tQpNm3aRLt27fQIJ5pP6JkDvWNX2i4aDP0nTJ1KUv3+tNz5k1f37y8nTxoVGNddB9Onl3BjpxOeeQZGjSLvqm70uOEI69Uh5t41ly7xXXwSr6Zdbor6zNYtztplLzo6mq5du+qkWdOCXFJKEqTVB6BOx/PPxloWVKgAjz8OP/wAm0s6X67dbgzNMW4cIYlrWPRxOn2OVqDndz2Zv3u+T+LVNO0MnThrmqZpZcL6w+uJSG5GDZKJ7tYy0OFckqeeMkqWH3/c6PdXYoMHw7JlWEPDmTrqCP9MiqbPuN58kvhJiUbbSM9N58ctP/L8vOe5b8Z9DJs7jIkbJuqxojXtAnSvG03TNK1MWLp/KRWO3ExTtsI11wQ6nEsSGwvvvgsPPQSvvQYvvmhMGFgibdrAqlXI4MG8OPkX+jWN4cldjzGn52w+vGk0dSvWPe9mSimW7V/GmLVjmLJpClnOLEKsIcQ6YknNSiXXnUuEPYJnuj7DsG7DiAyJvOT7q2nlhU6cNU3TtKCXmZfJwt2LCTnRmGZhkyHh2kCHdMkeeMAYLOOll2DPHnj/faOMo0RiYmDWLPjsM1q++ioLvoVTk35mR0w9civEERteGYcjGk/dOqQ0r8uMtuF8uWsqW45tITIkksEtB/PXln/lilpXYLfa8SgPy/YvY9TKUby2+DXGrBnDG9e9wd2t78Yi+k9qTdOJs6Zpmhb05u+ej/NIbZzuSNq2yYNyMKSnCHz7rdFR8M034ddfYfx4uPrqEu7IYoFHH0WGDIHZs7H8OovQpCUcPr6f/blHCU2FhutXUn88DHZA7m31qPLkl9ze8o5zWpMtYuHK2ldyZe0rebLzkzw952nunXEvo1aO4oPrP+DK2ld67wHQtDKo7H/yaJqmaeXeLzt+IWFzNwA6DqwT4Gi8x2IxxnRevhwiI+Gmm2D9+lLuLCICbr+dyDHfkbByN1dsyyZs4RJ2zviaWQs+Z9GUt6nQpjPPj9vNfSN+JjKv6N11rdWVZfcvY/yA8aRkptD96+7cMvEWft7+M9nO7FIGqWllmx6OTtM0zcf0cHSXRilF/Y/q0+zd11my/xbSjoOtUpRX9h1MkpOhSxcIDYV164xE+nx++QUmTIDq1eHZZ6Fq1RIcRCn48ENjwxYtYPZsiI+/6GZZzizeXfYuH6/8mKNZRwmxhlC/Un0qhVXCarEiCCKCIFjEQsOYhvSs35N+TfoRatMjFmllix6OTtM0TSuzZv05i2OH9rDlwBX0qLmtXCbNADVrGqUau3YZeW1hSsHw4XDjjcYEKu+/Dx07wp9/luAgIsaQHr/8Art3Q+fORpZ+EQ67gxevfpH9T+9n1p2zeKrzU7So0gKH3UGINeR08qxQ5LpzmbJpCoOmDSL+/XiGzR3GrrRdJQhS04KXbnHWNE3zMd3iXHpKKTqP6UzXceF8tGQRnw3fw9A36l56gEHsn/+E//wH/vtfo3QDwOMxhq779FNjJI5Ro2DjRujTxyj3mDevFDOQb9hgZOEnTsC0acbOvMTtcTN/93w+W/0ZM7bOwKM89Gvaj6e7PE332t2REg8homn+o1ucNU3TLhMicr2IbBORHSLy/HluDxWRyebtK0SkboHbhpvLt4lIn+Lu05emb5nO0U2JZC8bTIjFyV+eq3vRbcq6V1+FVq3g/vth/35jpsHBg42k+bnn4LPPjHlQ2raFJUvAZjM6FE6YAJmZRe97506jOmP1alAtWhrF1Q0aQN++xrh4TqdX7oPVYqVXg15Mv306e5/ay/Arh7N472Ku/uZqmo5uyrC5w/hxy48cTD/oleMVS3IyfPGF8SA+8AD8618weTKkpfkvBq3M0y3OmqZpPuavFmcRsQJ/Ar2AA0AicKdSanOBdR4FWimlHhaRO4BblVKDRKQ5MBHoBNQA5gGNzc2K3Of5eOMz+4/9f3DXqB6M/qopNx9bwX1/y+Wz78tnmUZhGzbAFVcYLc1WK2RkwIgR8Pzz5473vGsXDBhgdCoUgRo1oF49aN0aunWDhARYu9ZIuJcvP7NdvXrw2GNw38BTVPrnQ0YSmZDA/nv/zbyIfvyxxqhN7tgR+vfJJu7YFti0yWjq3r0bsrPB4YBataBZM+OACQkQHn7e+5TlzGLChglM3DiRxXsX4/K4AIhzxNG4cuPznkKsIZf2QB47ZsxrPnGiMfafUhAWhqdSReToMcTlQlksZF3VBdftfyFi0GBsMbGXdsxCMvMySU5PJvlUMocyDmERCw67g8iQSBrFNCI+Ol63wAeZoj6zdeKsaZrmY35MnLsCLyul+pjXhwMopd4ssM4cc50/RMQGHAbigOcLrpu/nrlZkfs8n9J+ZqfnprNxz0qWzv6M9Bk/0WJFL/6R9ylZFaqzYWtIyTrClXHbthmza+flwcMPQ7t2F17X4zGGs1u50kikd+40kuWCLdBNmsCDDxrJ9LZtMHas0WLtcEDXrhB24jCbklzscRqdBWMsJ7BYFMdclbCTx03M4h6+4Vrb70Q1qGJsmJFhNIvn5BgHsViMA7VuDVWqGE3jGRlGs/mpU0aynZuLx+PmpMPK4ZA89oXnsT0sk/W2VDbZT5AcDYciwWO30rhyYxKqJNAirgUJVRKoX6k+MeExVAqrRIg1BIXC7XFzKvcUaWmHyEzejWvbFkLWJlFl6Vri1+/B6vawr0YEP7ePZkoCrHCkkeXJweKBTslw8zYYtAkapEGuFRY2C2dj6+qcSGhAaL1GRMTXQ2zG6L1Ot5NsVzZZziyyndlku7LJceXg8rhwKzdujxu3cnM8+zgpGSkczjjMydyTRT7P0aHRNI9rTkJcAk1jm9K4cmMaxjQkzhFHpfBK2CznjhyslCIjL4OTuSc5mXOSI5lHOJJ5hJTMFOM8I4VMZyY2iw2bxUZkSCSVwytT2VGZyuGViQmPOX25sqMyUSFRQZO8K6VwepzkufPOOQGEWEPOOdktdq/GrxNnTdO0APJj4jwQuF4p9YB5/S6gs1Lq8QLrbDTXOWBe3wl0xkiSlyulvjeXjwV+MTcrcp/nU9LPbI/yMGhoZcZ8d4IKuXAb05jJLbiwU6u6k59m2YtMHLVzuVyQlGQkyQ0aGC3HhXOL9evh88+N0o28PGjQQHFltZ1c55pDi5TfwOkkydmMcen9Gbe5HUdOhgFQqZIxZfgvv0Dzph4jW1+/3jitW2c0maelGTuNjj5zcjggxGxFTkuD1FQ4fBhyc8+J32m3kmcTcqwesiwePAIWdf5TiBuiCg2vl1QF5jSzsaBjHEcb1SAuogpxEXFUcRjncY444iLiEISTOScIWbOe6rMW0WzBRmKOZ53ej1vgVChk2WH4dTCuDdgtdhx2B+H2cMJsYdgtdixiwWqxYhUrlcIrUS2yGlUjqlIjqgY1o2pSM7om1SOrIyJkObM4kXOCrce2sunIJjYdNU7Hso6d8zhE2COwiOX0yaM8pOel41Ge8z7vFrEQ54gjKjQKl8eF0+08nWRfiN1ip1J4pdMJaP4oKcBZI6bknysUSik8ynP6csFzj/Kcs6w46zs9ztP/RJSU3WLHbrWfk1SPHzCeLvFdSrSvcpE4i8hRYG+g4/CCWODcd4YWaPp5CT7l6Tmpo5SK8/VBAp04i8hDwEPm1SbANq/fyTOC7fWh4ymajqdoOp6L82dMF/zMLjMzB/rjS8cfRGTV5da7vizQz0vw0c9JqSQDtQpcjzeXnW+dA2apRgUg9SLbXmyfACilvgC+KG3wJRFsrw8dT9F0PEXT8VxcsMSkR9XQNE0rPxKBRiJST0RCgDuAmYXWmQkMMS8PBOYr46/HmcAd5qgb9YBGwMpi7lPTNO2yUGZanDVN07SiKaVcIvI4MAewAl8ppTaJyKvAKqXUTGAsME5EdgDHMRJhzPWmAJsBF/CYUsoNcL59+vu+aZqmBQOdOPufX/7G1EpMPy/BRz8npaCU+hn4udCyfxe4nAP85QLbjgBGFGefQSDYXh86nqLpeIqm47m4oIipzHQO1DRN0zRN07RA0jXOmqZpmqZpmlYMOnEuARFRIvJugevPicjLpdxXRXMGr9Jsu0dEvDu1URklIm4RWSciG0Vkqog4Srh9DRGZZl5uIyI3FrjtFn9PL1xWefO9cZHj/KvQ9WXePoYW/AI5Bbh5/K9E5Ig5tF/+shgRmSsi283zSn6Mp5aILBCRzSKySUSeDGRMIhImIitFZL0Zzyvm8nrmNO87xJj2/RKnBSxxXFYRWSsiswIdj/k9vsH8/lplLgvka6iiiEwTka0iskVEugbw9dPEfFzyT6dE5KlAPj4F6cS5ZHKBAV5KWisC502czSGitOLJVkq1UUq1APKAh0uysVLqoFJqoHm1DXBjgdtmKqXe8l6o5Zo33xtFOStxVkpd4ePjaUFGjGnFRwM3AM2BO8WYLtyfvgGuL7TseeA3pVQj4Dfzur+4gGeVUs2BLsBj5mMSqJhygR5KqdYYn6vXi0gXYCTwvlKqIZAG3O+nePI9CWwpcD3Q8Vxrfn/lD7EWyNfQh8D/lFJNgdYYj1NA4lFKbTMflzZAeyAL+DFQ8RSmE+eScWEUpz9d+AYRiROR6SKSaJ66mctfFpHnCqy3UUTqAm8BDcxfU2+LyDUiskREZmL0akdEfhKR1eYv9ocKH1M7xxKgofmr9CcRSRKR5SLSCkBEri7wC3atiESJSF3zOQkBXgUGmbcPEpF7RGSUiFQQkb0iYjH3EyEi+0XELiINROR/5vO0RESaBvD+B1Jp3htxZqvBJhEZYz7GseZt57z2ReQtINx8fsabyzLM80ki0rfAMb8RkYFmC9Pb5nGTRGSozx8Jzdc6ATuUUruUUnnAJKCfPwNQSi3GGJGkoH7At+blb4H+foznkFJqjXk5HSPpqRmomJQhw7xqN08K6AFM83c8ACISD/QFxpjXJZDxXEBAni8RqQBchTHiDkqpPKXUiUDFU8h1wE6l1N4giUcnzqUwGvib+UIr6EOMX64dgdsw35xFeB7jxdBGKfUPc1k74EmlVGPz+n1KqfZAB+AJEansnbtQ/pit9DcAG4BXgLVKqVYYLZTfmas9hzHEVhugO5Cdv735BfxvYLL5nEwucNtJYB1wtbnoJmCOUsqJkSz+3XyengM+8d29DHolfW+8hDGGcALGl1ftAtuc89pXSj3PmX8Y/lboGJOB2wHMH0HXAbMxWpBOmsfuCDwoxhjFWtlVE9hf4PoBc1mgVVVKHTIvHwaqBiIIs2GmLbAikDGZP1rXAUeAucBO4IRSKn8+ZX8/bx8Aw4D8eaorBzgeBfxqNhDkN4wF6vmqBxwFvjYblcaISEQA4ynoDmCieTkY4tHD0ZWUUuqUiHwHPEGBxAvoCTQXc253IFpEIku4+5VKqd0Frj8hIreal2thTEiQWoqwy7Nw88MZjBbnsRhfGLcBKKXmi0hlEYkGlgLvma2VPyilDhR4vi5mMjAIWIDxRv7EfH6vAKYW2E+oF+5TmVSK98aVwK3mtv8TkbQC25T0tf8L8KGIhGL8hb5YKZUtIr2BVmJMRQ3GLHmNgN0X2I+mXTKllBIRvw9ZZb6vpgNPme/HgMVkjgHeRkQqYvzNHrB/40TkJuCIUmq1iFwTqDgKuVIplSwiVYC5IrK14I1+fr5sGA13f1dKrRCRDylUBhGI17TZCHILMLzwbYF6j4FOnEvrA2AN8HWBZRagizlG6mki4uLslv2wIvabWWC7azASjq5KqSwRWXiRbS9X2WYL8mkXSoaVUm+JyGyMOualItIHyDnvyueaCbwhIjEYNVfzgQiMFos2RW55eSnJe+O8OyjNa18plWOu1wfjB86k/N1hfBnMKekd0YJWcaYVD4QUEamulDokItUxWlr9RkTsGEnzeKXUD8EQE4BS6oSILAC6AhVFxGa28vrzeesG3CJG5+8wIBrjn7BAxYNSKtk8PyIiP2KUIAXq+ToAHFBKrTCvT8NInAP9+rkBWKOUSjGvBzoeQJdqlIpS6jgwhbM7EvwK/D3/iojkJ1N7MH7JISLtMP4SAUgHooo4TAUgzUwcmmJ0+NCKZwnwNzidhB0zW18aKKU2KKVGYkwjXLgF5ILPiVmvl4jxYTtLKeVWSp0CdovIX8xjiYi09sk9KiNK+N5Yypnyit5Afg/pol77TjNBOJ/JwL0YZTj/M5fNAR7J30ZEGpt/QWplV7BOAV5wKvMhwAx/Hdis1x0LbFFKvRfomMz+CxXNy+FAL4y66wUY07z7NR6l1HClVLxSqi7G62W+We4VkHjE6CcTlX8Z6A1sJEDPl1LqMLBfRJqYi67D6GsVsNe06U7OlGkQBPEYlFL6VMwTkFHgclWMnp4vm9djMb64kzBecJ+Zy8MxEodNwFcYHx51zdsmYLxZ3gauwUjI8vcfivH38xbgJ2AhcDPmT7EAAAY8SURBVI152x4gNtCPRzCcCj4nBZbFmI9ZErAcaGUu/9h8vJMw3oyhQF1gY4HtEjHqmQcB9wCjCux3IEZd2tUFltXDSNLWm8/7vwP9mAT6eSjBe6MKRs/ojcCXwCHzOSnqtT/SXD7+PMe1Y3TY+rrAMgvwBkbt+0aML8oKgX689OmSX283An9i1M2+EIDjTzRfr06M1rr7MWpmfwO2A/OAGD/Gc6X52ZRkfn6tMx+jgMQEtALWmvFszP9cBOoDK4EdwFQgNADP3TWY37WBisc87nrztCn/NRzg11AbYJX5nP2E0ZARyHgiMMrzKhRYFrB4Cp70zIGapgWEWY/sVkq5RKQr8KnSZS+apmlaENM1zpqmBUptYIoYw/zlAQ8GOB5N0zRNK5JucdY0TdM0TdO0YtCdAzVN0zRN0zStGHTirGmapmmapmnFoBNnTdM0TdM0TSsGnThrZYKIvCAim0QkSUTWiUjnUuyjjTkAfv71W0Tk+aK2uVQico2IXOHLY2iapmma5h96VA0t6JlDld0EtFNK5YpILBBSil21AToAPwMopWbi+4kTrgEygGU+Po6maVq5IiL3AB2UUo8H6PhtgBpKqZ8DcXwtOOkWZ60sqI4x+18ugFLqmFLqoIi0F5FFIrJaROaYU3AiIgtFZKSIrBSRP0WkuznD2KvAILPFepCI3CMio8xtvhGRT0VkuYjsMluKvxKRLSLyTX4gItJbRP4QkTUiMlVEIs3le0TkFXP5BhFpKiJ1gYeBp81jdvfng6ZpmlaWiIg10DEU0gZjIhlNO00nzlpZ8CtQy0yCPxGRq80plD8GBiql2mPMyjiiwDY2pVQn4CngJaVUHvBvYLJSqo1SavJ5jlMJ6Ao8jdES/T6QALQ0yzxigf8Deiql2mHMsvRMge2Pmcs/BZ5TSu0BPgPeN4+5xEuPh6ZpWlARkX+IyBPm5fdFZL55uYeIjBeRO81GhY0iMrLAdhki8q6IrAe6isi95mf9SqDbRY5ZVUR+FJH15ukKc/kz5nE2ishT5rK6IrKxwLbPicjL5uViNbZ49QHTyixdqqEFPaVUhoi0B7oD12JM3/w60AKYKyIAVowpcPP9YJ6vxphWuzj+q5RSIrIBSFFKbQAQkU3mPuKB5sBS85ghwB8XOOaA4t9DTdO0Mm8J8CzwEUZJXKjZwNEdY3r0kUB7IA34VUT6K6V+wphaeYVS6lnzX8P/b+9uXqyq4ziOvz/gkOT0gJVtI+yB3AiCEplC9Qc4UIvZhLpSAoUIogJtE9EmopVEmxYpEqUwCIXIzOTCDBURNGihblxIm8SEnvTr4vyM8WHmHkxxxnm/Nvfcc39Pi8s5X373+z13Z2t3ARin++vu6XwOTFbVSNutHm73ig3AKiDA4SSTbd6ZLKiqla0OZntVvZZkG/cwVUSzk4Gz5oSqugxMABMtsH0LOFlVL07T5a/2epn+3/Nrfa5MOb72fkEba39Vjd7BOSXpfnAUWJHkYbpr4TG6APplYAyYqKrfAJJ8DawB9tJdL79tY6y6od1u4NkZ5nwFeBP+u0dcSLIa2FNVl9oY37U1DKpnuZ3NFs1Dpmpo1kvyXJJnppxaDvwCPNEKB0kylGTZgKEuAg/9j6X8BLyUZGmbc1GSmS7qd2JOSZr1quof4Aywnq4Y+iDdL4RLgbMzdP2zBb13279cH/MsvOFzNz7Ui4Gz5oJh4Kskp5KcoEuX2Aa8DnzScuOOA4Me+zYOvHC7+WptF2Q9sKut4xDw/IBuY8CIxYGS5oGDwDvAj+14E12qxc/A2iSPt5SKUWDyFv0Pt3aPtTSPNwbMdwDYDF1hYZJH2rzrkjyYZBEw0s6dB5a0sR+ge1LTIG586Capqnu9BkmSNMcleRX4Hni0qi4l+RXYUVWfJhkF3qfLO95XVe+2Pn9U1fCUMTYA7wG/022I/D1djnGSJ4EvgKfpdoo3V9WhJG8DG1uzL6vqs9Z+C7AVOAecBs5W1YdJJugKuo+0IvAjVfVUksXAD8AQ8PE0ReWaZwycJUmSpB5M1ZAkSZJ6MAFekiTNWkk+4OZ852+q6qNbtZfuJlM1JEmSpB5M1ZAkSZJ6MHCWJEmSejBwliRJknowcJYkSZJ6MHCWJEmSergKae6GNWjAMuYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "basic_feature_plots(\"Sentiment\",\"word_count\",final_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "3d715662",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "3d715662",
