assignment-automatic-ticket-classification.ipynb

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    "## Problem Statement \n",
    "\n",
    "You need to build a model that is able to classify customer complaints based on the products/services. By doing so, you can segregate these tickets into their relevant categories and, therefore, help in the quick resolution of the issue.\n",
    "\n",
    "You will be doing topic modelling on the <b>.json</b> data provided by the company. Since this data is not labelled, you need to apply NMF to analyse patterns and classify tickets into the following five clusters based on their products/services:\n",
    "\n",
    "* Credit card / Prepaid card\n",
    "\n",
    "* Bank account services\n",
    "\n",
    "* Theft/Dispute reporting\n",
    "\n",
    "* Mortgages/loans\n",
    "\n",
    "* Others \n",
    "\n",
    "\n",
    "With the help of topic modelling, you will be able to map each ticket onto its respective department/category. You can then use this data to train any supervised model such as logistic regression, decision tree or random forest. Using this trained model, you can classify any new customer complaint support ticket into its relevant department."
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    "## Pipelines that needs to be performed:\n",
    "\n",
    "You need to perform the following eight major tasks to complete the assignment:\n",
    "\n",
    "1.  Data loading\n",
    "\n",
    "2. Text preprocessing\n",
    "\n",
    "3. Exploratory data analysis (EDA)\n",
    "\n",
    "4. Feature extraction\n",
    "\n",
    "5. Topic modelling \n",
    "\n",
    "6. Model building using supervised learning\n",
    "\n",
    "7. Model training and evaluation\n",
    "\n",
    "8. Model inference"
   ]
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   "source": [
    "## Importing the necessary libraries"
   ]
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   "source": [
    "import json \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re, nltk, spacy, string\n",
    "import en_core_web_sm\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from plotly.offline import plot\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer\n",
    "from pprint import pprint\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "nlp = en_core_web_sm.load()"
   ]
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   "source": [
    "## Loading the data\n",
    "\n",
    "The data is in JSON format and we need to convert it to a dataframe."
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   "source": [
    "# Opening JSON file \n",
    "file_path = open('../input/automatic-ticket-classification/complaints-2021-05-14_08_16.json') # Write the path to your data file and load it \n",
    "  \n",
    "# returns JSON object as  \n",
    "# a dictionary \n",
    "data = json.load(file_path)\n",
    "df = pd.json_normalize(data)"
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   "source": [
    "## Data preparation"
   ]
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       "      <th></th>\n",
       "      <th>_index</th>\n",
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       "      <th>_id</th>\n",
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       "      <td>2019-04-13T12:00:00-05:00</td>\n",
       "      <td>CA</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>JPMORGAN CHASE &amp; CO.</td>\n",
       "      <td>Web</td>\n",
       "      <td>2019-04-13T12:00:00-05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>Credit card debt</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td>Debt is not yours</td>\n",
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       "      <th>1</th>\n",
       "      <td>complaint-public-v2</td>\n",
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       "      <td>3229299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Servicemember</td>\n",
       "      <td>319XX</td>\n",
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       "      <td>2019-05-01T12:00:00-05:00</td>\n",
       "      <td>GA</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Debt collection</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>JPMORGAN CHASE &amp; CO.</td>\n",
       "      <td>Web</td>\n",
       "      <td>2019-05-01T12:00:00-05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>Credit card debt</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good morning my name is XXXX XXXX and I apprec...</td>\n",
       "      <td>Didn't receive enough information to verify debt</td>\n",
       "      <td>Consent provided</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>complaint-public-v2</td>\n",
       "      <td>complaint</td>\n",
       "      <td>3199379</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>77069</td>\n",
       "      <td>3199379</td>\n",
       "      <td>Other features, terms, or problems</td>\n",
       "      <td>2019-04-02T12:00:00-05:00</td>\n",
       "      <td>TX</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Credit card or prepaid card</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>JPMORGAN CHASE &amp; CO.</td>\n",
       "      <td>Web</td>\n",
       "      <td>2019-04-02T12:00:00-05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>General-purpose credit card or charge card</td>\n",
       "      <td>Yes</td>\n",
       "      <td>I upgraded my XXXX XXXX card in XX/XX/2018 and...</td>\n",
       "      <td>Problem with rewards from credit card</td>\n",
       "      <td>Consent provided</td>\n",
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       "      <td>2673060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>48066</td>\n",
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       "      <td>Trouble during payment process</td>\n",
       "      <td>2017-09-13T12:00:00-05:00</td>\n",
       "      <td>MI</td>\n",
       "      <td>N/A</td>\n",
       "      <td>Mortgage</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>JPMORGAN CHASE &amp; CO.</td>\n",
       "      <td>Web</td>\n",
       "      <td>2017-09-14T12:00:00-05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>Conventional home mortgage</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
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       "      <td>Consent not provided</td>\n",
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       "      <td>3203545</td>\n",
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       "      <td>10473</td>\n",
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       "      <td>Fees or interest</td>\n",
       "      <td>2019-04-05T12:00:00-05:00</td>\n",
       "      <td>NY</td>\n",
       "      <td>N/A</td>\n",
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       "      <td>2019-04-05T12:00:00-05:00</td>\n",
       "      <td>None</td>\n",
       "      <td>General-purpose credit card or charge card</td>\n",
       "      <td>Yes</td>\n",
       "      <td></td>\n",
       "      <td>Charged too much interest</td>\n",
       "      <td>N/A</td>\n",
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       "                _index      _type      _id  _score   _source.tags  \\\n",
       "0  complaint-public-v2  complaint  3211475     0.0           None   \n",
       "1  complaint-public-v2  complaint  3229299     0.0  Servicemember   \n",
       "2  complaint-public-v2  complaint  3199379     0.0           None   \n",
       "3  complaint-public-v2  complaint  2673060     0.0           None   \n",
       "4  complaint-public-v2  complaint  3203545     0.0           None   \n",
       "\n",
       "  _source.zip_code _source.complaint_id                       _source.issue  \\\n",
       "0            90301              3211475   Attempts to collect debt not owed   \n",
       "1            319XX              3229299     Written notification about debt   \n",
       "2            77069              3199379  Other features, terms, or problems   \n",
       "3            48066              2673060      Trouble during payment process   \n",
       "4            10473              3203545                    Fees or interest   \n",
       "\n",
       "       _source.date_received _source.state _source.consumer_disputed  \\\n",
       "0  2019-04-13T12:00:00-05:00            CA                       N/A   \n",
       "1  2019-05-01T12:00:00-05:00            GA                       N/A   \n",
       "2  2019-04-02T12:00:00-05:00            TX                       N/A   \n",
       "3  2017-09-13T12:00:00-05:00            MI                       N/A   \n",
       "4  2019-04-05T12:00:00-05:00            NY                       N/A   \n",
       "\n",
       "               _source.product _source.company_response       _source.company  \\\n",
       "0              Debt collection  Closed with explanation  JPMORGAN CHASE & CO.   \n",
       "1              Debt collection  Closed with explanation  JPMORGAN CHASE & CO.   \n",
       "2  Credit card or prepaid card  Closed with explanation  JPMORGAN CHASE & CO.   \n",
       "3                     Mortgage  Closed with explanation  JPMORGAN CHASE & CO.   \n",
       "4  Credit card or prepaid card  Closed with explanation  JPMORGAN CHASE & CO.   \n",
       "\n",
       "  _source.submitted_via _source.date_sent_to_company  \\\n",
       "0                   Web    2019-04-13T12:00:00-05:00   \n",
       "1                   Web    2019-05-01T12:00:00-05:00   \n",
       "2                   Web    2019-04-02T12:00:00-05:00   \n",
       "3                   Web    2017-09-14T12:00:00-05:00   \n",
       "4              Referral    2019-04-05T12:00:00-05:00   \n",
       "\n",
       "  _source.company_public_response                         _source.sub_product  \\\n",
       "0                            None                            Credit card debt   \n",
       "1                            None                            Credit card debt   \n",
       "2                            None  General-purpose credit card or charge card   \n",
       "3                            None                  Conventional home mortgage   \n",
       "4                            None  General-purpose credit card or charge card   \n",
       "\n",
       "  _source.timely                    _source.complaint_what_happened  \\\n",
       "0            Yes                                                      \n",
       "1            Yes  Good morning my name is XXXX XXXX and I apprec...   \n",
       "2            Yes  I upgraded my XXXX XXXX card in XX/XX/2018 and...   \n",
       "3            Yes                                                      \n",
       "4            Yes                                                      \n",
       "\n",
       "                                  _source.sub_issue  \\\n",
       "0                                 Debt is not yours   \n",
       "1  Didn't receive enough information to verify debt   \n",
       "2             Problem with rewards from credit card   \n",
       "3                                              None   \n",
       "4                         Charged too much interest   \n",
       "\n",
       "  _source.consumer_consent_provided  \n",
       "0              Consent not provided  \n",
       "1                  Consent provided  \n",
       "2                  Consent provided  \n",
       "3              Consent not provided  \n",
       "4                               N/A  "
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 78313 entries, 0 to 78312\n",
      "Data columns (total 22 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   _index                             78313 non-null  object \n",
      " 1   _type                              78313 non-null  object \n",
      " 2   _id                                78313 non-null  object \n",
      " 3   _score                             78313 non-null  float64\n",
      " 4   _source.tags                       10900 non-null  object \n",
      " 5   _source.zip_code                   71556 non-null  object \n",
      " 6   _source.complaint_id               78313 non-null  object \n",
      " 7   _source.issue                      78313 non-null  object \n",
      " 8   _source.date_received              78313 non-null  object \n",
      " 9   _source.state                      76322 non-null  object \n",
      " 10  _source.consumer_disputed          78313 non-null  object \n",
      " 11  _source.product                    78313 non-null  object \n",
      " 12  _source.company_response           78313 non-null  object \n",
      " 13  _source.company                    78313 non-null  object \n",
      " 14  _source.submitted_via              78313 non-null  object \n",
      " 15  _source.date_sent_to_company       78313 non-null  object \n",
      " 16  _source.company_public_response    4 non-null      object \n",
      " 17  _source.sub_product                67742 non-null  object \n",
      " 18  _source.timely                     78313 non-null  object \n",
      " 19  _source.complaint_what_happened    78313 non-null  object \n",
      " 20  _source.sub_issue                  32016 non-null  object \n",
      " 21  _source.consumer_consent_provided  77305 non-null  object \n",
      "dtypes: float64(1), object(21)\n",
      "memory usage: 13.1+ MB\n"
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01 Column:\u001b[1m _index \u001b[0m\n",
      "02 Column:\u001b[1m _type \u001b[0m\n",
      "03 Column:\u001b[1m _id \u001b[0m\n",
      "04 Column:\u001b[1m _score \u001b[0m\n",
      "05 Column:\u001b[1m _source.tags \u001b[0m\n",
      "06 Column:\u001b[1m _source.zip_code \u001b[0m\n",
      "07 Column:\u001b[1m _source.complaint_id \u001b[0m\n",
      "08 Column:\u001b[1m _source.issue \u001b[0m\n",
      "09 Column:\u001b[1m _source.date_received \u001b[0m\n",
      "10 Column:\u001b[1m _source.state \u001b[0m\n",
      "11 Column:\u001b[1m _source.consumer_disputed \u001b[0m\n",
      "12 Column:\u001b[1m _source.product \u001b[0m\n",
      "13 Column:\u001b[1m _source.company_response \u001b[0m\n",
      "14 Column:\u001b[1m _source.company \u001b[0m\n",
      "15 Column:\u001b[1m _source.submitted_via \u001b[0m\n",
      "16 Column:\u001b[1m _source.date_sent_to_company \u001b[0m\n",
      "17 Column:\u001b[1m _source.company_public_response \u001b[0m\n",
      "18 Column:\u001b[1m _source.sub_product \u001b[0m\n",
      "19 Column:\u001b[1m _source.timely \u001b[0m\n",
      "20 Column:\u001b[1m _source.complaint_what_happened \u001b[0m\n",
      "21 Column:\u001b[1m _source.sub_issue \u001b[0m\n",
      "22 Column:\u001b[1m _source.consumer_consent_provided \u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Print the column names\n",
    "def print_column_names():\n",
    "    i = 1\n",
    "    for col in df.columns:\n",
    "        print(str(\"{:02d}\".format(i)) +\" Column:\\033[1m\", col, '\\033[0m')\n",
    "        i += 1\n",
    "        \n",
    "print_column_names()"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01 Column:\u001b[1m index \u001b[0m\n",
      "02 Column:\u001b[1m type \u001b[0m\n",
      "03 Column:\u001b[1m id \u001b[0m\n",
      "04 Column:\u001b[1m score \u001b[0m\n",
      "05 Column:\u001b[1m tags \u001b[0m\n",
      "06 Column:\u001b[1m zip_code \u001b[0m\n",
      "07 Column:\u001b[1m complaint_id \u001b[0m\n",
      "08 Column:\u001b[1m issue \u001b[0m\n",
      "09 Column:\u001b[1m date_received \u001b[0m\n",
      "10 Column:\u001b[1m state \u001b[0m\n",
      "11 Column:\u001b[1m consumer_disputed \u001b[0m\n",
      "12 Column:\u001b[1m product \u001b[0m\n",
      "13 Column:\u001b[1m company_response \u001b[0m\n",
      "14 Column:\u001b[1m company \u001b[0m\n",
      "15 Column:\u001b[1m submitted_via \u001b[0m\n",
      "16 Column:\u001b[1m date_sent_to_company \u001b[0m\n",
      "17 Column:\u001b[1m company_public_response \u001b[0m\n",
      "18 Column:\u001b[1m sub_product \u001b[0m\n",
      "19 Column:\u001b[1m timely \u001b[0m\n",
      "20 Column:\u001b[1m complaint_what_happened \u001b[0m\n",
      "21 Column:\u001b[1m sub_issue \u001b[0m\n",
      "22 Column:\u001b[1m consumer_consent_provided \u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Assign new column names\n",
    "new_columns = {}\n",
    "for col in df.columns:\n",
    "    new_columns[col] = re.sub(\"^_\", \"\", col).replace(\"source.\", \"\")\n",
    "\n",
    "df.rename(columns = new_columns, inplace = True)\n",
    "\n",
    "print_column_names()"
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of rows:\u001b[1m 78313 \u001b[0m\n",
      "No of cols:\u001b[1m 22 \u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Get the shape and size of the dataset\n",
    "print('No of rows:\\033[1m', df.shape[0], '\\033[0m')\n",
    "print('No of cols:\\033[1m', df.shape[1], '\\033[0m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c807d11c",
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    "execution": {
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   "outputs": [],
   "source": [
    "# Assign nan in place of blanks in the complaints column\n",
    "df.complaint_what_happened.replace(r'', np.nan, regex=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea9bf9c7",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remaining Rows:\u001b[1m 21072 \u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# Remove all rows where complaints column is nan\n",
    "df = df[df.complaint_what_happened.notna()]\n",
    "print('Remaining Rows:\\033[1m', len(df), '\\033[0m')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85edc83",
   "metadata": {
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   "source": [
    "## Prepare the text for topic modeling\n",
    "\n",
    "Once you have removed all the blank complaints, you need to:\n",
    "\n",
    "* Make the text lowercase\n",
    "* Remove text in square brackets\n",
    "* Remove punctuation\n",
    "* Remove words containing numbers\n",
    "\n",
    "\n",
    "Once you have done these cleaning operations you need to perform the following:\n",
    "* Lemmatize the texts\n",
    "* Use POS tags to get relevant words from the texts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2f6507d8",
   "metadata": {
    "execution": {
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   "source": [
    "# Write your function here to clean the text and remove all the unnecessary elements.\n",
    "def cleaned_text(sentence):\n",
    "    sentence = sentence.lower()\n",
    "    sentence = re.sub(\"[\\[].*?[\\]]\", \"\", sentence)\n",
    "    sentence = re.sub(r'[^\\w\\s]','', sentence) \n",
    "    sentence = re.sub(r'[0-9]', '', sentence, re.I|re.A)\n",
    "    return sentence"
   ]
  },
  {
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   "id": "b411fe29",
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   },
   "outputs": [],
   "source": [
    "# Write your function to Lemmatize the texts\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import wordnet, stopwords\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "stop = set(stopwords.words('english'))\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "# function to convert nltk tag to wordnet tag\n",
    "def nltk_tag_to_wordnet_tag(nltk_tag):\n",
    "    if nltk_tag.startswith('J'):\n",
    "        return wordnet.ADJ\n",
    "    elif nltk_tag.startswith('V'):\n",
    "        return wordnet.VERB\n",
    "    elif nltk_tag.startswith('N'):\n",
    "        return wordnet.NOUN\n",
    "    elif nltk_tag.startswith('R'):\n",
    "        return wordnet.ADV\n",
    "    else:          \n",
    "        return None\n",
    "\n",
    "def lemmatized_text(sentence):\n",
    "    # tokenize into words\n",
    "    words = nltk.word_tokenize(sentence)\n",
    "    \n",
    "    # remove stop words (this is optional but generally done)\n",
    "    words = [word for word in words if word not in stopwords.words(\"english\")]\n",
    "    \n",
    "    # tokenize the sentence and find the POS tag for each token\n",
    "    nltk_tagged = nltk.pos_tag(words)\n",
    "    \n",
    "    # tuple of (token, wordnet_tag)\n",
    "    wordnet_tagged = map(lambda x: (x[0], nltk_tag_to_wordnet_tag(x[1])), nltk_tagged)\n",
    "    lemmatized_sentence = []\n",
    "    for word, tag in wordnet_tagged:\n",
    "        if tag is None:\n",
    "            # if there is no available tag, append the token as is\n",
    "            lemmatized_sentence.append(word)\n",
    "        else:        \n",
    "            # else use the tag to lemmatize the token\n",
    "            lemmatized_sentence.append(lemmatizer.lemmatize(word, tag))\n",
    "    return \" \".join(lemmatized_sentence)"
   ]
  },
  {
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   "execution_count": 12,
   "id": "463e7880",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 21072/21072 [17:35<00:00, 19.96it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "# Create a dataframe('df_clean') that will have only the complaints and the lemmatized complaints \n",
