Drug_Review_Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNYR5WgFTIyo"
      },
      "source": [
        "## Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "0hQBDBXjrN38",
        "outputId": "4b5cb40b-bce6-4588-d6bc-7594726edf3c"
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      "outputs": [],
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# Plotting\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import re\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Sentiment Analysis\n",
        "from transformers import pipeline\n",
        "\n",
        "\n",
        "# LDA\n",
        "\n",
        "\n",
        "\n",
        "import os\n",
        "import nltk\n",
        "import re\n",
        "import string\n",
        "import gensim\n",
        "import numpy as np\n",
        "from gensim.models import Phrases\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# for tokenization\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download(\"punkt\")\n",
        "nltk.download('wordnet')\n",
        "\n",
        "# for stopword removal\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "\n",
        "# for lemmatization and POS tagging\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import wordnet\n",
        "nltk.download('averaged_perceptron_tagger')\n",
        "import spacy\n",
        "\n",
        "# for LDA\n",
        "from gensim import corpora\n",
        "from gensim.models import LdaModel\n",
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "import gensim.corpora as corpora\n",
        "\n",
        "# for LDA evaluation\n",
        "from gensim.models import CoherenceModel\n",
        "import pyLDAvis\n",
        "import pyLDAvis.gensim_models as gensimvisualize\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_fp1GWbvTIy9"
      },
      "source": [
        "## Getting the Scraped Drug Review Dataset from UCI ML"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "nF3KliCJnvwG",
        "outputId": "2548f47d-f4f7-4cc3-f2a1-ff3ab8b54e0b"
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'uci_id': 462, 'name': 'Drug Reviews (Drugs.com)', 'repository_url': 'https://archive.ics.uci.edu/dataset/462/drug+review+dataset+drugs+com', 'data_url': 'https://archive.ics.uci.edu/static/public/462/data.csv', 'abstract': 'The dataset provides patient reviews on specific drugs along with related conditions and a 10 star patient rating reflecting overall patient satisfaction.', 'area': 'Health and Medicine', 'tasks': ['Classification', 'Regression', 'Clustering'], 'characteristics': ['Multivariate', 'Text'], 'num_instances': 215063, 'num_features': 6, 'feature_types': ['Integer'], 'demographics': [], 'target_col': None, 'index_col': ['id'], 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 2018, 'last_updated': 'Wed Apr 03 2024', 'dataset_doi': '10.24432/C5SK5S', 'creators': ['Surya Kallumadi', 'Felix Grer'], 'intro_paper': {'title': 'Aspect-Based Sentiment Analysis of Drug Reviews Applying Cross-Domain and Cross-Data Learning', 'authors': 'F. Gräßer, Surya Kallumadi, H. Malberg, S. Zaunseder', 'published_in': 'Digital Humanities Conference', 'year': 2018, 'url': 'https://www.semanticscholar.org/paper/4d7c25fe6131a79dfec9f45b70b270f400ac2b4f', 'doi': '10.1145/3194658.3194677'}, 'additional_info': {'summary': \"The dataset provides patient reviews on specific drugs along with related conditions and a 10 star patient rating reflecting overall patient satisfaction. The data was obtained by crawling online pharmaceutical review sites. The intention was to study \\r\\n\\r\\n(1) sentiment analysis of drug experience over multiple facets, i.e. sentiments learned on specific aspects such as effectiveness and side effects,\\r\\n(2) the transferability of models among domains, i.e. conditions, and \\r\\n(3) the transferability of models among different data sources (see 'Drug Review Dataset (Druglib.com)').\\r\\n\\r\\nThe data is split into a train (75%) a test (25%) partition (see publication) and stored in two .tsv (tab-separated-values) files, respectively.\\r\\n\\r\\n\\r\\nImportant notes:\\r\\n\\r\\nWhen using this dataset, you agree that you\\r\\n1) only use the data for research purposes\\r\\n2) don't use the data for any commerical purposes\\r\\n3) don't distribute the data to anyone else\\r\\n4) cite us\\r\\n\", 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': '1. drugName (categorical): name of drug\\r\\n2. condition (categorical): name of condition\\r\\n3. review (text): patient review\\r\\n4. rating (numerical): 10 star patient rating\\r\\n5. date (date): date of review entry\\r\\n6. usefulCount (numerical): number of users who found review useful', 'citation': None}}\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# fetch dataset\n",
        "drug_reviews_drugs_com = fetch_ucirepo(id=462)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "X = drug_reviews_drugs_com.data.features\n",
        "y = drug_reviews_drugs_com.data.targets\n",
        "\n",
        "# metadata\n",
        "print(drug_reviews_drugs_com.metadata)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "dd_ZcalUqANf"
      },
      "outputs": [],
      "source": [
        "# Renaming for personal reference\n",
        "df=X[X['condition']=='Birth Control']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LdvlP-y2TIy_"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h1fJqpdOn9uz",
        "outputId": "ca686813-d6d9-4744-b9a7-69f8eedcb244"
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      "source": [
        "# Check dimensions\n",
        "df.shape"
      ]
    },
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      "execution_count": 26,
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              "      <th></th>\n",
              "      <th>drugName</th>\n",
              "      <th>condition</th>\n",
              "      <th>review</th>\n",
              "      <th>rating</th>\n",
              "      <th>date</th>\n",
              "      <th>usefulCount</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lybrel</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"I used to take another oral contraceptive, wh...</td>\n",
              "      <td>5</td>\n",
              "      <td>14-Dec-09</td>\n",
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              "      <td>Nexplanon</td>\n",
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              "      <td>9</td>\n",
              "      <td>11-Aug-14</td>\n",
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            "text/plain": [
              "                              drugName      condition  \\\n",
              "2                               Lybrel  Birth Control   \n",
              "3                           Ortho Evra  Birth Control   \n",
              "9   Ethinyl estradiol / levonorgestrel  Birth Control   \n",
              "14                           Nexplanon  Birth Control   \n",
              "22                        Etonogestrel  Birth Control   \n",
              "\n",
              "                                               review  rating       date  \\\n",
              "2   \"I used to take another oral contraceptive, wh...       5  14-Dec-09   \n",
              "3   \"This is my first time using any form of birth...       8   3-Nov-15   \n",
              "9   \"I had been on the pill for many years. When m...       8   8-Dec-16   \n",
              "14  \"Started Nexplanon 2 months ago because I have...       3   7-Aug-14   \n",
              "22  \"Nexplanon does its job. I can have worry free...       9  11-Aug-14   \n",
              "\n",
              "    usefulCount  \n",
              "2            17  \n",
              "3            10  \n",
              "9             1  \n",
              "14           10  \n",
              "22           11  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# First five rows\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "1a_5ArJCq-r6"
      },
      "outputs": [],
      "source": [
        "# Convert date to datetime\n",
        "df['date']=pd.to_datetime(df.date)"
      ]
    },
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      "execution_count": 28,
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 38436,\n  \"fields\": [\n    {\n      \"column\": \"drugName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 181,\n        \"samples\": [\n          \"Skyla\",\n          \"Mononessa\",\n          \"Lillow\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Birth Control\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19524,\n        \"samples\": [\n          \"\\\"By far, the WORST birth control that I have ever taken. I made the mistake of switching from the ring to this pill and it was practically ruining my life. My sex drive was gone, I had major stomach cramps along with constipation, my mood was terrible and I was always angry or sad, I gained a lot of weight due to the fact that my appetite increased majorly, my periods were abnormal and my skin was the worst it has ever been. Overall, not the right birth control for my body and I stopped in the middle of my pack and I feel 100% better than I was. The only thing it did do good, for me, is I didn&#039;t &#039;t get pregnant. Going back to the ring after this nightmare.\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2008-02-27 00:00:00\",\n        \"max\": \"2017-12-12 00:00:00\",\n        \"num_unique_values\": 3107,\n        \"samples\": [\n          \"2016-01-18 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"usefulCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 0,\n        \"max\": 1247,\n        \"num_unique_values\": 145,\n        \"samples\": [\n          41\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <th></th>\n",
              "      <th>drugName</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lybrel</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"I used to take another oral contraceptive, wh...</td>\n",
              "      <td>5</td>\n",
              "      <td>2009-12-14</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ortho Evra</td>\n",
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              "      <td>\"This is my first time using any form of birth...</td>\n",
              "      <td>8</td>\n",
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              "      <th>9</th>\n",
              "      <td>Ethinyl estradiol / levonorgestrel</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"I had been on the pill for many years. When m...</td>\n",
              "      <td>8</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Nexplanon</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"Started Nexplanon 2 months ago because I have...</td>\n",
              "      <td>3</td>\n",
              "      <td>2014-08-07</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Etonogestrel</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"Nexplanon does its job. I can have worry free...</td>\n",
              "      <td>9</td>\n",
              "      <td>2014-08-11</td>\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e8e338f4-ea2c-4274-863a-ea46e046c78e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                              drugName      condition  \\\n",
              "2                               Lybrel  Birth Control   \n",
              "3                           Ortho Evra  Birth Control   \n",
              "9   Ethinyl estradiol / levonorgestrel  Birth Control   \n",
              "14                           Nexplanon  Birth Control   \n",
              "22                        Etonogestrel  Birth Control   \n",
              "\n",
              "                                               review  rating       date  \\\n",
              "2   \"I used to take another oral contraceptive, wh...       5 2009-12-14   \n",
              "3   \"This is my first time using any form of birth...       8 2015-11-03   \n",
              "9   \"I had been on the pill for many years. When m...       8 2016-12-08   \n",
              "14  \"Started Nexplanon 2 months ago because I have...       3 2014-08-07   \n",
              "22  \"Nexplanon does its job. I can have worry free...       9 2014-08-11   \n",
              "\n",
              "    usefulCount  \n",
              "2            17  \n",
              "3            10  \n",
              "9             1  \n",
              "14           10  \n",
              "22           11  "
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check if the date is in datetime\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "1C6Er-Z1jnfg",
        "outputId": "9f650d8b-5bce-45ff-9827-68bcbcc70845"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\"This is my first time using any form of birth control. I&#039;m glad I went with the patch, I have been on it for 8 months. At first It decreased my libido but that subsided. The only downside is that it made my periods longer (5-6 days to be exact) I used to only have periods for 3-4 days max also made my cramps intense for the first two days of my period, I never had cramps before using birth control. Other than that in happy with the patch\"'"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# View one review\n",
        "df['review'].iloc[1]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uB6sV_Der3Vb",
        "outputId": "d7d1df45-c382-41fb-a970-277178d3ee7f"
      },
      "outputs": [
        {
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"drugName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Levonorgestrel\",\n          \"Apri\",\n          \"Etonogestrel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Birth Control\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"\\\"I first would like to thank all of you that posted comments. After reading them, I felt I&#039;m not alone. It&#039;s not just happening to me, there is nothing wrong with me. I&#039;m 34, with one child and had Mirena for 5 months. The insertion was almost painless and the first month was fine. Right now, I have gained 10lbs, loss of sex drive, have headaches, back pain, insomnia, constipation, hair loss and general depression - OF COURSE that&#039;s going to cause to have mood swings. I&#039;m waiting on an appointment to have it removed. ANOTHER IMPORTANT ASPECT is that I had suffered from postpartum depression after the birth of my child 4 years ago and honestly I was worried that these side effects (especially  depression) were a sign that I was still not okay.\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 3,\n        \"max\": 9,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2010-01-20 00:00:00\",\n        \"max\": \"2014-08-01 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2010-01-20 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"usefulCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58,\n        \"min\": 3,\n        \"max\": 140,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          140\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <td>Apri</td>\n",