    "outputId": "77f6744b-a913-43e3-e67c-5c72d637cce2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ylW7Hr7nmGr785S+zbNkyduzYwYUXXsgbb7zR5+vcdNNNbNy4kfXhB1m1ahXr1q1j48aNzJs3D4Af//jHVFZW0tTURHV1NZdeeikTJ04c/IdMkhJnEZE84u6PAI/0OPb1mOdrCFo44t27nTiDCd393NRGmVn798cfGAhdFWclziKDN3bsWK666ipuu+02ysvLO48/+eSTbNrUNRvmwYMHuy2VnYjFixd3Js0At912Gw8++CAAO3fu5O2331biLCIikiqNjTAr7o8KXRVntWrIcJdIZTidrr32Wk499VQ++9nPdh6LRCKsXr2asrKybtcWFRURiXTNiNnc3Nzn644aNarz+apVq3jyySd54YUXqKio4Oyzz+733nRQj7OIiOS16ODAeNSqIZIalZWVXH755dx1112dxy644AJuv/32zv1oC8bcuXNZt24dAOvWrWPbtm0AjBkzhkOHDvX5HgcOHGDChAlUVFTw5ptvsnr16nR8lH4pcZYRz+INtReRvKHBgSKZcf3113ebXeO2225j7dq1LFy4kPnz53PHHXcAcOmll9LQ0MCJJ57Id7/7Xd773vcCMHHiRJYuXcqCBQu44YYber3+8uXLaW9v54QTTmDlypUsWbIkMx8shlo1REQkbzU1QUtL3xXn6NzOqjiLDE5sz/LUqVM5erRrwdFJkyZx33339bqnvLycJ554Iu7r/eIXv+i2f/bZZ3c+Ly0t5dFHH4173/bt25OIevBUcRYRkby1f3+w7aviXFgIo0crcRaRxChxFhGRvBVd2KSvxBmCPme1aohIIpQ4i4hI3opWnPtq1YCgz1kVZxFJhBJnERHJW6o4S75z92yHMGwN5r9d2hNnM9tuZq+Z2XozWxseqzSz35vZ2+G2n680ERGRwUmk4jx2rCrOMjyVlZWxb98+Jc+D4O7s27ev1xzTA8nUrBrnuPvemP2VwFPufpOZrQz3v5qhWEREZIRIpOI8bhzs2pWZeERSadasWdTU1FBfX5/tUIalsrIyZvW1OlIfsjUd3ceAs8Pn9wCrUOIsIiIplmjFWa0aMhwVFxd3W45a0i8TPc4OPGFmL5vZivDYVHevDZ/vAaZmIA4RERlhDh+G0lIoLu77Gg0OFJFEZaLivMzdd5nZFOD3ZvZm7El3dzOL25wTJtorAObMmZP+SEVEJK8cPhzM09yfsWPh0CHo6AjmdRYR6UvaK87uvivc1gEPAouBd81sOkC4revj3jvdfZG7L5o8eXK6QxURkTyTaOIcvVZEpD9pTZzNbJSZjYk+By4ANgIPA58JL/sM8Jt0xiEiIiPT4cMwalT/14wbF2zV5ywiA0l3q8ZU4EEzi77XL9z9MTNbA9xvZp8D3gEuT3McIiIyAh05knjFWX3OIjKQtCbO7r4VODnO8X3Ah9L53iIiIom0akQrzkqcRWQgWjlQRETyVjI9zmrVEJGBKHGWEU8rLonkL/U4i0gqKXEWEZG8lUjFecyYYHvoUPrjEZHhTYmziIjkrUQGBypxFpFEKXEWEZG85J5YxTl6XomziAxEibOIiOSlpqYgeR4ocS4shIoKJc4iMjAlziIikpeiKwEONDgQupbdFhHpjxJnERHJS9HEeaCKMwR9zprHWUQGosRZRjxNRyeSn44cCbaJJs6qOIvIQJQ4i4TCpeFFJE8kW3FW4iwiA1HiLBJS5VkkvyTT46zEWUQSocRZJKSKs0h+SabirMGBIpIIJc4iIpKXNDhQRFJNibNISK0aIvlFgwNFJNWUOIuE1Kohkl+S7XFuaoL29vTGJCLDmxJnEZE8YmbLzewtM9tsZivjnD/LzNaZWbuZXdbjXIeZrQ8fD8ccn2dmL4aveZ+ZlWTiswxVNHGuqBj42rFju98jIhKPEmeRkFo1ZLgzs0Lge8CHgfnAJ81sfo/LdgBXA7+I8xJN7l4VPi6OOX4z8B13Pw5oBD6X8uDT4PDhIGkuLBz42jFjgq36nEWkP0qcRUJq1ZA8sBjY7O5b3b0VuBf4WOwF7r7d3TcAkURe0IK/GOcCD4SH7gEuSV3I6XP4cGL9zdCVOKvPWUT6o8RZJKSKs+SBmcDOmP2a8FiiysxsrZmtNrNocjwR2O/u0e7fZF8za44cUeIsIqlVlO0ARLJNlWaRTse4+y4zOxZ42sxeAw4kerOZrQBWAMyZMydNISbu8OHEBgaCEmcRSYwqziKhjo6ObIcgMlS7gNkx+7PCYwlx913hdiuwCjgF2AeMN7NooaXP13T3O919kbsvmjx5cvLRp1gyiXN0cKASZxHpjxJnGfGiLRpKnCUPrAGOD2fBKAGuBB4e4B4AzGyCmZWGzycBS4FNHvwFeQaIzsDxGeA3KY88DZqbobw8sWs1OFBEEqHEWSSkxFmGu7AP+UvA48AbwP3u/rqZfcvMLgYws2ozqwE+AfzQzF4Pbz8BWGtmrxIkyje5+6bw3FeB68xsM0HP812Z+1SD19wMZWWJXatWDRFJhHqcRULtWvlA8oC7PwI80uPY12OeryFot+h53/PASX285laCGTuGFSXOIpJqqjiLhJQ4i+SXlpbEE+fSUiguVuIsIv1T4iwjXnRWDSXOIvmluTlIiBM1dqwSZxHpnxJnGfE0OFAkPyXTqgFBu4YGB4pIf5Q4i4QikYQWUhORYWIwibMqziLSHyXOIiG1aojkl2R6nEGJs4gMTImzSEhLbovkj0gEWluT63FW4iwiA1HiLCIieaelJdgmU3EeO1Y9ziLSPyXOIiKSd5qbg61aNUQklZQ4i4QKCvTXQSRfDKbirMRZRAaiTEEkVFhYmO0QRCRFBltxPnwYNNxBRPqixFkkpMRZJH9EE+dkF0CJRODo0fTEJCLDnxJnkVBRUVG2QxCRFBlsxRk0QFBE+qbEWSRUUlKS7RBEJEWGkjirz1lE+qLEWUY8MwOguLg4y5GISKoMdnAgKHEWkb4pcRYJKXEWyR+D6XFW4iwiA1HiLBLS4ECR/DGYVo2xY4OtEmcR6YsSZxERyTsaHCgi6ZCRxNnMCs3sFTP7bbg/z8xeNLPNZnafmWlUloiIpIx6nEUkHTJVcb4GeCNm/2bgO+5+HNAIfC5DcYiIyAigHmcRSYe0J85mNgv4CPAf4b4B5wIPhJfcA1yS7jhERGTkGEyrxqhRYKbEWUT6lomK863AV4BIuD8R2O/u7eF+DTAzA3GIiMgIMZjE2SyoOitxFpG+pDVxNrOLgDp3f3mQ968ws7Vmtra+vj7F0YmISL6K9jgn06oBQeKswYEi0pd0V5yXAheb2XbgXoIWjX8HxptZdH3jWcCueDe7+53uvsjdF02ePDnNoYqISL5oboaiouCRDFWcRaQ/aU2c3f1r7j7L3ecCVwJPu/ungGeAy8LLPgP8Jp1xiIjIyNLcnHy1GZQ4i0j/sjWP81eB68xsM0HP811ZikMEd892CCKSYs3NyfU3RylxFpH+JPlLrMFz91XAqvD5VmBxpt5bpD/BRC8ikk8GmziPHQtbtqQ+HhHJD1o5UERE8k5LiyrOIpJ6SpxFRCTvqMdZRNJBibOIiOQd9TiLSDoocZYRT4MDRfLPUHqcW1u75oEWEYmlxFkkpARaJH8MpccZVHUWkfiUOIuElDiL5I+h9DiDEmcRiS/hxNnMnkrkmMhwVVCgnyNl+DOz5Wb2lpltNrOVcc6fZWbrzKzdzC6LOV5lZi+Y2etmtsHMrog5d7eZbTOz9eGjKlOfZ7CG0uMMSpxFJL4B53E2szKgAphkZhOA6KS3Y4GZaYxNJCM0j7PkCzMrBL4HnA/UAGvM7GF33xRz2Q7gauDvetx+FLjK3d82sxnAy2b2uLvvD8/f4O4PpPcTpI4SZxFJh0QWQPlr4FpgBvAyXYnzQeC7aYpLRESStxjYHC4yhZndC3wM6Eyc3X17eC4Se6O7/znm+W4zqwMmA/sZhgbb4zx2bLBV4iwi8Qz4u2l3/3d3nwf8nbsf6+7zwsfJ7q7EWYY99TZLHpkJ7IzZr2EQvxk0s8VACRC7ht7/CVs4vmNmg+gezqyhVpwPHkxtPCKSHxJectvdbzezDwBzY+9z9/9MQ1wiIpIFZjYd+CnwGXePVqW/BuwhSKbvBL4KfCvOvSuAFQBz5szJSLx90eBAEUmHhBNnM/sp8B5gPdARHnZAibOISG7YBcyO2Z8VHkuImY0Ffgf8g7uvjh5399rwaYuZ/YTe/dHR6+4kSKxZtGhRVn+Vox5nEUmHhBNnYBEw3/V7bclTGiQoeWANcLyZzSNImK8E/kciN5pZCfAg8J89BwGa2XR3r7XgL8klwMbUhp1akQi0tQ0tcT5wILUxiUh+SGb+rY3AtHQFIiIiQ+Pu7cCXgMeBN4D73f11M/uWmV0MYGbVZlYDfAL4oZm9Ht5+OXAWcHWcaed+bmavAa8Bk4B/zuDHSlp01b/BJM5FRTB6tBJnEYkvmYrzJGCTmb0EdC5G6u4XpzwqkQyKVpr1yxTJB+7+CPBIj2Nfj3m+hqCFo+d9PwN+1sdrnpviMNOquTnYDqbHGWD8eNg/LOcSEZF0SyZxvjFdQYjkArVqiOSHaOI8mIozwLhxqjiLSHzJzKrxbDoDERERSYWhJs6qOItIX5KZVeMQwSwaEExJVAwccfex6QhMJNPUqiGSH4bS4wxBxXnPntTFIyL5I5mK85jo83Bk9ceAJekISiSTlDCL5JdU9Di/9Vbq4hGR/JHMrBqdPPAQcGGK4xHJGvU4i+SHVPQ4q1VDROJJplXj4zG7BQTzOjenPCKRLFHlWSQ/pKLH+cABcAf9PC0isZKZVeOjMc/bge0E7Roiw5oqzSL5JRU9zu3tcPQojBqVurhEZPhLpsf5s+kMREREJBVSUXGGoOqsxFlEYiXc42xms8zsQTOrCx+/MrNek+iLiIhk01AHB44bF2zV5ywiPSUzOPAnwMPAjPDx3+ExkWFNvc0i+SWVFWcRkVjJJM6T3f0n7t4ePu4GJqcpLpGMU6+zSH5IRY8zqOIsIr0lkzjvM7NPm1lh+Pg0sC9dgYlkmirPIvlBFWcRSZdkEuf/BVwO7AFqgcsADRiUvKGKs0h+SKTHeXPDZi76xUU8v/P5XudUcRaRviQzq8Y7wMVpjEVERGTIBkqctzVu45x7zqHmYA3ratex/m/WM2XUlM7z0YqzEmcR6SmZWTXuMbPxMfsTzOzH6QlLJPPUqiGSH5qboagoeMTzL8/9C41Njfz0L39KY3Mj1//6b+C//gtWrwagvDy4V60aItJTMgugLHT3zp+/3b3RzE5JQ0wiWaFWDZH80NLSf3/zU9ue4rxjz+PTCz/Na9tf4uNf/C7UPBicvO027G//lvHjVXEWkd6S6XEuMLMJ0R0zqyS5xFtERCTtmpv7btN4Z/87bG3cyjlzzwHgC6+WcHqNs3rlp+Gii+C66+DVVxk3ThVnEektmcT5X4EXzOyfzOyfgOeB/5uesEQyT60aIvmhubnvivMz258B4Nx550Ikwpz/+CUvHVvKNxfshbvvDkYGfu1rjB8PjY2Zi1lEhoeEE2d3/0/g48C74ePj7v7T6PnYarTIcKRWDZH80F/i/PS2p5lcMZkTp5wI69ZhO3aw7dIP8eTWJ2moMLjhBnj0USqLDipxFpFekqk44+6b3P274WNTj9NPpTAuERGRQemvx/nZd57l7LlnU2AF8PDDUFDA3E99kfZIO6u2r4K/+RsoL6dy71s0NGQ0bBEZBpJKnAegcp0Ma2rVkFxiZr82s4+YWSq/p0eEvirOR1qPsOPADqqmVQUHnnoKTj+dUxacR0VxBc9seyZo1bj8cip3vErDPn0niEh3qfxC1jeMiEjqfB/4H8DbZnaTmb0v2wENF30NDtzSuAWA4yqPg9ZWWLcOzjiDksISls1Z1tn/zOc/T2XbHhobnUgkg4GLSM5TJUMk1NbWlu0QRDq5+5Pu/ingVGA78KSZPW9mnzWz4uxGl9v6qjhvbtgMhInzhg3BhaefDsA5c8/h9frXqTtSB0uXUjm5iIgXcPBgJiMXkVynVg0Z8aKDApU455bf/e53bNiwIdthZJWZTQSuBj4PvAL8O0Ei/fsshpXz+upxjibO75nwHnjppeBgmDifPfdsgKDP2YyJy04AoOHNuuKw1g0AACAASURBVHSHKyLDyICJs5lV9veIufRDaYxTJO2UOOeWm2++meuuuy7bYWSNmT0I/BGoAD7q7he7+33u/rfA6OxGl9v6qzhPrpjMuLJxsHFjsLb2nDkALJqxiFHFo/jDO38AoPK8UwFoePDZjMUtIrkvkQVMXiboXzZgDtAYPh8P7ADmAbi7xh/LsBQdFNja2prlSKSnEf5n8iN3fyT2gJmVunuLuy/KVlDDQV89zpsbNgdtGgCbNsH8+RD+xqmooIjFMxfzQs0LAFSePBuAhsfXwM2fyEjcIpL7Bqw4u/s8dz8WeJKg6jHJ3ScCFwFP9HevmZWZ2Utm9qqZvW5m3wyPzzOzF81ss5ndZ2YlqfgwIkOhinPu6OjoyHYIueCf4xx7IeNRDEP9VZx7Jc4xzph1Bq/ueZWjbUepDH+f2rChBurUriEigWR6nJfEVj/c/VHgAwPc0wKc6+4nA1XAcjNbAtwMfMfdjyOoYH8uubBFUk+Jc+5oaWnJdghZY2bTzOw0oNzMTjGzU8PH2QRtGzKAeIlzU1sTOw/uDBLn+vrg0TNxnn0GHd7B2t1ruxJnHw8PPZShyEUk1yWTOO82s380s7nh4x+A3f3d4IHD4W5x+HDgXOCB8Pg9wCVJxi2SMtFWjfb29ixHIlHNzc3ZDiGbLgRuAWYB/wb8a/i4Dvj7LMY1bMQbHLht/zYgnFHjzTeDgyec0O2a02cGAwVf2PkCE8K1cBsmHg8PPICICCTW4xz1SeAbwIMEye8fwmP9MrNCgj7p44DvAVuA/e4ezVJqgJl93LsCWAEwJxzAIZJq0YRZFefcMZITZ3e/B7jHzC51919lO57hKF6P85aGYA7n90x4D6wLE+fjjut2zeRRkzmu8jhW71pNSQmMHg0Nx1bD0zfAvn0wcWImwheRHJZQ4hwmv7eHc4omxd07gCozG0+QdL8/iXvvBO4EWLRokRZYkbSIJs7qq80dTU1N2Q4ha8zs0+7+M2CumfWaVsTd/y0LYQ0bHR3Q1ta74rzr0C4AZo2dBVsfgYKCzhk1Yi2ZtYTfb/k97k5lpdEw5f3Biz76KHz605n4CCKSwxJq1QiT32OGMojP3fcDzwBnAOPNLJq0zwJ2DfZ1RYYqmjArcc4dIzlxBkaF29HAmDgP6Ue0Pb5n4lx7qBbDmDp6KmzdCrNmQUnvf9LOmHUG7x55l+37t1NZCQ02EaZNg4cfzkD0IpLrkmnV2Ar8ycweBo5ED/ZX/TCzyUCbu+83s3LgfIKBgc8AlwH3Ap8BfjOI2EVSIpowq8c5d4zkwYHu/sNw+83B3G9mywkWSikE/sPdb+px/izgVmAhcKW7PxBz7jPAP4a7/xy2jRAOVrwbKAceAa7x6OCAHNNn4ny4lsmjJlNUUBQkzsceG/f+M2adAcDqmtVUVs6jodHgox+Fe+8NlumOk2yLyMiRzODALcBvw3sSrX5MB54xsw3AGuD37v5b4KvAdWa2GZgI3JVs4CKp0t4e9Dar4pw7Yn+IGak/0JjZ/zWzsWZWbGZPmVm9mfXbKxC21X0P+DAwH/ikmc3vcdkOgtUIf9Hj3kqCcSynA4uBb5hZOESOHwB/BRwfPpYP6cOlUbQ9Pl7iPH309GCnn8T5pKknUVFcwQs1LwQV5wbg4ovh0CF4VouhiIx0CVeco9UPMxsd7h/u/w5w9w3AKXGObyX4YhbJug71OOecSCTS+TxHC5uZcIG7f8XM/hLYDnycYFD2z/q5ZzGwOfyOxczuBT4GbIpe4O7bw3ORHvdeSFDcaAjP/55gCtFVwFh3Xx0e/0+CmZAeHeLnS4to4txzcGDtoVpmjJkBTU2wZw/MnRv3/qKCIqpnVLO6ZjWnVAZjAvnQh6C8PGjXOP/8tMYvIrkt4YqzmS0ws1eA14HXzexlMzsxfaGJZEa0ojlSK5u5bgQnztHCxkeAX7r7gQTumQnsjNnvc9aiJO6dGT4fzGtm3IAV513hkJrZs/t8jTNmncEre15h7Pg2GhogUloeJMwPPwwj9/9HESG5Vo07gevc/Rh3Pwa4HvhResISyZz2cBo6TUeXOwoLCzufFxUlMxQjr/zWzN4ETgOeCseM5PQ8fWa2wszWmtna+vr6rMQQr8e5I9LBu4ffZfqY6VAT/gzQT+K8ZNYS2iPttJTtpL0d9u8naNfYsQNeey19wYtIzksmcR7l7s9Ed9x9FV2jv0WGrdbW1m5byb6SmAFYBQXJfE3lD3dfSbA66yJ3byMYlP2xAW7bBcRmhMnMWtTXvbvC5wO+prvf6e6L3H3R5MmTE3zb1IpXca4/Wk+HdwQV551hUX3WrN43h5bMWgLAPgvme66vBy66CMw0u4bICJfMv0hbzex/x6wc+I8EM22IDGutYaVZiXPuKO3ZoDpyvR+4wsyuIpiJ6IIBrl8DHG9m88LpQ68EEs30HgcuMLMJ4aDAC4DH3b0WOGhmS8zMgKvI4ZmQ4vU41x6qBehece4ncZ46eipzxs1hV8d6IEycp06FxYuVOIuMcMkkzv8LmAz8OnxMCo+JDGvRqc9G8hRouaa8vDzbIWSdmf2UYOntZUB1+FjU3z3hiqxfIkiC3wDud/fXzexbZnZx+LrVZlYDfAL4oZm9Ht7bAPwTQfK9BvhWdKAg8AXgP4DNBDMs5eTAQIhfca49HCbOo8PEecIEGNX/L0wXz1zM281/AqCuLjx48cWwZg3s3p3qsEVkmEimebDS3f/ftEUikiUtYaW5pXlEL7qRU8p6juwamRYB85OdL9ndHyGYazn22Ndjnq+he+tF7HU/Bn4c5/haYEEycWRL3MQ5tuK8c2e//c1Ri2cs5oHIrUBYcYYgcf6Hf4Df/Q7+6q9SGbaIDBPJVJx/bGZbzOxeM/uimZ2UtqhEMiQSidDWFsym0dyc0+OuRhQlzgBsBKZlO4jhJt7gwGjFedroaUG1eMaMAV9n8czFULEXiEmcTzwxmMZO7RoiI1Yy8zh/MOyZqwbOBn5nZqPdvTJdwYmkW2xfs3qcc4cSZyBoh9tkZi8BnX1E7n5x9kLKfX31OE8om0BZUVkwh/NJA9d9TptxGgXF7RSPaqauLvz/0cJVBH/0o+CN9P+pyIiTcOJsZsuAM8PHeIJVBP+YprhEMiI2WW5rU+KcK0q0rDHAjdkOYDjqq8d5+pjpwRzMdXXBQL8BjC4ZzfzJ89k6eh/19THTVi9fDrffDs89B+edl+LoRSTXJdPjvAp4GfgX4BF3V5Yhw140cTac1lbN45wrRuoUdLHc/VkzOwY43t2fNLMKoHCg+0a6vqajmzJqCjQ2QltbQokzBH3Ob5bVUF8/A7Dg4Ac/CMXF8PvfK3EWGYGS+ddpEvAt4AzgMTN70sz+KT1hiWRGdJnt0kItuS25xcz+CngA+GF4aCbwUPYiGh7i9TjvPbqXSRWT4N13gwPTEmsdXzxzMe1ltdTUxtSJRo2CpUvhiSdSFLGIDCcJJ87uvp9g3uZtQC3wHuCsNMUlkhHRZLmowOnoiGQ5GpFuvggsBQ4CuPvbwJSsRjQMxOtx3nt0L5PKYxLnRCvOMxfDqHrerevx3XD++bB+fcw8dSIyUiScOJvZVuBfgQnAD4D3ufsH0xWYSCZEIsE/iEXW9VwkR7TEtsSZWRGQ1NR0I1FzMxQVQXTV9o5IBw1NDd0rzgkmzgumLKBwTAMHG0vo9vVwQbgOzZNPpi5wERkWkmnVOM7d/8Ld/8Xdn+vZ42xmX0txbCJpF50it7DAcVfiLDnlWTP7e6DczM4Hfgn8d5Zjynk9J7vY37yfiEeCxHnPnuBggolzcWExs6eXEekoZP/+mBOnnAKVlUGfs4iMKMm0agyUVXxiiLGIZFy0VaPQoCOiYp7klJVAPfAa8NcEi5r8Y1YjGgZaWnr3NwNdFefCwiDpTdD75wTX7qqNGTxcWBgMDHziiWCmDhEZMVI5dN1S+FoiGRGtOKtVQ3JNWKx4CPiCu1/m7j9KdhXBkahnxblX4jxlCiQxa8upxwVT0b341vbuJ84/P1hMZdOmoYYsIsNIKhNnfaHLsNPWFlSRSgqD/33b29uzGY6ERnJ+aIEbzWwv8BbwlpnVm9nXB7pXgsS558BAiEmcE2zTiFp24nEAvPjmju4nPvShYPuHPww6VhEZflRxzrC9e/dy4