project_word2Vec_oversample_RNN_v_s__LTSM.ipynb

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        "<a href=\"https://colab.research.google.com/github/bhusserl-davis/ECS171-Group1-Project/blob/main/project_word2Vec_oversample_RNN_v_s__LTSM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gntRQBTvsH61"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[K     |████████████████████████████████| 371 kB 15.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 24.2 MB 1.4 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# Download Needed Packages\n",
        "import sys\n",
        "!{sys.executable} -m pip install -q pandas\n",
        "!{sys.executable} -m pip install -q nltk\n",
        "!{sys.executable} -m pip install -q sklearn\n",
        "!{sys.executable} -m pip install -q wordcloud==1.8.1\n",
        "!{sys.executable} -m pip install -q gensim==3.8.3\n",
        "!{sys.executable} -m pip install -q keras\n",
        "!{sys.executable} -m pip install -q numpy\n",
        "!{sys.executable} -m pip install -q matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "XUBwYzkusH7D"
      },
      "outputs": [],
      "source": [
        "# Import needed libraries and download needed data\n",
        "import string\n",
        "import pandas as pd\n",
        "import nltk\n",
        "nltk.download('stopwords', quiet=True)\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('punkt', quiet=True)\n",
        "from nltk.tokenize import word_tokenize\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from wordcloud import WordCloud"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "duXaTruMsH7F"
      },
      "source": [
        "## Data Exploration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "8RVGvjx5sH7G",
        "outputId": "55e46903-9681-440b-d61d-579bde10965c"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "17880 observations, 18 features\n"
          ]
        },
        {
          "output_type": "display_data",
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              "   job_id                                      title            location  \\\n",
              "0       1                           Marketing Intern    US, NY, New York   \n",
              "1       2  Customer Service - Cloud Video Production      NZ, , Auckland   \n",
              "2       3    Commissioning Machinery Assistant (CMA)       US, IA, Wever   \n",
              "3       4          Account Executive - Washington DC  US, DC, Washington   \n",
              "4       5                        Bill Review Manager  US, FL, Fort Worth   \n",
              "\n",
              "  department salary_range                                    company_profile  \\\n",
              "0  Marketing          NaN  We're Food52, and we've created a groundbreaki...   \n",
              "1    Success          NaN  90 Seconds, the worlds Cloud Video Production ...   \n",
              "2        NaN          NaN  Valor Services provides Workforce Solutions th...   \n",
              "3      Sales          NaN  Our passion for improving quality of life thro...   \n",
              "4        NaN          NaN  SpotSource Solutions LLC is a Global Human Cap...   \n",
              "\n",
              "                                         description  \\\n",
              "0  Food52, a fast-growing, James Beard Award-winn...   \n",
              "1  Organised - Focused - Vibrant - Awesome!Do you...   \n",
              "2  Our client, located in Houston, is actively se...   \n",
              "3  THE COMPANY: ESRI – Environmental Systems Rese...   \n",
              "4  JOB TITLE: Itemization Review ManagerLOCATION:...   \n",
              "\n",
              "                                        requirements  \\\n",
              "0  Experience with content management systems a m...   \n",
              "1  What we expect from you:Your key responsibilit...   \n",
              "2  Implement pre-commissioning and commissioning ...   \n",
              "3  EDUCATION: Bachelor’s or Master’s in GIS, busi...   \n",
              "4  QUALIFICATIONS:RN license in the State of Texa...   \n",
              "\n",
              "                                            benefits  telecommuting  \\\n",
              "0                                                NaN              0   \n",
              "1  What you will get from usThrough being part of...              0   \n",
              "2                                                NaN              0   \n",
              "3  Our culture is anything but corporate—we have ...              0   \n",
              "4                              Full Benefits Offered              0   \n",
              "\n",
              "   has_company_logo  has_questions employment_type required_experience  \\\n",
              "0                 1              0           Other          Internship   \n",
              "1                 1              0       Full-time      Not Applicable   \n",
              "2                 1              0             NaN                 NaN   \n",
              "3                 1              0       Full-time    Mid-Senior level   \n",
              "4                 1              1       Full-time    Mid-Senior level   \n",
              "\n",
              "  required_education                   industry              function  \\\n",
              "0                NaN                        NaN             Marketing   \n",
              "1                NaN  Marketing and Advertising      Customer Service   \n",
              "2                NaN                        NaN                   NaN   \n",
              "3  Bachelor's Degree          Computer Software                 Sales   \n",
              "4  Bachelor's Degree     Hospital & Health Care  Health Care Provider   \n",
              "\n",
              "   fraudulent  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  "
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              "      <td>Experience with content management systems a m...</td>\n",
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              "      <td>Success</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Organised - Focused - Vibrant - Awesome!Do you...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Valor Services provides Workforce Solutions th...</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>Account Executive - Washington DC</td>\n",
              "      <td>US, DC, Washington</td>\n",
              "      <td>Sales</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Our passion for improving quality of life thro...</td>\n",
              "      <td>THE COMPANY: ESRI – Environmental Systems Rese...</td>\n",
              "      <td>EDUCATION: Bachelor’s or Master’s in GIS, busi...</td>\n",
              "      <td>Our culture is anything but corporate—we have ...</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>SpotSource Solutions LLC is a Global Human Cap...</td>\n",
              "      <td>JOB TITLE: Itemization Review ManagerLOCATION:...</td>\n",
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import Data\n",
        "df = pd.read_csv(\"./fake_job_postings.csv\")\n",
        "print(f'{df.shape[0]} observations, {df.shape[1]} features')\n",
        "display(df.head(5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YyMeutk0grON",
        "outputId": "7629b9a8-e908-4769-f6d9-f67d6e7d4c96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column\t\t    NaNs   Type\n",
            "job_id              0      int64\n",
            "title               0      object\n",
            "location            346    object\n",
            "department          11547  object\n",
            "salary_range        15012  object\n",
            "company_profile     3308   object\n",
            "description         1      object\n",
            "requirements        2695   object\n",
            "benefits            7210   object\n",
            "telecommuting       0      int64\n",
            "has_company_logo    0      int64\n",
            "has_questions       0      int64\n",
            "employment_type     3471   object\n",
            "required_experience 7050   object\n",
            "required_education  8105   object\n",
            "industry            4903   object\n",
            "function            6455   object\n",
            "fraudulent          0      int64\n"
          ]