    "df_clean = pd.DataFrame()\n",
    "\n",
    "for x in tqdm(df.complaint_what_happened):\n",
    "    df_clean = df_clean.append({'complaint_what_happened':x, 'lemmatized_complaints':lemmatized_text(cleaned_text(x))}, ignore_index=True)"
   ]
  },
  {
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>complaint_what_happened</th>\n",
       "      <th>lemmatized_complaints</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Good morning my name is XXXX XXXX and I apprec...</td>\n",
       "      <td>good morning name xxxx xxxx appreciate could h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I upgraded my XXXX XXXX card in XX/XX/2018 and...</td>\n",
       "      <td>upgraded xxxx xxxx card xxxx tell agent upgrad...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chase Card was reported on XX/XX/2019. However...</td>\n",
       "      <td>chase card report xxxx however fraudulent appl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>On XX/XX/2018, while trying to book a XXXX  XX...</td>\n",
       "      <td>xxxx try book xxxx xxxx ticket come across off...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>my grand son give me check for {$1600.00} i de...</td>\n",
       "      <td>grand son give check deposit chase account fun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21067</th>\n",
       "      <td>After being a Chase Card customer for well ove...</td>\n",
       "      <td>chase card customer well decade offer multiple...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21068</th>\n",
       "      <td>On Wednesday, XX/XX/XXXX I called Chas, my XXX...</td>\n",
       "      <td>wednesday xxxxxxxx call chas xxxx xxxx visa cr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21069</th>\n",
       "      <td>I am not familiar with XXXX pay and did not un...</td>\n",
       "      <td>familiar xxxx pay understand great risk provid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21070</th>\n",
       "      <td>I have had flawless credit for 30 yrs. I've ha...</td>\n",
       "      <td>flawless credit yrs ive chase credit card chas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21071</th>\n",
       "      <td>Roughly 10+ years ago I closed out my accounts...</td>\n",
       "      <td>roughly year ago close account jp morgan chase...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21072 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 complaint_what_happened  \\\n",
       "0      Good morning my name is XXXX XXXX and I apprec...   \n",
       "1      I upgraded my XXXX XXXX card in XX/XX/2018 and...   \n",
       "2      Chase Card was reported on XX/XX/2019. However...   \n",
       "3      On XX/XX/2018, while trying to book a XXXX  XX...   \n",
       "4      my grand son give me check for {$1600.00} i de...   \n",
       "...                                                  ...   \n",
       "21067  After being a Chase Card customer for well ove...   \n",
       "21068  On Wednesday, XX/XX/XXXX I called Chas, my XXX...   \n",
       "21069  I am not familiar with XXXX pay and did not un...   \n",
       "21070  I have had flawless credit for 30 yrs. I've ha...   \n",
       "21071  Roughly 10+ years ago I closed out my accounts...   \n",
       "\n",
       "                                   lemmatized_complaints  \n",
       "0      good morning name xxxx xxxx appreciate could h...  \n",
       "1      upgraded xxxx xxxx card xxxx tell agent upgrad...  \n",
       "2      chase card report xxxx however fraudulent appl...  \n",
       "3      xxxx try book xxxx xxxx ticket come across off...  \n",
       "4      grand son give check deposit chase account fun...  \n",
       "...                                                  ...  \n",
       "21067  chase card customer well decade offer multiple...  \n",
       "21068  wednesday xxxxxxxx call chas xxxx xxxx visa cr...  \n",
       "21069  familiar xxxx pay understand great risk provid...  \n",
       "21070  flawless credit yrs ive chase credit card chas...  \n",
       "21071  roughly year ago close account jp morgan chase...  \n",
       "\n",
       "[21072 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "07c54b1b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:47:10.165279Z",
     "iopub.status.busy": "2022-01-13T18:47:10.164563Z",
     "iopub.status.idle": "2022-01-13T18:51:23.757878Z",
     "shell.execute_reply": "2022-01-13T18:51:23.758352Z",
     "shell.execute_reply.started": "2022-01-13T18:05:34.548087Z"
    },
    "id": "Kk7fc4DuJrF1",
    "papermill": {
     "duration": 255.64144,
     "end_time": "2022-01-13T18:51:23.758546",
     "exception": false,
     "start_time": "2022-01-13T18:47:08.117106",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 21072/21072 [04:13<00:00, 83.27it/s]\n",
      "100%|██████████| 21072/21072 [00:00<00:00, 45122.98it/s]\n"
     ]
    }
   ],
   "source": [
    "from textblob import TextBlob\n",
    "\n",
    "tqdm.pandas()\n",
    "\n",
    "# Write your function to extract the POS tags \n",
    "def extract_pos_tag(sentence):\n",
    "    # Create a textblob object\n",
    "    blob = TextBlob(sentence)\n",
    "    \n",
    "    # extract words with tags 'NN', join them and return\n",
    "    return ' '.join([ word for (word,tag) in blob.tags if tag == \"NN\"])\n",
    "    \n",
    "df_clean[\"complaint_POS_removed\"] = df_clean.progress_apply(lambda x: extract_pos_tag(x.lemmatized_complaints), axis=1)\n",
    "df_clean[\"length\"] = df_clean.progress_apply(lambda x: len(x.complaint_POS_removed), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c5f736b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:51:29.191671Z",
     "iopub.status.busy": "2022-01-13T18:51:29.180425Z",
     "iopub.status.idle": "2022-01-13T18:51:29.196339Z",
     "shell.execute_reply": "2022-01-13T18:51:29.195692Z",
     "shell.execute_reply.started": "2022-01-13T18:09:36.646992Z"
    },
    "id": "AjxfchvFJrF2",
    "papermill": {
     "duration": 2.740563,
     "end_time": "2022-01-13T18:51:29.196501",
     "exception": false,
     "start_time": "2022-01-13T18:51:26.455938",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>complaint_what_happened</th>\n",
       "      <th>lemmatized_complaints</th>\n",
       "      <th>complaint_POS_removed</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Good morning my name is XXXX XXXX and I apprec...</td>\n",
       "      <td>good morning name xxxx xxxx appreciate could h...</td>\n",
       "      <td>morning name appreciate chase bank cardmember ...</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I upgraded my XXXX XXXX card in XX/XX/2018 and...</td>\n",
       "      <td>upgraded xxxx xxxx card xxxx tell agent upgrad...</td>\n",
       "      <td>xxxx card tell agent date information order up...</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chase Card was reported on XX/XX/2019. However...</td>\n",
       "      <td>chase card report xxxx however fraudulent appl...</td>\n",
       "      <td>card report application submit identity consen...</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>On XX/XX/2018, while trying to book a XXXX  XX...</td>\n",
       "      <td>xxxx try book xxxx xxxx ticket come across off...</td>\n",
       "      <td>try book ticket come offer apply ticket card i...</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>my grand son give me check for {$1600.00} i de...</td>\n",
       "      <td>grand son give check deposit chase account fun...</td>\n",
       "      <td>son check deposit chase account fund bank acco...</td>\n",
       "      <td>163</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>21067</th>\n",
       "      <td>After being a Chase Card customer for well ove...</td>\n",
       "      <td>chase card customer well decade offer multiple...</td>\n",
       "      <td>chase card customer decade offer solicitation ...</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21068</th>\n",
       "      <td>On Wednesday, XX/XX/XXXX I called Chas, my XXX...</td>\n",
       "      <td>wednesday xxxxxxxx call chas xxxx xxxx visa cr...</td>\n",
       "      <td>call chas xxxx credit card provider claim purc...</td>\n",
       "      <td>702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21069</th>\n",
       "      <td>I am not familiar with XXXX pay and did not un...</td>\n",
       "      <td>familiar xxxx pay understand great risk provid...</td>\n",
       "      <td>pay risk consumer chase bank chase year trust ...</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21070</th>\n",
       "      <td>I have had flawless credit for 30 yrs. I've ha...</td>\n",
       "      <td>flawless credit yrs ive chase credit card chas...</td>\n",
       "      <td>flawless credit chase credit card chase freedo...</td>\n",
       "      <td>563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21071</th>\n",
       "      <td>Roughly 10+ years ago I closed out my accounts...</td>\n",
       "      <td>roughly year ago close account jp morgan chase...</td>\n",
       "      <td>year account jp bank order line credit pay acc...</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21072 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 complaint_what_happened  \\\n",
       "0      Good morning my name is XXXX XXXX and I apprec...   \n",
       "1      I upgraded my XXXX XXXX card in XX/XX/2018 and...   \n",
       "2      Chase Card was reported on XX/XX/2019. However...   \n",
       "3      On XX/XX/2018, while trying to book a XXXX  XX...   \n",
       "4      my grand son give me check for {$1600.00} i de...   \n",
       "...                                                  ...   \n",
       "21067  After being a Chase Card customer for well ove...   \n",
       "21068  On Wednesday, XX/XX/XXXX I called Chas, my XXX...   \n",
       "21069  I am not familiar with XXXX pay and did not un...   \n",
       "21070  I have had flawless credit for 30 yrs. I've ha...   \n",
       "21071  Roughly 10+ years ago I closed out my accounts...   \n",
       "\n",
       "                                   lemmatized_complaints  \\\n",
       "0      good morning name xxxx xxxx appreciate could h...   \n",
       "1      upgraded xxxx xxxx card xxxx tell agent upgrad...   \n",
       "2      chase card report xxxx however fraudulent appl...   \n",
       "3      xxxx try book xxxx xxxx ticket come across off...   \n",
       "4      grand son give check deposit chase account fun...   \n",
       "...                                                  ...   \n",
       "21067  chase card customer well decade offer multiple...   \n",
       "21068  wednesday xxxxxxxx call chas xxxx xxxx visa cr...   \n",
       "21069  familiar xxxx pay understand great risk provid...   \n",
       "21070  flawless credit yrs ive chase credit card chas...   \n",
       "21071  roughly year ago close account jp morgan chase...   \n",
       "\n",
       "                                   complaint_POS_removed  length  \n",
       "0      morning name appreciate chase bank cardmember ...     175  \n",
       "1      xxxx card tell agent date information order up...     117  \n",
       "2      card report application submit identity consen...      81  \n",
       "3      try book ticket come offer apply ticket card i...     429  \n",
       "4      son check deposit chase account fund bank acco...     163  \n",
       "...                                                  ...     ...  \n",
       "21067  chase card customer decade offer solicitation ...     232  \n",
       "21068  call chas xxxx credit card provider claim purc...     702  \n",
       "21069  pay risk consumer chase bank chase year trust ...     695  \n",
       "21070  flawless credit chase credit card chase freedo...     563  \n",
       "21071  year account jp bank order line credit pay acc...     252  \n",
       "\n",
       "[21072 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The clean dataframe should now contain the raw complaint, lemmatized complaint and the complaint after removing POS tags.\n",
    "df_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf6dee5",
   "metadata": {
    "id": "_7Un1AElJrF2",
    "papermill": {
     "duration": 2.722454,
     "end_time": "2022-01-13T18:51:34.551572",
     "exception": false,
     "start_time": "2022-01-13T18:51:31.829118",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Exploratory data analysis to get familiar with the data.\n",
    "\n",
    "Write the code in this task to perform the following:\n",
    "\n",
    "*   Visualise the data according to the 'Complaint' character length.\n",
    "*   Using a word cloud find the top 40 words by frequency among all the articles after processing the text.\n",
    "*   Find the top unigrams, bigrams and trigrams by frequency among all the complaints after processing the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "33443706",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:51:39.856512Z",
     "iopub.status.busy": "2022-01-13T18:51:39.855794Z",
     "iopub.status.idle": "2022-01-13T18:51:40.184613Z",
     "shell.execute_reply": "2022-01-13T18:51:40.184098Z",
     "shell.execute_reply.started": "2022-01-13T18:09:36.671248Z"
    },
    "id": "q-zaqJF6JrF2",
    "papermill": {
     "duration": 3.014492,
     "end_time": "2022-01-13T18:51:40.184761",
     "exception": false,
     "start_time": "2022-01-13T18:51:37.170269",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAphE0AAAAANMIGgAAAIBpBA0AAADANIIGAAAAYBpBAwAAADCNoAEAAACYZtN6d4D7p82nXzJlPzedecKU/QAAADCHKxoAAACAaQQNAAAAwDSCBgAAAGAaQQMAAAAwjaABAAAAmEbQAAAAAEwjaAAAAACmETQAAAAA0wgaAAAAgGkEDQAAAMA0ggYAAABgGkEDAAAAMI2gAQAAAJhG0AAAAABMs2m9OwD3xebTL5m2r5vOPGHavgAAAO6vXNEAAAAATCNoAAAAAKYRNAAAAADTCBoAAACAaTwMEoZZD5b0UEkAAOD+bM2vaKiq46vq+qraWlWnr/XxAQAAgD1nTYOGqtovyR8meWaSo5L8TFUdtZZ9AAAAAPactb514pgkW7v7xiSpqguSnJjk2jXuB+wxs27BmMWtHAAAwFpa66Dh0CQ3L1m/JckT17gPcL+ytwUfG51gBwCA+7u97mGQVXVqklPH6uer6vr17M+99PAkn1rvTrBPMWY2iHrlmh3KmGG1jBlWy5hhtYwZVsuY2ff9i50VrnXQcGuSw5esHzbK7tHdZyc5ey07NVtVbenuo9e7H+w7jBlWy5hhtYwZVsuYYbWMGVbLmNm41vqtE1ckObKqjqiq/ZOclOTiNe4DAAAAsIes6RUN3X13Vf1Kkncm2S/Jud19zVr2AQAAANhz1vwZDd39jiTvWOvjrrF9+tYP1oUxw2oZM6yWMcNqGTOsljHDahkzG1R193r3AQAAANgg1voZDQAAAMAGJmiYqKqOr6rrq2prVZ2+3v1h/VTV4VV1WVVdW1XXVNULR/nDqurSqrphfD5olFdVvXqMnaur6glL9nXyaH9DVZ28XufE2qiq/arqqqp6+1g/oqouH2PjzeNBuqmqB4z1raN+85J9vGSUX19Vz1inU2ENVNWBVXVRVX2kqq6rqh8wz7ArVfXr4+fSh6vqTVX1QPMMO6qqc6vq9qr68JKyaXNLVX1fVX1obPPqqqq1PUNmW2bM/M74+XR1Vf1JVR24pG6nc8hyf08tN0+x9xI0TFJV+yX5wyTPTHJUkp+pqqPWt1eso7uTvKi7j0pybJLTxng4Pcm7uvvIJO8a68li3Bw5Pk5Nclay+KGe5IwkT0xyTJIztv9gZ8N6YZLrlqy/MsmruvtRSe5McsooPyXJnaP8VaNdxjg7Kcljkhyf5DVjfmJj+oMkf97d35nkcVmMHfMMO1VVhyb51SRHd/djs3gw90kxz/DNXp/F93apmXPLWUl+Ycl2Ox6Lfc/r883fx0uTPLa7vyfJ3yd5SbL8HLKbv6eWm6fYSwka5jkmydbuvrG7v5zkgiQnrnOfWCfdfVt3v38sfy6LX/4PzWJMnDeanZfkOWP5xCRv6IX3Jjmwqh6R5BlJLu3uO7r7ziwmbD+MN6iqOizJCUleN9YryVOTXDSa7Dhmto+li5IcN9qfmOSC7v5Sd380ydYs5ic2mKo6IMmTk5yTJN395e7+TMwz7NqmJA+qqk1JHpzktphn2EF3/1WSO3YonjK3jLp/2t3v7cXD4t6wZF/so3Y2Zrr7L7r77rH63iSHjeXl5pCd/j21m9+H2EsJGuY5NMnNS9ZvGWXcz41LTR+f5PIkh3T3baPqE0kOGcvLjR/j6v7l95O8OMnXxvq3JfnMkh/SS7//94yNUX/XaG/M3H8ckWRbkj+uxe02r6uqh8Q8wzK6+9Ykv5vk41kEDHcluTLmGVZm1txy6FjesZyN7d8k+bOxvNoxs6vfh9hLCRpgD6qqhyZ5S5Jf6+7PLq0bKb7XvpAkqapnJ7m9u69c776wz9iU5AlJzuruxyf5x3z9UuYk5hm+0bhs/cQsQqpvT/KQuHqFe8HcwmpU1b/P4rbi89e7L6wdQcM8tyY5fMn6YaOM+6mq+tYsQobzu/uto/iT45LBjM+3j/Llxo9xdf/xpCQ/VlU3ZXGp4FOzuP/+wHGJc/KN3/97xsaoPyDJp2PM3J/ckuSW7r58rF+URfBgnmE5T0vy0e7e1t1fSfLWLOYe8wwrMWtuuTVfv4R+aTkbUFX9XJJnJ3neCKiS1Y+ZT2f5eYq9lKBhniuSHDmeiLp/Fg84uXid+8Q6GfeSnZPkuu7+vSVVFyfZ/tTlk5O8bUn588eTm49Ncte4PPGdSZ5eVQeN/0Q9fZSxwXT3S7r7sO7enMX88e7ufl6Sy5I8dzTbccxsH0vPHe17lJ80nhZ/RBYP2XrfGp0Ga6i7P5Hk5qp69Cg6Lsm1Mc+wvI8nObaqHjx+Tm0fM+YZVmLK3DLqPltVx45x+Pwl+2IDqarjs7gl9Me6+wtLqpabQ3b699SYd5abp9hLbdp9E1aiu++uql/JYlLdL8m53X3NOneL9fOkJD+b5ENV9YFR9ptJzkxyYVWdkuRjSX5q1L0jybOyeBjOF5L8fJJ09x1V9bIsJt4keWl37/hwJja230hyQVW9PMlVGQ/+G5/fWFVbs3j40klJ0t3XVNWFWfzxcHeS07r7q2vfbdbIC5KcP34huzGLueNbYp5hJ7r78qq6KMn7s5gfrkpydpJLYp5hiap6U5KnJHl4Vd2SxdsjZv4O88tZvKXgQVnct7/93n32UcuMmZckeUCSSxeZUt7b3b+0qzlkF39PLff7EHup+voVLAAAAAD3jVsnAAAAgGkEDQAAAMA0ggYAAABgGkEDAAAAMI2gAQAAAJhG0AAAe0BV/fOquqCq/qGqrqyqd1TVv9yDx3tKVb19N22OrqpX76bNgVX1y7uo3+l5reT4s1XVb07Yx+ur6rm7b3mv9/+UqvrBtToeAOwNBA0AMFktXhj+J0ne092P7O7vy+J94oesZ7+6e0t3/+pumh2YxTvuv8mePK+q2nQvNlt10FBV+92L49wXT0nyg7trBAAbiaABAOb7kSRf6e4/2l7Q3R/s7r+uhd+pqg9X1Yeq6qeTe/7z/ZdV9baqurGqzqyq51XV+0a7R452r6+qP6qqLVX191X17B0PXlXHVNXfVdVVVfV/qurRS47x9rH8W1V1blW9ZxxvewBxZpJHVtUHqup3VnpeY/WhVXVRVX2kqs4fwUSq6j9V1RXjnM9eUv6eqvr9qtqS5IVV9aNVdfno9/+uqkNGu4dW1R+Pr8PVVfUTVXVmkgeNfp4/2v3r8fX6QFW9dnuoUFWfr6r/WlUfTPIDu/vmVdV+43t0xTjeLy75+r1nmXN81ii7sqpeXVVvr6rNSX4pya+PPv3wOMSTx/flRlc3ALAR3Zv/HgAAu/bYJFcuU/evknxvkscleXiSK6rqr0bd45J8V5I7ktyY5HXdfUxVvTDJC5L82mi3OckxSR6Z5LKqetQOx/hIkh/u7rur6mlJ/nOSn9hJX74zi/DgnyS5vqrOSnJ6ksd29/eu8ryS5PFJHpPk/yb52yRPSvI3Sf57d780SarqjUmeneRPxzb7d/fRo+6gJMd2d1fVv03y4iQvSvIfk9zV3d+9vV13v6WqfmV7P6vqu5L8dJIndfdXquo1SZ6X5A1JHpLk8u5+0S76vtQp43jfX1UPSPK3VfUXy53jCEpem+TJ3f3RqnpTknT3TVX1R0k+392/O/p5SpJHJPmhLL7+Fye5aIX9AoB9gqABANbWDyV5U3d/Ncknq+ovk3x/ks8muaK7b0uSqvqHJNv/uP1QFoHAdhd299eS3FBVN2bxB+tSByQ5r6qOTNJJvnWZvlzS3V9K8qWquj33/RaI93X3LaP/H8giEPmbJD9SVS9O8uAkD0tyTb4eNLx5yfaHJXlzVT0iyf5JPjrKn5bkpO2NuvvOnRz7uCTfl0VwkyQPSnL7qPtqkres4jyenuR7llxtcECSI5N8eZlz/HySG7t7e3/flOTUXez/f43v37Xbr9oAgI3ErRMAMN81WfzRu1pfWrL8tSXrX8s3/nOgd9hux/WXJbmsux+b5EeTPHAFx/tqdv8PiN2d1zftr6oemOQ1SZ47rkj4Hzv05x+XLP+3LK5++O4kv7iLfu9MJTmvu793fDy6u39r1H1xBDur2dcLluzriO7eHvqs9mu2M0v3UfdiewDYqwkaAGC+dyd5QFXd81/tqvqecY/+Xyf56fEcgIOTPDnJ+1a5/5+sqm8Zz234jiTX71B/QJJbx/LPrXLfn8viVoqd2dV5LWd7WPCpqnpokl09k2Bpv09eUn5pktOWHPOgsfiVqtp+tca7kjy3qv7ZaPOwqvoXuzjWrrwzyb/bvu9avFXjIbtof32S7xjPZEgWt3Bst6uvJwBsSIIGAJisuzvJjyd5Wi1eA3lNkt9O8oks3tpwdZIPZvGH+4u7+xOrPMTHswgn/izJL3X3F3eo/y9Jfruqrsoq/+Pe3Z/O4pkEH97xYZC7Oa/l9veZLK5i+HAWf8BfsYvD/1aS/1lVVyb51JLylyc5aPTpg/n6bSRnJ7m6qs7v7muT/Ickf1FVV2cRTjxihaf92qq6ZXz8XZLXJbk2yfur6sNZPH9h2a9jd/+/LN7U8eej759Lcteo/tMkP77DwyABYEOrxe8MAMC+oKpen+Tt3e0BgnuRqnpod39+vIXiD5Pc0N2vWu9+AcB6cEUDAMB99wvj4ZDXZHELyGvXtzsAsH5c0QAAAABM44oGAAAAYBpBAwAAADCNoAEAAACYRtAAAAAATCNoAAAAAKYRNAAAAADT/H9E3II9ixrpKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write your code here to visualise the data according to the 'Complaint' character length\n",
    "plt.figure(figsize=(18,5))\n",
    "plt.hist(df_clean.length, bins=50)\n",
    "plt.xlabel(\"Complaint Character Length\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ce655a98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:51:45.567957Z",
     "iopub.status.busy": "2022-01-13T18:51:45.567230Z",
     "iopub.status.idle": "2022-01-13T18:51:46.002651Z",
     "shell.execute_reply": "2022-01-13T18:51:46.003125Z",
     "shell.execute_reply.started": "2022-01-13T18:09:37.016629Z"
    },
    "papermill": {
     "duration": 3.152928,
     "end_time": "2022-01-13T18:51:46.003310",
     "exception": false,
