2_5_2_Spam_Email_Detection.ipynb

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      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Spam Email Detection Project Overview</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">1. Project Objective</span>\n",
        "The objective of this project is to classify emails as either spam or ham (non-spam) using machine learning techniques. This involves preprocessing the data, conducting exploratory data analysis (EDA), and training a Support Vector Machine (SVM) model for classification.\n",
        "\n",
        "## <span style=\"color: #28B463;\">2. Dataset Description</span>\n",
        "- **Dataset Source**: The dataset used for this project is a CSV file containing emails labeled as spam or ham.\n",
        "- **Key Features**:\n",
        "  - `label`: Indicates whether an email is spam (1) or ham (0).\n",
        "  - `message`: The content of the email.\n",
        "\n",
        "## <span style=\"color: #28B463;\">3. Data Preprocessing</span>\n",
        "- Loaded the dataset and inspected its structure.\n",
        "- Checked for missing values and duplicates.\n",
        "- Cleaned the dataset by dropping unnecessary columns and renaming others for clarity.\n",
        "- Mapped labels to binary values for easier processing.\n",
        "\n",
        "## <span style=\"color: #28B463;\">4. Exploratory Data Analysis (EDA)</span>\n",
        "- Visualized the distribution of spam and ham emails using count plots and pie charts.\n",
        "- Analyzed message lengths with histograms and violin plots to understand the differences between spam and ham emails.\n",
        "- Examined for missing values using a heatmap and identified any outliers.\n",
        "\n",
        "## <span style=\"color: #28B463;\">5. Feature Engineering</span>\n",
        "- Vectorized email messages using `CountVectorizer` to convert text into numerical features.\n",
        "- Calculated message lengths and scaled these features for improved model performance.\n",
        "\n",
        "## <span style=\"color: #28B463;\">6. Model Training and Evaluation</span>\n",
        "- Split the dataset into training and testing sets.\n",
        "- Combined text vector features with message length features.\n",
        "- Trained a linear Support Vector Classifier on the training data.\n",
        "- Made predictions on the test data and evaluated the model using accuracy, classification reports, and confusion matrices.\n",
        "\n",
        "## <span style=\"color: #28B463;\">7. Results</span>\n",
        "- Provided metrics such as accuracy percentage and detailed classification reports.\n",
        "- Visualized the confusion matrix to assess model performance.\n",
        "\n",
        "## <span style=\"color: #28B463;\">8. Outputs</span>\n",
        "- Plots and figures saved as PDFs for further analysis.\n",
        "- Classification report saved as a CSV file for documentation and review.\n",
        "\n",
        "## <span style=\"color: #28B463;\">9. Conclusion</span>\n",
        "This project successfully demonstrated the ability to classify spam emails using a SVM model. Future work could involve experimenting with different algorithms and further optimizing the model for improved accuracy."
      ],
      "metadata": {
        "id": "xTy-c4G4y6o1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RQEyWqm2zUQb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yrj_79U_zUU8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V9LVg8mgzUni"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 1: Import Necessary Libraries</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "In this step, we import the libraries essential for data manipulation, visualization, machine learning, and model evaluation. Each library serves a specific purpose in the project.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Libraries Imported</span>\n",
        "- **Pandas**: Used for data manipulation and analysis, enabling easy handling of data frames.\n",
        "- **Matplotlib**: A plotting library that helps in creating static, interactive, and animated visualizations.\n",
        "- **Seaborn**: Built on top of Matplotlib, it provides a high-level interface for drawing attractive statistical graphics.\n",
        "- **Scikit-learn**: A machine learning library that offers tools for model training, evaluation, and preprocessing.\n",
        "- **SciPy**: Provides additional functionalities for numerical operations, including sparse matrix operations."
      ],
      "metadata": {
        "id": "6EQyp_przVYU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries"
      ],
      "metadata": {
        "id": "fQ38UqwIrJ2E"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd  # For data manipulation and analysis"
      ],
      "metadata": {
        "id": "56QMqt5fvFSP"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt  # For data visualization"
      ],
      "metadata": {
        "id": "VXeFxWPQvFVM"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns  # For enhanced data visualization"
      ],
      "metadata": {
        "id": "sg_U6bozvFXx"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  # To split the dataset into training and testing sets"
      ],
      "metadata": {
        "id": "XFKVjo3PvFaI"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer  # To convert text into numerical data"
      ],
      "metadata": {
        "id": "n2W74uIwvFjl"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC  # Support Vector Classifier for modeling"
      ],
      "metadata": {
        "id": "2ANwYz7CvFl0"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix  # For model evaluation"
      ],
      "metadata": {
        "id": "cx092W8CvFnK"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler  # For feature scaling"
      ],
      "metadata": {
        "id": "zqT6WCkPvFpJ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.sparse as sp  # For sparse matrix operations"
      ],
      "metadata": {
        "id": "NPkIHR9zvFyf"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from wordcloud import WordCloud  # Import the WordCloud library"
      ],
      "metadata": {
        "id": "odBhXWj43rzt"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter  # Import Counter for counting words"
      ],
      "metadata": {
        "id": "F6M92af837TX"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qCtKKi-nvgp4"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rOK_QNztvgs3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dFHDEyZCvgwD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 2: Load the Dataset</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "This step involves loading the dataset into a DataFrame for further analysis. We specify the file path and read the CSV file while ensuring proper encoding.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Key Actions</span>\n",
        "- Defined the file path for the dataset.\n",
        "- Loaded the dataset using `pd.read_csv()` to create a DataFrame, enabling us to manipulate and analyze the data efficiently."
      ],
      "metadata": {
        "id": "GciTWJRuzZXD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset"
      ],
      "metadata": {
        "id": "BNymoZ2CvgzE"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = '/content/spam.csv'  # Define the file path for the dataset"
      ],
      "metadata": {
        "id": "_8hai1R7vg3L"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(file_path, encoding='ISO-8859-1')  # Read the dataset into a DataFrame"
      ],
      "metadata": {
        "id": "tWTt3Wsavg6H"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7X0UcvO3vhED"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YLYG5uxIwH9_"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7aYustoAwICd"
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      "execution_count": null,
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    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 3: Inspect the Dataset</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "In this step, we inspect the structure and contents of the dataset to understand its features and identify any potential issues.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Key Actions</span>\n",
        "- Displayed the first five rows using `df.head()` to get a quick overview of the data.\n",
        "- Used `df.info()` to check the data types and non-null counts for each column.\n",
        "- Generated summary statistics with `df.describe()` to summarize the central tendency, dispersion, and shape of the dataset's distribution."