        }
      ],
      "source": [
        "# Print count of NaNs and type of each column\n",
        "print(\"Column\\t\\t    NaNs   Type\")\n",
        "for column in df.columns:\n",
        "    print(f'{column.ljust(20)}{str(df[column].isna().sum()).ljust(7)}{df[column].dtype}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VOXcRkmhgwrZ",
        "outputId": "fa323394-8a50-4832-ba57-9563c8321136"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column\t\t       NaNs\n",
            "job_id                 0\n",
            "title                  0\n",
            "location               0\n",
            "department             0\n",
            "salary_range           0\n",
            "company_profile        0\n",
            "description            0\n",
            "requirements           0\n",
            "benefits               0\n",
            "telecommuting          0\n",
            "has_company_logo       0\n",
            "has_questions          0\n",
            "employment_type        0\n",
            "required_experience    0\n",
            "required_education     0\n",
            "industry               0\n",
            "function               0\n",
            "fraudulent             0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#Replacing NAN Values\n",
        "new_df = df.copy()\n",
        "NAN_Cols=[\n",
        "    \"location\",\"department\",\"salary_range\",\n",
        "    \"company_profile\",\"description\",\"requirements\",\n",
        "    \"benefits\",\"employment_type\",\"required_experience\",\n",
        "    \"required_experience\",\"required_education\",\"industry\",\"function\"\n",
        "]\n",
        "print(\"Column\\t\\t       NaNs\")\n",
        "for i in NAN_Cols:\n",
        "    new_df[i].fillna(\"\",inplace=True)\n",
        "print(new_df.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87mjUtgkg6ek",
        "outputId": "9140f001-56f6-4a52-a9e6-c1eedb428287"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "job_id: [    1     2     3 ... 17878 17879 17880]\n",
            "telecommuting: [0 1]\n",
            "has_company_logo: [1 0]\n",
            "has_questions: [0 1]\n",
            "fraudulent: [0 1]\n"
          ]
        }
      ],
      "source": [
        "# Get numeric columns unique values\n",
        "numeric_atts = ['job_id', 'telecommuting', 'has_company_logo', 'has_questions', 'fraudulent']\n",
        "for column in numeric_atts:\n",
        "    print(f'{column}: {new_df[column].unique()}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
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          "height": 0
        },
        "id": "7s618PyahBU2",
        "outputId": "8bf1f750-46d8-4543-e0b1-df640e33ad30"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   job_id                                      title            location  \\\n",
              "0       1                           Marketing Intern    US, NY, New York   \n",
              "1       2  Customer Service - Cloud Video Production      NZ, , Auckland   \n",
              "2       3    Commissioning Machinery Assistant (CMA)       US, IA, Wever   \n",
              "3       4          Account Executive - Washington DC  US, DC, Washington   \n",
              "4       5                        Bill Review Manager  US, FL, Fort Worth   \n",
              "\n",
              "  department salary_range                                    company_profile  \\\n",
              "0  Marketing               We're Food52, and we've created a groundbreaki...   \n",
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          "metadata": {},
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        "new_df[:5]"
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    },
    {
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      "execution_count": 9,
      "metadata": {
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          "execution_count": 9
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            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "new_df['fraudulent'].value_counts().plot.bar()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15sW_btIsH7Q"
      },
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mB7PDS_jwyvR"
      },
      "source": [
        "### covert string to word token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "JN48DwVxhqxE",
        "outputId": "6c030166-63fb-48da-b689-8eb3c309c66f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   job_id                                      title            location  \\\n",
              "0       1                           marketing intern    us, ny, new york   \n",
              "1       2  customer service - cloud video production      nz, , auckland   \n",
              "2       3    commissioning machinery assistant (cma)       us, ia, wever   \n",
              "3       4          account executive - washington dc  us, dc, washington   \n",
              "4       5                        bill review manager  us, fl, fort worth   \n",
              "\n",
              "  department salary_range                                    company_profile  \\\n",
              "0  marketing               we're food52, and we've created a groundbreaki...   \n",
              "1    success               90 seconds, the worlds cloud video production ...   \n",
              "2                          valor services provides workforce solutions th...   \n",
              "3      sales               our passion for improving quality of life thro...   \n",
              "4                          spotsource solutions llc is a global human cap...   \n",
              "\n",
              "                                         description  \\\n",
              "0  food52, a fast-growing, james beard award-winn...   \n",
              "1  organised - focused - vibrant - awesome!do you...   \n",
              "2  our client, located in houston, is actively se...   \n",
              "3  the company: esri – environmental systems rese...   \n",
              "4  job title: itemization review managerlocation:...   \n",
              "\n",
              "                                        requirements  \\\n",
              "0  experience with content management systems a m...   \n",
              "1  what we expect from you:your key responsibilit...   \n",
              "2  implement pre-commissioning and commissioning ...   \n",
              "3  education: bachelor’s or master’s in gis, busi...   \n",
              "4  qualifications:rn license in the state of texa...   \n",
              "\n",
              "                                            benefits  telecommuting  \\\n",
              "0                                                                 0   \n",
              "1  what you will get from usthrough being part of...              0   \n",
              "2                                                                 0   \n",
              "3  our culture is anything but corporate—we have ...              0   \n",
              "4                              full benefits offered              0   \n",
              "\n",
              "   has_company_logo  has_questions employment_type required_experience  \\\n",
              "0                 1              0           other          internship   \n",
              "1                 1              0       full-time      not applicable   \n",
              "2                 1              0                                       \n",
              "3                 1              0       full-time    mid-senior level   \n",
              "4                 1              1       full-time    mid-senior level   \n",
              "\n",
              "  required_education                   industry              function  \\\n",
              "0                                                           marketing   \n",
              "1                     marketing and advertising      customer service   \n",
              "2                                                                       \n",
              "3  bachelor's degree          computer software                 sales   \n",
              "4  bachelor's degree     hospital & health care  health care provider   \n",
              "\n",
              "   fraudulent  \n",
              "0           0  \n",
              "1           0  \n",
              "2           0  \n",