     "start_time": "2022-01-13T18:51:42.850382",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBsAAAE9CAYAAABKj2pIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAfA0lEQVR4nO3dfbRmVX0f8O9PRtGKSzBMKAXSIYa8oIloJmiicRmNiJAUbYzBlSbURYtJINXWLjO62mpMbCYvxsTWN1QiZhmRaqxESJT4EmMagUERASWMOBYoAoqi1oYE/PWP54w+jnNn5s7suW/z+az1rOecffazzz53cx7mfu8+51R3BwAAAGCU+yx3BwAAAIC1RdgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDrlrsDu3L44Yf3hg0blrsbAAAAwA6uvPLKz3f3+p1tW9Fhw4YNG7Jly5bl7gYAAACwg6r67ELbXEYBAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQ61b7g6wsA2bLl7uLnybbZtPXe4uAAAAsMKZ2QAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoXYbNlTV/avq8qr6eFVdW1W/PpUfW1WXVdXWqnpbVd1vKj94Wt86bd8w19YLp/Lrq+op++2oAAAAgGWzJzMb7k7yxO5+RJITkpxcVY9J8ttJXtHd35Pki0nOnOqfmeSLU/krpnqpquOTnJ7kYUlOTvLqqjpo4LEAAAAAK8Buw4ae+eq0et/p1UmemOTtU/n5SZ42LZ82rWfa/qSqqqn8gu6+u7s/k2RrkhNHHAQAAACwcuzRPRuq6qCquirJ7UkuTfLpJF/q7numKjcnOWpaPirJTUkybb8ryXfMl+/kMwAAAMAasUdhQ3ff290nJDk6s9kI37+/OlRVZ1XVlqracscdd+yv3QAAAAD7yaKeRtHdX0rygSQ/muTQqlo3bTo6yS3T8i1JjkmSafuDk3xhvnwnn5nfx7ndvbG7N65fv34x3QMAAABWgD15GsX6qjp0Wn5Akicn+WRmocMzpmpnJHnXtHzRtJ5p+/u7u6fy06enVRyb5Lgklw86DgAAAGCFWLf7KjkyyfnTkyPuk+TC7n53VV2X5IKq+s0kH0vyxqn+G5P8cVVtTXJnZk+gSHdfW1UXJrkuyT1Jzu7ue8ceDgAAALDcdhs2dPfVSR65k/Ibs5OnSXT33yf52QXaelmSly2+mwAAAMBqsah7NgAAAADsjrABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGCodcvdAVaXDZsuHtLOts2nDmkHAACAlcfMBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQ+02bKiqY6rqA1V1XVVdW1XPncpfUlW3VNVV0+uUuc+8sKq2VtX1VfWUufKTp7KtVbVp/xwSAAAAsJzW7UGde5I8v7s/WlUPSnJlVV06bXtFd//efOWqOj7J6UkeluSfJfnLqvreafOrkjw5yc1Jrqiqi7r7uhEHAgAAAKwMuw0buvvWJLdOy1+pqk8mOWoXHzktyQXdfXeSz1TV1iQnTtu2dveNSVJVF0x1hQ0AAACwhizqng1VtSHJI5NcNhWdU1VXV9V5VXXYVHZUkpvmPnbzVLZQOQAAALCG7HHYUFWHJHlHkud195eTvCbJQ5OckNnMh5eP6FBVnVVVW6pqyx133DGiSQAAAGAJ7VHYUFX3zSxoeEt3/2mSdPdt3X1vd389yevzzUslbklyzNzHj57KFir/Ft19bndv7O6N69evX+zxAAAAAMtsT55GUUnemOST3f37c+VHzlV7epJrpuWLkpxeVQdX1bFJjktyeZIrkhxXVcdW1f0yu4nkRWMOAwAAAFgp9uRpFI9N8gtJPlFVV01lL0ryrKo6IUkn2ZbkOUnS3ddW1YWZ3fjxniRnd/e9SVJV5yR5T5KDkpzX3dcOOxIAAABgRdiTp1F8OEntZNMlu/jMy5K8bCfll+zqcwAAAMDqt6inUQAAAADsjrABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGCodcvdAQ5MGzZdPKSdbZtPHdIOAAAA45jZAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMNRuw4aqOqaqPlBV11XVtVX13Kn8IVV1aVXdML0fNpVXVb2yqrZW1dVV9ai5ts6Y6t9QVWfsv8MCAAAAlsuezGy4J8nzu/v4JI9JcnZVHZ9kU5L3dfdxSd43rSfJU5McN73OSvKaZBZOJHlxkkcnOTHJi7cHFAAAAMDasduwobtv7e6PTstfSfLJJEclOS3J+VO185M8bVo+Lcmbe+YjSQ6tqiOTPCXJpd19Z3d/McmlSU4eeTAAAADA8lvUPRuqakOSRya5LMkR3X3rtOlzSY6Ylo9KctPcx26eyhYq33EfZ1XVlqracscddyymewAAAMAKsMdhQ1UdkuQdSZ7X3V+e39bdnaRHdKi7z+3ujd29cf369SOaBAAAAJbQHoUNVXXfzIKGt3T3n07Ft02XR2R6v30qvyXJMXMfP3oqW6gcAAAAWEP25GkUleSNST7Z3b8/t+miJNufKHFGknfNlf/i9FSKxyS5a7rc4j1JTqqqw6YbQ540lQEAAABryLo9qPPYJL+Q5BNVddVU9qIkm5NcWFVnJvlskmdO2y5JckqSrUm+luTZSdLdd1bVbyS5Yqr30u6+c8RBAAAAACvHbsOG7v5wklpg85N2Ur+TnL1AW+clOW8xHQQAAABWl0U9jQIAAABgd4QNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAICh1i13B2BfbNh08bC2tm0+dVhbAAAABzIzGwAAAIChhA0AAADAUMIGAAAAYCj3bNgPRt5HAAAAAFYbMxsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKF2GzZU1XlVdXtVXTNX9pKquqWqrppep8xte2FVba2q66vqKXPlJ09lW6tq0/hDAQAAAFaCPZnZ8KYkJ++k/BXdfcL0uiRJqur4JKcnedj0mVdX1UFVdVCSVyV5apLjkzxrqgsAAACsMet2V6G7P1RVG/awvdOSXNDddyf5TFVtTXLitG1rd9+YJFV1wVT3usV3GQAAAFjJ9uWeDedU1dXTZRaHTWVHJblprs7NU9lC5QAAAMAas7dhw2uSPDTJCUluTfLyUR2qqrOqaktVbbnjjjtGNQsAAAAskb0KG7r7tu6+t7u/nuT1+ealErckOWau6tFT2ULlO2v73O7e2N0b169fvzfdAwAAAJbRXoUNVXXk3OrTk2x/UsVFSU6vqoOr6tgkxyW5PMkVSY6rqmOr6n6Z3UTyor3vNgAAALBS7fYGkVX11iRPSHJ4Vd2c5MVJnlBVJyTpJNuSPCdJuvvaqrowsxs/3pPk7O6+d2rnnCTvSXJQkvO6+9rRBwMAAAAsvz15GsWzdlL8xl3Uf1mSl+2k/JIklyyqdwAAAMCqsy9PowAAAAD4NsIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGWrfcHYCVYsOmi4e0s23zqUPaAQAAWK3MbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhK2AAAAAAMJWwAAAAAhhI2AAAAAEMJGwAAAIChhA0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADLXbsKGqzquq26vqmrmyh1TVpVV1w/R+2FReVfXKqtpaVVdX1aPmPnPGVP+Gqjpj/xwOAAAAsNz2ZGbDm5KcvEPZpiTv6+7jkrxvWk+SpyY5bnqdleQ1ySycSPLiJI9OcmKSF28PKAAAAIC1ZbdhQ3d/KMmdOxSfluT8afn8JE+bK39zz3wkyaFVdWSSpyS5tLvv7O4vJrk03x5gAAAAAGvA3t6z4YjuvnVa/lySI6blo5LcNFfv5qlsoXIAAABgjVm3rw10d1dVj+hMklTVWZldgpHv+q7vGtUsLJkNmy4e0s62zacOaQcAAGCp7e3MhtumyyMyvd8+ld+S5Ji5ekdPZQuVf5vuPre7N3b3xvXr1+9l9wAAAIDlsrdhw0VJtj9R4owk75or/8XpqRSPSXLXdLnFe5KcVFWHTTeGPGkqAwAAANaY3V5GUVVvTfKEJIdX1c2ZPVVic5ILq+rMJJ9N8syp+iVJTkmyNcnXkjw7Sbr7zqr6jSRXTPVe2t073nQSAAAAWAN2GzZ097MW2PSkndTtJGcv0M55Sc5bVO8AAACAVWdvL6MAAAAA2ClhAwAAADCUsAEAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGWrfcHQB2bsOmi4e0s23zqUPaAQAA2FNmNgAAAABDCRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAwlLABAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQwgYAAABgKGEDAAAAMJSwAQAAABhq3XJ3ANi/Nmy6eFhb2zafOqwtAABg7TKzAQAAABhK2AAAAAAMJWwAAAAAhtqnsKGqtlXVJ6rqqqraMpU9pKouraobpvfDpvKqqldW1daqurqqHjXiAAAAAICVZcTMhp/o7hO6e+O0vinJ+7r7uCTvm9aT5KlJjpteZyV5zYB9AwAAACvM/riM4rQk50/L5yd52lz5m3vmI0kOraoj98P+AQAAgGW0r2FDJ3lvVV1ZVWdNZUd0963T8ueSHDEtH5XkprnP3jyVfYuqOquqtlTVljvuuGMfuwcAAAAstXX7+PnHdfctVfWdSS6tqk/Nb+zurqpeTIPdfW6Sc5Nk48aNi/osAAAAsPz2aWZDd98yvd+e5J1JTkxy2/bLI6b326fqtyQ5Zu7jR09lAAAAwBqy1zMbquqBSe7T3V+Zlk9K8tIkFyU5I8nm6f1d00cuSnJOVV2Q5NFJ7pq73AJYBTZsunhIO9s2nzqkHQAAYGXal8sojkjyzqra3s6fdPdfVNUVSS6sqjOTfDbJM6f6lyQ5JcnWJF9L8ux92DcAAACwQu112NDdNyZ5xE7Kv5DkSTsp7yRn7+3+AAAAgNVhfzz6EgAAADiACRsAAACAoYQNAAAAwFDCBgAAAGAoYQMAAAAw1L48+hJgr2zYdPGQdrZtPnVIOwAAwFhmNgAAAABDCRsAAACAoVxGAaxaoy7HSFySAQAAI5nZAAAAAAxlZgNA3LQSAABGMrMBAAAAGErYAAAAAAzlMgqAgVyOAQAAZjYAAAAAgwkbAAAAgKGEDQAAAMBQ7tkAsAK59wMAAKuZmQ0AAADAUMIGAAAAYChhAwAAADCUsAEAAAAYStgAAAAADOVpFABr2KinWiSebAEAwJ4zswEAAAAYStgAAAAADOUyCgD2yKhLMlyOAQCw9gkbAFhSQgsAgLXPZRQAAADAUMIGAAAAYCiXUQCwKrkcAwBg5VrysKGqTk7yh0kOSvKG7t681H0AgO1GhRYjCUAAgNVuScOGqjooyauSPDnJzUmuqKqLuvu6pewHAKxkKy0AEX4AAIu11DMbTkyytbtvTJKquiDJaUmEDQCwQo0MP0YFFystkFmJhEQALKelDhuOSnLT3PrNSR69xH0AAJaJkGDp+FkzgoBwdVpp47YSw8+V9t/kSvwZ7asVd4PIqjoryVnT6ler6vrl7M9eOjzJ55e7E+wz47h2GMu1wTiuDcZx7TCWa8Mux7F+ewl7wr74lnFcaeO20vqzEk0/o9X4vfrPF9qw1GHDLUmOmVs/eir7hu4+N8m5S9mp0apqS3dvXO5+sG+M49phLNcG47g2GMe1w1iuDcZxbTCOa8NaG8f7LPH+rkhyXFUdW1X3S3J6kouWuA8AAADAfrSkMxu6+56qOifJezJ79OV53X3tUvYBAAAA2L+W/J4N3X1JkkuWer9LbFVfBsI3GMe1w1iuDcZxbTCOa4exXBuM49pgHNeGNTWO1d3L3QcAAABgDVnqezYAAAAAa5ywYbCqOrmqrq+qrVW1abn7w65V1baq+kRVXVVVW6ayh1TVpVV1w/R+2FReVfXKaWyvrqpHLW/vD1xVdV5V3V5V18yVLXrcquqMqf4NVXXGchzLgWyBcXxJVd0ynZNXVdUpc9teOI3j9VX1lLly37vLqKqOqaoPVNV1VXVtVT13KndOrjK7GEvn5SpSVfevqsur6uPTOP76VH5sVV02jcnbppu1p6oOnta3Tts3zLW10/Fl/9vFOL6pqj4zdz6eMJX7bl3BquqgqvpYVb17Wj8wzsfu9hr0yuyml59O8t1J7pfk40mOX+5+ee1yzLYlOXyHst9Jsmla3pTkt6flU5L8eZJK8pgkly13/w/UV5LHJ3lUkmv2dtySPCTJjdP7YdPyYct9bAfSa4FxfEmS/7iTusdP36kHJzl2+q49yPfu8r+SHJnkUdPyg5L83TRezslV9trFWDovV9FrOrcOmZbvm+Sy6Vy7MMnpU/lrk/zytPwrSV47LZ+e5G27Gt/lPr4D5bWLcXxTkmfspL7v1hX8SvIfkvxJkndP6wfE+Whmw1gnJtna3Td29z8kuSDJacvcJxbvtCTnT8vnJ3naXPmbe+YjSQ6tqiOXoX8HvO7+UJI7dyhe7Lg9Jcml3X1nd38xyaVJTt7vnecbFhjHhZyW5ILuvru7P5Nka2bfub53l1l339rdH52Wv5Lkk0mOinNy1dnFWC7EebkCTefWV6fV+06vTvLEJG+fync8J7efq29P8qSqqiw8viyBXYzjQny3rlBVdXSSU5O8YVqvHCDno7BhrKOS3DS3fnN2/T9pll8neW9VXVlVZ01lR3T3rdPy55IcMS0b35VtseNmPFeuc6YpoOdtn3of47gqTNM9H5nZX+Cck6vYDmOZOC9XlWnK9lVJbs/sl8tPJ/lSd98zVZkfk2+M17T9riTfEeO47HYcx+7efj6+bDofX1FVB09lzseV6w+SvCDJ16f178gBcj4KGzjQPa67H5XkqUnOrqrHz2/s2bwlj2xZZYzbqvaaJA9NckKSW5O8fFl7wx6rqkOSvCPJ87r7y/PbnJOry07G0nm5ynT3vd19QpKjM/vr5/cvb4/YGzuOY1U9PMkLMxvPH8ns0ohfW74esjtV9VNJbu/uK5e7L8tB2DDWLUmOmVs/eipjheruW6b325O8M7P/Id+2/fKI6f32qbrxXdkWO27GcwXq7tumf1x9Pcnr880pgsZxBauq+2b2y+lbuvtPp2Ln5Cq0s7F0Xq5e3f2lJB9I8qOZTatfN22aH5NvjNe0/cFJvhDjuGLMjePJ0+VO3d13J/mjOB9Xuscm+RdVtS2zS8qemOQPc4Ccj8KGsa5Ictx0d9H7ZXZTj4uWuU8soKoeWFUP2r6c5KQk12Q2Ztvv1HtGkndNyxcl+cXpbr+PSXLX3BRhlt9ix+09SU6qqsOmKcEnTWUsox3ug/L0zM7JZDaOp093aT42yXFJLo/v3WU3XUv6xiSf7O7fn9vknFxlFhpL5+XqUlXrq+rQafkBSZ6c2f03PpDkGVO1Hc/J7efqM5K8f5qNtND4sgQWGMdPzYW4ldl1/vPno+/WFaa7X9jdR3f3hsy+C9/f3T+fA+R8XLf7Kuyp7r6nqs7J7AQ+KMl53X3tMneLhR2R5J2z7+qsS/In3f0XVXVFkgur6swkn03yzKn+JZnd6Xdrkq8lefbSd5kkqaq3JnlCksOr6uYkL06yOYsYt+6+s6p+I7N/FCfJS7t7T29WyAALjOMTavYYr87saTHPSZLuvraqLkxyXZJ7kpzd3fdO7fjeXV6PTfILST4xXVucJC+Kc3I1Wmgsn+W8XFWOTHJ+VR2U2R8WL+zud1fVdUkuqKrfTPKxzIKlTO9/XFVbM7tp7+nJrseXJbHQOL6/qtZn9tSJq5L80lTfd+vq8ms5AM7HmgUlAAAAAGO4jAIAAAAYStgAAAAADCVsAAAAAIYSNgAAAABDCRsAAACAoYQNALAfVNU/raoLqurTVXVlVV1SVd+7H/f3hKp6927qbKyqV+6mzqFV9Su72L7T49qT/Y9WVS8a0MabquoZu6+51+0/oap+bKn2BwArhbABAAarqkryziQf7O6HdvcPJ3lhkiOWs1/dvaW7/91uqh2aZKdhw/48rqpatxcfW3TYMD2zfik9IcmP7a4SAKw1wgYAGO8nkvxjd792e0F3f7y7/7pmfreqrqmqT1TVzyXf+Av4X1XVu6rqxqraXFU/X1WXT/UeOtV7U1W9tqq2VNXfVdVP7bjzqjqxqv62qj5WVf+rqr5vbh/vnpZfUlXnVdUHp/1tDyE2J3loVV1VVb+7p8c1rR5SVW+vqk9V1VumcCJV9V+q6orpmM+dK/9gVf1BVW1J8tyq+umqumzq919W1RFTvUOq6o+mn8PVVfUzVbU5yQOmfr5lqvevpp/XVVX1uu3BQlV9tapeXlUfT/Kjuxu8qjpoGqMrpv09Z+7n98EFjvGUqezKqnplVb27qjYk+aUk/37q049Pu3j8NC43muUAwFq1N39FAAB27eFJrlxg279MckKSRyQ5PMkVVfWhadsjkvxAkjuT3JjkDd19YlU9N8mvJnneVG9DkhOTPDTJB6rqe3bYx6eS/Hh331NVP5nkvyb5mZ305fszCxAelOT6qnpNkk1JHt7dJyzyuJLkkUkeluT/JPmbJI9N8uEk/727X5okVfXHSX4qyZ9Nn7lfd2+cth2W5DHd3VX1b5K8IMnzk/znJHd19w9ur9fd76iqc7b3s6p+IMnPJXlsd/9jVb06yc8neXOSBya5rLufv4u+zztz2t+PVNXBSf6mqt670DFOYcnrkjy+uz9TVW9Nku7eVlWvTfLV7v69qZ9nJjkyyeMy+/lflOTte9gvAFg1hA0AsLQel+St3X1vktuq6q+S/EiSLye5ortvTZKq+nSS7b/gfiKzUGC7C7v760luqKobM/uldd6Dk5xfVccl6ST3XaAvF3f33Unurqrbs++XQ1ze3TdP/b8qs1Dkw0l+oqpekOSfJHlIkmvzzbDhbXOfPzrJ26rqyCT3S/KZqfwnk5y+vVJ3f3En+35Skh/OLLxJkgckuX3adm+SdyziOE5K8kNzsw4enOS4JP+wwDF+NcmN3b29v29NctYu2v+f0/hdt332BgCsNS6jAIDxrs3sF9/Funtu+etz61/Pt/6BoHf43I7rv5HkA9398CQ/neT+e7C/e7P7P0Ls7ri+rb2qun+SVyd5xjQz4fU79Of/zi3/t8xmQfxgkufsot87U0nO7+4Tptf3dfdLpm1/P4U7i2nrV+faOra7twc/i/2Z7cx8G7UXnweAFU/YAADjvT/JwVX1jb9uV9UPTdfs/3WSn5vuC7A+yeOTXL7I9n+2qu4z3cfhu5Ncv8P2Bye5ZVr+14ts+yuZXVaxM7s6roVsDww+X1WHJNnVPQrm+33GXPmlSc6e2+dh0+I/VtX2WRvvS/KMqvrOqc5Dquqf72Jfu/KeJL+8ve2aPW3jgbuof32S757u0ZDMLufYblc/TwBYs4QNADBYd3eSpyf5yZo9IvLaJL+V5HOZPc3h6iQfz+yX9xd09+cWuYv/nVlA8edJfqm7/36H7b+T5Leq6mNZ5F/eu/sLmd2j4JodbxC5m+NaqL0vZTab4ZrMfom/Yhe7f0mS/1FVVyb5/Fz5byY5bOrTx/PNS0rOTXJ1Vb2lu69L8p+SvLeqrs4soDhyDw/7dVV18/T62yRvSHJdko9W1TWZ3Y9hwZ9jd/+/zJ7g8RdT37+S5K5p858lefoON4gEgDWvZv9uAABWg6p6U5J3d7ebCq4gVXVId391ejrFq5Lc0N2vWO5+AcByMbMBAGDf/dvphpHXZnY5yOuWtzsAsLzMbAAAAACGMrMBAAAAGErYAAAAAAwlbAAAAACGEjYAAAAAQwkbAAAAgKGEDQAAAMBQ/x+U/qgvB/hucAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zoomed to see the figure upto 4000\n",
    "plt.figure(figsize=(18,5))\n",
    "plt.hist([l for l in df_clean.length if l < 4000], bins=50)\n",
    "plt.xlabel(\"Complaint Character Length\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cc445b3",
   "metadata": {
    "id": "T9jD_6SeJrF3",
    "papermill": {
     "duration": 2.696766,
     "end_time": "2022-01-13T18:51:51.314851",
     "exception": false,
     "start_time": "2022-01-13T18:51:48.618085",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Find the top 40 words by frequency among all the articles after processing the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20df93e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:51:56.637438Z",
     "iopub.status.busy": "2022-01-13T18:51:56.636711Z",
     "iopub.status.idle": "2022-01-13T18:51:57.172205Z",
     "shell.execute_reply": "2022-01-13T18:51:57.172705Z",
     "shell.execute_reply.started": "2022-01-13T18:09:37.465252Z"
    },
    "id": "QcfdvtfZJrF3",
    "papermill": {
     "duration": 3.241022,
     "end_time": "2022-01-13T18:51:57.172875",
     "exception": false,
     "start_time": "2022-01-13T18:51:53.931853",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using a word cloud find the top 40 words by frequency among all the articles after processing the text\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "wordcloud = WordCloud(\n",
    "                          background_color='lemonchiffon',\n",
    "                          stopwords=stop,\n",
    "                          max_words=40,\n",
    "                          max_font_size=40, \n",
    "                          random_state=42\n",
    "                         ).generate(str(df_clean['complaint_POS_removed']))\n",
    "\n",
    "fig = plt.figure(figsize=(20,15))\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "59d188b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:52:02.526606Z",
     "iopub.status.busy": "2022-01-13T18:52:02.516378Z",
     "iopub.status.idle": "2022-01-13T18:52:02.528673Z",
     "shell.execute_reply": "2022-01-13T18:52:02.529290Z",
     "shell.execute_reply.started": "2022-01-13T18:09:37.983952Z"
    },
    "id": "OkSmc3UaJrF4",
    "papermill": {
     "duration": 2.688131,
     "end_time": "2022-01-13T18:52:02.529464",
     "exception": false,
     "start_time": "2022-01-13T18:51:59.841333",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Removing -PRON- from the text corpus\n",
    "df_clean['Complaint_clean'] = df_clean['complaint_POS_removed'].str.replace('-PRON-', '')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7b0232",
   "metadata": {
    "id": "5DfCSbbmJrF4",
    "papermill": {
     "duration": 2.701116,
     "end_time": "2022-01-13T18:52:07.926848",
     "exception": false,
     "start_time": "2022-01-13T18:52:05.225732",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Find the top unigrams,bigrams and trigrams by frequency among all the complaints after processing the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fe508474",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:52:13.360102Z",
     "iopub.status.busy": "2022-01-13T18:52:13.359122Z",
     "iopub.status.idle": "2022-01-13T18:52:21.692970Z",
     "shell.execute_reply": "2022-01-13T18:52:21.692329Z",
     "shell.execute_reply.started": "2022-01-13T18:09:38.016632Z"
    },
    "id": "5mbk5DS5JrF4",
    "papermill": {
     "duration": 11.09332,
     "end_time": "2022-01-13T18:52:21.693155",
     "exception": false,
     "start_time": "2022-01-13T18:52:10.599835",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write your code here to find the top 30 unigram frequency among the complaints in the cleaned datafram(df_clean). \n",
    "def get_top_n_words(corpus, n=None):\n",
    "    vec = CountVectorizer(stop_words='english').fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "\n",
    "# Top 30 unigrams by frequency among all the complaints\n",
    "common_words = get_top_n_words(df_clean['complaint_POS_removed'].values.astype('U'), 30)\n",
    "df2 = pd.DataFrame(common_words, columns = ['unigram' , 'count'])\n",
    "\n",
    "# Plot the top 30 unigrams\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.barplot(x='unigram', y='count', data=df2, palette=\"coolwarm\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Top 30 unigrams in the Complaint text after removing stop words and lemmatization\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cbc205d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:52:27.044153Z",
     "iopub.status.busy": "2022-01-13T18:52:27.043156Z",
     "iopub.status.idle": "2022-01-13T18:52:27.047798Z",
     "shell.execute_reply": "2022-01-13T18:52:27.048322Z",
     "shell.execute_reply.started": "2022-01-13T18:09:44.824671Z"
    },
    "id": "YX7fedm1JrF8",
    "papermill": {
     "duration": 2.660764,
     "end_time": "2022-01-13T18:52:27.048511",
     "exception": false,
     "start_time": "2022-01-13T18:52:24.387747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unigram</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chase</td>\n",
       "      <td>54088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>account</td>\n",
       "      <td>43603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>credit</td>\n",
       "      <td>34084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bank</td>\n",
       "      <td>28639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>card</td>\n",
       "      <td>26599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>payment</td>\n",
       "      <td>21175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>charge</td>\n",
       "      <td>17070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>time</td>\n",
       "      <td>16065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>xxxx</td>\n",
       "      <td>14380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>day</td>\n",
       "      <td>13243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unigram  count\n",
       "0    chase  54088\n",
       "1  account  43603\n",
       "2   credit  34084\n",
       "3     bank  28639\n",
       "4     card  26599\n",
       "5  payment  21175\n",
       "6   charge  17070\n",
       "7     time  16065\n",
       "8     xxxx  14380\n",
       "9      day  13243"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the top 10 words in the unigram frequency\n",
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "72369525",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:52:32.533760Z",
     "iopub.status.busy": "2022-01-13T18:52:32.533020Z",
     "iopub.status.idle": "2022-01-13T18:52:43.701807Z",
     "shell.execute_reply": "2022-01-13T18:52:43.702341Z",
     "shell.execute_reply.started": "2022-01-13T18:09:44.836690Z"
    },
    "id": "aV7kD7w8JrF8",
    "papermill": {
     "duration": 13.920037,
     "end_time": "2022-01-13T18:52:43.702529",
     "exception": false,
     "start_time": "2022-01-13T18:52:29.782492",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write your code here to find the top 30 bigram frequency among the complaints in the cleaned datafram(df_clean). \n",
    "def get_top_n_bigram(corpus, n=None):\n",
    "    vec = CountVectorizer(ngram_range=(2, 2), stop_words='english').fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "\n",
    "# Top 30 bigrams by frequency among all the complaints\n",
    "common_words = get_top_n_bigram(df_clean['Complaint_clean'].values.astype('U'), 30)\n",
    "df3 = pd.DataFrame(common_words, columns = ['bigram' , 'count'])\n",
    "\n",
    "# Plot the top 30 bigrams\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.barplot(x='bigram', y='count', data=df3, palette=\"coolwarm\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Top 30 bigrams in the Complaint text after removing stop words and lemmatization\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b6dcb6e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:52:49.132984Z",
     "iopub.status.busy": "2022-01-13T18:52:49.132314Z",
     "iopub.status.idle": "2022-01-13T18:52:49.142731Z",
     "shell.execute_reply": "2022-01-13T18:52:49.141990Z",
     "shell.execute_reply.started": "2022-01-13T18:09:55.590576Z"
    },
    "id": "NPnMNIpyJrF9",
    "papermill": {
     "duration": 2.752729,
     "end_time": "2022-01-13T18:52:49.142913",
     "exception": false,
     "start_time": "2022-01-13T18:52:46.390184",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bigram</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>credit card</td>\n",
       "      <td>12610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chase bank</td>\n",
       "      <td>5456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>credit report</td>\n",
       "      <td>4044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>check account</td>\n",
       "      <td>3734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>customer service</td>\n",
       "      <td>3074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>chase credit</td>\n",
       "      <td>2992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>bank account</td>\n",
       "      <td>2510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>account chase</td>\n",
       "      <td>2284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chase account</td>\n",
       "      <td>2066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>account account</td>\n",
       "      <td>1861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             bigram  count\n",
       "0       credit card  12610\n",
       "1        chase bank   5456\n",
       "2     credit report   4044\n",
       "3     check account   3734\n",
       "4  customer service   3074\n",
       "5      chase credit   2992\n",
       "6      bank account   2510\n",
       "7     account chase   2284\n",
       "8     chase account   2066\n",
       "9   account account   1861"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the top 10 words in the bigram frequency\n",
    "df3.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c0934e80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:52:55.417169Z",
     "iopub.status.busy": "2022-01-13T18:52:54.519854Z",
     "iopub.status.idle": "2022-01-13T18:53:09.833633Z",
     "shell.execute_reply": "2022-01-13T18:53:09.832666Z",
     "shell.execute_reply.started": "2022-01-13T18:09:55.602908Z"
    },
    "id": "Xkh7vtbtJrF-",
    "papermill": {
     "duration": 17.983477,
     "end_time": "2022-01-13T18:53:09.833783",
     "exception": false,
