Support_Mail_BOT_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MeU5waEpqlfl"
      },
      "source": [
        "# Section: 1 - Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Name: Lata\n",
        "# Project: Barclays: Problem Statement - Support Mail/BOT Prediction\n",
        "# Dataset: https://www.kaggle.com/datasets/wcukierski/enron-email-dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUZZMmUVDxst",
        "outputId": "b557ad1d-b998-45a6-ce55-fc0a49e254d5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /home/lata/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /home/lata/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Importing necessary libraries for text processing and machine learning\n",
        "\n",
        "import re\n",
        "import string\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer , TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
        "import pandas as pd\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import nltk\n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxIDoC86q0SR"
      },
      "source": [
        "#Section: 2 - Data Loading\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjeCMZUXFIhg",
        "outputId": "408dc10a-65cb-43db-98d1-2be35eb5efcd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Defaulting to user installation because normal site-packages is not writeable\n",
            "Requirement already satisfied: kaggle in /home/lata/.local/lib/python3.10/site-packages (1.6.14)\n",
            "Requirement already satisfied: six>=1.10 in /usr/lib/python3/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /home/lata/.local/lib/python3.10/site-packages (from kaggle) (2024.6.2)\n",
            "Requirement already satisfied: python-dateutil in /home/lata/.local/lib/python3.10/site-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /home/lata/.local/lib/python3.10/site-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /home/lata/.local/lib/python3.10/site-packages (from kaggle) (4.66.1)\n",
            "Requirement already satisfied: python-slugify in /home/lata/.local/lib/python3.10/site-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /home/lata/.local/lib/python3.10/site-packages (from kaggle) (1.26.18)\n",
            "Requirement already satisfied: bleach in /home/lata/.local/lib/python3.10/site-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /home/lata/.local/lib/python3.10/site-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /home/lata/.local/lib/python3.10/site-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /home/lata/.local/lib/python3.10/site-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests->kaggle) (3.3)\n",
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /home/lata/.kaggle/kaggle.json'\n",
            "Dataset URL: https://www.kaggle.com/datasets/wcukierski/enron-email-dataset\n",
            "License(s): copyright-authors\n",
            "Downloading enron-email-dataset.zip to /home/lata/Downloads/Support-Mail-BOT-Prediction\n",
            "100%|████████████████████████████████████████| 358M/358M [54:17<00:00, 94.5kB/s]\n",
            "100%|█████████████████████████████████████████| 358M/358M [54:17<00:00, 115kB/s]\n",
            "Archive:  enron-email-dataset.zip\n",
            "  inflating: enron/emails.csv        \n"
          ]
        }
      ],
      "source": [
        "# Install Kaggle API\n",
        "!pip install kaggle\n",
        "\n",
        "# Download the dataset\n",
        "!kaggle datasets download -d wcukierski/enron-email-dataset\n",
        "\n",
        "# Unzip the dataset\n",
        "!unzip enron-email-dataset.zip -d enron"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHEcA8lwLP1J",
        "outputId": "a3e3be15-dd1e-485b-b95c-bec46af615a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                       file                                            message\n",
            "0     allen-p/_sent_mail/1.  Message-ID: <18782981.1075855378110.JavaMail.e...\n",
            "1    allen-p/_sent_mail/10.  Message-ID: <15464986.1075855378456.JavaMail.e...\n",
            "2   allen-p/_sent_mail/100.  Message-ID: <24216240.1075855687451.JavaMail.e...\n",
            "3  allen-p/_sent_mail/1000.  Message-ID: <13505866.1075863688222.JavaMail.e...\n",
            "4  allen-p/_sent_mail/1001.  Message-ID: <30922949.1075863688243.JavaMail.e...\n",
            "                       file                                            message\n",
            "0     allen-p/_sent_mail/1.  Message-ID: <18782981.1075855378110.JavaMail.e...\n",
            "1    allen-p/_sent_mail/10.  Message-ID: <15464986.1075855378456.JavaMail.e...\n",
            "2   allen-p/_sent_mail/100.  Message-ID: <24216240.1075855687451.JavaMail.e...\n",
            "3  allen-p/_sent_mail/1000.  Message-ID: <13505866.1075863688222.JavaMail.e...\n",
            "4  allen-p/_sent_mail/1001.  Message-ID: <30922949.1075863688243.JavaMail.e...\n",
            "                                   file  \\\n",
            "191198        jones-t/notes_inbox/2744.   \n",
            "351399        nemec-g/notes_inbox/2302.   \n",
            "371461  ring-r/eesirenewableenergy/450.   \n",
            "225277            kaminski-v/sent/4086.   \n",
            "185228      jones-t/all_documents/4562.   \n",
            "\n",
            "                                                  message  \n",
            "191198  Message-ID: <29345819.1075847136775.JavaMail.e...  \n",
            "351399  Message-ID: <3668489.1075842834659.JavaMail.ev...  \n",
            "371461  Message-ID: <30216915.1075861005838.JavaMail.e...  \n",
            "225277  Message-ID: <21504162.1075856932321.JavaMail.e...  \n",
            "185228  Message-ID: <17301.1075847025295.JavaMail.evan...  \n"
          ]
        }
      ],
      "source": [
        "# Load the dataset\n",
        "df = pd.read_csv('/home/lata/Downloads/Support-Mail-BOT-Prediction/enron/emails.csv')\n",
        "\n",
        "# Check the first few rows of the dataset\n",
        "print(df.head())\n",
        "\n",
        "# View the first few rows of the DataFrame\n",
        "print(df.head())\n",
        "\n",
        "# View a random sample of rows from the DataFrame\n",
        "print(df.sample(5))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHXPKPWUvUWs"
      },
      "source": [
        "### Exploring dataset Content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZ9sauMBN7FI",
        "outputId": "a5f30f5e-830c-4967-af5a-091e77160bf3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    Message-ID: <18782981.1075855378110.JavaMail.e...\n",
            "1    Message-ID: <15464986.1075855378456.JavaMail.e...\n",
            "2    Message-ID: <24216240.1075855687451.JavaMail.e...\n",
            "3    Message-ID: <13505866.1075863688222.JavaMail.e...\n",
            "4    Message-ID: <30922949.1075863688243.JavaMail.e...\n",
            "Name: message, dtype: object\n",
            "Message-ID: <18782981.1075855378110.JavaMail.evans@thyme>\n",
            "Date: Mon, 14 May 2001 16:39:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: tim.belden@enron.com\n",
            "Subject: \n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: Tim Belden <Tim Belden/Enron@EnronXGate>\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Jan2002_1\\Allen, Phillip K.\\'Sent Mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen (Non-Privileged).pst\n",
            "\n",
            "Here is our forecast\n",
            "\n",
            " \n",
            "Message-ID: <15464986.1075855378456.JavaMail.evans@thyme>\n",
            "Date: Fri, 4 May 2001 13:51:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: john.lavorato@enron.com\n",
            "Subject: Re:\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: John J Lavorato <John J Lavorato/ENRON@enronXgate@ENRON>\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Jan2002_1\\Allen, Phillip K.\\'Sent Mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen (Non-Privileged).pst\n",
            "\n",
            "Traveling to have a business meeting takes the fun out of the trip.  Especially if you have to prepare a presentation.  I would suggest holding the business plan meetings here then take a trip without any formal business meetings.  I would even try and get some honest opinions on whether a trip is even desired or necessary.\n",
            "\n",
            "As far as the business meetings, I think it would be more productive to try and stimulate discussions across the different groups about what is working and what is not.  Too often the presenter speaks and the others are quiet just waiting for their turn.   The meetings might be better if held in a round table discussion format.  \n",
            "\n",
            "My suggestion for where to go is Austin.  Play golf and rent a ski boat and jet ski's.  Flying somewhere takes too much time.\n",
            "\n",
            "Message-ID: <24216240.1075855687451.JavaMail.evans@thyme>\n",
            "Date: Wed, 18 Oct 2000 03:00:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: leah.arsdall@enron.com\n",