              "3           0  \n",
              "4           0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c9edd055-da5b-46a7-84ff-c25e0f7cd5ab\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>job_id</th>\n",
              "      <th>title</th>\n",
              "      <th>location</th>\n",
              "      <th>department</th>\n",
              "      <th>salary_range</th>\n",
              "      <th>company_profile</th>\n",
              "      <th>description</th>\n",
              "      <th>requirements</th>\n",
              "      <th>benefits</th>\n",
              "      <th>telecommuting</th>\n",
              "      <th>has_company_logo</th>\n",
              "      <th>has_questions</th>\n",
              "      <th>employment_type</th>\n",
              "      <th>required_experience</th>\n",
              "      <th>required_education</th>\n",
              "      <th>industry</th>\n",
              "      <th>function</th>\n",
              "      <th>fraudulent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>marketing intern</td>\n",
              "      <td>us, ny, new york</td>\n",
              "      <td>marketing</td>\n",
              "      <td></td>\n",
              "      <td>we're food52, and we've created a groundbreaki...</td>\n",
              "      <td>food52, a fast-growing, james beard award-winn...</td>\n",
              "      <td>experience with content management systems a m...</td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>other</td>\n",
              "      <td>internship</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>marketing</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>customer service - cloud video production</td>\n",
              "      <td>nz, , auckland</td>\n",
              "      <td>success</td>\n",
              "      <td></td>\n",
              "      <td>90 seconds, the worlds cloud video production ...</td>\n",
              "      <td>organised - focused - vibrant - awesome!do you...</td>\n",
              "      <td>what we expect from you:your key responsibilit...</td>\n",
              "      <td>what you will get from usthrough being part of...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>full-time</td>\n",
              "      <td>not applicable</td>\n",
              "      <td></td>\n",
              "      <td>marketing and advertising</td>\n",
              "      <td>customer service</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>commissioning machinery assistant (cma)</td>\n",
              "      <td>us, ia, wever</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>valor services provides workforce solutions th...</td>\n",
              "      <td>our client, located in houston, is actively se...</td>\n",
              "      <td>implement pre-commissioning and commissioning ...</td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>account executive - washington dc</td>\n",
              "      <td>us, dc, washington</td>\n",
              "      <td>sales</td>\n",
              "      <td></td>\n",
              "      <td>our passion for improving quality of life thro...</td>\n",
              "      <td>the company: esri – environmental systems rese...</td>\n",
              "      <td>education: bachelor’s or master’s in gis, busi...</td>\n",
              "      <td>our culture is anything but corporate—we have ...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>full-time</td>\n",
              "      <td>mid-senior level</td>\n",
              "      <td>bachelor's degree</td>\n",
              "      <td>computer software</td>\n",
              "      <td>sales</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>bill review manager</td>\n",
              "      <td>us, fl, fort worth</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>spotsource solutions llc is a global human cap...</td>\n",
              "      <td>job title: itemization review managerlocation:...</td>\n",
              "      <td>qualifications:rn license in the state of texa...</td>\n",
              "      <td>full benefits offered</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>full-time</td>\n",
              "      <td>mid-senior level</td>\n",
              "      <td>bachelor's degree</td>\n",
              "      <td>hospital &amp; health care</td>\n",
              "      <td>health care provider</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9edd055-da5b-46a7-84ff-c25e0f7cd5ab')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c9edd055-da5b-46a7-84ff-c25e0f7cd5ab 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-c9edd055-da5b-46a7-84ff-c25e0f7cd5ab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Convert Everything to lowercase\n",
        "new_df = new_df.applymap(lambda s: s.lower() if type(s) == str else s)\n",
        "display(new_df.head(5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "HMDJhqYdh2Js",
        "outputId": "33c929e9-1a99-49f5-8060-984630669e2e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   job_id                                      title            location  \\\n",
              "0       1                           marketing intern    us  ny  new york   \n",
              "1       2  customer service   cloud video production      nz    auckland   \n",
              "2       3    commissioning machinery assistant  cma        us  ia  wever   \n",
              "3       4          account executive   washington dc  us  dc  washington   \n",
              "4       5                        bill review manager  us  fl  fort worth   \n",
              "\n",
              "  department salary_range                                    company_profile  \\\n",
              "0  marketing               we re food52  and we ve created a groundbreaki...   \n",
              "1    success               90 seconds  the worlds cloud video production ...   \n",
              "2                          valor services provides workforce solutions th...   \n",
              "3      sales               our passion for improving quality of life thro...   \n",
              "4                          spotsource solutions llc is a global human cap...   \n",
              "\n",
              "                                         description  \\\n",
              "0  food52  a fast growing  james beard award winn...   \n",
              "1  organised   focused   vibrant   awesome do you...   \n",
              "2  our client  located in houston  is actively se...   \n",
              "3  the company  esri – environmental systems rese...   \n",
              "4  job title  itemization review managerlocation ...   \n",
              "\n",
              "                                        requirements  \\\n",
              "0  experience with content management systems a m...   \n",
              "1  what we expect from you your key responsibilit...   \n",
              "2  implement pre commissioning and commissioning ...   \n",
              "3  education  bachelor’s or master’s in gis  busi...   \n",
              "4  qualifications rn license in the state of texa...   \n",
              "\n",
              "                                            benefits  telecommuting  \\\n",
              "0                                                                 0   \n",
              "1  what you will get from usthrough being part of...              0   \n",
              "2                                                                 0   \n",
              "3  our culture is anything but corporate—we have ...              0   \n",
              "4                              full benefits offered              0   \n",
              "\n",
              "   has_company_logo  has_questions employment_type required_experience  \\\n",
              "0                 1              0           other          internship   \n",
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              "2                 1              0                                       \n",
              "3                 1              0       full time    mid senior level   \n",
              "4                 1              1       full time    mid senior level   \n",
              "\n",
              "  required_education                   industry              function  \\\n",
              "0                                                           marketing   \n",