      ],
      "metadata": {
        "id": "IfuvZytLzh27"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the dataset"
      ],
      "metadata": {
        "id": "ZwI8Qt7PwIFv"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"First 5 rows of the dataset:\")  # Print a message before displaying the first 5 rows\n",
        "print(df.head(), '\\n')  # Display the first 5 rows of the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9kjgMxKtwIKF",
        "outputId": "6cba4e16-52b9-4f95-b4e5-669a962f4f8f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 5 rows of the dataset:\n",
            "     v1                                                 v2 Unnamed: 2  \\\n",
            "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
            "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
            "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
            "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
            "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
            "\n",
            "  Unnamed: 3 Unnamed: 4  \n",
            "0        NaN        NaN  \n",
            "1        NaN        NaN  \n",
            "2        NaN        NaN  \n",
            "3        NaN        NaN  \n",
            "4        NaN        NaN   \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "smwZSJNbwINA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rbNgbTwmwIQF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Dataset Info:\")  # Print a message before displaying dataset info\n",
        "print(df.info(), '\\n')  # Display information about the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NVepoTUjwITN",
        "outputId": "54084b5f-f8c2-47df-c849-eaa811f5f1ca"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   v1          5572 non-null   object\n",
            " 1   v2          5572 non-null   object\n",
            " 2   Unnamed: 2  50 non-null     object\n",
            " 3   Unnamed: 3  12 non-null     object\n",
            " 4   Unnamed: 4  6 non-null      object\n",
            "dtypes: object(5)\n",
            "memory usage: 217.8+ KB\n",
            "None \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P31EgMylwPjv"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S1rHYiHrwPq8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Summary Statistics:\")  # Print a message before displaying summary statistics\n",
        "print(df.describe(include='all'), '\\n')  # Display summary statistics for all columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "agkU18OJwPuL",
        "outputId": "1337eae2-9e04-487f-a2c3-89b7aef9e8a2"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary Statistics:\n",
            "          v1                      v2  \\\n",
            "count   5572                    5572   \n",
            "unique     2                    5169   \n",
            "top      ham  Sorry, I'll call later   \n",
            "freq    4825                      30   \n",
            "\n",
            "                                               Unnamed: 2  \\\n",
            "count                                                  50   \n",
            "unique                                                 43   \n",
            "top      bt not his girlfrnd... G o o d n i g h t . . .@\"   \n",
            "freq                                                    3   \n",
            "\n",
            "                   Unnamed: 3 Unnamed: 4  \n",
            "count                      12          6  \n",
            "unique                     10          5  \n",
            "top      MK17 92H. 450Ppw 16\"    GNT:-)\"  \n",
            "freq                        2          2   \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wLc_eZDpwPyD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qruRof7ZwP2w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XWcab5Khzqqm"
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      "execution_count": null,
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    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 4: Check for Missing Values and Duplicates</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "This step is crucial for data quality assessment. We identify missing values and duplicate entries that may affect model performance.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Key Actions</span>\n",
        "- Counted missing values in each column using `df.isnull().sum()`.\n",
        "- Identified and reported the number of duplicate rows in the dataset."
      ],
      "metadata": {
        "id": "KFAHLMCqzrCi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values"
      ],
      "metadata": {
        "id": "LbNKlhDswP6Y"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values = df.isnull().sum()  # Count missing values in each column"
      ],
      "metadata": {
        "id": "F_H97_RRwP-I"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Missing values in each column:\")  # Print a message before displaying missing values\n",
        "print(missing_values[missing_values > 0], '\\n')  # Display columns with missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5DRtJ9xvwQB5",
        "outputId": "85bd8cb7-eb08-4367-c0d0-8e5889eb3486"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values in each column:\n",
            "Unnamed: 2    5522\n",
            "Unnamed: 3    5560\n",
            "Unnamed: 4    5566\n",
            "dtype: int64 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fyP-5F4nwby1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Yx-DZCs6wb2Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicates"
      ],
      "metadata": {
        "id": "9kCM8nkTwb6h"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicates = df.duplicated().sum()  # Count the number of duplicate rows"
      ],
      "metadata": {
        "id": "19WTkr7uwb8t"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of duplicate rows: {duplicates}\\n\")  # Print the number of duplicates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7rVxPR4wb-t",
        "outputId": "64813e4f-a171-423e-899b-51234b711204"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of duplicate rows: 403\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T-1x_n-VwcBL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_7M1RHXdwcEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ltDlk8jfzxLL"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 5: Unique Values and Special Characters</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "Understanding the unique values in each column helps gauge the diversity of the data. Additionally, checking for special characters can assist in cleaning the text data.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Key Actions</span>\n",
        "- Calculated the number of unique values for each column.\n",
        "- Counted messages containing special characters to assess data cleanliness."
      ],
      "metadata": {
        "id": "drB9uvEXzxji"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for unique values in each column"
      ],
      "metadata": {
        "id": "0Mxa9RrLwcIu"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = {col: df[col].unique() for col in df.columns}  # Store unique values for each column"
      ],
      "metadata": {
        "id": "SQgAH0zhwkZc"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Unique values in each column:\")  # Print a message before displaying unique values\n",
        "for col, unique in unique_values.items():  # Iterate over columns and unique values\n",
        "    print(f\"{col}: {len(unique)} unique values\\n\")  # Display unique value counts for each column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aIpwreagwkcb",
        "outputId": "61ece3e3-4e40-403c-a82a-0877a1fdf2a2"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in each column:\n",
            "v1: 2 unique values\n",
            "\n",
            "v2: 5169 unique values\n",
            "\n",
            "Unnamed: 2: 44 unique values\n",
            "\n",
            "Unnamed: 3: 11 unique values\n",
            "\n",
            "Unnamed: 4: 6 unique values\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hHq6dP2CwkfQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W96pC3jRwkh5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for special characters in the messages"
      ],
      "metadata": {
        "id": "XFK2BbHHwkkN"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "special_characters = df['v2'].str.contains(r'[^a-zA-Z0-9\\s]', regex=True).sum()  # Count messages with special characters\n"
      ],
      "metadata": {
        "id": "HNxuMF6lwkml"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of messages with special characters: {special_characters}\\n\")  # Print the count of special character messages\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9tabRVdywkpl",
        "outputId": "96faf209-1334-4a75-a796-acac0b334a25"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of messages with special characters: 5113\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AC7EqJbfwktZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "94vrBdBQwQIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "39CAuUm1wIVr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 6: Clean the Dataset</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "Data cleaning is a critical step to prepare the dataset for analysis. This involves removing unnecessary columns and renaming for clarity.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Key Actions</span>\n",
        "- Dropped unnecessary columns that do not contribute to the analysis.\n",
        "- Renamed the remaining columns for better understanding.\n",
        "- Mapped categorical labels to binary values for easier processing in machine learning algorithms."
      ],
      "metadata": {
        "id": "i_qh2x01z1IK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean the dataset"
      ],
      "metadata": {
        "id": "f5E8j4uzvF1O"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned = df.drop(columns=['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'])  # Drop unnecessary columns"
      ],
      "metadata": {
        "id": "iJjavx0RvF5m"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned.columns = ['label', 'message']  # Rename columns for clarity"
      ],
      "metadata": {
        "id": "M-tiJK_EvF8e"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned['label'] = df_cleaned['label'].map({'ham': 0, 'spam': 1})  # Map labels to binary values"
      ],
      "metadata": {
        "id": "NcXrZrRLvF-f"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zaAAl8KavGCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6hEDngQAw5az"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-lL_ErU3w5eQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 7: Exploratory Data Analysis (EDA)</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "EDA helps to understand the distribution and characteristics of the data. This step includes various visualizations to analyze the dataset comprehensively.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Key Actions</span>\n",
        "- Created count plots and pie charts to visualize the distribution of spam and ham emails.\n",
        "- Analyzed message lengths through histograms and violin plots to reveal patterns in the data.\n",
        "- Generated heatmaps to check for missing values and identified outliers in message lengths."