            "Subject: Re: test\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: Leah Van Arsdall\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "test successful.  way to go!!!\n"
          ]
        }
      ],
      "source": [
        "#Exploring data set\n",
        "\n",
        "print(df['message'].head())\n",
        "print(df['message'].iloc[0])  # Print the first message\n",
        "print(df['message'].iloc[1])  # Print the second message\n",
        "print(df['message'].iloc[2])  # Print the third message\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gxsm8KYNvK43"
      },
      "source": [
        "### Reading Email body"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2onhkwbOQxG",
        "outputId": "5f2f090b-fe00-4ccf-d9ee-eb367b25682c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0                            Here is our forecast\\n\\n \n",
            "1    Traveling to have a business meeting takes the...\n",
            "2                       test successful.  way to go!!!\n",
            "3    Randy,\\n\\n Can you send me a schedule of the s...\n",
            "4                  Let's shoot for Tuesday at 11:45.  \n",
            "Name: message, dtype: object\n"
          ]
        }
      ],
      "source": [
        "# Extract email bodies from the 'message' column\n",
        "email_bodies = df['message'].apply(lambda x: x.split('\\n\\n', 1)[-1])\n",
        "\n",
        "# Print the first few email bodies to verify\n",
        "print(email_bodies.head())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6p3Cqm0vEl-"
      },
      "source": [
        "### Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a677y2o3Pq6V",
        "outputId": "94c592d3-e0d6-431e-aa43-bc76751f87d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0                                 Here is our forecast\n",
            "1    Traveling to have a business meeting takes the...\n",
            "2                           test successful  way to go\n",
            "3    Randy   Can you send me a schedule of the sala...\n",
            "4                       Lets shoot for Tuesday at 1145\n",
            "Name: message, dtype: object\n"
          ]
        }
      ],
      "source": [
        "# Function to clean the email bodies\n",
        "def clean_text(text):\n",
        "    # Remove newline characters and extra whitespace\n",
        "    text = text.replace('\\n', ' ').strip()\n",
        "    # Remove non-alphanumeric characters and punctuation\n",
        "    text = re.sub(r'[^a-zA-Z0-9\\s]', '', text)\n",
        "    return text\n",
        "\n",
        "# Apply the clean_text function to each email body\n",
        "cleaned_email_bodies = email_bodies.apply(clean_text)\n",
        "\n",
        "# Print the first few cleaned email bodies\n",
        "print(cleaned_email_bodies.head())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9RY3o7BRvcRp"
      },
      "source": [
        "# Section: 3 - Categorizing Emails Based on Keywords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxBEYJxlRBgT",
        "outputId": "ba799db4-4381-4614-8861-ec57d790b107"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Category Distribution:\n",
            "category\n",
            "Meetings         198060\n",
            "Uncategorized    152552\n",
            "Applications      62362\n",
            "HR                41268\n",
            "Resumes           39814\n",
            "Spam              23345\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Define a dictionary with categories as keys and lists of keywords (including regex patterns) as values\n",
        "\n",
        "category_keywords = {\n",
        "    'Resumes': [re.compile(r\"(resume|CV)(.docx|.pdf)\"), 'experience', 'skills', 'qualifications', 'cover letter', 'references', 'portfolio', 'rseume', r\"my resume\", r\"my cv\", 'cv', 'education'],\n",
        "    'Applications': ['application', 'apply', 'job', 'position', 'hire', 'candidate', r\"job title: (.*?)\", r\"location: (.*?)\", 'interview', 'screening', 'assessment'],\n",
        "    'Meetings': ['meeting', 'schedule', 'agenda', 'conference', 'call', 'appointment', 'minutes', 'location', 'time', r\"brainstorming meeting\", r\"performance review\", 'stakeholders', 'team'],\n",
        "    'HR': ['HR', 'human resources', 'personnel', 'employee', 'training', 'benefits', 'payroll', 'compensation', 'performance review', 'health insurance', 'paid time off', 'grievance', 'disciplinary action', 'workforce', 'manpower', 'staffing', 'onboarding', 'termination'],\n",
        "    'Spam': ['offer', 'free', 'discount', 'sale', 'limited time', 'exclusive', 'urgent', 'confidential', 'account verification', 'win a prize', 'money back guarantee', '.exe', '.zip', 'click here to win', 'unsubscribe', 'too good to be true', 'act now', '1000', '1000000', 'money']\n",
        "}\n",
        "\n",
        "#label email\n",
        "def label_category(email_body):\n",
        "    \"\"\"\n",
        "    Labels an email based on keywords in the category_keywords dictionary.\n",
        "\n",
        "    Args:\n",
        "        email_body (str): The body text of the email.\n",
        "\n",
        "    Returns:\n",
        "        str: The category label or 'Uncategorized' if no match is found.\n",
        "    \"\"\"\n",
        "    email_body_lower = email_body.lower()\n",
        "    for category, keywords in category_keywords.items():\n",
        "        for keyword in keywords:\n",
        "            if isinstance(keyword, str):  # Check if keyword is a string\n",
        "                if keyword.lower() in email_body_lower:\n",
        "                    return category\n",
        "            else:  # Keyword is a compiled regular expression\n",
        "                if re.search(keyword, email_body_lower):\n",
        "                    return category\n",
        "    return 'Uncategorized'\n",
        "\n",
        "\n",
        "# Apply the function to label emails\n",
        "df['category'] = email_bodies.apply(label_category)\n",
        "\n",
        "\n",
        "# Display the counts of emails in each category\n",
        "print(\"Category Distribution:\")\n",
        "print(df['category'].value_counts())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PN46_kHwG6m"
      },
      "source": [
        "### Checking if Emails are lebelled correctly or not"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EjvFMgE7Useu",
        "outputId": "ffa964c3-d2d6-4ce7-a8f6-9380a69fc6ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                        file  \\\n",
            "0      allen-p/_sent_mail/1.   \n",
            "1     allen-p/_sent_mail/10.   \n",
            "2    allen-p/_sent_mail/100.   \n",
            "3   allen-p/_sent_mail/1000.   \n",
            "12   allen-p/_sent_mail/105.   \n",
            "13   allen-p/_sent_mail/106.   \n",
            "25   allen-p/_sent_mail/117.   \n",
            "26   allen-p/_sent_mail/118.   \n",
            "58   allen-p/_sent_mail/147.   \n",
            "80   allen-p/_sent_mail/167.   \n",
            "94    allen-p/_sent_mail/18.   \n",
            "95   allen-p/_sent_mail/180.   \n",
            "\n",
            "                                              message       category  \n",
            "0   Message-ID: <18782981.1075855378110.JavaMail.e...  Uncategorized  \n",
            "1   Message-ID: <15464986.1075855378456.JavaMail.e...       Meetings  \n",
            "2   Message-ID: <24216240.1075855687451.JavaMail.e...  Uncategorized  \n",
            "3   Message-ID: <13505866.1075863688222.JavaMail.e...       Meetings  \n",
            "12  Message-ID: <13116875.1075855687561.JavaMail.e...   Applications  \n",
            "13  Message-ID: <2707340.1075855687584.JavaMail.ev...   Applications  \n",
            "25  Message-ID: <19034252.1075855687825.JavaMail.e...        Resumes  \n",
            "26  Message-ID: <719350.1075855687850.JavaMail.eva...        Resumes  \n",
            "58  Message-ID: <26254346.1075855688486.JavaMail.e...           Spam  \n",
            "80  Message-ID: <27058154.1075855688919.JavaMail.e...             HR  \n",
            "94  Message-ID: <22040365.1075855378763.JavaMail.e...             HR  \n",
            "95  Message-ID: <29919154.1075855689201.JavaMail.e...           Spam  \n",
            "Category: Uncategorized\n",
            "Message:\n",
            " Message-ID: <18782981.1075855378110.JavaMail.evans@thyme>\n",
            "Date: Mon, 14 May 2001 16:39:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: tim.belden@enron.com\n",
            "Subject: \n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: Tim Belden <Tim Belden/Enron@EnronXGate>\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Jan2002_1\\Allen, Phillip K.\\'Sent Mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen (Non-Privileged).pst\n",
            "\n",
            "Here is our forecast\n",
            "\n",
            " \n",
            "\n",
            "\n",
            "Category: Meetings\n",
            "Message:\n",