MCBbIchoWjiXF6kxDmXjPA/hy8TzKZR7e6V4eqspwNLzezL2Q0t9w1YcU5wKrqoxe+bDcBrW/Z2PzF3brB093PPDSVcERlmUpk4/zKFr5W3VqxYwXXXXZftMCTUmTiHFefovmRXc3ROsZHpfwKfdPdt0QPuvhX4NHBV1qIaJgbscU6y4lxZaRQUt7Jl55HuJ8xg2TL4oxbQFRlJkpmO7r1m9pSZbQz3F5pZ50AVd/92OgLMN3v37uXtt9/OdhgSiiZoY0q8275kV1NTU7ZDyKZid9/b86C71wPFWYhnWIlXcS4rKqOiqHxQibMZjKk8wt66Yg63Hu5+ctky2LkTduyIf7OI5J1kKs4/Ar4GtAG4+wbgynQEJZIp0QRtfEkwMPDo0aPZDEdCI/zPoXWQ54Q4Pc5NwaqBduAAtLYmnTgDTJsOHJrGutp13U+ceWawVbuGyIiRTOJc4e4v9Tg2ohsRZfiLJs7jworzCK905owRnjifbGYH4zwOASdlO7hcF6/iPJjFT2K9Z3Y5HJrOS7t6/BN40kkwZowSZ5ERpCiJa/ea2XsIZ88ws8sIlt4WGbY6E+fSoOKsVo3ccORIVz9pJBKhIInpw4Y7dy/MdgzDWc/Eed/RfUNOnOfOLqPgqZm9E+fCQjjjDCXOIiNIMv8afRH4IfB+M9sFXAv8P2mJSiRDoonzWFWcc0ps4qw/E0lGvMGBkyomQV1dcGDKlKRfc9o0iBydwIvvrO998vTT4fXX4ciR3udEJO8ks3LgVnc/D5gMvN/dl7n79rRFJpIBzc3NGDC6OKg4K0nLDbF/DiO8bUOSFG8e50nlk2DfvuDApElJv+b06cF2x64W3j38bveT1dUQicArrwwyYhEZThJu1eg5+b5ZMG2zu38rxTGJZExbWxtFhVBc0LUv2RebOKt9RpIR26rRHmmnsbkxqDg3NAQHk1huOyqaOHN4Gmt2r+Gi917UdbK6Oti+9FIwy4aI5LVkWjWOxDw6gA8Dc9MQk0jGtLe3U1RgFFnXvmRfS0tL53P9FkCSEZs4NzQFyfLEiolBxbmionsfR4KiibMdjtPnPG0azJ4Na9YMJWwRGSYSrji7+7/G7pvZLcDjKY9IJIM6OjooMCgs0MqBuaS1tWvWNf0WQJIR2+McTZwryyuh4eVBVZuhK3Gewam8tCvOQMDqaiXOIiPEUIaqVwCzUhWISLbErhUfbUGS7IpNnGOfi/SnowPa2roS58amRiBMnPftg4kTB/W6U6ZAQQFMiZzMS7tewt27X1BdDVu2dLWDiEjeSmblwNfMbEP4eB14C7g1faGJpJ+Z4UAk/HdwJE17lstiK//6LYAkKtrhEx0c2NgcJM4TyiYESe0gE+fCwiB5HtV6HI3NjWxt3Nr9gmif89q1g3p9ERk+kpnHOWY0BO3Au+6uf9FkWCsoKCDi4eTkkjM6OjriPhfpT3Qcac9WjQnlE4KK84IFg37tadOAwzMAWLN7De+pfE/XyVNPDbavvAIXXDDo9xCR3JdMee1QzKMJGGtmldFHWqITSbNo4hyJBC0aRUXJ/Cwp6aKKswxGtOLcs1VjqBVnCPqcjzSMpayorPcAwQkT4JhjYH2ceZ5FJK8kkyWsA2YDjQRtoeOBHeE5B45NbWj5R4Occk9RUREdEac9LDkXFmrRtlwQW2VW4iyJ6llxjrZqjC8dFyTOgxwcCEHivH69ccq0U1izO85AwKoqJc4iI0AyFeffAx9190nuPpGgdeMJd5/n7kqaE6D5aHNPYWEhHZGuHmclzrkhNllWq4YkKvoV29nj3NTImJIxFB9pgvb2IVec6+rgtKmLWVe7jvZIjx/oqqrgrbe0gqBInksmcV7i7o9Ed9z9UeADqQ8pfykByD0FBQXh4EDr3JfsU6uGDEa8ivOE8glDWvwkasaMYNaO91Us42jbUd6of6P7BVVV4A4bNw76PUQk9yWTJew2s380s7nh4x+A3ekKLB9pqrPcFR0cqD+j3NDW1gaFMc9FEhBvcOCEsgldy20PoeI8IxgXyEyCGTR69TlXVQXbV18d9HuISO5LJnH+JDAZeDB8TAmPSYKKi4uzHYL0EK0wR1s1lDjnhtbWViiOeS6SgF6DA1NccQYoODybcaXjevc5H3MMjBunPmeRPJfMyoENwDVpjCXvlUYb7yRnRCIRAAqt+75kV0tLC5QAzd2X3xbpT7we5/dOfG9KKs4zZwbbPbUFLJqxqHfibAYnn6zEWSTPDVhxNrNbw+1/m9nDPR/pDzF/aOBZ7uno6KAwZslt9aHnhpaWFrw4+DNRxTk5ZrbczN4ys81mtjLO+VIzuy88/6KZzQ2Pf8rM1sc8ImZWFZ5bFb5m9NyUzH6qxMTrcQ6W2w4rzkNInKdODVYP3LULqmdUs+HdDTS39xjwXVUFGzYEzdAikpcSqTj/NNzeks5ARLKhvb2dgoKuirMGouWGlpaWoFXDVHFOhpkVAt8DzgdqgDVm9rC7b4q57HNAo7sfZ2ZXAjcDV7j7z4Gfh69zEvCQu8eWTz/l7jm9NN6APc4TJgz6tYuKguR51y64aOZi2iPtrN+zniWzlnRdVFUVzKqxZQu8972Dfi8RyV0DVpzd/eVw+2y8R/pDFEmf1tZWSgqM4gJVN3NJS2sLFIIVmf5MkrMY2OzuW929FbgX+FiPaz4G3BM+fwD4kPVu7v9keO+wEtvj3NzeTHN7c1eP89ixMMRxJjNnwu7dUD0zGCC4ZlePdg0NEBTJewkPDjSzpWb2ezP7s5ltNbNtZrZ1gHtmm9kzZrbJzF43s2vC45Xha70dbgdfBhAZgpaWFkoKoaSga1+yr6WlBS9wrFCJc5JmAjtj9mvCY3Gvcfd24ADQs4fhCuC/ehz7Sdim8b/jJNo5Ibbi3G3VwH37hjQwMGrGjKDiPHPMTKaNnta7z3n+/KA0rT5nkbyVzKwadwH/BiwDqoFF4bY/7cD17j4fWAJ80czmAyuBp9z9eOCpcF8k45qbmykudEoKg4qzEufc0NbWFnw7FWg6ukwzs9OBo+4eOyHxp9z9JODM8PE/+7h3hZmtNbO19fX1GYi2u9jBgdFVAzsrzkPob46KVpzNjMUzF/dOnEtL4YQT4JVXhvxeIpKbkkmcD7j7o+5e5+77oo/+bnD3WndfFz4/BLxBUO2I/VXhPcAlg4hdZMhaWlooLXCKVXHOKR0dHcG3k2nAZpJ2AbNj9meFx+JeY2ZFwDgg9rv8SnpUm919V7g9BPyCoCWkF3e/090XufuiyZMnD+FjDE68inNleWVKK8779gXvUz2jmjf3vsmB5gPdL6qqUquGSB5LJnF+xsz+PzM7w8xOjT4SvTkcuX0K8CIw1d1rw1N7gKl93JPV6oXkv6DHOUKBQXGhEudcEfFwWkDTFIFJWgMcb2bzzKyEIAnuOfvRw8BnwueXAU+7uwOYWQFwOTH9zWZWZGaTwufFwEVATi6P19QUbMvKgoGBENOqkaKKM0BtbZA4A7xc+3L3i6qqgrK0/s0SyUsJz+MMnB5uTwu3RrDg2rkD3Whmo4FfAde6+8HY9jh3dzPzePe5+53AnQCLFi2Ke43IUDQ3N1NsQWJWWmg0NzcPcIdkTE520eY2d283sy8BjxOsvfhjd3/dzL4FrHX3hwna7n5qZpuBBoLkOuosYKe7x45fKQUeD5PmQuBJ4EcZ+DhJa2qCkpJg2rherRopqjhD0Oe86NRFQDBA8Nx5Mf8Mxg4QPO+8Ib+niOSWZBLnVXGODZjMhl+2vwJ+7u6/Dg+/a2bT3b3WzKYDdUnEIZIyrS3NlITTaxcXalaNXGFY57dLjo5Dy1nu/gjwSI9jX4953gx8oo97VxGMR4k9doSugklOa26G8vLgeefgwOKx0NiY0orz7t2wbNlE3jPhPb37nE8+OdiuX6/EWSQPJdOqcTjm0Q4sB+b2d0M48vou4A13/7eYU7G/KvwM8Jsk4hBJmdaWls6p6IoLXAPRckRnsuxKnCVxTU0xiXNYcR7fYuCeksQ5tuIMwbR0vRLniRNh1iz1OYvkqWSW3P7X2H0zu4Xg14H9WUow+vo1M4vOz/P3wE3A/Wb2OeAdgp66vBe2EUoOaW1tZdeRQn76VjnF5upxzhEFBQWdFeeCgmR+vpeRrFvi3NTIuNJxFDbuDw6koFVjwoSgf3r37mB/8YzF3LvxXvYc3sO00dO6Lqyq0pR0InkqmVaNnioIRmz3yd2fo+9OxQ8N4b2HJSVluaeltZWWduOdQ4WqOOcQs7BVQxVnSUJs4tzQ3NDV3wwpqTibdc3lDHDG7DMAeGHnC/zlCX/ZdeHJJ8Ojjwa9I9FlDEUkLySzAMprZrYhfLwOvAXcmr7Q8s/hw4c7n2tp59zQ2tra+ZNdcYEqzrlIv6mRRPWsOHdbbjsFFWfomssZ4JRpp1BSWMLzO5/vflFVFXR0wKZNvV9ARIa1ZCrOF8U8bwfeDVedkgTFJs5Hjhxh3LhxWYxGIEicS8PMudgitLRoVo1cEIlEgt9VmRJnSVxTU1eBt7G5MeUVZwgqzi+HM9CVFpWyaMYiXqh5oftF0Zk11q+HUxOetVVEhoGEK87u/k7MY5eS5uQdOnQo7nPJDnenubmFgnA2xLJCp+no0SxHJQBt7Vo5UJKXyYpz9Oe5M2adwdrda2ntiJmR59hjYfRo9TmL5CGNusmg2IqzEufsa25uJuLe+ZegrMg5qsQ5J7Q2t0IReKFrbm1JWLfp6Jobu1YNNIPx41PyHjNmwNGjcCBcMPADsz9AS0cLr9TGLLNdUAALF2pmDZE8pMQ5g2KT5dgkWrIjmiQXhK0a5YWo4pwDOjo6gl7zIogURThy5Ei2Q5JhIlpxdncamhqCinNDQ5A0Fxam5D2iczl3DhCcFQwQjNvnvH49aOVLkbyixDmDYhMAJQPZF/1BpjBMnEcVRzh4+Ih6arOs8wfMEqAYDkRLeyIDiCbOTe1NtHa0Bj3OKVpuOyp2ERSA6WOmM3f8XJ6v6ZE4n3YaHDwImzen7L1FJPuUOGdQU1NT3OeSHfv3B/O7FoYLoIwpdiKRiH4bkGXRPxdKwUud/Qf2ZzcgGTaiiXPnqoHRinMKE+eei6BAUHV+fufz3X/orq4Otmt6LJAiIsOaEucMip3qTNOeZV+0khmtOI8p8W7HJTsawlkQvMyhLEikI/p1tyQgOqtGdNXAzopzigYGQlfiHK04Q9DnvPvQbnYe3Nl1cP58GDUKXnopZe8tItmnxDmDYudu1kwB2RdN0IrCvwXjSiLdjkt27N27N3hSBpRDpCPSVYUW6UfPinNleWXKK87l5cEKgj0rzhAshNKpsDBo11DiLJJXlDhnUOwKaFpGOPv27t2LGRSF09GNL410Hpfs6fzvXx5WndGfiQysvT1Yc6S8HBqagh9+O6ejS2HFGbovggKwcOpCKooreg8QrK6GV16B1lZEJD8oe8ugkpKSuM8lO/bt28f40q4fZiaUKknLBfX19VixQTFQ3nVMpD/RYSPl5TGtGkWjgwF6Kaw4Q/dltwGKC4upnlHde4Dg4sXQ0gIbN6b0/UUke5Q4Z1BZdEkroLS0NIuRCEBdXR2VpV3tM6OKnOLC4LhkT319PVYR/kBTEWz0w4wMpFviHG3VaAr/P0pzxRngzDln8krtKxxqiZmjf/HiYKt2DZG8ocQ5g8qjM/P3eC7ZUfduLZWlHZ37ZjCxTNXNbKurq6Mj+udSBph+mJGB9aw4G8bYI+FYkjRUnPfsCVpDos465iw6vKN7u8Yxx8DUqfDccyl9fxHJHiXOGRRbZVbFObvcnbq6vUws7T5bQ2VJG+/uqc1SVAJQV18HTWDrDQwKKgr0w4wMKJo4l5UFFefxZeMpaAwHlaah4tzRAbE/z31g9gcoKiji2Xee7TpoBh/8IDz7bNca3SIyrClxziANDswdBw8epLmlhUnl3RPnSWUR3t2zJ0tRSXt7O40NjdABtj/4+xIpi6jiLAOKrTg3NDd0TUUHaak4Q/c+51Elozht+mn84Z0/dL/47LOhpga2bk1pDCKSHcreMkjT0eWOPWFyPKmse+I8uTzCvsb9mmc7S/bt2xcsIhHzzRQpj1Cr3wLIAJqbg220x7lz8RNIeeLcc/XAqA8e80Fe2vUSR9uOdh0855xgu2pVSmMQkexQ4pxBR492fZlq5cDs6itxju6rwpkdtbVhglwYc3AU1NfVaxEU6VfPHuduFecUt2rEqzhD0OfcFmljdc3qroPve1/Q5/zMMymNQUSyQ4lzBsUmzkeOHMliJLI7LBVNqejodjy6v7tnKUkyYlc0E+mROLe3t6vPWfrVc1aNzopzYSGMHZvS95o6FQoKelecl81ZRoEVdG/XMAvaNVatUp+zSB5Q4pxBqjjnjl27djGmxKgo6n58StjzvKtnKUkyYseOHcG3Usw3k4/xrnMifehZca4sr+xa/CRmfEkqFBbCtGm9K87jysZRNa2q+wBBCNo1du2CN95IaRwiknlKnDOora2t8wtcPc7ZtWtXDVPK23sdH1/ilBYaNTU1WYhKtm3bho0NZtPoNLbrnEhfoolzaanT0NTQVXFOcX9z1MyZvRNngLPmnMXqmtW0tMeMk/jIR4Ltf/93WmIRkcxR4pxB7g5W0PVcsmbHO9uZHidxNoNpozrYuXNnFqKSt/78Fh1ju7fPUAYF5QVs3rw5O0HJsBBNnL3oKO2R9q4e5xT3N0fFWwQF4INzP0hzezNrdq/pOjhrFpx2GvzmN2mJRUQyR4lzBhUUFECko+u5ZMXRo0ep39vA9FHxL81gKAAAIABJREFUB5tNL29nxzvbMxuUsHfv3mAqujh5Tsf4Dja9sSnzQcmwEZ1Vo8WCuZsnlIWJc5oqzj2X3Y5aNmcZAM9u79GucfHFsHo1vPtuWuIRkcxQ9pZBluI+OxmcaDV5eo+BgVHTKzrY826dpqTLsA0bNgDglb1/G+MTnR3v7ODAgQOZDkuGiWjFuYlgCrrK8sqgVSONFefGxq73jZpUMYkFUxbwhx095nP+2MeCwYG//W1a4hGRzFDinEGtra1YYTGgHudsivbKzhodP3GeOboDd9dgtAx75ZVXsGKDCb3P+WTvvEYknmgCe9SDxLmzVSONFWeI365x1pyz+NOOP9HWEfM9v3BhsAT3r3+dlnhEJDOUOGfQwYMHoaQcKyoOnktWbNu2jaICmFoev1Vj9qiOzuskM9yd5194nsjkSPxvpUqwEuPFF1/MeGwyPDQ1QWkp7G8JK85UwNGjaR0cCH33OR9pO8Ire2J+0DODyy+HJ57oWphFRIYdJc4ZtHt3Le3Fo/CS0V0LPUjGbdu2jRmjIhT28X//1Irg3FYtkZsxb7/9NvV19fj0PgbNFkDHlA7++Kc/0tER/zcFMrI1NUFZWTAVHcDE5rA1Lk2tGn0tggLBQigQp8/5iiugvR0efDAtMYlI+ilxzhB3Z+u2rUTKxtNeNp7NW7ZkO6QRa/Pbf2b2qL5bZYoKYOaoCFv0Z5QxTz/9NBj4zL5nm/FZzsH9B1m/fn0GI5Phoqmpa/ETgPFHw98oZaFVY9roabx34nt7z+d86qlw3HFw771piUlE0k+Jc4bU1tbS3NREZNREIhWV7Kmt1eqBWbB//3727mtgzpj+q5ZzRrWx+c9vZSiqka29vZ1HH38Un+ZQ2s+FM4J2jUceeSRjscnw0dzctfhJoRUy+lA4uDdNFefx44MKd1+/PPzgMR/kuR3P0RGJ+a4xC6rOTz8NdXVpiUtE0kuJc4ZEq5eRikoiFUEFRK0AmRf9c5jTx8DAqDljOtjXuJ/GxsZMhDWiPf/88zTuayQyL37PeadC6JjdwTOrnmH//v2ZCU6GjdiK8/iy8Vj0726aKs5mMH16/IozwNlzz+ZAy4Hufc4AV14JkQg88EBa4hKR9FLinCHRGRoiZeOJlI8D0CIbWfDWW0EVee4AFefo+ej1kj73338/NspgxsDX+nFOe1s7v9FCEn0ys+Vm9paZbTazlXHOl5rZfeH5F81sbnh8rpk1mdn68HFHzD3/P3vnHR5Vlf7xz5mWXiAJgRRCKKFDKNIRFRBQlAVFsHd0BXVX0dVdfxbUVVfXgl0QsbAIgigK0kGU3kIInYSWQCCFtEmZzMz5/XEnEEKAJMxkJsn5PM88M/fcc89979w7M9/7znvet4cQYpdjm6nCA3Nrlgnn7OLscxk1wGUeZ9DCNS7mcb4u9joAVqasPH9Fp07QoQPMmeMyuxQKhetQwrmWOH36NMLoDQYT0uQPwCmVCL/W2bdvH2G+EGC6dOXGFoFWBEo4u5pdu3aRmJiIrbXt/DLbFyMQZDPJnLlzKKqYQFeBEEIPfAyMADoAtwshOlTo9iBwRkrZGngPeKvcumQpZbzj8Wi59k+Bh4E2jsdwVx1DTTGbwc9P8zifLbcNLvM4w6U9zk39m9IxrCMrD6+8cOX48fDHH5Ca6jLbFAqFa1DCuZbIyspCGn20BZ0eYfJWYQBuYN/e3cT6Wy7bz9cAzfwle/aoanWu5KuvvkJ4C2TLqpegt7ezU5BfwE8//eRCy+osvYBDUsoUKaUF+B4YVaHPKOBrx+t5wOBLeZCFEM2AQCnlRimlBL4B/uJ806+MggKHcC4+c87jbDKBr6/L9nkpjzNoXuc/j/1JibVCMaVx47RiKD/84DLbFAqFa1DCuZbIzs7Gpvc+uyyNvmSV/ZWoqBUyMzNJP5VBmyBrlfq3CrCwZ3cSmlZQOJvExES2bt2KLc4GhmpsGAqEw3ezvqOwsNBV5tVVIoHyMWCpjrZK+0gprUAuUOaWjRVC7BBC/C6EGFiuf3nXaGVjuh2zGfz9NY9zY5/G54qfuDCqpFkzyMvT9l0Zg2MHU2QtYmPqxvNXxMVBt24qXEOhqIMo4VxLnDp9Gmk65/mw6X04nZHhRosaHklJSQDEBVdNOMcFW8nNyydV/Z3qdKSUfDHtC4SPQLaq/o2JrZON/Lx85s+f7wLrGiwngeZSym7AU8D/hBCB1RlACDFBCLFVCLE1o5a/386GahSXC9VwYZgGaMIZLpFZo8UgdEJXebjGuHGwaROoQksKRZ1CCedawGKxkJmRgd373G+Q3TuA1OOpyptZiyQmJmLUQ8xlJgaWEefwTCcmJrrSrAbJ9u3bSdyZiK1tNb3NZTTWYp1n/W8WBQUFTrevDpMGRJdbjnK0VdpHCGEAgoAsKWWJlDILQEq5DUgG4hz9oy4zJo7tvpBS9pRS9gwLC3PC4VQdLVRDnotxzspy6cRAOJfL+WLCOdg7mB7NerDq8KoLV44bpz3Pnesa4xQKhUtQwrkWOHz4MFJK7D7BZ9vsPo0wmwuoba9MQ2b7tq3EBVkxVPGqj/CzE+SliTyFc/nqq68QvtWLba6IvaOdQnOh8jqfzxagjRAiVghhAsYDCyv0WQjc63h9K7BKSimFEGGOyYUIIVqiTQJMkVKeBPKEEH0csdD3AB6X1sRsBqOPBZu0aTHOtehxvtgEQdDCNTalbaLAUuEGr0UL6N1bFUNRKOoYSjjXAvv27QPA7nfOA1P2eu/evW6xqaFx5swZUg4foWOji1cMrIgQ0D7YwvZtW9Q/A04kKSlJy6QRZwP9FQzU6FyGjZKSksv3bwA4YpYnAUuBvcBcKeVuIcQUIcTNjm5fAiFCiENoIRllKeuuBhKFEAlokwYflVI6UlPwGDAdOITmif6tVg6oipSWgsUCOpOWacVTPM6gTRC02q2sPbr2wpXjx0NCAqjsPQpFnUEJ51pg586dCC9fpFfA2Ta7XwhCZ1BhALVEmde4Q+OqC2eAjo1LycrO4ciRIy6wqmHyww8/IEwCGXvlNyP2tlqGjWXLljnBsvqBlHKxlDJOStlKSvm6o+1FKeVCx+tiKeVYKWVrKWUvKWWKo32+lLKjIxVddynlL+XG3Cql7OQYc5L0sDvJssl5wqS9aFwmnF3scW7UCLy8Lu1x7t+8Pya9qfJwjbFjtTt0NUlQoagzKOHsYqSUbN+xg1K/8PNnd+v0WP3D2L5jx8U3VjiNdevWEWCCloFVi28uo0uIJrTXr1/vCrMaHDk5Ofy+9ndsLWoY21yRUBDBgp8XelzkgKIWKRPO0pgPQKj00VzQoaEu3W9Z9cBLeZx9jb70i+7H8pTlF66MjISBA7VwDc+6F1EoFBdBCWcXk56eTnZWFraAphesswU0JSU5GfPFchkpnILVamXjhvXEh5Sgq2ZmqhBvSWygnXV//uEa4xoYK1aswG6zI1s4SSQIsMXYOLD/gPpXoAFTNj/UasgFILQsS6GLhTNcughKGcNbDSfxVCJpeZXMqbz1Vti7F1JSXGOgQqFwKi4VzkKIGUKI00KIpHJtjYUQy4UQBx3PjVxpg7vZtWsXAPZKhXM4Ukp2795d22Y1KBISEigwF9I9rHphGmV0Dy1h9569ZGZmOtmyhsfKVSsRwULL4+AkZHNNhK9aVclf4YoGQZnvwaLXikqFmB03ZrUknC/lcQa4oc0NACw5tOTClUOHas8rK0lZp1AoPA5Xe5xncmFp1ueAlVLKNsBKzk1MqZfs3r0bYTBi973w/sDu1+RsH4XrWLZsGT5GQdeQmgnn3uEWpJSsVD9sV0RmZiZ7du/BFlm9cJnL4g2Eweo1q507rqLOcE44a3MZg82OXO21IJwjIi7vce7UpBORAZEsPrT4wpVt22qDqO8XhaJO4FLhLKVcC2RXaC5f7vVrPLB0qzNJStqN1TcMRCVvtcEEfo2VcHYhxcXF/L5mNb3CijFVksHh2/0+HM3XczRfz2tb/fl2v88FfSL87LQMtLN0iUclEqhzrFu3DiklMtL5sZz2CDtHjxzl+PHjl++sqHeUhWoUkoGv0RfTmTytoZY8zrm5cKkilkIIbmhzA8uTl1NqK624EgYPhlWrwG53rbEKheKKcUeMc7gjLyhAOhDuBhtqhZKSEpJTkrE5Us+Zjm7AdHTDeX1KfUPZvWevSnfmItauXUtRcQn9m1oqXX80X0+RTUeRTce+HCNH8yvPj9a/aTGHklM4dOiQK82t16xYuQIRKKBateiqhozSPj+rVyuvc0OkzONcKDII8QmBsrCqWvI4Q9XCNfIt+aw7vu7ClYMHazY7QvsUCoXn4tbJgY6URhdVjO4s3+oMkpOTsdts2P21L2+dOQudOeu8Pna/MMwF+aSnp7vDxHrPgh/n08xP0q5R1cpsX4z+zSwY9bBgwQInWdawSE9P1yoFRtmgmhM0q4QvEApLli5RN6ENkDKPcwHphPg6hLNeD0FODKa/CFUpggJaIRSjzshvByv552rwYO15xQrnGqdQKJyOO4TzKSFEMwDH8+mLdXRn+VZncPDgQQDsvhf3etj9Qs7rq3Ae+/fvZ/eevQyOLKp2No2K+Bsl/cJLWL5sKfn5+c4xsAHxyy+/aGEazsqmUQn2WDupx1NJSEhw2T4UnkmZxznPfvKcxzkkBHSu/4mLdhQ4T029dL8ArwAGxgysPM45KgratIG1lRRJUSgUHoU7hHP5cq/34oGlW53F/v37EUZvpJf/RfvYfRuDEOxXlaOczvz58/HSCwY2qzxMo7pcH11CcYmFRYsWOWW8hoLZbObHBT8iIyT4uW4/MloivAWz/jfLdTtReCRlwjlHpp7zONdCmAZATIz2XJVsiDe0voGk00kcz60kFn/AAFi3TuVzVig8HFeno5sNbADaCiFShRAPAm8CQ4UQB4EhjuV6SdLu3Vh9Q88vfFIRnQHpG6ImCDqZjIwMVixfzqCIIvyMzvkhigmw0b6RlXk/zMFqvbLQj4bEvHnzMBeYsbev2sQnkSAgB8gB3RqdtlwV9GBrY2Pzps0kJSVdvr+i3lBQoH3NZpemnfM415Jw9vPTdlUV4TyizQgAFh+sxOs8YIBW7VA5URQKj8bVWTVul1I2k1IapZRRUsovpZRZUsrBUso2UsohUsqKWTfqBbm5uRw9cgRbwOXnPlr9m5C0ezcWi3M8owpNrNntNoY3L3HquDfEFHE6I0vlDK4iGRkZfDfrOy2TRuOqbSNyBKLU8cgQiJyqx9nI1hLhI5j64VTsKkNBg8FsBl9fSU7JmVoXzgAtWlRNOLcPbU9scCwLDyy8cGX//trzn3860zSFQuFkVOVAF7FlyxaklNgCIy7b1xYUiaWkhMTExFqwrP5TUFDAzz//xFVNLDTxca546hpiJdJfMvt/s9QktCrw0UcfYSm1YO9SSyLWALZONvbt3cfixZV49RT1koIC8POT2KWdxj6N3SKcjx69fD8hBGPaj2F58nJyi3PPXxkXp9mshLNC4dEo4ewiVq5chfDyw+5/+UmNtsBIhN6ovJhO4qeffqKwsIiRMcVOH1sn4MbmhSSnHGbjxo1OH78+sWHDBlavXo2tnQ0uHubvdGSMhDD46OOPyM6ul39oKSpgNoOXrxY+FeJG4VyVe+kx7cdQai/l1wO/nr9CiHNxzgqFwmNRwtkFpKWlsX79Okoat6q88ElF9AYsjWNZumwZOTk5rjewHlNcXMzcOd/TJaSU2EAnV6hz0K+phVAf+Obrr5XX+SIUFhby9jtvI4IEsl0tv0cCbD1sFBUV8cEHH9TuvhVuwWwGLx+tsEi41RtstloXzsXFcOrU5fv2iepDREAE8/fOv3Bl//5w6BCo9KQKhceihLOTkVLy4Ycfgk6PtWmnKm9X2qwLpaWlfP755y60rv7zyy+/kJObx80tnO9tLsOg07zOu/fsYceOHS7bT11m2rRpZGZkYu1hdc+3TADYOthYvXo169evd4MBitqkoAAM3tockSZFjguuFoVzdTJr6ISO0e1G89uh38gvqZDacsAA7Vl5nRUKj0UJZyfz008/sX79eoojuyNNvlXeTvoEY2namUWLFqmQjRpSXFzMrO++pX0j6xUXPLkcgyJKaOQNM778UnmdK7B//35+/PFH7K3sEOI+O2RbiQgSvPPuOxQVFbnPEIXLMZvB4KWd49Cy0te17HGGqsU5A9ze6XaKrcUs2FehoFL37uDtrYSzQuHBKOHsRNasWcP777+PrVHzanmbyyiN6o49sCmvvf46W7dudYGF9Zuff/6Z7DM53NLS9SLJpIebYwpJ3LWLbdu2uXx/dQUpJe+9/x54gezk5hsKHVi7Wck8ncns2bPda4vCpRQUgDBpijm4wHHT7AaP8+HDVevfL7ofLYJbMGtXhZzjJhP06gV//OFcAxUKhdNQwtlJLFy4kJdeegmbfxOKW11btdjmiugMFLUegtUUwDPPPsuaNWucbmd9pbCwkFnffUunxq73NpdxTWQJIT4w7YvPldfZwZo1a9izew+2jjYwudsaIAzsUXZm/W8WGRkZ7rZG4SLMZpBGMzqhwy/PceNci8I5IADCw6GqBWCFENzR6Q5WpKwgvaBCPPOAAbBjx7mqLgqFwqNQwvkKsVqtTJ06lXfeeQdrUBRFbUeA3ljzAY3emNvdSKlPCC+++CIzZ85U+WirwOzZs8nJzWNsq8LLd3YSRh2MiTWzd99+dZMD2Gw2pn85XZsQGOs5NxKys6TUWsqsWaqiYH3FbAa7MY9G3o3QZZ/RGmtROAN06ADVqWN1V5e7sEs73yV+d/6KAQO0yY2bNzvXQIVC4RSUcL4C8vPzeebZZ5k3bx6lTTtRHDcU9IYrH9jgRVG7EZSGtmbGjBm8/PLLFBe7brJbXSczM5M538+mT7iFVkGuyaRxMQY2sxAdYOfzzz6ltLS0Vvftafzxxx8cP3YcW3sbVL1mievxB3uMnYULF6r0dPWUggKwGnK1ctunT4OXF/jXYg5ENOG8Z0/VK2a3D2vPgOYD+GLbF9hlOedI375aajqVz1mh8EiUcK4hJ06cYMKER9i2fTslsQOxxPSpWXjGxdAZsLQchCW6F2vW/M5jEyeSlZXlvPHrEV9++SWlpRZua1X7E8B0Asa3MnPiZDoLFiy4/Ab1mNnfz0YECGSU53iby5BtJVartcGfo/qI1ap5nC3GDJr4NdFSuYWHa+KzFunYEfLzIS2t6ts80uMRDmYfZPXh1ecag4OhUyclnBUKD0UJ5xpw8OBBHnn0UU6czqCo7QisTdq6ZkdCUBrRheK4oSSnHOHRR//K8ePHXbOvOsqBAwdYvHgR10cV08TXPSEtXUKsdA6xMvOrGeTm5l5+g3rIvn372LtnL7ZWHuZtLiMAZDPJgp8XNPh/BuobZR+5Iv1JTTifOgVNm9a6HR06aM979lR9m1s73Epjn8Z8tu2z81cMGADr12t3BQqFwqNQwrmaJCQkMGnS4+QWWTG3H4k9sJnL92lr1JzCdiM4nZ3LXx+byMGqzkCp50gp+ejDqfgb4S+x7gtlEQLuaGOmsLCQmTNnus0Od7Jw4UKEQSBbeJ63uQx7azt5OXmsXbvW3aYonEiZcDbrThDuF37O41zL1EQ4exu8uT/+fn7a9xMn80+eWzFggBZ/smuXc41UKBRXjBLO1eD333/n6acnUyRMFLYfifRpVGv7tvs3wdz+RvJLrEya9LhKgQasXbuWhJ2J3NLSjJ/RvYIt2t/OtZHFLFiwgCNVqYJQjygsLGT5iuXYomxwBfNiXU44CD/Br7/+evm+ijpDmXAuEGlu9TiHhWnzEaszQRBgQo8JWO1WZuyYca5RFUJRKDwWJZyrgJSSOXPm8H8vvojFOxhzu5FIr+pNPDEd3YCuMAtdYRbee37FdHRD9e3w0fZdJLyYPHkyv/32W7XHqC9YLBY+/eRjIv0l10ZY3G0OALe0LMZLL/n0k0/cbUqtsmrVKkqKS5AtPdfbDGiluFvY2LZtGydOnHC3NQonkZPjeOF9hnDvUG1yoBs8zqDFOScmVm+buJA4rou9jmnbp2GzOyY3N28O0dEqzlmh8ECUcL4MJSUlvPHGG3z88cdYG8VQ2PYGMHpXexydOQthK0XYStHnp6Mz12yin/Tyx9x+JBa/cN544w0++ugjrA0wDm7BggWcOJnOHa0L0F/BVVxkFfj4+HDrrbfi4+NDkbXmAbqBJsmoGDMbNm5ky5YtNTeqjvHrol8RQQIaO2nAUs47JzgxJLkslGTJkiXOG1ThVs5OK/DOJarUB+x2twnn3r21FMzVLVT5WM/HOJp79PxKgv37a4VQVI54hcKjUML5Ehw+fJiHHnqYJUuWYInsRknrwc5JN3elGLwobjuc0vAOzJ07l8cem9igPGi5ubl8PfMrOodY6Rp6ZTcNhVbBjTfeyBNPPMGNN95I4RUIZ4BhzUsI84VPPv4Im612U+O5g+PHj2sFT5o7cVJgKeedE2cKZ3yBcFj822KVH72ecFY4e+USYXb8pLkhVAM0rVtaCtUt/PqXdn+hTeM2vPHnG+eKKQ0YACdOVL2Ot0KhqBWUcK6EkpISZsyYwQMPPsixk6c1kRrVo9bTG10SnQ5Li34Ut76O/YeSuffe+5g7d26D8D5/++23FBYWckebK6+s5WuQLFq0iKlTp7Jo0SJ8DVfm3THqYFzLApJTDrNs2bIrts/TWbZsGQiQMU70ihk575w4O27aHmPn9KnTJFb3P3WFR3IuVCOHsALHzZCbPM79+mnP1Q1N1uv0PNv/Wbaf3M7ylOVaY1mcswrXUCg8CiWcyyGlZM2aNdx7333MnDmTkqDmFHT6C7bgKHebdlFsIS0xdxpNoU8YH330EQ8+9BCbN2+utyWgT5w4wY/z5zOwWQnR/lfuMfQxSIqKipg3bx5FRUX4XKFwBugdXkrLIBvTvvi8XheukVKydNlSaAL4OHFgI+edE2cLZxkpEQbBihUrnDuwwi2c8zjn0TjXMd/BTR7n0FBo27Zmc/ru7nI3EQERvPHnG1pDp04QGKgmCCoUHoYSzmgCYPPmzUx45BFefPFFTpwppLjtcEpaXwdGX3ebd1mkVwDFcddT3Howh09kMnnyZJ588m/10qM2ffp0dNi5pWXtFzupKkLAHa0LyczKZt68ee42x2UkJyeTfjIde1QdC3kwgK2ZjTVr16hwjXpATg6YfEowGAW+2QVao5s8zqB5ndev10Ktq4OXwYun+jzFmiNr2Ji6EfR6rYqg8jgrFB5FgxbONpuNNWvW8NBDDzN58mT2H06lpOXVmDuN9mgvc6UIgS0kFnPnWyiJ6cvOPfuYNGkSEydOYtOmTfXCA33w4EFWrFjBsOhCGnt79vG0a2SlW2gp/5v1Hfn5+e42xyVs2KBlhpERnn0uKiUC8nLy2Ldvn7stUVwhublg8DXTxK8JulOnwMcHAgLcZs/QoZCdDRuqnziJCT0m0Mi70Tmv88CBkJQEGRnONVKhUNSYBimczWYzP/zwA+Nvv4MXX3yRg6mnKIkdiLnLWKxhcc4tnV3b6PRYm3akoMs4SmL6kHQwhWeeeYZ77rmXhQsX1unQgWnTvsDPJBgZU+JuU6rE2FZFmM2FzJo1y92muIRdu3Zp2TSqn2TG7cgwTewnJSW52RLFlZKbCzrvgnM5nN1Qbrs8N94IJhPMn1/9bQO8Avhbn7+xcP9CNqdthiFDtBWrVjnXSIVCUWPqsEKsPseOHeODDz5g9JgxfPjhh5w02ylufR3mzrdoZbN1eneb6Dz0BqxNO1HQeSwlLQdxJLOAd955h9FjbuHTTz8lPT3d3RZWiz179rBx4yZGNnd/sZOq0jzARr+mJcz74Qdyzs5gqj8cOHQAW3AdzRziAzofHSkpKe62xOkIIYYLIfYLIQ4JIZ6rZL2XEGKOY/0mIUQLR/tQIcQ2IcQux/N15bZZ4xgzwfFoUntHdGlycgDvHE04p6e7Lb65jMBAzev84481yyT39z5/J8w3jOdWPIfs3h2CgkDF4ysUHkO9F85SSrZs2cJTTz3NXXfdxfwFCyjwjaSo4yiKOtyELaRl3fYwXw6dHmtYGwo7jqKo/UhyjSHM/v57xo0bxz//+U921ZGSrvPnz8fHIBgSVTe8zWXc1KIYS2kpixcvdrcpTic/Lx+83G1FzZHektyzM8vqB0IIPfAxMALoANwuhOhQoduDwBkpZWvgPeAtR3smcJOUsjNwL/Bthe3ulFLGOx6nXXYQ1SQ3F2ym7PM9zm7mllu0LHKbN1d/2wCvAF64+gVWH1nNsqOr4NprYflylc9ZofAQ6q1ilFKyceNGHn30rzz99NNs27UHS1QPzPHjKWl9DXb/MHebWLsIgT2wKSVxQyjsOo6SZl1Yt2krEydO5G9/+xsJCQnutvCinDlzhjWrVzGwWRE+HpBGuzpE+dtp18jKgh/n17u8zjqdDury3Dq74xjqF72AQ1LKFCmlBfgeGFWhzyjga8frecBgIYSQUu6QUpYlhN8N+AghPP7WKDcXLMYMmvg6PM4eIJzHjAE/P/jss5pt/0iPR4gNjuUfK/6BffBgTYXXw39HFIq6SL371QDIz8/n5Zdf5tlnn2Xv4eOUtOhPQZfbKI3s5r4sGTbL+dXQbO4rEy29/CmNvoqCruMoad6bHUn7eOKJJ3j77bc9MgZ69erVlFptDK5j3uYyhkQWc+p0Rp3x7leVyIhIRJ4H5TavDjagACIiItxtibOJBI6XW051tFXaR0ppBXKBkAp9bgG2SynLf+i+coRp/J8QnpPU/kyOHZspk3CvEMjM9AjhHBQE99wDs2dDVg2KxHoZvHj12lfZeWoni8rmdCxf7lwjFQpFjah3wjk7O5sHHniQ1WvWYInqibnzWKzh7d0evyyslvOqoQmr+4TzWfRGrM06U9D1NizNuvDLL78w4ZFHMJtrgF6DAAAgAElEQVSvvLCIM8nMzEQvIMK3bro3owM0T3NWTX5BPZh+/fohMgQUutuS6iNSBdIm6dOnj7tN8TiEEB3RwjceKdd8pyOEY6DjcfdFtp0ghNgqhNiaUQuZIKR05HH2yqW1xU9riPKMjEgTJ0JJCXz+ec22v73z7cQ3jWfiwfexxzSH335zroEKhaJG1CvhLKXk9X//m9MZmRS1H0lpZDx4yF+x0mA6rxqaNJjcbdI5dAZKm/eiuO0wjhw5wnvvvedui84jPz8fP5PwqMKN1cHPUVSlvqWlGzlyJDqdDpEooC6FX5aCfo+eqOgounfv7m5rnE0aEF1uOcrRVmkfIYQBCAKyHMtRwALgHillctkGUso0x3M+8D+0kJALkFJ+IaXsKaXsGRbm+nC44mIotejAO5cW+Q7nSHT0pTeqJTp2hBtugHffhZp89HVCx4cjPuR4fiobu4VpHufCOniXqlDUMzxDVTqJ1NRUtmzeTElkN+wB7v+77jz0pvOroek9SDg7sAVHUxreiWXLlpGXl+duc85SXFyMlJJSFzicYwJs+Ojt+OjttAsuJSbA+XHIpXZN8RcVeW7RlpoQERHBA/c/gO64DpHivLsaGSyRRscjTCKDnajK7SC2CjDD8889jwdFHDiLLUAbIUSsEMIEjAcWVuizEG3yH8CtwCoppRRCBAOLgOeklGfL1QkhDEKIUMdrIzAS8Ig8fmfndnrn0Cy7VHvdvLnb7KnISy9poRoffliz7Qc0H8C9Xe/l1cAEKCqClSuda6BCoag29Uo4JydrDhJbYDM3W1J3KXvvPClN19ChQ8m3wMpU589TurttETEBNmICbLzQs4C72zpf3C5I8cZo0DNo0CCnj+1u7rzzTnr17oVuuw5xyDkiVMZLCAaCwX6NXVt2BnYQmwS6VB2PPvoonTt3ds64HoQjZnkSsBTYC8yVUu4WQkwRQtzs6PYlECKEOAQ8BZSlrJsEtAZerJB2zgtYKoRIBBLQPNbTau+oLs65ctu5hGQ5vLEe4nEG6NULRo6EN9/U5i3WhLeGvMXWNn6YvfXIn392roEKhaLa1Cvh3KJFCwD0eXUrR7Enocs/CZx7Lz2BXr16cVXPniw44oe5tG55CI/m6/njpBdjbrm1Pk5EQ6/X8+/X/02/fv3Q7dAhdght4p2nUQT6tXp0qTr++te/cvvtt7vbIpchpVwspYyTUraSUr7uaHtRSrnQ8bpYSjlWStlaStlLSpniaH9NSulXLuVcvJTytJTSLKXsIaXsIqXsKKV8UkrpEWf5zBnt2T/QiiktXZuVFxjoXqMq8N//aiElz12QUbtqhPuH8+LQ1/i1lY3in+dXv5a3QqFwKvVKOMfExNCzZ09MaVvR5xy//AaK89BnHsJ0MokhQ4YSHBzsbnPO47GJEykshfcT/cmz1A3xfMKs46OkAPz9/bjnnnvcbY7LMJlMvPbaa4wdOxbdIR2GNQbwpHDuE2BYYcAr34sXXnihXovmhkZmpvYc3kQPx455lLe5jLg4eOop+PrrmpXhBvjrVX8lsU8sPpk55Kz41bkGKhSKalGvhLMQgldeeYUWMTF471+K6ehGsFvdbZbnY7NgSlmLd/IaOnbsyD/+8ay7LbqAVq1a8c9//pNDBd68uCWYw3meXeVx22kjL20JpsgQxOv/foOAgAB3m+RSDAYDjz/+OK+//jo+JT4YlhsQe4V78zyXgNgo0K/TE9MshunTpnP99de70SCFsykTzlFNveH4cY+Kby7PCy9ARAQ88UTNHMYGnYHx//wfZiNse+dp5xuoUCiqTL0SzgABAQF8/tlnjBo1CmN6Ev6JP2BIT3K7gLb7hSD1RqTeiC2gKXa/imlT3YDVgjEtAf+dczFlHuSuu+5i6tQP8PLyzJoHw4YN45NPPkXnH8qUbUH8edLzJljaJcxP9ua9RH+at2zNtOlfEh8f726zao2BAwcy67tZXD3ganRJOgwrDeD6rGTnI0GkCAxLDRhPGLn//vv5cvqXxMTE1LIhCldTJpxjmvlpHmcPFc7+/vCf/8DWrTBzZs3G6NyyD8lXd6bbH4f4Zdd8p9qnUCiqTr0TzgA+Pj48/fTTTJ06lS7t2+B1dKMmDo9uQmfOckvpUktMX+y+Idh9QyjuMBJLTN9atwEAKdHln8J0ZD3+iXMwpW6lV/d4Pv/8cyZMmIDRaHSPXVWkbdu2TP9yBh07d+Gz3X68t9Of04WecRkfytXzytZAFhz2Yfjw4Xz08SeEe0AxhtomJCSEV199lddff50QYwj6NXrEZgG1UVsnG/Sr9Oi26egU14kZM2Zw//33e/x1ragZp07bQF9Cm6AgLX2FB4ZqlHHHHdCvnxbrnJNTszHaP/kqjYth/nsPk12U7VwDFQpFlahjBYyrR3x8PB9OnUpCQgJz5sxh48aN2NJ3gW8jLI1isQVHYfcLBeEZwstl2O3ozKfR5xzHdOYwFOVhNJoYOHAA48aNo3379u62sFoEBwfz7rvv8f333/PN11/zj01GRkQXcXOLYrzdcEXnlAjmHPLhj5NeNG4UzAsvTGLo0KH1MdVZtRg4cCA9e/bku+++43+z/4f9pB1bBxuylXT+LbsFxC6BLkVHcKNgJqlz0CBIPVUIvnm0LfLRGjzU4wwghJaWrmdPeOMNeOut6o9hHH4DpSGNGLUxh0d+fYS5t85V17hCUcvUa+FcRnx8PPHx8eTm5rJmzRqWLF3K7qTtkLYdYfSmNKAZtqBIbIGRSC9/6myljTKkRBTnoc9LQ5+bhjH/JNJqQQhBfHw3hg8fxtVXX42fn5+7La0xBoOBu+66i+HDh/Ppp5+ycPly/kz3YXzrAvqGl9bKKbTaYdlxLxYc9qNU6rjjjnHcc889+Pq6qay7B+Lj48PDDz/M8OHDefe9d9m2dRviqMDawwqNnLADCeKYQJ+ohxK45dZbeOCBB/D393fC4ApP58QpC/hmelzxk4vRvTvceacmoJ98Uot7rhZGI8YHH2bUf9/h8Q3zmNFqBg92f9AltioUisoR0g1hCzWhZ8+ecuvWrU4bLycnh23btrFlyxY2bNzEmWytHLLw8qfUPxxbQFNsAU2RPsFOE9Lee7TZ0MUdRjplvLNIia4wG11+Ovr8dIwFp5AWLadpk/Bw+vTuzVVXXUX37t3r7SS1Xbt28cH773Hg4CHigm3cHWcmNrBqGbNe26qJrBd6FlR5fwmZBmYd9OekWdC3Tx8mPf440R7+o+1upJT8/vvvvPfBe5zJPoO9rR3ZQUIl8zx1azSXtP2aS8ykKkTLH31S0L5De56Z/AytW7d2kfVXhhBim5Syp7vtqE2c/Z1dGe17ZLDvTCLHH19H1FMvQUoKxMa6dJ9XSkoKtG0LEybAxx/XYIDDh5GtWvHdzTE82us02ydsp21oW6fbqVA0ZC71nd1ghXN5pJQcOXKEHTt2kJiYyPYdO8hxJAgVJh9K/cKxBUZgC2x2RULaacK5TCjnnUCfdxKj+RSytASAkNAwenTvRteuXYmPjycqKqrB/JVns9n47bff+OLzz8jNzWNQRAljWxcRZLr0NV4d4XzCrGPWQV92ZhqJjoxk0hNP0Levm+LV6yj5+fl88sknLFq0CBEksPaxQoXUu5cVzmlg2GrAgIFHJjzCLbfcgl7vuZlWlHB2DU2aZ5MRuJzCUdvxeeNtLWGyyfMmDVfkscdg2jTYvx9atqzBADfdhG3zJqKetBER2oJ1D6zD2+DtdDsVioaKEs7VREpJWloaO3fuJCEhgW3bd5CZcRpwCGn/ptiCorAFRyNNVf9b/kqEsygpQJ9zTAu9KDiFLNVmWjWLiKBH9+507dqVrl270rRp02qPXd8oKChg5syZzJ8/D5NOMrqFmeujSzBcJK62KsK50AoLUnxYdtwbL28f7n/gAcaMGaMmnV0BmzZtYsqrUygoLMDW04aMPvdddFHhbHfEMh/Q0SauDa+8/ApRUVG1aXaNUMLZNXj5FSG6fU1x642wfDmkpbl0f87ixAlo1QrGjoVvvqnBAEuXwvDhJLw2iW7Wj7i36718NeqrBuMkUShcjRLOV4iUkpMnT7Jjxw4SEhLYsnUr2VlaaIf0b0JpcDTWRjFI38aXHKdawllKdIVZ6LOPYMw9jjBr+wsPb0rPnj3o1q0b8fHxNGnS5MoOrh5z7NgxPvxwKps2baZlkJ2JHfMJ973Qg/ntfm1i0cXKbR/I0fPJ7kCyiuGGG27k4YcfpnHjS59rRdXIyMjgpZdfImlXEvZ4O7KN9n0kEjQBcF65bRvoNukQaYLRo0czceJETHXAuwhKOLuCwkLw84OoMR9z/NRsMBhgzRqX7c/ZPPssvPMO7NoFHTtWc2MptXreWVlM+eJOXlr3GlOHT+Xx3o+7xFaFoqGhhLOTkVKSnJzMunXr+HPdOvbv26e1+4dhCY3DGtIKDBf+oFdJOJcWY8g8hFfmASjMRghBp06dGTCgP/369aN58+bKq1BNfv/9d9568w1sJUU82C6fPk1Lq7SdXcKvR7yZl+JD0/BwXnz5FTp06OBiaxseFouFKVOmsHbtWuyd7ch2lXwn2UG/Tg/pMGnSJG677bbaN/QKUMLZ+Rw+rIU59Jn4ORt+eBFuugmmT3fZ/pxNVpYWjj10KMyvSVrmX36Bm2/GPuNLRvv8zKIDi1h+93Kujb3W6bYqFA2NS31nuy2rhhBiOPAB2tSg6VLKN91lS3URQtC6dWtat27NvffeS1ZWFqtWrWLhL79w9Mg6vI9vwhLWDktEVzD6VG3MkgKMJxIwZh4Eu424tm25aeT9XHPNNQQFBbn4iOo3gwYNom3btkx55WU+StrD7jMl3BVXiNclQmJzSwSf7vYnKdvAddddy+TJz6hMDS7CZDLxyiuv8Nprr7Fy5Ups/jaoEH0hEgSkw+TJk7n55pvdY6jCozh63AKYaNfEBKdPQ5s27japWoSEwN/+Bq++CgkJUO06SSNHQvfu6F5+hW8TNtE761pumXsLfz7wJx3C1A2+QuEq3JLAWAihBz4GRgAdgNuFEHX2kx4SEsLYsWP55uuv+eyzz7h+yGBMp3bjv3MuxtRtYL9EdgebFdPRjfgm/oB31kFG3TSSr776iunTpjFq1Cglmp1E06ZNmfrhR9x1112sTvPi3Z0BWC8y7yzfIpiyPYgD+T4888wzvPTSy0o0uxi9Xs9zzz1Hu/btMGw1QPmomTTQJesYP368Es2KsySmaPNOuns5qsLWMeEM8Pe/Q1AQvPJKDTYWAt57D44dI/C9T1h8x2K8DF4M+24Yx3OPO91WhUKh4a7KH72AQ1LKFCmlBfgeGOUmW5yGEIIOHTrwr3/9i2+++YZBA/tjStuB795fEcW5F/YvzMZvz88Y05O4YfgwZs+ezdNPP02rVq3cYH39x2AwMGHCBJ5//nl2Zxv4er/vBUUkS+3wwa4Asi1G3nv/fW666SYVGlNLeHl58fJLL6NHj9jpeM+tYNhpILZlLBMmTHCvgQqPYu9hLfNRd7tZa6iDwrlRI83r/NNPsGNHDQa4+mq4/Xb4z3+IzbSy5M4l5JXkMey7YWQVZjndXoVC4T7hHAmUvyVOdbSdhxBighBiqxBia0ZGRq0Z5wxiYmKYMmUKU6ZMwV8W4bf3V+xeAdj9QgDQmTPx2/srwV7w7rvv8txzz6mMGLXEiBEjuPvuu1md5sVvx7zOtksJM/b6su+Mnuef/yedO3d2o5UNk4iICG4ffzu64zoo0IqbSLPkySeexGBoEPWaFFXk4OFi0FnompsGOl2dFM6gCeegIHj55RoO8Pbb4OMDd99N19COLBy/kJQzKQz7bhhnis4401SFQoH7hHOVkFJ+IaXsKaXsGRYW5m5zasQ111zDZ599ip+XEWPBKSxRV0FpMb4HlhESHMS0L76gZ88GNWfII3jwwQcZNGgQsw/6klagfQy2nDbyx0kv7r//foYMGeJmCxsuo0ePRqfTIQ4L9If1xLaMpVu3bu42S+FhHDsGuuAT+B1Mgdatwbtu5jEODoann4aFC2HbthoMEBkJn30GmzbBlCkMajGI+bfNZ9fpXVz/3fVKPCsUTsZdwjkNKF9mLcrRVi+JiYnh9ddeheI8jCcTMaVtR1iL+c9/3iI8PNzd5jVIdDodTz/9NAajgWXHtR/cJcd9iGjWlHvvvdfN1jVsQkND6dq1K7rDOmS2ZOiQoSpcRnEBGSd98A3NQuzeXYN8bp7Fk09qYRs19jqPGwf33afNNJw/nxvjbmT+bfNJPJWoxLNC4WTcJZy3AG2EELFCCBMwHljoJltqhW7dutGvXz9M6bswZuxn+PDhHlseuKEQHBzMkCFD+TPdm93ZBg7k6Blzy63odB79R0yDoGfPnqAVw6RHjx7uNUbhkeRnBBMeboaDB+u8cA4M1LzOv/4Kf/5Zw0E+/RT69oW774YNGxgZN/KseL565tWk5dVb35RCUau4RSFIKa3AJGApsBeYK6Xc7Q5bapPrr78ebKVgt2mvFW5nzJgxlNgk/03wx8tkZMSIEe42SQHn3VS2rFFNYkV95kTuaWw5TekYVAJ2O9SD+QhPPAEtWsBdd0FOTg0G8PaGBQu00I1hw2DjRkbGjeS3O3/jaM5R+s3ox77Mfc42W6FocLjNtSalXCyljJNStpJSvu4uO2qTTp06nX2tCml4BnFxcfj5+mCxC+Li2hIQEOBukxRokwTL8PLyukRPRUNk2fY9IA1cZXAozHowTyQgAGbPhtRUGD0aCgpqMEh4OKxeDU2anBXP18Vex5r71lBsLWbAjAGsOrzK6bYrFA0J9Z90LVJ+gqN3HZ3IUt8QQhAbq3k0W6o0gB5DaGgogLqRUVTKmu0nAOhjOaQFB8fGutki59CnD3z9NfzxB1x3HZw4UYNBoqK00uNhYTBkCCxdSvdm3Vn/wHqa+DVh6LdDeW3ta1jtVmebr1A0CJRwrkWEEFx33XWMHj3a3aYoyhHrCAVoU0fTWdVH/Pz8ePvtt5k6daq7TVF4IDuSNHdsx+NrNG9zPZo8eued8OOPsGePdmibNtVgkKgoLVi6dWutwuCsWbRq3IpND23ito638X+r/48+0/uw7tg6p9uvUNR3hKxYAcJD6dmzp9y6dau7zVDUQ/Lz80lKSqJHjx6YTCZ3m6Oohwghtkkp6348QTVw5Xd2wMCvKN46HoslAPH8c/Daay7ZjzvZtQtGjYK0NPjtN80DXW1yc+Evf9E80O+9pyWNBubunsvfl/6dE/knuLbFtUzoMYERrUcQ5K0q1SoUcOnvbOVxVjR4AgIC6Nu3rxLNCkUdIMOcQcGJKJqHnkLYbTBokLtNcgmdO8OWLZrTeNw4LW91tQkK0lT3mDFafe/nnwcpua3jbRyYdID/DPkPh7IPcfv82wl9O5Sh3w7lv+v/S0J6AnZpd/oxKRT1ASWcFQqFQlFnWHv0DzjZjasCjoBer6Vgq6eEhGhhGyUlcO+9WgKRauPtDXPnwiOPwJtvwoMPgsWCn8mPZ/o/w+EnD/Pn/X/yVJ+nSMtLY/LyyXT7vBvh74Qzbt44vk74muyibKcfm0JRV1E1bBUKhUJRZ5i7fj0UjWGAeRNcdRX4+7vbJJfStq0WZfHQQ/D55/DXv9ZgEL1ey/PctCm88grs2wc//ACRkeh1evo370//5v15a+hbpOalsurwKlakrGDl4ZXM3T0XvdBzbey1jGk3hpFxI4kOiq50NyXWEo7kHCHlTAq5JbnohI6m/k3pGt5VhYEo6g0qxlmhUChcjIpxdg52aSfkgQfJmfkV6+hHv3/fpIUf1HOkhOHDYd06SErS8j3XmLlz4YEHNDH92muaIvfxuch+JdtObuPHvT8yf+98DmQdACAqMIq4kDgaeTfCareSlp9GWl4a6QXpSC7UFAJBn6g+3Bd/H+M6jlMiWuHxXOo7WwlnhUKhcDFKODuHbSe20fOW3zFtnUSuNRDvXVuhXH78+syxY9qhXnUVrFhxhYlEDh3SXNcrVkBwMIwYAe3ba2EdFgvk5Z17GI3QpAmyY0eS24axmINsPrGF5DPJ5JXkoRd6IgIiiAyIJDoomlaNWhHbKJYQnxBs0kZaXhobUzcyd89c9mTswcfgw9iOY3mo20MMaD4AUY8yoijqD0o4KxQKhRtRwtk5vLzmZV654yYGFUjWRN+ruV8bkPCaNg0mTID//heeeuoKB5NSy7YxfbqWuq787EMvL60OeGCgJqRPn9YCrQFatdKE9vDhcPXVWuWWKu1OsuXEFmbsmMHspNnkleQRFxLHyDYj6RnRk5jgGJoHNaeJXxNMejVRW+FelHBWKBQKN6KE85VTYi0h5tW+nJqyndf4F/96uxFMnuy08esCUmpVBX/+WdOtUVFaee7iYoiL03JAd+9+4XYFBbB8OWRmQkwM9OtXSWh4cTFYrZqHuWK1TqsVDhzQhPbixbBqFRQVgcEAvXrBwIHQoYPmtW7X7rJi2mwxM2/PPL5N/JY/jv2BxWY5b72/yZ9Q31DaNG5D78jeDGs9jN6RvTHqjdV/0xSKGqCEs0KhULgRJZyvnBk7ZvDgv7bDbx+xx7s77Y8vA0eFyYZEaSm88QZ8+y2YzZpT2MtL07XFxXDttfD441rxlKQkLaT5hx+0vmX4+2tZOv7+d82BXG2Ki7WA65Urtcf27Zq4LiMmRosriY/XCrD06gW6ypN4FZUWcTjnMEdzjnIs9xiZhZlkFWVxynyKvRl72XlqJ3ZpJ9ArkBGtRzC63WhuaHMDAV6qqqjCdSjhrFAoFG5ECecro8RaQpdPu3Lq1Z+JzShkx9Oz4J13nDJ2fSEnB778Untb0tPPtfv7a3mg775bq0y+fz989x18/72W3u6xx+D//u8K70FKSyElBfbu1UoeJiVpj717NUHdrBncdx9MnAiRkdU7ruIcVqasZMmhJSw8sJDT5tMYdUbim8bTN6ovvSJ70SGsA3EhcfiZ/K7gIBSKcyjhrFAoFG6kNoWzEGI48AGgB6ZLKd+ssN4L+AboAWQB46SURxzrngceBGzAE1LKpVUZszKc+Z09cdFEZnyVRvH8n/gy8O88cPQlbVKb4gIsFti4UdOvrVpB//7g63thv5Mntcx006ZpkRXPPQeTJlU/u19RkeZMrhjdAWhqfvFimDMHfvlFy+Qxbhw8/TR061btY7PZbSSsnk3ujE8J25xE8+N5+FsgwxcSw2F9l2D2X9OZsLh42oW2o1OTTvSM6ImvsZI3oA6SmpfK/sz9ZBRmoBM6DDoDfkY/YhvFEhMUg5ehspOgqAlKOCsUCoUbqS3hLITQAweAoUAqsAW4XUq5p1yfx4AuUspHhRDjgdFSynFCiA7AbKAXEAGsAOIcm11yzMpwxne2zW7j1bWv8tHczzBO205j6xl2LknHMGzwFY2rOMfu3fCPf8CiRVrBlcmTtQx1l/JA792rea3nzIHkZE04t2unxV+PHQtdumhzNm02bc7h0aNgOJ1GmxWfET77fS3oevBgLaZk2DAtm8elyMjQdvbNN1o5Rb0eevfG1i2eLIMF87Fk/BJ20+TwaWwCVrXW82UXGz+3A6uXgW5Nu9Evut/ZR1RglHPfxEqQUpJZmMnJgpP4GHwI8g4i2Du4yhMfLTYLO07uYP3x9WxI3cCG1A2k5qVetL9A0DyoOXEhccSFxNE2pK32HNqW6MBo9Dq9sw6tQaCEs0KhULiRWhTOfYGXpZTDHMvPA0gp3yjXZ6mjzwYhhAFIB8KA58r3Levn2OySY1ZGTb+zi63FJGccYNvO31j162xKl7dlw8E3SJdNWffmn3T7x/XVHlNxeTZu1DzQS5Zo8wNvuAGGDNEKsHh7ax7qnTs1gb1zpyaWhw6FAQO0SI0//9TmDtrtEBGhxV4fOaKFQ5enYzsbQ0J2MHTvVLpmryLCPx/djSOgd2+tvnjjxlp4x6lTmkL//Xf44w+trWtXLTj7jjsgPPzCg9i/H2bNQn79NeLYMUoD/NjXLZpV0VYW+B3joL+FdH+IDI7mqsiraBvSljaN2xAdFE2obyghPiGE+obiY6w8r3UZVruVrMIsMgozSC9I51jusbOP43nHz74uthZfsG0TvyZEBUZpjwDt2dvgTbG1GHOpmeQzyezJ2MO+zH1nJ03GBMXQN7ovfaP60iW8C038mpy1I7c4l8M5h0nOTubQmUMcyDrA/sz95Fvyz+7TS+9Fm5A2ZwV186DmRAREnH008m6Et8HbrakBpZRY7VZK7aVYbBYsNgultnOvrXYrBp0Bo96ISW/CpDdh1J17bdAZnGq/Es4KhULhRmpRON8KDJdSPuRYvhvoLaWcVK5PkqNPqmM5GeiNJpI3Sim/c7R/Cfzm2OySY1ZGdb+zpZQ0/W9TPvrqNO33tOU61pKBJhBa+qbz3fRi+t7eosrjKWrGrl0wc6Y2ofD48fPX6fXaPL/x4+G227RChOXJyIAFCzSdW1SkFWpp106LrbZaISFBS8ixdu05Qe2lLyVQ5uJnz2clg2nJ4fMH7dpV80rfdRd07ly1g7DbNRX/7bfa5MVyB2LXCUpMegqMkjf7S97tc2Edc4POcFaUGfVGdEJ3VsSVCbuKCATNAprRPKg5zYOaEx0YTfOg5jTzb0aJrYSc4hyyCrNIy08jNS/17ONM8ZnzxmgR3IIOYR3oENaB3pG96Rvdl4iAiKodtwMpJafMp9ifuV8T0lna84GsAySfScZqt16wjUDga/TF1+iLUW9EIBBCnH0u61O+TaC126UdidSepfZcWVvZcsU2q91a6XtaXcoL6bJzZ9KbmDVmFn2i+lRrrHohnIUQGcBRd9vhBEKBTHcbobgAdV48j/p0TmKklGGu3om7hbMQYgIwwbHYFtjv9IM8n7p4jdQ1m+uavVD3bK5r9kL9t/mi39kG59njWmrjR6c2EEJsbWiz6+sC6rx4Huqc1Ig0ILrccpSjrW7anVAAAA7VSURBVLI+qY5QjSC0SYKX2vZyYwIgpfwC+KKmxleXuniN1DWb65q9UPdsrmv2QsO2ufLEigqFQqGoi2wB2gghYoUQJmA8sLBCn4XAvY7XtwKrpPbX40JgvBDCSwgRC7QBNldxTIVCoWgQ1BmPs0KhUCgujZTSKoSYBCxFSx03Q0q5WwgxBdgqpVwIfAl8K4Q4BGSjCWEc/eYCewArMFFKaQOobMzaPjaFQqHwBJRwrn1q7W9MRbVQ58XzUOekBkgpFwOLK7S9WO51MTD2Itu+DrxelTE9hLp4jdQ1m+uavVD3bK5r9kIDtrnOTA5UKBQKhUKhUCjciYpxVigUCoVCoVAoqoASztVACCGFEP8ttzxZCPFyDccKdlTwqsm2R4QQl6jr1HAQQtiEEAlCiCQhxA9CiGrVVhVCRAgh5jlexwshbii37mYhxHPOtrk+4szPxmX2888Ky+udvQ+F5yOEGC6E2C+EOOSpn1EhxAwhxGlH+r+ytsZCiOVCiIOO50butLEiQohoIcRqIcQeIcRuIcSTjnaPtFsI4S2E2CyE2Omw9xVHe6wQYpPj+pjjmNTqUQgh9EKIHUKIXx3LHm2zQ3fscvzebnW0eeR1AWc11jwhxD4hxF4hRF9n2auEc/UoAcY4SbQGA5UKZ0eKKEXVKJJSxkspOwEW4NHqbCylPCGlvNWxGA/cUG7dQinlm84ztV7jzM/GpThPOEsp+7l4fwoPQ2hlxT8GRgAdgNuFVi7c05gJDK/Q9hywUkrZBljpWPYkrMDTUsoOQB9gouO99VS7S4DrpJRd0b6/hwsh+gBvAe9JKVsDZ4AH3WjjxXgS2FtuuS7YfK3j97YspZunXhcAHwBLpJTtgK5o77VT7FXCuXpY0YLL/15xhRAiTAgxXwixxfHo72h/WQgxuVy/JCFEC+BNoJXj7u1tIcQ1Qog/hBAL0Wa1I4T4SQixzXEnPaHiPhUX8AfQ2nFX+ZMQIlEIsVEI0QVACDHI8X4nOO70A4QQLRznxARMAcY51o8TQtwnhPhICBEkhDgqhNA5xvETQhwXQhiFEK2EEEsc5+kPIUQ7Nx6/O6nJZyPMcde/Wwgx3fEehzrWXXDtCyHeBHwc52eWo63A8fy9EOLGcvucKYS41eHVedux30QhxCMufycUrqYXcEhKmSKltADfA6PcbNMFSCnXomUtKc8o4GvH66+Bv9SqUZdBSnlSSrnd8TofTWxE4qF2S40Cx6LR8ZDAdcA8R7vH2FuGECIKuBGY7lgWeLjNF8EjrwshRBBwNVoGIaSUFillDk6yVwnn6vMxcKfjxJTnA7S7xauAW3B8IC7Bc0Cy4+7tGUdbd+BJKWWcY/kBKWUPoCfwhBAixDmHUP9weOlHALuAV4AdUsouaB7KbxzdJqOl2IoHBgJFZds7foBfBOY4zsmccutygQRgkKNpJLBUSlmKJhYfd5ynycAnrjtKj6e6n42X0HIId0T7wWhebpsLrn0p5XOc+4fhzgr7mAPcBuC4CRoMLELz2uQ69n0V8LDQchQr6i6RQPli0KmOtrpAuJTypON1OhDuTmMuhcPB0w3YhAfb7bg5TgBOA8uBZCBHSllWV9oTr4/3gWeBsnrfIXi+zRJY5nBolDnyPPW6iAUygK8cTrLpQgg/nGSvCgmoJlLKPCHEN8ATlBNewBCgg3DUdAcChRD+1Rx+s5TycLnlJ4QQox2vo9EKEmTVwOz6jI/jSxM0j/OXaF/0twBIKVcJIUKEEIHAOuBdh7fyRyllarnzdTnmAOOA1Wh5bz9xnN9+wA/lxvFywjHVSWrw2RgAjHZsu0QIcabcNtW99n8DPhBCeKH9Pb5WSlkkhLge6CK0UtSgVclrAxy+yDgKRa0gpZRCCI9Ma+X4fM4H/ub4XJ9d52l2O3KNxwshgoEFgEf/6yeEGAmcllJuE0Jc4257qsEAKWWaEKIJsFwIsa/8Sg+7LgxojsjHpZSbhBAfUCEs40rsVcK5ZrwPbAe+KtemA/o4cqSeRQhh5XzPvvclxjWX2+4aNMHRV0pZKIRYc5ltGypFDg/yWS4mhqWUbwohFqHFMa8TQgwDiivtfCELgX8LIRoDPYBVgB+alyD+kls2LKrz2ah0gJpc+1LK/2/v/oOsrOo4jr8/BqEFxgDZ0KhRphgUIJBFiC2YNDXJTAZjRhnkmNOkRg5NP3QUm6GGmnRGZ8SoDGIYBUxTsUFIFgGTH6L8EDCsYQu0yPXHACWGy7c/zrlw2e7u3l123Wfp85ph7nOfH+ece3n22bPn+Z7nezDv92nSHzj3loojXTwfbe0HscKqJq14Ue2V1D8i/i6pP2mUtFAkdSd1mhdExP15deHbHRGvSaoFRgG9JXXLI7hFOz9GAxOUJqKfDJxKuitX5DYTES/k139KeoAUMlXU82IPsCci1uX395E6zu3SXodqtEFEvAIs4tjg/WXAtaU3kkqdqTrSXz5IGk66hQCwH+jVTDXvAl7NHYdzSRM1rDqrgclwpBNWn0dNzoqIrRExi5RGuPHIRJP/JzmObgPpArckIhoiYh+wS9KkXJckDe2QT9RFtPJn4wmOhleMB0oznJs79w/lX+yVLASmksJwluZ1jwLfKB0j6Zx8y866rq6cArw83flXgQc7sS3/I8fa/grYERG3lm0qZLvzPIneefkU4GJSXHYtKZ08FKi9ABHx/Yg4PSIGkM7dFTn0rLBtVprX06u0DIwHnqWg50VE/APYLWlgXnURae5Yu7TXHee2+xlQ/gSB64CReQLSdo4+3eG3QB9J24BrgJ0AEfEyadTzWUk/rVD+UqCbpB2kiYRrO+hznIhmACMkbSF9d6UflGn5+94CHCLd3i9XSwop2CTpsgrlLgS+nF9LJgNXStoMbKOAk5Q6QbU/G7cA45Ue1zWJFHO2n+bP/TnAlhxu09gyUhz6H3LMOqR46u3A07men+M7bV1aHpErpQDfASwqYgpwSfcATwIDJe2RdCXpfL5Y0vOkuypFe2rPaOArwDgdnUj9WYrb7v5Abb6mbwCWR8QS4LvA9Upp5fuSJ4kVXJHb/B5gTf49tx54JCKWUtzzAtJgzYJ8bgwDfkQ7tdeZA82sU+R45IaIeFPSKGC2w17MzKzIPPJiZp3lTGCR0mP+/gNc1cntMTMza5ZHnM3MzMzMquAYZzMzMzOzKrjjbGZmZmZWBXeczczMzMyq4I6zdQmSbpC0LT/SbJOkj7WhjGH50Uql9xMkfa+5Y46XpBpJn+jIOszMzOyt4Y6zFV5+VNnngOERMYT0/MXdbShqGClrIAAR8VBEdPRzJ2tIabnNzLosSSMl3d7Z7Tgekuok9Wt5zw6rf5qkd3RW/dY+/FQNKzxJlwJTI+KSRutHALcCPYF6YEpOpbkSWAeMBXqTstitA/4MnEJKZfrjvDwyIq6RNBd4HTgPOA34GnAFKX3ruoiYkuscT0rc0QP4S27XAUl1wDzgEqA7KaHHQVLyjgbgJVLq59Xt++2YmVljZemry9fVka759Z3Upk6t39qHR5ytK1gGnCFpp6Q7JX0yp1C+A5gYESOAu4GZZcd0i4jzgWnAzTmT3E3AwogYFhELG1dCSvk8Cvg2KTXnbcBg4CM5zKMfcCPwqYgYDjwFXF92fH1ePxuYHhF1wF3AbblOd5rNrBAkDcjZNEvvp0uaIWmlpFmS1udr7pi8vUbSkrzcV9KyHD73S0l/ldSvqTLz8lmSlkraKGm1pHObaNfbJO1S0ltSg6QL87ZVks6W1EfS73Lo3lpJQ/L2GZLmS3oCmN+4nYBa+E6uyGVuljS/7Htakdc/JunMvH6upIllxx4o+55WSrpP0nOSFuTPch3wXlKmw9pW/ndZgbjjbIUXEQeAEcDXSSO3C4GrgQ8DyyVtInVoTy877P78uhEYUGVVD0e6BbMV2BsRWyPiMCmV9gDg48AgUqr0TaRU3u87zjrNzIrmmIGHCttvBtZExGDgAVIyo5bMId11GwFMB+6stFNENAB/Il1rLwCeBsbkTKNnRMTzpLt+z+TQvR8AvykrYhBpcOPy1rRT0mDS75FxETEU+FbedAcwL9e1AKgmXOU80nc3CPgAMDoibgdeBMZGxNgqyrCCcuZA6xLyxXQlsFLSVuCbwLaIGNXEIW/k1waqP89LxxwuWy6975bLWp4vyO1Vp5lZ0bQ0CHAhcClARDwi6dXmCpPUkzTXY7F0ZNC3RzOHrM51vJ8UVncV8DiwIW+/APhCrn9FHlk+NW97KCJeb0M7xwGLS2EUEfFKXj+qVAYwH/hJc581Wx8RewDyIMsAYE0Vx1kX4BFnKzxJAyWdXbZqGLADeHeeOIik7nnEoDn7gV7H0ZS1wGhJH8x1vlPSOR1cp5lZR3iTY/sAJ5ctt3UQoKkyTwJeyyFrpX8faqacVcAY4Hzg96S5KjWkDnVL/tWK9h6PI59V0knA28u2lQ+8eCDlBOOOs3UFPYF5krZL2kK6/XUTMBGYJWkzsImWn15RCwzKj7O7rLWNiIiXgCnAPbkdTwIV4/TKPAx8Ptc5prV1mpl1kL3AaXm0tgfpyUXVWgV8CUDSZ0jzQ5osMyL2AbskTcrHSNLQZspfT7qeH46Ig6Tr+9W5Xkgd6Mm5rBrS/JJ9rWhnJSuASZL65v375PV/BL6YlydztPNeRwohBJhAmhTeEg+knAD8V5AVXkRspHKnuJ50K67x/jVly/XkW4351ttHG+0+N2+bUnZMHSl+mgrbVlQog4gYULb8FGl0hIjYCQyp9LnMzDpLRByS9ENSJ/UF4LlWHH4LaQBhG6lj+bcqypwMzJZ0I6mTeS+wuYm2vSFpN+kuH6TO6uWk+ScAM4C78wDGv0nzTapuZxN1bpM0E3hcUgPwDGmg5Frg15K+Q5pjMzUf8gvgwTxws5TqRrrnAEslveg4567Lj6MzMzOzNpMfs2b/RxyqYWZmZmZWBY84m5mZ2VtO0g2kZFHlFkfEzEr7t1OdfYHHKmy6KCJe7qh67cThjrOZmZmZWRUcqmFmZmZmVgV3nM3MzMzMquCOs5mZmZlZFdxxNjMzMzOrgjvOZmZmZmZV+C+analULQ0I0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "basic_feature_plots(\"Sentiment\",\"unique_word_count\",final_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "dba05a4a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "dba05a4a",
    "outputId": "8c8f13c3-44dc-45e6-8c61-43f68c0c007a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "basic_feature_plots(\"Sentiment\",\"avg_wordlength\",final_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "57a2eee5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "57a2eee5",
    "outputId": "bc675275-33a8-4a31-d0e5-cf45ab8eacab"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "basic_feature_plots(\"Sentiment\",\"unique_vs_words\",final_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842a8ab7",
   "metadata": {
    "id": "842a8ab7"
   },
   "source": [
    "# Aspect based sentiments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "c8b17d6a",
   "metadata": {
    "id": "c8b17d6a"
   },
   "outputs": [],
   "source": [
    "def aspect_plots(data,col_name,aspect_name):\n",
    "    \"\"\"\n",
    "    For a given aspect, this function plots the pie chart and line plot \n",
    "    \n",
    "    \"\"\"\n",
    "    s1 = data.groupby(by = col_name)\n",
    "    s2 = s1.get_group(aspect_name)\n",
    "    plt.figure(figsize=(14, 5))\n",
    "    plt.title(\"Sentiment for {} acorss a time period\".format(aspect_name))\n",
    "    sns.lineplot(x=\"Date\", y=\"Score\", data=s2, hue=\"Sentiment\", palette=\"Dark2\")\n",
    "\n",
    "    fig = px.pie(s2, values= list(dict(s2[\"Sentiment\"].value_counts()).values()),\n",
    "             names=list(dict(s2[\"Sentiment\"].value_counts()).keys()), title = (\"Sentiment for {}\".format(aspect_name)),\n",
    "            width = 400, height = 400)\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "9df43687",
   "metadata": {
    "id": "9df43687"
   },
   "outputs": [],
   "source": [
    "data = final_train\n",
    "col_name = \"Aspect\"\n",
    "aspect_name = \"display\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "ef927386",
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     "height": 750
    },
    "id": "ef927386",
    "outputId": "480eb41e-c337-4a69-a093-c2ac153219aa"
   },
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aspect_plots(data,col_name,aspect_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "2858a9e5",
   "metadata": {
    "id": "2858a9e5"
   },
   "outputs": [],
   "source": [
    "def display_reviews(data,aspect_name):\n",
    "    \"\"\"\n",
    "    This function helps us to see the reviews which talk about a particular aspect\n",
    "    \n",
    "    \"\"\"\n",
    "    face = [x for x,value in enumerate(data['Review'].values) if aspect_name in value]\n",
    "\n",
    "    get_reviews =[data['Review'].values[k] for k in face]\n",
    "    get_sentiments = [data['Sentiment'].values[k] for k in face]\n",
    "\n",
    "    data = pd.DataFrame({\"Review\" : get_reviews, \"Sentiment\" : get_sentiments})\n",
    "    data_display = data.drop_duplicates(subset = [\"Review\"])\n",
    "    data_display_ = data_display.reset_index().drop([\"index\"], axis=1)\n",
    "    return data_display_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "e8754c63",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "e8754c63",
    "outputId": "dc0c4aa5-9cde-4a4e-89dc-c71c04d48c4c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-4d9f08cc-f6b1-4564-8530-3d8080322f6f\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Review</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>really impressed display</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pixel pepper display play important role</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>worth because not premium display</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>display more le good</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>trust display good phone same chipset pro vers...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>display missing amoled</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>go iphone getting beautiful display experience</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>iphone display not bad beat k flagship</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>display quality worst</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>camera display awesomebattery health not upto ...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>173 rows × 2 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d9f08cc-f6b1-4564-8530-3d8080322f6f')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-4d9f08cc-f6b1-4564-8530-3d8080322f6f button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-4d9f08cc-f6b1-4564-8530-3d8080322f6f');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                                                Review Sentiment\n",