              "1                     marketing and advertising      customer service   \n",
              "2                                                                       \n",
              "3  bachelor s degree          computer software                 sales   \n",
              "4  bachelor s degree     hospital   health care  health care provider   \n",
              "\n",
              "   fraudulent  \n",
              "0           0  \n",
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              "      <th>3</th>\n",
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              "      <td>account executive   washington dc</td>\n",
              "      <td>us  dc  washington</td>\n",
              "      <td>sales</td>\n",
              "      <td></td>\n",
              "      <td>our passion for improving quality of life thro...</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>bill review manager</td>\n",
              "      <td>us  fl  fort worth</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>spotsource solutions llc is a global human cap...</td>\n",
              "      <td>job title  itemization review managerlocation ...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff3d8ed2-5280-4fe9-be13-3d22defc4e78')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ff3d8ed2-5280-4fe9-be13-3d22defc4e78 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-ff3d8ed2-5280-4fe9-be13-3d22defc4e78');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Remove Punctuation\n",
        "punc_list = list(string.punctuation)\n",
        "def remove_punctuation(text, punct_list):\n",
        "    for punc in punct_list:\n",
        "        if punc in text:\n",
        "            text = text.replace(punc, ' ')\n",
        "    return text\n",
        "\n",
        "new_df = new_df.applymap(lambda s: remove_punctuation(s, punc_list) if type(s) == str else s)\n",
        "display(new_df.head(5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "-zl9Z_UEh9Mp",
        "outputId": "927ce840-0c51-42c2-ebfd-ba6a6a43c927"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   job_id                                          title  \\\n",
              "0       1                            [marketing, intern]   \n",
              "1       2  [customer, service, cloud, video, production]   \n",
              "2       3     [commissioning, machinery, assistant, cma]   \n",
              "3       4           [account, executive, washington, dc]   \n",
              "4       5                        [bill, review, manager]   \n",
              "\n",
              "                location   department salary_range  \\\n",
              "0    [us, ny, new, york]  [marketing]           []   \n",
              "1         [nz, auckland]    [success]           []   \n",
              "2        [us, ia, wever]           []           []   \n",
              "3   [us, dc, washington]      [sales]           []   \n",
              "4  [us, fl, fort, worth]           []           []   \n",
              "\n",
              "                                     company_profile  \\\n",
              "0  [we, re, food52, and, we, ve, created, a, grou...   \n",
              "1  [90, seconds, the, worlds, cloud, video, produ...   \n",
              "2  [valor, services, provides, workforce, solutio...   \n",
              "3  [our, passion, for, improving, quality, of, li...   \n",
              "4  [spotsource, solutions, llc, is, a, global, hu...   \n",
              "\n",
              "                                         description  \\\n",
              "0  [food52, a, fast, growing, james, beard, award...   \n",
              "1  [organised, focused, vibrant, awesome, do, you...   \n",
              "2  [our, client, located, in, houston, is, active...   \n",
              "3  [the, company, esri, –, environmental, systems...   \n",
              "4  [job, title, itemization, review, managerlocat...   \n",
              "\n",
              "                                        requirements  \\\n",
              "0  [experience, with, content, management, system...   \n",
              "1  [what, we, expect, from, you, your, key, respo...   \n",
              "2  [implement, pre, commissioning, and, commissio...   \n",
              "3  [education, bachelor, ’, s, or, master, ’, s, ...   \n",
              "4  [qualifications, rn, license, in, the, state, ...   \n",
              "\n",
              "                                            benefits  telecommuting  \\\n",
              "0                                                 []              0   \n",
              "1  [what, you, will, get, from, usthrough, being,...              0   \n",
              "2                                                 []              0   \n",
              "3  [our, culture, is, anything, but, corporate—we...              0   \n",
              "4                          [full, benefits, offered]              0   \n",
              "\n",
              "   has_company_logo  has_questions employment_type   required_experience  \\\n",
              "0                 1              0         [other]          [internship]   \n",
              "1                 1              0    [full, time]     [not, applicable]   \n",
              "2                 1              0              []                    []   \n",
              "3                 1              0    [full, time]  [mid, senior, level]   \n",
              "4                 1              1    [full, time]  [mid, senior, level]   \n",
              "\n",
              "      required_education                       industry  \\\n",
              "0                     []                             []   \n",
              "1                     []  [marketing, and, advertising]   \n",
              "2                     []                             []   \n",
              "3  [bachelor, s, degree]           [computer, software]   \n",
              "4  [bachelor, s, degree]       [hospital, health, care]   \n",
              "\n",
              "                   function  fraudulent  \n",
              "0               [marketing]           0  \n",
              "1       [customer, service]           0  \n",
              "2                        []           0  \n",
              "3                   [sales]           0  \n",
              "4  [health, care, provider]           0  "
            ],
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              "\n",
              "  <div id=\"df-2f0bbb5b-2f56-4afa-8475-6ffd29c77618\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        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>job_id</th>\n",
              "      <th>title</th>\n",
              "      <th>location</th>\n",
              "      <th>department</th>\n",
              "      <th>salary_range</th>\n",
              "      <th>company_profile</th>\n",
              "      <th>description</th>\n",
              "      <th>requirements</th>\n",
              "      <th>benefits</th>\n",
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              "      <th>required_education</th>\n",
              "      <th>industry</th>\n",
              "      <th>function</th>\n",
              "      <th>fraudulent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>[marketing, intern]</td>\n",
              "      <td>[us, ny, new, york]</td>\n",
              "      <td>[marketing]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[we, re, food52, and, we, ve, created, a, grou...</td>\n",
              "      <td>[food52, a, fast, growing, james, beard, award...</td>\n",
              "      <td>[experience, with, content, management, system...</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>[other]</td>\n",
              "      <td>[internship]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[marketing]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>[customer, service, cloud, video, production]</td>\n",
              "      <td>[nz, auckland]</td>\n",
              "      <td>[success]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[90, seconds, the, worlds, cloud, video, produ...</td>\n",
              "      <td>[organised, focused, vibrant, awesome, do, you...</td>\n",
              "      <td>[what, we, expect, from, you, your, key, respo...</td>\n",
              "      <td>[what, you, will, get, from, usthrough, being,...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>[full, time]</td>\n",