            " Message-ID: <15464986.1075855378456.JavaMail.evans@thyme>\n",
            "Date: Fri, 4 May 2001 13:51:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: john.lavorato@enron.com\n",
            "Subject: Re:\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: John J Lavorato <John J Lavorato/ENRON@enronXgate@ENRON>\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Jan2002_1\\Allen, Phillip K.\\'Sent Mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen (Non-Privileged).pst\n",
            "\n",
            "Traveling to have a business meeting takes the fun out of the trip.  Especially if you have to prepare a presentation.  I would suggest holding the business plan meetings here then take a trip without any formal business meetings.  I would even try and get some honest opinions on whether a trip is even desired or necessary.\n",
            "\n",
            "As far as the business meetings, I think it would be more productive to try and stimulate discussions across the different groups about what is working and what is not.  Too often the presenter speaks and the others are quiet just waiting for their turn.   The meetings might be better if held in a round table discussion format.  \n",
            "\n",
            "My suggestion for where to go is Austin.  Play golf and rent a ski boat and jet ski's.  Flying somewhere takes too much time.\n",
            "\n",
            "\n",
            "\n",
            "Category: Uncategorized\n",
            "Message:\n",
            " Message-ID: <24216240.1075855687451.JavaMail.evans@thyme>\n",
            "Date: Wed, 18 Oct 2000 03:00:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: leah.arsdall@enron.com\n",
            "Subject: Re: test\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: Leah Van Arsdall\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "test successful.  way to go!!!\n",
            "\n",
            "\n",
            "Category: Meetings\n",
            "Message:\n",
            " Message-ID: <13505866.1075863688222.JavaMail.evans@thyme>\n",
            "Date: Mon, 23 Oct 2000 06:13:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: randall.gay@enron.com\n",
            "Subject: \n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: Randall L Gay\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "Randy,\n",
            "\n",
            " Can you send me a schedule of the salary and level of everyone in the \n",
            "scheduling group.  Plus your thoughts on any changes that need to be made.  \n",
            "(Patti S for example)\n",
            "\n",
            "Phillip\n",
            "\n",
            "\n",
            "Category: Applications\n",
            "Message:\n",
            " Message-ID: <13116875.1075855687561.JavaMail.evans@thyme>\n",
            "Date: Mon, 9 Oct 2000 07:16:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: keith.holst@enron.com\n",
            "Subject: Consolidated positions: Issues & To Do list\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: Keith Holst\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "---------------------- Forwarded by Phillip K Allen/HOU/ECT on 10/09/2000 \n",
            "02:16 PM ---------------------------\n",
            "\n",
            "\n",
            "Richard Burchfield\n",
            "10/06/2000 06:59 AM\n",
            "To: Phillip K Allen/HOU/ECT@ECT\n",
            "cc: Beth Perlman/HOU/ECT@ECT \n",
            "Subject: Consolidated positions: Issues & To Do list\n",
            "\n",
            "Phillip,\n",
            " Below is the issues & to do list as we go forward with documenting the \n",
            "requirements for consolidated physical/financial positions and transport \n",
            "trade capture. What we need to focus on is the first bullet in Allan's list; \n",
            "the need for a single set of requirements. Although the meeting with Keith, \n",
            "on Wednesday,  was informative the solution of creating a infinitely dynamic \n",
            "consolidated position screen, will be extremely difficult and time \n",
            "consuming.  Throughout the meeting on Wednesday, Keith alluded to the \n",
            "inability to get consensus amongst the traders on the presentation of the \n",
            "consolidated position, so the solution was to make it so that a trader can \n",
            "arrange the position screen to their liking (much like Excel). What needs to \n",
            "happen on Monday from 3 - 5 is a effort to design a desired layout for the \n",
            "consolidated position screen, this is critical. This does not exclude \n",
            "building a capability to create a more flexible position presentation for the \n",
            "future, but in order to create a plan that can be measured we need firm \n",
            "requirements. Also, to reiterate that the goals of this project is a project \n",
            "plan on consolidate physical/financial positions and transport trade capture. \n",
            "The other issues that have been raised will be capture as projects on to \n",
            "themselves, and will need to be prioritised as efforts outside of this \n",
            "project.\n",
            "\n",
            "I have been involved in most of the meetings and the discussions have been \n",
            "good. I believe there has been good communication between the teams, but now \n",
            "we need to have focus on the objectives we set out to solve.\n",
            "\n",
            "Richard  \n",
            "---------------------- Forwarded by Richard Burchfield/HOU/ECT on 10/06/2000 \n",
            "08:34 AM ---------------------------\n",
            "\n",
            "\n",
            "Allan Severude\n",
            "10/05/2000 06:03 PM\n",
            "To: Richard Burchfield/HOU/ECT@ECT\n",
            "cc: Peggy Alix/HOU/ECT@ECT, Russ Severson/HOU/ECT@ECT, Scott \n",
            "Mills/HOU/ECT@ECT, Kenny Ha/HOU/ECT@ECT \n",
            "Subject: Consolidated positions: Issues & To Do list\n",
            "\n",
            "\n",
            "From our initial set of meetings with the traders regarding consolidated \n",
            "positions, I think we still have the following issues:\n",
            "We don't have a single point of contact from the trading group.  We've had \n",
            "three meetings which brought out very different issues from different \n",
            "traders.  We really need a single point of contact to help drive the trader \n",
            "requirements and help come to a consensus regarding the requirements.\n",
            "We're getting hit with a lot of different requests, many of which appear to \n",
            "be outside the scope of position consolidation.\n",
            "\n",
            "Things left to do:\n",
            "I think it may be useful to try to formulate a high level project goal to \n",
            "make it as clear as possible what we're trying to accomplish with this \n",
            "project.  It'll help determine which requests fall under the project scope.\n",
            "Go through the list of requests to determine which are in scope for this \n",
            "project and which fall out of scope.\n",
            "For those in scope, work to define relative importance (priority) of each and \n",
            "work with traders to define the exact requirements of each.\n",
            "Define the desired lay out of the position manager screen: main view and all \n",
            "drill downs.\n",
            "Use the above to formulate a project plan.\n",
            "\n",
            "Things requested thus far (no particular order):\n",
            "Inclusion of Sitara physical deals into the TDS position manager and deal \n",
            "ticker.\n",
            "Customized rows and columns in the position manager (ad hoc rows/columns that \n",
            "add up existing position manager rows/columns).\n",
            "New drill down in the position manager to break out positions by: physical, \n",
            "transport, swaps, options, ...\n",
            "Addition of a curve tab to the position manager to show the real-time values \n",
            "of all curves on which the desk has a position.\n",
            "Ability to split the current position grid to allow daily positions to be \n",
            "shown directly above monthly positions.  Each grouped column in the top grid \n",
            "would be tied to a grouped column in the bottom grid.\n",
            "Ability to properly show curve shift for float-for-float deals; determine the \n",
            "appropriate positions to show for each:\n",
            "Gas Daily for monthly index,\n",
            "Physical gas for Nymex,\n",
            "Physical gas for Inside Ferc,\n",
            "Physical gas for Mid market.\n",
            "Ability for TDS to pull valuation results based on a TDS flag instead of \n",
            "using official valuations.\n",
            "Position and P&L aggregation across all gas desks.\n",
            "Ability to include the Gas Price book into TDS:\n",
            "Inclusion of spread options in our systems.  Ability to handle volatility \n",