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              "    <tr>\n",
              "      <th>215060</th>\n",
              "      <td>Levonorgestrel</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"I&amp;#039;m married, 34 years old and I have no ...</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-11-15</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cab8905f-f6f3-440e-bd18-1beb39320c6a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cab8905f-f6f3-440e-bd18-1beb39320c6a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cab8905f-f6f3-440e-bd18-1beb39320c6a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f168c2c6-3041-4d66-91c4-110baf90b87b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f168c2c6-3041-4d66-91c4-110baf90b87b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f168c2c6-3041-4d66-91c4-110baf90b87b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                     drugName      condition  \\\n",
              "215017           Etonogestrel  Birth Control   \n",
              "215038         Levonorgestrel  Birth Control   \n",
              "215056  Microgestin Fe 1 / 20  Birth Control   \n",
              "215057                   Apri  Birth Control   \n",
              "215060         Levonorgestrel  Birth Control   \n",
              "\n",
              "                                                   review  rating       date  \\\n",
              "215017  \"So I got Nexplanon just under a year ago. I o...       9 2013-11-05   \n",
              "215038  \"I first would like to thank all of you that p...       3 2010-01-20   \n",
              "215056  \"I was on Microgestin for about 3 years. Over ...       6 2014-08-01   \n",
              "215057  \"I started taking Apri about 7 months ago. My ...       9 2010-08-25   \n",
              "215060  \"I&#039;m married, 34 years old and I have no ...       8 2010-11-15   \n",
              "\n",
              "        usefulCount  \n",
              "215017            3  \n",
              "215038          140  \n",
              "215056           15  \n",
              "215057           18  \n",
              "215060            7  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# View the last five rows\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "1D6XooU8sCjp",
        "outputId": "9ed4e5c4-189d-452c-ed8d-8f21740fa478"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 38436.0,\n        \"max\": 38436.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          38436.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000006\",\n        \"max\": \"2015-02-07 04:47:41.567280640\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6.077973774586325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00\",\n        \"max\": \"2008-02-27 00:00:00\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000002\",\n        \"max\": \"2013-09-16 00:00:00\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000005\",\n        \"max\": \"2015-11-07 00:00:00\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000009\",\n        \"max\": \"2016-10-22 00:00:00\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"2016-10-22 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000010\",\n        \"max\": \"2017-12-12 00:00:00\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3.3095818356049174,\n        \"max\": 17.68238561988182,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          17.68238561988182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
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            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "        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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>rating</th>\n",
              "      <td>38436.0</td>\n",
              "      <td>6.077974</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.309582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <td>38436</td>\n",
              "      <td>2015-02-07 04:47:41.567280640</td>\n",
              "      <td>2008-02-27 00:00:00</td>\n",
              "      <td>2013-09-16 00:00:00</td>\n",
              "      <td>2015-11-07 00:00:00</td>\n",
              "      <td>2016-10-22 00:00:00</td>\n",
              "      <td>2017-12-12 00:00:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>usefulCount</th>\n",
              "      <td>38436.0</td>\n",
              "      <td>7.848944</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1247.0</td>\n",
              "      <td>17.682386</td>\n",
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              "</div>\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e25a8208-ed98-4805-9d61-21a6a06f831e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e25a8208-ed98-4805-9d61-21a6a06f831e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1603671b-871f-442b-9e95-913f09d38611')\"\n",
              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1603671b-871f-442b-9e95-913f09d38611 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "               count                           mean                  min  \\\n",
              "rating       38436.0                       6.077974                  1.0   \n",
              "date           38436  2015-02-07 04:47:41.567280640  2008-02-27 00:00:00   \n",
              "usefulCount  38436.0                       7.848944                  0.0   \n",
              "\n",
              "                             25%                  50%                  75%  \\\n",
              "rating                       3.0                  7.0                  9.0   \n",
              "date         2013-09-16 00:00:00  2015-11-07 00:00:00  2016-10-22 00:00:00   \n",
              "usefulCount                  2.0                  5.0                  9.0   \n",
              "\n",
              "                             max        std  \n",
              "rating                      10.0   3.309582  \n",
              "date         2017-12-12 00:00:00        NaN  \n",
              "usefulCount               1247.0  17.682386  "
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check Descriptive Statistics of the numerical columns\n",
        "df.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rCfZXye1sWSj",
        "outputId": "997a2273-a71d-434a-ea3a-9a29e488b9d3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "181"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# The distinct count of drugs\n",
        "df.drugName.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kB6A34n1uS8x",
        "outputId": "812dfd61-6c9a-4505-f6d1-9434869ec433"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "drugName\n",
              "Etonogestrel                          4394\n",
              "Ethinyl estradiol / norethindrone     3081\n",
              "Levonorgestrel                        2884\n",
              "Nexplanon                             2883\n",
              "Ethinyl estradiol / levonorgestrel    2107\n",
              "                                      ... \n",
              "Norlyda                                  1\n",
              "Larin 24 Fe                              1\n",
              "Loestrin 21 1.5 / 30                     1\n",
              "Lillow                                   1\n",
              "Cyclafem 7 / 7 / 7                       1\n",
              "Name: count, Length: 181, dtype: int64"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# The distribution of the drugs\n",
        "df.drugName.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5GgbFOxnTIzK"
      },
      "source": [
        "## Subsetting Data for Analysis\n",
        "- Focused on top five most reviewed birth control drugs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "e3UOZ2i2gQU7"
      },
      "outputs": [],
      "source": [
        "\n",
        "drug_counts = df['drugName'].value_counts()\n",
        "top_drugs = drug_counts.head(5).index\n",
        "\n",
        "#Subset the DataFrame to include only the top 5 drugs\n",
        "df = df[df['drugName'].isin(top_drugs)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqddWd7qzTAo",
        "outputId": "fcb24405-3cdc-4a97-8016-4ec484b67fbc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# New number of unique drugs\n",
        "df.drugName.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jsQMEobTIzN"
      },
      "source": [
        "### Distribution of the top 5 drugs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "108E1KeBpb6a",
        "outputId": "f685c993-0b9d-4548-eab3-234d856cd5d4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "drugName\n",
              "Etonogestrel                          4394\n",
              "Ethinyl estradiol / norethindrone     3081\n",
              "Levonorgestrel                        2884\n",
              "Nexplanon                             2883\n",
              "Ethinyl estradiol / levonorgestrel    2107\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.drugName.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BViEIMA6TIzO",
        "outputId": "2643d972-8dcf-48f0-ee51-7907e7cec23e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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AqeOkk05y9erVcxs3bqzymMGDB7vs7Oz4V20FQeDatGnjJLlbb711m7epqKhwd955p9tnn31cbm6ua9Sokdtvv/3czTffHP+6J+fcNr9ua4u//OUvbq+99nK5ubmuY8eObvz48Vt93ZNzzm3cuNFddtllrnHjxq6goMCdeuqpbsGCBU6Su+OOOxKO/eabb9xll13m2rRp47Kzs12LFi3c0Ucf7R599NFqa9W2bVt3wgknbPO6xx9/3Ely48ePd8459/nnn7u+ffu6goIC17RpU3fhhRe6OXPmJBzjnHOVlZXu8ssvd82aNXOe5yU8N1XxlWGrV69OeOwtX5u1dOnSWtVk06ZNbuTIka5bt26uQYMGLj8/33Xr1s09+OCD1dbCOedmzZrl+vXr5woKClz9+vXdkUce6T744IOEY3bkK8O2bFlZWa5x48bugAMOcNdff33CV63973MvLi7e5n2+9dZbbt9993U5OTmuQ4cO7m9/+9sO/wztbL0AZBbPuR2YtAEAABASs2fPVo8ePfS3v/1NAwcOtF5OSqAmtUO9AOwMPtMNAADSRnl5+Vb77r//fkUiER122GEGK7JHTWqHegGoa3ymGwAApI277rpLH3/8sY488khlZWVp8uTJmjx5si666KKkTNEOA2pSO9QLQF3j9HIAAJA2pkyZoptvvlmff/65NmzYoN13313nnnuubrjhBmVlZeZ7DdSkdqgXgLpG0w0AAAAAQJLwmW4AAAAAAJKEphsAAAAAgCThgykAqhQEgb7++ms1aNBAnudZLwcAAABIGc45fffdd9ptt90UiVT9fjZNN4Aqff3110xqBQAAALbjyy+/VOvWrau8nqYbQJUaNGggSVq6dKkaN25svJrMVFlZqU8++UQ9evRgaq4RMrBF/e2RgT0ysEX97aVqBuvXr1ebNm3ivzNXJXVWDCDlbDmlvGHDhmrYsKHxajJTZWWl8vPz1bBhw5T6RyaTkIEt6m+PDOyRgS3qby/VM6juY5gMUgMAAAAAIElougFUiyFqdjzPU2FhIRkYIgNb1N8eGdgjA1vU317YM/Ccc856EQBS0/r161VYWKhYLMbp5QAAAMBP1PR3Zd7pBlCtIAisl5CxgiDQihUryMAQGdii/vbIwB4Z2KL+9sKeAU03gGqF9QUuHYT9H5l0QAa2qL89MrBHBraov72wZ0DTDQAAAABAktB0AwAAAACQJDTdAKoVifBSYSUSiahZs2ZkYIgMbFF/e2RgjwxsUX97Yc+A6eUAqsT0cgAAAGDbmF4OoM6EdWhFOgiCQKWlpWRgiAxsUX97ZGCPDGxRf3thz4CmG0C1wvoClw6CINDq1avJwBAZ2KL+9sjAHhnYov72wp4BTTcAAAAAAElC0w0AAAAAQJLQdAOoVlgnRaaDSCSi1q1bk4EhMrBF/e2RgT0ysEX97YU9A6aXA6gS08sBAACAbWN6OYA64/u+9RIylu/7KikpIQNDZGCL+tsjA3tkYIv62wt7BjTdAKrFCTF2nHOKxWJkYIgMbFF/e2RgjwxsUX97Yc+AphsAAAAAgCSh6QYAAAAAIElougFUK6yTItNBJBJRUVERGRgiA1vU3x4Z2CMDW9TfXtgzYHo5gCoxvRwAAADYNqaXA6gzYZ0UmQ5839ecOXPIwBAZ2KL+9sjAHhnYov72wp4BTTeAanFCjB3nnMrLy8nAEBnYov72yMAeGdii/vbCngFNNwAAAAAASULTDQAAAABAktB0A6hWNBq1XkLGikaj6tixIxkYIgNb1N8eGdgjA1vU317YM2B6OYAqMb0cAAAA2DamlwOoM5WVldZLyFiVlZUqLi4mA0NkYIv62yMDe2Rgi/rbC3sGNN0AkOLC+vUY6YQMbFF/e2RgjwxsUX97Yc6AphsAAAAAgCSh6QYAAAAAIEkYpAagSluGQ5SVlamwsNB6ORnJOafy8nLl5eXJ8zzr5WQkMrBF/e2RgT0ysEX97aVqBgxSA4A0kZOTY72EjEcGtqi/PTKwRwa2qL+9MGdA0w2gWs2a+fI8sRls9er5Gj16purVIwMyyMyN+ttvZGC/kQH1z/RtWxmECU03AAAAAABJQtMNAAAAAECS0HQDAAAAAJAkTC8HUKUtExmlMklML7fhlJPjq6IiKilkH2BKG2Rgi/rbIwN7ZGCL+tvbOoNU6GKZXg4AacDzpIYNK0I3MCSdkIEt6m+PDOyRgS3qby/sGdB0A6hWdrZvvYSMlZ3ta+jQuWRgiAxsUX97ZGCPDGxRf3thz4CmGwAAAACAJKHpBgAAAAAgSWi6ASDF/Tg0BJbIwBb1t0cG9sjAFvW3F+YMmF4OoEr/nV4ek1T1REYAAADg55QKXSzTywHUGc9LgVe1DBWJOBUVlSkSIQMrZGCL+tsjA3tkYIv62wt7BjTdAKqVlRXOSZHpICvL14AB88nAEBnYov72yMAeGdii/vbCngFNNwAAAAAASULTDQAAAABAktB0A6iWc571EjKWc57WrMkjA0NkYIv62yMDe2Rgi/rbC3sGTC8HUCWmlwMAACAVpUIXy/RyAHXG8wLrJWSsSCRQ9+6rFImQgRUysEX97ZGBPTKwRf3thT0Dmm4A1crKCucLXDrIygp0wglLyMAQGdii/vbIwB4Z2KL+9sKeAU03AAAAAABJQtMNAAAAAECS0HQDqFZYJ0WmA+c8LVlSSAaGyMAW9bdHBvbIwBb1txf2DJheDqBKTC8HAABAKkqFLpbp5QDqTFgnRaaDaDTQYYetUDRKBlbIwBb1t0cG9sjAFvW3F/YMaLoBVCusL3DpIBoNdOih4f1HJh2QgS3qb48M7JGBLepvL+wZ0HQDAAAAAJAkNN0AAAAAACQJTTeAagUBLxVWgiCi2bObkYEhMrBF/e2RgT0ysEX97YU9A6aXA6gS08sBAACQilKhi2V6OYAE7777rjzPU1lZWa1vG9ahFekgKyvQCSeUKiuLDKyQgS3qb48M7JGBLepvL+wZ0HQj6QYPHqxTTz3VehmhdMQRR2jEiBHWy+ArwwxFIoG6d19NBobIwBb1t0cG9sjAFvW3F/YMaLqBHVBRUWG9hDjnnCorK62XAQAAAGAbaLph6tNPP9Vxxx2ngoICNW/eXOeee67WrFkjSXr00Ue12267KQgS/6J1yimn6Pzzz49ffuihh9S+fXvl5OSoQ4cOeuqppxKO9zxPjz32mE477TTVr19fe+21l15++eWEY6ZPn679999fubm5atmypa677rqERvaII47QsGHDNGLECDVt2lT9+vWTJL388svaa6+9VK9ePR155JF64okntjqF+/3339ehhx6qvLw8tWnTRsOHD9fGjRvj1z/44IPx+2jevLlOP/10ST+eITB9+nQ98MAD8jxPnudp2bJl8dPEJ0+erP3220+5ubl6//33FQSBxowZoz322EN5eXnq1q2b/vGPf+xEOgAAAAB2mgOSbNCgQe6UU07Zav+6detcs2bN3PXXX+9KSkrcrFmz3DHHHOOOPPJI55xza9eudTk5Oe7tt9+O3+bbb79N2Ddp0iSXnZ3t/vznP7sFCxa4sWPHumg06t555534bSS51q1bu6efftotWrTIDR8+3BUUFLhvv/3WOefcihUrXP369d2ll17qSkpK3AsvvOCaNm3qRo0aFb+Pww8/3BUUFLiRI0e6+fPnu/nz57slS5a47Oxsd80117j58+e7v//9765Vq1ZOklu3bp1zzrnFixe7/Px8d99997mFCxe6GTNmuB49erjBgwc755wrLi520WjUPf30027ZsmVu1qxZ7oEHHnDOOVdWVuYOOuggd+GFF7qVK1e6lStXusrKSjdt2jQnyXXt2tW99dZbbvHixe7bb791t956q+vYsaN74403XGlpqRs/frzLzc117777rnPOxW+3ZW01EYvFnCQXiaxzP46rYPu5t2jUd4cd9qWLRn3ztWTqRgbUP9M3MrDfyID6Z/q2rQxSwZbflWOx2HaPS5HlIp1V1XTfcsst7thjj03Y9+WXXzpJbsGCBc4550455RR3/vnnx69/5JFH3G677eZ833fOOXfwwQe7Cy+8MOE+zjjjDHf88cfHL0tyN954Y/zyhg0bnCQ3efJk55xzv/3tb12HDh1cEATxY/785z+7goKC+OMcfvjhrkePHgmPc+2117p99903Yd8NN9zgftrYDhkyxF100UUJx7z33nsuEom48vJyN3HiRNewYUO3fv36reqz5XGvuOKKhH1bmucXX3wxvu+HH35w9evXdx988EHCsUOGDHEDBgxIuN32mu4ffvjBxWKx+LYlDylm/mLLxsbGxsbGxsbGtmVLBTVtujm9HGbmzJmjadOmqaCgIL517NhRklRaWipJGjhwoCZOnKhNmzZJkiZMmKCzzz5bkciPP7olJSXq06dPwv326dNHJSUlCfu6du0a/+/8/Hw1bNhQq1atit/HQQcdJM/zEu5jw4YNWrFiRXzffvvtl3CfCxYsUO/evRP27b///ls9x8cffzzhOfbr109BEGjp0qU65phj1LZtWxUVFencc8/VhAkT9P3339eofr169Yr/9+LFi/X999/rmGOOSXisJ598Ml7LmhgzZowKCwvjW5s2bSRJWVl+je8DdSs729eAASXKziYDK2Rgi/rbIwN7ZGCL+tsLewZZ1gtA5tqwYYNOOukk3XnnnVtd17JlS0nSSSedJOecXnvtNfXu3Vvvvfee7rvvvlo/VnZ2dsJlz/O2+qx4dfLz82v9uBs2bNDQoUM1fPjwra7bfffdlZOTo1mzZundd9/VW2+9pd/97ncaPXq0iouLtcsuu9R4PRs2bJAkvfbaa2rVqlXCcbm5uTVe7/XXX6+rrroqfnn9+vVq06aNPM/V+D5QtzzPqagoRgaGyMAW9bdHBvbIwBb1txf2DGi6YaZnz56aOHGi2rVrp6ysbf8o1qtXT7/4xS80YcIELV68WB06dFDPnj3j13fq1EkzZszQoEGD4vtmzJihzp0