     "start_time": "2022-01-13T18:52:51.850306",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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H03wedzH4PN77zg9elO9iUX5Y8L3GTPe/7cqyUwa8fimyELcw14/e97x+Z9mozwW9ffOP9F2nyNaqICsC+4c+LUVeg09aiO/lKLLSepkB8Q+e4j3bs4ACIVmR1avkeMtCbM9bynue0Lf8ChaiQEhWYtxKdrNdfcDrexUU7+ss6/1fpw14/TLls527oP9hXLuMblF+z9dEHxH/J+nPwAaSVomIblfHO8la8X4nl9+PWpiNiIh3AO+QdG/ywvnfZLP5eyPiQ9O/u7rbyBqSmer976f1PxERV0q6ijxYBzlrqpWWz2YfsrvBQ8ixBeq85P5TvHXugGW9xBHnDHiul9GxO4XH90rsH0n6PtlienpE/GGq7V0Eg7bzqvJ7tSHGWZhYjy2/15siVfpG5ffDyFrtGhb1GL1piu/nGrKGearvfmlyfFZ/Zs9fRsTdA95zMtl96VFkN6EplQzBbyO7Lj4IuHffS6bahwc5PwZ3W7qKie+ttseSF5hdJO0y4PllgTUl3Scibugs35dsNeglD/pIRBw3zA2TtC7Z9esp5A3IvfpesjCf9XR6n/UTJW014Pm1yJuchzD/Pjfl+W4BroiIQd1LF2dbFuc8eWX5e1GO1UPIbsIvJhOiIWltsrX7vIhYmGvPTM9r012j7pbUy+I9E6eQn8U7Svf6o8kuXlMdnwNJWonshn51DE4u0ftMB91bnBt9Q0iKk8kKzkeRn/OUyrX5crJL5BxKgYS81p1C3vM8hYkhBmLy99zbrl9GJlAbtP0vK687tO+5+Y6Dzudx1RTn8ZPJrtxdw/ouhnmvMWj/6x0n810vIuIuSaeR3V4Xx6jPBT2DPvveuv6vf78t//91TL7/AkDSs8kW/C3JbpP95ZY1GG4Sti+RQ4y+GBGfGrA9Dyev508gu4su3/eSxb3GbEx2GT89Iv4+4PlfkA0wg84J832HEfGf8tku8H5yXAuEq5TfU+1E15I3E6syeezb36Y4wfylb70LJSJuJWvVXyppdeCDko6LiLPLS3rbMNX6e8tvWpT4U7g+SvXCDPW24bopnr+OqQuEfxm0UNIy5M7/GLI5/ntkF4XehWc/sgl/kEFjFu+cwXPL9BZExFmSHk8mIdmZMtZL0qXA+yPiO1PEXhg3TbMtSw1h/YsS6z7l96Cb/a4VZxDzWrLguLAnyUU9Rqcaq3onQF/hcdJzdL77jqn25xkd82UswNlkYfQs8qbo7yXmquQNyMLMv3jTFMvvpN28svchrx39N2b9ViS7iwEQOX7sZ+SYrTvJsVJDI+lB5Ge8Gtk19Thyf7iLPPe8goX7rKfTO0betoDXDTpGBp7vZmCq9y3ytizgeJjpsbIox+qhZE33KygFQjIh2dIsoBAzwE3TbGf3vDaTa9SMRMQ/JG1DjhF6HlmQBfibpC+SrUYzGV8+k88O8rPrt1jnpo4TyV5GW5DX1jWBEyPiFklnMzFucL7xgyze9g/anxf0Hc33nmF8F4t5r3HTgGWLsv8t6nmha9Tngp75Xh8Rd0qaal299U1al6R9yPFzN5LDev5EdlkOMp/BZgzvnI6kd5JjLX9GJn3rf34bcj9ZmjwOjiK7DN/NxHj0xd2exTmmbpriPXcyg/vJcS0Q9nbI+5Lj//qt0/e6njUkLTWgUHjfKV6/KI4hB1o/kbyRhOzKAlPXXvZabP5vCPF7FqYwCHlQQGb7umTA82svQqwdyRP0wRGxR/cJSeuw4BvSxRYRvwKeUwa6P5r8bt4IfLskIjmh9jaMQG8/3jEijlrMdZ1GJlB5CvCNRdiGhT1Gh22q/Xamx/yrycLg+6NvWpwyuH6fxdq60biZ7Ea7+sK8SdLjyBuVv5G1ugdKeuZCVjxN5y3kDdEeEXFwX+zdyMLHsMyrpIuIf0z7yj6L8f9O9b5F3pYhWehjNSL+LOkXwFM7CU1eQd6Af7vSdnavUYNMd42aT0T8GXiV8i53E/I8tzc5NGEOOWZqQbqf3SDTnecW99zU8wuyQPhUJpK5ndh57p2lovopZZ3ndt67ONs/aH/uvW5B/9vkFS3+d9HiXmOR/rdFjDGqc8HQSFqa7Jb7F2CLyOmYus8PtUdMSX72IbJx5sVTNP68h+x18qSIOLnv/e8k96PFtTjH1GJpVaN8T3Ne+b19/xMlw9YDgD9GxE19Ty9Ndnnq11vPeQOeW1i9lpQ7O8vOJDMdbVe6VMxTunk8vTw8aYYx7irvHWYLVO9/f1z/E5LWIzM2LawHl99HDnjuiYuwvkUWEbdHxBkR8T4mao6GcfAPQ+/ENazv88zy+/FDWNdB5E3eCyVtMt0LS6G7Z1GP0WF7XDnG+vW2a0HHfG8f/sGA52rvw4u6XyzofWcCq5WuMzNSus1+h9wXnkxmDnw62b1zYeNPZdif9d3TbMMwj5HFNeptWdRj9eDy+xWSNiczGf48Iv5aZSunv0bNYfC1fYEiXRIRnyezakO2XvRMuT+XrnN/AO4vaaP+58lumjC5ENazRf/9QLF9+T3T+5HeOLGnkMfm5TExZ/OJ5H3iy8mK55P7bpTnfablBn5htn8+5fO4jPw8BnWf3H4B71/QdzGVFvcavc9gvvWVe7H59stFMOpzwTCtQbaCnTGgMLgiE11wuxbp2iFpO7JnwtXkdDf/nOKlDwb+3l8YLKbaT+5ayO25lGwF3az0MOq3UMfUwhjXAuGB5fd7JK3ZW1gOyk+Sn8tUrRkf6d64lpqz95SHBy0osKTlJG02xXNbkX2l76KTcrvsnIeRY4/273vbG8juUMdGxExSK8NEN651p33Vwvk2WYh9o6R5hb9SY/cRFq2wckX5vX13YekaNijV8VBJ2lZS/xgkmKjh+1ftbZihYX+fPyZvUvaW9KxBL5D02JLue1rlxmJ/clzZzyRtOcX6eunHexbnGB2mjci04vNI2pE8+V9Gdk2czhXl9/Z963gUmV2wpkXdL24kbxCnet+ny++vSbpf/5OS7l261nQdRBYM3hwRFwGvJz+/DypTsQ9ju68ov7fv255nkC21C+sGpq7I+gJZuP20crqgSSQtW7qbtzDqbVnUY/VIstXuZWSiEZg8xcewnU6e154kaYe+5/Zk5uMHkfTwMuax36Brw4L25wPJsWqf6FbSSlqDiZatAwe8bxWyBay7XVuSXW9vJhOILFAZl3oJmfTkCUzuEnoGmU+gd676Rd97/0x25VufifT6vW3ZGngJeT6Z0bYUB5H7zMe6lXHK6S0GdeNbmO9iKleU39v3rXuY9xpnkDf7TyjXkK43sPjjB2H054Jhup787h5dCoDAvO69nyULjP0WdO2aT6m0+hHZOv7siLhmmpdfQU5dtmnfOl7FRFflfjeQY+oH3UvOJ3IqrW+RY1i7U75QKkneRH7Hh81kfQtjLLuMRsQZkj5OztdxsTJhyK3kvEiPILu5fWLAW68l+wdfLOkosr/zzmQT7hcj4tQZhL8XcL6kC8m+6n8mB5A+jIm54N42YID5u8iT1VtKbepZ5T07kgfOwsyBeCI5PuxISUeTrY9XRsQi72AR8QdJ7yOn0LhAOdfUzWQt3erkvDCbTrOKQX5C3jS+RdIjydrIdcnsZz9juAXaQf4f8GRJvySzZv0TeDi5n9xIzvVzT3AS2ZrxEUmPILeNiPifRVlZGYS8E5lp62fKZAvnkyfnB5LzQz2I3O8XeLGNiA+X2uP9gLPL+uaSn+fa5E3IRnQGRC/GMTpsxwAHlBvIC5iYh/A2MvPaoIQzXYeS3SQ/I+lJZAa9jch9+EhyvqpafkV+P/uWFrreGJXPTzFeBMgKKEm/Bh4v6VtkV/S7gKMi4sKIOFHSO8iKnt+Xc8gfyXEp65GF5dPI7tVI2pfMBPiDiPhyiXFL6aLzK+A7yjmSblyc7SbHou0BHFH2l2vIfeWZZObEhf2sTwReLOknZG3sf4BTI+LUiPidcr6vA4FLJB1TPqdlyPPS48kxSA9dyJgLbdTbsqjHakT8W9IRZCbNvcgbp5/V2MYS725JryaP6aMk/YAsIG5KXqd+XrZ5Qcc05fWfkPQr8rO+nqzw2LG8v/v/Xkq2PLxYOafbleRN62ERcSVZaN6hvPeCcjytQF6j1wI+HhHzJcIhpwx6dSl4nc7EPIRzyGmcFqbL4Inkd9X7G8ieMZJOZ/D4wZ7XlfifUM71OpeJeQjvJrtwD0p+M5UDyFa9FwLnKudZXpWcuuZUcpxg18J8F1Opfq8REVEKDseT8+J25yF8ChPDhRYnxj3mvLS4yvH6OXIewouU80cvS7aQrU7e9zyp7z3TXrumCPU5snD5C2Cncu/Tvy37lz8/Qxb8TpN0OHmPuyXZuvt9sjzQ70TynukYSaeS2ZUviIifDHhtzzvI7+oNpaHoJCbmIVyJzAr/x2nev2hiMVPy3tN/GDDtROe5F5MXq1vIG7xLyAQiy0+xnivIWrn/JU/wt5MTb76JAfPXTLE9y5AtiseThcHbyALZZeTN49bTvLc3KeaVZG3GteSBP9+8KAvYhqXIgtvl5E1Of9rbK5g+Te7+TJEemUy8cl75v/4KfJOcu+5iMgNk97Xbs+AUwA8ka0uuLp/TJeSNx9L92z2DbdudqVPMz7ctZJe2g8ipE24mb3IuJU8g683ws16fAWmQGZAyemE+lwHveRlZaPt3eW8sbizyZuSj5bv7F1mA+z154nsZffOhzWAbHwZ8vqzvH519+OfkTeF88xqyCMfoFLFP7n4mC/ouup8LmbnthLLNt5DJSraa6XFBjmk5irxRuZXM2Pbqhdk3pnrtgv4/8gbjV+W7i6n2gwHvezB5k3QDeVM133FDXgQPJwted5DH+/nAp4Aty2seTZ4nr6BvzsLy/JvKun80pO3elryw31i+q9PIm8t53+dCnBPWIns+XEfeVAx6/yPL93Vl+T//Tu7fX6FvipPp9sEZ/F/znesGvGYo2zJo/1vQPr6wx2rfPtT7fgfOszvV/7+AbVmfKY4XcvLt48t23kIe248lW1eCzly002zPw8p+Prfs9719/Pt05gzsvH4r8sbwZiaOp+07zy9PVvpeTJ7De/vubtP9b2U7fkzu7/8iC2bzTV01g/+nN3XD3fRNbcLEPIl/meb99yezM/buTf5GtroMOk/uzhTHXOc1K5fP9+qyL/2OzE78oP7vdWG/i2liDvNeY7r979Fk4a9//5tyfYtwnI7kXDDd/z3Vcdx57gr6rt/ls38LeQ/2b7Jy8DCy4nHgtjHNtYvB93knM3EOGvjTt/7nkF1zbyGTuBxHVmrvzoD9muzZ9yXyfv/O/s9nqs+ErAT5GHnPdXuJdTyDp2qa7/9a0Gc76EflxbYAkq4AiIj1R7slSx5JK5M3VedHRKvU+GaLTNL2ZK3cfMlgzGz2KS1hW5MJOW4d9fZMRdL6ZIv8IRGx+2i3xsxmi3EdQ2gVSFqz9O/uLlua7AKyPAs3jsDMzGxoJK2gAYkaJO1Oti4fd08uDJqZ1TKWYwitmhcCH5B0Ajkx6+pkU/pDyK5knx/dppmZ2ZhbFzhP0vHkMI2lyQmeH0d2yXrr6DbNzGx0XCC0Yfo1Oe7hCUxMkPpHcm6Xj0XEv0e1YWZmNvauI8eJPZFMSLEcOS7pIOBDETFoHkUzs1nPYwjNzMzMzMzGlMcQmpmZmZmZjalZ2WV0jTXWiPXXX3/Um2FmZmZmZjYS55xzzt8iYs0FvW5WFgjXX3995s6du+AXmpmZmZmZzUKSrpzJ69xl1MzMzMzMbEy5QGhmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSkXCM3MzMzMzMaUC4RmZmZmZmZjygVCMzMzMzOzMeUCoZmZmZmZ2ZhygdDMzMzMzGxMuUBoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY2rpUW9Abbu+9oRq6z78K0+ttm4zMzMzM7Pa3EJoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYcoHQzMzMzMxsTLlAaGZmZmZmNqZcIDQzMzMzMxtTLhCamZmZmZmNKRcIzczMzMzMxpQLhGZmZmZmZmPKBUIzMzMzM7Mx5QKhmZmZmZnZmKpWIJT0QEknSfqNpEsk7VOWry7peEm/L79XK8sl6XOSLpN0oaQtOut6RXn97yW9otY2m5mZmZmZjZOaLYR3Am+NiE2AbYC9JW0CvAM4MSI2Ak4sjwF2ADYqP3sCX4IsQAL7AVsDjwH26xUizczMzMzMbNFVKxBGxLURcW75+xbgt8D9gR2BQ8rLDgGeX/7eETg00pnAqpLWAZ4BHB8Rf4+IG4HjgWfW2m4zMzMzM7Nx0WQMoaT1gUcBvwbWjohry1N/AdYuf98fuKrztj+XZVMtNzMzMzMzs8VQvUAoaUXgB8C+EfGP7nMREUAMKc6ekuZKmvvXv/51GKs0MzMzMzOb1aoWCCUtQxYGvxURR5bF15WuoJTf15flVwMP7Lz9AWXZVMsniYivRsSWEbHlmmuuOdx/xMzMzMzMbBaqmWVUwDeA30bEpzpPHQX0MoW+AvhxZ/nLS7bRbYCbS9fSY4GnS1qtJJN5ellmZmZmZmZmi2HpiuveDvgv4CJJ55dl7wI+Chwu6VXAlcCu5bmjgWcBlwH/AvYAiIi/S/ogcHZ53Qci4u8Vt9vMzMzMzGwsVCsQRsRpgKZ4+ikDXh/A3lOs60DgwOFtnZmZmZmZmTXJMmpmZmZmZmb3PC4QmpmZmZmZjSkXCM3MzMzMzMaUC4RmZmZmZmZjygVCMzMzMzOzMeUCoZmZmZmZ2ZhygdDMzMzMzGxMuUBoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYcoHQzMzMzMxsTLlAaGZmZmZmNqZcIDQzMzMzMxtTLhCamZmZmZmNKRcIzczMzMzMxpQLhGZmZmZmZmPKBUIzMzMzM7Mx5QKhmZmZmZnZmHKB0MzMzMzMbEy5QGhmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSkXCM3MzMzMzMaUC4RmZmZmZmZjygVCMzMzMzOzMeUCoZmZmZmZ2ZhygdDMzMzMzGxMuUBoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYcoHQzMzMzMxsTLlAaGZmZmZmNqZcIDQzMzMzMxtTLhCamZmZmZmNKRcIzczMzMzMxpQLhGZmZmZmZmPKBUIzMzMzM7Mx5QKhmZmZmZnZmHKB0MzMzMzMbEy5QGhmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSkXCM3MzMzMzMaUC4RmZmZmZmZjygVCMzMzMzOzMeUCoZmZmZmZ2ZhygdDMzMzMzGxMuUBoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYcoHQzMzMzMxsTLlAaGZmZmZmNqaqFQglHSjpekkXd5btL+lqSeeXn2d1nnunpMskXSrpGZ3lzyzLLpP0jlrba2ZmZmZmNm5qthAeDDxzwPJPR8Tm5edoAEmbAC8GHl7e80VJS0laCvhfYAdgE2C38lozMzMzMzNbTEvXWnFEnCpp/Rm+fEfguxFxO/BHSZcBjynPXRYRlwNI+m557W+Gvb1mZmZmZmbjZhRjCN8g6cLSpXS1suz+wFWd1/y5LJtquZmZmZmZmS2m1gXCLwEbApsD1wIHDGvFkvaUNFfS3L/+9a/DWq2ZmZmZmdms1bRAGBHXRcRdEXE38DUmuoVeDTyw89IHlGVTLR+07q9GxJYRseWaa645/I03MzMzMzObZZoWCCWt03n4AqCXgfQo4MWSlpO0AbARcBZwNrCRpA0kLUsmnjmq5TabmZmZmZnNVtWSykj6DrA9sIakPwP7AdtL2hwI4ArgtQARcYmkw8lkMXcCe0fEXWU9bwCOBZYCDoyIS2pts5mZmZmZ2TipmWV0twGLvzHN6z8EfGjA8qOBo4e4aWZmZmZmZsZosoyamZmZmZnZPYALhGZmZmZmZmPKBUIzMzMzM7Mx5QKhmZmZmZnZmHKB0MzMzMzMbEy5QGhmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSkXCM3MzMzMzMaUC4RmZmZmZmZjygVCMzMzMzOzMeUCoZmZmZmZ2ZhygdDMzMzMzGxMuUBoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYcoHQzMzMzMxsTLlAaGZmZmZmNqZcIDQzMzMzMxtTLhCamZmZmZmNKRcIzczMzMzMxpQLhGZmZmZmZmPKBUIzMzMzM7Mx5QKhmZmZmZnZmHKB0MzMzMzMbEy5QGhmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSkXCM3MzMzMzMaUC4RmZmZmZmZjygVCMzMzMzOzMeUCoZmZmZmZ2ZhygdDMzMzMzGxMzahAKOnEmSwzMzMzMzOzJcfS0z0paXlgBWANSasBKk+tDNy/8raZmZmZmZlZRdMWCIHXAvsC9wPOYaJA+A/gC/U2y8zMzMzMzGqbtkAYEZ8FPivpjRHx+UbbZGZmZmZmZg0sqIUQgIj4vKRtgfW774mIQyttl5mZmZmZmVU2owKhpMOADYHzgbvK4gBcIDQzMzMzM1tCzahACGwJbBIRUXNjzMzMzMzMrJ2ZzkN4MXDfmhtiZmZmZmZmbc20hXAN4DeSzgJu7y2MiOdV2SozMzMzMzOrbqYFwv1rboSZmZmZmZm1N9Mso6fU3hAzMzMzMzNra6ZZRm8hs4oCLAssA9waESvX2jAzMzMzMzOra6YthCv1/pYkYEdgm1obZWZmZmZmZvXNNMvoPJF+BDxj+JtjZmZmZmZmrcy0y+hOnYdzyHkJb6uyRWZmZmZmZtbETLOMPrfz953AFWS3UTMzMzMzM1tCzXQM4R61N8TMzMzMzMzamtEYQkkPkPRDSdeXnx9IekDtjTMzMzMzM7N6ZppU5iDgKOB+5ecnZZmZmZmZmZktoWZaIFwzIg6KiDvLz8HAmhW3y8zMzMzMzCqbaYHwBkkvk7RU+XkZcEPNDTMzMzMzM7O6ZlogfCWwK/AX4FpgZ2D3SttkZmZmZmZmDcx02okPAK+IiBsBJK0OfJIsKJqZmZmZmdkSaKYthJv2CoMAEfF34FF1NsnMzMzMzMxamGmBcI6k1XoPSgvhTFsXzczMzMzM7B5opoW6A4BfSTqiPN4F+FCdTTIzMzMzM7MWZtRCGBGHAjsB15WfnSLisOneI+nAMon9xZ1lq0s6XtLvy+/VynJJ+pykyyRdKGmLznteUV7/e0mvWJR/0szMzMzMzOY30y6jRMRvIuIL5ec3M3jLwcAz+5a9AzgxIjYCTiyPAXYANio/ewJfgnldU/cDtgYeA+zX7bpqZmZmZmZmi27GBcKFFRGnAn/vW7wjcEj5+xDg+Z3lh0Y6E1hV0jrAM4DjI+LvJanN8cxfyDQzMzMzM7NFUK1AOIW1I+La8vdfgLXL3/cHruq87s9l2VTLzczMzMzMbDGNLFNoRISkGNb6JO1Jdjdl3XXXHdZqF9rub/1VlfUefMBjq6zXzMzMzMzGV+sWwutKV1DK7+vL8quBB3Ze94CybKrl84mIr0bElhGx5Zprrjn0DTczMzMzM5ttWhcIjwJ6mUJfAfy4s/zlJdvoNsDNpWvpscDTJa1Wksk8vSwzMzMzMzOzxVSty6ik7wDbA2tI+jOZLfSjwOGSXgVcCexaXn408CzgMuBfwB4AEfF3SR8Ezi6v+0BE9CeqMTMzMzMzs0VQrUAYEbtN8dRTBrw2gL2nWM+BwIFD3DQzMzMzMzOjfZdRMzMzMzMzu4dwgdDMzMzMzGxMuUBoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYWnrUG2CLZ6/9L6iy3i/uv1mV9ZqZmZmZ2T2HWwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYcoHQzMzMzMxsTLlAaGZmZmZmNqZcIDQzMzMzMxtTLhCamZmZmZmNKRcIzczMzMzMxpQLhGZmZmZmZmPKBUIzMzMzM7Mx5QKhmZmZmZnZmHKB0MzMzMzMbEy5QGhmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSkXCM3MzMzMzMaUC4RmZmZmZmZjygVCMzMzMzOzMeUCoZmZmZmZ2ZhygdDMzMzMzGxMuUBoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYWnrUG2BLlrd96vdV1vuJt2xUZb1mZmZmZjY1txCamZmZmZmNKRcIzczMzMzMxpQLhGZmZmZmZmPKBUIzMzMzM7Mx5QKhmZmZmZnZmHKB0MzMzMzMbEy5QGhmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSkXCM3MzMzMzMaUC4RmZmZmZmZjygVCMzMzMzOzMeUCoZmZmZmZ2ZhygdDMzMzMzGxMuUBoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYcoHQzMzMzMxsTLlAaGZmZmZmNqZcIDQzMzMzMxtTLhCamZmZmZmNqZEUCCVdIekiSedLmluWrS7peEm/L79XK8sl6XOSLpN0oaQtRrHNZmZmZmZms80oWwifFBGbR8SW5fE7gBMjYiPgxPIYYAdgo/KzJ/Cl5ltqZmZmZmY2C92TuozuCBxS/j4EeH5n+aGRzgRWlbTOCLbPzMzMzMxsVhlVgTCA4ySdI2nPsmztiLi2/P0XYO3y9/2Bqzrv/XNZZmZmZmZmZoth6RHFfVxEXC1pLeB4Sb/rPhkRISkWZoWlYLknwLrrrju8LTUzMzMzM5ulRlIgjIiry+/rJf0QeAxwnaR1IuLa0iX0+vLyq4EHdt7+gLKsf51fBb4KsOWWWy5UYdLuufb7yp+qrPf9rx1cafCxb15XJd7bX7b2wOVfOPLvVeK9YafVq6zXzMzMzGaX5l1GJd1b0kq9v4GnAxcDRwGvKC97BfDj8vdRwMtLttFtgJs7XUvNzMzMzMxsEY2ihXBt4IeSevG/HRHHSDobOFzSq4ArgV3L648GngVcBvwL2KP9JpuZmZmZmc0+zQuEEXE5sNmA5TcATxmwPIC9G2yamZmZmZnZWLknTTthZmZmZmZmDblAaGZmZmZmNqZcIDQzMzMzMxtTLhCamZmZmZmNKRcIzczMzMzMxpQLhGZmZmZmZmPKBUIzMzMzM7Mx5QKhmZmZmZnZmGo+Mb2Zjc5Bx95cZb17PGOVKus1MzMzs7rcQmhmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSmPITSzKr53yi3V1v2iJ65Ubd1mZmZm48QFQjObFY4685/V1v28bVastm4zMzOzUXKXUTMzMzMzszHlAqGZmZmZmdmYcpdRM7NFcPx59cZIPu1RHiNpZmZmbbhAaGa2BDj9kpurrHe7h68ycPk5l95QJd6jN75PlfWamZnZonGXUTMzMzMzszHlFkIzMxu5iy/7S5X1PuLB962yXjMzs9nCBUIzMxs7//eHP1VZ70M2XHfg8isvu7RKvPUevPHA5df89rwq8e73sEdVWa+ZmY2Ou4yamZmZmZmNKRcIzczMzMzMxpS7jJqZmdliuf7CM6qsd61Nt62yXjMzm+ACoZmZmS0x/nb28dXWvcZWT6u2bjOzeyoXCM3MzMymcMMvf1ht3fd5/AuqrdvMbKZcIDQzMzO7h/jbcd+stu41nv6y+ZZd/6OvVIm11vNfO3D5td/6VJV467z0LVXWazYOnFTGzMzMzMxsTLmF0MzMzMxmpT99+QNV1rvu695XZb1mo+AWQjMzMzMzszHlFkIzMzMzsyG47JNvr7LeB//3xwYu/81+b6gSb5P3f6HKeu2eyS2EZmZmZmZmY8othGZmZmZmtkDnvnmPKuvd4tMHVVmvzYwLhGZmZmZmdo9z2u67Vlnv4w4+fL5lxz3/OVViATz9Rz+ttu5hcIHQzMzMzMysoSMf96Rq697ptJMW6vUeQ2hmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSkXCM3MzMzMzMaUC4RmZmZmZmZjygVCMzMzMzOzMeUCoZmZmZmZ2ZhygdDMzMzMzGxMuUBoZmZmZmY2plwgNDMzMzMzG1MuEJqZmZmZmY0pFwjNzMzMzMzGlAuEZmZmZmZmY8oFQjMzMzMzszHlAqGZmZmZmdmYcoHQzMzMzMxsTLlAaGZmZmZmNqZcIDQzMzMzMxtTLhCamZmZmZmNKRcIzczMzMzMxpQLhGZmZmZmZmPKBUIzMzMzM7Mx5QKhmZmZmZnZmHKB0MzMzMzMbEy5QGhmZmZmZjamXCA0MzMzMzMbUy4QmpmZmZmZjSkXCM3MzMzMzMbUElMglPRMSZdKukzSO0a9PWZmZmZmZku6JaJAKGkp4H+BHYBNgN0kbTLarTIzMzMzM1uyLREFQuAxwGURcXlE3AF8F9hxxNtkZmZmZma2RFtSCoT3B67qPP5zWWZmZmZmZmaLSBEx6m1YIEk7A8+MiFeXx/8FbB0Rb+i8Zk9gz/JwY+DSRQi1BvC3xdxcx3M8x3O8UcZyPMdzvPGJN5v/N8dzPMdb/FjrRcSaC3rR0ouw4lG4Gnhg5/EDyrJ5IuKrwFcXJ4ikuRGx5eKsw/Ecz/Ecb5SxHM/xHG984s3m/83xHM/x2sVaUrqMng1sJGkDScsCLwaOGvE2mZmZmZmZLdGWiBbCiLhT0huAY4GlgAMj4pIRb5aZmZmZmdkSbYkoEAJExNHA0ZXDLFaXU8dzPMdzvHtALMdzPMcbn3iz+X9zPMdzvEaxloikMmZmZmZmZjZ8S8oYQjMzMzMzMxsyFwjNzMzMzMzGlAuEDUl6ZON4u8xkmd0zSdpuJsuGGO+wmSwbYrym+6ekfWaybEkk6UhJz5Y0K8/pkpabybIhxvvYTJYNMd6g/2/1WvFGRdIKo96GYZG0xXQ/FeOeOJNlS2q81iQ9esCy51SM1/TcYsMn6V6SNh71dgzbWI4hlLTTdM9HxJGV4v4SWA44GPhWRNxcI04n3rkRscWClg0x3grAW4F1I+I1kjYCNo6In1aKtzbwYeB+EbGDpE2Ax0bEN4Yc5y3TPR8RnxpmvE7c1t/fpHVLWgq4KCI2aRFvqmWV450XEY8aYoyfAFOeVCPiecOK1Rf3qcAewDbAEcBBEXFpjVglXpNjrxPvnrCvXBgRm1aK9zPg+RHxn/J4HeCnETHfzepixhnVuWxb4OvAihGxrqTNgNdGxF5DjnML0x9/Kw8x1knlz+WBLYELAAGbAnMj4rHDilXiLQ+sAJwEbF9iAawMHBMRD13C443q3Hku8PKIuLg83g3YNyK2rhWv8bnlIAZ8rhHxykrxXj5oeUQcWile6/vO5wKfBJaNiA0kbQ58YJj756iOhSUmy+iQPbf8XgvYFvhFefwk4AygSoEwIh5fdtZXAudIOou8cTt+mHEk7QA8C7i/pM91nloZuHOYsfocBJwD9C6EV5M3p1UOTLJgfRDw7vL4/4DvAcO+KV2p/N4Y2IqJOTCfC5w15FhIeiy5X67ZdwO3MjntyrDjvRN4F3AvSf/oLQbuoEJWq9b7Z7nAvwTYQFJ3/tKVgL8POdwny++dgPsC3yyPdwOuG3KseSLiBOAESauUWCdIugr4GvDNXkFjiA6mwbEn6b7A/cl981FMvikdemuTpNcDewEPknRh56mVgNOHHa/jR8DhknYGHkieY/67Qpym57KOTwPP6MWLiAskPWHYQSJiJQBJHwSuBQ4j95mXAusMOdaTSqwjgS0i4qLy+BHA/sOMVbwW2Be4H3md7R0L/wC+MAvijeTcCewMfF/SS4DHAy8Hnj7sIJ1zy4aNzy3d+6/lgRcA11SMt1VfvKcA5wJVCoS0v+/cH3gMcDJARJwvaYMhxxjNsRARY/sDHAes03m8DnBsg7hLAS8kd9zfAr8Ddhri+jcDXgFcWX73fnYCVqv4f80tv8/rLLugYryzB8Q7v2K8U4GVOo9XAk6tEOeJwH7kDc1+nZ+3ABtV+t/mkPN7Vvns+mI13T+B9cga7l+Vz7b3swWwdKX/ce5Mlg055n2AfYC55I33i4DPAydXiNXk2Cv7xUnALWTF3Unl58fDPGd24q0CrA98p+w3vZ/Va353JfbewE+Ai4BtK8dqci7rrP/XA/aXmteG+dZdKx5wyUyWDTHeG2vuG/eAeKM4dz4E+A1wDHCvSjFGdm7p2445wBkN461KtihX3V8anlvOHBDvwpr/24KWDetnXFsIex4YEdd2Hl8HrFsrmKRNyW5dzwaOB54bEedKuh95szqUlsmIuAC4QNK3Y/gtA9O5Q9K9KE3dkjYEbq8Y71ZJ9+nE2wao2Q13bbLVrOeOsmyoIuIU4BRJB0fElcNe/xQx75a01YJfOZRYTffP8hleyUQNYgv3lvSgiLgcoNQg3rtWMEk/JFt9DiPPK73z2vckza0QssmxFxGHAIdIemFE/GDY6x8Q72by/9itdJlem+xJs6KkFSPiT8OM19cDQOT153xgG0nbRKUunDQ6l3VcVbqNhqRlyIqL31aMd6uklwLfJffR3YBbK8W6SNLXmajFfylw4TSvXywR8fnyWa5Pp5dXVOqS1zoejc6dki5icre81cnK+l9LIobchTMibpb0T+BRra7rU9iI7B3Xyq3AsFvQulrfd15SWpOXKj3+3kT2LKyh6X3EuBcIT5R0LFljA1mjfkLFeJ8nu1S9KyL+3VsYEddIek+FeI+RtD9ZC7U0ecMREfGgCrEgW7GOAR4o6VvAdsDulWJBtpgdRXbBOB1Yk+z+UcuhwFnl5hvg+WTXuaGS9JmI2Bf4gqRBff+r9B8HzpW0VUScXWn9/Zrsn5JOi4jHDRhf1Is3tHFFHfsCJ0u6vMRZD9izQpyez0XESYOeiIgtK8Rrfew9WtKJEXETgKTVgLdGRI3zJpLeQHYNug64uywOcnzYMK3U9/jIKZYPW5NzWcfrgM+S3X+vJnvn7F0x3ktKvM+S39vpZVkNuwOvJwu5kK2vX6oUq5foa0Oy4uCusjio1CWvdTzanTurJY6ZSkTcJelSSesOu3JpKp3rnsrvvwBvrxivO/5tDrAJcHiteLS/73wjOVTiduDbwLHA/1SKtS8N7yPGMqlMl6QXAL2xDKdGxA+ne/2SRNLvgDeT/at7J3Ii4oaKMe9DJrYQ2bT+t1qxSrylyZYRAZfWanGSJOAB5I3v48viUyPivAqxHh0R50h64qDnSwvi0JX95cFka9qtTBSYag12b75/tqDM9Lkz2a2xl3jhdxFRrdZS0t5koqqbyuPVgN0i4osVYzY59kqs86Iv+c+g5AxDjHcZsPWSvi8O0vJcNgqlZfdjEVFjDOagWCdEGU/YgqTfAptEo5u3lvFGce4scbu9AQCoVWCTdCrwKHLM7rxW64oVvU313bfcCVwZEX+uHLPpfWcLI7mPGNcCYTkBXBJDzpS1gJjbkbXOTVrsJP06KmXKmiLeduQ4olslvYwco/XZWt0jWt8ES7ooIppOHdKSpPUGLa/4/TXZP7WA9P0RMezEMkiaW6llbqp450fE5n3L5itEDTHeoEzNN5NZaa+vEO9CYKvexbB0EZobEQ8fdqyy/pOAp0VEzSRc3XgPIZPIrM/km9InV4rX9Fwm6eNkLfq/ydr8TYE3R8Q3p33josc7MyK2qbHuAbFOJMezVs0a3ol3BPCmvuEusyle63PnG8lWpkm9ASpWhDat6C0xVyO7ii7fiXdqrXgtlUadX/SOP0mrAttHxI8qxTse2KXvvvO7EfGMCrHaHgvjWiAEkPRjcsB0q6b7pi0ikj5K9ok/kk6f6og4t1K8C8mEIZuSmZ++AewaEQNPgEOI1/om+BDgC7W7VA4Y2zBJrQtVib0ZE60Gvyzj/WrFarJ/SvojE11m1gVuLH+vCvwpIoY+vqH8b38jM292a4GHXvgs8S4CNu3V4pcKrwsrFph+Ro7J7HVT3Z48r21ApuAe6vyVkt5OZsI8qCzaAzgqIj4+zDideN8gWz9/xuR9s9a0DBcAX2b+a8M5leI1OZd14p0fEZuXm7fnkF2OT42IzSrF+xLZPfUIJh9/Q88gXu4jHkXmBejGetOwY5V4JwGbky1M3X2z1rQMreO1Pnc27w2gnLanN2b/rBqVaJ1Yrya7Mz+AMj4Z+FXFyqZtyOFRDwOWJa/xt1YamjGK+8BBvVWqxGt9LIz7GMLVyAGirZrub46In1da9yC91pduDUMAVU4EwJ0REZJ2BP43Ir4h6VWVYkEO6lXfTfCyFeNtDbxUUu0ulb2xDb0xNr2b65cxTUFxcSknaX8NE+OYvinpqxHx+Uohm+yfvQKfpK8BP4yIo8vjHcixUzW8qPzujpMKoNb43WPIBDJfKY9fW5bVsjTwsIi4Dubd4BxKfqenMrHPDkVEfKxUOD2lLPpgRBw7zBh9/lR+lqXuOaXnzoioNu5sgFbnsp7evcazgSMiE2xUCgVkS8gNTD6XBHWmlDqy0nqnsn/DWKOI1/rceRV1k9FNImlX4BPktAUCPi/pbRHx/Uoh9yELn2dGxJMkPZScQ7aWLwAvJitjtiSn8XhIxXhzBiyrWba5uzsGtPSsqnVf1vRYGPcWwtZjtJq22LUm6RTyJnQPclzm9WT63ypdkyR9gux+270Jvioi3lopXusula3HTV1ITi5+a3l8b7ImsVqLZEuDusnNlm7AZbzBa5koMB0PfD0i7pr6XYsV7zcRsUnnscgu+JvUrJ2drZTJla4Hfsjka0OtVpHW57KPkpUv/ybn8FoV+GnLIQ2zSfn+NoqIE5QTcy8VEbfMlngtjag3wNN6rYKS1iTHodZqLT87IraSdD7ZEnq7pEsq9h6ZGxFbSrqwd+9QucXuQOAm4H/Lor3JqTx2rxTvmeT8zKeQBfrHA3tWrqBsYqxbCGv22Z5C0xY7Se8btDwiPlAjHlmb8RLgVRHxF0nrkjVhtbydvAl+fXl8PPD1WsF6N0uS1qLTF78iSdouIk4vD7ZlcG3Y0OLR6a5W/q5WjT+C/bOXzbebHr7aBL3KCao3YfK4jVqp4e8mMxu2amU6WdJPyVpgyHlVTy6VCDcNO9gIuiGdxIBa31rdrMj5FgHe1g1HpZrg1ueyiHhHGUd4c2SmxVuBHWvFk7Q88Crg4Uw+/l5ZIdZGwEeY/1ivlRvgNWSmwdXJ7J/3J7sbP2W69y0p8UrMZudO2vcGmNPXRfQG6l7X/1zG1f0IOF7SjWTiuFr+JWlZ4PxyzF9L3f/vjcB7yW6VkPeB1TIYR8QxkrYgu94C7BsVk9i0PBbGvYWw6U1Ga5K6LWXLk10Rf1vjojgOJD0POAC4H1mbvx75edaqaXs0cCA5oS3kjfYrK44BfQt5Y/pDsiC4I3BwRHymUrym+6cyucx+ZOt1kF0bP1CjFUbSfuS4uk2Ao4EdgNMiosrUDGqcsKrEfCHwuPLwdOAHUemCopxLcb5uSBHxzkrxHt15uDxZ4L0zIv5fjXittT6XlZjtbmwyEcrvyArKD5CVP7+NiH2mfeOixTqNPK98mhznugd50z+wwmsI8c4nW1l/3Wt1qdnTYQTxmp47Wys9mzZl8nRnF0ZEtakgOrGfSN5PHBMRdyzo9YsYYz0yQc+yZM6MVYAvRsRlNeK1IumhEfG7UhicT437sub3EWNeIGx6k1FiPpv5ay1rtYj0x14OODYitq+0/kEF7H9GxCrTvnHh4xweEbtqiuQrtbo4lq4eTya7dzxK0pOAl0VEzXGSSFoF5k2aXVU52T2O/FxPi4ap6Gvvn5049+51i60Y4yIywdJ5EbFZGWP3zYh4WqV4zRJWaTQZmpt2Q5piG86KiMdUXH/LAlPTc9kIKkjOK//XhRGxqaRlyCRZQ888KumciHh0t5DUWzbsWGXdv46IrTv/49LAuRWve63jtT53rgn8P+a/L6vVGwBlluZeZdovo/J0Z5IeR3b5Paj8vytGxB8rxrsXsG5EXForRidWkwzNynwKe5beI/2ixv7S+lgY6y6jABFxmaSlIsfaHCTpPKBWrfOXgRWAJ5FdG3cmM3e1sgKZaaqWVoOJe7W8rSeW/U9E3CBpjqQ5EXGSpM/UClYO/g8D94uIHSRtQo7x+0atmL3QTGTlbKnq/lm63H4dWBFYV5lR9bURsVeFcP+OiLsl3SlpZbIV5oEV4vQ0S1gVI5hcmcbdkDR5qpI5wKOZaKmvEW9ggYl6k383PZeR17rejc0evRubivF6c2LeVArafwHWqhTrduUY3t9LegNwNXmOqeUUSe8C7iXpacBewE9mUbzW585vkd0NnwO8juwl89eK8QDOICvu7gZqZy3fj7wf25jM0rwMeextVynec4FPko0CG0janOyJUytZ4xFkF+avM3nIy1BFxJ7ld7M5R2l8LIx7gbB1X+dtS23lhRHxfkkHANVu4vpa0JYiJyKu2hrZooAdE/MhvZCc/6XaOLA+N0lakexq+C1J19PJTlvBweQJ/N3l8f+RF64qBULlmL5dgB+QhcGDJB0REf9TKV7r/fPTwDOAowAi4gJJT6gUa65y3MbXyFa7fwK/qhQL4KTSFalVwqrWGZr/izw3v4FsCX0gefzXcg4TlSJ3An8kx6TV0rrA1Ppc1vom/6vK+cHeQx7vKwJVunCSFZQrAG8CPkhW+L5i2ncsnneQ++JF5Bj6o6k4dn4E8VqfO+8TmRF9n8i8EqdIqlZIU04D8T7gF0xkGf1ARBxYKeQLyGlRzgWIiGskrVQpFuTQhceQWVSJiPMlDX1qp46mGZqVyfe+AxweEX+oHK7psTDuXUab9nXudL04E9iJHEx8SUQ8uFK8bia5O4HrouJEy5JOBZ5KXiz+Qhawd4962bP2A3YF/k4WlI6Ikga/Urx7k1ny5pBjUlYBvlWjW16J18sONq9rnAbMuTPEeJcCm0XEbeXxvYDzI2LjSvFa75+Tuj6VZRfU2j87cdcHVo6ICyvGaNaNpcRrPrnybNbrjirpHLJAcQs55q1Kt9wRnMu+CLyL7EHyVvLG5vyI2KNGPJsdGp07z4yIbSQdC3yOTDT2/YjYsFK8S8nGgRvK4/sAZ1S8zvbOLedGxBaqnD2883l2r7MXVoy3P+0zNL+o/NxN3nseXru3TItjYdxbCP8G3FFugN+vHBuzXMV4Py2l/U+QtTVBlvyriIgrNXmi8VOBajsTjWvxI+L95Pe2KXlwniLpzxHx1Eoh1wKuLfvLIaXAtDZZsK/h1nKx6M2zuA1150u6hhxDcVt5vBzZ/amKEeyfV5Vuo6EcU7QP8NsagZQTcP8iIm6OiCskrSrp+RHxoxrxGndjmfUFv7J/vJ5MQARZ2/2ViPjPlG9aPK1bRZqeyzrdsr8s6Rjq3+R/GPh4RNxUHq8GvDUi3lMh1vHALn2xvhsRzxh2rLL+55Atkf0JpGpl3G0dr+m5E/gf5Tj9t5I5EFYm719quYGs8Om5hXr3EACHK+enXVWZMfaVVLzvJHuOvIScJ3ojsuX8jIrxRpGh+ePAx8v/917gY2Qvp6FqfSyMewvhmcBTI+Kf5fGKwHERsW2D2MsBy0fFRCGaf6LxFwA1JxofCUn3Jbs6vhhYqWJN1FyyZu+O8nhZ4PSI2KpSvC3IC9QjgIvJLpU717qRkvQjcgLb48kT6tPIMa5/BoiINw05XtP9U9IawGfJVmwBxwH71GgVGdSSq7pzMTUdb6rZn6H56+RYm0PKov8C7oqIVzeIvT71C0xNz2Ulxv2ZKFQAEBGnVoo137GmSnO4ThGr5rF+GdnD6KJocAM3gnhNz52tSToUeCTwY/I6uyNZEXohDHf+Q0kix+U/FHg6ed07NiKOH1aMATFXIIe5PL0sOhb4n17Po9mgr5XwLuB7EXFAhThNj4VxbyFcvlcYBIiIf5aduQrl3Eh70cniKOlLFQ+UV5ETkfYmGv8YWetc64a7P/U9UHU+pr3ILqNrkgOLXxMRv6kRq1g6OqmaI+KOciM1dKW1+onlZ2PyRH5pxRYKyC4X3WxnJ1eMBQ33z/J5fjYiXjrsdU9h0Fjkmufbg2k43pR2CaRGZau+rsS/UGbmrEYTmQeDTChTs7W82bkM5h3bLwJ+w0Tih97ULzUsJWm5iLi9xL8X9Xr/3K1OgqVys1iz4HQVcHGLwtmI4jU9d0p6EFlR+FiyC+CvgDdHxOWVQv6h/PT8uPwe+ri+iAhJR0dmv61WCOwp19mflR4r717Q64cYt2WG5l+TlYWHkz0Dau0n0PhYGPcC4a2StoiSeEE599S/K8Y7lOwe0LvhfQlwGNm6VUPTicbJm8/5Ut9X9AByUtDzG8QC+Kuk50XEUQCSdiS7HQ9dZCbH3SLi08AlNWIMiNnrOtYkXTQN98/yea4nadmoNP9Sn7mSPgX8b3m8N3lc1LJGRBwu6Z0AEXGnpKrHYLTN0PwT5r/JvhmYS3blHHal2l2SNoySNKDcNFb7PJVj7B7MxNxkr5X01IioNcFys3NZ8Xxg414BrYFvASdKOqg83oOJ1t5hezdZuXsKef56PDmRey3/Dzi6xOuOmRpay9KI47U+d367xHpBefxi8jjcukawMtQFSStExL9qxOhzrqStIqJqNlOYd529W9IqNXu/dalhhmZlNuEjI+Jjw173FJoeC+NeINwXOELSNeSJ/L5kLWYtj4iITTqPT5JUs0XrIODXknqtPs+nXosBNEx9X2qidoqId7WIV7yOzMj3hfL4z2RXslpOL7G+x+RMjrUmpm+dLrr1/nk5+ZkexeTPs8aNzRvJsQXfIwsyx5Mn81pajzdtnaH5crInQHcy51vIVsmvMfzj8G3k+fly8tqwHlmoqOXJwMN6rTCSDqFuRVDrc9nlZK16kwJhRHystOj2xpN/MCKOrRTrmNK9vzfH4b4RUbNw/SFyjOny5Lm6ttbxWp87V4iIwzqPvynpbVO+ejFJeix5nWsx/RFkwfalkq4kr3u9MaBVhtaQ+8pFyrG13evsUIecdDTL0ByZKXkXcsxgC02PhbEeQwjzkgf0sjtV7ZIn6ZvAFyLizPJ4a2DviHh5xZi9icYhJ0Ad+kTjJQZk982laJT6XtKPgTdGu7nQenFXhOxiXDlO68yR55A3pifHRHawiyPiETXilfVX3z87sfYbtLxXY7skG8F409YZms/uH9+miSy8l0TEwyvEXI7J14ZqhRlJPyWvBVeWx+uR14rn1opZ4lQ9l0n6PHkjc3/ypu1EJl8bat0kzlq1z8mjjteKJuYafTtwI/Bdcl99EbBaRNTq7fBrshBzVIvrrCZn856nd66pEG/glCsRUaWFXu0zNH+U7E3RX1FfJatpS+PeQkgpAF7cKNyjgTMk9Qow6wKXqszHNuwam9JKcEmnS+zKkraOiF8PMw7QP5h2y87fQRYyamg9F1pv/VULgp04TTNHkpNV35zj0Oe5u1awhvsnMDsKflOJiHOVU0E0GW/auZm4DWjxua7YN05rXSYm/x56F2BJe5PTMFxYHq8m6VUR8cUhx+l1hV0J+G05lwVZq3/WMGMN0uBcNrf8Pocy/6cttqMlPT0ijpul8VrpzjUKOcdiT1Cp+ztARFzVd52t2b2/aatPrYLfNFpnaO71Iuy21FXLatrS2LcQtjRVTU3PsGtsypieLTrdkOYAc6NCprVR0CyfC00NU6eX9X+DrMF/BzldyJuAZSLidZXiNd0/1Tg9fEvKhCT9biYzA15fIV7rBFLPAr5MJmMQsAGZoOtkMpnUZ4Ycr0l2t6nOYT2z6Fx2b+C2Mt601+V/uUZjqGYVSbcA9yZbWv8D1aeBaBpvtpP0feBTZGKurcnpj7aMiBdXincREwXf5clz56U1elWUeH9kQCG01rWhL/b6VM7QPJu5QNhYuQl9IJNvomp1qRx0U1NzgtCmBZgSYz1go4g4QZkhdqmIuGVB71vEWPOy1k23bIjx5rsBVaXU6WXdTdNFj2D/bJbCWdJ2EXH6gpYNMd7PyCx5vW7G25O1pRuQ40APm+KtixrvdwxIIBWVJjYvMZcj06dD3tBUS2NebqI27VRWLAVcWOsmqrURnMuaTvEkaZ+I+OyClg0p1mER8V8LWmYz0/rc2ZoaTn80RfwtgL2i0hQ6yrHsPcuTSRNXj4j3DTnOtPdBFe+rVwDeQibf21M5F+HGEfHTCrGaHgs1kwDc40k6cSbLhhjvg2Qq8c+R3SwPIJN41HK5pDdJWqb87EMO7q9lh15hECAibgSeVSuYcpLV7wNfKYvuD/yoVjwGd0Oo2TVhqXITDFA1dbom0kW/OyK2Kj/vqXnTTfv9867S1RCYV5lQq0Zs0NQZNef/XJpMSvLCiHghmXGt1/Xw7RXi3RwRP4+I6yPiht5PhThdjwYeTo5F21VStbHXwDHA9yQ9RdJTyGQ2x1SM11rrc9l8UzwB1aZ4YmKy6q7dK8WaVElQzqWPrhRrFPctP5D0rNKDo4XW585m1Jn+KCLWjoi1IuJlrQqDMK+gVCWDaln/DZ2fq0vvjWdXCHXAND8176sPIocp9Cqzrgb+p1KspsfCWI4hVM4HuAKwRmnF6nXmXpksVNSyK7BhtEl7D5lJ7nPAe8ibwxOpmw675dxPkH24HwP8GiAifi9prWEHUU58f3/gXpIexeT9peZNTbPU6TGCdNG03z+rp4dXZpDbFlhT0ls6T61MJlyq5YERcV3n8fVl2d8lDW0sYadW9iRJn6BdAqnDgA2B85k8j12VuabIQvSewOvL4+OBr1eK1cwIz2VNpniStBs5ndMGymzCPSsBQ036oJzi5V3kZ/mP3mLyZvGrw4xV4o3qvuVL5LXn85KOAA6KCtMSjfDc2Uy0n/6Ivs9yDrAFcE3FeN2WuzlkTomhlzWifY6Fng0j4kXlXENE/EuaPCB0cY3qWBjLAiE5eHhf4H5A9wbmH2S/7louBlYlb9aqK2OHqvRLn0LLuZ8Abo+cUBkASUtTp8XnGWTt8gPIvv89t5A3BFVEw9TpRdN00a33z2iTHn5ZMtnJ0kyeaPgfZGa5Wk5WZqo8ojx+YVl2b+CmIcYZVQKpLYFNel04G7gX8LWI+DJMjHkDlvQxbyM5l9FuiqczyClQ1mDyvnoL2TtnaCLiI8BHJH0kKmWk7NO9bzmHiQJh1fuWiDgBOEHSKsBu5e+ryCQe34zhJa8ayblT0okR8ZQFLRuiltMfweTP8k7gZ8APKsWCycfdncAVZGNINWo4MT1wR2ns6A0n2JDhT6czmmNhnMcQSnpjRDTriiBpS+DHZMGwW6teJSumci6rffrG9B0QEa+sEa/EeCYTBZjjaxZglPOf3QS8nJyvZS/gNxHx7krxXhgRNU+kI6X26aKb75+tSFovKqX1niKeyELgdmXR6cAPGhagqiotE2+KiGsbxWsy5k0TCR8Gqjietvm5TA2neGpB0kMj4ndTjWWq0VpeKibeFREfHPa6FxD3PsDLyLkqryErfx8HPDIith9yrCbnzk6L60nkmOtui+sxUW/agpFMfyRp5QxTJ8fCqGiKiekjokrBSdLTyF5Nm5DjP7cDdo+IkyvEansfMUvuFxaKpCdHxC80ODMfEXFkpbiXkOPdLqKTzj8qZZLT4KQk8y1bUpUxDa8ik6CITILy9WHfBEt6WUR8U9JbGZw9q1bN3qw2G/dPSZ+JiH01MZ3AJLUqf1pT+wy4JwGbk1MxtKhMG5SAaL5lQ4jTyzzdS2HeS/7zUoCIeMeQ483qc5mk0yLiccrMmN3/b+iZMSV9LSJeo/bzxTY9R0r6IVmQPww4uFspI2luRGw55ZsXLk7Tc6dyzPq+ZIvr1Uxucf1aRNTsLdZMaYg4iImWppuBV0XE3KnftVjx7gPsR1YYBHAamdisyjjJUqnWm5h+M5WJ6SPiaTXilZj3IXsaCThz2D2NRnUfMa5dRp8I/AIYNOlvkONiavhXRHyu0roHmSNptcjkLignYp0133lE3E12W/la+d8eUKlF5N7l94rTvsoW1mzcP3s39DUHtc9HOafj54GHkd1NlgJuHeYNcJ8dImJeF8OIuFE5NUStjML7V1rvVJqMeYuJieif1neT/w5J55JTwAzTrD6XRcTjyu+VFvTaIcR6TfndeizTiZJeCBzZqAfA5yJiUKGXYRUGi6bnzsiMs59t3VNsBA4ks4r+EkDS48qyKr0PgO8Cp5I9ViArt77HRM+xYft3RNwt6c7SCno9mcm/CuWUS+dHxM8kvQx4l6TPDrklbzT3EePYQjgqkj5F1m4fRZtEDC8nx4UcQdZk7Ax8KIacgn5UJJ0MPI8sRJxDngjOiIg3j3K7bGZa75+axenhJc0lx2MeQY63eznwkFpjmyRdCGwVkxNIzY3ZMy3DVuSNzaQxbxFxTqV45wN7R0knLmlb4IvDbpGc7Uql0pQiYmiJZabqYdSJVaunUW9ewDuB26jQ+jkgZssxWs2V4219Jk8HNiv+vyl64tScvuriiHhE37KLIuKRleJ9kbyPeDHwVjIXwvkRsUeleBeSLZKbki2v3wB2jYhp55RdEoxlgVCTs/bMp1a3mdZdS0rMhwO9GsxfRMRvasVqrXeik/RqMqPifqowj52kaVt1Y8hJV0Y4rmiXiDhiQcuGHLPZ/tl/ESzjcS6KiE2GGGNU393ciNiyu//X7Fom6e1kD4tuAqmjIuLjQ47TrAvggNjNxryVcWgHAauURTcBrxx2ZWHrc1knbpPEHZqYFFvAusCN5e9VgT9FxAZDjNXb99ciMwL+ojx+Elkx+ZxhxRqlVmO0RnjuHJjBuOKx0GRuuc7Y1peTSbK+Q36+LwJui4hp74MXI+6nyK79h5dFOwOPiYj/rhGvL/b6VJ6YvncfIel9wNUR8Y1hF7BHdSws6d2zFlWvO8nGwFZkix3kDc5ZtYKOoGsJEXGJpL9SavYkrRsRfxpmjFHtvMDSktYhM1hVSSRT9FoFtiMvit8rj3cBahRgejcSA8cVVfROJrJUTrdsaBrtny3Tw0/13b2MenMeAvxL0rLA+cpkS9dScZ7ZaJQBt2UXwAE2ZqJVZAtJVVoNSsXEE8v4l1UAot7UL03PZWo8VUKvwCfpa8API+Lo8ngH4PlDjrVHWfdxZAbca8vjdYCDhxmrX/ksN2Jyi92plcLtzMQYrT16Y7QqxBnVubN1BuPPk1M/LGjZ4urPCN1NZjP0/7VTaSdybGbv+1uKbLWrViAsrfXdMYvVCoTALeWe4mXAE5S5LJYZcoyRHAtj2ULYI+lU4NlRsi5JWomcnPsJleKtQh6UvfWfQg62rXLxl/Q88qRwP7I75XrAb4fdrUuNEyN04u4CvJesrdxL0oOAT0ROzF0j3pnA4yLizvJ4GeCXEbHN9O9c5HhNunqUm6VnkQXr73WeWpm8UD5mmPE6cZvsnyXWHDLhUJMMpiPoprMecB05fvDNZEvTFyPishrxWmvd3bdVq0gn3lm1jrMp4jU5l2lEiTsGdVGr1W1N0m8j4mGdx3OAS7rLhhzv1cA+5NQh55PJLX5Vq6dRb9+UdA7Z+nkLeZ6ulYWz9bmzSQZjTcwtty/w6c5TKwMviIjNasafrUqX0QeTLaCQLaB/iIi9p37XYsW7LznX6dkR8UtJ6wLbV6osbHosjGsLYc/aZCtBzx1lWS0HklNO9OZk+S+ym9C0YxEWwwfJi8UJpWvlk8gahqGK9okRenGPoNN6FRGXMzGQuYbVyJN3bxzKimVZLep2JSnjHGq0+lwDzCXHY3bHSN1CFi5qabJ/QiYgUo4La6XVdwdMHIPkmKKq6ctHZFIlgXLO0UdXjNeqVaTndElfICtkunOTVRlfTqNzWYwuccc1kt7DxHf2UupNxn2ipGOZfEN6QqVYkIXBrcjshk+S9FDgwxXjzZW0KpnA7RyytedXFeM1PXeSc1b+RlLtDMajmqN2tnsy8LBeC69yOqtLagWLiL/QmcO19GiqNd606bEw7gXCQ4GzlGmVIbuU1JxIfcO+1qv3K5MJ1PKfiLhB0hxJcyLiJEmfqRiv9Ym8tY8C5ynHgops6d2/YrxXAgf1upFRxhUNO0hEXABcIOlbvRaDRlrvn+dK2ioizq4Yo+dVwIG1v7vZrnF3366mmevIKTUAPtBZFuTNTg1Nz2UR8Xm1TdyxG9kb54fk53hqWTZ0EfEGSS9goufPVyPih9O9ZzHdFhG3SULScpFzIW684LctmojYq/z5ZUnHUHmMFu3PnftXXPc8kdOLnSLp4Gg4t9wYuIwcL9z7TB9Yls0GTY+Fse4yCvMG3j6+PDw1Is6rGOtXwNsi4rTyeDvgkxHx2ErxTiALuR8ha8GuJzMDDnVy5U68JokRRql0F9i6PPx1qS2qEWcpshvLp2uPK5J0eETsqinGgtYaAzqC/fN3ZNeSK8lWmF5iklpjXKn93Y2D1t19S8ymmetGodW5rMRqmrijE/feEXHrgl+52HHWAzaKiBMkrQAsFZUmAC8V2HuQXQ+fTCbOWSYinlUjXok5aYxW5QJvL+asOndqTOaobaXzOa5CtpifVR5vDZwVEduPbuuGq9Wx4AJhzsmyUUQcJGlNYMWI+GOlWJuTLZC9AtONwO6lhaZGvHuTc2fNIbvMrAJ8KypMENqyAFPizQF2jojDF/ji4cUU+Tk+KCI+UPqO3zciqiQiajWuSNI6EXGtJsaCTlKrNrPl/lniNfv/ShfDDwP3i4gdJG0CPDYivjHsWC1NVWnQU7HyoFra8hnEXp/6rSJIejbZNbabKOQDU79jsWK1Ppf9loaJO0pr5NfJ6/m6kjYDXttp7RpmrNcAewKrR8SGkjYCvhxDzqA6RewnkufNYyLijgW9fhFjtB6j1fTcqckZjJclE4QMfQ5XSY+OiHPKdzaf0oJYRcvWeTUa7z3V59hT8/NspfmxMM4FQmXigC2BjSPiIZLuBxwREdtVjrsyQET8Y0GvXZK0KsB04s2N4U6Mu6B4XwLuBp4cEQ9TZno7LiKqjE2T9Gny4tRqXNGsV24Mez0CflmxMubnZGv5uyOzRy5NjkerNRfToFrnm8mxoV+JiNuGFGdUCaQOAb7QqLtvc5K+TGbjfBJZkNmZrOV+VaV4rc9lTRJ3dOL9mvwMj4oyrl0D5kcbUqzzgceQray9WFUrMEpvnF6L3ek1rwmlZ0V3jFbtpDlNz519sQXsCGxT61zWWuvWeTWY3mkURlEZ2vpYGPcxhC8AHgWcCxAR1ygzjVYh6cPAxyPipvJ4NeCtEfGeWjEba50Y4QRJ/z0g3tAmH+6zdeT8M+eVODcqU/3Xsnn5XXVckeaf422SYdeUjooy4+FrgN6E0d+U9NWok+xijYg4XDkGjoi4U9JdC3rTYrgcWJPJtfi3AA8hk0EMpXY2RpRAiuwG9FJJzbr7NrZtRGyqnEfy/ZIOAH5eMV7rc1mrxB3zRMRVeX8/T63j7/aIuKMXq9y01UsNn/Of7cLEeewgSUdExP9UCtl6jFbrc+c8pdD7o9JYMNRz2ah6V9BoWg2Nbrx3K6OYDqzpsTDuBcI7IiIk9Wq+7l053g4R8a7eg3IRfhYwWwqEm5ffrRIjvKj87nZdCeBBleL9p9R29faXNcla9iqi0byVUeZ4k/RBcv66w8gT+UuBdVpsQyOvIm+EbwWQ9DEyW16NAuGtku7DxL6yDdliV8u2fa07P5F0dkRsJalGxjWpbQKpZ1Rc9z3Bv8vvf5WeKjdQ99hrei6jUeKOjqvKPhnKKTX2AX5bKdYpkno3wk8D9gJ+UikW5Hl5s16rv6SPkq0/tQqEKwG/LYX5IFtD50o6CqoU6pueO5XjI3vmkAWoofSo6DOqeRYvBu5LXturiYiPAB+R9JGIeGfNWKMwosrQpsfCuBcID5f0FWDVMg7glWRtei1LKbOC3Q4g6V7AchXjNdWqANOJt0HLeMDnyKx1a0n6ENklqWphXg3HFQHPi8lzIX1JOfn4+2oE642p6Fv2nIj4aY14ZCG3W7t2V1lWw1uAo4ANJZ1Ott7VTC2+oqR1I1NgoxwTtmJ5rsbYoiYZcHsi4spW3X0BJL2qf5yGpI9W7Eb2U2Vq/0+QPVaC7DpaS9Nz2QjG87wO+Cxwf3L+w+OYXHE4TG8HXg1cBLyWnLey5nd3DXk96BValiP/x1qqnP+n0frc+dzO33cCV5DdRodqmgLF2yv3rmjSOi/poRHxO+CI0qV5klk01KVlZWjTY2FsC4Slr/j3gIeS88BsDLwvIo6vGPZb5JxFB5XHe1BhmosRdk1onRhhBfKAWTci9lQO5t+4RoGijJv4I/D/gKeQBYnnR0StWucpxxXVikfWRr0U+C65/+xGpytuBV+T9PKIuBhA0m5k5rxaBcKDgF8rs/T1xooMfXB2aXl5YvnZuMS6NCL+M+xYHW8FTpP0hxJvA2Cv0uthqOeY3v9XxjS0yX7WtrsvwAsl3RYR3yrx/5fOOW3YIuKD5c8fSPopsHytz3RE57ImiTtKrKWAz0ZEza5c3ViXRE7SXrMyuetm4BJJx5Of6dPI6bM+BzDssWEtC/OjOHdG+8zBrXtX7F9x3V1vJc/RBwx4rsZQl1Hd5zapDB3FsTDuSWWaZ66T9EzgqeXh8RFxbIUYo0r80DoxwvfIiXJfHhGPKAXEMyJi80rxzuur2auqjCfatPN7ReDnEfH4Bb550eKtT9aqb0dJVgDsGxFXVIr3IOD7wEvIlp+XA8+pWbjQ5GQMp0WlaWbUOMFSibkcWcEFeeGo0e2pF6t1AqkLyexqve6+9wZ+VeuiX3pvHAUcCDwTuCki9qkRq8Tbm8ywe1N5vBqwW0R8sVK8pueyvtjVE3dIOo1MmFMl82ZfrB8Db+y1zjeI94rpno+ImnMpVzeCc8sDyGEDvWSCvwT2iYg/V4r3aPK80mx6LmW2yt6QgrMi4vpasVoZxX2u2mfTb3ssjHmBcLZnrpvvoq++DFBDjte6ADM3Irbs/p+SLujr9jjMeJ8kx5wdWXuAdon364jYWtKZwE7kuKJLIuLBtWO3IukhwI+APwEviIh/T/+OxY7Xm3f0bipm59MIMsSqbWrxpv9fqQ3eqjNuanng7GFX6ElavfNwJXLfPJ3SbS4qJaySdH5/RVbNQlvrc9kU21Dz/zsUeBhZqO/un5+qEOtUMjndWX2xqiXMKRUW60bEpbVijMoIzi3HA99m8pi+l0bE02rE68Rt1btiV7Ir+slkK9Pjyfmwvz/kODtN93xEHDnd84sRt/V9brNCWutjYWy7jBazPXNd664JrRMj3FEujL0BtxvS6SNfwWvJLqp3SrqNif2lVhbOpuOKSuHsS8DapcV1U3Jc4VCTFQzo6rE6sBTZnbPmXHa97Hw/IL+7mtn5Ni+/myRY0hSpxYEqBULaJ5Bq0t2X7HEQJUbv97PLT82EVUtJUq9wVmqia2b9bHouU7vEHT1/KD9zyIJ9Te+tvP5JJD0X+CS5f2ygnN/4AzULoI1tXn63OresGREHdR4fLGnfSrF6rXUt56h9N1mZdn2JvyZwAtk7Z5h6YzHXArYFflEePwk4g4nu/sPW+j63ZTb9zcvvNvcRY95C2HQi7tZKa8hBNOqaIOm9ZNeLpwD/SynARESVC6Yyo9t7gE3IpAHbAbtHxMk14o1S6Q5YbVxRiXEK8DZy3rpqc3dNddz11Dr+JF3K5Ox89wLOj4iNa8RrSY0n/h6FVt19R0HSJ4D1gK+URa8FroqIt45uq4ZHE+PmYSJxx9dmSde1DYBr+84ra1fsan8OeUN4cuXz9MhyEbQk6UTyPqk3Zc9uwB4R8ZRK8VrPUTtpaJRyDPEFFeMdB7wiypyjktYBDo6IKpmiR3Cfe9KAxRERtSosmhn3FsJ1yC54twAoJ4x/GBPz7SyxNILED9EwMUKJd7wyO9c2ZA33PhHxt1rxJL0A+EXvfyqtd9tHxI8qxZs3rigibpe0gqS9ao0rAlaIiLM0ee6uO4cdJCayrW1D2+OvWXY+tZ9ztElq8S61zYA7LywTLXd1AzXsgktmqtwTeH15fDx1ewM0PZdF48QdpRvgLn3H33cr3ZQeQbaI9NxVlm01+OWL7T8RcXPfebrGlCGjmHdtFOfOV5IV2Z8mzy1nkAn/amk9z+Ixko5l8hy1R1eM98BeYbC4jpzHcuhGdJ/bLJt+62Nh3FsIzwO26HTTmQPMHXbf41HVtLXs61ziNU2MUGJsyvw3bbX6qrce59M63s+BNwBHRE5avTPwqojYoVK8JsdfJ96PyJu0Sdn5gD/DcLPzjWBcw0lk95ImE3+rfQKp/u6+zyf30ypzr03VBXeY+8gojeDc0jpxR7P/b4pYNceyfwM4kZym4IXAm4BlIuJ1leK1Ppc1jdeapJPJ7+34cp3dBvhYRDyxYswX0jn2IuKHFWN9AdiIyQXQyyLijZXijSKBW5PK0NbHwri3EM4bswEQEXeX5vthG0lNG237OgO8JiL+txPnRuX8jrUy5R0IbApcwkQNaVCvr/qgfuk1j6HW44r2Br4KPFTS1WRq+pr7aKvjr+eH5afn5IqxWs85un/FdQ+ybUwkkHq/pAOAn1eM13oy7i2Z3V1wW5/LDiITd+xSHr+sLKuVuOMuTZ6Xcz3qTf79V0nPi4ijSqwdgWo9VYA3kuPCbidvuo8FPjjtOxZP6zFaTc+dyuSC+/RVZB8QEbXmVW09zyIR8QOyMq26iHhD6YHwhLLoqzULoDS+z52qMrRGLBofC+NeILxc0pvIRBoAewGXDztITD0h6TtUd0LSzcvvVoOzWxdgtomITSquv99cSZ8ix0dCFqDOmeb1i+sY4HuSuuOKjqkRqHxXe0XEU5Up/ef0unJW1OT464mIQ9QuO1+TOUd7ov3E360TSLWejLt5F9zGWp/LmibuIAtMpynHRfcyK+5ZKdbrgG+Vm1LIHgf/VSkWEfEv8v97d60YfZrMu9bR9NwJbNorDMK8iuxaLeXN5paTdFpEPE6T5wCFygmkinOBWyLihDLUZaWK9xObl9+t7nNbVoY2PRbGvcvoWsDnyB0nyG4Y+0alge6Szgf27qtp+2J/d5MllRonRihdZw6IiN/UWP+AePcmM8o9ldxfjgc+FGVutArx5pA3MfPmrSST9FQZbyDpzIjYpsa6p4jX+vibl50vIjZQ5ex8ajPn6Egu+mqfQOpHNOruW+K17oK7S0QcsaBlQ4zX+lzWNHFHibkGOb4c4MyoOL68xFsRICL+WWn9P2H6oSdD3zfVeN61Ttzq585OrAvI8bM3lserA6dEvaQrzbs4tlR6he0JrB4RG0raCPhyzWO9JTWeDqzpsTDOBcLW1DgbUonZLPHDCAowTyS7XvyFvGmbbdOGNCXpS8D9yYQI3a4XtbrgNqVG2fnGjdpkwG06GXc5twyKU6UldtC4kFk2bmo9sgLhsUwk7nhTNJrMfTbo7JM7ka3X3yyPdwOui4g3V4o72wswLwfeRV73ILs1fygiDpv6XYsVr/U8i4dFxH8taNkQ450PPAb4dec6e1GtAnZZf8v73KaVoS25QNjIKGrapurrHJUSP7Qm6TKyP/5FdLKsxSyZNqQ1TU4N3xO1xlKo0byHnXhnRsQ26gzULt0+lvgKhBFc9EeRQGrWTcYtaQfgWcCu5A1iz8rkGMZZeyNui0bS3IjYckHLhhivaQFmFJRzAfa6GP6iZq8jNZ62oL9iSTlO/8Jaw206LWjnRcSjSrxza11nR3mf26IytCUXCBtqXdPWu9nt/F4R+HlEPL7VNtQk6VcR8dhRb4ctGjWa97ATr2l2vpZGcNFvnaWydXffbcha4IeR46CXAm4ddhdcSZuRXVM/ALyv89QtwEm9bmxLOrVP3DFrKeccfXZEXF4ebwAcHREPqxRv1s67BvOO9fmmP4qIX492yxaPclqLdwH3Av7VWwzcQSZ6eWeluB8ne7+9nEyAtBfwm4ioMua19X3uKCpDW6mZKcrmd7qkL0h6vKQtej8V4/UnfvgPdRM/tHaepG9L2k3STr2fUW/UsEjaZSbLhhjvEOV8ZL3HqykzudayQkT0Z+ca+ryHHW8ku5XcTmY8vBnYt0YgSfvMZNkQ4ryzjB/cVNI/ys8t5NxPPx52vI6lpImJ0FQ/gdT+ZDekmwAi4nzgQRXjfYHsivd78obq1UwkYBmaiLigdHfdMCIO6fwcWbMwWMZJ9S/boFY8BiTuAGpMAbH6dD/DjldiniNp73Jj2MKbgZMlnVwq1U4Chn5u6YmIJw34qVoYlHQvSRvXjNHxJaA77vOfTCQ6GzpJHx5wnR16r5iI+EhErAR8IiJWLj8rRcR9ahUGi7cDfyV7br2WnPOw1hyS0P4+9zUDzmWvqRivmbHOMippbeDDwP0iYofSbeCxEfGNSiE3L79bZUP6aTnxfILM+hTUney4aWIE8kbtduDpnWXVpp1o3cUReCcT4xqmWzYszbKtFX+TtCElUYJy3sMqWR1LgeVnkZPKtsjO9wrgs33Ldh+wbLFExEckfYwcw9CytaVZBtyi1WTc80TEZZKWihwDfZBy3syh3khJOjwidiUrt+brrlOxO/NPJO0QEf8o27EJcDhQazztHEmrxeTEHTXuP84hzyciJ8O+sfy9KvAnoEah90Vk9r+zJc0l8wQcF5W6X0XEMcpEHQ8ti34XJS19LWo7RmtebwCgem8AaD790Q4R8a5OvBslPYt6haazJK3S69ZY7gm3j4gfDTtQuc5eEhEPBb427PVPoel9Lg2z6Ut6DjmlzHrk+bJusrhx7jKqnIj7IODdEbFZOQmcFxUHv45Ki77O/d3Wplq2pGrVxVEjGlek9tnWHkTOe7gteeP2R+BlEXFFpXgnAjtVPgZ2A14CPI6cfLtnJeDuqJRpTZUH7Q+I1zqBVOvJuE8l/7evk0mrrgV2jyFPNi5pnYi4Vpl0ZT5RaTx0ucH/f8CzyfT3hwIvLS2vNeK1TtzxNeCHEXF0ebwD8PyIeG2NeCXGHHLO4S8Bd5H3Fp+NiL/XitmCGo/R0uDkX9XOb5KOJOek7U5/9KSIeH6leBcCW8XkueXmRsTDK8Vr3b3/x8AbYwQJoxrd5zbLpq/Mk7ETcFGtCqausW4hBNaIiMOVfa2JiDslVbmh6Wlc0zavr3NE3K6cD2avYfd17hRg7i/pc52nVqZiF0BJDyDH+WxXFv2SHKfy50ohV4iIs/paKWr8f9cAc4HnMXlusFvI7kK1HAD8StKkm7ZawcoYmJbzHv4TuEjS8UxOjjDMKQvOIAsPa5CfZ88twIVDjNPvXElbRcTZFWPMExF3A18uPy10J+P+NjkZd62Wech55OYAbyCPuQeSBdGhiohry++mibAi4meSlgGOIysrXhAR/1cx3qGl9azXG2anqDtd0DYRMa8bV0T8XDm2qYrSW2QP8jr4A3L+sMcBv2CiZ9CSquW8azC4N0DNm+HXkdMfvYeJ6Y9qzVkJ7edZHDQ0rOa9/2rAJZLOYvJ1ttZ47yb3uR1vJ/eP15fHx1OvRfIq4OIWhUFwC+HJ5EX++IjYQjm4+GMRMTDl+BDita5pa1IzpBElRig39t8GerXMLyNruZ9WKd7PyRvEI8r+sjPwqojYoVK8pSOi5pi6QTFbZltbjjz+1qdzgapYQTJw6oIY8pQFoyDpd8CDgSvJi/CsmYKldMk5oXT3bRl3WbJbXpCTR99RIUb//JGTDLtrkKTP98V7CvAH4IoSb6jzOXbiNk3cIelYsoKwNzXDS4EnRMQzKsQ6hxzb+g3gB93um5KOjIglely72s+7NmuTf/Wo7TyLB5L7Z28M9N7kHIG7V4rXesqepi2gLUnaiuwyegqT58P9VJV4Y14g3IJsYXoEcDGwJrBzRFSpyVf7bEgXkePCun2dL6zYNaFpAWaKE8F8y4YYr0kXx964ovL9tRxX1JSkY8jELueQXawAiIgDpnzTEkKZ3OhjwFpk4az2RPFNuxy21qK7b1+8Z5Otn38gv7sNgNdGRJWWEUkfJFuWDyvxXgqsExHvm/aNCx+n6XyOnbjnAVt0rkVzyG5yVYYTlO7u+wFPIM+hp5Lj0IbefVPSg0pvh6q0gAR0UW8eu6bzrklagewN8HTyWDgW+GBE3FYp3vLAq5i/59asyIBbeuC8lyyABtmi9aGIuHXaNy56vA2Aa3vfV+kSu/aw75M68Zre57Yk6ThKzyYmT632/irxxrlACFmIIcdQiKwF/k/FWK1r2pr0dR5VAabcJB4EfKcs2g3YIyqN0+rErdrFcVTjilrTLJ4UvvT9f25E/LZhzM2AXuXSLyPigoqxmiaQKuNSHkXezNTq7tuN9zvgORFxWXm8IZmU6KHTv3OR410QfeMTBy1bUk1ReVdlDtByQ3hoRLx02OueJmb1oSCamP5heWBL4ALyvmVTsnBdfQqmFmO0+uItBdw7SvKjSjGOAH5Hjv3+AFkZ89uIqJa5dRQk3btWIbAvzlyym/Ed5fGywOkRsVWleM3G9LXW+h5prKedUKbwv1dEXAI8n8yaVzMBSn82pCuYKMzU8HZyDMPry8+JZCKBYeudOJ8DPHfATy2vJBOv9JI+7ExmcqxC0lskvYU84bymPH6VMgva0HTHFQ36GWasETtD0qxL4FRc17gwuA85NmWt8vNNSW+sGHJQts2aqcyPJGu5TyVblHs/tdzSKwwWl5Nd4Gu5VdJLJS0laY6kl9Ip+A6bpO0kHS/p/yRdLumPkmq2cl0u6U2Slik/+5Cf6dBFJjZar9yIVleGgryIHOcqcuz1wMq8xRFlygfyWrdFRGwZEY8mK0quHna8HuWUGquWbbidzBi7V8V435a0cql4vQj4jaS31YoHPLi0dt5aWsifDWxdMV5TkraV9Bvgt+XxZpJqzpm3dLd7ffm75rHY6j4XmFduWOCyITla0tMX/LLhGOsWwk7XzceR/XQ/CbwvIqqfDFrXtM1GkraLiNMXtGyI8b5N1sz+pCx6DpkoZH1yXOFQkhao8biiTtw3At+MypNhd1qSlwY2Im8Mb6fyuLeWrVqSPgvcF/gRk/v+15oS5UJyypxby+N7A78a9mepEWXAbUUT85g+jbypP5zcV3cB/hQRVW6EJa1PTkmyXYl3OrBvxW5WvyOT5fR3176hUry1yMQdT2Yicce+EXF9pXiHAg8DjmJyi/LQx96MYCjIJf3d4QYtG2K81lkqz4+IzUulyBbkWMJzKl4XzoqIxygzC+9FVjCfFRFV5jmVtE9EfHZBy4YY79dkZflRUTE7eife8cDnI+Ko8nhH4E21e261oobZ9Mu94L2BO8j5FaHi0JNxzzLauxA+G/haZOa1apnr1D4bUhOjKsCQ4xr6D8JBy4blAWTN7D8BJO0H/Iwcp3IOMJQCYeRkslOOKxpGjCmsTc6ldS5wIHBsr1/+kD2nwjpnouW8jisD/6LRHJnk/tHNkHxXWTZso8qA20q3R8N1QC9Bwl/pdAcctlLw27HW+ge4OSqNhxykFPxe3CoeOfbzD2QvqJUqx+qfGPsG6p6nL5T0dSYnzKmZwbjZvGvFMsoMuM8HvhAR/9GAOTqH6KuSViOzjB4FrEj2RqilyRy1XRFxlSZnba2ZTf91wLckfaE8/jOZtXmJphFk0+/dC7Yy7gXCq5UTKz8N+FhptavZjfY1EdHL9NSbkPQ1wBJdIGxdgJH0WDKxy5qlC2fPysBSw47XsRad1h6yxmbtiPi3pBoTAz+vbwzRl5RzBQ410URPRLxHmUDg6WQq7C9IOhz4RkT8YYhxrgSmzDxIZsocmhGdyPeosd5pHAT8WtIPyWNvRzLr4VCVcYkXSPpWNM6A28IIvjcAJD2EnAdt7Yh4hHIag+dFRK0KypPK2JsjmdyCXSsxSdPEHVEp6cIUBk2MXXNS7j3IrnG9oRqnMjGHXg3HkMNpumO0jqkY7yvkcJoLgFOVY+mrjSGMiN6UAacCVVoFATQxR+0Gko7qPLUSUHOuyqskbQtEKWjvQ+k+WkO5V9imtJTTq0CfBUZSGSrpeWSjA+TcnD+tFmvMu4yuADyTnPTx95LWAR4ZEcdVitc662frxA9NEiMo0xpvT9ZEdedBuwX4SUT8fpjxOnHfC7wA+HFZ9FyyRvEA4KvDTmIg6Qwyq9t3yZuM3YC9I2LbYcYZEHcz8qbjmcBJwDZkauyh9stXo8yDGsG0KMo5pgYlWKqWuU45/vlxJe5pEXFehRijSiDV9FzWmqRTgLcBX2nUreukAYsjIp48YPkw4jVN3CFpTXIcUX8BtMr/14k764aClPPynnSmSSCzjFads7lvG5pPwTRspWC7AfARshtszy3kfWCV/0/SGmTr41PJysLjyC6cNQuhzYzgPrfZvijpo8BWZH4AyHvAuRFRZbz+WBcIe8r4hu5F40+V4jTNhtSyr3NZd9MCjKT1Oq1Nc4AVo1I2MmV/iweQ3Sq3K4tPj4i5NeKVmOvTdlzRPsDLgb+R82T+qHTXmQP8PiI2HHK8ZpkHy7pbnsi7k5gvT1YkXBOVsmKWmFuQWUbvJvfNobf2aEQZcFufy1qTdHZEbNUdmzXo+FhS9f4vTYyzW4bMhLtNpXjHkWNc/5usOHwF8NeIeHuFWMuTY8/mVcYAX4p60yT8kcGVMdVat1qSNLAHTFSan3a2U+NcC621ujaMojJUmRtg84i4uzxeCjiv1j3SWHcZLU2xBwD3A64H1iVrMWvNX/J2sqbt9eXx8eSN91CNootc8RKyAPNZJgowL6kY7yOSXkf2hz8bWFnSZyPiE8MOFBEh6eiIeCTZbaC6EYwrWo2c623STX1E3C2pxri/yyW9iYnuTntRIfNg70QOnDdoLEqNk2tE/KBvG75D3ihWUW6idgF+QNYCHyTpiGF3OYxOBtxhrncqIzyXtfY35dQWvdbyncnu99WowVQJHb2ECDdJegSZuGOtSrEA7hMR31Am6zgFOEXS2ZViHUq28ny+PH4JOWyiVubBLTt/L1/irF4p1ih0s+suT445r9bFUZoYH9lZtlxkRtUa8ZrOUUvjXAuDPrsan+cIrg3dbPotrcpEl+JVagYa6xZC5XisJwMnlNrLJ5ETjb9qxJu2WEbRRW4U1D4b2SHkIPdaNxb98ZqNKyo1T5dEpXnWpojZJPPgqFq1+rZhY3Ieu1pzjl4KbBaTJwM+PyI2HnKcpgmkRnUuk7Q28GHgfhGxg6RNyCyuQx+XWeI9CPgqOTb6RuCPwEsrtrh+GVgBeBJZKbkzmVmxyrVP0qvJyopHAgdTEndExFeme99ixDszIraRdCx5jrkG+P6wezmUWL+JiE0WtKwmSedETkEx65RuuMdGxPaV1n9gtyu/cuzbj6NSVkw1mqNWE7kW9gU+3XlqZeAFUWmO04YtdrP+Plc57vSj5NAdkWMJ3xER35v2jYtorFsIgf9ExA3KeZ/mRMRJkj4z6o1aXDGixA8tCzBF62xkWwMvlXQlWYtZdZoEMjHB2yhdjCPiQuXUF0P/PCPiLkmXSlq3VpfpATGbZB5s3aoFkwpOKr//QvYQqOUasja9101tOSrMTRaNE0iN6lxGFloOAt5dHv8f2QVx6AXCUhmzV0Q8VTldyJwoiZYq2jYmpkp4v6QDgGpZR6NR4o6O/5G0CvBWsjVkZeolfjhX0jYRcSaApK2p2ItEk+dKnkO2GFa7l9Pox++uQA7XqOXPkr4YEXsps43+jLpJgVrNUbssWfGyNJMz7f6DrAAaKkn3Be4P3EvSo2BeluuVye9wqFpfG1pXhpZ1fkfSyeQ4QoC3R8Rfhh2nZ9wLhDeV2qBTyTS511NxMuBWRtFFrmhWgCmaZiMDnlFx3YOsEBFnaXK66JonvtWASySdxeS5u55XMWZ1IzqRN00XDdxMfnfHk//r04Czel1pYvhjF5tkwB3huWyNiDhc0jtLnDslVUmiUSpjHlf+bnX9aT1VQlMxkYnvZrIVtKZHA2dI6lWkrQtc2htrVGEfPaDz953kNXDXIcfoajldTy/5Xu9YXwpYk2wFqiIi3ifp46XV/NHAR/u7/A/ZXEnfo/IctZ2u0gdHxJWqn/XzGeT0GQ8g99Hejcs/gHcNO1jra0PLytC+Sh/IqTsA7ifpflEpG/S4Fwh3JGvU30x+qatQ8cTTsKZtVH2dmxZgIuJzZHegnitLt99a8XoJbCYlIaqo9biimnMvjUzrVq0eNUwXDfyw/PScXDEWwK2lq3Y3gVSNwsyozmW3SroPE8feNmThopbzlKnoj2ByZUyteStbT5XQVOnev09E3FQerwYcEHWy/D6zwjqnFBG1C7jASMfvdo/1O8kWtaHHU47l6/k1ef07i5yeYaeKx17rOWpXUmb0Xh1A0t+AV0TExcMMEhGHSDoM2C0ivrXANyy+UV0bWlSG9ip9lid7AFxA3rNsSvY+eOwQY80z1mMIW2vVt3pUJP0ceANwRERsUQowr4qIHSrFa5qNTPMnIVqPTJ1ea9qQpuOKZjs1mhalrHdQuuizI2LoNaWdmPcC1o2IS2vF6MRan4YZcFsrNbSfBx4BXEy2UuwcEVUmAFdOU9IvKhVg+mNXnypBap6447wo2VqnW7akUoOEQLN9jNYUx1xPk2OvBWX293dHxEnl8fbAh6Ne9ve5EbHlgl+5ZFLDbPqSjgT2i4iLyuNHAPtHxNC7/MKYtxCqUban1jVto+giV+xNFmAeKulqSgGmUixonI0M+CA5J9+kJEQ1ArUcVyTptIh43ID9pmr2MzVO3EG7Vi3I472bLvoQ4DwqdJ0p638u8Ely3MgGkjYHPlCru280yoA7qnNZRJyrnO90Y/I4uDQi/rOAty1OvD1qrXsQDZgqQVK1qRLIsZfzJe4AqiTuAOZIWq1XaJG0OrPkfkdTJAQadpwRjt9tovUx16P2c9Teu1cYLHFOLvcUtZwg6b/JMdfd3g5DnfdwhPe5LbPpb9wrDAJExMWSHlYp1uw4QS6Gj9Mg2xOZ8GEu8DzgnM7yW6gw0H0UXeRaFmB6IqI7lgJJnwSOrRiyWRKiluOKIqIXp/W4t4NplLijaD0tyqo0ShcN7A88htJVNCLOLy3MVahRAqkRdvfdBTgmIi6R9B5gC0n/U2vsRuMujtB+qoTWiTsOAH4lqTccYxfgQxXjtdQkIVDrMVqjMoJjrzt0YN4ctZViQU7v9F7y+IasxB769E4dLyq/9+4sC4acTGpU14ZWlaHFhZK+DnyzPH4pUKWXCox5l1FJp0fEdgt+5dDiNZsYu8Rr1kWurPvMqDTR8Azjr0Z2y6uV2v8EMqPpR4A1yG6jW1XsevElMmtXk3FFktYdtDwqZR3VLJ6MW43TRWsizX73s7yw1k2bpFMoCaQ68S6OiEdUitf6XNabQP1xZM+ATwLvi4itK8Vr2sVRI5gqQdLHyV4xLRJ3UHocPLk8/EVE/KZmvFYknRURj5F0JrATmRDokmFf93QPmK6nhVF3L5Y0Bzit4n3EasD7megN8Evg/Ut6l9+eEVwbWk4Htjw5b3kvF8GpQLWeHGPZQqiJwcRNsj2NsKatZRc5aJwYQY2zkdE4CRFZe3gDEzc1UHfw+c/6Ym8AXEqOVamhaeKOlifyaJwumsww+hJgKUkbAW8CzqgYr3UG3Nbnsl5G0WcDX4uIn0mqlS0Z2ndxbDJVgkaXuINSAJwVhcA+P1GDhEAxgul6RmTU3Ys3IoctDV3puXVkNEpEVGIuw+RCzMlkxWGtLvetrw0tpwO7rXQRPzoa5AYYywIh8NzO3y2yPY0qG1LrLnKtCzBNspH19HXdPKRWnE68pmMcIuKR3cfKxBp7VQz5FuAoYENJp1MSd1SM1+xELukFZKvEUeXxqpKeHxE/Gnas4o1k19vbgW+TXadrFmBaZ8BtfS67WtJXyOk7PqZMvDKnYrwmXRw7lWjLMDFVQpAJsn437HhMvtZCjqNdpiyveW2YlUpr0omle+MPJP2USgmBRjhGq7Wm3YvVcI7aMvTkbkmr1NhHpvAl8hj/Ynn8X2XZqyvFa31taFYZqkxk+Aka5QYY6y6jtmQrLUqX9MYqSloJ2CQifl0pXpMkRJ14rcc2DNqGi/oLikNe/9I0StzRsovqoPXW6oZUaoFPaFwLPKsz4EpagZxO4KKI+L2kdYBHRsRxFWNW7+I4Vfe/ntny/c1mLbszlngDx2hFxFDnHB2l2dq9GEDSj4FHAcczuefWsOem7cVr2oWzNTXMpi/pHHK/PLlzz1LtnmxcWwiBdjfco6ppa9lFrsRrXYD5EtCdsuPWAcuGqVUSop5Ne58lQETcKKnajYCkt3QeziE/x2qD3dU4cQdtW7UGtSZVOd+2rgXWCBJItT6XRcS/gCMlraWJsbU1WtC6Mat3cRxVge+eULk1i5wo6YVkV8AWNfot5l0btdWBWyPiIElrStogIv5YK5jazlF7JG1b4u+StGFE/AHmVR7etYD3LLLW1wbaZtP/T0Tc3NcaWe2YH+sCIY1uuGNE2ZBo2EWuaFqAIVu45x0cEXF3aXGq5bqGhUFoP7ahm2X0TnJMYc3ED++NiCOUiTueQibu+BJQJXEHbU/kcyV9ipyvqBf7nGlev7j+CVwkqXotcDTMgNvR9Fym+eccXZcsENYaTzvbtb42zGavJbvb3ynpNir3VKH9GK2mJO1HTv69MZn1ehkyq2OVhIOaf47afSRtG5XmqI2cMH5Z4KHk93dpRNxRI1bxNuAkSZeT++Z6QM3hLy2HgrSuDG2aG2DcC4Stb7hb17S1TvzQ+vO8XNKbyEIE5Hi3oadTVuMkRB1NxzZExPtrrXsKzRJ3jOBE/kYygcb3yIvw8UxOwz1srWuBmyaQov25rNmco2Ni1Ik7lniStouI04E1o958kYO0HqPV2gvILpXnAkTENWX4SS2t56h9FllY+gNZQNtA0msjYuhTlQBExIml8LJxWXRpRNw+3XsWU7NrwwgqQ5vmBhj3E3LruYpa17S1TvzQ+vN8HfA54D3k/3gisGeFOK2TEOWKIw6VNJeJsQ071RzbIOknTN+1edgDmZsl7mh9Ii8x3lE7Tide9SRHfVonkGp9Lms25+iYmM3zArbyOXLKjjOoNyxiPtF23rVRuCMiQiUDvOpO2t6zKu3mqP0U8KSIuAygnEd/RoW5K8v6lycr5+dNcyHpyxUrMVpfG5pVhpahC++W9KHyd1Vjn1Sm5WBiSeuTtWzbMVHTtm854daI1zzxw2wenD3bSfoscF8mJkHdDbiObBElIk4ZcrymiTvUcF7H0nVzl74xU9+NiGcMO9Y4aH0uU+M5R8eBrw2LRznv4IXkfvnd/ucrJglpPUarKUn/TU798DTyeH8l8O2I+HyleK3nqD07IrbqPBZwVnfZkOMdDtzCxH3ES4BVI2KXSvFaXxsOGrA4aoyHlrQt8HVgxYhYV9JmwGsjokr297EvEM5WpYvcxyLiv1slfpjtZntiBElzI2LLBS2rEHctssUJgIj4U6U4LU/k58UIJzuureWxMIpzWYnTG5/Vm3P0WxFxQ824s1lpod+ol7iDvMmplrhjtpG0BvBUMtP1fMNMavUSkHQKZYxWTGQ6vDgiHlEj3ihIehrZ80fAsRFxfOV46zAxR+1ZUXGO2lIRuh5wONkQsQvwJ+AEqDLv9m8iYpMFLRtSrFl9nyvp1+RUXEe1OPbGvctoUy1r2kaU+GG2m+2JEe4t6UERcTmApA2Aat1n1DhxR7Sd1/FuSev2CrfKdP/Vat8k7RIRRyxo2RA1OxZGcS6LxnOOznZqnLhjNoqIvwHflfTbiLigYejW43ebKwXAqoXAHrWfo3Z5sqfPE8vjvwL3ot5coOdK2iYizgSQtDUwd8gxgNFcG1o3DETEVX3HXrWMrS4QttU662frxA+z3WxPjPBm4GRNzg722orxmibuaHwifzdwWqldF/B46oxv7XkneZwvaNmwtD4Wmp7L1HjO0THQOnHHrNW4MAjtx2g1NYJjfb+I+GHvQUTcVCpMflQjWOOKUCjjXCX1evqsC1wq6aLcnNh0yPFa3+e2bBi4qnQbDUnLAPsA1TLdz6ab2SVB65q21okfmpK0NvBh4H4RsUMZo/LYiPhGpZCzOjFCRByjzA720LLod1E3O1jrxB0tW7WOkbQFWeCFHCv8t2HHkbQDmbXu/pI+13lqZeqeW1ofC63PZa3nHJ3tRpG4w4aj5XQ9o9D6WG82R+2IPLNxvNbXhpaVoa8j847cH7gaOI6K2cpn0064JGha0zaCmqHWDia7H727PP4/Ms1/lQJhNM76OSKPBtYnzw2bSSIiDq0U6yZJKwKnAt+SdD11s+62btW6i+wKuzywSfksTx1yjGvI7jjPY/I8h7eQLb5VtD4WRnAuaz3n6Gx3uDKj8KqSXkMm7vjaiLfJFkDtp+sZhdbHeus5apuKiCv7xguvAaxUa7zwCK4NTSpDy7H32YhoVvnipDINjSAb0mxPgnJ2RGzVTdYh6fyI2HzEm7ZEknQYsCFwPhP91CPqZa9rmrhD0svJuZ4mncgj4rAKsV5Ndu94APl5bgP8KiKePN37FiPe0hExq8b1dLU6l2liztEnkhl3f0SbOUdnvdaJO2ar1j1jJJ0ZEdss+JVLllEd6+W6914yQVBvjtoP1RgDJ2kOsHNEHD7sdU8Tc9544Yh4iKT7AUdERJXxwqO4z1WjjMmSTgOeHBF31Fj/fPFcIGxjFNmQNPszHZ4MvBA4PiK2kLQN+Rk/cfp32iCSfgtsErP4pNDwRH4RmUXuzIjYXNJDgQ9HxE4LeOvCxjk8Inbtjc/of77CeI2RaHUu0+BMtD0xWyrTbMkl6eeUnjERsZmkpYHzIuKRleI1m66npXE51tUgU3hfvPMp44U7FfUX1roWzeb7XEmHAg8DjmLysfepGvHcZbSRGE3Wz9meBOUt5IGyoaTTgTXJFL22aC4ma0qbJAwYReKOUgBs0c33toi4TRKSlouI30nauEKcfcrv51RY9z1Jk3PZGHSzHwkn6RmqNSLicEnvBIiIOyVVyzzILM1FMKpjXe3nqD1BOdfi95hcqPh7pXitxwvP5vvcP5SfOUD1JFyz5UNbUrTOhjTbk6CcK+mJZCpzAZdGxH9GvFlLsjWA30g6i8ldZ55XKd5sTtzxZ0mrkt2Qjpd0IzD0ruERcW35XaXb+T1I03PZbO9uPwKz+Vhv7VZJ92EiF8E2wM21gs32SpIRHOtrxPzJzdaqFAvgReV3NxlJAA+qFK/1eOFZd58r6bCI+C/gpoj4bLO4s7h32D3OFF0UqnZNaNVFbhQk7QIcExG3SHoPsAXwPxFx7og3bYlUCtfziYhTKsU7vda4gnuS8rmuQu6rQx0LIOkWppnfcDa1wLQ8l83mbkijMC7HegvK7MWfBx5B9upYkxwndmGleLO6cqT1sS7pHOAFMXmO2h9GxBY14rUkSeS4+YfScLzwbLvPlfQbcozpz4Htyc9xnlqtuy4Q2hKr1y+9dMX9IPBJ4H0RsfWIN82m4cQdwyfpg2RX38OYSNKzTkS8b6QbtoSSdAGwfV83pFNqjdOarXys11HGDTbpGTPbK0daH+uSnkkmF5w0R21EHFsp3grk8Jp1I2JP5dRSG0fETyvFu8jnycUj6U3A68lW3KuZXCCMiKjSuusCYUOzvaattd5FSdJHgIsi4tuz6ULViqTTIuJxA1qbqozzGZfB/C1JuiAiNlvQMpsZNcxIO5v5WB++1j1jZnvlyCiOdeVUDL3MrWdGhTlqO7G+R05r8fKIeEQpIJ4RlbKxl/vcL0TE2TXWP04kfSkiXt8snguE7cz2mrbWJP2UrD15GnlR/Ddwlm+CbdxIOoOc1+q7ZKF+N2DviNh2pBu2BJtt3ZBsdmjdM2YcKkdaH+ulMWAjMmEPQI05anux5kbElpo8PVe1ykJJvwMeTI6Zv5WJiuVZkfF6NnNSmbZmczakUdgVeCbwyYi4SdI6wNtGvE02Q24xH6qXAJ8tPwGcXpbZIop2GWlnPR/rQ9XLKPps4GsR8TNJ/1MrWEQcKmkuEwWmnWZb5UjLY11TzFHL5Cyuw3SHpHsxkYRoQzrdtiuolS3VKnMLYUPjUNM2CiVDV7em7U8j3BybIbeYm40HH+vD454xSzY1mqO2E+9pwHuATYDjgO2A3SPi5BrxbMk1Z9QbME4i4lBgJ+C68rOTC4OLTtLzJP0e+CM5QPuPZFYmWzLMKS0FgFvMF4ekh0g6UdLF5fGmZXyR2T2Bj/Xh2RU4FnhGaXFdHfeMWZLcFhG3AfPmqCUTBFVRMnzuBOwOfAfY0oVBG8Qn5MbcDWmoPkh2tzihJJd5EvCyEW+Tzdysmz9ohL5G3hR+BSAiLpT0baBaVzKzheBjfUgi4l/AkZLWkrRuWfy7UW6TLZQmc9T2uT+wFHnP/wRJzvBr83GXUVtidQZLXwA8KiLudmbFJYsTdwyHpLMjYqu+xAHn18okZ7awfKwPh6TnkQXs+wHXA+sCv4uIh490w2yh1ZyjthPjQGBT4BLg7rLYGX5tPm4htCXZTZJWBE4FviXpejKrlS0h3GI+NH8ryQJ6iQN2JuclNLtH8LE+NO4ZM0tExCkNwmwTEZs0iGNLOI8htCXZjuSA+jcDxwB/AJ470i0yG429ye6iD5V0NbAv8LqRbpGZ1fCfiLiBHJc5JyJOArYc9UbZPdavSuu82bTcQmhLrIjotgYeMrINMRshSUsBe0XEUyXdG5gTEbeMervMrAr3jLGFcShZKPwLOd2E5wW0gTyG0JZYknYCPgasRZ7keie6lUe6YWaNSTozIrYZ9XaYWV2l0uc28nr3UnIM2rdKq6HZJJIuA94CXMTEGEIionYiG1vCuEBoS6xyontuRPx21NtiNkqSvkRmkjuCTmuBM8mZmY0vSb+KiMeOejvsns9dRm1Jdp0Lg2YALA/cwEQWR8gEMy4Qms0i7hljC+m8MgXRT8guo4ArC21+biG0JU65IAI8EbgvOZ+PT3RmZjaruWeMLQxJBw1Y7GknbD4uENoSZ4oTXI9PdDZ2JB0C7BMRN5XHqwEH+Fgwm10knR4R2416O8xsdnGB0MxsCdedkH66ZWa2ZHLPGFsUpQJ9vht9VxZaP48htCWWW0XM5pkjabWIuBFA0ur4/G42m3Tn2P0X8PTOY48Xtqn8tPP38sALgGtGtC12D+YbBluSbdorDAJExI2S3CJi4+gAcq6pI8rjXYAPjXB7zGyIImKPUW+DLXki4gfdx5K+A5w2os2xe7A5o94As8Uwp7QKAm4VsfEVEYcCOwHXlZ+dIuKw0W6VmQ2bpEMkrdp5vJqkA0e4SbZk2YjMUGs2iW+ebUnmVhGzIiJ+A/xm1NthZlW5Z4zNmKRbmDyG8C/A20e0OXYP5gKhLbEi4lBJc5mYe22nclNsZmY2G3m8sM1YRKw06m2wJYNPIrZEc6uImZmNEfeMsRmTtB1wfkTcKullwBbAZyPiyhFvmt3DeNoJMzMzsyWEpE2Y6BnzC/eMsalIuhDYDNgUOBj4OrBrRDxxlNtl9zwuEJqZmZmZzTKSzo2ILSS9D7g6Ir7RWzbqbbN7FncZNTMzMzObfW6R9E7gZcATJM0BlhnxNtk9kKedMDMzMzObfV4E3A68KiL+AjwA+MRoN8nuidxl1MzMzMzM/n979xNyaVXHAfz7HS1cODGToBJCQhBiU4zuxBZS1K4WRqtMpKJFSOs2LqSCEFyI7oJJl2Jq+Gdri9DQIAamdKFg5MKmGPyvRTW/FveO3HfQaRhmvNd7Px94uM95znlffhfezfc9zzmHHWWGEAAAtkzbm9u+2PaNtm+2favtm+uui81jhhAAALZM25eSfGNmXlh3LWw2M4QAALB9jguDnA0zhAAAsGXa3pPkyiS/yWJzmSTJzDyyrprYTI6dAACA7fOpJO8m+frKs0kiELKHGUIAAIAdZQ0hAABsmbZXtX207d+X18Ntr1p3XWwegRAAALbPr5I8luQzy+vx5TPYwyujAACwZdoenZnD/+8ZmCEEAIDtc6LtLW0vWl63JDmx7qLYPGYIAQBgy7T9bJJ7k9yQxe6izyT58cz8da2FsXEEQgAAgB3llVEAANgybR9oe2ClfbDtkTWWxIYSCAEAYPt8aWZeP9WYmdeSXLe+cthUAiEAAGyffW0Pnmq0/XSSi9dYDxvKHwUAAGyfu5P8vu1Dy/a3k/x8jfWwoWwqAwAAW6jttUm+smw+NTPPr7MeNpNACAAAsKOsIQQAANhRAiEAAMCOEggBYEXbA21/dIb+Zz7KegDgQrKGEABWtL06yRMzc+i05xfPzH/O4fddNDP/PV/1AcD55NgJANjrF0k+1/Zokn8n+WeS15Jck+Tzbd+emUvb7ktyXxY7+L2yHHtkZn7d9i9JHkzytSR3td2f5IdJPpnkpSTfnZl3296f5L0sDou+PMn3ktya5IYkz87MbR/JNwZgZwmEALDXT5IcmpnDbW9K8uSy/fJp425OcnWSa7MIcy8kObLSf2Jmrk+StpfNzC+X9z9L8v0k9y7HHcwiAH4zyWNJbkzygyR/aHt4Zo6e5+8HAO+zhhAAzuy5DwiDSfLlJA/NzMmZ+VuS357W/+DK/aG2v2t7LMl3knxhpe/xWazfOJbk+Mwcm5mTSf6cReAEgAtGIASAM3vnPPzc/Ulun5kvJrkzySUrff9afp5cuT/V9iYPABeUQAgAe72VZP9ZjHs6ybfa7mt7RZKbzjB2f5JX234iixlCANgI/vMIACtm5kTbp9v+KYsNX45/yNCHk3w1yfNZbCrzxyRvfMjYO5I8m+Qfy8+zCZwAcME5dgIAzlHbS2fm7baXJXkuyY3L9YQA8LFghhAAzt0TbQ9kcZzET4VBAD5uzBACAADsKJvKAAAA7CiBEAAAYEcJhAAAADtKIAQAANhRAiEAAMCOEggBAAB21P8A3k+pDP+UfcMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write your code here to find the top 30 trigram frequency among the complaints in the cleaned datafram(df_clean). \n",