              "      <td>[not, applicable]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[marketing, and, advertising]</td>\n",
              "      <td>[customer, service]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>[commissioning, machinery, assistant, cma]</td>\n",
              "      <td>[us, ia, wever]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[valor, services, provides, workforce, solutio...</td>\n",
              "      <td>[our, client, located, in, houston, is, active...</td>\n",
              "      <td>[implement, pre, commissioning, and, commissio...</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>[account, executive, washington, dc]</td>\n",
              "      <td>[us, dc, washington]</td>\n",
              "      <td>[sales]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[our, passion, for, improving, quality, of, li...</td>\n",
              "      <td>[the, company, esri, –, environmental, systems...</td>\n",
              "      <td>[education, bachelor, ’, s, or, master, ’, s, ...</td>\n",
              "      <td>[our, culture, is, anything, but, corporate—we...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>[full, time]</td>\n",
              "      <td>[mid, senior, level]</td>\n",
              "      <td>[bachelor, s, degree]</td>\n",
              "      <td>[computer, software]</td>\n",
              "      <td>[sales]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>[bill, review, manager]</td>\n",
              "      <td>[us, fl, fort, worth]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[spotsource, solutions, llc, is, a, global, hu...</td>\n",
              "      <td>[job, title, itemization, review, managerlocat...</td>\n",
              "      <td>[qualifications, rn, license, in, the, state, ...</td>\n",
              "      <td>[full, benefits, offered]</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>[full, time]</td>\n",
              "      <td>[mid, senior, level]</td>\n",
              "      <td>[bachelor, s, degree]</td>\n",
              "      <td>[hospital, health, care]</td>\n",
              "      <td>[health, care, provider]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f0bbb5b-2f56-4afa-8475-6ffd29c77618')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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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",
              "    [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-2f0bbb5b-2f56-4afa-8475-6ffd29c77618 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-2f0bbb5b-2f56-4afa-8475-6ffd29c77618');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Tokenize each text column\n",
        "token_df = new_df.copy()\n",
        "for column in new_df.columns:\n",
        "    token_df[column] = new_df[column].apply(lambda s: word_tokenize(s) if type(s) == str else s)\n",
        "display(token_df.head(5))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AILw9JOa5438"
      },
      "source": [
        "### tokenizer "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "1I2njQ_SoAMj"
      },
      "outputs": [],
      "source": [
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.utils import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Embedding, LSTM, Conv1D, MaxPool1D, SimpleRNN\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "KJpGeXjnn3Cb"
      },
      "outputs": [],
      "source": [
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(token_df['description'])\n",
        "\n",
        "X = tokenizer.texts_to_sequences(token_df['description'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujcQnLiTou0k",
        "outputId": "a0a92419-5890-41d5-d035-8e4b1560b9c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3025, 5, 149, 96, 5426, 7320, 808, 759, 173, 547]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "X[0][:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMtBEjyCo0UR",
        "outputId": "5cc3d75d-f569-4f9f-9a8a-a93f9fdec176"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "and -> 1\n",
            "the -> 2\n",
            "to -> 3\n",
            "of -> 4\n",
            "a -> 5\n",
            "in -> 6\n",
            "for -> 7\n",
            "with -> 8\n",
            "you -> 9\n",
            "our -> 10\n"
          ]
        }
      ],
      "source": [
        "word_index = tokenizer.word_index\n",
        "for word, num in word_index.items():\n",
        "    print(f\"{word} -> {num}\")\n",
        "    if num == 10:\n",
        "        break    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "6DH_pb9Co2H7",
        "outputId": "01c91e6c-03bd-40be-e6c7-fa2e742f5654"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.hist([len(x) for x in X], bins=500)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "poDO1hGlo5pA",
        "outputId": "0a71fb9c-36d8-4321-a132-9736b737440b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17772"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "import numpy as np\n",
        "nos = np.array([len(x) for x in X])\n",
        "len(nos[nos  < 700])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "U1op4Orbo9Ez"
      },
      "outputs": [],
      "source": [
        "#Lets keep all descriptions to 700, add padding to descriptions with less than 700 words and truncating long ones\n",
        "maxlen = 700 \n",
        "\n",
        "#Making all descriptions of size maxlen defined above\n",
        "X = pad_sequences(X, maxlen=maxlen)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lrU_nBMpBJY",
        "outputId": "8980cdb8-8183-46e4-8cf7-39c53437639b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "700"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "#all descriptions has 700 words (in numerical form now). If they had less words, they have been padded with 0\n",
        "# 0 is not associated to any word, as mapping of words started from 1\n",
        "# 0 will also be used later, if unknows word is encountered in test set\n",
        "len(X[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "anrg5N5tpHfj"
      },
      "outputs": [],
      "source": [
        "# Adding 1 because of reserved 0 index\n",
        "# Embedding Layer creates one more vector for \"UNKNOWN\" words, or padded words (0s). This Vector is filled with zeros.\n",
        "# Thus our vocab size inceeases by 1\n",
        "vocab_size = len(tokenizer.word_index) + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "zVJwgGMD-_Vt"
      },
      "outputs": [],
      "source": [
        "y = df['fraudulent']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-P0pUyzwvU6"
      },
      "source": [
        "### oversample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "SvZkpfoOxPH8"
      },
      "outputs": [],
      "source": [
        "from imblearn.over_sampling import RandomOverSampler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "9wM-e7Cnw8cq"
      },
      "outputs": [],
      "source": [
        "oversample = RandomOverSampler(sampling_strategy='minority')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "TBez8HEky-4g",
        "outputId": "6de975aa-7019-4311-93e0-b6cfc3a91581"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd890e6ed30>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "y.value_counts().plot.bar()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "qVJFp664_BQg"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "0mODa4qnxQ43"
      },
      "outputs": [],
      "source": [
        "X_train, y_train = oversample.fit_resample(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "sgL7tOG5yyHh",
        "outputId": "3c954adb-39d1-4ec9-c8bc-40ddbfd27e10"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd890e6e280>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "y_train.value_counts().plot.bar()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ClgnzAl25lfR"
      },
      "source": [
        "## Model Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHW90M8qA5s8"
      },
      "source": [
        "### model with no embedding layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uf4B9u6b-iIj"
      },
      "outputs": [],
      "source": [
        "#Defining Neural Network\n",
        "model = Sequential()\n",
        "#Non-trainable embeddidng layer\n",
        "#LSTM \n",
        "model.add(LSTM(units=128,input_shape=(700, 1)))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFOBTX2t-xzK",