            "skew and correlations.\n",
            "Ability to revalue all options incrementally throughout the trading day.  \n",
            "Approximate delta changes between valuations using instantaneous gamma or a \n",
            "gamma grid.\n",
            "Valuation of Gas Daily options.\n",
            "A new position screen for options (months x strike x delta).  TBD.\n",
            "Inclusion of positions for exotic options currently managed in spreadsheets.\n",
            "Ability to isolate the position change due to changed deals in the position \n",
            "manager.\n",
            "Ability to view change deal P&L in the TDS deal ticker.  Show new deal terms, \n",
            "prior deal terms, and net P&L affect of the change.\n",
            "Eliminate change deals with no economic impact from the TDS deal ticker.\n",
            "Position drill down in the position manager to isolate the impact of \n",
            "individual deals on the position total in a grid cell.\n",
            "Benchmark positions in TDS.\n",
            "Deployment of TDS in Canada. Currency and volume uom conversions. Implicit \n",
            "and explicit position break out issues.\n",
            "\n",
            "-- Allan.\n",
            "\n",
            "PS: Colleen is setting up a meeting tomorrow to discuss the direction for \n",
            "transport.  Hopefully we'll know much better where that part stands at that \n",
            "point.\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Category: Applications\n",
            "Message:\n",
            " Message-ID: <2707340.1075855687584.JavaMail.evans@thyme>\n",
            "Date: Mon, 9 Oct 2000 07:00:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: keith.holst@enron.com\n",
            "Subject: Consolidated positions: Issues & To Do list\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: Keith Holst\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "---------------------- Forwarded by Phillip K Allen/HOU/ECT on 10/09/2000 \n",
            "02:00 PM ---------------------------\n",
            "\n",
            "\n",
            "Richard Burchfield\n",
            "10/06/2000 06:59 AM\n",
            "To: Phillip K Allen/HOU/ECT@ECT\n",
            "cc: Beth Perlman/HOU/ECT@ECT \n",
            "Subject: Consolidated positions: Issues & To Do list\n",
            "\n",
            "Phillip,\n",
            " Below is the issues & to do list as we go forward with documenting the \n",
            "requirements for consolidated physical/financial positions and transport \n",
            "trade capture. What we need to focus on is the first bullet in Allan's list; \n",
            "the need for a single set of requirements. Although the meeting with Keith, \n",
            "on Wednesday,  was informative the solution of creating a infinitely dynamic \n",
            "consolidated position screen, will be extremely difficult and time \n",
            "consuming.  Throughout the meeting on Wednesday, Keith alluded to the \n",
            "inability to get consensus amongst the traders on the presentation of the \n",
            "consolidated position, so the solution was to make it so that a trader can \n",
            "arrange the position screen to their liking (much like Excel). What needs to \n",
            "happen on Monday from 3 - 5 is a effort to design a desired layout for the \n",
            "consolidated position screen, this is critical. This does not exclude \n",
            "building a capability to create a more flexible position presentation for the \n",
            "future, but in order to create a plan that can be measured we need firm \n",
            "requirements. Also, to reiterate that the goals of this project is a project \n",
            "plan on consolidate physical/financial positions and transport trade capture. \n",
            "The other issues that have been raised will be capture as projects on to \n",
            "themselves, and will need to be prioritised as efforts outside of this \n",
            "project.\n",
            "\n",
            "I have been involved in most of the meetings and the discussions have been \n",
            "good. I believe there has been good communication between the teams, but now \n",
            "we need to have focus on the objectives we set out to solve.\n",
            "\n",
            "Richard  \n",
            "---------------------- Forwarded by Richard Burchfield/HOU/ECT on 10/06/2000 \n",
            "08:34 AM ---------------------------\n",
            "\n",
            "\n",
            "Allan Severude\n",
            "10/05/2000 06:03 PM\n",
            "To: Richard Burchfield/HOU/ECT@ECT\n",
            "cc: Peggy Alix/HOU/ECT@ECT, Russ Severson/HOU/ECT@ECT, Scott \n",
            "Mills/HOU/ECT@ECT, Kenny Ha/HOU/ECT@ECT \n",
            "Subject: Consolidated positions: Issues & To Do list\n",
            "\n",
            "\n",
            "From our initial set of meetings with the traders regarding consolidated \n",
            "positions, I think we still have the following issues:\n",
            "We don't have a single point of contact from the trading group.  We've had \n",
            "three meetings which brought out very different issues from different \n",
            "traders.  We really need a single point of contact to help drive the trader \n",
            "requirements and help come to a consensus regarding the requirements.\n",
            "We're getting hit with a lot of different requests, many of which appear to \n",
            "be outside the scope of position consolidation.\n",
            "\n",
            "Things left to do:\n",
            "I think it may be useful to try to formulate a high level project goal to \n",
            "make it as clear as possible what we're trying to accomplish with this \n",
            "project.  It'll help determine which requests fall under the project scope.\n",
            "Go through the list of requests to determine which are in scope for this \n",
            "project and which fall out of scope.\n",
            "For those in scope, work to define relative importance (priority) of each and \n",
            "work with traders to define the exact requirements of each.\n",
            "Define the desired lay out of the position manager screen: main view and all \n",
            "drill downs.\n",
            "Use the above to formulate a project plan.\n",
            "\n",
            "Things requested thus far (no particular order):\n",
            "Inclusion of Sitara physical deals into the TDS position manager and deal \n",
            "ticker.\n",
            "Customized rows and columns in the position manager (ad hoc rows/columns that \n",
            "add up existing position manager rows/columns).\n",
            "New drill down in the position manager to break out positions by: physical, \n",
            "transport, swaps, options, ...\n",
            "Addition of a curve tab to the position manager to show the real-time values \n",
            "of all curves on which the desk has a position.\n",
            "Ability to split the current position grid to allow daily positions to be \n",
            "shown directly above monthly positions.  Each grouped column in the top grid \n",
            "would be tied to a grouped column in the bottom grid.\n",
            "Ability to properly show curve shift for float-for-float deals; determine the \n",
            "appropriate positions to show for each:\n",
            "Gas Daily for monthly index,\n",
            "Physical gas for Nymex,\n",
            "Physical gas for Inside Ferc,\n",
            "Physical gas for Mid market.\n",
            "Ability for TDS to pull valuation results based on a TDS flag instead of \n",
            "using official valuations.\n",
            "Position and P&L aggregation across all gas desks.\n",
            "Ability to include the Gas Price book into TDS:\n",
            "Inclusion of spread options in our systems.  Ability to handle volatility \n",
            "skew and correlations.\n",
            "Ability to revalue all options incrementally throughout the trading day.  \n",
            "Approximate delta changes between valuations using instantaneous gamma or a \n",
            "gamma grid.\n",
            "Valuation of Gas Daily options.\n",
            "A new position screen for options (months x strike x delta).  TBD.\n",
            "Inclusion of positions for exotic options currently managed in spreadsheets.\n",
            "Ability to isolate the position change due to changed deals in the position \n",
            "manager.\n",
            "Ability to view change deal P&L in the TDS deal ticker.  Show new deal terms, \n",
            "prior deal terms, and net P&L affect of the change.\n",
            "Eliminate change deals with no economic impact from the TDS deal ticker.\n",
            "Position drill down in the position manager to isolate the impact of \n",
            "individual deals on the position total in a grid cell.\n",
            "Benchmark positions in TDS.\n",