    "def get_top_n_trigram(corpus, n=None):\n",
    "    vec = CountVectorizer(ngram_range=(3, 3), stop_words='english').fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "\n",
    "# Top 30 trigrams by frequency among all the complaints\n",
    "common_words = get_top_n_trigram(df_clean['Complaint_clean'].values.astype('U'), 30)\n",
    "df4 = pd.DataFrame(common_words, columns = ['trigram' , 'count'])\n",
    "\n",
    "# Plot the top 30 unigrams\n",
    "plt.figure(figsize=(15,6))\n",
    "sns.barplot(x='trigram', y='count', data=df4, palette=\"coolwarm\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Top 30 trigrams in the Complaint text after removing stop words and lemmatization\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "54c64ae3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:53:15.202384Z",
     "iopub.status.busy": "2022-01-13T18:53:15.201398Z",
     "iopub.status.idle": "2022-01-13T18:53:15.211172Z",
     "shell.execute_reply": "2022-01-13T18:53:15.211695Z",
     "shell.execute_reply.started": "2022-01-13T18:10:10.357333Z"
    },
    "id": "REcVxNfvJrF-",
    "papermill": {
     "duration": 2.680038,
     "end_time": "2022-01-13T18:53:15.211868",
     "exception": false,
     "start_time": "2022-01-13T18:53:12.531830",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trigram</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chase credit card</td>\n",
       "      <td>2143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>credit card company</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>credit card account</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>credit card chase</td>\n",
       "      <td>703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chase customer service</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>charge credit card</td>\n",
       "      <td>491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>credit card credit</td>\n",
       "      <td>490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>inquiry credit report</td>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>chase check account</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>credit report credit</td>\n",
       "      <td>375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  trigram  count\n",
       "0       chase credit card   2143\n",
       "1     credit card company   1000\n",
       "2     credit card account    906\n",
       "3       credit card chase    703\n",
       "4  chase customer service    494\n",
       "5      charge credit card    491\n",
       "6      credit card credit    490\n",
       "7   inquiry credit report    411\n",
       "8     chase check account    381\n",
       "9    credit report credit    375"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the top 10 words in the trigram frequency\n",
    "df4.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a04f55",
   "metadata": {
    "id": "yUXzFji0JrF_",
    "papermill": {
     "duration": 2.667278,
     "end_time": "2022-01-13T18:53:20.613826",
     "exception": false,
     "start_time": "2022-01-13T18:53:17.946548",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## The personal details of customer has been masked in the dataset with xxxx. Let's remove the masked text as this will be of no use for our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1dfe97e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:53:25.985122Z",
     "iopub.status.busy": "2022-01-13T18:53:25.984386Z",
     "iopub.status.idle": "2022-01-13T18:53:26.023604Z",
     "shell.execute_reply": "2022-01-13T18:53:26.022993Z",
     "shell.execute_reply.started": "2022-01-13T18:10:10.372824Z"
    },
    "id": "wKda-a_IJrF_",
    "papermill": {
     "duration": 2.762797,
     "end_time": "2022-01-13T18:53:26.023750",
     "exception": false,
     "start_time": "2022-01-13T18:53:23.260953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_clean['Complaint_clean'] = df_clean['Complaint_clean'].str.replace('xxxx','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4f459500",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:53:31.425893Z",
     "iopub.status.busy": "2022-01-13T18:53:31.425195Z",
     "iopub.status.idle": "2022-01-13T18:53:31.436190Z",
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>complaint_what_happened</th>\n",
       "      <th>lemmatized_complaints</th>\n",
       "      <th>complaint_POS_removed</th>\n",
       "      <th>length</th>\n",
       "      <th>Complaint_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Good morning my name is XXXX XXXX and I apprec...</td>\n",
       "      <td>good morning name xxxx xxxx appreciate could h...</td>\n",
       "      <td>morning name appreciate chase bank cardmember ...</td>\n",
       "      <td>175</td>\n",
       "      <td>morning name appreciate chase bank cardmember ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I upgraded my XXXX XXXX card in XX/XX/2018 and...</td>\n",
       "      <td>upgraded xxxx xxxx card xxxx tell agent upgrad...</td>\n",
       "      <td>xxxx card tell agent date information order up...</td>\n",
       "      <td>117</td>\n",
       "      <td>card tell agent date information order upgrad...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chase Card was reported on XX/XX/2019. However...</td>\n",
       "      <td>chase card report xxxx however fraudulent appl...</td>\n",
       "      <td>card report application submit identity consen...</td>\n",
       "      <td>81</td>\n",
       "      <td>card report application submit identity consen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>On XX/XX/2018, while trying to book a XXXX  XX...</td>\n",
       "      <td>xxxx try book xxxx xxxx ticket come across off...</td>\n",
       "      <td>try book ticket come offer apply ticket card i...</td>\n",
       "      <td>429</td>\n",
       "      <td>try book ticket come offer apply ticket card i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>my grand son give me check for {$1600.00} i de...</td>\n",
       "      <td>grand son give check deposit chase account fun...</td>\n",
       "      <td>son check deposit chase account fund bank acco...</td>\n",
       "      <td>163</td>\n",
       "      <td>son check deposit chase account fund bank acco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21067</th>\n",
       "      <td>After being a Chase Card customer for well ove...</td>\n",
       "      <td>chase card customer well decade offer multiple...</td>\n",
       "      <td>chase card customer decade offer solicitation ...</td>\n",
       "      <td>232</td>\n",
       "      <td>chase card customer decade offer solicitation ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21068</th>\n",
       "      <td>On Wednesday, XX/XX/XXXX I called Chas, my XXX...</td>\n",
       "      <td>wednesday xxxxxxxx call chas xxxx xxxx visa cr...</td>\n",
       "      <td>call chas xxxx credit card provider claim purc...</td>\n",
       "      <td>702</td>\n",
       "      <td>call chas  credit card provider claim purchase...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21069</th>\n",
       "      <td>I am not familiar with XXXX pay and did not un...</td>\n",
       "      <td>familiar xxxx pay understand great risk provid...</td>\n",
       "      <td>pay risk consumer chase bank chase year trust ...</td>\n",
       "      <td>695</td>\n",
       "      <td>pay risk consumer chase bank chase year trust ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21070</th>\n",
       "      <td>I have had flawless credit for 30 yrs. I've ha...</td>\n",
       "      <td>flawless credit yrs ive chase credit card chas...</td>\n",
       "      <td>flawless credit chase credit card chase freedo...</td>\n",
       "      <td>563</td>\n",
       "      <td>flawless credit chase credit card chase freedo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21071</th>\n",
       "      <td>Roughly 10+ years ago I closed out my accounts...</td>\n",
       "      <td>roughly year ago close account jp morgan chase...</td>\n",
       "      <td>year account jp bank order line credit pay acc...</td>\n",
       "      <td>252</td>\n",
       "      <td>year account jp bank order line credit pay acc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21072 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 complaint_what_happened  \\\n",
       "0      Good morning my name is XXXX XXXX and I apprec...   \n",
       "1      I upgraded my XXXX XXXX card in XX/XX/2018 and...   \n",
       "2      Chase Card was reported on XX/XX/2019. However...   \n",
       "3      On XX/XX/2018, while trying to book a XXXX  XX...   \n",
       "4      my grand son give me check for {$1600.00} i de...   \n",
       "...                                                  ...   \n",
       "21067  After being a Chase Card customer for well ove...   \n",
       "21068  On Wednesday, XX/XX/XXXX I called Chas, my XXX...   \n",
       "21069  I am not familiar with XXXX pay and did not un...   \n",
       "21070  I have had flawless credit for 30 yrs. I've ha...   \n",
       "21071  Roughly 10+ years ago I closed out my accounts...   \n",
       "\n",
       "                                   lemmatized_complaints  \\\n",
       "0      good morning name xxxx xxxx appreciate could h...   \n",
       "1      upgraded xxxx xxxx card xxxx tell agent upgrad...   \n",
       "2      chase card report xxxx however fraudulent appl...   \n",
       "3      xxxx try book xxxx xxxx ticket come across off...   \n",
       "4      grand son give check deposit chase account fun...   \n",
       "...                                                  ...   \n",
       "21067  chase card customer well decade offer multiple...   \n",
       "21068  wednesday xxxxxxxx call chas xxxx xxxx visa cr...   \n",
       "21069  familiar xxxx pay understand great risk provid...   \n",
       "21070  flawless credit yrs ive chase credit card chas...   \n",
       "21071  roughly year ago close account jp morgan chase...   \n",
       "\n",
       "                                   complaint_POS_removed  length  \\\n",
       "0      morning name appreciate chase bank cardmember ...     175   \n",
       "1      xxxx card tell agent date information order up...     117   \n",
       "2      card report application submit identity consen...      81   \n",
       "3      try book ticket come offer apply ticket card i...     429   \n",
       "4      son check deposit chase account fund bank acco...     163   \n",
       "...                                                  ...     ...   \n",
       "21067  chase card customer decade offer solicitation ...     232   \n",
       "21068  call chas xxxx credit card provider claim purc...     702   \n",
       "21069  pay risk consumer chase bank chase year trust ...     695   \n",
       "21070  flawless credit chase credit card chase freedo...     563   \n",
       "21071  year account jp bank order line credit pay acc...     252   \n",
       "\n",
       "                                         Complaint_clean  \n",
       "0      morning name appreciate chase bank cardmember ...  \n",
       "1       card tell agent date information order upgrad...  \n",
       "2      card report application submit identity consen...  \n",
       "3      try book ticket come offer apply ticket card i...  \n",
       "4      son check deposit chase account fund bank acco...  \n",
       "...                                                  ...  \n",
       "21067  chase card customer decade offer solicitation ...  \n",
       "21068  call chas  credit card provider claim purchase...  \n",
       "21069  pay risk consumer chase bank chase year trust ...  \n",
       "21070  flawless credit chase credit card chase freedo...  \n",
       "21071  year account jp bank order line credit pay acc...  \n",
       "\n",
       "[21072 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All masked texts has been removed\n",
    "df_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602f504c",
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "## Feature Extraction\n",
    "Convert the raw texts to a matrix of TF-IDF features\n",
    "\n",
    "**max_df** is used for removing terms that appear too frequently, also known as \"corpus-specific stop words\"\n",
    "max_df = 0.95 means \"ignore terms that appear in more than 95% of the complaints\"\n",
    "\n",
    "**min_df** is used for removing terms that appear too infrequently\n",
    "min_df = 2 means \"ignore terms that appear in less than 2 complaints\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c561f2f6",
   "metadata": {
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   "source": [
    "# Write your code here to initialise the TfidfVectorizer \n",
    "tfidf = TfidfVectorizer(max_df=0.95, min_df=2, stop_words=\"english\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3aaa6aa9",
   "metadata": {
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   "source": [
    "#### Create a document term matrix using fit_transform\n",
    "\n",
    "The contents of a document term matrix are tuples of (complaint_id,token_id) tf-idf score:\n",
    "The tuples that are not there have a tf-idf score of 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6f32c1e0",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [],
   "source": [
    "# Write your code here to create the Document Term Matrix by transforming the complaints column present in df_clean.\n",
    "dtm = tfidf.fit_transform(df_clean.Complaint_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36de1ca6",
   "metadata": {
    "id": "7Q9lwvNEJrGB",
    "papermill": {
     "duration": 2.700309,
     "end_time": "2022-01-13T18:54:00.396740",
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     "start_time": "2022-01-13T18:53:57.696431",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Topic Modelling using NMF\n",
    "\n",
    "Non-Negative Matrix Factorization (NMF) is an unsupervised technique so there are no labeling of topics that the model will be trained on. The way it works is that, NMF decomposes (or factorizes) high-dimensional vectors into a lower-dimensional representation. These lower-dimensional vectors are non-negative which also means their coefficients are non-negative.\n",
    "\n",
    "In this task you have to perform the following:\n",
    "\n",
    "* Find the best number of clusters \n",
    "* Apply the best number to create word clusters\n",
    "* Inspect & validate the correction of each cluster wrt the complaints \n",
    "* Correct the labels if needed \n",
    "* Map the clusters to topics/cluster names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cfa80539",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import NMF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f515cd45",
   "metadata": {
    "id": "0wYR1xUTJrGD",
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   },
   "source": [
    "## Manual Topic Modeling\n",
    "You need to do take the trial & error approach to find the best num of topics for your NMF model.\n",
    "\n",
    "The only parameter that is required is the number of components i.e. the number of topics we want. This is the most crucial step in the whole topic modeling process and will greatly affect how good your final topics are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e3858b6b",
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    "execution": {
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   "source": [
    "# Load your nmf_model with the n_components i.e 5\n",
    "num_topics = 5 # write the value you want to test out\n",
    "\n",
    "# keep the random_state = 40\n",
    "nmf_model = NMF(n_components=num_topics, random_state=40) # write your code here"
   ]
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    {
     "data": {
      "text/plain": [
       "7292"
      ]
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   ],
   "source": [
    "nmf_model.fit(dtm)\n",
    "len(tfidf.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "53e841a5",
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mTHE TOP 15 WORDS FOR TOPIC #1 \u001b[0m\n",
      "['business', 'tell', 'claim', 'transfer', 'number', 'transaction', 'branch', 'day', 'fund', 'deposit', 'chase', 'money', 'check', 'bank', 'account']\n",
      "\n",
      "\n",
      "\u001b[1mTHE TOP 15 WORDS FOR TOPIC #2 \u001b[0m\n",
      "['year', 'application', 'letter', 'limit', 'debt', 'bureau', 'information', 'score', 'company', 'account', 'chase', 'inquiry', 'report', 'card', 'credit']\n",
      "\n",
      "\n",
      "\u001b[1mTHE TOP 15 WORDS FOR TOPIC #3 \u001b[0m\n",
      "['bank', 'document', 'foreclosure', 'sale', 'house', 'time', 'rate', 'letter', 'year', 'property', 'modification', 'chase', 'home', 'mortgage', 'loan']\n",
      "\n",
      "\n",
      "\u001b[1mTHE TOP 15 WORDS FOR TOPIC #4 \u001b[0m\n",
      "['balance', 'statement', 'pay', 'service', 'refund', 'fraud', 'claim', 'merchant', 'purchase', 'transaction', 'dispute', 'chase', 'card', 'fee', 'charge']\n",
      "\n",
      "\n",
      "\u001b[1mTHE TOP 15 WORDS FOR TOPIC #5 \u001b[0m\n",
      "['mortgage', 'year', 'account', 'credit', 'chase', 'date', 'time', 'auto', 'day', 'fee', 'statement', 'month', 'balance', 'pay', 'payment']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the Top15 words for each of the topics\n",
    "for index,topic in enumerate(nmf_model.components_):\n",
    "    print(f'\\033[1mTHE TOP 15 WORDS FOR TOPIC #{index + 1}', '\\033[0m')\n",
    "    print([tfidf.get_feature_names()[i] for i in topic.argsort()[-15:]])\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
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    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 3, 4, 4])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the best topic for each complaint in terms of integer value 0,1,2,3 & 4\n",
    "topic_results = nmf_model.transform(dtm)\n",
    "topic_results[0].round(2)\n",
    "topic_results[0].argmax()\n",
    "topic_results.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
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    "#Assign the best topic to each of the cmplaints in Topic Column\n",
    "df_clean['Topic'] = topic_results.argmax(axis = 1) # write your code to assign topics to each rows."