      ],
      "metadata": {
        "id": "0hQHZGvdz7HE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Eplanatory Data Analysis"
      ],
      "metadata": {
        "id": "jdfe_KmNw5jl"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " # Create a count plot for labels"
      ],
      "metadata": {
        "id": "Ni5_8Jo41XRs"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))  # Set the figure size for the plot\n",
        "sns.countplot(x='label', data=df_cleaned)  # Create a count plot for labels\n",
        "plt.title('Count of Spam and Ham Emails')  # Set the title of the plot\n",
        "plt.xlabel('Label (0: Ham, 1: Spam)')  # Set the x-axis label\n",
        "plt.ylabel('Count')  # Set the y-axis label\n",
        "plt.savefig('/content/spam_count_plot.pdf')  # Save the plot as a PDF\n",
        "plt.show()  # Display the plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "cEGi7Hrkw5m5",
        "outputId": "c1095bcf-3f07-4424-8522-911b6b3f555f"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a9nne5rgw5p3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_rCF5rq8w5sW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Pie chart"
      ],
      "metadata": {
        "id": "DX9zu6yYw5vW"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 8))  # Set the figure size for the pie chart\n",
        "df_cleaned['label'].value_counts().plot.pie(autopct='%1.1f%%', startangle=90, colors=['lightblue', 'salmon'])  # Create a pie chart for label distribution\n",
        "plt.title('Distribution of Spam and Ham Emails')  # Set the title of the pie chart\n",
        "plt.ylabel('')  # Hide the y-label\n",
        "plt.savefig('/content/spam_pie_chart.pdf')  # Save the pie chart as a PDF\n",
        "plt.show()  # Display the pie chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "mrbdWPNlvGG7",
        "outputId": "7f1d787a-24e1-4835-8d9f-0719524d97a1"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7Ur_fR7ZvGJe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Fq07JR4yvGLp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Histograms of message lengths"
      ],
      "metadata": {
        "id": "4FVNlFDavGN-"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned['message_length'] = df_cleaned['message'].apply(len)  # Calculate message lengths and add as a new column\n"
      ],
      "metadata": {
        "id": "4HIyucvdvGQT"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the histogram\n",
        "sns.histplot(df_cleaned['message_length'], bins=30, kde=True)  # Create a histogram of message lengths\n",
        "plt.title('Histogram of Message Lengths')  # Set the title of the histogram\n",
        "plt.xlabel('Message Length')  # Set the x-axis label\n",
        "plt.ylabel('Frequency')  # Set the y-axis label\n",
        "plt.savefig('/content/message_length_histogram.pdf')  # Save the histogram as a PDF\n",
        "plt.show()  # Display the histogram"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "KUVSayq4vGSi",
        "outputId": "d16b0146-3abe-42ea-8361-24be8d7885c0"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KlcaNFVYvGUq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rO5qmz4kvGW4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Heatmap for missing values"
      ],
      "metadata": {
        "id": "IsqynTUivGZX"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the heatmap\n",
        "sns.heatmap(df_cleaned.isnull(), cbar=False, cmap='viridis')  # Create a heatmap for missing values\n",
        "plt.title('Missing Values Heatmap')  # Set the title of the heatmap\n",
        "plt.savefig('/content/missing_values_heatmap.pdf')  # Save the heatmap as a PDF\n",
        "plt.show()  # Display the heatmap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "rqjNAhqtvGb0",
        "outputId": "286571fa-73ff-4d27-ef7f-dc70e82914ca"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "32-rgXWNvGeQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D4TJpCjmvGgo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Violins for message length by label"
      ],
      "metadata": {
        "id": "YN15O1CYvGlj"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the violin plot\n",
        "sns.violinplot(x='label', y='message_length', data=df_cleaned)  # Create a violin plot for message length by label\n",
        "plt.title('Message Length Distribution by Label')  # Set the title of the violin plot\n",
        "plt.xlabel('Label (0: Ham, 1: Spam)')  # Set the x-axis label\n",
        "plt.ylabel('Message Length')  # Set the y-axis label\n",
        "plt.savefig('/content/message_length_violin.pdf')  # Save the violin plot as a PDF\n",
        "plt.show()  # Display the violin plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "lUJPqWZ3vGoG",