        "outputId": "794d99cb-68b5-4128-d2f7-84038b77b296"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 128)               66560     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 66,689\n",
            "Trainable params: 66,689\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_xfsYuc-zr9",
        "outputId": "b589f1c3-6650-456e-ce17-2cc174953e7e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "558/558 [==============================] - 28s 38ms/step - loss: 0.5727 - acc: 0.7244 - val_loss: 0.9599 - val_acc: 0.1352\n",
            "Epoch 2/6\n",
            "558/558 [==============================] - 21s 38ms/step - loss: 0.5433 - acc: 0.7355 - val_loss: 1.0332 - val_acc: 0.1055\n",
            "Epoch 3/6\n",
            "558/558 [==============================] - 22s 40ms/step - loss: 0.5084 - acc: 0.7564 - val_loss: 0.6318 - val_acc: 0.7098\n",
            "Epoch 4/6\n",
            "558/558 [==============================] - 23s 41ms/step - loss: 0.4374 - acc: 0.7967 - val_loss: 0.9041 - val_acc: 0.4859\n",
            "Epoch 5/6\n",
            "558/558 [==============================] - 21s 38ms/step - loss: 0.3898 - acc: 0.8292 - val_loss: 0.6997 - val_acc: 0.6645\n",
            "Epoch 6/6\n",
            "558/558 [==============================] - 21s 38ms/step - loss: 0.3444 - acc: 0.8544 - val_loss: 0.6345 - val_acc: 0.7034\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fc330361640>"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(X_train, y_train, validation_split=0.3, epochs=6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zc6XbQwb-12I",
        "outputId": "9bc57573-5c58-408d-bb65-59770ab9df6f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "140/140 [==============================] - 2s 14ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = (model.predict(X_test) >= 0.5).astype(\"int\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwBysA1I-5hS",
        "outputId": "c91ca76d-1398-4f2a-e035-43f757be815e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.93      0.95      4272\n",
            "           1       0.26      0.51      0.35       198\n",
            "\n",
            "    accuracy                           0.92      4470\n",
            "   macro avg       0.62      0.72      0.65      4470\n",
            "weighted avg       0.94      0.92      0.93      4470\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dbxj69bwBD5X"
      },
      "source": [
        "### model with word2Vec embedding layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "w5PZaepOeKER"
      },
      "outputs": [],
      "source": [
        "EMBEDDING_DIM = 100"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyl5l88R6LFb"
      },
      "source": [
        "#### word2vec"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "2Miwj-OX6LFc"
      },
      "outputs": [],
      "source": [
        "import gensim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "5-YvXYn-6LFd"
      },
      "outputs": [],
      "source": [
        "sentence = token_df['description']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NT9wCqIa6LFd",
        "outputId": "c5cff6f4-da52-40f4-fb7c-15a7d4cacf24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "(17880,)\n",
            "0        [food52, a, fast, growing, james, beard, award...\n",
            "1        [organised, focused, vibrant, awesome, do, you...\n",
            "2        [our, client, located, in, houston, is, active...\n",
            "3        [the, company, esri, –, environmental, systems...\n",
            "4        [job, title, itemization, review, managerlocat...\n",
            "                               ...                        \n",
            "17875    [just, in, case, this, is, the, first, time, y...\n",
            "17876    [the, payroll, accountant, will, focus, primar...\n",
            "17877    [experienced, project, cost, control, staff, e...\n",
            "17878    [nemsia, studios, is, looking, for, an, experi...\n",
            "17879    [who, are, we, vend, is, an, award, winning, w...\n",
            "Name: description, Length: 17880, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(type(sentence))\n",
        "print(sentence.shape)\n",
        "print(sentence)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YoFarlOR6LFe",
        "outputId": "7d05533f-60d6-4a59-eb3e-d34f8c1465a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['organised', 'focused', 'vibrant', 'awesome', 'do']"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "sentence[1][:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWZ-zxly6LFf",
        "outputId": "74d508ae-5024-4c0b-bd0c-24e1cb5e230d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "'reproducing' in sentence[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "Bcel78Bt6LFf"
      },
      "outputs": [],
      "source": [
        "# initialize a model\n",
        "w2v_model = gensim.models.Word2Vec(sentence, size=100, min_count=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Vfv3pUa6LFg",
        "outputId": "d8a869e3-c303-4c85-b7a7-9ed78b1a7714"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.00062329  0.0498212   0.08530529 -0.00125262 -0.01489186  0.04034812\n",
            "  0.03812296  0.03571906 -0.06341068 -0.0420344   0.01132633 -0.04271222\n",
            " -0.03024082 -0.036109   -0.03750932  0.01320946  0.02575871  0.0686506\n",
            "  0.06655494  0.06486227 -0.01068479  0.02766676  0.02643735  0.01772205\n",
            " -0.01145937 -0.00095719 -0.04017467  0.07008585 -0.0098391  -0.07565887\n",
            "  0.03428091  0.03498233  0.02872053  0.03321368  0.00490183 -0.00067558\n",
            " -0.02519242  0.02093745  0.03296743 -0.05252251 -0.0607527  -0.00417768\n",
            " -0.03699403 -0.00500933 -0.00663219  0.01455477  0.05356229 -0.02537\n",
            " -0.08610583 -0.0888812  -0.02052267 -0.1078166   0.00723931 -0.05310535\n",
            "  0.05855199 -0.02231228  0.05708353 -0.02434276  0.0276176   0.01733991\n",
            " -0.01073983 -0.03219206 -0.06353445  0.03739585 -0.02180521  0.01912833\n",
            "  0.05018204  0.02616036  0.03640198  0.00782739 -0.02195341 -0.03325364\n",
            "  0.08248302  0.01426963  0.03989118  0.01956808 -0.04609757  0.00938743\n",
            "  0.08777583  0.00449176  0.10819012 -0.04985386 -0.02429663  0.02862748\n",
            "  0.07442554 -0.0021985  -0.0169454   0.01363772 -0.04563374 -0.06237229\n",
            "  0.0241743  -0.11279841  0.10626285 -0.06148292 -0.16401356 -0.06158196\n",
            "  0.04173446  0.02277612 -0.02578465  0.01404049]\n"
          ]
        }
      ],
      "source": [
        "print(w2v_model.wv[\"reproducing\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "BiUEDogJ6LFh"
      },
      "outputs": [],
      "source": [
        "def most_similar(w2v_model, words, topn=10):\n",
        "    similar_df = pd.DataFrame()\n",
        "    for word in words:\n",
        "        try:\n",
        "            similar_words = pd.DataFrame(w2v_model.wv.most_similar(word, topn=topn), columns=[word, 'cos'])\n",
        "            similar_df = pd.concat([similar_df, similar_words], axis=1)\n",
        "        except:\n",
        "            print(word, \"not found in Word2Vec model!\")\n",
        "    return similar_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Ay6-6o5z6LFh",
        "outputId": "cc8c0efb-2d31-4059-ce15-cd6d834d41f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      our       cos                      title       cos  \\\n",
              "0                     its  0.647316     assistantdescriptionan  0.667890   \n",
              "1                     the  0.575469  descriptionadministrative  0.631626   \n",
              "2                   their  0.526456            analystlocation  0.608782   \n",
              "3               cargobarn  0.469371                  shoreland  0.607702   \n",
              "4  reactivationpartnering  0.440518                    summary  0.577561   \n",
              "5                testsyou  0.438143                         jr  0.577309   \n",
              "6                 success  0.424167                description  0.576535   \n",
              "7                  global  0.422468                     escrow  0.561300   \n",
              "8      resultsinteracting  0.419508                  paralegal  0.558223   \n",