713gdnTp10sSJE+Wci7/bPWPGDDVo0ECtW7eu8nYdOnTQ66+/nrCvuLh4q+f4+eefa88996zyfrKystS3b1/17dtXo0aN0i677KJ33nlHv/jFL5STkyPfr/4vep07d1Zubq6++OILHX744dUeX5Xc3NxaNekAAAAAto+mGz+LWCym2bNnJ+y76KKLNG7cOA0YMEC/+c1v1LhxYy1evFjPPPOMHnvsMUWjUUk/nmJ+4okn6rPPPtM555yTcB8jR47UmWeeqR49eqhv37565ZVXNGnSJL399ts1Xtull16q+++/X5dffrmGDRumBQsWaNSoUbrqqqvip7Fvy9ChQ3Xvvffq2muv1ZAhQzR79mw9/vjjkhRv3q+99lodeOCBGjZsmC644ALl5+fr888/15QpU/SnP/1Jr776qpYsWaLDDjtMjRo10uuvv64gCNShQwdJUrt27fTvf/9by5YtU0FBgRo3brzNtTRo0EDXXHONrrzySgVBoEMOOUSxWEwzZsxQw4YNE/4oAQAAAOBn9LN8whwZbdCgQU7SVtuQIUPcwoUL3WmnneZ22WUXl5eX5zp27OhGjBiRMNTM933XsmVLJ8mVlpZudf8PPvigKyoqctnZ2W7vvfd2Tz75ZML1ktwLL7yQsK+wsNCNHz8+fvndd991vXv3djk5Oa5Fixbu2muvdZs3b45fv62BZs4599JLL7k999zT5ebmuiOOOMI99NBDTpIrLy+PH/PRRx+5Y445xhUUFLj8/HzXtWtXd9tttznnfhyqdvjhh7tGjRq5vLw817VrV/fss8/Gb7tgwQJ34IEHury8PCfJLV26tMqBaEEQuPvvv9916NDBZWdnu2bNmrl+/fq56dOnO+d2bnq5560zH5aRqVsk4rvu3b9xkQgTU8kgMzfqb7+Rgf1GBtQ/07dtZZAKajpIzXPOOZNuH0hDt912mx5++GF9+eWX1kupE+vXr1dhYaGkmKSG1ssBAAAAJP3Yelvb8rtyLBZTw4ZV/67M9HJgJzz44IMqLi7WkiVL9NRTT+nuu+9Oy1O5mV5uJzvb19Chc0I7rTMdkIEt6m+PDOyRgS3qby/sGfCZbmAnLFq0SLfeeqvWrl2r3XffXVdffbWuv/5662XVubBOikwHnufUtGk5GRgiA1vU3x4Z2CMDW9TfXtgzoOkGdsJ99923Q19hBgAAACAzcHo5AAAAAABJwiA1AFXaMhzC88rkXKH1cjJSJOLUrl1My5YVKgg86+VkJDKwRf3tkYE9MrBF/e1tK4NU6GJrOkiNphtAlZheDgAAgFSUCl0s08sB1Jns7ErrJWSsnJxKjRxZrJwcMrBCBraovz0ysEcGtqi/vbBnQNMNACkuJyecX4+RTsjAFvW3Rwb2yMAW9bcX5gxougEAAAAASBKabgAAAAAAkoRBagCq9N9BamWSmF5uwfOcmjQp17ff5sk5JqZaIANb1N8eGdgjA1vU3962MkiFLpZBagCQBpyT1q/PSYl/WDIVGdii/vbIwB4Z2KL+9sKeAU03gGplZ4d3cEXY5eT4GjlyZqiHh4QdGdii/vbIwB4Z2KL+9sKeAU03AAAAAABJQtMNAAAAAECS0HQDAAAAAJAkTC8HUCWml6cCp5wcXxUVUUlMTLVBBraovz0ysEcGtqi/va0zSIUulunlAJAGPE9q2LBCHv/GmyEDW9TfHhnYIwNb1N9e2DOg6QZQLaaX28nO9jV06FwyMEQGtqi/PTKwRwa2qL+9sGdA0w0AAAAAQJLQdAMAAAAAkCQ03QCQ4n4cGgJLZGCL+tsjA3tkYIv62wtzBkwvB1Cl/04vj0mqeiIjAAAA8HNKhS6W6eUA6oznpcCrWoaKRJyKisoUiZCBFTKwRf3tkYE9MrBF/e2FPQOabgDVysoK56TIdJCV5WvAgPlkYIgMbFF/e2RgjwxsUX97Yc+AphsAAAAAgCSh6QYAAAAAIElougFUyznPegkZyzlPa9bkkYEhMrBF/e2RgT0ysEX97YU9A6aXA6gS08sBAACQilKhi63p9PKsn3FNAEJq3bpAu+xivYrMFASB1qxZo6ZNmyoS4eQkC2Rgi/rbIwN7ZGCL+tsLewbhWzGAn10QBNZLyFhBEGjJkiVkYIgMbFF/e2RgjwxsUX97Yc+AphsAAAAAgCSh6QYAAAAAIElougFUy/PCOSkyHXiep8LCQjIwRAa2qL89MrBHBraov72wZ8D0cgBVqulERgAAACDT1PR3Zd7pBlCtsA6tSAdBEGjFihVkYIgMbFF/e2RgjwxsUX97Yc+AphtAtcL6ApcOwv6PTDogA1vU3x4Z2CMDW9TfXtgzoOkGAAAAACBJaLoBAAAAAEgSmm4A1YpEeKmwEolE1KxZMzIwRAa2qL89MrBHBraov72wZ8D0cgBVYno5AAAAsG1MLwdQZ8I6tCIdBEGg0tJSMjBEBraovz0ysEcGtqi/vbBnQNMNoFphfYFLB0EQaPXq1WRgiAxsUX97ZGCPDGxRf3thz4CmGwAAAACAJKHpBgAAAAAgSWi6AVQrrJMi00EkElHr1q3JwBAZ2KL+9sjAHhnYov72wp4B08sBVInp5QAAAMC2Mb0cQJ3xfd96CRnL932VlJSQgSEysEX97ZGBPTKwRf3thT0Dmm4A1eKEGDvOOcViMTIwRAa2qL89MrBHBraov72wZ0DTDQAAAABAktB0AwAAAACQJDTdAKoV1kmR6SASiaioqIgMDJGBLepvjwzskYEt6m8v7BkwvRxAlZheDgAAAGwb08sB1JmwTopMB77va86cOWRgiAxsUX97ZGCPDGxRf3thz4CmG0C1OCHGjnNO5eXlZGCIDGxRf3tkYI8MbFF/e2HPgKYbAAAAAIAkoekGAAAAACBJaLoBVCsajVovIWNFo1F17NiRDAyRgS3qb48M7JGBLepvL+wZML0cQJWYXg4AAABsG9PLAdSZyspK6yVkrMrKShUXF5OBITKwRf3tkYE9MrBF/e2FPQOabgBIcWH9eox0Qga2qL89MrBHBraov70wZ0DTDQAAAABAktB0AwAAAACQJAxSA1ClLcMhysrKVFhYaL2cjOScU3l5ufLy8uR5nvVyMhIZ2KL+9sjAHhnYov72UjUDBqkBQJrIycmxXkLGIwNb1N8eGdgjA1vU316YM6DpBlCtMA+uCDvf9zVz5kwyMEQGtqi/PTKwRwa2qL+9sGdA0w0AAAAAQJLQdAMAAAAAkCQ03QAAAAAAJAnTywFUienl9pxz8n1f0Wg0paZ1ZhIysEX97ZGBPTKwRf3tpWoGTC8HgDRRUVFhvYSMRwa2qL89MrBHBraov70wZ0DTDaBaYZ0UmQ5839fcuXPJwBAZ2KL+9sjAHhnYov72wp4BTTcAAAAAAElC0w0AAAAAQJLQdANAiotGo9ZLyHhkYIv62yMDe2Rgi/rbC3MGTC8HUKWaTmQEAAAAMg3TywHUGf42Z8c5p7KyMjIwRAa2qL89MrBHBraov72wZ0DTDaBaYZ0UmQ5839f8+fPJwBAZ2KL+9sjAHhnYov72wp4BTTcAAAAAAElC0w0AAAAAQJLQdAOolud51kvIWJ7nKS8vjwwMkYEt6m+PDOyRgS3qby/sGTC9HECVmF4OAAAAbBvTywHUmSAIrJeQsYIg0KpVq8jAEBnYov72yMAeGdii/vbCngFNN4BqhfUFLh0EQaAlS5aQgSEysEX97ZGBPTKwRf3thT0Dmm4AAAAAAJKEphsAAAAAgCSh6QZQrbBOikwHnuepsLCQDAyRgS3qb48M7JGBLepvL+wZML0cQJWYXg4AAABsG9PLAdSZsA6tSAdBEGjFihVkYIgMbFF/e2RgjwxsUX97Yc+AphtAtcL6ApcOwv6PTDogA1vU3x4Z2CMDW9TfXtgzoOkGAAAAACBJaLoBAAAAAEgSmm4A1YpEeKmwEolE1KxZMzIwRAa2qL89MrBHBraov72wZ8D0cgBVYno5AAAAsG1MLwdQZ8I6tCIdBEGg0tJSMjBEBraovz0ysEcGtqi/vbBnQNMNoFphfYFLB0EQaPXq1WRgiAxsUX97ZGCPDGxRf3thz4CmGwAAAACAJKHpBgAAAAAgSWi6AVQrrJMi00EkElHr1q3JwBAZ2KL+9sjAHhnYov72wp4B08sBVGnLREYpJonp5QAAID3QAaEuML0cQJ3JyvKtl5CxsrN9DRhQouxsMrBCBraovz0ysEcGtnzfV0lJiXyf+lsJewY03QCq5Xn8OdiK5zkVFcXIwBAZ2KL+9sjAHhnYcs4pFouJE4TthD0Dmm4AAAAAAJKEphsAAAAAgCSh6QZQrcpKXiqsVFZG9NprRWRgiAxsUX97ZGCPDGxFIhEVFRWFdnJ2Ogh7BkwvB1AlppcDAIB0RAeEusD0cgB1hunldrKzfQ0dOoeJtYbIwBb1t0cG9sjAlu/7mjNnTmgnZ6eDsGdA0w2gWkxLteN5Tk2blpOBITKwRf3tkYE9MrDlnFN5eXloJ2eng7BnQNMNAAAAAECS0HQDAAAAAJAkDFIDUKUtwyE8r0zOFVovJyNFIk7t2sW0bFmhgsCzXk5GIgNb1N8eGdgjg7pXmw7IOadYLPb/vxNRfwupmkFNB6nRdAOoEtPLAQBAOqIDQl1gejmAOpOdXWm9hIyVk1OpkSOLlZNDBlbIwBb1t0cG9sjAVmVlpYqLi1VZSf2thD0Dmm4ASHE5OeH8eox0Qga2qL89MrBHBrbC+lVV6STMGdB0AwAAAACQJDTdAAAAAAAkCYPUAFTpv4PUyiQxvdyC5zk1aVKub7/Nk3OpM60zk5CBLepvjwzskUHdq+308vLycuXl5aXU5OxMkqoZMEgNANKAc9L69TlMWTVEBraovz0ysEcG9nJycqyXkPHCnAFNN4BqZWeHd3BF2OXk+Bo5ciYDdAyRgS3qb48M7JGBLd/3NXPmzFAP8gq7sGdA0w0AAAAAQJLQdAMAAAAAkCQ03QAAAAAAJAnTywFUienlqcApJ8dXRUVUUupM68wsZGCL+tsjA3tkUNdqO73c931Fo9GUmpydSVI1A6aXA0Aa8DypYcMKpdC/LxmHDGxRf3tkYI8M7FVUVFgvIeOFOQOabgDVYnq5nexsX0OHziUDQ2Rgi/rbIwN7ZGDL933NnTs3tJOz00HYM6DpBgAAAAAgSWi6AQAAAABIEppuAEhxPw7OgSUysEX97ZGBPTKwFY1Sf2thzoDp5QCq9N/p5TFJVU9kBAAACBM6INSFtJ5ePnr0aHXv3n27xxxxxBEaMWJEnT5uu3btdP/999fpff6cli1bJs/zNHv2bEnSu+++K8/zVFZWVuP7qIsaDB48WKeeeupO3Qd23I78v+F5/MtkJRJxKioqUyRCBlbIwBb1t0cG9sjAlnNOZWVl4r1KO2HPwLTpHjx4sDzP22rr379//BjP8/Tiiy/W+r4nTZqkW265pQ5Xm3w7+lx31MEHH6yVK1f+/zuZdevmm2/WOeecU+f3m4l25I8jdS0rK5yTItNBVpavAQPmk4EhMrBF/e2RgT0ysOX7vubPnx/aydnpIOwZZFkvoH///ho/fnzCvtzc3J2+38aNG+/0faSiiooK5eTk1Ml95eTkqEWLFnVyX//rpZde0nXXXZeU+04Vzjn5vq+sLPP/jSTV7c8GAAAAgLphfnp5bm6uWrRokbA1atRI0o+nMkvSaaedJs/z4pe3eOqpp9SuXTsVFhbq7LPP1nfffRe/7n9PoW3Xrp1uv/12nX/++WrQoIF23313Pfroo/HrjzrqKA0bNizh/levXq2cnBxNnTq1xs/nscceU6dOnVSvXj117NhRDz74YPy6iooKDRs2TC1btlS9evXUtm1bjRkzZrvPdcup9I899pj22GMP1atXT5L0xhtv6JBDDtEuu+yiJk2a6MQTT1RpaWnCWj766CP16NFD9erVU69evfTJJ58kXL+td1AnTpyoffbZR7m5uWrXrp3Gjh1b4+e+xZdffqnPPvss4YyF7QmCQGPGjNEee+yhvLw8devWTf/4xz/i17Vu3VoPPfRQwm0++eQTRSIRLV++XJL0xRdf6JRTTlFBQYEaNmyoM888U9988038+C113N7PzKZNmzR8+HDtuuuuqlevng455BAVFxdvVa/Jkydrv/32U25urt5//3199913GjhwoPLz89WyZUvdd999W/38bdq0Sddcc41atWql/Px8HXDAAXr33Xfj1y9fvlwnnXSSGjVqpPz8fO2zzz56/fXXtWzZMh155JGSpEaNGsnzPA0ePFjSjz/jw4YN04gRI9S0aVP169dPkvTpp5/quOOOU0FBgZo3b65zzz1Xa9asqVEWAAAAAOqWedO9PVsanvHjx2vlypUJDVBpaalefPFFvfrqq3r11Vc1ffp03XHHHdu9v7Fjx8abz0svvVSXXHKJFixYIEm64IIL9PTTT2vTpk3x4//2t7+pVatWOuqoo2q03gkTJuh3v/udbrvtNpWUlOj222/XTTfdpCeeeEKS9Ic//EEvv/yynnvuOS1YsEATJkyIN9fbe66LFy/WxIkTNWnSpPjnsTdu3KirrrpKM2fO1NSpUxWJRHTaaacpCAJJ0oYNG3TiiSeqc+fO+vjjjzV69Ghdc801213/xx9/rDPPPFNnn3225s2bp9GjR+umm27S448/XqPnv8XLL7+sI444YrvDBH5qzJgxevLJJ/Xwww/rs88+05VXXqlzzjlH06dPVyQS0YABA/T0008n3GbChAnq06eP2rZtqyAIdMopp2jt2rWaPn26pkyZoiVLluiss85KuE11PzO/+c1vNHHiRD3xxBOaNWuW9txzT/Xr109r165NuJ/rrrtOd9xxh0pKStS1a1ddddVVmjFjhl5++WVNmTJF7733nmbNmpVwm2HDhunDDz/UM888o7lz5+qMM85Q//79tWjRIknSZZddpk2bNumf//yn5s2bpzvvvFMFBQVq06aNJk6cKElasGCBVq5cqQceeCB+v0888YRycnI0Y8YMPfzwwyorK9NRRx2lHj16aObMmXrjjTf0zTff6Mwzz6xRFlVxztup22PHOedpzZo8MjBEBraovz0ysEcGtjzPU15enjyP+lsJfQbO0KBBg1w0GnX5+fkJ22233RY/RpJ74YUXEm43atQoV79+fbd+/fr4vpEjR7oDDjggfvnwww93V1xxRfxy27Zt3TnnnBO/HASB23XXXd1DDz3knHOuvLzcNWrUyD377LPxY7p27epGjx6dcB/33Xdflc+nffv27umnn07Yd8stt7iDDjrIOefc5Zdf7o466igXBME2b1/Vc83OznarVq2q8nGdc2716tVOkps3b55zzrlHHnnENWnSxJWXl8ePeeihh5wk98knnzjnnJs2bZqT5NatW+ecc+5Xv/qVO+aYYxLud+TIka5z587xy9XVwDnnjjnmGPenP/2pyusHDRrkTjnlFOeccz/88IOrX7++++CDDxKOGTJkiBswYIBzzrlPPvnEeZ7nli9f7pxzzvd916pVq3h2b731lotGo+6LL76I3/6zzz5zktxHH33knKv+Z2bDhg0uOzvbTZgwIX59RUWF22233dxdd92VUK8XX3wxfsz69etddna2e/755+P7ysrKXP369eM/f8uXL3fRaNR99dVXCc/x6KOPdtdff71zzrkuXbok/Kz91P/mtMXhhx/uevTokbDvlltucccee2zCvi+//NJJcgsWLIjf7qf/b/zUDz/84GKxWHzbclsp5n6c88nGxsbGxsbGFv4NqAuxWMxJcrFYbLvHmb/TfeSRR2r27NkJ28UXX1zt7dq1a6cGDRrEL7ds2VKrVq3a7m26du0a/2/P89SiRYv4berVq6dzzz1Xf/3rXyVJs2bN0qeffho/lbc6GzduVGlpqYYMGaKCgoL4duutt8ZP+x48eLBmz56tDh06aPjw4XrrrbdqdN9t27ZVs2bNEvYtWrRIAwYMUFFRkRo2bBh/x/yLL76QpPi7sFtOR5ekgw46aLuPU1JSoj59+iTs69OnjxYtWlTjoQXr16/X9OnTdfLJJ9fo+MWLF+v777/XMccck1C3J598Ml637t27q1OnTvF3u6dPn65Vq1bpjDPOiK+7TZs2atOmTfx+O3furF122UUlJSXxfdv7mSktLdXmzZsTnn92drb233//hPuQpF69esX/e8mSJdq8ebP233//+L7CwkJ16NAhfnnevHnyfV977713wnOcPn16/DkOHz5ct956q/r06aNRo0Zp7ty5Narffvvtl3B5zpw5mjZtWsLjdOzYMf4cqzNmzBgVFhbGty019bygRutB3YtEAnXvvkqRCBlYIQNb1N8eGdgjA1tBEGjVqlXxM0rx8wt7BuYToPLz87XnnnvW+nbZ2dkJlz3PqzaE6m5zwQUXqHv37lqxYoXGjx+vo446Sm3btq3RejZs2CBJGjdunA444ICE67Z8kXvPnj21dOlSTZ48WW+//bbOPPNM9e3bN/755ark5+dvte+kk05S27ZtNW7cOO22224KgkD77ruvKioqarTeZJk8ebI6d+6c0ABvz5a6vfbaa2rVqlXCdT8dqDdw4EA9/fTTuu666/T000+rf//+atKkSa3WtiM/M9uyrTy2Z8OGDYpGo/r444/jPwtbFBQUSPrxZ69fv3567bXX9NZbb2nMmDEaO3asLr/88lqtZcOGDTrppJN05513bnVsy5Ytq13r9ddfr6uuuip+ef369WrTpo2ysgJt3lztzZEEWVmBTjhhiT7/vLEqKsz/TpqRyMAW9bdHBvbIwFYQBFqyZIkaN26sSIT6Wwh7Bim/4uzs7J9tNHyXLl3Uq1cvjRs3Tk8//bTOP//8Gt+2efPm2m233bRkyRLtueeeCdsee+wRP65hw4Y666yzNG7cOD377LOaOHFi/DPDNX2u3377rRYsWKAbb7xRRx99tDp16qR169YlHNOpUyfNnTtXP/zwQ3zfv/71r+3eb6dOnTRjxoyEfTNmzNDee++9VbNYlZdeekmnnHJKjY6VfnxHOjc3V1988cVWdftp4/6rX/1Kn376qT7++GP94x//0MCBAxPW/eWXX+rLL7+M7/v8889VVlamzp0712gd7du3j382eovNmzeruLh4u/dRVFSk7OzshM/gx2IxLVy4MH65R48e8n1fq1at2uo5/nR6fJs2bXTxxRdr0qRJuvrqqzVu3DhJik8kr8nPRs+ePfXZZ5+pXbt2Wz1WTf5YkJubq4YNGyZsAAAAAHac+TvdmzZt0n/+85+EfVlZWWratKmkH08Jnjp1qvr06aPc3Nz4ZPNkueCCCzRs2DDl5+frtNNOq9Vtb775Zg0fPlyFhYXq37+/Nm3apJkzZ2rdunW66qqrdO+996ply5bq0aOHIpGInn/+ebVo0UK77LKLpJo/10aNGqlJkyZ69NFH1bJlS33xxRdbfT3Xr371K91www268MILdf3112vZsmW65557trv+q6++Wr1799Ytt9yis846Sx9++KH+9Kc/JUxg357KykpNnjy52oFtP9WgQQNdc801uvLKKxUEgQ455BDFYjHNmDFDDRs21KBBgyT9WJuDDz5YQ4YMke/7Caev9+3bV126dNHAgQN1//33q7KyUpdeeqkOP/zwhFPBtyc/P1+XXHKJRo4cqcaNG2v33XfXXXfdpe+//15DhgzZ7voHDRoUv92uu+6qUaNGKRKJxAc97L333ho4cKDOO+88jR07Vj169NDq1as1depUde3aVSeccIJGjBih4447TnvvvbfWrVunadOmqVOnTpJ+/HiB53l69dVXdfzxxysvLy/+Dvn/uuyyyzRu3DgNGDBAv/nNb9S4cWMtXrxYzzzzjB577LEa//EEAAAAQN0wf6f7jTfeUMuWLRO2Qw45JH792LFjNWXKFLVp00Y9evRI+noGDBigrKwsDRgwIOHz0DVxwQUX6LHHHtP48ePVpUsXHX744Xr88cfj73Q3aNBAd911l3r16qXevXtr2bJlev311+OnSNT0uUYiET3zzDP6+OOPte++++rKK6/U3XffnXBMQUGBXnnlFc2bN089evTQDTfcsM1Tjn+qZ8+eeu655/TMM89o33331e9+9zv9/ve/r/Hn2qdPn66CggL17NmzRsdvccstt+imm27SmDFj1KlTJ/Xv31+vvfZawhkC0o+nmM+ZM0ennXaa8vLy4vs9z9NLL72kRo0a6bDDDlPfvn1VVFSkZ599tlbruOOOO/TLX/5S5557rnr27KnFixfrzTffrPYPPffee68OOuggnXjiierbt6/69OkT/9q4LcaPH6/zzjtPV199tTp06KBTTz1VxcXF2n333SX9+C72ZZddFn/+e++9d/yPHa1atdLNN9+s6667Ts2bN9/qq+1+arfddtOMGTPk+76OPfZYdenSRSNGjNAuu+yyU6fiMC3VjnOeliwpJANDZGCL+tsjA3tkYMvzPBUWFoZ3cnYaCHsGnnPOWS8ilSxbtkzt27dXcXFxrZvHTDd8+HBVVlbW+J3xdLVx40a1atVKY8eO3e675GGwfv16FRYWSopJ4lRzAACQHuiAUBe2/K4ci8W2+7FM83e6U8XmzZv1n//8RzfeeKMOPPBAGu4dsO++++qSSy6xXsbP7pNPPtHf//53lZaWatasWfHPm9fms+2pjmmpdqLRQIcdtkLRKBlYIQNb1N8eGdgjA1tBEGjFihWhnZydDsKeAU33/5sxY4Zatmyp4uJiPfzww9bLCaWLLrpIXbp0sV6GiXvuuUfdunVT3759tXHjRr333nvxuQTpgH/k7USjgQ49lF+0LJGBLepvjwzskYGtsDd86SDsGZgPUksVRxxxhDjTHjuiR48e+vjjj62XAQAAACAF8U43AAAAAABJQtMNoFpBwEuFlSCIaPbsZmRgiAxsUX97ZGCPDGxFIhE1a9Zsp74JBjsn7BkwvRxAlZheDgAA0hEdEOoC08sB1BkGt9jJygp0wgmlysoiAytkYIv62yMDe2RgKwgClZaWhnaIVzoIewY03QCqxVeG2YlEAnXvvpoMDJGBLepvjwzskYGtIAi0evXq0DZ86SDsGdB0AwAAAACQJDTdAAAAAAAkCU03gGr5Pi8VVnw/ovfea00GhsjAFvW3Rwb2yMBWJBJR69atQzs5Ox2EPQOmlwOoEtPLAQBAOqIDQl1gejmAOpOV5VsvIWNlZ/saMKBE2dlkYIUMbFF/e2Rgjwxs+b6vkpIS+T71txL2DGi6AVTL8/hzsBXPcyoqipGBITKwRf3tkYE9MrDlnFMsFhMnCNsJewY03QAAAAAAJAlNNwAAAAAASULTDaBalZW8VFiprIzotdeKyMAQGdii/vbIwB4Z2IpEIioqKgrt5Ox0EPYMmF4OoEpMLwcAAOmIDgh1genlAOoM08vtZGf7Gjp0DhNrDZGBLepvjwzskYEt3/c1Z86c0E7OTgdhz4CmG0C1mJZqx/OcmjYtJwNDZGCL+tsjA3tkYMs5p/Ly8tBOzk4HYc+AphsAAAAAgCSh6QYAAAAAIEkYpAagSluGQ3hemZwrtF5ORopEnNq1i2nZskIFgWe9nIxEBraovz0ysEcGda82HZBzTrFY7P9/J6L+FlI1g5oOUqPpBlAlppcDAIB0RAeEusD0cgB1Jju70noJGSsnp1IjRxYrJ4cMrJCBLepvjwzskYGtyspKFRcXq7KS+lsJewY03QCQ4nJywvn1GOmEDGxRf3tkYI8MbIX1q6rSSZgzoOkGAAAAACBJaLoBAAAAAEgSBqkBqNJ/B6mVSWJ6uQXPc2rSpFzffpsn51JnWmcmIQNb1N8eGdgjg7pX2+nl5eXlysvLS6nJ2ZkkVTOo6SC1rJ9xTQBCqqxMKqTnNuGc5Ps5ikalFPo3JqOQgS3qb48M7JGBvZycHOslZLwwZ8Dp5QCqFebBFWHn+75mzpxJBobIwBb1t0cG9sjAFvW3F/YMaLoBAAAAAEgSmm4AAAAAAJKEphsAAAAAgCRhejmAKm2ZyFhWVvb/U8zxc3POyfd9RaPRlJrWmUnIwBb1t0cG9sjAFvW3l6oZ1HR6Oe90A0CKq6iosF5CxiMDW9TfHhnYIwNb1N9emDOg6QZQrbBOikwHvu9r7ty5ZGCIDGxRf3tkYI8MbFF/e2HPgKYbAAAAAIAkoekGAAAAACBJaLoBIMVFo1HrJWQ8MrBF/e2RgT0ysEX97YU5A6aXA6hSTScyAgAAAJmG6eUA6gx/m7PjnFNZWRkZGCIDW9TfHhnYIwNb1N9e2DOg6QZQrbBOikwHvu9r/vz5ZGCIDGxRf3tkYI8MbFF/e2HPgKYbAAAAAIAkoekGAAAAACBJaLoBVMvzPOslZCzP85SXl0cGhsjAFvW3Rwb2yMAW9bcX9gyYXg6gSkwvBwAAALaN6eUA6kwQBNZLyFhBEGjVqlVkYIgMbFF/e2RgjwxsUX97Yc+AphtAtcL6ApcOgiDQkiVLyMAQGdii/vbIwB4Z2KL+9sKeAU03AAAAAABJQtMNAAAAAECS0HQDqFZYJ0WmA8/zVFhYSAaGyMAW9bdHBvbIwBb1txf2DJheDqBKTC8HAAAAto3p5QDqTFiHVqSDIAi0YsUKMjBEBraovz0ysEcGtqi/vbBnQNMNoFphfYFLB2H/RyYdkIEt6m+PDOyRgS3qby/sGdB0AwAAAACQJDTdAAAAAAAkCU03gGpFIrxUWIlEImrWrBkZGCIDW9TfHhnYIwNb1N9e2DNgejmAKjG9HAAAANg2ppcDqDNhHVqRDoIgUGlpKRkYIgNb1N8eGdgjA1vU317YM6DpBlCtsL7ApYMgCLR69WoyMEQGtqi/PTKwRwa2qL+9sGdA0w0AAAAAQJLQdAMAAAAAkCQ03QCqFdZJkekgEomodevWZGCIDGxRf3tkYI8MbFF/e2HPgOnlAKrE9HIAAABg25heDqDO+L5vvYSM5fu+SkpKyMAQGdii/vbIwB4Z2KL+9sKeAU03gGpxQowd55xisRgZGCIDW9TfHhnYIwNb1N9e2DOg6QYAAAAAIElougEAAAAASBKabgDVCuukyHQQiURUVFREBobIwBb1t0cG9sjAFvW3F/YMmF4OoEpMLwcAAAC2jenlAOpMWCdFpgPf9zVnzhwyMEQGtqi/PTKwRwa2qL+9sGdA0w2gWpwQY8c5p/LycjIwRAa2qL89MrBHBraov72wZ0DTDQAAAABAktB0AwAAAACQJDTdAKoVjUatl5CxotGoOnbsSAaGyMAW9bdHBvbIwBb1txf2DJheDqBKTC8HAAAAto3p5QDqTGVlpfUSMlZlZaWKi4vJwBAZ2KL+9sjAHhnYov72wp4BTTcApLiwfj1GOiEDW9TfHhnYIwNb1N9emDOg6QYAAAAAIElougEAAAAASBIGqQGo0pbhEGVlZSosLLReTkZyzqm8vFx5eXnyPM96ORmJDGxRf3tkYI8MbFF/e6maAYPUACBN5OTkWC8h45GBLepvjwzskYEt6m8vzBnQdAOoVpgHV4Sd7/uaOXMmGRgiA1vU3x4Z2CMDW9TfXtgzoOkGAAAAACBJaLoBAAAAAEgSmm4AAAAAAJKE6eUAqsT0cnvOOfm+r2g0mlLTOjMJGdii/vbIwB4Z2KL+9lI1A6aXA0CaqKiosF5CxiMDW9TfHhnYIwNb1N9emDOg6QZQrbBOikwHvu9r7ty5ZGCIDGxRf3tkYI8MbFF/e2HPgKYbAAAAAIAkoekGAAAAACBJaLoBIMVFo1HrJWQ8MrBF/e2RgT0ysEX97YU5A6aXA6hSTScyAgAAAJmG6eUA6gx/m7PjnFNZWRkZGCIDW9TfHhnYIwNb1N9e2DOg6QZQrbBOikwHvu9r/vz5ZGCIDGxRf3tkYI8MbFF/e2HPYIea7vfee0/nnHOODjroIH311VeSpKeeekrvv/9+nS4OAAAAAIAwq3XTPXHiRPXr1095eXn65JNPtGnTJklSLBbT7bffXucLBAAAAAAgrGrddN966616+OGHNW7cOGVnZ8f39+nTR7NmzarTxQFIDZ7nWS8hY3mep7y8PDIwRAa2qL89MrBHBraov72wZ5BV2xssWLBAhx122Fb7CwsLVVZWVhdrApBiGjcO71c0hF9UUjfrRWQ4MrBF/e2RgT0y2Fk7M38rGo2qWzfqbynsGdT6ne4WLVpo8eLFW+1///33VVRUVCeLApBaPC+wXkLGikQCde++SpEIGVghA1vU3x4Z2CMDW0EQaNWqVQoC6m8l7BnUuum+8MILdcUVV+jf//63PM/T119/rQkTJuiaa67RJZdckow1AjCWlRXOF7h0kJUV6IQTlpCBITKwRf3tkYE9MrAVBIGWLFkS2oYvHYQ9g1qfXn7dddcpCAIdffTR+v7773XYYYcpNzdX11xzjS6//PJkrBEAAAAAgFCqddPteZ5uuOEGjRw5UosXL9aGDRvUuXNnFRQUJGN9AAAAAACEVq2b7i1ycnLUuXPnulwLgBTlXDgnRaYD5zwtWVJIBobIwBb1t0cG9sjAlud5KiwsDO3k7HQQ9gw852o3y++HH37QH//4R02bNm2bH2bna8OA9LF+/XoVFhZKiklqaL0cAACAHbIz08uBqmz5XTkWi6lhw6p/V671O91DhgzRW2+9pdNPP137779/aP/aAKDmIpFAIZ1bEXrRaKA+fb7WjBm7yfdrPfsSdYAMbFF/e2RgjwxsBUGgr7/+WrvttpsiEepvIewZ1LrpfvXVV/X666+rT58+yVgPgBQUjdJ0W4lGAx166Ar9618t+EXLCBnYov72yMAeGdgKgkArVqxQixYtQtnwpYOwZ1DrFbdq1UoNGjRIxloAAAAAAEgrtW66x44dq2uvvVbLly9PxnoAAAAAAEgbtT69vFevXvrhhx9UVFSk+vXrKzs7O+H6tWvX1tniAKSGIAjfaTzpIggimj27GRkYIgNb1N8eGdgjA1uRSETNmjUL5WnN6SLsGdR6ennfvn31xRdfaMiQIWrevPlWg9QGDRpUpwsEYIfp5QAAIB0wvRzJkLTp5R988IE+/PBDdevWbacWCCA8otFAvm+9isyUlRWoX7+levPNPVRZGc6/7oYdGdii/vbIwB4Z2AqCQEuXLtUee+wR2ndawy7sGdR6xR07dlR5eXky1gIgRUUijC63EokE6t59NRkYIgNb1N8eGdgjA1tBEGj16tUK+CoXM2HPoNZN9x133KGrr75a7777rr799lutX78+YQMAAAAAAD+q9enl/fv3lyQdffTRCfudc/I8Tz7noAIAAAAAIGkHmu5p06YlYx0AUpjvh++zM+nC9yN6773WZGCIDGxRf3tkYI8MbEUiEbVu3TqUnyVOF2HPoNbTywFkDqaXAwCAdEDHg2So6fTyHf5Twffff6/58+dr7ty5CRuA9JOVxcdGrGRn+xowoETZ2WRghQxsUX97ZGCPDGz5vq+SkhI+Rmso7BnU+vTy1atX69e//rUmT568zevDWggAVfM8/jxsxfOciopiZGCIDGxRf3tkYI8MbDnnFIvFxAnCdsKeQa3f6R4xYoTKysr073//W3l5eXrjjTf0xBNPaK+99tLLL7+cjDUCAAAAABBKtX6n+5133tFLL72kXr16KRKJqG3btjrmmGPUsGFDjRkzRieccEIy1gkAAAAAQOjU+p3ujRs3atddd5UkNWrUSKtXr5YkdenSRbNmzarb1QFICZWV4ZwUmQ4qKyN67bUiMjBEBraovz0ysEcGtiKRiIqKikI7OTsdhD2DWq+6Q4cOWrBggSSpW7dueuSRR/TVV1/p4YcfVsuWLet8gQDsORfOF7h0EAQRzZ69q4KADKyQgS3qb48M7JGBrUgkol133TW0DV86CHsGtV71FVdcoZUrV0qSRo0apcmTJ2v33XfXH/7wB91+++11vkAA9phebic729fQoXOYWGuIDGxRf3tkYI8MbPm+rzlz5jAw2lDYM6j1Z7rPOeec+H/vt99+Wr58uebPn6/dd99dTZs2rdPFAUgNTEu143lOTZuWk4EhMrBF/e2RgT0ysOWcU3l5eWgnZ6eDsGdQ66b7f9WvX189e/asi7UAAAAAAJBWatx0//73v6/Rcb/73e92eDEAAAAAAKQTz9XwPfoePXps9/qFCxfqhx9+CO159gC2tn79ehUWFsrzyuRcofVyMlIk4tSuXUzLlhUqCDzr5WQkMrBF/e2RgT0y2Hk7c1ayc06xWOz/fyei/hZSNYMtvyvHYjE1bNiwyuNq3HRXZfbs2bruuuv0zjvv6Pzzz9fDDz+8M3cHIImOOOIIde/eXffff3+Njt/yQiLFJFX9QgIAAJDKQvpRYKS4mjbdOzxzfenSpTrnnHPUu3dvFRYW6rPPPqPhRlIMHjxYnudttfXv31+S5HmeXnzxRdtFJsm7774rz/NUVlZmuo7s7ErTx89kOTmVGjmyWDk5ZGCFDGxRf3tkYI8MbFVWVqq4uFiVldTfStgzqPUgtTVr1ujmm2/Wo48+qkMOOUQffPCBevfunYy1AXH9+/fX+PHjE/bl5uYarSb1VFRUKCcnx3oZSJKcHD62Y40MbFF/e2Rgjwxs8RFae2HOoMbvdG/cuFE333yz2rdvrw8++ECvvPKKpk6dSsONn0Vubq5atGiRsDVq1Ejt2rWTJJ122mnyPC9+WZIeeughtW/fXjk5OerQoYOeeuqphPv0PE+PPfaYTjvtNNWvX1977bWXXn755YRjpk+frv3331+5ublq2bKlrrvuuoS/sH333XcaOHCg8vPz1bJlS91333064ogjNGLEiPgxmzZt0jXXXKNWrVopPz9fBxxwgN5999349cuXL9dJJ52kRo0aKT8/X/vss49ef/11LVu2TEceeaQkqVGjRvI8T4MHD5b042niw4YN04gRI9S0aVP169dPkvTpp5/quOOOU0FBgZo3b65zzz1Xa9as2cnqAwAAANhRNW6627dvr7vuukuXXHKJ/vrXv6pFixaaO3fuVhvwcyouLpYkjR8/XitXroxffuGFF3TFFVfo6quv1qeffqqhQ4fq17/+taZNm5Zw+5tvvllnnnmm5s6dq+OPP14DBw7U2rVrJUlfffWVjj/+ePXu3Vtz5szRQw89pL/85S+69dZb47e/6qqrNGPGDL388suaMmWK3nvvPc2aNSvhMYYNG6YPP/xQzzzzjObOnaszzjhD/fv316JFiyRJl112mTZt2qR//vOfmjdvnu68804VFBSoTZs2mjhxoiRpwYIFWrlypR544IH4/T7xxBPKycnRjBkz9PDDD6usrExHHXWUevTooZkzZ+qNN97QN998ozPPPLOOqw4AAACgxlwNeZ4X3yKRyDYvRyKRmt4dUGODBg1y0WjU5efnJ2y33Xabc845Se6FF15IuM3BBx/sLrzwwoR9Z5xxhjv++OPjlyW5G2+8MX55w4YNTpKbPHmyc8653/72t65Dhw4uCIL4MX/+859dQUGB833frV+/3mVnZ7vnn38+fn1ZWZmrX7++u+KKK5xzzi1fvtxFo1H31VdfJazl6KOPdtdff71zzrkuXbq40aNHb/O5T5s2zUly69atS9h/+OGHux49eiTsu+WWW9yxxx6bsO/LL790ktyCBQvit9uytm354YcfXCwWi29bbi+VuR9HkLD93JvnBa5p043O8wLztWTqRgbUP9M3MrDfyGDnt50RBIHbuHFjwu+E+HmlagaxWMxJcrFYbLvH1fgz3UuXLk1K0w/UxJFHHqmHHnooYV/jxo2rPL6kpEQXXXRRwr4+ffokvFMsSV27do3/d35+vho2bKhVq1bF7+Oggw5K+FqCPn36aMOGDVqxYoXWrVunzZs3a//9949fX1hYqA4dOsQvz5s3T77va++990543E2bNqlJkyaSpOHDh+uSSy7RW2+9pb59++qXv/xlwrqqst9++yVcnjNnjqZNm6aCgoKtji0tLd1qDdsyZswY3XzzzdUeh5+Pc9L69TlyznolmYsMbFF/e2RgjwzsMTvHXpgzqPHp5W3btq3RBiRDfn6+9txzz4Rte013TWVnZydc9jxPQRDs9P1usWHDBkWjUX388ceaPXt2fCspKYn/AeCCCy7QkiVLdO6552revHnq1auX/vjHP1Z73/n5+Vs91kknnZTwOLNnz9aiRYt02GGH1Wi9119/vWKxWHz78ssvJUnZ2eEdXBF2OTm+Ro6cyQAdQ2Rgi/rbIwN7ZGDL933NnDkz1IO8wi7sGezwV4YBqSI7O3ur/wE7deqkGTNmJOybMWOGOnfuXOP77dSpkz788EO5n/xZecaMGWrQoIFat26toqIiZWdnxz9HLkmxWEwLFy6MX+7Ro4d839eqVau2+qNBixYt4se1adNGF198sSZNmqSrr75a48aNk/Tfv+jV5AWmZ8+e+uyzz9SuXbutHut/G/Sq5ObmqmHDhgkbAAAAgB1H041Q2LRpk/7zn/8kbFumcrdr105Tp07Vf/7zH61bt06SNHLkSD3++ON66KGHtGjRIt17772aNGmSrrnmmho/5qWXXqovv/xSl19+uebPn6+XXnpJo0aN0lVXXaVIJKIGDRpo0KBBGjlypKZNm6bPPvtMQ4YMUSQSiZ+Svvfee2vgwIE677zzNGnSJC1dulQfffSRxowZo9dee02SNGLECL355ptaunSpZs2apWnTpqlTp06SfjzDxPM8vfrqq1q9erU2bNhQ5Xovu+wyrV27VgMGDFBxcbFKS0v15ptv6te//nVo/yoIAAAAhB1NN0LhjTfeUMuWLRO2Qw45RJI0duxYTZkyRW3atFGPHj0kSaeeeqoeeOAB3XPPPdpnn330yCOPaPz48TriiCNq/JitWrXS66+/ro8++kjdunXTxRdfrCFDhujGG2+MH3PvvffqoIMO0oknnqi+ffuqT58+6tSpk+rVqxc/Zvz48TrvvPN09dVXq0OHDjr11FNVXFys3XffXdKP72Jfdtll6tSpk/r376+9995bDz74YHwNN998s6677jo1b95cw4YNq3K9u+22m2bMmCHf93XssceqS5cuGjFihHbZZRdFIvyvDgAAAFjwnGMkA1BXNm7cqFatWmns2LEaMmSI9XJ22vr161VYWCipTFKh8WoylVNOjq+Kiqgkr9qjkQxkYIv62yMDe2Sws3am43HOyfd9RaPRhAG7+PmkagZbfleOxWLb/Vgmb38BO+GTTz7R3//+d5WWlmrWrFkaOHCgJOmUU04xXhnShedJDRtWKIX+fck4ZGCL+tsjA3tkYK+iosJ6CRkvzBnUuunu0aOHevbsudW23377qU+fPho0aJCmTZuWjLUCKemee+5Rt27d1LdvX23cuFHvvfeemjZtar2sOsX0cjvZ2b6GDp1LBobIwBb1t0cG9sjAlu/7mjt3LjNyDIU9g1o33f3799eSJUuUn5+vI488UkceeaQKCgpUWlqq3r17a+XKlerbt69eeumlZKwXSCk9evTQxx9/rA0bNmjt2rWaMmWKunTpYr0sAAAAACkiq7Y3WLNmja6++mrddNNNCftvvfVWLV++XG+99ZZGjRqlW265hVNsAQAAAAAZrdbvdD/33HMaMGDAVvvPPvtsPffcc5KkAQMGaMGCBTu/OgDA/w/OgSUysEX97ZGBPTKwFY1Sf2thzqDW08ubN2+uu+++W+edd17C/ieffFIjR47UN998o88//1yHH364Vq9eXaeLBfDz+u/08pikqicyAgAApDK+rwnJUNPp5bU+vfzyyy/XxRdfrI8//li9e/eWJBUXF+uxxx7Tb3/7W0nSm2++qe7du+/YygGkHM9z/GNlJBJxatcupmXLChUEjK21QAa2qL89MrBHBracc4rFYiosLEypr6vKJGHPoNanl994440aN26cPvroIw0fPlzDhw/XRx99pHHjxumGG26QJF188cV65ZVX6nyxAGxkZYVzUmQ6yMryNWDAfDIwRAa2qL89MrBHBrZ839f8+fNDOzk7HYQ9g1q/0y1JAwcOjH8f8bbk5eXt8IIAAAAAAEgXtX6nGwAAAAAA1Eyt3+mORCLbPY8+rG/5A6iac+H77Ey6cM7TmjV5ZGCIDGxRf3tkYI8MbHmep7y8vFB+ljhdhD2DWk8vf+mllxIub968WZ988omeeOIJ3XzzzRoyZEidLhCAHaaXAwCAdMBAWCRDTaeX17rprsrTTz+tZ599dqumHEB4bXkh8bx1cm4X6+VkpEgkUNeuazR3blMFAZ8IskAGtqi/PTKwRwY7b2c6niAItGbNGjVt2lSRCPW3kKoZ1LTprrMVH3jggZo6dWpd3R2AFJKVFVgvIWNlZQU64YQlZGCIDGxRf3tkYI8MbAVBoCVLligIqL+VsGdQJ013eXm5/vCHP6hVq1Z1cXcAAAAAAKSFWg9Sa9SoUcIH2J1z+u6771S/fn397W9/q9PFAQAAAAAQZrVuuu+///6Ey5FIRM2aNdMBBxygRo0a1dW6AKQQpqXacc7TkiWFZGCIDGxRf3tkYI8MbHme9/8zbqi/lbBnUKtBapWVlbr99tt1/vnnq3Xr1slcF4AUwPRyAACQDphejmRIyiC1rKws3X333aqsrNzpBQIIj0gknEMr0kE0Guiww1YoGiUDK2Rgi/rbIwN7ZGArCAKtWLEitEO80kHYM6j1ILWjjjpK06dPT8ZaAKQo/pG3E40GOvRQftGyRAa2qL89MrBHBrbC3vClg7BnUOvPdB933HG67rrrNG/ePO23337Kz89PuP7kk0+us8UBAAAAABBmtW66L730UknSvffeu9V1nufJ9/2dXxUAAAAAAGmg1k13WN/SB7DjgqDWn0RBHQmCiGbPbkYGhsjAFvW3Rwb2yMDWlm9rikSov5WwZ1Cr6eUAMgvTywEAQDqg40EyJGV6eRAE+utf/6oTTzxR++67r7p06aKTTz5ZTz75pOjdgfTF4BY7WVmBTjihVFlZZGCFDGxRf3tkYI8MbAVBoNLSUs74NRT2DGrcdDvndPLJJ+uCCy7QV199pS5dumifffbR8uXLNXjwYJ122mnJXCcAQ3xlmJ1IJFD37qvJwBAZ2KL+9sjAHhnYCoJAq1evDm3Dlw7CnkGNP9P9+OOP65///KemTp2qI488MuG6d955R6eeeqqefPJJnXfeeXW+SAAAAAAAwqjG73T//e9/129/+9utGm7px+/uvu666zRhwoQ6XRwAAAAAAGFW46Z77ty56t+/f5XXH3fccZozZ06dLApAavH9cE6KTAe+H9F777UmA0NkYIv62yMDe2RgKxKJqHXr1qGdnJ0Owp5BjU8vX7t2rZo3b17l9c2bN9e6devqZFEAUsu6dRFtZyAjkioiqbX1IjIcGdii/vbIwB4ZWNrS8MFO2DOo8Z8KfN9XVlbVPXo0GlVlZWWdLApAavF933oJGcv3fZWUlJCBITKwRf3tkYE9MrBF/e2FPYMav9PtnNPgwYOVm5u7zes3bdpUZ4sCkFr4SkA7zjnFYjEyMEQGtqi/PTKwRwa2qL+9sGdQ46Z70KBB1R7D5HIAAAAAAP6rxk33+PHjk7kOAAAAAADSTjjHvwH4WYV1UmQ6iEQiKioqIgNDZGCL+tsjA3tkYIv62wt7Bp4L64nxAJJu/fr1KiwsVCwWU0PGlwMAAABxNf1dOZx/KgDwswrrpMh04Pu+5syZQwaGyMAW9bdHBvbIwBb1txf2DGi6AVSLE2LsOOdUXl5OBobIwBb1t0cG9sjAFvW3F/YMaLoBAAAAAEgSmm4AAAAAAJKEphtAtaLRqPUSMlY0GlXHjh3JwBAZ2KL+9sjAHhnYov72wp4B08sBVInp5QAAAMC2Mb0cQJ2prKy0XkLGqqysVHFxMRkYIgNb1N8eGdgjA1vU317YM6DpBoAUF9avx0gnZGCL+tsjA3tkYIv62wtzBjTdAAAAAAAkCU03AAAAAABJwiA1AFXaMhyirKxMhYWF1svJSM45lZeXKy8vT57nWS8nI5GBLepvjwzskYEt6m8vVTNgkBoApImcnBzrJWQ8MrBF/e2RgT0ysEX97YU5A5puANUK8+CKsPN9XzNnziQDQ2Rgi/rbIwN7ZGCL+tsLewY03QAAAAAAJAlNNwAAAAAASULTDQAAAABAkjC9HECVmF5uzzkn3/cVjUZTalpnJiEDW9TfHhnYIwNb1N9eqmbA9HIASBMVFRXWS8h4ZGCL+tsjA3tkYIv62wtzBjTdAKoV1kmR6cD3fc2dO5cMDJGBLepvjwzskYEt6m8v7BnQdAMAAAAAkCQ03QAAAAAAJAlNNwCkuGg0ar2EjEcGtqi/PTKwRwa2qL+9MGfA9HIAVarpREYAAAAg0zC9HECd4W9zdpxzKisrIwNDZGCL+tsjA3tkYIv62wt7BjTdAKoV1kmR6cD3fc2fP58MDJGBLepvjwzskYEt6m8v7BnQdAMAAAAAkCQ03QAAAAAAJAlNN4BqeZ5nvYSM5Xme8vLyyMAQGdii/vbIwB4Z2KL+9sKeAdPLAVSJ6eUAAADAtjG9HECdCYLAegkZKwgCrVq1igwMkYEt6m+PDOyRgS3qby/sGdB0A6hWWF/g0kEQBFqyZAkZGCIDW9TfHhnYIwNb1N9e2DOg6QYAAAAAIElougEAAAAASBKabgDVCuukyHTgeZ4KCwvJwBAZ2KL+9sjAHhnYov72wp4B08sBVInp5QAAAMC2Mb0cQJ0J69CKdBAEgVasWEEGhsjAFvW3Rwb2yMAW9bcX9gxougFUK6wvcOkg7P/IpAMysEX97ZGBPTKwRf3thT0Dmm4AAAAAAJKEphsAAAAAgCSh6QZQrUiElworkUhEzZo1IwNDZGCL+tsjA3tkYIv62wt7BkwvB1AlppcDAAAA28b0cgB1JqxDK9JBEAQqLS0lA0NkYIv62yMDe2Rgi/rbC3sGNN0AqhXWF7h0EASBVq9eTQaGyMAW9bdHBvbIwBb1txf2DGi6AQAAAABIEppuAAAAAACShKYbQLXCOikyHUQiEbVu3ZoMDJGBLepvjwzskYEt6m8v7BkwvRxAlZheDgAAAGwb08sB1Bnf962XkLF831dJSQkZGCIDW9TfHhnYIwNb1N9e2DOg6QZQLU6IseOcUywWIwNDZGCL+tsjA3tkYIv62wt7BjTdAAAAAAAkCU03AAAAAABJQtMNoFphnRSZDiKRiIqKisjAEBnYov72yMAeGdii/vbCngHTywFUienlAAAAwLYxvRxAnQnrpMh04Pu+5syZQwaGyMAW9bdHBvbIwBb1txf2DGi6AVSLE2LsOOdUXl5OBobIwBb1t0cG9sjAFvW3F/YMaLoBAAAAAEgSmm4AAAAAAJKEphtAtaLRqPUSMlY0GlXHjh3JwBAZ2KL+9sjAHhnYov72wp4B08sBVInp5QAAAMC2Mb0cQJ2prKy0XkLGqqysVHFxMRkYIgNb1N8eGdgjA1vU317YM6DpBoAUF9avx0gnZGCL+tsjA3tkYIv62wtzBjTdAAAAAAAkCU03AAAAAABJwiA1AFXaMhxCKpNUaLyazOR5Tk2alOvbb/PknGe9nIxEBraovz0ysJfpGVh3K845lZeXKy8vT56XefVPBamaAYPUACANOCetX59j/gtHJiMDW9TfHhnYIwN7OTk51kvIeGHOgKYbQLWys8M7uCLscnJ8jRw5Uzk5ZGCFDGxRf3tkYI8MbPm+r5kzZ4Z6kFfYhT0Dmm4AAAAAAJKEphsAAAAAgCSh6QYAAAAAIEmYXg6gSkwvTwVOOTm+KiqiklJnWmdmIQNb1N8eGdjL7AysuxXnnHzfVzQaTanJ2ZkkVTNgejkApAHPkxo2rFAK/fuSccjAFvW3Rwb2yMBeRUWF9RIyXpgzoOkGUC2ml9vJzvY1dOhcMjBEBraovz0ysEcGtnzf19y5c0M7OTsdhD0Dmm4AAAAAAJKEphsAAAAAgCSh6QaAFPfj4BxYIgNb1N8eGdgjA1vRKPW3FuYMmF4OoEr/nV4ek1T1REYAAIBkoVtBqmJ6OYA643n8a2clEnEqKipTJEIGVsjAFvW3Rwb2yMCWc05lZWXivUo7Yc+AphtAtbKywjkpMh1kZfkaMGA+GRgiA1vU3x4Z2CMDW77va/78+aGdnJ0Owp4BTTcAAAAAAElC0w0AAAAAQJLQdAOolnOe9RIylnOe1qzJIwNDZGCL+tsjA3tkYMvzPOXl5cnzqL+VsGfA9HIAVWJ6OQAAsEa3glTF9HIAdcbzAuslZKxIJFD37qsUiZCBFTKwRf3tkYE9MrAVBIFWrVqlIKD+VsKeAU03gGplZYXzBS4dZGUFOuGEJWRgiAxsUX97ZGCPDGwFQaAlS5aEtuFLB2HPgKYbAAAAAIAkoekGAAAAACBJaLoBVItpqXac87RkSSEZGCIDW9TfHhnYIwNbnuepsLAwtJOz00HYM2B6OYAqMb0cAABYo1tBqmJ6OYA6w7RUO9FooMMOW6FolAyskIEt6m+PDOyRga0gCLRixYrQDvFKB2HPgKYbQLX4R95ONBro0EP5RcsSGdii/vbIwB4Z2Ap7w5cOwp4BTTcAAAAAAElC0w0AAAAAQJLQdAOoVhDwUmElCCKaPbsZGRgiA1vU3x4Z2CMDW5FIRM2aNVMkQv2thD0DppcDPwPP8/TCCy/o1FNPtV5KrTC9HAAAWKNbQapiejky3uDBg+V5nu64446E/S+++GJov+PPCoNb7GRlBTrhhFJlZZGBFTKwRf3tkYE9MrAVBIFKS0tDO8QrHYQ9A5pupLV69erpzjvv1Lp166yXEmp8ZZidSCRQ9+6rycAQGdii/vbIwB4Z2AqCQKtXrw5tw5cOwp4BTTfSWt++fdWiRQuNGTOmymPef/99HXroocrLy1ObNm00fPhwbdy4UZL05JNPqqCgQIsWLYoff+mll6pjx476/vvvJUnt2rXTLbfcogEDBig/P1+tWrXSn//85+2u69prr9Xee++t+vXrq6ioSDfddJM2b94cv3706NHq3r27nnrqKbVr106FhYU6++yz9d1338WP2bRpk4YPH65dd91V9erV0yGHHKLi4uL49e+++648z9PUqVPVq1cv1a9fXwcffLAWLFhQuyICAAAA2GE03Uhr0WhUt99+u/74xz9qxYoVW11fWlqq/v3765e//KXmzp2rZ599Vu+//76GDRsmSTrvvPN0/PHHa+DAgaqsrNRrr72mxx57TBMmTFD9+vXj93P33XerW7du+uSTT3Tdddfpiiuu0JQpU6pcV4MGDfT444/r888/1wMPPKBx48bpvvvu22ptL774ol599VW9+uqrmj59esKp8r/5zW80ceJEPfHEE5o1a5b23HNP9evXT2vXrk24nxtuuEFjx47VzJkzlZWVpfPPP7/KdW3atEnr169P2AAAAADsBAekqUGDBrlTTjnFOefcgQce6M4//3znnHMvvPCC2/KjP2TIEHfRRRcl3O69995zkUjElZeXO+ecW7t2rWvdurW75JJLXPPmzd1tt92WcHzbtm1d//79E/adddZZ7rjjjotfluReeOGFKtd69913u/322y9+edSoUa5+/fpu/fr18X0jR450BxxwgHPOuQ0bNrjs7Gw3YcKE+PUVFRVut912c3fddZdzzrlp06Y5Se7tt9+OH/Paa685SfHn9r9GjRrlJG21RSLr3I9jTNh+7i0a9d1hh33polHffC2ZupEB9c/0jQzst0zPwJrv++7LL790vu9bLyVjpWoGsVjMSXKxWGy7x/FONzLCnXfeqSeeeEIlJSUJ++fMmaPHH39cBQUF8a1fv34KgkBLly6VJDVq1Eh/+ctf9NBDD6l9+/a67rrrtrr/gw46aKvL//tYP/Xss8+qT58+atGihQoKCnTjjTfqiy++SDimXbt2atCgQfxyy5YttWrVKkk/vgu+efNm9enTJ359dna29t9//60et2vXrgn3ISl+P//r+uuvVywWi29ffvmlJL4yzJLvR/TPf7aW75OBFTKwRf3tkYE9MrAViUTUunXr0H5dVToIewbhXDVQS4cddpj69eun66+/PmH/hg0bNHToUM2ePTu+zZkzR4sWLVL79u3jx/3zn/9UNBrVypUr45/33lEffvihBg4cqOOPP16vvvqqPvnkE91www2qqKhIOC47Ozvhsud5OzQ84qf3s2Vqe1X3k5ubq4YNGyZskpSV5df6cVE3srN9DRhQouxsMrBCBraovz0ysEcGtnzfV0lJiXyf+lsJewY03cgYd9xxh1555RV9+OGH8X09e/bU559/rj333HOrLScnR5L0wQcf6M4779Qrr7yigoKC+Oe9f+pf//rXVpc7deq0zXV88MEHatu2rW644Qb16tVLe+21l5YvX16r59K+fXvl5ORoxowZ8X2bN29WcXGxOnfuXKv7qgnPc3V+n6gZz3MqKoqRgSEysEX97ZGBPTKw5ZxTLBaTc9TfStgzyLJeAPBz6dKliwYOHKg//OEP8X3XXnutDjzwQA0bNkwXXHCB8vPz9fnnn2vKlCn605/+pO+++07nnnuuhg8fruOOO06tW7dW7969ddJJJ+n000+P38+MGTN011136dRTT9WUKVP0/PPP67XXXtvmOvbaay998cUXeuaZZ9S7d2+99tpreuGFF2r1XPLz83XJJZdo5MiRaty4sXbffXfddddd+v777zVkyJAdKxAAAACAOsc73cgov//97xNOre7ataumT5+uhQsX6tBDD1WPHj30u9/9Trvttpsk6YorrlB+fr5uv/12ST827rfffruGDh2qr776Kn4/V199tWbOnKkePXro1ltv1b333qt+/fptcw0nn3yyrrzySg0bNkzdu3fXBx98oJtuuqnWz+WOO+7QL3/5S5177rnq2bOnFi9erDfffFONGjWq9X0BAAAASA7PhfU9eiBFtGvXTiNGjNCIESOsl1Ln1q9fr8LCQnneOjm3i/VyMlIkEqhr1zWaO7cpA+2MkIEt6m+PDOxlegbW3UoQBFqzZo2aNm0a2kFeYZeqGWz5XTkWi8VnIW0Lp5cDqBZfdGAnCCKaPXtX62VkNDKwRf3tkYE9MrAViUS0667U31LYM+A3aQDVYnq5nexsX0OHzmFirSEysEX97ZGBPTKw5fu+5syZE9rJ2ekg7BnwTjewk5YtW2a9hKRjWqodz3Nq2rScDAyRgS3qb48M7JGBLeecysvLQzs5Ox2EPQPe6QYAAAAAIElougEAAAAASBKmlwOo0n+nl5fJuULr5WSkSMSpXbuYli0rVBB41svJSGRgi/rbIwN7mZ6BdbfinFMsFvv/34kyr/6pIFUzqOn0cppuAFXa8kIixSRV/UICAACQLHQrSFU1bbo5vRxAtbKzK62XkLFycio1cmSxcnLIwAoZ2KL+9sjAHhnYqqysVHFxsSorqb+VsGdA0w0AKS4nJ5xfj5FOyMAW9bdHBvbIwFZYv6oqnYQ5A5puAAAAAACShKYbAAAAAIAkYZAagCr9d5BamSSml1vwPKcmTcr17bd5ci51pnVmEjKwRf3tkYG9TM/Aultxzqm8vFx5eXkpNTk7k6RqBgxSA4A04Jy0fn2O+S8cmYwMbFF/e2Rgjwzs5eTkWC8h44U5A5puANXKzg7v4Iqwy8nxNXLkTAboGCIDW9TfHhnYIwNbvu9r5syZoR7kFXZhz4CmGwAAAACAJKHpBgAAAAAgSWi6AQAAAABIEqaXA6gS08tTgVNOjq+Kiqik1JnWmVnIwBb1t0cG9jI7A+tuxTkn3/cVjUZTanJ2JknVDJheDgBpwPOkhg0rlEL/vmQcMrBF/e2RgT0ysFdRUWG9hIwX5gxougFUi+nldrKzfQ0dOpcMDJGBLepvjwzskYEt3/c1d+7c0E7OTgdhz4CmGwAAAACAJKHpBgAAAAAgSWi6ASDF/Tg4B5bIwBb1t0cG9sjAVjRK/a2FOQOmlwOo0n+nl8ckVT2REQAAIFnoVpCqmF4OoM54Hv/aWYlEnIqKyhSJkIEVMrBF/e2RgT0ysOWcU1lZmXiv0k7YM6DpBlCtrKxwTopMB1lZvgYMmE8GhsjAFvW3Rwb2yMCW7/uaP39+aCdnp4OwZ0DTDQAAAABAktB0AwAAAACQJDTdAKrlnGe9hIzlnKc1a/LIwBAZ2KL+9sjAHhnY8jxPeXl58jzqbyXsGTC9HECVajqREQAAAMg0TC8HUGeCILBeQsYKgkCrVq0iA0NkYIv62yMDe2Rgi/rbC3sGNN0AqhXWF7h0EASBlixZQgaGyMAW9bdHBvbIwBb1txf2DGi6AQAAAABIEppuAAAAAACShKYbQLXCOikyHXiep8LCQjIwRAa2qL89MrBHBraov72wZ8D0cgBVYno5AAAAsG1MLwdQZ8I6tCIdBEGgFStWkIEhMrBF/e2RgT0ysEX97YU9A5puANUK6wtcOgj7PzLpgAxsUX97ZGCPDGxRf3thz4CmGwAAAACAJKHpBgAAAAAgSWi6AVQrEuGlwkokElGzZs3IwBAZ2KL+9sjAHhnYov72wp4B08sBVInp5QAAAMC2Mb0cQJ0J69CKdBAEgUpLS8nAEBnYov72yMAeGdii/vbCngFNN4BqhfUFLh0EQaDVq1eTgSEysEX97ZGBPTKwRf3thT0Dmm4AAAAAAJKEphsAAAAAgCSh6QZQrbBOikwHkUhErVu3JgNDZGCL+tsjA3tkYIv62wt7BkwvB1AlppcDAAAA28b0cgB1xvd96yVkLN/3VVJSQgaGyMAW9bdHBvbIwBb1txf2DGi6AVSLE2LsOOcUi8XIwBAZ2KL+9sjAHhnYov72wp4BTTcAAAAAAElC0w0AAAAAQJLQdAOoVlgnRaaDSCSioqIiMjBEBraovz0ysEcGtqi/vbBnwPRyAFViejkAAACwbUwvB1BnwjopMh34vq85c+aQgSEysEX97ZGBPTKwRf3thT0Dmm4A1eKEGDvOOZWXl5OBITKwRf3tkYE9MrBF/e2FPQOabgAAAAAAkoSmGwAAAACAJKHpBlCtaDRqvYSMFY1G1bFjRzIwRAa2qL89MrBHBraov72wZ8D0cgBVYno5AAAAsG1MLwdQZyorK62XkLEqKytVXFxMBobIwBb1t0cG9sjAFvW3F/YMaLoBIMWF9esx0gkZ2KL+9sjAHhnYov72wpwBTTcAAAAAAElC0w0AAAAAQJIwSA1AlbYMhygrK1NhYaH1cjKSc07l5eXKy8uT53nWy8lIZGCL+tsjA3tkYIv620vVDBikBgBpIicnx3oJGY8MbFF/e2RgjwxsUX97Yc6AphtAtcI8uCLsfN/XzJkzycAQGdii/vbIwB4Z2KL+9sKeAU03AAAAAABJQtMNAAAAAECS0HQDAAAAAJAkTC8HUCWml9tzzsn3fUWj0ZSa1plJyMAW9bdHBvbIwBb1t5eqGTC9HADSREVFhfUSMh4Z2KL+9sjAHhnYov72wpwBTTeAaoV1UmQ68H1fc+fOJQNDZGCL+tsjA3tkYIv62wt7BjTdAAAAAAAkCU03AAAAAABJQtMNACkuGo1aLyHjkYEt6m+PDOyRgS3qby/MGTC9HECVajqREQAAAMg0TC8HUGf425wd55zKysrIwBAZ2KL