        "outputId": "3b586472-1303-4479-f8bb-8e43541d7657"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IrxGZYagvGrm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XsFHynBd2Q_9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EMSlsK-H2RNN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color:blue;\">Additional EDA Graphs</span>\n",
        "\n",
        "## <span style=\"color:green;\">1. Word Cloud for Spam Messages</span>  \n",
        "This visualization displays the most frequently occurring words in spam messages. By generating a word cloud, we can easily identify which words are predominant in spam communications. The size of each word in the cloud represents its frequency in the dataset.\n",
        "\n",
        "## <span style=\"color:green;\">2. Bar Chart of Top Words in Spam and Ham Messages</span>  \n",
        "This bar chart compares the top 20 most common words in spam and ham messages. The plot allows us to visually differentiate between frequently used words in both categories, highlighting the distinct language patterns used in spam versus legitimate communications.\n",
        "\n",
        "## <span style=\"color:green;\">3. Box Plot of Message Lengths by Label</span>  \n",
        "This box plot visualizes the distribution of message lengths for both spam and ham messages. It helps identify the range, median, and potential outliers in message lengths for each category, providing insights into how message length varies between spam and legitimate messages.\n",
        "\n",
        "## <span style=\"color:green;\">4. Heatmap of Correlation (if applicable)</span>  \n",
        "This heatmap visualizes the correlation between different features in the dataset. It provides a quick overview of how variables are related to one another, which can be crucial for feature selection and understanding the dataset's structure.\n",
        "\n",
        "## <span style=\"color:green;\">5. Density Plot of Message Lengths</span>  \n",
        "This density plot compares the distribution of message lengths between spam and ham messages. It allows us to observe the density and spread of message lengths, helping to identify any significant differences between the two categories."
      ],
      "metadata": {
        "id": "ls5A-Mq14iRR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  Box Plot of Message Lengths by Label"
      ],
      "metadata": {
        "id": "Dx5uuEG32wIc"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the plot\n",
        "sns.boxplot(x='label', y='message_length', data=df_cleaned)  # Create a box plot for message length by label\n",
        "plt.title('Boxplot of Message Lengths by Label')  # Set the title of the plot\n",
        "plt.xlabel('Label (0: Ham, 1: Spam)')  # Set the x-axis label\n",
        "plt.ylabel('Message Length')  # Set the y-axis label\n",
        "plt.savefig('/content/message_length_boxplot.pdf')  # Save the box plot as a PDF\n",
        "plt.show()  # Display the box plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "A6V3bET62wLi",
        "outputId": "31943c2b-c885-49b5-eca3-f8f136121dcb"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-Exm9XRb3QKi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-w1bU8m03QOL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Distribution of Message Lengths"
      ],
      "metadata": {
        "id": "qJqoZfcB3QbT"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the density plot\n",
        "sns.kdeplot(df_cleaned[df_cleaned['label'] == 0]['message_length'], label='Ham', color='lightblue', fill=True)  # Density plot for ham messages\n",
        "sns.kdeplot(df_cleaned[df_cleaned['label'] == 1]['message_length'], label='Spam', color='salmon', fill=True)  # Density plot for spam messages\n",
        "plt.title('Density Plot of Message Lengths')  # Set the title of the plot\n",
        "plt.xlabel('Message Length')  # Set the x-axis label\n",
        "plt.ylabel('Density')  # Set the y-axis label\n",
        "plt.legend()  # Display legend\n",
        "plt.savefig('/content/message_length_density_plot.pdf')  # Save the density plot as a PDF\n",
        "plt.show()  # Display the density plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "xtVBWFdG2wOn",
        "outputId": "09b194f6-cba3-4f04-b7b1-10109b3773ab"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6858C_8X2RQZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-Fu1KG4w3Yk9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Word Cloud"
      ],
      "metadata": {
        "id": "tL53PJDs3Yod"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate word cloud for spam messages"
      ],
      "metadata": {
        "id": "w60Ol0F43Yto"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spam_words = ' '.join(df_cleaned[df_cleaned['label'] == 1]['message'])  # Combine spam messages into a single string\n"