              "9                cleanweb  0.402541                          ―  0.552016   \n",
              "\n",
              "          client       cos       company       cos  \n",
              "0       customer  0.683258  organization  0.660439  \n",
              "1        clients  0.631586  organisation  0.626861  \n",
              "2   teamconsumer  0.562217          firm  0.524735  \n",
              "3  customersplan  0.558160        agency  0.511640  \n",
              "4      customers  0.507048    department  0.495721  \n",
              "5    membersplan  0.492424       startup  0.459227  \n",
              "6      strategic  0.492030            uk  0.459029  \n",
              "7        partner  0.480728      industry  0.421131  \n",
              "8         vendor  0.480097         group  0.419480  \n",
              "9       employee  0.475023           csm  0.414177  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>our</th>\n",
              "      <th>cos</th>\n",
              "      <th>title</th>\n",
              "      <th>cos</th>\n",
              "      <th>client</th>\n",
              "      <th>cos</th>\n",
              "      <th>company</th>\n",
              "      <th>cos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>its</td>\n",
              "      <td>0.647316</td>\n",
              "      <td>assistantdescriptionan</td>\n",
              "      <td>0.667890</td>\n",
              "      <td>customer</td>\n",
              "      <td>0.683258</td>\n",
              "      <td>organization</td>\n",
              "      <td>0.660439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>the</td>\n",
              "      <td>0.575469</td>\n",
              "      <td>descriptionadministrative</td>\n",
              "      <td>0.631626</td>\n",
              "      <td>clients</td>\n",
              "      <td>0.631586</td>\n",
              "      <td>organisation</td>\n",
              "      <td>0.626861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>their</td>\n",
              "      <td>0.526456</td>\n",
              "      <td>analystlocation</td>\n",
              "      <td>0.608782</td>\n",
              "      <td>teamconsumer</td>\n",
              "      <td>0.562217</td>\n",
              "      <td>firm</td>\n",
              "      <td>0.524735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cargobarn</td>\n",
              "      <td>0.469371</td>\n",
              "      <td>shoreland</td>\n",
              "      <td>0.607702</td>\n",
              "      <td>customersplan</td>\n",
              "      <td>0.558160</td>\n",
              "      <td>agency</td>\n",
              "      <td>0.511640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>reactivationpartnering</td>\n",
              "      <td>0.440518</td>\n",
              "      <td>summary</td>\n",
              "      <td>0.577561</td>\n",
              "      <td>customers</td>\n",
              "      <td>0.507048</td>\n",
              "      <td>department</td>\n",
              "      <td>0.495721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>testsyou</td>\n",
              "      <td>0.438143</td>\n",
              "      <td>jr</td>\n",
              "      <td>0.577309</td>\n",
              "      <td>membersplan</td>\n",
              "      <td>0.492424</td>\n",
              "      <td>startup</td>\n",
              "      <td>0.459227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>success</td>\n",
              "      <td>0.424167</td>\n",
              "      <td>description</td>\n",
              "      <td>0.576535</td>\n",
              "      <td>strategic</td>\n",
              "      <td>0.492030</td>\n",
              "      <td>uk</td>\n",
              "      <td>0.459029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>global</td>\n",
              "      <td>0.422468</td>\n",
              "      <td>escrow</td>\n",
              "      <td>0.561300</td>\n",
              "      <td>partner</td>\n",
              "      <td>0.480728</td>\n",
              "      <td>industry</td>\n",
              "      <td>0.421131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>resultsinteracting</td>\n",
              "      <td>0.419508</td>\n",
              "      <td>paralegal</td>\n",
              "      <td>0.558223</td>\n",
              "      <td>vendor</td>\n",
              "      <td>0.480097</td>\n",
              "      <td>group</td>\n",
              "      <td>0.419480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>cleanweb</td>\n",
              "      <td>0.402541</td>\n",
              "      <td>―</td>\n",
              "      <td>0.552016</td>\n",
              "      <td>employee</td>\n",
              "      <td>0.475023</td>\n",
              "      <td>csm</td>\n",
              "      <td>0.414177</td>\n",
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              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "most_similar(w2v_model,['our','title','client','company'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfQKHgMp77ke"
      },
      "source": [
        "#### create embedding_vectors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "S6g-8dSwpMdx"
      },
      "outputs": [],
      "source": [
        "# Function to create weight matrix from word2vec gensim model\n",
        "def get_weight_matrix(model, vocab):\n",
        "    # total vocabulary size plus 0 for unknown words\n",
        "    vocab_size = len(vocab) + 1\n",
        "    # define weight matrix dimensions with all 0\n",
        "    weight_matrix = np.zeros((vocab_size, EMBEDDING_DIM))\n",
        "    # step vocab, store vectors using the Tokenizer's integer mapping\n",
        "    for word, i in vocab.items():\n",
        "        weight_matrix[i] = model.wv[word]\n",
        "    return weight_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKf5oMMgpjxb",
        "outputId": "4c51cbda-2532-4419-9355-553e378be20d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'dict'>\n",
            "15409\n"
          ]
        }
      ],
      "source": [
        "print(type(word_index))\n",
        "print(word_index[\"reproducing\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "vdqUrGHKpOaa"
      },
      "outputs": [],
      "source": [
        "#Getting embedding vectors from word2vec and usings it as weights of non-trainable keras embedding layer\n",
        "embedding_vectors = get_weight_matrix(w2v_model, word_index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRPVMZUcooGP",
        "outputId": "17e215dc-474f-4f25-9466-ba79e85b7e87"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.1093884 ,  0.92519796, -0.7353275 , -0.7073498 , -1.3840168 ,\n",
              "        0.51579756,  0.17734994, -1.3891793 , -0.06611987,  0.8886675 ,\n",
              "       -0.53825825, -0.52612054,  1.0277138 ,  1.8273603 ,  1.3067213 ,\n",
              "       -1.1652528 ,  1.0488411 ,  1.0230304 ,  0.7986655 ,  0.7438682 ,\n",
              "        0.17770427, -1.1002121 , -0.9038848 , -0.19892673,  0.6156057 ,\n",
              "        0.6356144 , -0.46962327, -0.18428124,  0.15815845, -0.10241646,\n",
              "        2.9565368 ,  0.07031235, -0.81419283, -1.2041827 , -1.1232411 ,\n",
              "        0.34675652, -0.07988142,  1.3514696 ,  0.11408135,  0.6743372 ,\n",
              "        1.8243078 , -0.65693194, -1.0702657 ,  0.8004537 ,  1.1839561 ,\n",
              "       -0.5220152 ,  0.6987609 , -0.612823  , -0.44561833, -0.49618235,\n",
              "       -0.48599806,  0.7307539 ,  0.5528368 , -0.93728465,  0.1997045 ,\n",
              "        1.5242621 ,  2.4989257 ,  0.23858106,  0.29177734,  0.8033563 ,\n",
              "       -0.12028384, -1.0350748 , -0.15074453, -0.8519465 ,  1.2636303 ,\n",
              "        0.20200518, -0.40440446, -0.24812585,  0.9848704 ,  0.26746762,\n",
              "        2.1273472 ,  0.3652984 ,  0.6684529 , -0.9322324 ,  0.581935  ,\n",
              "       -0.5104699 , -0.8620387 , -0.06435682, -0.9892307 ,  0.88499814,\n",
              "        0.52471846,  0.22145915, -0.7118506 ,  0.4300239 ,  0.7422194 ,\n",
              "       -0.36004043, -1.0145965 ,  0.8785093 ,  0.05617242,  0.92842835,\n",
              "       -0.22866066,  0.5682152 , -1.2191311 ,  0.08185615,  1.5254725 ,\n",