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       "0  Good morning my name is XXXX XXXX and I apprec...   \n",
       "1  I upgraded my XXXX XXXX card in XX/XX/2018 and...   \n",
       "2  Chase Card was reported on XX/XX/2019. However...   \n",
       "3  On XX/XX/2018, while trying to book a XXXX  XX...   \n",
       "4  my grand son give me check for {$1600.00} i de...   \n",
       "\n",
       "                               lemmatized_complaints  \\\n",
       "0  good morning name xxxx xxxx appreciate could h...   \n",
       "1  upgraded xxxx xxxx card xxxx tell agent upgrad...   \n",
       "2  chase card report xxxx however fraudulent appl...   \n",
       "3  xxxx try book xxxx xxxx ticket come across off...   \n",
       "4  grand son give check deposit chase account fun...   \n",
       "\n",
       "                               complaint_POS_removed  length  \\\n",
       "0  morning name appreciate chase bank cardmember ...     175   \n",
       "1  xxxx card tell agent date information order up...     117   \n",
       "2  card report application submit identity consen...      81   \n",
       "3  try book ticket come offer apply ticket card i...     429   \n",
       "4  son check deposit chase account fund bank acco...     163   \n",
       "\n",
       "                                     Complaint_clean  Topic  \n",
       "0  morning name appreciate chase bank cardmember ...      0  \n",
       "1   card tell agent date information order upgrad...      1  \n",
       "2  card report application submit identity consen...      1  \n",
       "3  try book ticket come offer apply ticket card i...      1  \n",
       "4  son check deposit chase account fund bank acco...      0  "
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       "      <td>0</td>\n",
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       "      <td>I upgraded my XXXX XXXX card in XX/XX/2018 and...</td>\n",
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       "      <td>card report application submit identity consen...</td>\n",
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       "      <td>xxxx try book xxxx xxxx ticket come across off...</td>\n",
       "      <td>try book ticket come offer apply ticket card i...</td>\n",
       "      <td>429</td>\n",
       "      <td>try book ticket come offer apply ticket card i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Can you please remove inquiry</td>\n",
       "      <td>please remove inquiry</td>\n",
       "      <td>inquiry</td>\n",
       "      <td>7</td>\n",
       "      <td>inquiry</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>I have a Chase credit card which is incorrectl...</td>\n",
       "      <td>chase credit card incorrectly report data cred...</td>\n",
       "      <td>chase credit card credit report company issue</td>\n",
       "      <td>45</td>\n",
       "      <td>chase credit card credit report company issue</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>I am XXXX military member ( XXXX ) with the XX...</td>\n",
       "      <td>xxxx military member xxxx xxxx chase auto fina...</td>\n",
       "      <td>member auto finance refuse pay gap insurancewa...</td>\n",
       "      <td>561</td>\n",
       "      <td>member auto finance refuse pay gap insurancewa...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>My name is XXXX XXXX. I have missed a payment ...</td>\n",
       "      <td>name xxxx xxxx miss payment xxxx day past due ...</td>\n",
       "      <td>name payment day time loan modification loss m...</td>\n",
       "      <td>136</td>\n",
       "      <td>name payment day time loan modification loss m...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>I bought a new XXXX XXXX on XX/XX/XXXX from XX...</td>\n",
       "      <td>buy new xxxx xxxx xxxxxxxx xxxx xxxx xxxx xxxx...</td>\n",
       "      <td>pay loan release time resolution time release ...</td>\n",
       "      <td>105</td>\n",
       "      <td>pay loan release time resolution time release ...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>I have wired through one of Chase branches in ...</td>\n",
       "      <td>wire one chase branch xxxx xxxx exact reinstat...</td>\n",
       "      <td>wire chase branch reinstatement amount bank or...</td>\n",
       "      <td>408</td>\n",
       "      <td>wire chase branch reinstatement amount bank or...</td>\n",
       "      <td>2</td>\n",
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       "      <th>23</th>\n",
       "      <td>I have been trying to do a loan modification w...</td>\n",
       "      <td>try loan modification chase give runaround im ...</td>\n",
       "      <td>try loan modification chase runaround im mo pa...</td>\n",
       "      <td>51</td>\n",
       "      <td>try loan modification chase runaround im mo pa...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>To whom it may concern, Chase bank charged wro...</td>\n",
       "      <td>may concern chase bank charge wrongly overdraf...</td>\n",
       "      <td>concern bank charge fee alert balance fee bala...</td>\n",
       "      <td>171</td>\n",
       "      <td>concern bank charge fee alert balance fee bala...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>XXXX XXXX a sofa, love seat, table and chairs ...</td>\n",
       "      <td>xxxx xxxx sofa love seat table chair never del...</td>\n",
       "      <td>sofa love seat chair charge bank claim furnitu...</td>\n",
       "      <td>54</td>\n",
       "      <td>sofa love seat chair charge bank claim furnitu...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>My chase amazon card was declined for a cateri...</td>\n",
       "      <td>chase amazon card decline cater order place br...</td>\n",
       "      <td>chase decline cater order place amount contact...</td>\n",
       "      <td>643</td>\n",
       "      <td>chase decline cater order place amount contact...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>On XXXX XX/XX/2019, I made a {$300.00} payment...</td>\n",
       "      <td>xxxx xxxx make payment online retailer use cha...</td>\n",
       "      <td>payment online retailer use pay chase website ...</td>\n",
       "      <td>1265</td>\n",
       "      <td>payment online retailer use pay chase website ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>I made a purchase with XXXX XXXX XXXX on XX/XX...</td>\n",
       "      <td>make purchase xxxx xxxx xxxx xxxx amount charg...</td>\n",
       "      <td>purchase amount charge transaction place dispu...</td>\n",
       "      <td>570</td>\n",
       "      <td>purchase amount charge transaction place dispu...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>I made a purchase of {$260.00} on XX/XX/XXXX. ...</td>\n",
       "      <td>make purchase xxxxxxxx make payment xxxxxxxx x...</td>\n",
       "      <td>purchase payment xxxxxxxx payment closing date...</td>\n",
       "      <td>370</td>\n",
       "      <td>purchase payment  payment closing date please ...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Action Taken By The Company Between the dates ...</td>\n",
       "      <td>action take company date xxxxxxxx xxxxxxxx ind...</td>\n",
       "      <td>action company date xxxxxxxx group contact cha...</td>\n",
       "      <td>1679</td>\n",
       "      <td>action company date  group contact chase claim...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>I recently called to ask Chase bank why they r...</td>\n",
       "      <td>recently call ask chase bank report credit bur...</td>\n",
       "      <td>ask bank report credit day payment call show b...</td>\n",
       "      <td>292</td>\n",
       "      <td>ask bank report credit day payment call show b...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>During the summer months, I experience a decli...</td>\n",
       "      <td>summer month experience decline income due emp...</td>\n",
       "      <td>summer month experience income employment mont...</td>\n",
       "      <td>605</td>\n",
       "      <td>summer month experience income employment mont...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>In XXXX of this year Chase withdrew money from...</td>\n",
       "      <td>xxxx year chase withdraw money escrow account ...</td>\n",
       "      <td>year chase money escrow property tax money pay...</td>\n",
       "      <td>257</td>\n",
       "      <td>year chase money escrow property tax money pay...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                              complaint_what_happened  \\\n",
       "0   Good morning my name is XXXX XXXX and I apprec...   \n",
       "4   my grand son give me check for {$1600.00} i de...   \n",
       "6   With out notice J.P. Morgan Chase restricted m...   \n",
       "10  mishandling of this account by Chase auto and ...   \n",
       "12  I opened an account with chase bank on XXXX an...   \n",
       "1   I upgraded my XXXX XXXX card in XX/XX/2018 and...   \n",
       "2   Chase Card was reported on XX/XX/2019. However...   \n",
       "3   On XX/XX/2018, while trying to book a XXXX  XX...   \n",
       "5                       Can you please remove inquiry   \n",
       "9   I have a Chase credit card which is incorrectl...   \n",
       "47  I am XXXX military member ( XXXX ) with the XX...   \n",
       "41  My name is XXXX XXXX. I have missed a payment ...   \n",
       "32  I bought a new XXXX XXXX on XX/XX/XXXX from XX...   \n",
       "30  I have wired through one of Chase branches in ...   \n",
       "23  I have been trying to do a loan modification w...   \n",
       "13  To whom it may concern, Chase bank charged wro...   \n",
       "16  XXXX XXXX a sofa, love seat, table and chairs ...   \n",
       "14  My chase amazon card was declined for a cateri...   \n",
       "8   On XXXX XX/XX/2019, I made a {$300.00} payment...   \n",
       "19  I made a purchase with XXXX XXXX XXXX on XX/XX...   \n",
       "24  I made a purchase of {$260.00} on XX/XX/XXXX. ...   \n",
       "57  Action Taken By The Company Between the dates ...   \n",
       "33  I recently called to ask Chase bank why they r...   \n",
       "7   During the summer months, I experience a decli...   \n",
       "72  In XXXX of this year Chase withdrew money from...   \n",
       "\n",
       "                                lemmatized_complaints  \\\n",
       "0   good morning name xxxx xxxx appreciate could h...   \n",
       "4   grand son give check deposit chase account fun...   \n",
       "6   notice jp morgan chase restrict account debit ...   \n",
       "10                  mishandle account chase auto xxxx   \n",
       "12  open account chase bank xxxx use code xxxx bon...   \n",
       "1   upgraded xxxx xxxx card xxxx tell agent upgrad...   \n",
       "2   chase card report xxxx however fraudulent appl...   \n",
       "3   xxxx try book xxxx xxxx ticket come across off...   \n",
       "5                               please remove inquiry   \n",
       "9   chase credit card incorrectly report data cred...   \n",
       "47  xxxx military member xxxx xxxx chase auto fina...   \n",
       "41  name xxxx xxxx miss payment xxxx day past due ...   \n",
       "32  buy new xxxx xxxx xxxxxxxx xxxx xxxx xxxx xxxx...   \n",
       "30  wire one chase branch xxxx xxxx exact reinstat...   \n",
       "23  try loan modification chase give runaround im ...   \n",
       "13  may concern chase bank charge wrongly overdraf...   \n",
       "16  xxxx xxxx sofa love seat table chair never del...   \n",
       "14  chase amazon card decline cater order place br...   \n",
       "8   xxxx xxxx make payment online retailer use cha...   \n",
       "19  make purchase xxxx xxxx xxxx xxxx amount charg...   \n",
       "24  make purchase xxxxxxxx make payment xxxxxxxx x...   \n",
       "57  action take company date xxxxxxxx xxxxxxxx ind...   \n",
       "33  recently call ask chase bank report credit bur...   \n",
       "7   summer month experience decline income due emp...   \n",
       "72  xxxx year chase withdraw money escrow account ...   \n",
       "\n",
       "                                complaint_POS_removed  length  \\\n",
       "0   morning name appreciate chase bank cardmember ...     175   \n",
       "4   son check deposit chase account fund bank acco...     163   \n",
       "6   jp restrict account debit card tuesday thursda...     564   \n",
       "10                                  account auto xxxx      17   \n",
       "12  account bank xxxx use code bonus call term tel...     132   \n",
       "1   xxxx card tell agent date information order up...     117   \n",
       "2   card report application submit identity consen...      81   \n",
       "3   try book ticket come offer apply ticket card i...     429   \n",
       "5                                             inquiry       7   \n",
       "9       chase credit card credit report company issue      45   \n",
       "47  member auto finance refuse pay gap insurancewa...     561   \n",
       "41  name payment day time loan modification loss m...     136   \n",
       "32  pay loan release time resolution time release ...     105   \n",
       "30  wire chase branch reinstatement amount bank or...     408   \n",
       "23  try loan modification chase runaround im mo pa...      51   \n",
       "13  concern bank charge fee alert balance fee bala...     171   \n",
       "16  sofa love seat chair charge bank claim furnitu...      54   \n",
       "14  chase decline cater order place amount contact...     643   \n",
       "8   payment online retailer use pay chase website ...    1265   \n",
       "19  purchase amount charge transaction place dispu...     570   \n",
       "24  purchase payment xxxxxxxx payment closing date...     370   \n",
       "57  action company date xxxxxxxx group contact cha...    1679   \n",
       "33  ask bank report credit day payment call show b...     292   \n",
       "7   summer month experience income employment mont...     605   \n",
       "72  year chase money escrow property tax money pay...     257   \n",
       "\n",
       "                                      Complaint_clean  Topic  \n",
       "0   morning name appreciate chase bank cardmember ...      0  \n",
       "4   son check deposit chase account fund bank acco...      0  \n",
       "6   jp restrict account debit card tuesday thursda...      0  \n",
       "10                                      account auto       0  \n",
       "12  account bank  use code bonus call term tell ev...      0  \n",
       "1    card tell agent date information order upgrad...      1  \n",
       "2   card report application submit identity consen...      1  \n",
       "3   try book ticket come offer apply ticket card i...      1  \n",
       "5                                             inquiry      1  \n",
       "9       chase credit card credit report company issue      1  \n",
       "47  member auto finance refuse pay gap insurancewa...      2  \n",
       "41  name payment day time loan modification loss m...      2  \n",
       "32  pay loan release time resolution time release ...      2  \n",
       "30  wire chase branch reinstatement amount bank or...      2  \n",
       "23  try loan modification chase runaround im mo pa...      2  \n",
       "13  concern bank charge fee alert balance fee bala...      3  \n",
       "16  sofa love seat chair charge bank claim furnitu...      3  \n",
       "14  chase decline cater order place amount contact...      3  \n",
       "8   payment online retailer use pay chase website ...      3  \n",
       "19  purchase amount charge transaction place dispu...      3  \n",
       "24  purchase payment  payment closing date please ...      4  \n",
       "57  action company date  group contact chase claim...      4  \n",
       "33  ask bank report credit day payment call show b...      4  \n",
       "7   summer month experience income employment mont...      4  \n",
       "72  year chase money escrow property tax money pay...      4  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first 5 Complaint for each of the Topics\n",
    "df_clean5 = df_clean.groupby('Topic').head(5)\n",
    "df_clean5.sort_values('Topic')"
   ]
  },
  {
   "cell_type": "markdown",
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    "#### After evaluating the mapping, if the topics assigned are correct then assign these names to the relevant topic:\n",
    "* Bank Account services\n",
    "* Credit card or prepaid card\n",
    "* Theft/Dispute Reporting\n",
    "* Mortgage/Loan\n",
    "* Others"
   ]
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   "source": [
    "import warnings as warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Create the dictionary of Topic names and Topics\n",
    "Topic_names = {0:\"Bank Account services\",\n",
    "               1:\"Credit card or prepaid card\", \n",
    "               2:\"Others\",\n",
    "               3:\"Theft/Dispute Reporting\",\n",
    "               4:\"Mortgage/Loan\"}\n",
    "\n",
    "# Replace Topics with Topic Names\n",
    "df_clean['Topic'] = df_clean['Topic'].map(Topic_names)"
   ]
  },
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       "      <th></th>\n",
       "      <th>complaint_what_happened</th>\n",
       "      <th>lemmatized_complaints</th>\n",
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       "      <td>Good morning my name is XXXX XXXX and I apprec...</td>\n",
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       "      <td>morning name appreciate chase bank cardmember ...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I upgraded my XXXX XXXX card in XX/XX/2018 and...</td>\n",
       "      <td>upgraded xxxx xxxx card xxxx tell agent upgrad...</td>\n",
       "      <td>xxxx card tell agent date information order up...</td>\n",
       "      <td>117</td>\n",
       "      <td>card tell agent date information order upgrad...</td>\n",
       "      <td>Credit card or prepaid card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chase Card was reported on XX/XX/2019. However...</td>\n",
       "      <td>chase card report xxxx however fraudulent appl...</td>\n",
       "      <td>card report application submit identity consen...</td>\n",
       "      <td>81</td>\n",
       "      <td>card report application submit identity consen...</td>\n",
       "      <td>Credit card or prepaid card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>On XX/XX/2018, while trying to book a XXXX  XX...</td>\n",
       "      <td>xxxx try book xxxx xxxx ticket come across off...</td>\n",
       "      <td>try book ticket come offer apply ticket card i...</td>\n",
       "      <td>429</td>\n",
       "      <td>try book ticket come offer apply ticket card i...</td>\n",
       "      <td>Credit card or prepaid card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>my grand son give me check for {$1600.00} i de...</td>\n",
       "      <td>grand son give check deposit chase account fun...</td>\n",
       "      <td>son check deposit chase account fund bank acco...</td>\n",
       "      <td>163</td>\n",
       "      <td>son check deposit chase account fund bank acco...</td>\n",
       "      <td>Bank Account services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21067</th>\n",
       "      <td>After being a Chase Card customer for well ove...</td>\n",
       "      <td>chase card customer well decade offer multiple...</td>\n",
       "      <td>chase card customer decade offer solicitation ...</td>\n",
       "      <td>232</td>\n",
       "      <td>chase card customer decade offer solicitation ...</td>\n",
       "      <td>Credit card or prepaid card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21068</th>\n",
       "      <td>On Wednesday, XX/XX/XXXX I called Chas, my XXX...</td>\n",
       "      <td>wednesday xxxxxxxx call chas xxxx xxxx visa cr...</td>\n",
       "      <td>call chas xxxx credit card provider claim purc...</td>\n",
       "      <td>702</td>\n",
       "      <td>call chas  credit card provider claim purchase...</td>\n",
       "      <td>Theft/Dispute Reporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21069</th>\n",
       "      <td>I am not familiar with XXXX pay and did not un...</td>\n",
       "      <td>familiar xxxx pay understand great risk provid...</td>\n",
       "      <td>pay risk consumer chase bank chase year trust ...</td>\n",
       "      <td>695</td>\n",
       "      <td>pay risk consumer chase bank chase year trust ...</td>\n",
       "      <td>Theft/Dispute Reporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21070</th>\n",
       "      <td>I have had flawless credit for 30 yrs. I've ha...</td>\n",
       "      <td>flawless credit yrs ive chase credit card chas...</td>\n",
       "      <td>flawless credit chase credit card chase freedo...</td>\n",
       "      <td>563</td>\n",
       "      <td>flawless credit chase credit card chase freedo...</td>\n",
       "      <td>Mortgage/Loan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21071</th>\n",
       "      <td>Roughly 10+ years ago I closed out my accounts...</td>\n",
       "      <td>roughly year ago close account jp morgan chase...</td>\n",
       "      <td>year account jp bank order line credit pay acc...</td>\n",
       "      <td>252</td>\n",
       "      <td>year account jp bank order line credit pay acc...</td>\n",
       "      <td>Mortgage/Loan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21072 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 complaint_what_happened  \\\n",
       "0      Good morning my name is XXXX XXXX and I apprec...   \n",
       "1      I upgraded my XXXX XXXX card in XX/XX/2018 and...   \n",
       "2      Chase Card was reported on XX/XX/2019. However...   \n",
       "3      On XX/XX/2018, while trying to book a XXXX  XX...   \n",
       "4      my grand son give me check for {$1600.00} i de...   \n",
       "...                                                  ...   \n",
       "21067  After being a Chase Card customer for well ove...   \n",
       "21068  On Wednesday, XX/XX/XXXX I called Chas, my XXX...   \n",
       "21069  I am not familiar with XXXX pay and did not un...   \n",
       "21070  I have had flawless credit for 30 yrs. I've ha...   \n",
       "21071  Roughly 10+ years ago I closed out my accounts...   \n",
       "\n",
       "                                   lemmatized_complaints  \\\n",
       "0      good morning name xxxx xxxx appreciate could h...   \n",
       "1      upgraded xxxx xxxx card xxxx tell agent upgrad...   \n",
       "2      chase card report xxxx however fraudulent appl...   \n",
       "3      xxxx try book xxxx xxxx ticket come across off...   \n",
       "4      grand son give check deposit chase account fun...   \n",
       "...                                                  ...   \n",
       "21067  chase card customer well decade offer multiple...   \n",
       "21068  wednesday xxxxxxxx call chas xxxx xxxx visa cr...   \n",
       "21069  familiar xxxx pay understand great risk provid...   \n",
       "21070  flawless credit yrs ive chase credit card chas...   \n",
       "21071  roughly year ago close account jp morgan chase...   \n",
       "\n",
       "                                   complaint_POS_removed  length  \\\n",
       "0      morning name appreciate chase bank cardmember ...     175   \n",
       "1      xxxx card tell agent date information order up...     117   \n",
       "2      card report application submit identity consen...      81   \n",
       "3      try book ticket come offer apply ticket card i...     429   \n",
       "4      son check deposit chase account fund bank acco...     163   \n",
       "...                                                  ...     ...   \n",
       "21067  chase card customer decade offer solicitation ...     232   \n",
       "21068  call chas xxxx credit card provider claim purc...     702   \n",
       "21069  pay risk consumer chase bank chase year trust ...     695   \n",
       "21070  flawless credit chase credit card chase freedo...     563   \n",
       "21071  year account jp bank order line credit pay acc...     252   \n",
       "\n",
       "                                         Complaint_clean  \\\n",
       "0      morning name appreciate chase bank cardmember ...   \n",
       "1       card tell agent date information order upgrad...   \n",
       "2      card report application submit identity consen...   \n",
       "3      try book ticket come offer apply ticket card i...   \n",
       "4      son check deposit chase account fund bank acco...   \n",
       "...                                                  ...   \n",
       "21067  chase card customer decade offer solicitation ...   \n",
       "21068  call chas  credit card provider claim purchase...   \n",
       "21069  pay risk consumer chase bank chase year trust ...   \n",
       "21070  flawless credit chase credit card chase freedo...   \n",
       "21071  year account jp bank order line credit pay acc...   \n",
       "\n",
       "                             Topic  \n",
       "0            Bank Account services  \n",
       "1      Credit card or prepaid card  \n",
       "2      Credit card or prepaid card  \n",
       "3      Credit card or prepaid card  \n",
       "4            Bank Account services  \n",
       "...                            ...  \n",
       "21067  Credit card or prepaid card  \n",
       "21068      Theft/Dispute Reporting  \n",
       "21069      Theft/Dispute Reporting  \n",
       "21070                Mortgage/Loan  \n",
       "21071                Mortgage/Loan  \n",
       "\n",
       "[21072 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean"
   ]
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   "source": [
    "## Supervised model to predict any new complaints to the relevant Topics.\n",
    "\n",
    "You have now build the model to create the topics for each complaints.Now in the below section you will use them to classify any new complaints.\n",
    "\n",
    "Since you will be using supervised learning technique we have to convert the topic names to numbers(numpy arrays only understand numbers)"
   ]
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    "# Create the dictionary again of Topic names and Topics\n",
    "Topic_names = {\"Bank Account services\":0,\n",
    "               \"Credit card or prepaid card\":1,\n",
    "               \"Others\":2,\n",
    "               \"Theft/Dispute Reporting\":3,\n",
    "               \"Mortgage/Loan\":4}\n",
    "\n",
    "# Replace Topics with Topic Names\n",
    "df_clean['Topic'] = df_clean['Topic'].map(Topic_names)"
   ]
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21067</th>\n",
       "      <td>After being a Chase Card customer for well ove...</td>\n",
       "      <td>chase card customer well decade offer multiple...</td>\n",
       "      <td>chase card customer decade offer solicitation ...</td>\n",
       "      <td>232</td>\n",
       "      <td>chase card customer decade offer solicitation ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21068</th>\n",
       "      <td>On Wednesday, XX/XX/XXXX I called Chas, my XXX...</td>\n",
       "      <td>wednesday xxxxxxxx call chas xxxx xxxx visa cr...</td>\n",
       "      <td>call chas xxxx credit card provider claim purc...</td>\n",
       "      <td>702</td>\n",
       "      <td>call chas  credit card provider claim purchase...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21069</th>\n",
       "      <td>I am not familiar with XXXX pay and did not un...</td>\n",
       "      <td>familiar xxxx pay understand great risk provid...</td>\n",
       "      <td>pay risk consumer chase bank chase year trust ...</td>\n",
       "      <td>695</td>\n",
       "      <td>pay risk consumer chase bank chase year trust ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21070</th>\n",
       "      <td>I have had flawless credit for 30 yrs. I've ha...</td>\n",
       "      <td>flawless credit yrs ive chase credit card chas...</td>\n",
       "      <td>flawless credit chase credit card chase freedo...</td>\n",
       "      <td>563</td>\n",
       "      <td>flawless credit chase credit card chase freedo...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21071</th>\n",
       "      <td>Roughly 10+ years ago I closed out my accounts...</td>\n",
       "      <td>roughly year ago close account jp morgan chase...</td>\n",
       "      <td>year account jp bank order line credit pay acc...</td>\n",
       "      <td>252</td>\n",
       "      <td>year account jp bank order line credit pay acc...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21072 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 complaint_what_happened  \\\n",
       "0      Good morning my name is XXXX XXXX and I apprec...   \n",
       "1      I upgraded my XXXX XXXX card in XX/XX/2018 and...   \n",
       "2      Chase Card was reported on XX/XX/2019. However...   \n",
       "3      On XX/XX/2018, while trying to book a XXXX  XX...   \n",
       "4      my grand son give me check for {$1600.00} i de...   \n",
       "...                                                  ...   \n",
       "21067  After being a Chase Card customer for well ove...   \n",
       "21068  On Wednesday, XX/XX/XXXX I called Chas, my XXX...   \n",
       "21069  I am not familiar with XXXX pay and did not un...   \n",
       "21070  I have had flawless credit for 30 yrs. I've ha...   \n",
       "21071  Roughly 10+ years ago I closed out my accounts...   \n",
       "\n",
       "                                   lemmatized_complaints  \\\n",
       "0      good morning name xxxx xxxx appreciate could h...   \n",
       "1      upgraded xxxx xxxx card xxxx tell agent upgrad...   \n",
       "2      chase card report xxxx however fraudulent appl...   \n",
       "3      xxxx try book xxxx xxxx ticket come across off...   \n",
       "4      grand son give check deposit chase account fun...   \n",
       "...                                                  ...   \n",
       "21067  chase card customer well decade offer multiple...   \n",
       "21068  wednesday xxxxxxxx call chas xxxx xxxx visa cr...   \n",
       "21069  familiar xxxx pay understand great risk provid...   \n",
       "21070  flawless credit yrs ive chase credit card chas...   \n",
       "21071  roughly year ago close account jp morgan chase...   \n",
       "\n",
       "                                   complaint_POS_removed  length  \\\n",
       "0      morning name appreciate chase bank cardmember ...     175   \n",
       "1      xxxx card tell agent date information order up...     117   \n",
       "2      card report application submit identity consen...      81   \n",
       "3      try book ticket come offer apply ticket card i...     429   \n",
       "4      son check deposit chase account fund bank acco...     163   \n",
       "...                                                  ...     ...   \n",
       "21067  chase card customer decade offer solicitation ...     232   \n",
       "21068  call chas xxxx credit card provider claim purc...     702   \n",
       "21069  pay risk consumer chase bank chase year trust ...     695   \n",
       "21070  flawless credit chase credit card chase freedo...     563   \n",
       "21071  year account jp bank order line credit pay acc...     252   \n",
       "\n",
       "                                         Complaint_clean  Topic  \n",
       "0      morning name appreciate chase bank cardmember ...      0  \n",
       "1       card tell agent date information order upgrad...      1  \n",
       "2      card report application submit identity consen...      1  \n",
       "3      try book ticket come offer apply ticket card i...      1  \n",
       "4      son check deposit chase account fund bank acco...      0  \n",
       "...                                                  ...    ...  \n",
       "21067  chase card customer decade offer solicitation ...      1  \n",
       "21068  call chas  credit card provider claim purchase...      3  \n",
       "21069  pay risk consumer chase bank chase year trust ...      3  \n",