      ],
      "metadata": {
        "id": "XThr3lSzvGt4"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wordcloud_spam = WordCloud(width=800, height=400, background_color='white').generate(spam_words)  # Create word cloud\n"
      ],
      "metadata": {
        "id": "3q6aiSUZvGwQ"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the plot\n",
        "plt.imshow(wordcloud_spam, interpolation='bilinear')  # Display the word cloud\n",
        "plt.axis('off')  # Hide axes\n",
        "plt.title('Word Cloud for Spam Messages')  # Set the title of the plot\n",
        "plt.savefig('/content/spam_wordcloud.pdf')  # Save the word cloud as a PDF\n",
        "plt.show()  # Display the word cloud"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "id": "TO_IoRFZ3yUe",
        "outputId": "a10dcdcd-7556-4702-8534-a52f0a8cf4be"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y-zbSKWS32Vh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kf9Q03pq4CLh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar Chart of Top Words"
      ],
      "metadata": {
        "id": "tI37oPDE4CPe"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Count words in spam and ham messages"
      ],
      "metadata": {
        "id": "ECfzOu6t4CTR"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spam_counts = Counter(' '.join(df_cleaned[df_cleaned['label'] == 1]['message']).split())  # Count words in spam messages\n"
      ],
      "metadata": {
        "id": "_j-Wj0Pj32Zu"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ham_counts = Counter(' '.join(df_cleaned[df_cleaned['label'] == 0]['message']).split())  # Count words in ham messages\n"
      ],
      "metadata": {
        "id": "GxglY2I032si"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Nv4jiPpA4NMH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the most common words"
      ],
      "metadata": {
        "id": "QDza-kkd4NP6"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spam_top_words = spam_counts.most_common(20)  # Get top 20 words in spam"
      ],
      "metadata": {
        "id": "Gzp7CqYE4NTl"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ham_top_words = ham_counts.most_common(20)  # Get top 20 words in ham"
      ],
      "metadata": {
        "id": "7xlg8ssY4NXY"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dkc7V2wn4URo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create DataFrames for plotting"
      ],
      "metadata": {
        "id": "xDRCU4Qr4UVX"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spam_df = pd.DataFrame(spam_top_words, columns=['word', 'count'])  # Create DataFrame for spam words"
      ],
      "metadata": {
        "id": "TSR49Pi84bE1"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ham_df = pd.DataFrame(ham_top_words, columns=['word', 'count'])  # Create DataFrame for ham words"
      ],
      "metadata": {
        "id": "C-h5Gg2W4UY0"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ubfp-YgE4UdJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))  # Set the figure size for the plot\n",
        "sns.barplot(x='count', y='word', data=spam_df, color='salmon', label='Spam')  # Bar plot for spam words\n",
        "sns.barplot(x='count', y='word', data=ham_df, color='lightblue', label='Ham', alpha=0.7)  # Bar plot for ham words\n",
        "plt.title('Top Words in Spam and Ham Messages')  # Set the title of the plot\n",
        "plt.xlabel('Word Count')  # Set the x-axis label\n",
        "plt.ylabel('Words')  # Set the y-axis label\n",
        "plt.legend()  # Display legend\n",
        "plt.savefig('/content/top_words_bar_chart.pdf')  # Save the bar chart as a PDF\n",
        "plt.show()  # Display the bar chart"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "id": "k3Vw3Z_o4Ugk",
        "outputId": "74a8da01-df52-434c-8a8f-c4dba66a8931"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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NjS3Q9/fff9eTTz6patWqycfHR6Ghoapdu7YkFfkz9Pb2VtWqVQu0F+V34s/69u2rAwcOaO/evfrggw/04IMPFnrJxuHDh2UYhp555hmFhoY6LPmPmszMzJQkPfvsszp9+rTq1aunxo0ba+zYsfr666/t2zKbzZo+fbqWLVumatWqqV27dpoxY4ZOnDjhsM/9+/erZ8+eslgsCgwMVGhoqP2mnfmfU/75q1u3rsN7PTw8HL5QkqRDhw5p//79BfLn/47n53/ggQcUHx+vRx55RNWqVdODDz6ojz/+mCIfQIXHNfYAgDLx5+vsvby8FBISogYNGkiSmjVrJl9fX23cuFFHjx516F9cZrNZbm7X/r117969deutt+qTTz7RihUrNHPmTE2fPl1LlizR7bffXuztXe5O+cZfbmp3NZGRkXr44YfVs2dP1alTRwsXLtTUqVOLtY0NGzbo7rvvVrt27fT666+revXq8vT01Pz58wvciO1K2a/1mIYPH6758+crMTFRrVu3lsVikclk0oMPPlho4VVSn11J6t27tzZv3qyxY8eqWbNm8vf3V15enrp161bkYyip47r55psVHR2txMREHT16tMDTE/Ll5xozZsxlb6yXX1y3a9dOR44c0WeffaYVK1boX//6l2bPnq033njDPvsgMTFRd911lz799FN9+eWXeuaZZ5ScnKw1a9aoefPmOn36tNq3b6/AwEA9++yzio6Olre3t3bt2qWnnnrqmorsvLw8NW7cWC+++GKh6/O/bPLx8dH69eu1du1affHFF1q+fLk++ugjdezYUStWrCjRJ1cAQHlCYQ8AlVhkZKRWrVqlM2fOOIza50+Vzb+BW77Cpud+99138vX1VWho6BX3ddNNN9mLd7PZrNatW9tHFz08PNSyZUtt2rRJR48eVVhYmH0kLj/DwYMH7dNu8x08eLBAxssdZ15eno4cOeIwSn/w4MFC+1evXl1Dhw7V0KFDlZmZqZtuuknPP//8NRX2JS04OFjR0dHat2+fQ3teXp6+//57++cm/XFuJNlHPxcvXixvb299+eWXMpvN9n7z588v/eCS/vOf/2jAgAEOd4C/ePGiTp8+fU3byz/3hw4dcpit8OuvvxZ79LsoTp06pdWrV2vKlCmaOHGivf1y09bLQp8+fTR16lQ1bNhQzZo1K7RP/mfj6elpv3nllYSEhGjQoEEaNGiQzp49q3bt2mny5MkOlxVER0dr9OjRGj16tA4dOqRmzZrphRde0Pvvv6/U1FSdPHlSS5YscbgZYP6Xdvnyz9/hw4fVoUMHe/ulS5eUnp6uJk2aOOxv79696tSp01VvJOnm5qZOnTqpU6dOevHFF/XPf/5TTz/9tNauXVuk4wcAV8RUfACoxO644w7l5ubq1VdfdWifPXu2TCZTgUJ2y5YtDtcRZ2Rk6LPPPlOXLl2uOhLm4eGhm2++WZs2bdKmTZvs19fna9OmjdavX6+tW7c6POItLi5OYWFheuONNxyuK1+2bJnS0tIKvbP9X+Ufx8svv+zQ/tJLLzm8zs3NLTCdOiwsTDVq1Cj0mvbStHfv3kLv1P7DDz/owIEDhV5G8OfzaBiGXn31VXl6eqpTp06S/hgpNplMDvdDSE9Pt99RvLS5u7sXGJV+5ZVXrnp/hsvp3LmzPD099corrzhs96/ntaTk/47/9RhKa39F8cgjj2jSpElXfFxeWFiYEhIS9Oabb+r48eMF1v/5Mpq/PrbP399fdevWtf/+nz9/XhcvXnToEx0drYCAAHufwj6n7Oxsvf766w7vi4uLU5UqVTRv3jxdunTJ3r5w4cICX8z07t1bP/30k+bNm1cg/4ULF3Tu3DlJf1wq8Vf5X3iU9b9hAChLjNgDQCV21113qUOHDnr66aeVnp6upk2basWKFfrss8+UmJhY4Jr0Ro0aqWvXrg6Pu5OkKVOmFGl/bdu2td/k6q/PZ2/Tpo2Sk5Pt/fJ5enpq+vTpGjRokNq3b68+ffrYH3cXFRWlkSNHXnW/zZo1U58+ffT6668rKytLbdq00erVq3X48GGHfmfOnFHNmjV1//33q2nTpvL399eqVau0ffv2Yj1nvCSsXLlSkyZN0t13361bbrnF/pz6d955RzabTZMnT3bo7+3treXLl2vAgAG6+eabtWzZMn3xxRf6xz/+YZ9N0b17d7344ovq1q2b+vbtq8zMTL322muqW7euw3XUpeXOO+/Ue++9J4vFotjYWG3ZskWrVq1SlSpVrml7oaGhGjNmjP0xfHfccYd2796tZcuWFbiGvSQEBgbarynPycnRDTfcoBUrVhQYiS5LkZGRBX4XCvPaa6+pbdu2aty4sR599FHVqVNHv/zyi7Zs2aIff/xRe/fulfTHzR4TEhLUokULhYSEaMeOHfbHP0p/zALp1KmTevfurdjYWHl4eOiTTz7RL7/8ogcffFDSH/+Wg4ODNWDAAI0YMUImk0nvvfdegS9EvLy8NHnyZA0fPlwdO3ZU7969lZ6erpSUFEVHRzuMzPfv318ff/yx/v73v2vt2rWKj49Xbm6uvv32W3388cf68ssvFRcXp2effVbr169X9+7dFRkZqczMTL3++uuqWbPmNV/eAwCugMIeACoxNzc3ff7555o4caI++ugjzZ8/X1FRUZo5c6ZGjx5doH/79u3VunVrTZkyRceOHVNsbKxSUlIcpsxeSf4f1vlT7/+sTZs2MplMMgyjwB/gAwcOlK+vr6ZNm6annnpKfn5+6tmzp6ZPn+7wDPsreeeddxQaGqqFCxfq008/VceOHfXFF1843AjO19dXQ4cO1YoVK7RkyRLl5eWpbt26ev311wvcab+03XfffTpz5oxWrFihNWvW6Pfff1dwcLBatWql0aNHO0xdlv4YJV2+fLmGDBmisWPHKiAgQJMmTXKYMt6xY0e9/fbbmjZtmhITE1W7dm1Nnz5d6enpZVLYz5kzR+7u7lq4cKEuXryo+Ph4rVq16rLXfRfF1KlT5e3trTfeeENr167VzTffrBUrVhRpJse1+OCDDzR8+HC99tprMgxDXbp00bJly1SjRo1S2V9JiY2N1Y4dOzRlyhSlpKTo5MmTCgsLU/PmzR1+R0aMGKHPP/9cK1askM1mU2RkpKZOnWp/QkBERIT69Omj1atX67333pOHh4caNGigjz/+WPfdd58kqUqVKlq6dKlGjx6tCRMmKDg4WA899JA6depU4Fw/8cQTMgxDL7zwgsaMGaOmTZvq888/14gRIxyeguHm5qZPP/1Us2fP1rvvvqtPPvlEvr6+qlOnjp588kn7JSh333230tPT9c477+i3335T1apV1b59e02ZMsV+k0cAqIhMhjPvQAMAcBkmk0nDhg0rMG0fzjdw4ED95z//0dmzZ50dBbhueXl5Cg0N1b333lvo1HsAQEFcYw8AAACnuHjxYoEp+u+++65+//13JSQkOCcUALggpuIDAADAKbZu3aqRI0eqV69eqlKlinbt2qW3335bjRo1Uq9evZwdDwBcBoU9AAAAnCIqKkoRERF6+eWX9fvvvyskJER/+9vfNG3aNHl5eTk7HgC4DK6xBwAAAADAhXGNPQAAAAAALozCHgAAAAAAF8Y19kWQl5enn3/+WQEBATKZTM6OAwAAAACo4AzD0JkzZ1SjRg25uV15TJ7Cvgh+/vlnRUREODsGAAAAAKCSycjIUM2aNa/Yh8K+CAICAiRJiz9eLD9fPyenAYCy4xUZ7ewIAAAAJaZFrTBnRygyq9WqiIgIez16JRT2RZA//d7P109+fhT2ACoPcxH+RwIAAOAqAgMDnR2h2IpyOTg3zwMAAAAAwIVVuMI+ISFBiYmJzo4BAAAAAECZqHCFPQAAAAAAlUmFKuwHDhyodevWac6cOTKZTDKZTEpPT9e6devUqlUrmc1mVa9eXePHj9elS5ecHRcAAAAAgOtWoQr7OXPmqHXr1nr00Ud1/PhxHT9+XJ6enrrjjjvUsmVL7d27V3PnztXbb7+tqVOnXnY7NptNVqvVYQEAAAAAoDyqUHfFt1gs8vLykq+vr8LDwyVJTz/9tCIiIvTqq6/KZDKpQYMG+vnnn/XUU09p4sSJcnMr+N1GcnKypkyZUtbxAQAAAAAotgo1Yl+YtLQ0tW7d2uERAfHx8Tp79qx+/PHHQt+TlJSkrKws+5KRkVFWcQEAAAAAKJYKNWJfUsxms8xms7NjAAAAAABwVRVuxN7Ly0u5ubn21w0bNtSWLVtkGIa9bdOmTQoICFDNmjWdEREAAAAAgBJT4Qr7qKgobdu2Tenp6frtt980dOhQZWRkaPjw4fr222/12WefadKkSRo1alSh19cDAAAAAOBKKlxlO2bMGLm7uys2NlahoaHKycnR//73P3311Vdq2rSp/v73v2vw4MGaMGGCs6MCAAAAAHDdTMaf56ijUFarVRaLRVlZWQoMDHR2HAAAAABABVecOrTCjdgDAAAAAFCZUNgDAAAAAODCeNxdMWzZsEV+fn7OjgGUOXPtGGdHAIBKoWVkNWdHAAC4IEbsAQAAAABwYRT2AAAAAAC4MAp7AAAAAABcGIU9AAAAAAAurNIU9suXL1fbtm0VFBSkKlWq6M4779SRI0ecHQsAAAAAgOtSaQr7c+fOadSoUdqxY4dWr14tNzc39ezZU3l5eQX62mw2Wa1WhwUAAAAAgPKo0jzu7r777nN4/c477yg0NFQHDhxQo0aNHNYlJydrypQpZRkPAAAAAIBrUmlG7A8dOqQ+ffqoTp06CgwMVFRUlCTp2LFjBfomJSUpKyvLvmRkZJRxWgAAAAAAiqbSjNjfddddioyM1Lx581SjRg3l5eWpUaNGys7OLtDXbDbLbDY7ISUAAAAAAMVTKQr7kydP6uDBg5o3b55uvfVWSdLGjRudnAoAAAAAgOtXKQr74OBgValSRW+99ZaqV6+uY8eOafz48c6OBQAAAADAdasU19i7ublp0aJF2rlzpxo1aqSRI0dq5syZzo4FAAAAAMB1MxmGYTg7RHlntVplsViUlZWlwMBAZ8cBAAAAAFRwxalDK8WIPQAAAAAAFRWFPQAAAAAALqxS3DyvpGzZsEV+fn7OjoHLMNeOcXYEFFHLyGrOjgAAAABUGIzYAwAAAADgwijsAQAAAABwYRT2AAAAAAC4sEpd2BuGoUuXLjk7BgAAAAAA16zcFPbvvvuuqlSpIpvN5tDeo0cP9e/fX5I0d+5cRUdHy8vLS/Xr19d7771n75eeni6TyaQ9e/bY206fPi2TyaTU1FRJUmpqqkwmk5YtW6YWLVrIbDZr48aNpX5sAAAAAACUlnJT2Pfq1Uu5ubn6/PPP7W2ZmZn64osv9PDDD+uTTz7Rk08+qdGjR2vfvn16/PHHNWjQIK1du7bY+xo/frymTZumtLQ0NWnSpMB6m80mq9XqsAAAAAAAUB6Vm8Lex8dHffv21fz58+1t77//vmrVqqWEhATNmjVLAwcO1NChQ1WvXj2NGjVK9957r2bNmlXsfT377LO67bbbFB0drZCQkALrk5OTZbFY7EtERMR1HRsAAAAAAKWl3BT2kvToo49qxYoV+umnnyRJKSkpGjhwoEwmk9LS0hQfH+/QPz4+XmlpacXeT1xc3BXXJyUlKSsry75kZGQUex8AAAAAAJQFD2cH+LPmzZuradOmevfdd9WlSxft379fX3zxRZHe6+