       "0                             really impressed display  Positive\n",
       "1             pixel pepper display play important role  Positive\n",
       "2                    worth because not premium display  Positive\n",
       "3                                 display more le good  Positive\n",
       "4    trust display good phone same chipset pro vers...  Positive\n",
       "..                                                 ...       ...\n",
       "168                             display missing amoled  Negative\n",
       "169     go iphone getting beautiful display experience  Positive\n",
       "170             iphone display not bad beat k flagship  Positive\n",
       "171                              display quality worst  Negative\n",
       "172  camera display awesomebattery health not upto ...   Neutral\n",
       "\n",
       "[173 rows x 2 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_display = display_reviews(data,aspect_name)\n",
    "final_display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "52aea36a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "52aea36a",
    "outputId": "2f464321-1a9c-48de-a5a0-948e70931d31"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Positive    110\n",
       "Neutral      34\n",
       "Negative     29\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_display[\"Sentiment\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "6cf55afe",
   "metadata": {
    "id": "6cf55afe"
   },
   "outputs": [],
   "source": [
    "def wc(data,col_name,aspect_name):\n",
    "    \"\"\"\n",
    "    For a given aspect this function plots the wordcloud for the description\n",
    "    \n",
    "    \"\"\"\n",
    "    s1 = data.groupby(by = col_name)\n",
    "    s2 = s1.get_group(aspect_name)\n",
    "    s3 = s2[\"Description\"].values\n",
    "    \n",
    "    wordcloud = WordCloud(background_color = 'black', width = 512,height = 384,contour_color='black',contour_width=1,\n",
    "                         colormap='RdYlGn',collocations=True).generate(' '.join(s3))\n",
    "    plt.title(\"Descriptions of {}\".format(aspect_name), fontsize = 20)\n",
    "    plt.imshow(wordcloud) # image show\n",
    "    plt.axis('off') # to off the axis of x and y\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "f382de29",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "id": "f382de29",
    "outputId": "df526002-b7ea-4221-89bd-55b6dd4094d8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wc(data,col_name,aspect_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a535b61d",
   "metadata": {
    "id": "a535b61d"
   },
   "source": [
    "# Data Preprocessing:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16c54bf",
   "metadata": {},
   "source": [
    "- Month & Week----> **Cyclical Encoding**\n",
    "- Review----> **TFIDF**\n",
    "- Aspect & Description----> **Word2Vec**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "00c29378",
   "metadata": {
    "id": "00c29378"
   },
   "outputs": [],
   "source": [
    "# Select the features to be used for modeling:\n",
    "\n",
    "X_train = final_train[[\"Review\",\"Aspect\",\"Description\",\"Month\",\"Week\",\"char_count\",\"word_count\",\"unique_word_count\",\n",
    "                      \"avg_wordlength\",\"unique_vs_words\"]]\n",
    "X_test = final_test[[\"Review\",\"Aspect\",\"Description\",\"Month\",\"Week\",\"char_count\",\"word_count\",\"unique_word_count\",\n",
    "                      \"avg_wordlength\",\"unique_vs_words\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "e16593dc",
   "metadata": {
    "id": "e16593dc"
   },
   "outputs": [],
   "source": [
    "# Select the target variable:\n",
    "\n",
    "y_train = final_train[\"Score\"]\n",
    "y_test = final_test[\"Score\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03107c8",
   "metadata": {},
   "source": [
    "# Cyclical Encoding for Month & Week:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "286e6d76",
   "metadata": {
    "id": "286e6d76"
   },
   "outputs": [],
   "source": [
    "# Adding the encoded values to train set.\n",
    "X_train['month_sin'] = np.sin((X_train.Month -1)*(2.*np.pi/12))\n",
    "X_train['month_cos'] = np.cos((X_train.Month -1)*(2.*np.pi/12))\n",
    "X_train['weekday_sin'] = np.sin((X_train.Week-1)*(2.*np.pi/53))\n",
    "X_train['weekday_cos'] = np.cos((X_train.Week-1)*(2.*np.pi/53))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "f02f4ccd",
   "metadata": {
    "id": "f02f4ccd"
   },
   "outputs": [],
   "source": [
    "# Adding the encoded values to test set.\n",
    "X_test['month_sin'] = np.sin((X_test.Month -1)*(2.*np.pi/12))\n",
    "X_test['month_cos'] = np.cos((X_test.Month -1)*(2.*np.pi/12))\n",
    "X_test['weekday_sin'] = np.sin((X_test.Week-1)*(2.*np.pi/53))\n",
    "X_test['weekday_cos'] = np.cos((X_test.Week-1)*(2.*np.pi/53))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7b093d5",
   "metadata": {
    "id": "d7b093d5"
   },
   "source": [
    "# TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "de2886a0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "de2886a0",
    "outputId": "fd704e30-a987-44c7-8734-37cef7abf125"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data shape (9180, 500)\n",
      "====================================================================================================\n",
      "test data shape (2037, 500)\n"
     ]
    }
   ],
   "source": [
    "tfidf = TfidfVectorizer(min_df=10,ngram_range=(1,3), max_features=500)\n",
    "\n",
    "tfidf_train = tfidf.fit_transform(X_train['Review'].values)\n",
    "tfidf_test = tfidf.transform(X_test['Review'].values)\n",
    "\n",
    "print('train data shape',tfidf_train.shape)\n",
    "print('='*100)\n",
    "print('test data shape',tfidf_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "c8d6d6c1",
   "metadata": {
    "id": "c8d6d6c1"
   },
   "outputs": [],
   "source": [
    "tfidf_train = tfidf_train.toarray()\n",
    "tfidf_test = tfidf_test.toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30bdfb8e",
   "metadata": {
    "id": "30bdfb8e"
   },
   "source": [
    "#  W2V:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "6o68GR3sZiiS",
   "metadata": {
    "id": "6o68GR3sZiiS"
   },
   "outputs": [],
   "source": [
    "#import gensim.downloader as api\n",
    "\n",
    "#w2v_model = api.load('word2vec-google-news-300')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "3bcd78dc",
   "metadata": {
    "id": "3bcd78dc"
   },
   "outputs": [],
   "source": [
    "#w2v_model=KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "\n",
    "w2v_model_ = w2v_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "539a254f",
   "metadata": {
    "id": "539a254f"
   },
   "outputs": [],
   "source": [
    "def get_w2v(aspect):\n",
    "    \"\"\"\n",
    "    Given an aspect, this function returns  Word2Vec for the word\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    sent_vector = np.zeros(300) # since W2v gives 300 dimension representation for each word\n",
    "    try:\n",
    "        vector = w2v_model_[aspect] # getting w2v vector for the word\n",
    "        sent_vector += vector # adding this vector for performing Average W2v\n",
    "    except KeyError:\n",
    "        sent_vector += np.zeros(300)\n",
    "    sent_vector_final = sent_vector\n",
    "    return sent_vector_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "a3f409cb",
   "metadata": {
    "id": "a3f409cb"
   },
   "outputs": [],
   "source": [
    "# Getting the W2V vector for train set\n",
    "\n",
    "w2v_aspect_train = np.array([get_w2v(i) for i in X_train[\"Aspect\"].values])\n",
    "w2v_description_train = np.array([get_w2v(i) for i in X_train[\"Description\"].values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "0baa15b6",
   "metadata": {
    "id": "0baa15b6"
   },
   "outputs": [],
   "source": [
    "# Getting the W2V vector for test set\n",
    "\n",
    "w2v_aspect_test = np.array([get_w2v(i) for i in X_test[\"Aspect\"].values])\n",
    "w2v_description_test = np.array([get_w2v(i) for i in X_test[\"Description\"].values])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a0d6c2b",
   "metadata": {
    "id": "2a0d6c2b"
   },
   "source": [
    "# Concatenation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "d961e51a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d961e51a",
    "outputId": "d8b9fa20-5306-43b9-f1ef-4f4d5f28d59c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9180, 1109)"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating all the features \n",
    "\n",
    "training_data = np.hstack((tfidf_train,w2v_aspect_train,w2v_description_train,X_train[\"month_sin\"].values.reshape(-1,1),\n",
    "                X_train[\"month_cos\"].values.reshape(-1,1),X_train[\"weekday_sin\"].values.reshape(-1,1),\n",
    "                X_train[\"weekday_cos\"].values.reshape(-1,1),X_train['char_count'].values.reshape(-1,1),\n",
    "                X_train['word_count'].values.reshape(-1,1),X_train['unique_word_count'].values.reshape(-1,1),\n",
    "                X_train['avg_wordlength'].values.reshape(-1,1),X_train['unique_vs_words'].values.reshape(-1,1)))\n",
    "\n",