       "21070  flawless credit chase credit card chase freedo...      4  \n",
       "21071  year account jp bank order line credit pay acc...      4  \n",
       "\n",
       "[21072 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "edb61f17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:55:28.417996Z",
     "iopub.status.busy": "2022-01-13T18:55:28.416764Z",
     "iopub.status.idle": "2022-01-13T18:55:28.418844Z",
     "shell.execute_reply": "2022-01-13T18:55:28.419372Z",
     "shell.execute_reply.started": "2022-01-13T18:10:13.626653Z"
    },
    "id": "Xx-FrbkWJrGH",
    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Keep the columns\"complaint_what_happened\" & \"Topic\" only in the new dataframe --> training_data\n",
    "training_data = df_clean[[\"complaint_what_happened\",\"Topic\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "14d4c32e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:55:33.771775Z",
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    },
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     "start_time": "2022-01-13T18:55:31.132764",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>complaint_what_happened</th>\n",
       "      <th>Topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Good morning my name is XXXX XXXX and I apprec...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I upgraded my XXXX XXXX card in XX/XX/2018 and...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chase Card was reported on XX/XX/2019. However...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>On XX/XX/2018, while trying to book a XXXX  XX...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>my grand son give me check for {$1600.00} i de...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21067</th>\n",
       "      <td>After being a Chase Card customer for well ove...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21068</th>\n",
       "      <td>On Wednesday, XX/XX/XXXX I called Chas, my XXX...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21069</th>\n",
       "      <td>I am not familiar with XXXX pay and did not un...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21070</th>\n",
       "      <td>I have had flawless credit for 30 yrs. I've ha...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21071</th>\n",
       "      <td>Roughly 10+ years ago I closed out my accounts...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21072 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 complaint_what_happened  Topic\n",
       "0      Good morning my name is XXXX XXXX and I apprec...      0\n",
       "1      I upgraded my XXXX XXXX card in XX/XX/2018 and...      1\n",
       "2      Chase Card was reported on XX/XX/2019. However...      1\n",
       "3      On XX/XX/2018, while trying to book a XXXX  XX...      1\n",
       "4      my grand son give me check for {$1600.00} i de...      0\n",
       "...                                                  ...    ...\n",
       "21067  After being a Chase Card customer for well ove...      1\n",
       "21068  On Wednesday, XX/XX/XXXX I called Chas, my XXX...      3\n",
       "21069  I am not familiar with XXXX pay and did not un...      3\n",
       "21070  I have had flawless credit for 30 yrs. I've ha...      4\n",
       "21071  Roughly 10+ years ago I closed out my accounts...      4\n",
       "\n",
       "[21072 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac80b8a6",
   "metadata": {
    "id": "280Vbqk-7a8M",
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     "duration": 2.634713,
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     "start_time": "2022-01-13T18:55:36.515706",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Apply the supervised models on the training data created. In this process, you have to do the following:\n",
    "* Create the vector counts using Count Vectoriser\n",
    "* Transform the word vecotr to tf-idf\n",
    "* Create the train & test data using the train_test_split on the tf-idf & topics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "60b9466a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:55:44.530164Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Write your code to get the Vector count\n",
    "count_vect = CountVectorizer()\n",
    "X_train_counts = count_vect.fit_transform(training_data.complaint_what_happened)\n",
    "\n",
    "# Write your code here to transform the word vector to tf-idf\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5cbb904e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:55:55.910558Z",
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     "shell.execute_reply": "2022-01-13T18:55:55.930432Z",
     "shell.execute_reply.started": "2022-01-13T18:10:19.374744Z"
    },
    "papermill": {
     "duration": 2.628738,
     "end_time": "2022-01-13T18:55:55.931240",
     "exception": false,
     "start_time": "2022-01-13T18:55:53.302502",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train:\u001b[1m (15804, 29725) \u001b[0m\n",
      "Shape of y_train:\u001b[1m (15804,) \u001b[0m\n",
      "Shape of X_test:\u001b[1m (5268, 29725) \u001b[0m\n",
      "Shape of y_test:\u001b[1m (5268,) \u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Performing Train-Test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train_tfidf, training_data.Topic, test_size=0.25, random_state=42)\n",
    "\n",
    "print(f\"Shape of X_train:\\033[1m {X_train.shape}\", '\\033[0m')\n",
    "print(f\"Shape of y_train:\\033[1m {y_train.shape}\", '\\033[0m')\n",
    "print(f\"Shape of X_test:\\033[1m {X_test.shape}\", '\\033[0m')\n",
    "print(f\"Shape of y_test:\\033[1m {y_test.shape}\", '\\033[0m')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d40e613",
   "metadata": {
    "id": "uMU3vj6w-wqL",
    "papermill": {
     "duration": 2.711482,
     "end_time": "2022-01-13T18:56:01.285179",
     "exception": false,
     "start_time": "2022-01-13T18:55:58.573697",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "You have to try atleast 3 models on the train & test data from these options:\n",
    "* Logistic regression\n",
    "* Decision Tree\n",
    "* Random Forest\n",
    "* Naive Bayes (optional)\n",
    "\n",
    "**Using the required evaluation metrics judge the tried models and select the ones performing the best**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43564921",
   "metadata": {
    "papermill": {
     "duration": 2.718449,
     "end_time": "2022-01-13T18:56:06.625504",
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     "start_time": "2022-01-13T18:56:03.907055",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Model 1: Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8bf605bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:56:11.923994Z",
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     "start_time": "2022-01-13T18:56:09.296379",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a function to evaluate models\n",
    "def eval_model(y_test, y_pred, model_name):\n",
    "    # print classification report of classifier\n",
    "    print(f\"\\033[1mCLASSIFICATION REPORT for {model_name}\\n\", '\\033[0m')\n",
    "    print(classification_report(y_test, y_pred, target_names=[\"Bank Account services\", \"Credit card or prepaid card\", \"Others\", \"Theft/Dispute Reporting\",\"Mortgage/Loan\"]))\n",
    "    \n",
    "    # plot confusion matrix of the classifier\n",
    "    plt.figure(figsize=(10,6))\n",
    "    plt.title(f\"CONFUSION MATRIX for {model_name}\\n\")\n",
    "    matrix = confusion_matrix(y_test, y_pred)\n",
    "    sns.heatmap(matrix, annot=True, cbar=None, cmap=\"Blues\", fmt='d', xticklabels=[\"Bank Account services\", \"Credit card or prepaid card\", \"Others\", \"Theft/Dispute Reporting\",\"Mortgage/Loan\"], yticklabels=[\"Bank Account services\", \"Credit card or prepaid card\", \"Others\", \"Theft/Dispute Reporting\",\"Mortgage/Loan\"])\n",
    "    plt.show()\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9b7f5591",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:56:17.351596Z",
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     "iopub.status.idle": "2022-01-13T18:56:21.504026Z",
     "shell.execute_reply": "2022-01-13T18:56:21.505534Z",
     "shell.execute_reply.started": "2022-01-13T18:10:19.406179Z"
    },
    "papermill": {
     "duration": 6.897574,
     "end_time": "2022-01-13T18:56:21.505824",
     "exception": false,
     "start_time": "2022-01-13T18:56:14.608250",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
      "Wall time: 8.11 µs\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import f1_score, classification_report, confusion_matrix\n",
    "\n",
    "# Run the Logistic Regression model\n",
    "model_name = 'LOGISTIC REGRESSION'\n",
    "clf_lr = LogisticRegression(solver='liblinear')\n",
    "%time \n",
    "clf_lr.fit(X_train, y_train)\n",
    "y_pred_lr = clf_lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7009e6f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:56:26.853741Z",
     "iopub.status.busy": "2022-01-13T18:56:26.852696Z",
     "iopub.status.idle": "2022-01-13T18:56:26.861885Z",
     "shell.execute_reply": "2022-01-13T18:56:26.861365Z",
     "shell.execute_reply.started": "2022-01-13T18:10:23.882562Z"
    },
    "papermill": {
     "duration": 2.676346,
     "end_time": "2022-01-13T18:56:26.862050",
     "exception": false,
     "start_time": "2022-01-13T18:56:24.185704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9149934633757285"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate F1 Score using weighted average method\n",
    "f1_lr = f1_score(y_test, y_pred_lr, average=\"weighted\")\n",
    "f1_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3071f570",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:56:32.229185Z",
     "iopub.status.busy": "2022-01-13T18:56:32.228451Z",
     "iopub.status.idle": "2022-01-13T18:56:43.750491Z",
     "shell.execute_reply": "2022-01-13T18:56:43.749802Z",
     "shell.execute_reply.started": "2022-01-13T18:10:23.915832Z"
    },
    "papermill": {
     "duration": 14.190167,
     "end_time": "2022-01-13T18:56:43.750652",
     "exception": false,
     "start_time": "2022-01-13T18:56:29.560485",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs\n",
      "Wall time: 7.63 µs\n"
     ]
    }
   ],
   "source": [
    "# Run Logistic Regression on tuned hyperparameters\n",
    "clf_lr_tuned = LogisticRegression(C=1, penalty='l1',solver='saga')\n",
    "%time \n",
    "clf_lr_tuned.fit(X_train, y_train)\n",
    "y_pred_lr_tuned = clf_lr_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "10d77452",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:56:49.234007Z",
     "iopub.status.busy": "2022-01-13T18:56:49.233163Z",
     "iopub.status.idle": "2022-01-13T18:56:49.240276Z",
     "shell.execute_reply": "2022-01-13T18:56:49.240857Z",
     "shell.execute_reply.started": "2022-01-13T18:10:35.312216Z"
    },
    "papermill": {
     "duration": 2.671487,
     "end_time": "2022-01-13T18:56:49.241075",
     "exception": false,
     "start_time": "2022-01-13T18:56:46.569588",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9395908993282575"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate F1 Score of tuned model using weighted average method\n",
    "f1_lr_tuned = f1_score(y_test, y_pred_lr_tuned, average=\"weighted\")\n",
    "f1_lr_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b7487734",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:56:54.606800Z",
     "iopub.status.busy": "2022-01-13T18:56:54.606092Z",
     "iopub.status.idle": "2022-01-13T18:56:54.842591Z",
     "shell.execute_reply": "2022-01-13T18:56:54.843112Z",
     "shell.execute_reply.started": "2022-01-13T18:10:35.327425Z"
    },
    "papermill": {
     "duration": 2.911994,
     "end_time": "2022-01-13T18:56:54.843295",
     "exception": false,
     "start_time": "2022-01-13T18:56:51.931301",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mCLASSIFICATION REPORT for LOGISTIC REGRESSION\n",
      " \u001b[0m\n",
      "                             precision    recall  f1-score   support\n",
      "\n",
      "      Bank Account services       0.94      0.94      0.94      1371\n",
      "Credit card or prepaid card       0.94      0.96      0.95      1275\n",
      "                     Others       0.94      0.94      0.94       917\n",
      "    Theft/Dispute Reporting       0.95      0.92      0.93      1142\n",
      "              Mortgage/Loan       0.93      0.92      0.93       563\n",
      "\n",
      "                   accuracy                           0.94      5268\n",
      "                  macro avg       0.94      0.94      0.94      5268\n",
      "               weighted avg       0.94      0.94      0.94      5268\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the tuned Logistic Regression classifier\n",
    "eval_model(y_test, y_pred_lr_tuned, model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0ef3af69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:57:00.259342Z",
     "iopub.status.busy": "2022-01-13T18:57:00.253164Z",
     "iopub.status.idle": "2022-01-13T18:57:00.264369Z",
     "shell.execute_reply": "2022-01-13T18:57:00.264852Z",
     "shell.execute_reply.started": "2022-01-13T18:10:35.587029Z"
    },
    "papermill": {
     "duration": 2.699034,
     "end_time": "2022-01-13T18:57:00.265027",
     "exception": false,
     "start_time": "2022-01-13T18:56:57.565993",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>F1 Score (untuned)</th>\n",
       "      <th>F1 Score (tuned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  F1 Score (untuned)  F1 Score (tuned)\n",
       "0  Logistic Regression                0.91              0.94"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe to store F1 Scores of all models we will build\n",
    "summary = pd.DataFrame([{'Model': 'Logistic Regression','F1 Score (untuned)': round(f1_lr, 2), 'F1 Score (tuned)': round(f1_lr_tuned, 2)}])\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3053e064",
   "metadata": {
    "papermill": {
     "duration": 2.775672,
     "end_time": "2022-01-13T18:57:05.716235",
     "exception": false,
     "start_time": "2022-01-13T18:57:02.940563",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Model 2: Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "aaf22c3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:57:11.193760Z",
     "iopub.status.busy": "2022-01-13T18:57:11.193075Z",
     "iopub.status.idle": "2022-01-13T18:57:26.403917Z",
     "shell.execute_reply": "2022-01-13T18:57:26.404618Z",
     "shell.execute_reply.started": "2022-01-13T18:10:35.603345Z"
    },
    "papermill": {
     "duration": 17.975713,
     "end_time": "2022-01-13T18:57:26.404801",
     "exception": false,
     "start_time": "2022-01-13T18:57:08.429088",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
      "Wall time: 8.11 µs\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Run Decision Tree on default hyperparameters\n",
    "model_name = 'DECISION TREE'\n",
    "clf_dt = DecisionTreeClassifier()\n",
    "%time \n",
    "clf_dt.fit(X_train, y_train)\n",
    "y_pred_dt = clf_dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "050dd2a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:57:31.650928Z",
     "iopub.status.busy": "2022-01-13T18:57:31.650253Z",
     "iopub.status.idle": "2022-01-13T18:57:31.657236Z",
     "shell.execute_reply": "2022-01-13T18:57:31.657768Z",
     "shell.execute_reply.started": "2022-01-13T18:10:50.401533Z"
    },
    "papermill": {
     "duration": 2.63096,
     "end_time": "2022-01-13T18:57:31.657936",
     "exception": false,
     "start_time": "2022-01-13T18:57:29.026976",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7814976455129667"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate F1 Score using weighted average method\n",
    "f1_dt = f1_score(y_test, y_pred_dt, average=\"weighted\")\n",
    "f1_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "363dd886",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:57:37.066960Z",
     "iopub.status.busy": "2022-01-13T18:57:37.062093Z",
     "iopub.status.idle": "2022-01-13T18:57:44.550336Z",
     "shell.execute_reply": "2022-01-13T18:57:44.549730Z",
     "shell.execute_reply.started": "2022-01-13T18:10:50.415366Z"
    },
    "papermill": {
     "duration": 10.250168,
     "end_time": "2022-01-13T18:57:44.550480",
     "exception": false,
     "start_time": "2022-01-13T18:57:34.300312",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
      "Wall time: 7.39 µs\n"
     ]
    }
   ],
   "source": [
    "# Run Decision Tree on tuned hyperparameters\n",
    "clf_dt_tuned = DecisionTreeClassifier(criterion='gini', max_depth=30, min_samples_leaf=15, max_features=None)\n",
    "%time \n",
    "clf_dt_tuned.fit(X_train, y_train)\n",
    "y_pred_dt_tuned = clf_dt_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "68638753",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:57:50.031100Z",
     "iopub.status.busy": "2022-01-13T18:57:50.029981Z",
     "iopub.status.idle": "2022-01-13T18:57:50.037353Z",
     "shell.execute_reply": "2022-01-13T18:57:50.037921Z",
     "shell.execute_reply.started": "2022-01-13T18:10:57.838493Z"
    },
    "papermill": {
     "duration": 2.757368,
     "end_time": "2022-01-13T18:57:50.038113",
     "exception": false,
     "start_time": "2022-01-13T18:57:47.280745",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8000120029727986"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate F1 Score of tuned model using weighted average method\n",
    "f1_dt_tuned = f1_score(y_test, y_pred_dt_tuned, average=\"weighted\")\n",
    "f1_dt_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "642c526a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:57:55.399829Z",
     "iopub.status.busy": "2022-01-13T18:57:55.399084Z",
     "iopub.status.idle": "2022-01-13T18:57:55.635795Z",
     "shell.execute_reply": "2022-01-13T18:57:55.635281Z",
     "shell.execute_reply.started": "2022-01-13T18:10:57.850440Z"
    },
    "papermill": {
     "duration": 2.875229,
     "end_time": "2022-01-13T18:57:55.635955",
     "exception": false,
     "start_time": "2022-01-13T18:57:52.760726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mCLASSIFICATION REPORT for DECISION TREE\n",
      " \u001b[0m\n",
      "                             precision    recall  f1-score   support\n",
      "\n",
      "      Bank Account services       0.78      0.82      0.80      1371\n",
      "Credit card or prepaid card       0.83      0.83      0.83      1275\n",
      "                     Others       0.81      0.80      0.80       917\n",
      "    Theft/Dispute Reporting       0.77      0.77      0.77      1142\n",
      "              Mortgage/Loan       0.80      0.75      0.77       563\n",
      "\n",
      "                   accuracy                           0.80      5268\n",
      "                  macro avg       0.80      0.79      0.80      5268\n",
      "               weighted avg       0.80      0.80      0.80      5268\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the tuned Decision Tree classifier\n",
    "eval_model(y_test, y_pred_dt_tuned, model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6540ec8c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:58:01.019581Z",
     "iopub.status.busy": "2022-01-13T18:58:01.018558Z",
     "iopub.status.idle": "2022-01-13T18:58:01.029220Z",
     "shell.execute_reply": "2022-01-13T18:58:01.028645Z",
     "shell.execute_reply.started": "2022-01-13T18:10:58.098684Z"
    },
    "papermill": {
     "duration": 2.692965,
     "end_time": "2022-01-13T18:58:01.029378",
     "exception": false,
     "start_time": "2022-01-13T18:57:58.336413",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>F1 Score (untuned)</th>\n",
       "      <th>F1 Score (tuned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  F1 Score (untuned)  F1 Score (tuned)\n",
       "0  Logistic Regression                0.91              0.94\n",
       "1        Decision Tree                0.78              0.80"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update the summary table\n",
    "summary.loc[len(summary.index)] = ['Decision Tree', round(f1_dt, 2), round(f1_dt_tuned, 2)]\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39fabc74",
   "metadata": {
    "papermill": {
     "duration": 2.658042,
     "end_time": "2022-01-13T18:58:06.333338",
     "exception": false,
     "start_time": "2022-01-13T18:58:03.675296",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Model 3: Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ce2e76c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:58:11.726794Z",
     "iopub.status.busy": "2022-01-13T18:58:11.725807Z",
     "iopub.status.idle": "2022-01-13T18:58:56.098011Z",
     "shell.execute_reply": "2022-01-13T18:58:56.097410Z",
     "shell.execute_reply.started": "2022-01-13T18:10:58.118553Z"
    },
    "papermill": {
     "duration": 47.037831,
     "end_time": "2022-01-13T18:58:56.098215",
     "exception": false,
     "start_time": "2022-01-13T18:58:09.060384",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
      "Wall time: 7.87 µs\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Run the Random Forest model on default hyperparameters\n",
    "model_name = 'RANDOM FOREST'\n",
    "clf_rf = RandomForestClassifier()\n",
    "%time \n",
    "clf_rf.fit(X_train, y_train)\n",
    "y_pred_rf = clf_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fc0bbcfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:59:01.474154Z",
     "iopub.status.busy": "2022-01-13T18:59:01.473179Z",
     "iopub.status.idle": "2022-01-13T18:59:01.481522Z",
     "shell.execute_reply": "2022-01-13T18:59:01.482281Z",
     "shell.execute_reply.started": "2022-01-13T18:11:39.734671Z"
    },
    "papermill": {
     "duration": 2.735247,
     "end_time": "2022-01-13T18:59:01.482485",
     "exception": false,
     "start_time": "2022-01-13T18:58:58.747238",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.810319688622784"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate F1 Score using weighted average method\n",
    "f1_rf = f1_score(y_test, y_pred_rf, average=\"weighted\")\n",
    "f1_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2398d970",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T18:59:07.017941Z",
     "iopub.status.busy": "2022-01-13T18:59:07.017252Z",
     "iopub.status.idle": "2022-01-13T19:11:33.360374Z",
     "shell.execute_reply": "2022-01-13T19:11:33.361106Z",
     "shell.execute_reply.started": "2022-01-13T18:11:39.746125Z"
    },
    "papermill": {
     "duration": 749.187026,
     "end_time": "2022-01-13T19:11:33.361318",
     "exception": false,
     "start_time": "2022-01-13T18:59:04.174292",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 1e+03 ns, total: 4 µs\n",
      "Wall time: 7.39 µs\n"
     ]
    }
   ],
   "source": [
    "# Run Random Forest on tuned hyperparameters\n",
    "clf_rf_tuned = RandomForestClassifier(n_estimators=100, min_samples_split=5, min_samples_leaf=5, max_features=None, max_depth=30, criterion='gini')\n",
    "%time \n",
    "clf_rf_tuned.fit(X_train, y_train)\n",
    "y_pred_rf_tuned = clf_rf_tuned.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5aeaba51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T19:11:38.771428Z",
     "iopub.status.busy": "2022-01-13T19:11:38.770716Z",
     "iopub.status.idle": "2022-01-13T19:11:38.778224Z",
     "shell.execute_reply": "2022-01-13T19:11:38.778742Z",
     "shell.execute_reply.started": "2022-01-13T18:23:38.907830Z"
    },
    "papermill": {
     "duration": 2.699996,
     "end_time": "2022-01-13T19:11:38.778912",
     "exception": false,
     "start_time": "2022-01-13T19:11:36.078916",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8484537931957626"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate F1 Score of tuned model using weighted average method\n",
    "f1_rf_tuned = f1_score(y_test, y_pred_rf_tuned, average=\"weighted\")\n",
    "f1_rf_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "88ea8f93",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T19:11:44.478759Z",
     "iopub.status.busy": "2022-01-13T19:11:44.477987Z",
     "iopub.status.idle": "2022-01-13T19:11:44.728732Z",
     "shell.execute_reply": "2022-01-13T19:11:44.728134Z",
     "shell.execute_reply.started": "2022-01-13T18:23:38.918202Z"
    },
    "papermill": {
     "duration": 3.128231,
     "end_time": "2022-01-13T19:11:44.728934",
     "exception": false,
     "start_time": "2022-01-13T19:11:41.600703",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mCLASSIFICATION REPORT for RANDOM FOREST\n",
      " \u001b[0m\n",
      "                             precision    recall  f1-score   support\n",
      "\n",
      "      Bank Account services       0.86      0.86      0.86      1371\n",
      "Credit card or prepaid card       0.87      0.88      0.87      1275\n",
      "                     Others       0.86      0.85      0.86       917\n",
      "    Theft/Dispute Reporting       0.82      0.83      0.83      1142\n",
      "              Mortgage/Loan       0.82      0.79      0.80       563\n",
      "\n",
      "                   accuracy                           0.85      5268\n",
      "                  macro avg       0.85      0.84      0.84      5268\n",
      "               weighted avg       0.85      0.85      0.85      5268\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the tuned Random Forest classifier\n",
    "eval_model(y_test, y_pred_rf_tuned, model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0dfbe410",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T19:11:50.211399Z",
     "iopub.status.busy": "2022-01-13T19:11:50.210718Z",
     "iopub.status.idle": "2022-01-13T19:11:50.225581Z",
     "shell.execute_reply": "2022-01-13T19:11:50.226096Z",
     "shell.execute_reply.started": "2022-01-13T18:23:39.171685Z"
    },
    "papermill": {
     "duration": 2.752386,
     "end_time": "2022-01-13T19:11:50.226268",
     "exception": false,
     "start_time": "2022-01-13T19:11:47.473882",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>F1 Score (untuned)</th>\n",
       "      <th>F1 Score (tuned)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  F1 Score (untuned)  F1 Score (tuned)\n",
       "0  Logistic Regression                0.91              0.94\n",
       "1        Decision Tree                0.78              0.80\n",
       "2        Random Forest                0.81              0.85"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update the summary table\n",
    "summary.loc[len(summary.index)] = ['Random Forest', round(f1_rf, 2), round(f1_rf_tuned, 2)]\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e74a06db",
   "metadata": {
    "papermill": {
     "duration": 2.669304,
     "end_time": "2022-01-13T19:11:55.565589",
     "exception": false,
     "start_time": "2022-01-13T19:11:52.896285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### From the above summary table, we observe that the `tuned Logistic Regression` performs the best among all that we tried."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bdede1a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-13T19:12:00.948846Z",
     "iopub.status.busy": "2022-01-13T19:12:00.948170Z",
     "iopub.status.idle": "2022-01-13T19:12:00.953248Z",
     "shell.execute_reply": "2022-01-13T19:12:00.953804Z",
     "shell.execute_reply.started": "2022-01-13T18:28:06.547759Z"
    },
    "papermill": {
     "duration": 2.667122,
     "end_time": "2022-01-13T19:12:00.953973",
     "exception": false,
     "start_time": "2022-01-13T19:11:58.286851",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Function to predict a topic for given text\n",
    "def predict_topic(text):\n",
    "    \n",
    "    target_names = [\"Bank Account services\", \"Credit card or prepaid card\", \"Others\", \"Theft/Dispute Reporting\", \"Mortgage/Loan\"]\n",
    "\n",
    "    X_new_counts = count_vect.transform(text)\n",
    "    X_new_tfidf = tfidf_transformer.transform(X_new_counts)\n",
    "    predicted = clf_lr_tuned.predict(X_new_tfidf)\n",
    "\n",
    "    return target_names[predicted[0]]"
   ]
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       "      <th></th>\n",
       "      <th>complaints</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>I can not get from chase who services my mortg...</td>\n",
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       "      <td>The bill amount of my credit card was debited ...</td>\n",
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      "text/plain": [
       "                                          complaints\n",
       "0  I can not get from chase who services my mortg...\n",
       "1  The bill amount of my credit card was debited ...\n",
       "2  I want to open a salary account at your downto...\n",
       "3  Yesterday, I received a fraudulent email regar...\n",
       "4      What is the procedure to know my CIBIL score?\n",
       "5  I need to know the number of bank branches and..."
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe of some sample customer complaints\n",
    "df_new = pd.DataFrame({'complaints': [\"I can not get from chase who services my mortgage, who owns it and who has original loan docs\", \n",
    "                                  \"The bill amount of my credit card was debited twice. Please look into the matter and resolve at the earliest.\",\n",
    "                                  \"I want to open a salary account at your downtown branch. Please provide me the procedure.\",\n",
    "                                  \"Yesterday, I received a fraudulent email regarding renewal of my services.\",\n",
    "                                  \"What is the procedure to know my CIBIL score?\",\n",
    "                                  \"I need to know the number of bank branches and their locations in the city of Dubai\"]})\n",
    "df_new"
   ]
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    {
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       "  <thead>\n",
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       "      <th>predicted topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I can not get from chase who services my mortg...</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The bill amount of my credit card was debited ...</td>\n",
       "      <td>Credit card or prepaid card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I want to open a salary account at your downto...</td>\n",
       "      <td>Bank Account services</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yesterday, I received a fraudulent email regar...</td>\n",
       "      <td>Theft/Dispute Reporting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What is the procedure to know my CIBIL score?</td>\n",
       "      <td>Others</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>I need to know the number of bank branches and...</td>\n",
       "      <td>Bank Account services</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                          complaints  \\\n",
       "0  I can not get from chase who services my mortg...   \n",
       "1  The bill amount of my credit card was debited ...   \n",
       "2  I want to open a salary account at your downto...   \n",
       "3  Yesterday, I received a fraudulent email regar...   \n",
       "4      What is the procedure to know my CIBIL score?   \n",
       "5  I need to know the number of bank branches and...   \n",
       "\n",
       "               predicted topic  \n",
       "0                       Others  \n",
       "1  Credit card or prepaid card  \n",
       "2        Bank Account services  \n",
       "3      Theft/Dispute Reporting  \n",
       "4                       Others  \n",
       "5        Bank Account services  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column of predicted topics of each complaint, predicted using the tuned Logistic Regression model\n",
    "df_new['predicted topic'] = df_new['complaints'].apply(lambda x: predict_topic([x]))\n",
    "df_new"
   ]
  }
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