b2x3cUhmHY23Jycgrt6+fnd8Vtmc1mmc3mIqYGAAAAAMB5ytWIvSQ98sgjSklJ0fz589W5c2f7NPiGDRtq06ZNDn03bdqk2NhYSVJoaKgk6fjx4/b1f76RHgAAAAAAFVG5GrGXpL59+2rMmDGaN2+e3n33XXv72LFj1bt3bzVv3lydO3fWf//7Xy1ZskSrVq2S9Mc1+rfccoumTZum2rVrKzMzUxMmTHDWYQAAAAAAUCbK3Yi9xWLRfffdJ39/f/Xo0cPe3qNHD82ZM0ezZs3SjTfeqDfffFPz589XQkKCvc8777yjS5cuqUWLFkpMTNTUqVPL/gAAAAAAAChDJuPPF6WXE506ddKNN96ol19+2dlRJElWq1UWi0VZWVkKDAx0dhwAAAAAQAVXnDq0XE3FP3XqlFJTU5WamqrXX3/d2XEAAAAAACj3ylVh37x5c506dUrTp09X/fr1nR0HAAAAAIByr1wV9unp6c6OcEW/vjZDF729nR0DksJGcmNEAAAAAJDK4c3zAAAAAABA0VWKwn7y5Mlq1qyZ/fXAgQMd7rgPAAAAAICrqhSFPQAAAAAAFRWFPQAAAAAALsxlCvu8vDzNmDFDdevWldlsVq1atfT8889Lkp566inVq1dPvr6+qlOnjp555hnl5ORc875sNpusVqvDAgAAAABAeVSu7op/JUlJSZo3b55mz56ttm3b6vjx4/r2228lSQEBAUpJSVGNGjX0zTff6NFHH1VAQIDGjRt3TftKTk7WlClTSjI+AAAAAAClwiUK+zNnzmjOnDl69dVXNWDAAElSdHS02rZtK0maMOH/Hn0WFRWlMWPGaNGiRddc2CclJWnUqFH211arVREREddxBAAAAAAAlA6XKOzT0tJks9nUqVOnQtd/9NFHevnll3XkyBGdPXtWly5dUmBg4DXvz2w2y2w2X/P7AQAAAAAoKy5xjb2Pj89l123ZskX9+vXTHXfcoaVLl2r37t16+umnlZ2dXYYJAQAAAABwDpco7GNiYuTj46PVq1cXWLd582ZFRkbq6aefVlxcnGJiYvTDDz84ISUAAAAAAGXPJabie3t766mnntK4cePk5eWl+Ph4/frrr9q/f79iYmJ07NgxLVq0SC1bttQXX3yhTz75xNmRAQAAAAAoEy4xYi9JzzzzjEaPHq2JEyeqYcOGeuCBB5SZmam7775bI0eO1BNPPKFmzZpp8+bNeuaZZ5wdFwAAAACAMmEyDMNwdojyzmq1ymKxKCsr67puygcAAAAAQFEUpw51mRF7AAAAAABQEIU9AAAAAAAuzCVunldebNmwRX5+fs6OUamZa8c4OwIAAJDUMrKasyMAAP4/RuwBAAAAAHBhLl3Yp6amymQy6fTp086OAgAAAACAU7hUYZ+QkKDExERnxwAAAAAAoNxwqcIeAAAAAAA4cpnCfuDAgVq3bp3mzJkjk8kkk8mk9PR0SdLOnTsVFxcnX19ftWnTRgcPHnR472effaabbrpJ3t7eqlOnjqZMmaJLly5ddl82m01Wq9VhAQAAAACgPHKZwn7OnDlq3bq1Hn30UR0/flzHjx9XRESEJOnpp5/WCy+8oB07dsjDw0MPP/yw/X0bNmzQ3/72Nz355JM6cOCA3nzzTaWkpOj555+/7L6Sk5NlsVjsS/5+AAAAAAAob1ymsLdYLPLy8pKvr6/Cw8MVHh4ud3d3SdLzzz+v9u3bKzY2VuPHj9fmzZt18eJFSdKUKVM0fvx4DRgwQHXq1NFtt92m5557Tm+++eZl95WUlKSsrCz7kpGRUSbHCAAAAABAcVWI59g3adLE/nP16tUlSZmZmapVq5b27t2rTZs2OYzQ5+bm6uLFizp//rx8fX0LbM9sNstsNpd+cAAAAAAArlOFKOw9PT3tP5tMJklSXl6eJOns2bOaMmWK7r333gLv8/b2LpuAAAAAAACUEpcq7L28vJSbm1us99x00006ePCg6tatW0qpAAAAAABwHpcq7KOiorRt2zalp6fL39/fPip/JRMnTtSdd96pWrVq6f7775ebm5v27t2rffv2aerUqWWQGgAAAACA0uMyN8+TpDFjxsjd3V2xsbEKDQ3VsWPHrvqerl27aunSpVqxYoVatmypW265RbNnz1ZkZGQZJAYAAAAAoHSZDMMwnB2ivLNarbJYLMrKylJgYKCz4wAAAAAAKrji1KEuNWIPAAAAAAAcUdgDAAAAAODCXOrmec7262szdJFH5JW5sJETnB0BAAAAAMotRuwBAAAAAHBhFPYAAAAAALiwSl3YG4ahS5cuOTsGAAAAAADXrMIV9jabTSNGjFBYWJi8vb3Vtm1bbd++XZKUmpoqk8mkZcuWqUWLFjKbzdq4caOTEwMAAAAAcO0qXGE/btw4LV68WAsWLNCuXbtUt25dde3aVb///ru9z/jx4zVt2jSlpaWpSZMmBbZhs9lktVodFgAAAAAAyqMKVdifO3dOc+fO1cyZM3X77bcrNjZW8+bNk4+Pj95++217v2effVa33XaboqOjFRISUmA7ycnJslgs9iUiIqIsDwMAAAAAgCKrUIX9kSNHlJOTo/j4eHubp6enWrVqpbS0NHtbXFzcFbeTlJSkrKws+5KRkVFqmQEAAAAAuB6V8jn2fn5+V1xvNptlNpvLKA0AAAAAANeuQo3YR0dHy8vLS5s2bbK35eTkaPv27YqNjXViMgAAAAAASkeFGrH38/PTkCFDNHbsWIWEhKhWrVqaMWOGzp8/r8GDB2vv3r3OjggAAAAAQImqUIW9JE2bNk15eXnq37+/zpw5o7i4OH355ZcKDg52djQAAAAAAEqcyTAMw9khyjur1SqLxaLD/3xaAd7ezo5T6YSNnODsCAAAAABQpvLr0KysLAUGBl6xb4UbsS9NocPGXfUDBQAAAACgLFWom+cBAAAAAFDZUNgDAAAAAODCmIpfDFs2bJGfn5+zY1Qa5toxzo4AlJqWkdWcHQEAAAAVhEuP2BuGoccee0whISEymUzas2ePsyMBAAAAAFCmXHrEfvny5UpJSVFqaqrq1KmjqlWrOjsSAAAAAABlyqUL+yNHjqh69epq06bNNb3fMAzl5ubKw8OlPwYAAAAAQCXmslPxBw4cqOHDh+vYsWMymUyKioqSzWbTiBEjFBYWJm9vb7Vt21bbt2+3vyc1NVUmk0nLli1TixYtZDabtXHjRiceBQAAAAAA18dlh6rnzJmj6OhovfXWW9q+fbvc3d01btw4LV68WAsWLFBkZKRmzJihrl276vDhwwoJCbG/d/z48Zo1a5bq1Kmj4ODgAtu22Wyy2Wz211artUyOCQAAAACA4nLZEXuLxaKAgAC5u7srPDxcvr6+mjt3rmbOnKnbb79dsbGxmjdvnnx8fPT22287vPfZZ5/VbbfdpujoaIeCP19ycrIsFot9iYiIKKvDAgAAAACgWFy2sP+rI0eOKCcnR/Hx8fY2T09PtWrVSmlpaQ594+LirritpKQkZWVl2ZeMjIxSyQwAAAAAwPVy2an41+Nqz6I3m80ym81llAYAAAAAgGtXYUbso6Oj5eXlpU2bNtnbcnJytH37dsXGxjoxGQAAAAAApafCjNj7+flpyJAhGjt2rEJCQlSrVi3NmDFD58+f1+DBg50dDwAAAACAUlFhCntJmjZtmvLy8tS/f3+dOXNGcXFx+vLLLwu98z0AAAAAABWByTAMw9khyjur1SqLxaLlS5df9fp8lBxz7RhnRwBKTcvIas6OAAAAgHIsvw7NyspSYGDgFftWqBH70tb61tZX/UABAAAAAChLFebmeQAAAAAAVEYU9gAAAAAAuDCm4hfDr6/N0EVvb2fHqDTCRk5wdgQAAAAAKPcYsQcAAAAAwIVVisI+OTlZLVu2VEBAgMLCwtSjRw8dPHjQ2bEAAAAAALhulaKwX7dunYYNG6atW7dq5cqVysnJUZcuXXTu3DlnRwMAAAAA4LpUimvsly9f7vA6JSVFYWFh2rlzp9q1a+ekVAAAAAAAXL9KUdj/VVZWliQpJCSk0PU2m002m83+2mq1lkkuAAAAAACKq1JMxf+zvLw8JSYmKj4+Xo0aNSq0T3JysiwWi32JiIgo45QAAAAAABRNpSvshw0bpn379mnRokWX7ZOUlKSsrCz7kpGRUYYJAQAAAAAouko1Ff+JJ57Q0qVLtX79etWsWfOy/cxms8xmcxkmAwAAAADg2lSKwt4wDA0fPlyffPKJUlNTVbt2bWdHAgAAAACgRFSKwn7YsGH64IMP9NlnnykgIEAnTpyQJFksFvn4+Dg5HQAAAAAA165SXGM/d+5cZWVlKSEhQdWrV7cvH330kbOjAQAAAABwXSrFiL1hGM6OAAAAAABAqagUhX1JCR02ToGBgc6OAQAAAACAXaWYig8AAAAAQEVFYQ8AAAAAgAtjKn4xbNmwRX5+fs6OUSmYa8c4OwIAAAAAJ2gZWc3ZEVwOI/YAAAAAALgwCnsAAAAAAFwYhT0AAAAAAC6s3Bf27777rqpUqSKbzebQ3qNHD/Xv31+SNHfuXEVHR8vLy0v169fXe++9Z++Xnp4uk8mkPXv22NtOnz4tk8mk1NTUsjgEAAAAAABKTbkv7Hv16qXc3Fx9/vnn9rbMzEx98cUXevjhh/XJJ5/oySef1OjRo7Vv3z49/vjjGjRokNauXXvN+7TZbLJarQ4LAAAAAADlUbkv7H18fNS3b1/Nnz/f3vb++++rVq1aSkhI0KxZszRw4EANHTpU9erV06hRo3Tvvfdq1qxZ17zP5ORkWSwW+xIREVEShwIAAAAAQIkr94W9JD366KNasWKFfvrpJ0lSSkqKBg4