+9sjAHhnYov72wp4BTTeAaoV1UmQ68H1f8+fPJwNDZGCL+tsjA3tkYIv62wt7BjTdAAAAAAAkCU03AAAAAABJQtMNoFqe51kvIWN5nqe8vDwyMEQGtqi/PTKwRwa2qL+9sGfA9HIAVWJ6OQAAALBtTC8HUGeCILBeQsYKgkCrVq0iA0NkYIv62yMDe2Rgi/rbC3sGNN0AqhXWF7h0EASBlixZQgaGyMAW9bdHBvbIwBb1txf2DGi6AQAAAABIEppuAAAAAACShKYbQLXCOikyHXiep8LCQjIwRAa2qL89MrBHBraov72wZ8D0cgBVYno5AAAAsG1MLwdQZ8I6tCIdBEGgFStWkIEhMrBF/e2RgT0ysEX97YU9A5puANUK6wtcOgj7PzLpgAxsUX97ZGCPDGxRf3thz4CmGwAAAACAJKHpBgAAAAAgSWi6AVQrEuGlwkokElGzZs3IwBAZ2KL+9sjAHhnYov72wp4B08sBVInp5QAAAMC2Mb0cQJ0J69CKdBAEgUpLS8nAEBnYov72yMAeGdii/vbCngFNN4BqhfUFLh0EQaDVq1eTgSEysEX97ZGBPTKwRf3thT0Dmm4AAAAAAJKEphsAAAAAgCTJsl4AgNTXrFlEIT2bJ/Si0Yj69GmtGTMi8n3r1WQmMrBF/e2Rgb1MziAVRj5HIhG1bt06tJOz00HYM2B6OYAqbZnIKMUkMb0cAAD8vOhUkMqYXg6gzmRlZdif1VNIdravAQNKlJ1NBlbIwBb1t0cG9sjAlu/7KikpkZ9ppxmkkLBnQNMNoFqex5+ZrXieU1FRjAwMkYEt6m+PDOyRgS3nnGKxmDhB2E7YM6DpBgAAAAAgSWi6AQAAAABIEppuANWqrOSlwkplZUSvvVZEBobIwBb1t0cG9sjAViQSUVFRUWgnZ6eDsGfA9HIAVWJ6OQAAsESnglTG9HIAdYbp5Xays30NHTqHibWGyMAW9bdHBvbIwJbv+5ozZ05oJ2eng7BnQNMNoFpMS7XjeU5Nm5aTgSEysEX97ZGBPTKw5ZxTeXl5aCdnp4OwZ0DTDQAAAABAktB0AwAAAACQJAxSA1ClLcMhPK9MzhVaLycjRSJO7drFtGxZoYLAs15ORiIDW9TfHhnYy+QMUqFTcc4pFov9/+9EmVX/VJGqGdR0kBpNN4AqMb0cAABYolNBKmN6OYA6k51dab2EjJWTU6mRI4uVk0MGVsjAFvW3Rwb2yMBWZWWliouLVVlJ/a2EPQOabgBIcTk54fx6jHRCBraovz0ysEcGtsL6VVXpJMwZ0HQDAAAAAJAkNN0AAAAAACQJg9QAVOm/g9TKJDG93ILnOTVpUq5vv82Tc6kzrTOTkIEt6m+PDOxlcgap0Kk451ReXq68vLyUmpydSVI1AwapAUAacE5avz4nJX7pyFRkYIv62yMDe2RgLycnx3oJGS/MGdB0A6hWdnZ4B1eEXU6Or5EjZzJAxxAZ2KL+9sjAHhnY8n1fM2fODPUgr7ALewY03QAAAAAAJAlNNwAAAAAASULTDQAAAABAkjC9HECVmF6eCpxycnxVVEQlpc60zsxCBraovz0ysJe5GaRCp+Kck+/7ikajKTU5O5OkagZMLweANOB5UsOGFUqhf18yDhnYov72yMAeGdirqKiwXkLGC3MGNN0AqsX0cjvZ2b6GDp1LBobIwBb1t0cG9sjAlu/7mjt3bmgnZ6eDsGdA0w0AAAAAQJLQdAMAAAAAkCQ03QCQ4n4cnANLZGCL+tsjA3tkYCsapf7WwpxBaJvu0aNHq3v37ts95ogjjtCIESPq9HHbtWun+++/v07v8+e0bNkyeZ6n2bNnS5LeffddeZ6nsrKyGt9H2GtQleqe1//WbkfV5Gc31WzenGW9hIxVUZGlu+/urYoKMrBCBraovz0ysEcGtrKystS7d29lZVF/K2HPwLzpHjx4sDzP22rr379//BjP8/Tiiy/W+r4nTZqkW265pQ5Xm3w7+lx31MEHH6yVK1f+/9dC1a2bb75Z55xzTp3f7856/PHHtcsuu9T6dm3atNHKlSu177771v2iUpznpcD3dWSoSMSpqKhMkQgZWCEDW9TfHhnYIwNbzjmVlZWJb1q2E/YMzJtuSerfv79WrlyZsP3973/f6ftt3LixGjRoUAcrTC11OS4/JydHLVq0SMr33b300ks6+eST6/x+q+L7voIgSNr9R6NRtWjRIul/YUvFr0PIygrnpMh0kJXla8CA+WRgiAxsUX97ZGCPDGz5vq/58+eHdnJ2Ogh7BinRdOfm5qpFixYJW6NGjST9eMqvJJ122mnyPC9+eYunnnpK7dq1U2Fhoc4++2x999138ev+9/Tydu3a6fbbb9f555+vBg0aaPfdd9ejjz4av/6oo47SsGHDEu5/9erVysnJ0dSpU2v8fB577DF16tRJ9erVU8eOHfXggw/Gr6uoqNCwYcPUsmVL1atXT23bttWYMWO2+1y3nI782GOPaY899lC9evUkSW+88YYOOeQQ7bLLLmrSpIlOPPFElZaWJqzlo48+Uo8ePVSvXj316tVLn3zyScL12zq9fOLEidpnn32Um5urdu3aaezYsTV+7lt8+eWX+uyzzxLOWPipwYMH69RTT9U999yjli1bqkmTJrrsssu0efPm+DHr1q3Teeedp0aNGql+/fo67rjjtGjRovj1W96xfvnll9W5c2fl5ubqiy++0KZNm3TNNdeoVatWys/P1wEHHKB33303/nx//etfKxaLxc+qGD16dPw+v//++yp/Pqo6NX/q1Knq1auX6tevr4MPPlgLFixIeK533HGHmjdvrgYNGmjIkCH64YcftlmL2267Tbvttps6dOggSZo3b56OOuoo5eXlqUmTJrrooou0YcOGWtVwe7UAAAAAkHwp0XRvT3FxsSRp/PjxWrlyZfyyJJWWlurFF1/Uq6++qldffVXTp0/XHXfcsd37Gzt2bLz5vPTSS3XJJZfEm6QLLrhATz/9tDZt2hQ//m9/+5tatWqlo446qkbrnTBhgn73u9/ptttuU0lJiW6//XbddNNNeuKJJyRJf/jDH/Tyyy/rueee04IFCzRhwoR4c72957p48WJNnDhRkyZNijd9Gzdu1FVXXaWZM2dq6tSpikQiOu200+Lv9m7YsEEnnniiOnfurI8//lijR4/WNddcs931f/zxxzrzzDN19tlna968eRo9erRuuukmPf744zV6/lu8/PLLOuKII9SwYcMqj5k2bZpKS0s1bdo0PfHEE3r88ccTHmfw4MGaOXOmXn75ZX344Ydyzun4449PaCq///573XnnnXrsscf02Wefadddd9WwYcP04Ycf6plnntHcuXN1xhlnqH///lq0aJEOPvhg3X///WrYsGH8rIqf1mR7Px9VueGGGzR27FjNnDlTWVlZOv/88+PXPffccxo9erRuv/12zZw5Uy1btkz4I8wWU6dO1YIFCzRlyhS9+uqr2rhxo/r166dGjRqpuLhYzz//vN5+++2t/ihUXQ23V4tt2bRpk9avX5+wAQAAANgJztigQYNcNBp1+fn5Cdttt90WP0aSe+GFFxJuN2rUKFe/fn23fv36+L6RI0e6Aw44IH758MMPd1dccUX8ctu2bd0555wTvxwEgdt1113dQw895Jxzrry83DVq1Mg9++yz8WO6du3qRo8enXAf9913X5XPp3379u7pp59O2HfLLbe4gw46yDnn3OWXX+6OOuooFwTBNm9f1XPNzs52q1atqvJxnXNu9erVTpKbN2+ec865Rx55xDVp0sSVl5fHj3nooYecJPfJJ58455ybNm2ak+TWrVvnnHPuV7/6lTvmmGMS7nfkyJGuc+fO8cvV1cA554455hj3pz/9qcrrBw0a5Nq2besqKyvj+8444wx31llnOeecW7hwoZPkZsyYEb9+zZo1Li8vzz333HPOOefGjx/vJLnZs2fHj1m+fLmLRqPuq6++Sni8o48+2l1//fXx2xUWFm61pup+PpYuXbrN2r399tvx27z22mtOUrzmBx10kLv00ksTHueAAw5w3bp1S6hF8+bN3aZNm+L7Hn30UdeoUSO3YcOGhPuORCLuP//5T41qWJNa/K9Ro0Y5SVttWVlrneTYDLbs7Eo3dOhsl51dab6WTN3IgPpn+kYG9lsmZ5AKKisr3ezZsxN+58LPK1UziMViTpKLxWLbPS4l3uk+8sgjNXv27ITt4osvrvZ27dq1S/jMdsuWLbVq1art3qZr167x//Y8Ty1atIjfpl69ejr33HP117/+VZI0a9Ysffrppxo8eHCNnsfGjRtVWlqqIUOGqKCgIL7deuut8dO+Bw8erNmzZ6tDhw4aPny43nrrrRrdd9u2bdWsWbOEfYsWLdKAAQNUVFSkhg0bxt8x/+KLLyRJJSUl6tq1a/x0dEk66KCDtvs4JSUl6tOnT8K+Pn36aNGiRTX+DMX69es1ffr0aj/Pvc8++ySM/v9pfiUlJcrKytIBBxwQv75Jkybq0KGDSkpK4vtycnISMp03b55839fee++dkMH06dO3OvV+W7b381GT27Rs2VKSEp7HT5+DtO0MunTpopycnPjlkpISdevWTfn5+fF9ffr0URAECe+8b6+GO1KL66+/XrFYLL59+eWXkqTKyvB+RUPYbd4c1SOPdNPmzWRghQxsUX97ZGCPDGxFo1F169Yt1F9ZFXZhzyAlZq7n5+drzz33rPXtsrOzEy57nlftIK3qbnPBBReoe/fuWrFihcaPH6+jjjpKbdu2rdF6tnzedty4cVs1Wlt+QHr27KmlS5dq8uTJevvtt3XmmWeqb9+++sc//rHd+/5p87XFSSedpLZt22rcuHHabbfdFASB9t13X/NBXJMnT1bnzp3Vpk2b7R63I/n9r7y8vIQhcBs2bFA0GtXHH3+81f+UBQUF1d7fzv5MbVlLbZ/HtvKtie2td0dqkZubq9zc3K32e14g53ZoidhJkUigrl3XaO7cpgqClPg7acYhA1vU3x4Z2CMDW0EQaM2aNWratKkiEepvIewZhGLF2dnZP9ukui5duqhXr14aN26cnn766YTP51anefPm2m233bRkyRLtueeeCdsee+wRP65hw4Y666yzNG7cOD377LOaOHGi1q5dK6nmz/Xbb7/VggULdOONN+roo49Wp06dtG7duoRjOnXqpLlz5yYM7vrXv/613fvt1KmTZsyYkbBvxowZ2nvvvWv8l6WXXnpJp5xySo2O3d46Kisr9e9//zu+b8tz7ty5c5W369Gjh3zf16pVq7bKoEWLFpJ+fHf85/p56tSpU8JzkKrPYMvt5syZo40bN8b3zZgxQ5FIJD5orTo1qUVNZWUlbyo8ti8rK9AJJywhA0NkYIv62yMDe2RgKwgCLVmyJKnfkoPtC3sGKdF0b9q0Sf/5z38StjVr1sSvb9eunaZOnar//Oc/WzWWyXDBBRfojjvukHNOp512Wq1ue/PNN2vMmDH6wx/+oIULF2revHkaP3687r33XknSvffeq7///e+aP3++Fi5cqOeff14tWrSIf290TZ9ro0aN1KRJEz366KNavHix3nnnHV111VUJx/zqV7+S53m68MIL9fnnn+v111/XPffcs931X3311Zo6dapuueUWLVy4UE888YT+9Kc/VTuAbYvKykpNnjx5p78qbK+99tIpp5yiCy+8UO+//77mzJmjc845R61atdpuQ7/33ntr4MCBOu+88zRp0iQtXbpUH330kcaMGaPXXntN0o813rBhg6ZOnao1a9bo+++/36m1bs8VV1yhv/71rxo/frwWLlyoUaNG6bPPPqv2dgMHDlS9evU0aNAgffrpp5o2bZouv/xynXvuuWrevHmNHrsmtQAAAACQXCnRdL/xxhtq2bJlwnbIIYfErx87dqymTJmiNm3aqEePHklfz4ABA5SVlaUBAwYkfB66Ji644AI99thjGj9+vLp06aLDDz9cjz/+ePyd7gYNGuiuu+5Sr1691Lt3by1btkyvv/56/DSJmj7XSCSiZ555Rh9//LH23XdfXXnllbr77rsTjikoKNArr7yiefPmqUePHrrhhht05513bnf9PXv21HPPPadnnnlG++67r373u9/p97//fY0/1z59+nQVFBSoZ8+eNTp+e8aPH6/99ttPJ554og466CA55/T6669vdUr1tm533nnn6eqrr1aHDh106qmnqri4WLvvvrsk6eCDD9bFF1+ss846S82aNdNdd92102utyllnnaWbbrpJv/nNb7Tffvtp+fLluuSSS6q9Xf369fXmm29q7dq16t27t04//XQdffTR+tOf/lSrx6+uFgAAAACSy3OOT2r+r2XLlql9+/YqLi6uk+YxkwwfPlyVlZXb/FoshM/69etVWFiorKy1qqxsZL2cjJSd7ev00xfqH//YmwE6RsjAFvW3Rwb2MjmDVOhUfN/XwoULa/VxS9StVM1gy+/KsVhsu1+VTNP9E5s3b9a3336ra665RkuXLt3qs82o3qOPPqqDDjpIXbp0sV4K6sCWFxIpJqnqFxIAAIBkoFNBKqtp050Sp5enihkzZqhly5YqLi7Www8/bL2cULroootouNNQJBLOoRXpIBoNdNhhKxSNkoEVMrBF/e2RgT0ysBUEgVasWBHaIV7pIOwZ0HT/xBFHHCHnnBYsWEDjCPwE/8jbiUYDHXoov2hZIgNb1N8eGdgjA1thb/jSQdgzoOkGAAAAACBJaLoBAAAAAEgSmm4A1QoCXiqsBEFEs2c3IwNDZGCL+tsjA3tkYCsSiahZs2bxr/jFzy/sGTC9HECVmF4OAAAs0akglTG9HECdYXCLnaysQCecUKqsLDKwQga2qL89MrBHBraCIFBpaWloh3ilg7BnQNMNoFp8ZZidSCRQ9+6rycAQGdii/vbIwB4Z2AqCQKtXrw5tw5cOwp4BTTcAAAAAAElC0w0AAAAAQJLQdAOolu/zUmHF9yN6773WZGCIDGxRf3tkYI8MbEUiEbVu3Tq0k7PTQdgzYHo5gCoxvRwAAFiiU0EqY3o5gDqTleVbLyFjZWf7GjCgRNnZZGCFDGxRf3tkYI8MbPm+r5KSEvk+9bcS9gxougFUy/P4M7MVz3MqKoqRgSEysEX97ZGBPTKw5ZxTLBYTJwjbCXsGNN0AAAAAACQJTTcAAAAAAElC0w2gWpWVvFRYqayM6LXXisjAEBnYov72yMAeGdiKRCIqKioK7eTsdBD2DJheDqBKTC8HAACW6FSQypheDqDOML3cTna2r/9r796Do6zPNo5fmyw5CEkgQCARJJEzYpCEwEAiUAkK0gxhFGkaxqDQFgcPQCliWwuj1TBYarBaTk3BTKXgWBNaaGASBBSqkkSCKMgZAxIIpRyyUQvJPv0D2Xf2hTRgXX7Zfb6fmcy4h+xz732NCzfP7r0/+ckuNtYaRAZm0X/zyMA8MjCroaFBu3bt8tvN2YHA3zNg6AbQJLalmuNwWGrX7isyMIgMzKL/5pGBeWRglmVZ+uqrr/x2c3Yg8PcMGLoBAAAAAPARhm4AAAAAAHyERWoAGnVlOYTDcU6WFWW6HFsKCrIUH39eR49Gye12mC7HlsjALPpvHhmYZ+cMmsOkYlmWzp8//83fiezV/+aiuWZwvYvUGLoBNIrt5QAAwCQmFTRnbC8H8J1p0aLedAm2FRJSr5/9rEwhIWRgChmYRf/NIwPzyMCs+vp6lZWVqb6e/pvi7xkwdANAMxcS4p9fjxFIyMAs+m8eGZhHBmb561dVBRJ/zoChGwAAAAAAH2HoBgAAAADAR1ikBqBRV5ZDnDt37puFarjZLMvSV199pfDw8Ga1rdNOyMAs+m8eGZhHBmbRf/OaawYsUgOAABESEmK6BNsjA7Pov3lkYB4ZmEX/zfPnDBi6ATTJnxdX+LuGhgaVl5eTgUFkYBb9N48MzCMDs+i/ef6eAUM3AAAAAAA+wtANAAAAAICPMHQDAAAAAOAjbC8H0Ci2l5tnWZYaGhoUHBzcrLZ12gkZmEX/zSMD88jALPpvXnPNgO3lABAgLl68aLoE2yMDs+i/eWRgHhmYRf/N8+cMGLoBNMlfN0UGgoaGBn388cdkYBAZmEX/zSMD88jALPpvnr9nwNANAAAAAICPMHQDAAAAAOAjDN0A0MwFBwebLsH2yMAs+m8eGZhHBmbRf/P8OQO2lwNo1PVuZAQAAADshu3lAL4z/NucOZZl6dy5c2RgEBmYRf/NIwPzyMAs+m+ev2fA0A2gSf66KTIQNDQ06LPPPiMDg8jALPpvHhmYRwZm0X/z/D0Dhm4AAAAAAHyEoRsAAAAAAB9h6AbQJIfDYboE23I4HAoPDycDg8jALPpvHhmYRwZm0X/z/D0DtpcDaBTbywEAAIBrY3s5gO+M2+02XYJtud1u1dTUkIFBZGAW/TePDMwjA7Pov3n+ngFDN4Am+esLXCBwu906fPgwGRhEBmbRf/PIwDwyMIv+m+fvGTB0AwAAAADgIwzdAAAAAAD4CEM3gCb566bIQOBwOBQVFUUGBpGBWfTfPDIwjwzMov/m+XsGbC8H0Ci2lwMAAADXxvZyAN8Zf11aEQjcbreOHz9OBgaRgVn03zwyMI8MzKL/5vl7BgzdAJrkry9wgcDf/5AJBGRgFv03jwzMIwOz6L95/p4BQzcAAAAAAD7C0A0AAAAAgI8wdANoUlAQLxWmBAUFqX379mRgEBmYRf/NIwPzyMAs+m+ev2fA9nIAjWJ7OQAAAHBtbC8H8J3x16UVgcDtduvQoUNkYBAZmEX/zSMD88jALPpvnr9nwNANoEn++gIXCNxut06fPk0GBpGBWfTfPDIwjwzMov/m+XsGDN0AAAAAAPiI03QBAJqvKysfLly4IKeTlwsT6uvrVVdXRwYGkYFZ9N88MjCPDMyi/+Y11wwuXLgg6f/+ztyY5lMxgGbnzJkzkqSEhATDlQAAAADNU21traKiohq9naEbQKOio6MlSVVVVf/1hQS+c+HCBXXu3FnHjh1jg7whZGAW/TePDMwjA7Pov3nNNQPLslRbW6u4uLj/ej+GbgCNuvJdiFFRUc3qBc6OIiMjycAwMjCL/ptHBuaRgVn037zmmMH1nJhikRoAAAAAAD7C0A0AAAAAgI8wdANoVGhoqObOnavQ0FDTpdgWGZhHBmbRf/PIwDwyMIv+m+fvGTispvabAwAAAACAb4Uz3QAAAAAA+AhDNwAAAAAAPsLQDQAAAACAjzB0A2jUa6+9pvj4eIWFhWnQoEHasWOH6ZJs491331VGRobi4uLkcDhUVFRkuiRbyc3NVUpKiiIiIhQTE6PMzEzt27fPdFm2snjxYiUmJnq+k3Xw4MEqLi42XZZtzZ8/Xw6HQ9OnTzddim3MmzdPDofD66dXr16my7KdL774QhMnTlTbtm0VHh6uO++8U+Xl5abLso34+Pir/j9wOByaNm2a6dJuCEM3gGtas2aNZs6cqblz5+qjjz5Sv379dN9996mmpsZ0abZQV1enfv366bXXXjNdii1t3bpV06ZN0wcffKCSkhJdunRJ9957r+rq6kyXZhudOnXS/PnzVVFRofLyct1zzz0aO3asPv30U9Ol2U5ZWZmWLl2qxMRE06XYzh133KHq6mrPz7Zt20yXZCtnz55VamqqWrRooeLiYu3Zs0cLFy5UmzZtTJdmG2VlZV7/D5SUlEiSxo8fb7iyG8P2cgDXNGjQIKWkpOjVV1+VJLndbnXu3FlPPPGE5syZY7g6e3E4HCosLFRmZqbpUmzr9OnTiomJ0datWzV06FDT5dhWdHS0XnrpJU2ePNl0KbbhcrmUlJSk3//+9/r1r3+tu+66S3l5eabLsoV58+apqKhIlZWVpkuxrTlz5mj79u167733TJeCb0yfPl3r1q3TgQMH5HA4TJdz3TjTDeAqFy9eVEVFhdLT0z3XBQUFKT09Xe+//77BygAzzp8/L+ny0Iebr6GhQatXr1ZdXZ0GDx5suhxbmTZtmsaMGeP15wFungMHDiguLk633367srOzVVVVZbokW/nrX/+qAQMGaPz48YqJiVH//v21fPly02XZ1sWLF/WnP/1Jjz76qF8N3BJDN4Br+Oc//6mGhgZ16NDB6/oOHTro5MmThqoCzHC73Zo+fbpSU1PVt29f0+XYyu7du9WqVSuFhoZq6tSpKiwsVJ8+fUyXZRurV6/WRx99pNzcXNOl2NKgQYO0cuVKbdiwQYsXL9aRI0d09913q7a21nRptnH48GEtXrxY3bt318aNG/XYY4/pySef1Ouvv266NFsqKirSuXPnNGnSJNOl3DCn6QIAAGjOpk2bpk8++YTPUhrQs2dPVVZW6vz583rrrbeUk5OjrVu3MnjfBMeOHdNTTz2lkpIShYWFmS7HlkaPHu3578TERA0aNEhdunTRm2++yUcsbhK3260BAwboxRdflCT1799fn3zyiZYsWaKcnBzD1dlPfn6+Ro8erbi4ONOl3DDOdAO4Srt27RQcHKxTp055XX/q1Cl17NjRUFXAzff4449r3bp12rx5szp16mS6HNsJCQlRt27dlJycrNzcXPXr10+LFi0yXZYtVFRUqKamRklJSXI6nXI6ndq6dateeeUVOZ1ONTQ0mC7Rdlq3bq0ePXro4MGDpkuxjdjY2Kv+ka937968zd+Azz//XKWlpZoyZYrpUr4Vhm4AVwkJCVFycrI2bdrkuc7tdmvTpk18nhK2YFmWHn/8cRUWFuqdd95RQkKC6ZKgy69D//73v02XYQsjRozQ7t27VVlZ6fkZMGCAsrOzVVlZqeDgYNMl2o7L5dKhQ4cUGxtruhTbSE1NverrIvfv368uXboYqsi+VqxYoZiYGI0ZM8Z0Kd8Kby8HcE0zZ85UTk6OBgwYoIEDByovL091dXV65JFHTJdmCy6Xy+tsxpEjR1RZWano6GjddtttBiuzh2nTpmnVqlVau3atIiIiPLsMoqKiFB4ebrg6e3jmmWc0evRo3XbbbaqtrdWqVau0ZcsWbdy40XRpthAREXHVDoOWLVuqbdu27Da4SWbNmqWMjAx16dJFJ06c0Ny5cxUcHKysrCzTpdnGjBkzNGTIEL344ot66KGHtGPHDi1btkzLli0zXZqtuN1urVixQjk5OXI6/XN89c+qAfjchAkTdPr0af3qV7/SyZMnddddd2nDhg1XLVeDb5SXl+t73/ue5/LMmTMlSTk5OVq5cqWhquxj8eLFkqThw4d7Xb9ixQq/XODij2pqavTwww+rurpaUVFRSkxM1MaNGzVy5EjTpQE3xfHjx5WVlaUzZ86offv2SktL0wcffKD27dubLs02UlJSVFhYqGeeeUbPPfecEhISlJeXp+zsbNOl2Uppaamqqqr06KOPmi7lW+N7ugEAAAAA8BE+0w0AAAAAgI8wdAMAAAAA4CMM3QAAAAAA+AhDNwAAAAAAPsLQDQAAAACAjzB0AwAAAADgIwzdAAAAAAD4CEM3AAAAAAA+wtANAACA6zZp0iRlZmaaLgMA/AZDNwAACHjvv/++goODNWbMGNOl3BQOh8PzExkZqZSUFK1du/aGHuPo0aNyOByqrKz0un7RokVauXLld1csAAQ4hm4AABDw8vPz9cQTT+jdd9/ViRMnfHosy7JUX1/v02NcjxUrVqi6ulrl5eVKTU3Vgw8+qN27d//PjxsVFaXWrVv/7wUCgE0wdAMAgIDmcrm0Zs0aPfbYYxozZozXWdof/vCHmjBhgtf9L126pHbt2qmgoECS5Ha7lZubq4SEBIWHh6tfv3566623PPffsmWLHA6HiouLlZycrNDQUG3btk2HDh3S2LFj1aFDB7Vq1UopKSkqLS31OlZ1dbXGjBmj8PBwJSQkaNWqVYqPj1deXp7nPufOndOUKVPUvn17RUZG6p577tGuXbuafN6tW7dWx44d1aNHDz3//POqr6/X5s2bPbdv2LBBaWlpat26tdq2bavvf//7OnTokOf2hIQESVL//v3lcDg0fPhwSVe/vXz48OF68sknNXv2bEVHR6tjx46aN2+eVy2fffaZ0tLSFBYWpj59+qi0tFQOh0NFRUVNPg8A8HcM3QAAIKC9+eab6tWrl3r27KmJEyfqj3/8oyzLkiRlZ2frb3/7m1wul+f+Gzdu1Jdffqlx48ZJknJzc1VQUKAlS5bo008/1YwZMzRx4kRt3brV6zhz5szR/PnztXfvXiUmJsrlcun+++/Xpk2btHPnTo0aNUoZGRmqqqry/M7DDz+sEydOaMuWLfrLX/6iZcuWqaamxutxx48fr5qaGhUXF6uiokJJSUkaMWKE/vWvf13X86+vr1d+fr4kKSQkxHN9XV2dZs6cqfLycm3atElBQUEaN26c3G63JGnHjh2SpNLSUlVXV+vtt99u9Bivv/66WrZsqQ8//FALFizQc889p5KSEklSQ0ODMjMzdcstt+jDDz/UsmXL9Itf/OK6ageAgGABAAAEsCFDhlh5eXmWZVnWpUuXrHbt2lmbN2/2ulxQUOC5f1ZWljVhwgTLsizr66+/tm655RbrH//4h9djTp482crKyrIsy7I2b95sSbKKioqarOWOO+6wfve731mWZVl79+61JFllZWWe2w8cOGBJsl5++WXLsizrvffesyIjI62vv/7a63G6du1qLV26tNHjSLLCwsKsli1bWkFBQZYkKz4+3jpz5kyjv3P69GlLkrV7927LsizryJEjliRr586dXvfLycmxxo4d67k8bNgwKy0tzes+KSkp1tNPP21ZlmUVFxdbTqfTqq6u9txeUlJiSbIKCwsbrQcAAgVnugEAQMDat2+fduzYoaysLEmS0+nUhAkTPGd+nU6nHnroIb3xxhuSLp/9Xbt2rbKzsyVJBw8e1JdffqmRI0eqVatWnp+CggKvt2JL0oABA7wuu1wuzZo1S71791br1q3VqlUr7d2713Ome9++fXI6nUpKSvL8Trdu3dSmTRvP5V27dsnlcqlt27Zexz9y5MhVx///Xn75ZVVWVqq4uFh9+vTRH/7wB0VHR3tuP3DggLKysnT77bcrMjJS8fHxkuR1Jv56JSYmel2OjY31nLHft2+fOnfurI4dO3puHzhw4A0fAwD8ldN0AQAAAL6Sn5+v+vp6xcXFea6zLEuhoaF69dVXFRUVpezsbA0bNkw1NTUqKSlReHi4Ro0aJUmet52vX79et956q9djh4aGel1u2bKl1+VZs2appKREv/nNb9StWzeFh4frwQcf1MWLF6+7fpfLpdjYWG3ZsuWq25paZtaxY0d169ZN3bp104oVK3T//fdrz549iomJkSRlZGSoS5cuWr58ueLi4uR2u9W3b98bqu+KFi1aeF12OByet6kDgN0xdAMAgIBUX1+vgoICLVy4UPfee6/XbZmZmfrzn/+sqVOnasiQIercubPWrFmj4uJijR8/3jNE9unTR6GhoaqqqtKwYcNu6Pjbt2/XpEmTPJ8Nd7lcOnr0qOf2nj17qr6+Xjt37lRycrKky2fWz54967lPUlKSTp48KafT6TkT/W0MHDhQycnJeuGFF7Ro0SKdOXNG+/bt0/Lly3X33XdLkrZt2+b1O1c+/93Q0PCtjytdfp7Hjh3TqVOn1KFDB0lSWVnZ//SYAOBPGLoBAEBAWrdunc6ePavJkycrKirK67YHHnhA+fn5mjp1qqTLW8yXLFmi/fv3e234joiI0KxZszRjxgy53W6lpaXp/Pnz2r59uyIjI5WTk9Po8bt37663335bGRkZcjgcevbZZ73O/vbq1Uvp6en68Y9/rMWLF6tFixb66U9/qvDwcDkcDklSenq6Bg8erMzMTC1YsEA9evTQiRMntH79eo0bN+6qt7T/N9OnT9e4ceM0e/ZsxcbGqm3btlq2bJliY2NVVVWlOXPmeN0/JiZG4eHh2rBhgzp16qSwsLCr+ng9Ro4cqa5duyonJ0cLFixQbW2tfvnLX0qS53kCQCDjM90AACAg5efnKz09/ZqD4gMPPKDy8nJ9/PHHki5vMd+zZ49uvfVWpaamet33+eef17PPPqvc3Fz17t1bo0aN0vr16z1fqdWY3/72t2rTpo2GDBmijIwM3XfffV6f35akgoICdejQQUOHDtW4ceP0ox/9SBEREQoLC5N0eSj9+9//rqFDh+qRRx5Rjx499IMf/ECff/6556zx9Ro1apQSEhL0wgsvKCgoSKtXr1ZFRYX69u2rGTNm6KWXXvK6v9Pp1CuvvKKlS5cqLi5OY8eOvaHjXREcHKyioiK5XC6lpKRoypQpnu3lV54nAAQyh2V9850ZAAAAMOr48ePq3LmzSktLNWLECNPl+Mz27duVlpamgwcPqmvXrqbLAQCfYugGAAAw5J133pHL5dKdd96p6upqzZ49W1988YX2799/1XIyf1ZYWKhWrVqpe/fuOnjwoJ566im1adPmqs+RA0Ag4jPdAAAAhly6dEk///nPdfjwYUVERGjIkCF64403AmrglqTa2lo9/fTTqqqqUrt27ZSenq6FCxeaLgsAbgrOdAMAAAAA4CMsUgMAAAAAwEcYugEAAAAA8BGGbgAAAAAAfIShGwAAAAAAH2HoBgAAAADARxi6AQAAAADwEYZuAAAAAAB8hKEbAAAAAAAfYegGAAAAAMBH/gPLw/4jkEQ3SQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Group by drug name and calculate the mean rating\n",