            "Deployment of TDS in Canada. Currency and volume uom conversions. Implicit \n",
            "and explicit position break out issues.\n",
            "\n",
            "-- Allan.\n",
            "\n",
            "PS: Colleen is setting up a meeting tomorrow to discuss the direction for \n",
            "transport.  Hopefully we'll know much better where that part stands at that \n",
            "point.\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Category: Resumes\n",
            "Message:\n",
            " Message-ID: <19034252.1075855687825.JavaMail.evans@thyme>\n",
            "Date: Tue, 26 Sep 2000 09:28:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: kholst@enron.com\n",
            "Subject: Investment Structure\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: kholst@enron.com\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "---------------------- Forwarded by Phillip K Allen/HOU/ECT on 09/26/2000 \n",
            "04:28 PM ---------------------------\n",
            "\n",
            "\n",
            "\"George Richards\" <cbpres@austin.rr.com> on 09/26/2000 01:18:45 PM\n",
            "Please respond to <cbpres@austin.rr.com>\n",
            "To: \"Phillip Allen\" <pallen@enron.com>\n",
            "cc: \"Larry Lewter\" <retwell@mail.sanmarcos.net>, \"Claudia L. Crocker\" \n",
            "<clclegal2@aol.com> \n",
            "Subject: Investment Structure\n",
            "\n",
            "\n",
            "STRUCTURE:\n",
            "Typically the structure is a limited partnership with a corporate (or LLC)\n",
            "general partner.  The General Partner owns 1% of the project and carries the\n",
            "liability of construction.\n",
            "\n",
            "LAND OWNERSHIP & LOANS\n",
            "The property would be purchased in the name of the limited partnership and\n",
            "any land loans, land improvements loans and construction loans would be in\n",
            "the name of the limited partnership.  Each of the individual investors and\n",
            "all of the principals in Creekside would also personally guarantee the\n",
            "loans.  If the investor(s) do not sign on the loans, this generally means\n",
            "that a larger amount of cash is required and the investor's share of profits\n",
            "is reduced.\n",
            "\n",
            "All loans for residential construction, that are intended for re-sale, are\n",
            "full recourse loans.  If we are pursuing multifamily rental developments,\n",
            "the construction loans are still full recourse but the mortgage can often be\n",
            "non-recourse.\n",
            "\n",
            "USE OF INITIAL INVESTMENT\n",
            "The initial investment is used for land deposit, engineering &\n",
            "architectural design, soils tests, surveys, filing fees, legal fees for\n",
            "organization and condominium association formation,  and appraisals.  Unlike\n",
            "many real estate investment programs, none of the funds are used for fees to\n",
            "Creekside Builders, LLC.  These professional expenses will be incurred over\n",
            "the estimated 6 month design and approval period.\n",
            "\n",
            "EARLY LAND COSTS\n",
            "The $4,000 per month costs listed in the cash flow as part of land cost\n",
            "represent the extension fees due to the seller for up to 4 months of\n",
            "extensions on closing.  As an alternative, we can close into a land loan at\n",
            "probably 70% of appraised value.  With a land value equal to the purchase\n",
            "price of $680,000 this would mean a land loan of $476,000 with estimated\n",
            "monthly interest payments of $3,966, given a 10% annual interest rate, plus\n",
            "approximately 1.25% of the loan amount for closing costs and loan fees.\n",
            "\n",
            "EQUITY AT IMPROVEMENT LOAN\n",
            "Once the site plan is approved by the City of Austin, the City will require\n",
            "the development entity to post funds for fiscal improvements, referred to as\n",
            "the \"fiscals\".  This cost represents a bond for the completion of\n",
            "improvements that COA considers vital and these funds are released once the\n",
            "improvements have been completed and accepted by COA.  This release will be\n",
            "for 90% of the cost with the remaining 10% released one year after\n",
            "completion.  Releases can be granted once every 90 days and you should\n",
            "expect that the release would occur 6 months after the start of lot\n",
            "improvement construction.  These fiscals are usually posted in cash or an\n",
            "irrevocable letter of credit.  As such, they have to be counted as a\n",
            "development cost, even though they are not spent.  Because they are not\n",
            "spent no interest is charged on these funds.\n",
            "\n",
            "The lot improvement loan is typically 75% of the appraised value of a\n",
            "finished lot, which I suspect will be at least $20,000 and potentially as\n",
            "high as $25,000.  This would produce a loan amount of  $15,000 on $20,000\n",
            "per lot.  With estimated per lot improvement costs of $9,000, 'fiscals' at\n",
            "$2,000 and the land cost at $8,000 , total improved lot cost is $19,000\n",
            "which means $0 to $4,000 per lot in total equity.  The investment prior to\n",
            "obtaining the improvement loan would count towards any equity requirement\n",
            "provided it was for direct costs.  Thus, the additional equity for the\n",
            "improvement loan would be $0-$184,000.   Even if the maximum loan would\n",
            "cover all costs, it is unlikely the bank would allow reimbursement of funds\n",
            "spent. The higher estimates of equity investments are shown in the\n",
            "preliminary proforma to be on the safe side.  The engineer is preparing a\n",
            "tentative site layout with an initial evaluation of the phasing, which can\n",
            "significantly reduce the cash equity requirement.\n",
            "\n",
            "Phasing works as follows.  If the first phase was say 40 units, the total\n",
            "lot improvement cost might average $31,000 per lot.   Of this, probably\n",
            "$13,000 would be for improvements and $19,000 for the land cost.  The\n",
            "improvements are higher to cover large one time up front costs for design\n",
            "costs, the entry road, water treatment costs, perimeter fencing and\n",
            "landscaping, and so on, as well as for 100% of the land.  The land loan for\n",
            "undeveloped lots would be 70% of the appraised raw lot value, which I would\n",
            "estimate as $10,000 per lot for a loan value of $7,000 per lot.  Then the\n",
            "loan value for each improved lot would be $15,000 per lot.  This would give\n",
            "you a total loan of $992,000, total cost of $1,232,645 for equity required\n",
            "of $241,000.  This was not presented in the initial analysis as the phasing\n",
            "is depended on a more careful assessment by the Civil Engineer as the\n",
            "separate phases must each be able to stand on its own from a utility\n",
            "standpoint.\n",
            "\n",
            "CONSTRUCTION LOANS\n",
            "There are three types of  construction loans.  First, is a speculative\n",
            "(spec) loan that is taken out prior to any pre-sales activity.  Second,  is\n",
            "a construction loan for a pre-sold unit, but the loan remains in the\n",
            "builder/developers name.  Third, is a pre-sold unit with the construction\n",
            "loan in the name of the buyer.  We expect to have up to 8 spec loans to\n",
            "start the project and expect all other loans to be pre-sold units with loans\n",
            "in the name of the builder/developer.  We do not expect to have any\n",
            "construction loans in the name of the buyers, as such loans are too\n",
            "difficult to manage and please new buyers unfamiliar with the process.\n",
            "\n",
            "Spec loans will be for 70% to 75% of value and construction loans for\n",
            "pre-sold units, if the construction loan is from the mortgage lender,  will\n",
            "be from 80% to 95% of value.\n",
            "\n",
            "DISBURSEMENTS\n",
            "Disbursements will be handled by the General Partner to cover current and\n",
            "near term third party costs, then to necessary reserves, then to priority\n",
            "payments and then to the partners per the agreement.  The General Partner\n",
            "will contract with Creekside Builders, LLC to construct the units and the\n",
            "fee to CB will include a construction management and overhead fee equal to\n",
            "15% of the direct hard cost excluding land, financing and sales costs.\n",