    "training_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "5f7f34e6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5f7f34e6",
    "outputId": "f3b209c4-b93d-4e18-916e-4338fd3a2b92"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2037, 1109)"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data = np.hstack((tfidf_test,w2v_aspect_test,w2v_description_test,X_test[\"month_sin\"].values.reshape(-1,1),\n",
    "                X_test[\"month_cos\"].values.reshape(-1,1),X_test[\"weekday_sin\"].values.reshape(-1,1),\n",
    "                X_test[\"weekday_cos\"].values.reshape(-1,1),X_test['char_count'].values.reshape(-1,1),\n",
    "                X_test['word_count'].values.reshape(-1,1),X_test['unique_word_count'].values.reshape(-1,1),\n",
    "                X_test['avg_wordlength'].values.reshape(-1,1),X_test['unique_vs_words'].values.reshape(-1,1)))\n",
    "\n",
    "testing_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0827f4",
   "metadata": {
    "id": "6f0827f4"
   },
   "source": [
    "# Modeling:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b29d5e",
   "metadata": {
    "id": "f9b29d5e"
   },
   "source": [
    "# LGB:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b859479c",
   "metadata": {},
   "source": [
    "- The LGB Regressor model gives the best performance without using hyperparametre optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "261e399d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "261e399d",
    "outputId": "0d0d2379-24b0-4a08-a87f-441e5fc6cd61"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMRegressor()"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lgb = ltb.LGBMRegressor()\n",
    "lgb.fit(training_data, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "b8b251a5",
   "metadata": {
    "id": "b8b251a5"
   },
   "outputs": [],
   "source": [
    "lgb_predict_train = lgb.predict(training_data)\n",
    "lgb_predict_test = lgb.predict(testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "2fbc2d5e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2fbc2d5e",
    "outputId": "e79a94c5-3a74-4789-b5fd-86fbd2ef4781"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score on train set : 0.8454592420686778\n",
      "R2 Score on test set : 0.6462480419650449\n"
     ]
    }
   ],
   "source": [
    "r2_train = r2_score(y_train,lgb_predict_train)\n",
    "r2_test = r2_score(y_test,lgb_predict_test)\n",
    "\n",
    "print(\"R2 Score on train set :\",r2_train)\n",
    "print(\"R2 Score on test set :\",r2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "8f24dcaa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8f24dcaa",
    "outputId": "e38cef13-a3b4-4b8e-a72e-5cc480fb5111"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE train : 0.15208108051113706\n",
      "RMSE test : 0.2360249775532124\n"
     ]
    }
   ],
   "source": [
    "mse_train = mean_squared_error(y_train,lgb_predict_train)\n",
    "mse_test = mean_squared_error(y_test,lgb_predict_test)\n",
    "\n",
    "print(\"RMSE train :\",math.sqrt(mse_train))\n",
    "print(\"RMSE test :\",math.sqrt(mse_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f29a156c",
   "metadata": {
    "id": "f29a156c"
   },
   "source": [
    "# Interpretation of the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "672b0fc6",
   "metadata": {
    "id": "672b0fc6"
   },
   "outputs": [],
   "source": [
    "# stacking the actual and predicted results to form the dataframe\n",
    "\n",
    "interpret = np.hstack((np.array(y_test).reshape(-1,1),lgb_predict_test.reshape(-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "2bf8aa42",
   "metadata": {
    "id": "2bf8aa42"
   },
   "outputs": [],
   "source": [
    "interpret_df = pd.DataFrame(interpret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "8f5147f3",
   "metadata": {
    "id": "8f5147f3"
   },
   "outputs": [],
   "source": [
    "interpret_df[\"Actual_sentiment\"] = final_test[\"Sentiment\"] # adding the actual sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "37c30221",
   "metadata": {
    "id": "37c30221"
   },
   "outputs": [],
   "source": [
    "interpret_df.columns = [\"Actual\",\"Predicted\",\"Actual_sentiment\"] # renaming the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "c573aaac",
   "metadata": {
    "id": "c573aaac"
   },
   "outputs": [],
   "source": [
    "# Converting the precited scores to sentiments\n",
    "\n",
    "senti = [] # this list carries the sentiment for the review\n",
    "   \n",
    "for u in interpret_df[\"Predicted\"].values: \n",
    "    if u >= 0.05:\n",
    "        senti.append(\"Positive\")\n",
    "    elif u <=-0.05:\n",
    "        senti.append(\"Negative\")\n",
    "    else:\n",
    "        senti.append(\"Neutral\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "ea29e910",
   "metadata": {
    "id": "ea29e910"
   },
   "outputs": [],
   "source": [
    "interpret_df[\"Predicted_sentiment\"] = senti # adding the predicted sentiments to dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "eea1d82e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "eea1d82e",
    "outputId": "992d825a-905d-4957-d421-a6710022a794"
   },
   "outputs": [
    {
     "data": {
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       "      <th>Actual</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.027122</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Neutral</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0258</td>\n",
       "      <td>0.027122</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Neutral</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>Positive</td>\n",
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       "      <th>2033</th>\n",
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       "          document.querySelector('#df-8004ec32-8b73-4044-aa65-44c68c4fb9f9 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-8004ec32-8b73-4044-aa65-44c68c4fb9f9');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "      Actual  Predicted Actual_sentiment Predicted_sentiment\n",
       "0     0.0000   0.027122          Neutral             Neutral\n",
       "1     0.0258   0.027122          Neutral             Neutral\n",
       "2     0.0000   0.038314          Neutral             Neutral\n",
       "3     0.6369   0.575119         Positive            Positive\n",
       "4     0.6369   0.680089         Positive            Positive\n",
       "...      ...        ...              ...                 ...\n",
       "2032  0.1779   0.155613         Positive            Positive\n",
       "2033 -0.3412  -0.157369         Negative            Negative\n",
       "2034  0.0000   0.061564          Neutral            Positive\n",
       "2035  0.0000  -0.043740          Neutral             Neutral\n",
       "2036 -0.1027  -0.070874         Negative            Negative\n",
       "\n",
       "[2037 rows x 4 columns]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interpret_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "b0e6ffa5",
   "metadata": {
    "id": "b0e6ffa5"
   },
   "outputs": [],
   "source": [
    "# We will use the predicted sentiments as target variables, so label encoding the same.\n",
    "\n",
    "label_encoder = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "48202996",
   "metadata": {
    "id": "48202996"
   },
   "outputs": [],
   "source": [
    "# Applying the label encoding on target variable\n",
    "\n",
    "interpret_df['Actual_sentiment']= label_encoder.fit_transform(interpret_df['Actual_sentiment'])\n",
    "interpret_df['Predicted_sentiment']= label_encoder.fit_transform(interpret_df['Predicted_sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "5f72f386",
   "metadata": {
    "id": "5f72f386"
   },
   "outputs": [],
   "source": [
    "# Forming the actual & predicted arrays for using the Confusion Matrix and F1 score \n",
    "\n",
    "Y_test = interpret_df[\"Actual_sentiment\"].values.reshape(-1,1)\n",
    "Y_pred = interpret_df[\"Predicted_sentiment\"].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "679dbb71",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 458
    },
    "id": "679dbb71",
    "outputId": "cf24e56d-faf2-4fe2-e615-49778271d9c1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix:\n",
    "\n",
    "C = confusion_matrix(Y_test, Y_pred) \n",
    "labels = [\"Negative\",\"Neutral\",\"Positive\"] # since we have 9 classes\n",
    "\n",
    "# Confusion Matrix\n",
    "plt.figure(figsize=(20,7))\n",
    "sns.heatmap(C, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
    "plt.xlabel('Prediction')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "38b1eff6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "38b1eff6",
    "outputId": "363a6fde-9d82-4f52-ea17-8518d86bcfb8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('F1 score on test :', 0.7471017293943243)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\"F1 score on test :\",f1_score(Y_test,Y_pred,average = 'weighted'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "da95279d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "da95279d",
    "outputId": "2ecaa32b-e3e3-4bb6-c71f-2e1e93859c1b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('F1 score on test :', 0.7555228276877761)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\"F1 score on test :\",f1_score(Y_test,Y_pred,average = 'micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "0e546b8f",
   "metadata": {
    "id": "0e546b8f"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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