        "mean_ratings = df.groupby('drugName')['rating'].mean()\n",
        "\n",
        "# Plot the mean ratings\n",
        "plt.figure(figsize=(10, 8))\n",
        "mean_ratings.sort_values().plot(kind='barh', color='blue')\n",
        "plt.title('Average Ratings of Drugs')\n",
        "plt.xlabel('Average Rating')\n",
        "plt.ylabel('Drug Name')\n",
        "plt.grid(axis='x', linestyle='--', alpha=0.7)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Group by drug name and get value counts\n",
        "value_counts = df['drugName'].value_counts()\n",
        "\n",
        "# Plot the value counts\n",
        "plt.figure(figsize=(10, 8))\n",
        "value_counts.sort_values().plot(kind='barh', color='blue')\n",
        "plt.title('Value Counts of Drugs')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Drug Name')\n",
        "plt.grid(axis='x', linestyle='--', alpha=0.7)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rso2h5PuTIzP"
      },
      "source": [
        "## Sentiment Analysis With Fine-Tuned DistilBert"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnZYI9j1TIzP"
      },
      "source": [
        "### Text Preprocessing\n",
        "- No extensive steps were taken since the transformers pipeline was used, which automatically handles tokenization and other preprocessing steps."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T5QJRCq7pJFt"
      },
      "outputs": [],
      "source": [
        "# Function to clean text\n",
        "def clean_text(text):\n",
        "    # Remove HTML entities like '&#039 found in most of the reviews;'\n",
        "    text = re.sub(r'&#\\d+;', '', text)  # Remove HTML numeric character references\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xdpgNeoLpJ5n"
      },
      "outputs": [],
      "source": [
        "# Applying the function to the review column\n",
        "df['review'] = df['review'].apply(clean_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "SUxIFMM9pyRL",
        "outputId": "1abe185f-ed0b-4be5-fd9c-89126a6230bd"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\"I had been on the pill for many years. When my doctor changed my RX to chateal, it was as effective. It really did help me by completely clearing my acne, this takes about 6 months though. I did not gain extra weight, or develop any emotional health issues. I stopped taking it bc I started using a more natural method of birth control, but started to take it bc I hate that my acne came back at age 28. I really hope symptoms like depression, or weight gain do not begin to affect me as I am older now. Im also naturally moody, so this may worsen things. I was in a negative mental rut today. Also I hope this doesnt push me over the edge, as I believe I am depressed. Hopefully itll be just like when I was younger.\"'"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# View the cleaned review\n",
        "df['review'].iloc[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Q3QBleHTIzQ"
      },
      "source": [
        "### Sentiment Analysis Pipeline\n",
        "- Each review was processed using the distilbert-base-uncased-finetuned-sst-2-english pipeline to predict sentiment. Confidence scores were also obtained to ascertain the predictions."
      ]
    },
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "        element.innerHTML = '';\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-865a4a28-efe0-4f0d-96da-720ed208d4ca button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                 drugName      condition  \\\n",
              "108166                       Etonogestrel  Birth Control   \n",
              "160800                          Nexplanon  Birth Control   \n",
              "212792  Ethinyl estradiol / norethindrone  Birth Control   \n",
              "98158                           Nexplanon  Birth Control   \n",
              "213265                       Etonogestrel  Birth Control   \n",
              "7716                         Etonogestrel  Birth Control   \n",
              "5487                       Levonorgestrel  Birth Control   \n",
              "94972                           Nexplanon  Birth Control   \n",
              "89063                           Nexplanon  Birth Control   \n",
              "187909                     Levonorgestrel  Birth Control   \n",
              "\n",
              "                                                   review  rating       date  \\\n",
              "108166  \"I had no issues after insertion at six weeks ...       5 2015-12-24   \n",
              "160800  \"I had the original impalanon in for three yea...       1 2016-03-23   \n",
              "212792  \"Ive been on this pill for about 6 months and ...       2 2015-12-21   \n",
              "98158   \"I had Nexplanon inserted a week ago, it was p...      10 2014-04-29   \n",
              "213265  \"I had implanon inserted a few months ago due ...      10 2017-11-06   \n",
              "7716    \"I got nexplanon on Nov 11,2014 mostly because...       8 2016-07-01   \n",
              "5487    \"After several failed attempts by two differen...       5 2016-11-22   \n",
              "94972   \"I was on nexplanon for a year. Let me just sa...       1 2017-04-24   \n",
              "89063   \"Ive had this in for a few months and I love i...       8 2017-01-11   \n",
              "187909  \"I am 19, and the obgyn I go to strongly sugge...      10 2014-03-20   \n",
              "\n",
              "        usefulCount sentiment  confidence  \n",
              "108166            1  NEGATIVE    0.973629  \n",
              "160800            3  NEGATIVE    0.999488  \n",
              "212792            4  NEGATIVE    0.990531  \n",
              "98158             7  POSITIVE    0.949550  \n",
              "213265            4  NEGATIVE    0.970334  \n",
              "7716              3  NEGATIVE    0.989419  \n",
              "5487              0  NEGATIVE    0.949211  \n",
              "94972             7  NEGATIVE    0.999303  \n",
              "89063             5  POSITIVE    0.999134  \n",
              "187909           32  POSITIVE    0.974640  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Load the pre-trained sentiment analysis pipeline\n",
        "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "sentiment_analysis = pipeline(\"sentiment-analysis\", model=model_name, truncation=True)\n",
        "\n",
        "# Define a function to get sentiment and confidence scores\n",
        "def analyze_sentiment(text):\n",
        "    result = sentiment_analysis(text)[0]\n",
        "    sentiment = result['label']\n",
        "    confidence = result['score']\n",
        "    return pd.Series([sentiment, confidence], index=['sentiment', 'confidence'])\n",
        "\n",
        "\n",
        "# Apply the sentiment analysis function to the reviews column\n",
        "df[['sentiment', 'confidence']] = df['review'].apply(analyze_sentiment)\n",
        "\n",
        "# Print the DataFrame with new columns\n",
        "df.sample(10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9XyY5MmetaxF"
      },
      "outputs": [],
      "source": [
        "# save the data\n",
        "df.to_csv('drug_reviews.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvjDo_ffTIzR"
      },
      "source": [
        "## LDA Topic Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tumrbfd-TIzR"
      },
      "source": [
        "### Text Preprocessing\n",
        "1. Loaded the `en_core_web_sm` SpaCy model for NLP tasks.\n",
        "2. Converted text to lowercase for uniformity.\n",
        "3. Removed multiple white spaces and line breaks to clean the text.\n",
        "4. Tokenized the text into individual words using NLTK's `word_tokenize`.\n",
        "5. Removed non-alphabetic tokens to keep only words.\n",
        "6. Used NLTK's stopword list and added custom stopwords specific to the context.\n",
        "7. Removed these stopwords from the tokenized text.\n",
        "8. Created bigrams and trigrams using Gensim's `Phrases` and `Phraser`.\n",
        "9. Lemmatized the text using SpaCy, retaining only nouns, adjectives, verbs, and adverbs.\n",
        "10. Applied the defined preprocessing pipeline to the DataFrame's `review` column.\n",
        "11. Printed the first few processed reviews to verify the preprocessing steps.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Dx4Dw9Slvbq"
      },
      "outputs": [],
      "source": [
        "nlp = spacy.load('en_core_web_sm')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocNRsg4U_uFL",
        "outputId": "e7ab727e-33c2-46b9-e7da-6d1f29cb515b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "9     [many, year, doctor, change, chateal, effectiv...\n",
            "14    [start, month, ago, minimal, amount, contracep...\n",
            "22    [job, worry, free, sex, thing, period, sometim...\n",
            "74    [experience, great, yet, issue, cause, much, b...\n",
            "75    [ve, mine, year, noticed, weight, gain, mood, ...\n",
            "Name: processed_reviews, dtype: object\n"
          ]
        }
      ],
      "source": [
        "# Load SpaCy model\n",
        "nlp = spacy.load('en_core_web_sm')\n",
        "\n",
        "# Define preprocessing pipeline\n",
        "def txt_preprocess_pipeline(text):\n",
        "    # Standardize text to lowercase\n",
        "    standard_txt = text.lower()\n",
        "    # Remove multiple white spaces and line breaks\n",
        "    clean_txt = re.sub(r'\\n', ' ', standard_txt)\n",
        "    clean_txt = re.sub(r'\\s+', ' ', clean_txt)\n",
        "    clean_txt = clean_txt.strip()\n",
        "    # Tokenize text\n",
        "    tokens = word_tokenize(clean_txt)\n",
        "    # Remove non-alphabetic tokens\n",
        "    filtered_tokens_alpha = [word for word in tokens if word.isalpha()]\n",
        "    # Load NLTK stopword list and add custom stopwords\n",
        "    stop_words = stopwords.words('english')\n",
        "    stop_words.extend(['rx', 'pill', 'use', 'nexplanon', 'pregnancy', 'nexplon', 'birth control','swing'])\n",
        "    # Remove stopwords\n",
        "    filtered_tokens_final = [w for w in filtered_tokens_alpha if not w in stop_words]\n",
        "\n",
        "    # Create bigrams and trigrams\n",
        "    bigrams = Phrases([filtered_tokens_final], min_count=1, threshold=100)\n",
        "    bigram_mod = Phraser(bigrams)\n",
        "    bigram_tokens = bigram_mod[filtered_tokens_final]\n",
        "\n",
        "    trigrams = Phrases([bigram_tokens], threshold=100)\n",
        "    trigram_mod = Phraser(trigrams)\n",
        "    trigram_tokens = trigram_mod[bigram_tokens]\n",
        "\n",
        "    # Lemmatization using SpaCy, keeping only noun, adj, vb, adv\n",
        "    doc = nlp(' '.join(trigram_tokens))\n",
        "    lemma_tokens = [token.lemma_ for token in doc if token.pos_ in ['NOUN', 'ADJ', 'VERB', 'ADV']]\n",
        "\n",
        "    return lemma_tokens\n",
        "\n",
        "# Apply the preprocessing pipeline to the DataFrame\n",
        "df['processed_reviews'] = df['review'].apply(txt_preprocess_pipeline)\n",
        "\n",
        "# Check the processed reviews\n",
        "print(df['processed_reviews'].head())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ztq3-yBKn1df",
        "outputId": "c4ac4563-02c9-4d86-e58b-a87002745193"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['many', 'year', 'doctor', 'change', 'chateal', 'effective', 'really', 'help', 'completely', 'clear', 'acne', 'take', 'month', 'though', 'gain', 'extra', 'weight', 'develop', 'emotional', 'health', 'issue', 'stop', 'take', 'start', 'use', 'natural', 'method', 'birth', 'control', 'start', 'take', 'come', 'back', 'age', 'really', 'hope', 'symptom', 'depression', 'weight', 'gain', 'begin', 'affect', 'old', 'm', 'also', 'naturally', 'worsen', 'thing', 'negative', 'mental', 'rut', 'today', 'also', 'hope', 'push', 'edge', 'believe', 'depressed', 'hopefully', 'like', 'young']\n"
          ]
        }
      ],
      "source": [
        "# print first lemmatized instance\n",
        "print(df['processed_reviews'].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "eohzJP_1_7Bq",
        "outputId": "2277ad5a-bb47-4749-f34b-b6051a3b0770"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>drugName</th>\n",
              "      <th>condition</th>\n",
              "      <th>review</th>\n",
              "      <th>rating</th>\n",
              "      <th>date</th>\n",
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              "      <th>9</th>\n",
              "      <td>Ethinyl estradiol / levonorgestrel</td>\n",
              "      <td>Birth Control</td>\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2c69421b-b26d-4c1b-b391-58c72065ccb0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                              drugName      condition  \\\n",
              "9   Ethinyl estradiol / levonorgestrel  Birth Control   \n",
              "14                           Nexplanon  Birth Control   \n",
              "22                        Etonogestrel  Birth Control   \n",
              "74                        Etonogestrel  Birth Control   \n",
              "75                           Nexplanon  Birth Control   \n",
              "\n",
              "                                               review  rating       date  \\\n",
              "9   \"I had been on the pill for many years. When m...       8 2016-12-08   \n",
              "14  \"Started Nexplanon 2 months ago because I have...       3 2014-08-07   \n",
              "22  \"Nexplanon does its job. I can have worry free...       9 2014-08-11   \n",
              "74  \"My experience during the nexplon is great not...      10 2016-05-27   \n",
              "75  \"Ive had mine for over a year and noticed the ...       6 2016-06-19   \n",
              "\n",
              "    usefulCount sentiment  confidence  \\\n",
              "9             1  NEGATIVE    0.953042   \n",
              "14           10  NEGATIVE    0.999347   \n",
              "22           11  NEGATIVE    0.997134   \n",
              "74            1  NEGATIVE    0.999737   \n",
              "75            1  POSITIVE    0.802087   \n",
              "\n",
              "                                    processed_reviews  \n",
              "9   [many, year, doctor, change, chateal, effectiv...  \n",
              "14  [start, month, ago, minimal, amount, contracep...  \n",
              "22  [job, worry, free, sex, thing, period, sometim...  \n",
              "74  [experience, great, yet, issue, cause, much, b...  \n",
              "75  [ve, mine, year, noticed, weight, gain, mood, ...  "