            "These fees are the only monies to Creekside, Larry Lewter or myself prior to\n",
            "calculation of profit, except for a) direct reimbursement for partnership\n",
            "expenses and b) direct payment to CB for any subcontractor costs that it has\n",
            "to perform.  For example, if CB cannot find a good trim carpenter sub, or\n",
            "cannot find enough trim carpenters, etc., and it decides to undertake this\n",
            "function, it will charge the partnership the same fee it was able to obtain\n",
            "from third parties and will disclose those cases to the partnership.\n",
            "Finally, CB will receive a fee for the use of any of its equipment if it is\n",
            "used in lieu of leasing equipment from others.  At present CB does not own\n",
            "any significant equipment, but it is considering the purchase of a sky track\n",
            "to facilitate and speed up framing, cornice, roofing and drywall spreading.\n",
            "\n",
            "REPORTING\n",
            "We are more than willing to provide reports to track expenses vs. plan.\n",
            "What did you have in mind?  I would like to use some form of internet based\n",
            "reporting.\n",
            "\n",
            "BOOKKEEPING\n",
            "I am not sure what you are referring to by the question, \"Bookkeeping\n",
            "procedures to record actual expenses?\"  Please expand.\n",
            "\n",
            "INVESTOR INPUT\n",
            "We are glad to have the investor's input on design and materials.  As always\n",
            "the question will be who has final say if there is disagreement, but in my\n",
            "experience I have always been able to reach consensus. As you, and I presume\n",
            "Keith, want to be involved to learn as much as possible we would make every\n",
            "effort to be accommodating.\n",
            "\n",
            "CREEKSIDE PROCEEDURES\n",
            "CB procedures for dealing with subs, vendors and professionals is not as\n",
            "formal as your question indicates.  In the EXTREMELY tight labor market\n",
            "obtaining 3 bids for each labor trade is not feasible.  For the professional\n",
            "subs we use those with whom we have developed a previous rapport.  Finally,\n",
            "for vendors they are constantly shopped.\n",
            "\n",
            "PRE-SELECTED PROFESSIONALS, SUBS AND VENDORS\n",
            "Yes there are many different subs that have been identified and I can\n",
            "provide these if you are interested.\n",
            "\n",
            "I know I have not answered everything, but this is a starting point.  Call\n",
            "when you have reviewed and we can discuss further.\n",
            "\n",
            "Sincerely,\n",
            "\n",
            "George Richards\n",
            "President, Creekside Builders, LLC\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " - winmail.dat\n",
            "\n",
            "\n",
            "\n",
            "Category: Resumes\n",
            "Message:\n",
            " Message-ID: <719350.1075855687850.JavaMail.evans@thyme>\n",
            "Date: Tue, 26 Sep 2000 09:26:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: pallen70@hotmail.com\n",
            "Subject: Investment Structure\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: pallen70@hotmail.com\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "---------------------- Forwarded by Phillip K Allen/HOU/ECT on 09/26/2000 \n",
            "04:26 PM ---------------------------\n",
            "\n",
            "\n",
            "\"George Richards\" <cbpres@austin.rr.com> on 09/26/2000 01:18:45 PM\n",
            "Please respond to <cbpres@austin.rr.com>\n",
            "To: \"Phillip Allen\" <pallen@enron.com>\n",
            "cc: \"Larry Lewter\" <retwell@mail.sanmarcos.net>, \"Claudia L. Crocker\" \n",
            "<clclegal2@aol.com> \n",
            "Subject: Investment Structure\n",
            "\n",
            "\n",
            "STRUCTURE:\n",
            "Typically the structure is a limited partnership with a corporate (or LLC)\n",
            "general partner.  The General Partner owns 1% of the project and carries the\n",
            "liability of construction.\n",
            "\n",
            "LAND OWNERSHIP & LOANS\n",
            "The property would be purchased in the name of the limited partnership and\n",
            "any land loans, land improvements loans and construction loans would be in\n",
            "the name of the limited partnership.  Each of the individual investors and\n",
            "all of the principals in Creekside would also personally guarantee the\n",
            "loans.  If the investor(s) do not sign on the loans, this generally means\n",
            "that a larger amount of cash is required and the investor's share of profits\n",
            "is reduced.\n",
            "\n",
            "All loans for residential construction, that are intended for re-sale, are\n",
            "full recourse loans.  If we are pursuing multifamily rental developments,\n",
            "the construction loans are still full recourse but the mortgage can often be\n",
            "non-recourse.\n",
            "\n",
            "USE OF INITIAL INVESTMENT\n",
            "The initial investment is used for land deposit, engineering &\n",
            "architectural design, soils tests, surveys, filing fees, legal fees for\n",
            "organization and condominium association formation,  and appraisals.  Unlike\n",
            "many real estate investment programs, none of the funds are used for fees to\n",
            "Creekside Builders, LLC.  These professional expenses will be incurred over\n",
            "the estimated 6 month design and approval period.\n",
            "\n",
            "EARLY LAND COSTS\n",
            "The $4,000 per month costs listed in the cash flow as part of land cost\n",
            "represent the extension fees due to the seller for up to 4 months of\n",
            "extensions on closing.  As an alternative, we can close into a land loan at\n",
            "probably 70% of appraised value.  With a land value equal to the purchase\n",
            "price of $680,000 this would mean a land loan of $476,000 with estimated\n",
            "monthly interest payments of $3,966, given a 10% annual interest rate, plus\n",
            "approximately 1.25% of the loan amount for closing costs and loan fees.\n",
            "\n",
            "EQUITY AT IMPROVEMENT LOAN\n",
            "Once the site plan is approved by the City of Austin, the City will require\n",
            "the development entity to post funds for fiscal improvements, referred to as\n",
            "the \"fiscals\".  This cost represents a bond for the completion of\n",
            "improvements that COA considers vital and these funds are released once the\n",
            "improvements have been completed and accepted by COA.  This release will be\n",
            "for 90% of the cost with the remaining 10% released one year after\n",
            "completion.  Releases can be granted once every 90 days and you should\n",
            "expect that the release would occur 6 months after the start of lot\n",
            "improvement construction.  These fiscals are usually posted in cash or an\n",
            "irrevocable letter of credit.  As such, they have to be counted as a\n",
            "development cost, even though they are not spent.  Because they are not\n",
            "spent no interest is charged on these funds.\n",
            "\n",
            "The lot improvement loan is typically 75% of the appraised value of a\n",
            "finished lot, which I suspect will be at least $20,000 and potentially as\n",
            "high as $25,000.  This would produce a loan amount of  $15,000 on $20,000\n",
            "per lot.  With estimated per lot improvement costs of $9,000, 'fiscals' at\n",
            "$2,000 and the land cost at $8,000 , total improved lot cost is $19,000\n",
            "which means $0 to $4,000 per lot in total equity.  The investment prior to\n",
            "obtaining the improvement loan would count towards any equity requirement\n",
            "provided it was for direct costs.  Thus, the additional equity for the\n",
            "improvement loan would be $0-$184,000.   Even if the maximum loan would\n",
            "cover all costs, it is unlikely the bank would allow reimbursement of funds\n",
            "spent. The higher estimates of equity investments are shown in the\n",
            "preliminary proforma to be on the safe side.  The engineer is preparing a\n",
            "tentative site layout with an initial evaluation of the phasing, which can\n",
            "significantly reduce the cash equity requirement.\n",
            "\n",
            "Phasing works as follows.  If the first phase was say 40 units, the total\n",
            "lot improvement cost might average $31,000 per lot.   Of this, probably\n",