              "       -1.03916   ,  1.1234447 , -0.17669503, -0.39390326,  0.6454685 ],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "w2v_model.wv['and']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XVEbzMhZnaey",
        "outputId": "ee4d5892-9652-4376-976e-7942f7950fe9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.10938835,  0.92519796, -0.73532748, -0.70734978, -1.38401675,\n",
              "        0.51579756,  0.17734994, -1.38917935, -0.06611987,  0.88866752,\n",
              "       -0.53825825, -0.52612054,  1.02771378,  1.82736027,  1.30672133,\n",
              "       -1.1652528 ,  1.04884112,  1.0230304 ,  0.79866552,  0.74386817,\n",
              "        0.17770427, -1.1002121 , -0.90388483, -0.19892673,  0.61560571,\n",
              "        0.6356144 , -0.46962327, -0.18428124,  0.15815845, -0.10241646,\n",
              "        2.95653677,  0.07031235, -0.81419283, -1.20418274, -1.12324107,\n",
              "        0.34675652, -0.07988142,  1.35146964,  0.11408135,  0.67433721,\n",
              "        1.8243078 , -0.65693194, -1.07026565,  0.80045372,  1.18395615,\n",
              "       -0.52201521,  0.69876093, -0.61282301, -0.44561833, -0.49618235,\n",
              "       -0.48599806,  0.7307539 ,  0.55283678, -0.93728465,  0.1997045 ,\n",
              "        1.52426207,  2.49892569,  0.23858106,  0.29177734,  0.80335629,\n",
              "       -0.12028384, -1.03507483, -0.15074453, -0.85194647,  1.26363027,\n",
              "        0.20200518, -0.40440446, -0.24812585,  0.98487037,  0.26746762,\n",
              "        2.12734723,  0.36529839,  0.66845292, -0.93223238,  0.58193499,\n",
              "       -0.51046991, -0.86203867, -0.06435682, -0.98923069,  0.88499814,\n",
              "        0.52471846,  0.22145915, -0.71185058,  0.43002391,  0.74221939,\n",
              "       -0.36004043, -1.01459646,  0.87850928,  0.05617242,  0.92842835,\n",
              "       -0.22866066,  0.56821519, -1.21913111,  0.08185615,  1.52547252,\n",
              "       -1.03916001,  1.12344468, -0.17669503, -0.39390326,  0.64546847])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "embedding_vectors[1][:100]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7uUZzyH2CQT",
        "outputId": "bf4b8703-fa07-4622-ca47-83d70ca7ac3c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "embedding_vectors[1].size"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### RNN"
      ],
      "metadata": {
        "id": "pktaHMlXJ89U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Defining Neural Network\n",
        "model = Sequential()\n",
        "#Non-trainable embeddidng layer\n",
        "model.add(Embedding(vocab_size, output_dim=EMBEDDING_DIM, weights=[embedding_vectors], input_length=maxlen, trainable=False))\n",
        "#RNN\n",
        "model.add(SimpleRNN(units=128))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n"
      ],
      "metadata": {
        "id": "_7zS2KviKqtn"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, validation_split=0.3, epochs=6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xknNc6aaK061",
        "outputId": "72728aff-c981-4aab-faa0-f72edd8005fb"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/6\n",
            "559/559 [==============================] - 306s 546ms/step - loss: 0.3461 - acc: 0.8475 - val_loss: 0.3048 - val_acc: 0.8770\n",
            "Epoch 2/6\n",
            "559/559 [==============================] - 297s 531ms/step - loss: 0.1293 - acc: 0.9562 - val_loss: 0.0986 - val_acc: 0.9751\n",
            "Epoch 3/6\n",
            "559/559 [==============================] - 293s 525ms/step - loss: 0.0550 - acc: 0.9842 - val_loss: 0.0307 - val_acc: 0.9952\n",
            "Epoch 4/6\n",
            "559/559 [==============================] - 293s 525ms/step - loss: 0.1264 - acc: 0.9631 - val_loss: 0.4338 - val_acc: 0.8158\n",
            "Epoch 5/6\n",
            "559/559 [==============================] - 296s 529ms/step - loss: 0.0909 - acc: 0.9713 - val_loss: 0.0713 - val_acc: 0.9847\n",
            "Epoch 6/6\n",
            "559/559 [==============================] - 300s 538ms/step - loss: 0.0251 - acc: 0.9948 - val_loss: 0.0382 - val_acc: 0.9909\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd826424190>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = (model.predict(X_test) >= 0.5).astype(\"int\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y8ZxEO0wSB9A",
        "outputId": "09de6bcd-b4b0-4375-f3ce-0fc02b14bee2"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "140/140 [==============================] - 12s 87ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IuRoJBOCSEBn",
        "outputId": "b51412e6-d38e-4aac-de82-f0313edd407c"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.98      0.98      4254\n",
            "           1       0.68      0.62      0.65       216\n",
            "\n",
            "    accuracy                           0.97      4470\n",
            "   macro avg       0.83      0.80      0.82      4470\n",
            "weighted avg       0.97      0.97      0.97      4470\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8Rnmk4b8Uj-"
      },
      "source": [
        "#### LSTM model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "ybHc2FaQx72k"
      },
      "outputs": [],
      "source": [
        "#Defining Neural Network\n",
        "model = Sequential()\n",
        "#Non-trainable embeddidng layer\n",
        "model.add(Embedding(vocab_size, output_dim=EMBEDDING_DIM, weights=[embedding_vectors], input_length=maxlen, trainable=False))\n",
        "#LSTM \n",
        "model.add(LSTM(units=128))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQjZIk2iyFJA",
        "outputId": "20245418-1a88-4e7a-fe3a-e102452ecadc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 700, 100)          6429900   \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 128)               117248    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6,547,277\n",
            "Trainable params: 117,377\n",
            "Non-trainable params: 6,429,900\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBAjmlXByXMF",
        "outputId": "efbb08c3-700a-46c1-b357-07d2b33b2615"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/6\n",
            "559/559 [==============================] - 31s 43ms/step - loss: 0.2816 - acc: 0.8774 - val_loss: 0.2646 - val_acc: 0.8956\n",
            "Epoch 2/6\n",
            "559/559 [==============================] - 24s 43ms/step - loss: 0.1255 - acc: 0.9619 - val_loss: 0.1210 - val_acc: 0.9667\n",
            "Epoch 3/6\n",
            "559/559 [==============================] - 24s 43ms/step - loss: 0.0350 - acc: 0.9899 - val_loss: 0.0255 - val_acc: 0.9966\n",
            "Epoch 4/6\n",
            "559/559 [==============================] - 24s 43ms/step - loss: 0.0356 - acc: 0.9897 - val_loss: 0.1731 - val_acc: 0.9421\n",
            "Epoch 5/6\n",
            "559/559 [==============================] - 24s 43ms/step - loss: 0.0232 - acc: 0.9922 - val_loss: 0.0180 - val_acc: 0.9939\n",
            "Epoch 6/6\n",
            "559/559 [==============================] - 24s 43ms/step - loss: 0.0098 - acc: 0.9973 - val_loss: 0.0166 - val_acc: 0.9946\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd8264c4100>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "model.fit(X_train, y_train, validation_split=0.3, epochs=6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wENCZWEXysix",
        "outputId": "3551375e-324d-4683-bdaa-f580b23460ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "140/140 [==============================] - 3s 16ms/step\n"
          ]
        }
      ],
      "source": [
        "y_pred = (model.predict(X_test) >= 0.5).astype(\"int\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFZW7M7kyuUM",
        "outputId": "0dc43027-1eb8-46dc-cf3d-454e268233f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      4254\n",
            "           1       0.84      0.66      0.74       216\n",
            "\n",
            "    accuracy                           0.98      4470\n",
            "   macro avg       0.91      0.83      0.86      4470\n",
            "weighted avg       0.98      0.98      0.98      4470\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.10.7 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.7"
    },
    "vscode": {
      "interpreter": {
        "hash": "2a8dfe095fce2b5e88c64a2c3ee084c8e0e0d70b23e7b95b1cfb538be294c5c8"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}