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# First five rows\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcIybjMmTIzU"
      },
      "source": [
        "#### Additional Preprocessing Steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jc_0hb0HTIzU"
      },
      "source": [
        "- Created a dictionary (`id2word`) from the processed reviews using Gensim's `Dictionary` function. Filtered extremes to remove words that appear in less than 50 documents or more than 50% of the documents.\n",
        "\n",
        "- Used the processed reviews to create the corpus (`texts`).\n",
        "\n",
        "- Converted the processed reviews into a bag-of-words format (`corpus`) where each document is represented as a list of tuples with the word ID and its frequency in the document.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SpjMe-eaKEbw"
      },
      "outputs": [],
      "source": [
        "# Create Dictionary\n",
        "id2word = corpora.Dictionary(df['processed_reviews'])\n",
        "id2word.filter_extremes(no_below=50, no_above=0.5)\n",
        "\n",
        "# Create Corpus\n",
        "texts = df['processed_reviews']\n",
        "\n",
        "# Term Document Frequency\n",
        "corpus = [id2word.doc2bow(text) for text in texts]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "JI97aJVeL7hQ",
        "outputId": "e09986fc-21f8-4232-d2fd-f98e56621d72"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15349,\n  \"fields\": [\n    {\n      \"column\": \"drugName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Nexplanon\",\n          \"Ethinyl estradiol / norethindrone\",\n          \"Etonogestrel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Birth Control\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12461,\n        \"samples\": [\n          \"\\\"I starting taking this birth control because it allowed you to still have a period every month. Previously, I had been on a BC that only allowed a period every 2 months and I dont think my body liked that. However, I stop taking Generess and got an IUD. After I stopped using Generes, I have had the worst cystic acne on my face and body. I have been off of the pill for over a year and I still have terrible acne problems. It has gotten better over time, but it is still bad. I wish I would of never been prescribed this pill.\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2008-03-06 00:00:00\",\n        \"max\": \"2017-12-12 00:00:00\",\n        \"num_unique_values\": 2810,\n        \"samples\": [\n          \"2015-01-04 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"usefulCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 0,\n        \"max\": 1247,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"POSITIVE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"confidence\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09045413052421365,\n        \"min\": 0.5001012682914734,\n        \"max\": 0.9998834133148193,\n        \"num_unique_values\": 11961,\n        \"samples\": [\n          0.8805505633354187\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"processed_reviews\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
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            "text/html": [
              "\n",
              "  <div id=\"df-2809c816-1780-4a82-bdd5-bcf5b847f9f8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th>drugName</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Ethinyl estradiol / levonorgestrel</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"I had been on the pill for many years. When m...</td>\n",
              "      <td>8</td>\n",
              "      <td>2016-12-08</td>\n",
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              "      <td>NEGATIVE</td>\n",
              "      <td>0.953042</td>\n",
              "      <td>[many, year, doctor, change, chateal, effectiv...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Nexplanon</td>\n",
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              "      <td>3</td>\n",
              "      <td>2014-08-07</td>\n",
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              "      <td>NEGATIVE</td>\n",
              "      <td>0.999347</td>\n",
              "      <td>[start, month, ago, minimal, amount, contracep...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>[job, worry, free, sex, thing, period, sometim...</td>\n",
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              "    <tr>\n",
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              "      <td>Etonogestrel</td>\n",
              "      <td>Birth Control</td>\n",
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              "      <td>10</td>\n",
              "      <td>2016-05-27</td>\n",
              "      <td>1</td>\n",
              "      <td>NEGATIVE</td>\n",
              "      <td>0.999737</td>\n",
              "      <td>[experience, great, yet, issue, cause, much, b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Nexplanon</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"Ive had mine for over a year and noticed the ...</td>\n",
              "      <td>6</td>\n",
              "      <td>2016-06-19</td>\n",
              "      <td>1</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>0.802087</td>\n",
              "      <td>[ve, mine, year, noticed, weight, gain, mood, ...</td>\n",
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              "  </tbody>\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2809c816-1780-4a82-bdd5-bcf5b847f9f8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2809c816-1780-4a82-bdd5-bcf5b847f9f8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "</svg>\n",
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              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bb603d38-069d-4ccf-8b0f-34c185c3f373 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                              drugName      condition  \\\n",
              "9   Ethinyl estradiol / levonorgestrel  Birth Control   \n",
              "14                           Nexplanon  Birth Control   \n",
              "22                        Etonogestrel  Birth Control   \n",
              "74                        Etonogestrel  Birth Control   \n",
              "75                           Nexplanon  Birth Control   \n",
              "\n",
              "                                               review  rating       date  \\\n",
              "9   \"I had been on the pill for many years. When m...       8 2016-12-08   \n",
              "14  \"Started Nexplanon 2 months ago because I have...       3 2014-08-07   \n",
              "22  \"Nexplanon does its job. I can have worry free...       9 2014-08-11   \n",
              "74  \"My experience during the nexplon is great not...      10 2016-05-27   \n",
              "75  \"Ive had mine for over a year and noticed the ...       6 2016-06-19   \n",
              "\n",
              "    usefulCount sentiment  confidence  \\\n",
              "9             1  NEGATIVE    0.953042   \n",
              "14           10  NEGATIVE    0.999347   \n",
              "22           11  NEGATIVE    0.997134   \n",
              "74            1  NEGATIVE    0.999737   \n",
              "75            1  POSITIVE    0.802087   \n",
              "\n",
              "                                    processed_reviews  \n",
              "9   [many, year, doctor, change, chateal, effectiv...  \n",
              "14  [start, month, ago, minimal, amount, contracep...  \n",
              "22  [job, worry, free, sex, thing, period, sometim...  \n",
              "74  [experience, great, yet, issue, cause, much, b...  \n",
              "75  [ve, mine, year, noticed, weight, gain, mood, ...  "
            ]
          },
          "execution_count": 129,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# First five rows\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-RgF0ijTIzf"
      },
      "source": [
        "## LDA Model No of Topics Selection\n",
        "- The number of topics was selected based on coherence and perplexity scores. Three topics were chosen as they provided the highest coherence score. Although the perplexity was high, three topics were selected because they made the most sense after manually reviewing the representative documents in each topic.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g4e_vpR7KUhF"
      },
      "outputs": [],
      "source": [
        "def compute_coherence_perplexity(dictionary, corpus, texts, limit, start=2, step=3):\n",
        "    coherence_values = []\n",
        "    perplexity_values = []\n",
        "    model_list = []\n",
        "    for num_topics in range(start, limit, step):\n",
        "        model = gensim.models.LdaModel(corpus=corpus,\n",
        "                                       id2word=dictionary,\n",
        "                                       num_topics=num_topics,\n",
        "                                       random_state=100,\n",
        "                                       update_every=1,\n",
        "                                       chunksize=100,\n",
        "                                       passes=10,\n",
        "                                       alpha='auto',\n",
        "                                       per_word_topics=True)\n",
        "        model_list.append(model)\n",
        "\n",
        "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
        "        coherence_values.append(coherencemodel.get_coherence())\n",
        "\n",
        "        perplexity = model.log_perplexity(corpus)\n",
        "        perplexity_values.append(perplexity)\n",
        "\n",
        "    return model_list, coherence_values, perplexity_values\n",
        "\n",
        "# Define limit, start, and step\n",
        "limit = 8\n",
        "start = 2\n",
        "step = 1\n",
        "\n",
        "# Compute coherence and perplexity values for different numbers of topics\n",
        "model_list, coherence_values, perplexity_values = compute_coherence_perplexity(dictionary=id2word, corpus=corpus, texts=texts, start=start, limit=limit, step=step)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "Tg2D6lUXKYBJ",
        "outputId": "20e1deb2-9c3e-41df-ea5f-a5d26687610b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot coherence score\n",
        "x = range(start, limit, step)\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(x, coherence_values)\n",
        "plt.xlabel(\"Num Topics\")\n",
        "plt.ylabel(\"Coherence score\")\n",
        "plt.title(\"Coherence Scores\")\n",
        "\n",
        "# Plot perplexity score\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(x, perplexity_values)\n",
        "plt.xlabel(\"Num Topics\")\n",
        "plt.ylabel(\"Perplexity score\")\n",
        "plt.title(\"Perplexity Scores\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pz5i4--sKe2t",
        "outputId": "f594b172-800b-467c-9ea5-e6ac065344a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[(0, '0.030*\"year\" + 0.026*\"control\" + 0.026*\"birth\" + 0.025*\"gain\" + 0.025*\"weight\" + 0.020*\"first\" + 0.018*\"take\" + 0.018*\"side\" + 0.017*\"effect\" + 0.015*\"mood\"'), (1, '0.044*\"week\" + 0.034*\"day\" + 0.031*\"go\" + 0.024*\"start\" + 0.023*\"bleed\" + 0.023*\"bad\" + 0.021*\"feel\" + 0.020*\"time\" + 0.018*\"stop\" + 0.016*\"implant\"'), (2, '0.070*\"cramp\" + 0.048*\"pain\" + 0.040*\"insertion\" + 0.035*\"insert\" + 0.028*\"mirena\" + 0.028*\"painful\" + 0.027*\"day\" + 0.021*\"feel\" + 0.016*\"bad\" + 0.016*\"hurt\"')]\n"
          ]
        }
      ],
      "source": [
        "# Select the model with the highest coherence score\n",
        "optimal_model = model_list[coherence_values.index(max(coherence_values))]\n",
        "model_topics = optimal_model.show_topics(formatted=False)\n",
        "print(optimal_model.print_topics(num_words=10))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylzmL3FETIzh"
      },
      "source": [
        "## Topics Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "SdWloI9aF555",
        "outputId": "77caeb61-e27e-4388-df11-53d43a549a9a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el10211332003030882244898439970\" style=\"background-color:white;\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el10211332003030882244898439970_data = {\"mdsDat\": {\"x\": [-0.15428479924422642, -0.13602638914670817, 0.29031118839093467], \"y\": [0.23054888097834597, -0.2404224089951319, 0.009873528016785968], \"topics\": [1, 2, 3], \"cluster\": [1, 1, 1], \"Freq\": [49.41533862255672, 36.46869447056339, 14.115966906879896]}, \"tinfo\": {\"Term\": [\"cramp\", \"week\", \"pain\", \"insertion\", \"year\", \"day\", \"insert\", \"control\", \"birth\", \"gain\", \"weight\", \"bleed\", \"feel\", \"mirena\", \"painful\", \"go\", \"stop\", \"side\", \"bad\", \"start\", \"implant\", \"effect\", \"remove\", \"back\", \"hurt\", \"skyla\", \"review\", \"heavy\", \"acne\", \"time\", \"control\", \"birth\", \"gain\", \"weight\", \"side\", \"effect\", \"acne\", \"drive\", \"great\", \"use\", \"lose\", \"switch\", \"pill\", \"change\", \"pound\", \"notice\", \"breast\", \"skin\", \"issue\", \"clear\", \"effective\", \"implanon\", \"seem\", \"big\", \"increase\", \"face\", \"actually\", \"pack\", \"moody\", \"regular\", \"year\", \"love\", \"problem\", \"cycle\", \"swing\", \"mood\", \"try\", \"good\", \"ve\", \"however\", \"first\", \"light\", \"experience\", \"really\", \"work\", \"also\", \"take\", \"m\", \"little\", \"sex\", \"spot\", \"recommend\", \"bleed\", \"stop\", \"remove\", \"bleeding\", \"horrible\", \"depression\", \"anxiety\", \"life\", \"hair\", \"implant\", \"end\", \"cry\", \"wait\", \"hate\", \"straight\", \"soon\", \"tired\", \"constant\", \"depressed\", \"back\", \"awful\", \"finally\", \"constantly\", \"rsquo\", \"point\", \"stomach\", \"sleep\", \"husband\", \"call\", \"leave\", \"week\", \"heavy\", \"blood\", \"come\", \"arm\", \"go\", \"away\", \"start\", \"even\", \"ever\", \"day\", \"bad\", \"feel\", \"put\", \"time\", \"want\", \"thing\", \"doctor\", \"never\", \"make\", \"tell\", \"still\", \"take\", \"last\", \"m\", \"first\", \"pain\", \"insertion\", \"mirena\", \"painful\", \"skyla\", \"iud\", \"hurt\", \"hour\", \"child\", 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0.027927096958779634, 0.9997519059382761, 0.5714956208015377, 0.3259794515763284, 0.10255124221221053, 0.8454176425921417, 0.15402345715241447, 0.22591632922263805, 0.7737134460987692, 0.30206638748501524, 0.6978350012919128, 0.9983358723828274, 0.9785468365813083, 0.021527094444160774, 0.0031039483998146736, 0.0031039483998146736, 0.9963674363405103, 0.9985671016742216, 0.9995503500489364, 0.9089222465796867, 0.09095837620429907, 0.999361959095415, 0.10443371000449131, 0.8305666889559075, 0.06520978136430677, 0.010244381405110877, 0.9896305264187223, 0.9999047477290873, 0.8004036994918323, 0.1973353748635681, 0.0024979161375135203, 0.9982756368298051, 0.0016248382720404993, 0.9966819679275087], \"Term\": [\"acne\", \"actually\", \"afterwards\", \"ago\", \"ago\", \"ago\", \"also\", \"also\", \"anxiety\", \"arm\", \"arm\", \"away\", \"away\", \"away\", \"awful\", \"back\", \"back\", \"bad\", \"bad\", \"bad\", \"big\", \"birth\", \"bleed\", \"bleeding\", \"blood\", \"blood\", \"breast\", 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\"unbearable\", \"unbearable\", \"unbearable\", \"uncomfortable\", \"use\", \"ve\", \"ve\", \"wait\", \"want\", \"want\", \"want\", \"week\", \"week\", \"weight\", \"work\", \"work\", \"work\", \"year\", \"year\", \"yesterday\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 3]};\n",