            "$13,000 would be for improvements and $19,000 for the land cost.  The\n",
            "improvements are higher to cover large one time up front costs for design\n",
            "costs, the entry road, water treatment costs, perimeter fencing and\n",
            "landscaping, and so on, as well as for 100% of the land.  The land loan for\n",
            "undeveloped lots would be 70% of the appraised raw lot value, which I would\n",
            "estimate as $10,000 per lot for a loan value of $7,000 per lot.  Then the\n",
            "loan value for each improved lot would be $15,000 per lot.  This would give\n",
            "you a total loan of $992,000, total cost of $1,232,645 for equity required\n",
            "of $241,000.  This was not presented in the initial analysis as the phasing\n",
            "is depended on a more careful assessment by the Civil Engineer as the\n",
            "separate phases must each be able to stand on its own from a utility\n",
            "standpoint.\n",
            "\n",
            "CONSTRUCTION LOANS\n",
            "There are three types of  construction loans.  First, is a speculative\n",
            "(spec) loan that is taken out prior to any pre-sales activity.  Second,  is\n",
            "a construction loan for a pre-sold unit, but the loan remains in the\n",
            "builder/developers name.  Third, is a pre-sold unit with the construction\n",
            "loan in the name of the buyer.  We expect to have up to 8 spec loans to\n",
            "start the project and expect all other loans to be pre-sold units with loans\n",
            "in the name of the builder/developer.  We do not expect to have any\n",
            "construction loans in the name of the buyers, as such loans are too\n",
            "difficult to manage and please new buyers unfamiliar with the process.\n",
            "\n",
            "Spec loans will be for 70% to 75% of value and construction loans for\n",
            "pre-sold units, if the construction loan is from the mortgage lender,  will\n",
            "be from 80% to 95% of value.\n",
            "\n",
            "DISBURSEMENTS\n",
            "Disbursements will be handled by the General Partner to cover current and\n",
            "near term third party costs, then to necessary reserves, then to priority\n",
            "payments and then to the partners per the agreement.  The General Partner\n",
            "will contract with Creekside Builders, LLC to construct the units and the\n",
            "fee to CB will include a construction management and overhead fee equal to\n",
            "15% of the direct hard cost excluding land, financing and sales costs.\n",
            "These fees are the only monies to Creekside, Larry Lewter or myself prior to\n",
            "calculation of profit, except for a) direct reimbursement for partnership\n",
            "expenses and b) direct payment to CB for any subcontractor costs that it has\n",
            "to perform.  For example, if CB cannot find a good trim carpenter sub, or\n",
            "cannot find enough trim carpenters, etc., and it decides to undertake this\n",
            "function, it will charge the partnership the same fee it was able to obtain\n",
            "from third parties and will disclose those cases to the partnership.\n",
            "Finally, CB will receive a fee for the use of any of its equipment if it is\n",
            "used in lieu of leasing equipment from others.  At present CB does not own\n",
            "any significant equipment, but it is considering the purchase of a sky track\n",
            "to facilitate and speed up framing, cornice, roofing and drywall spreading.\n",
            "\n",
            "REPORTING\n",
            "We are more than willing to provide reports to track expenses vs. plan.\n",
            "What did you have in mind?  I would like to use some form of internet based\n",
            "reporting.\n",
            "\n",
            "BOOKKEEPING\n",
            "I am not sure what you are referring to by the question, \"Bookkeeping\n",
            "procedures to record actual expenses?\"  Please expand.\n",
            "\n",
            "INVESTOR INPUT\n",
            "We are glad to have the investor's input on design and materials.  As always\n",
            "the question will be who has final say if there is disagreement, but in my\n",
            "experience I have always been able to reach consensus. As you, and I presume\n",
            "Keith, want to be involved to learn as much as possible we would make every\n",
            "effort to be accommodating.\n",
            "\n",
            "CREEKSIDE PROCEEDURES\n",
            "CB procedures for dealing with subs, vendors and professionals is not as\n",
            "formal as your question indicates.  In the EXTREMELY tight labor market\n",
            "obtaining 3 bids for each labor trade is not feasible.  For the professional\n",
            "subs we use those with whom we have developed a previous rapport.  Finally,\n",
            "for vendors they are constantly shopped.\n",
            "\n",
            "PRE-SELECTED PROFESSIONALS, SUBS AND VENDORS\n",
            "Yes there are many different subs that have been identified and I can\n",
            "provide these if you are interested.\n",
            "\n",
            "I know I have not answered everything, but this is a starting point.  Call\n",
            "when you have reviewed and we can discuss further.\n",
            "\n",
            "Sincerely,\n",
            "\n",
            "George Richards\n",
            "President, Creekside Builders, LLC\n",
            "\n",
            "\n",
            "\n",
            "\n",
            " - winmail.dat\n",
            "\n",
            "\n",
            "\n",
            "Category: Spam\n",
            "Message:\n",
            " Message-ID: <26254346.1075855688486.JavaMail.evans@thyme>\n",
            "Date: Wed, 6 Sep 2000 08:54:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: pallen70@hotmail.com\n",
            "Subject: utilities roll\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: pallen70@hotmail.com\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "---------------------- Forwarded by Phillip K Allen/HOU/ECT on 09/06/2000 \n",
            "03:53 PM ---------------------------\n",
            "\n",
            "\n",
            "\"Lucy Gonzalez\" <stagecoachmama@hotmail.com> on 09/06/2000 09:06:45 AM\n",
            "To: pallen@enron.com\n",
            "cc:  \n",
            "Subject: utilities roll\n",
            "\n",
            "\n",
            "\n",
            "_________________________________________________________________________\n",
            "Get Your Private, Free E-mail from MSN Hotmail at http://www.hotmail.com.\n",
            "\n",
            "Share information about yourself, create your own public profile at\n",
            "http://profiles.msn.com.\n",
            "\n",
            " - utility.xls\n",
            " - utility.xls\n",
            "\n",
            "\n",
            "\n",
            "Category: HR\n",
            "Message:\n",
            " Message-ID: <27058154.1075855688919.JavaMail.evans@thyme>\n",
            "Date: Mon, 28 Aug 2000 06:40:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: matthew.lenhart@enron.com, mike.grigsby@enron.com, keith.holst@enron.com, \n",
            "\tfrank.ermis@enron.com, jay.reitmeyer@enron.com\n",
            "Subject: New Generation\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: Matthew Lenhart, Mike Grigsby, Keith Holst, Frank Ermis, Jay Reitmeyer\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "---------------------- Forwarded by Phillip K Allen/HOU/ECT on 08/28/2000 \n",
            "01:39 PM ---------------------------\n",
            "\n",
            "Kristian J Lande\n",
            "\n",
            "08/24/2000 03:56 PM\n",
            "\n",
            "To: Christopher F Calger/PDX/ECT@ECT, Jake Thomas/HOU/ECT@ECT, Frank W \n",
            "Vickers/HOU/ECT@ECT, Elliot Mainzer/PDX/ECT@ECT, Michael McDonald/SF/ECT@ECT, \n",
            "David Parquet/SF/ECT@ECT, Laird Dyer/SF/ECT@ECT, Jim Buerkle/PDX/ECT@ECT, Jim \n",
            "Gilbert/PDX/ECT@ECT, Terry W Donovan/HOU/ECT@ECT, Jeff G \n",
            "Slaughter/ENRON_DEVELOPMENT@ENRON_DEVELOPMENT, Ed Clark/PDX/ECT@ECT\n",
            "cc: Tim Belden/HOU/ECT@ECT, Robert Badeer/HOU/ECT@ECT, Matt \n",
            "Motley/PDX/ECT@ECT, Phillip K Allen/HOU/ECT@ECT \n",
            "Subject: New Generation\n",
            "\n",
            "\n",
            "\n",
            "Sorry, \n",
            "Report as of August 24, 2000\n",
            "\n",
            "\n",
            "\n",
            "Category: HR\n",
            "Message:\n",
            " Message-ID: <22040365.1075855378763.JavaMail.evans@thyme>\n",
            "Date: Mon, 30 Apr 2001 14:22:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: keith.holst@enron.com\n",
            "Subject: Request from Steve Kean\n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: Keith Holst <Keith Holst/HOU/ECT@ect>\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Jan2002_1\\Allen, Phillip K.\\'Sent Mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen (Non-Privileged).pst\n",
            "\n",
            "\n",
            "---------------------- Forwarded by Phillip K Allen/HOU/ECT on 04/30/2001 11:21 AM ---------------------------\n",
            "\n",
            "\n",
            "Alan Comnes\n",
            "04/27/2001 01:38 PM\n",
            "To:\tPhillip K Allen/HOU/ECT@ECT\n",
            "cc:\tJoe Hartsoe/Corp/Enron@ENRON \n",
            "Subject:\tRequest from Steve Kean\n",
            "\n",
            "Phillip,\n",
            "\n",
            "I got this request.  On the gas side, I think Kean/Lay need an update to a table you prepared for me a few months ago, which I've attached..  Can you oblige?  Thanks,\n",
            "\n",
            "Alan Comnes\n",
            "\n",
            " \n",
            "\n",
            "---------------------- Forwarded by Alan Comnes/PDX/ECT on 04/27/2001 01:42 PM ---------------------------\n",
            "\n",
            "\n",
            "Janel Guerrero@ENRON\n",
            "04/27/2001 12:44 PM\n",
            "To:\tAlan Comnes/PDX/ECT@ECT, Jeff Dasovich/NA/Enron@Enron\n",
            "cc:\tPaul Kaufman/PDX/ECT@ECT, Richard Shapiro/NA/Enron@Enron \n",
            "\n",
            "Subject:\tRequest from Steve Kean\n",
            "\n",
            "Alan,\n",
            "\n",
            "Steve has asked that you update the power point below so that it reflects all of the \"stupid regulatory/legislative decisions\" since the beginning of the year.  Ken wants to have this updated chart in his briefing book for next week's \"Ken Lay Tour\" to CA.  \n",
            "\n",
            "He also wants a forward price curve for both gas and power in CA.  Can we get these three documents by Monday afternoon?  \n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "Category: Spam\n",
            "Message:\n",
            " Message-ID: <29919154.1075855689201.JavaMail.evans@thyme>\n",
            "Date: Mon, 21 Aug 2000 09:40:00 -0700 (PDT)\n",
            "From: phillip.allen@enron.com\n",
            "To: mac.d.hargrove@rssmb.com\n",
            "Subject: \n",
            "Mime-Version: 1.0\n",
            "Content-Type: text/plain; charset=us-ascii\n",
            "Content-Transfer-Encoding: 7bit\n",
            "X-From: Phillip K Allen\n",
            "X-To: mac.d.hargrove@rssmb.com\n",
            "X-cc: \n",
            "X-bcc: \n",
            "X-Folder: \\Phillip_Allen_Dec2000\\Notes Folders\\'sent mail\n",
            "X-Origin: Allen-P\n",
            "X-FileName: pallen.nsf\n",
            "\n",
            "Mac,\n",
            "\n",
            " Thanks for the research report on EOG.  Here are my observations:\n",
            "\n",
            "  Gas Sales 916,000/day x 365 days = 334,340,000/year\n",
            "  \n",
            "  Estimated Gas Prices $985,721,000/334,340,000= $2.95/mcf\n",
            "\n",
            "  Actual gas prices are around $1.00/mcf higher and rising. \n",
            "\n",
            "   Recalc of EPS with more accurate gas prices:\n",
            "  (334,340,000 mct X $1/mcf)/116,897,000 shares outst = $2.86 additional EPS \n",
            "X 12 P/E multiple = $34 a share\n",
            "\n",
            "\n",
            " That is just a back of the envelope valuation based on gas prices.  I think \n",
            "crude price are undervalued by the tune of $10/share.\n",
            "\n",
            " Current price 37\n",
            " Nat. Gas 34\n",
            " Crude  10\n",
            "\n",
            " Total  81\n",
            "\n",
            " \n",
            " Can you take a look at these numbers and play devil's advocate?  To me this \n",
            "looks like the best stock to own Also can you send me a report on Calpine, \n",
            "Tosco, and SLB?\n",
            "\n",
            "\n",
            "Thank you,\n",
            "\n",
            " Phillip\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Group the DataFrame by the \"category\" column and select the first 5 rows of each group\n",
        "sample_data = df.groupby('category').head(2)\n",
        "\n",
        "# Display the sample data\n",
        "print(sample_data)\n",
        "\n",
        "\n",
        "# Display the sample data including the message content\n",
        "for index, row in sample_data.iterrows():\n",
        "    print(\"Category:\", row['category'])\n",
        "    print(\"Message:\\n\", row['message'])\n",
        "    print(\"\\n\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "mlYtEIaTxffZ",
        "outputId": "f8471af8-9e25-4297-b03e-86b06e1fda70"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize the class distribution\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='category', data=df)\n",
        "plt.title('Class Distribution')\n",
        "plt.xlabel('Category')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7i8uld8JwccU"
      },
      "source": [
        "# Section: 4- Splitting Data, Vectorizing Text, and Training a Classification Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5z9Gw9VxP4xz",
        "outputId": "e6f61552-047e-4944-d903-997714163843"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/lata/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Import the necessary library\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(cleaned_email_bodies, df['category'], test_size=0.2, random_state=42)\n",
        "\n",
        "# Vectorize the text data using TF-IDF\n",
        "vectorizer = TfidfVectorizer()\n",
        "X_train_vec = vectorizer.fit_transform(X_train)\n",
        "X_test_vec = vectorizer.transform(X_test)\n",
        "\n",
        "# Train a classification model (Logistic Regression)\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train_vec, y_train)\n",
        "\n",
        "from collections import Counter\n",
        "\n",
        "# Visualize the top TF-IDF features for each category\n",
        "def plot_top_tfidf_features(X, y, vectorizer, category, top_n=20, skip_n=10):\n",
        "    indices = (y == category)\n",
        "    X_cat = X[indices]\n",
        "    y_cat = y[indices]\n",
        "    tfidf_means = X_cat.mean(axis=0).A1\n",
        "    features = vectorizer.get_feature_names_out()\n",
        "    \n",
        "    # Take the top (N + skip_n) indices and then exclude the first skip_n of those\n",
        "    top_indices = tfidf_means.argsort()[-(top_n + skip_n):]\n",
        "    selected_indices = top_indices[:-(skip_n + 1):-1]  # This will give you the last 'top_n' elements, excluding the first 'skip_n'\n",
        "    \n",
        "    top_features = [features[i] for i in selected_indices]\n",
        "    top_scores = tfidf_means[selected_indices]\n",
        "    \n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.barplot(x=top_scores, y=top_features)\n",
        "    plt.title(f'Top {top_n} TF-IDF Features for {category} (excluding the top {skip_n})')\n",
        "    plt.xlabel('TF-IDF Score')\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "for category in df['category'].unique():\n",
        "    plot_top_tfidf_features(X_train_vec, y_train, vectorizer, category)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4qMfXyHyB5K"
      },
      "source": [
        "### Evaluating the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1YX54XJx6CX",
        "outputId": "6db2646e-31d0-4e8d-f819-0066f5d54a14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8858147872556315\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            " Applications       0.90      0.79      0.84     12382\n",
            "           HR       0.88      0.70      0.78      8120\n",
            "     Meetings       0.90      0.94      0.92     39525\n",
            "      Resumes       0.85      0.74      0.79      8027\n",
            "         Spam       0.83      0.65      0.73      4769\n",
            "Uncategorized       0.88      0.99      0.93     30658\n",
            "\n",
            "     accuracy                           0.89    103481\n",
            "    macro avg       0.87      0.80      0.83    103481\n",
            " weighted avg       0.89      0.89      0.88    103481\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model\n",
        "y_pred = model.predict(X_test_vec)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "id": "_5AozyUoww8x",
        "outputId": "6dd1411d-2a5d-494a-d1d1-9c18aec59758"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot confusion matrix\n",
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues', xticklabels=df['category'].unique(), yticklabels=df['category'].unique())\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Save the Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['tfidf_vectorizer.pkl']"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Save the trained model if needed\n",
        "import joblib\n",
        "\n",
        "joblib.dump(model, 'email_classification_model.pkl')\n",
        "joblib.dump(vectorizer, 'tfidf_vectorizer.pkl')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}