              "\n",
              "function LDAvis_load_lib(url, callback){\n",
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              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
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              "\n",
              "if(typeof(LDAvis) !== \"undefined\"){\n",
              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el10211332003030882244898439970\", ldavis_el10211332003030882244898439970_data);\n",
              "   }(LDAvis);\n",
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              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el10211332003030882244898439970\", ldavis_el10211332003030882244898439970_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el10211332003030882244898439970\", ldavis_el10211332003030882244898439970_data);\n",
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              "</script>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Prepare the pyLDAvis visualization\n",
        "pyLDAvis.enable_notebook()\n",
        "\n",
        "# Generate the visualization\n",
        "lda_display = gensimvis.prepare(optimal_model, corpus, id2word, sort_topics=False)\n",
        "\n",
        "# Display the visualization in a Jupyter Notebook\n",
        "pyLDAvis.display(lda_display)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R58bg60qTIzi"
      },
      "source": [
        "## Topic Assignment\n",
        "- Each observation was assigned its most dominant topic.\n",
        "- Themes were inferred from the three topics, namely: \"Weight Gain and Side Effects,\" \"Bleeding and Cycle Issues,\" and \"Pain and Insertion Issues.\"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "nY_AYPuI-bpY",
        "outputId": "f6ef5596-271b-4f7d-899f-8994c2de58cc"
      },
      "outputs": [
        {
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e1df0703-2625-4ecd-83c7-3895e9aba468 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                              drugName      condition  \\\n",
              "9   Ethinyl estradiol / levonorgestrel  Birth Control   \n",
              "14                           Nexplanon  Birth Control   \n",
              "22                        Etonogestrel  Birth Control   \n",
              "74                        Etonogestrel  Birth Control   \n",
              "75                           Nexplanon  Birth Control   \n",
              "\n",
              "                                               review  rating       date  \\\n",
              "9   \"I had been on the pill for many years. When m...       8 2016-12-08   \n",
              "14  \"Started Nexplanon 2 months ago because I have...       3 2014-08-07   \n",
              "22  \"Nexplanon does its job. I can have worry free...       9 2014-08-11   \n",
              "74  \"My experience during the nexplon is great not...      10 2016-05-27   \n",
              "75  \"Ive had mine for over a year and noticed the ...       6 2016-06-19   \n",
              "\n",
              "    usefulCount sentiment  confidence  \\\n",
              "9             1  NEGATIVE    0.953042   \n",
              "14           10  NEGATIVE    0.999347   \n",
              "22           11  NEGATIVE    0.997134   \n",
              "74            1  NEGATIVE    0.999737   \n",
              "75            1  POSITIVE    0.802087   \n",
              "\n",
              "                                    processed_reviews  Topic  \n",
              "9   [many, year, doctor, change, chateal, effectiv...      0  \n",
              "14  [start, month, ago, minimal, amount, contracep...      0  \n",
              "22  [job, worry, free, sex, thing, period, sometim...      0  \n",
              "74  [experience, great, yet, issue, cause, much, b...      1  \n",
              "75  [ve, mine, year, noticed, weight, gain, mood, ...      0  "
            ]
          },
          "execution_count": 138,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def get_dominant_topic(corpus, lda_model):\n",
        "    dominant_topics = []\n",
        "    for doc_bow in corpus:\n",
        "        # Get topic probabilities for the document\n",
        "        topic_probs = lda_model.get_document_topics(doc_bow)\n",
        "        # Check if topic_probs is not empty\n",
        "        if topic_probs:\n",
        "            # Sort probabilities and get the topic index with highest probability\n",
        "            sorted_probs = sorted(topic_probs, key=lambda x: x[1], reverse=True)\n",
        "            dominant_topic = sorted_probs[0][0]  # Topic ID with the highest probability\n",
        "        else:\n",
        "            dominant_topic = -1  # No topic assigned if topic_probs is empty\n",
        "        dominant_topics.append(dominant_topic)\n",
        "    return dominant_topics\n",
        "\n",
        "# Assign topics to each review\n",
        "df['Topic'] = get_dominant_topic(corpus, optimal_model)\n",
        "\n",
        "# Check the DataFrame with the new column\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "8iArS9kV-rjB",
        "outputId": "b5f5c713-89b4-40cc-e2cc-c0a8df52bc16"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15349,\n  \"fields\": [\n    {\n      \"column\": \"drugName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Nexplanon\",\n          \"Ethinyl estradiol / norethindrone\",\n          \"Etonogestrel\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"condition\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Birth Control\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12461,\n        \"samples\": [\n          \"\\\"I starting taking this birth control because it allowed you to still have a period every month. Previously, I had been on a BC that only allowed a period every 2 months and I dont think my body liked that. However, I stop taking Generess and got an IUD. After I stopped using Generes, I have had the worst cystic acne on my face and body. I have been off of the pill for over a year and I still have terrible acne problems. It has gotten better over time, but it is still bad. I wish I would of never been prescribed this pill.\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2008-03-06 00:00:00\",\n        \"max\": \"2017-12-12 00:00:00\",\n        \"num_unique_values\": 2810,\n        \"samples\": [\n          \"2015-01-04 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"usefulCount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 0,\n        \"max\": 1247,\n        \"num_unique_values\": 129,\n        \"samples\": [\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"POSITIVE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"confidence\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09045413052421365,\n        \"min\": 0.5001012682914734,\n        \"max\": 0.9998834133148193,\n        \"num_unique_values\": 11961,\n        \"samples\": [\n          0.8805505633354187\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"processed_reviews\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Topic_Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Weight Gain and Side Effects\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-ff3c8629-0ce6-46a1-8424-d4fadf140b25\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>drugName</th>\n",
              "      <th>condition</th>\n",
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              "      <th>9</th>\n",
              "      <td>Ethinyl estradiol / levonorgestrel</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"I had been on the pill for many years. When m...</td>\n",
              "      <td>8</td>\n",
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              "      <td>NEGATIVE</td>\n",
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              "      <td>Weight Gain and Side Effects</td>\n",
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              "      <th>14</th>\n",
              "      <td>Nexplanon</td>\n",
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              "      <td>3</td>\n",
              "      <td>2014-08-07</td>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
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              "      <td>Birth Control</td>\n",
              "      <td>\"Nexplanon does its job. I can have worry free...</td>\n",
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              "      <td>2014-08-11</td>\n",
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              "      <td>NEGATIVE</td>\n",
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              "      <td>[job, worry, free, sex, thing, period, sometim...</td>\n",
              "      <td>Weight Gain and Side Effects</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>Etonogestrel</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"My experience during the nexplon is great not...</td>\n",
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              "      <td>NEGATIVE</td>\n",
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              "      <td>[experience, great, yet, issue, cause, much, b...</td>\n",
              "      <td>Bleeding and Cycle Issues</td>\n",
              "    </tr>\n",
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              "      <th>75</th>\n",
              "      <td>Nexplanon</td>\n",
              "      <td>Birth Control</td>\n",
              "      <td>\"Ive had mine for over a year and noticed the ...</td>\n",
              "      <td>6</td>\n",
              "      <td>2016-06-19</td>\n",
              "      <td>1</td>\n",
              "      <td>POSITIVE</td>\n",
              "      <td>0.802087</td>\n",
              "      <td>[ve, mine, year, noticed, weight, gain, mood, ...</td>\n",
              "      <td>Weight Gain and Side Effects</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ff3c8629-0ce6-46a1-8424-d4fadf140b25 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ff3c8629-0ce6-46a1-8424-d4fadf140b25');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    cursor: pointer;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
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            ],
            "text/plain": [
              "                              drugName      condition  \\\n",
              "9   Ethinyl estradiol / levonorgestrel  Birth Control   \n",
              "14                           Nexplanon  Birth Control   \n",
              "22                        Etonogestrel  Birth Control   \n",
              "74                        Etonogestrel  Birth Control   \n",
              "75                           Nexplanon  Birth Control   \n",
              "\n",
              "                                               review  rating       date  \\\n",
              "9   \"I had been on the pill for many years. When m...       8 2016-12-08   \n",
              "14  \"Started Nexplanon 2 months ago because I have...       3 2014-08-07   \n",
              "22  \"Nexplanon does its job. I can have worry free...       9 2014-08-11   \n",
              "74  \"My experience during the nexplon is great not...      10 2016-05-27   \n",
              "75  \"Ive had mine for over a year and noticed the ...       6 2016-06-19   \n",
              "\n",
              "    usefulCount sentiment  confidence  \\\n",
              "9             1  NEGATIVE    0.953042   \n",
              "14           10  NEGATIVE    0.999347   \n",
              "22           11  NEGATIVE    0.997134   \n",
              "74            1  NEGATIVE    0.999737   \n",
              "75            1  POSITIVE    0.802087   \n",
              "\n",
              "                                    processed_reviews  \\\n",
              "9   [many, year, doctor, change, chateal, effectiv...   \n",
              "14  [start, month, ago, minimal, amount, contracep...   \n",
              "22  [job, worry, free, sex, thing, period, sometim...   \n",
              "74  [experience, great, yet, issue, cause, much, b...   \n",
              "75  [ve, mine, year, noticed, weight, gain, mood, ...   \n",
              "\n",
              "                      Topic_Name  \n",
              "9   Weight Gain and Side Effects  \n",
              "14  Weight Gain and Side Effects  \n",
              "22  Weight Gain and Side Effects  \n",
              "74     Bleeding and Cycle Issues  \n",
              "75  Weight Gain and Side Effects  "
            ]
          },
          "execution_count": 139,
          "metadata": {},
          "output_type": "execute_result"
        }
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        "# Define the mapping from topic IDs to descriptive names\n",
        "topic_names = {\n",
        "    0: \"Weight Gain and Side Effects\",\n",
        "    1: \"Bleeding and Cycle Issues\",\n",
        "    2: \"Pain and Insertion Issues\"\n",
        "}\n",
        "\n",
        "# Apply the mapping using a lambda function\n",
        "df['Topic_Name'] = df['Topic'].apply(lambda x: topic_names[x] if x in topic_names else None)\n",
        "\n",
        "# Drop the old 'Topic' column if you don't need it\n",
        "df = df.drop(columns=['Topic'])\n",
        "\n",
        "# Check the DataFrame with the new column\n",
        "df.head()\n"
      ]
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      "source": [
        "# Save the DataFrame to a CSV file\n",
        "df.to_csv('drug_reviews_with_topics.csv', index=False)"
      ]
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