cKJPJpLS0NMXHxzv0j4+PV1pa2jXvLykpSVlZWfYlIyPjuvIDAAAAAFBaXOI59s2bN1fTpk317rvvqkuXLtq/f7+++OKLIr3Xze2P7y4Mw7C35eTkXPE9ZrNZZrP52gMDAAAAAFBGXGLEXpIeeeQRpaSkaP78+ercubN9enzDhg21adMmh76bNm1SbGysJCk0NFSSdPz4cfv6P99IDwAAAAAAV+YSI/aS1LdvX40ZM0bz5s3Tu+++a28fO3asevfurebNm6tz587673//qyVLlmjVqlWS/rhG/5ZbbtG0adNUu3ZtZWZmasKECc46DAAAAAAASpTLjNhbLBbdd9998vf3V48ePeztPXr00Jw5czRr1izdeOONevPNNzV//nwlJCTY+7zzzju6dOmSWrRoocTERE2dOrXsDwAAAAAAgFLgMiP2kvTTTz+pX79+Ba5/HzJkiIYMGXLZ9zVs2FCbN292aPvzNfcAAAAAALgqlyjsT506pdTUVKWmpur11193Wo7Wt7ZWYGCg0/YPAAAAAMBfuURh37x5c506dUrTp09X/fr1nR0HAAAAAIBywyUK+/T0dGdHAAAAAACgXHKJwr682LJhi/z8/JwdwyWZa8c4OwKAcqhlZDVnRwAAAHB5LnNX/GuRkJCgxMREZ8cAAAAAAKDUVOgR+yVLlsjT09PZMQAAAAAAKDUVurAPCQlxdgQAAAAAAEpVpZmK//rrrysmJkbe3t6qVq2a7r//fueGAwAAAACgBFToEft8O3bs0IgRI/Tee++pTZs2+v3337Vhw4bL9rfZbLLZbPbXVqu1LGICAAAAAFBslaKwP3bsmPz8/HTnnXcqICBAkZGRat68+WX7Jycna8qUKWWYEAAAAACAa1Ohp+Lnu+222xQZGak6deqof//+Wrhwoc6fP3/Z/klJScrKyrIvGRkZZZgWAAAAAICiqxSFfUBAgHbt2qUPP/xQ1atX18SJE9W0aVOdPn260P5ms1mBgYEOCwAAAAAA5VGlKOwlycPDQ507d9aMGTP09ddfKz09XWvWrHF2LAAAAAAArkuluMZ+6dKl+v7779WuXTsFBwfrf//7n/Ly8lS/fn1nRwMAAAAA4LpUisI+KChIS5Ys0eTJk3Xx4kXFxMToww8/1I033ujsaAAAAAAAXBeTYRiGs0OUd1arVRaLRVlZWVxvDwAAAAAodcWpQyvNNfYAAAAAAFREFPYAAAAAALgwCnsAAAAAAFxYpbh5Xkn59bUZuujt7ewY5U7YyAnOjgAAAAAAlValGLFPSUlRUFCQ/fXkyZPVrFkzp+UBAAAAAKCkuERhf+LECQ0fPlx16tSR2WxWRESE7rrrLq1evdrZ0QAAAAAAcKpyPxU/PT1d8fHxCgoK0syZM9W4cWPl5OToyy+/1LBhw/Ttt986OyIAAAAAAE5T7gv7oUOHymQy6auvvpKfn5+9/cYbb9TDDz8sSXrxxRc1f/58ff/99woJCdFdd92lGTNmyN/f31mxAQAAAAAoE+V6Kv7vv/+u5cuXa9iwYQ5Ffb786+bd3Nz08ssva//+/VqwYIHWrFmjcePGXfN+bTabrFarwwIAAAAAQHlUrgv7w4cPyzAMNWjQ4Ir9EhMT1aFDB0VFRaljx46aOnWqPv7442veb3JysiwWi32JiIi45m0BAAAAAFCaynVhbxhGkfqtWrVKnTp10g033KCAgAD1799fJ0+e1Pnz569pv0lJScrKyrIvGRkZ17QdAAAAAABKW7ku7GNiYmQyma54g7z09HTdeeedatKkiRYvXqydO3fqtddekyRlZ2df037NZrMCAwMdFgAAAAAAyqNyXdiHhISoa9eueu2113Tu3LkC60+fPq2dO3cqLy9PL7zwgm655RbVq1dPP//8sxPSAgAAAABQ9sp1YS9Jr732mnJzc9WqVSstXrxYhw4dUlpaml5++WW1bt1adevWVU5Ojl555RV9//33eu+99/TGG284OzYAAAAAAGWi3Bf2derU0a5du9ShQweNHj1ajRo10m233abVq1dr7ty5atq0qV588UVNnz5djRo10sKFC5WcnOzs2AAAAAAAlAmTUdQ71FViVqtVFotFWVlZXG8PAAAAACh1xalDy/2IPQAAAAAAuDwKewAAAAAAXBiFPQAAAAAALszD2QFcyZYNW+Tn5+fsGOWGuXaMsyOgnGoZWc3ZEQAAAIBKgxF7AAAAAABcGIU9AAAAAAAujMIeAAAAAAAXViEKe5vNphEjRigsLEze3t5q27attm/fLklKTU2VyWTS6tWrFRcXJ19fX7Vp00YHDx50cmoAAAAAAK5fhSjsx40bp8WLF2vBggXatWuX6tatq65du+r333+393n66af1wgsvaMeOHfLw8NDDDz982e3ZbDZZrVaHBQAAAACA8sjlC/tz585p7ty5mjlzpm6//XbFxsZq3rx58vHx0dtvv23v9/zzz6t9+/aKjY3V+PHjtXnzZl28eLHQbSYnJ8tisdiXiIiIsjocAAAAAACKxeUL+yNHjignJ0fx8fH2Nk9PT7Vq1UppaWn2tiZNmth/rl69uiQpMzOz0G0mJSUpKyvLvmRkZJRSegAAAAAArk+leY69p6en/WeTySRJysvLK7Sv2WyW2Wwuk1wAAAAAAFwPlx+xj46OlpeXlzZt2mRvy8nJ0fbt2xUbG+vEZAAAAAAAlD6XH7H38/PTkCFDNHbsWIWEhKhWrVqaMWOGzp8/r8GDB2vv3r3OjggAAAAAQKlx+cJekqZNm6a8vDz1799fZ86cUVxcnL788ksFBwc7OxoAAAAAAKXKZBiG4ewQ5Z3VapXFYlFWVpYCAwOdHQcAAAAAUMEVpw51+WvsAQAAAACozCjsAQAAAABwYRXiGvuysmXDFvn5+Tk7Rrlgrh3j7AhwAS0jqzk7AgAAAFDhMWIPAAAAAIALc3phn5CQoMTERGfHAAAAAADAJTm9sAcAAAAAANeOwh4AAAAAABdWLgr7vLw8jRs3TiEhIQoPD9fkyZPt61588UU1btxYfn5+ioiI0NChQ3X27FlJfzzXz8fHR8uWLXPY3ieffKKAgACdP39ekpSRkaHevXsrKChIISEhuueee5Senl5WhwcAAAAAQKkpF4X9ggUL5Ofnp23btmnGjBl69tlntXLlSkmSm5ubXn75Ze3fv18LFizQmjVrNG7cOElSYGCg7rzzTn3wwQcO21u4cKF69OghX19f5eTkqGvXrgoICNCGDRu0adMm+fv7q1u3bsrOzi40j81mk9VqdVgAAAAAACiPTIZhGM4MkJCQoNzcXG3YsMHe1qpVK3Xs2FHTpk0r0P8///mP/v73v+u3336TJH366afq37+/fvnlF/n6+spqtapatWr65JNP1K1bN73//vuaOnWq0tLSZDKZJEnZ2dkKCgrSp59+qi5duhTYx+TJkzVlypQC7cuXLudxd/8fj7tDUfC4OwAAAODaWK1WWSwWZWVlKTAw8Ip9y8WIfZMmTRxeV69eXZmZmZKkVatWqVOnTrrhhhsUEBCg/v376+TJk/Zp9nfccYc8PT31+eefS5IWL16swMBAde7cWZK0d+9eHT58WAEBAfL395e/v79CQkJ08eJFHTlypNA8SUlJysrKsi8ZGRmldegAAAAAAFwXD2cHkCRPT0+H1yaTSXl5eUpPT9edd96pIUOG6Pnnn1dISIg2btyowYMHKzs7W76+vvLy8tL999+vDz74QA8++KA++OADPfDAA/Lw+OPQzp49qxYtWmjhwoUF9hsaGlpoHrPZLLPZXPIHCgAAAABACSsXhf3l7Ny5U3l5eXrhhRfk5vbH5IKPP/64QL9+/frptttu0/79+7VmzRpNnTrVvu6mm27SRx99pLCwsKtOXwAAAAAAwNWUi6n4l1O3bl3l5OTolVde0ffff6/33ntPb7zxRoF+7dq1U3h4uPr166fatWvr5ptvtq/r16+fqlatqnvuuUcbNmzQ0aNHlZqaqhEjRujHH38sy8MBAAAAAKDElevCvmnTpnrxxRc1ffp0NWrUSAsXLlRycnKBfiaTSX369NHevXvVr18/h3W+vr5av369atWqpXvvvVcNGzbU4MGDdfHiRUbwAQAAAAAuz+l3xXcFxbkbIQAAAAAA18vl7ooPAAAAAACuDYU9AAAAAAAurFzfFb+8+fW1Gbro7e3sGE4XNnKCsyMAAAAAAP4/RuwBAAAAAHBh5aawNwxDjz32mEJCQmQymbRnzx5nRwIAAAAAoNwrN1Pxly9frpSUFKWmpqpOnTqqWrWqsyMBAAAAAFDuXfeIvdVq1aeffqq0tLTr2s6RI0dUvXp1tWnTRuHh4fLwcPzOITs7+7q2DwAAAABARVTswr5379569dVXJUkXLlxQXFycevfurSZNmmjx4sXXFGLgwIEaPny4jh07JpPJpKioKCUkJOiJJ55QYmKiqlatqq5du0qS1q1bp1atWslsNqt69eoaP368Ll26ZN9WQkKChg8frsTERAUHB6tatWqaN2+ezp07p0GDBikgIEB169bVsmXLrikrAAAAAADlSbEL+/Xr1+vWW2+VJH3yyScyDEOnT5/Wyy+/rKlTp15TiDlz5ujZZ59VzZo1dfz4cW3fvl2StGDBAnl5eWnTpk1644039NNPP+mOO+5Qy5YttXfvXs2dO1dvv/12gf0uWLBAVatW1VdffaXhw4dryJAh6tWrl9q0aaNdu3apS5cu6t+/v86fP19oHpvNJqvV6rAAAAAAAFAeFbuwz8rKUkhIiKQ/rou/77775Ovrq+7du+vQoUPXFMJisSggIEDu7u4KDw9XaGioJCkmJkYzZsxQ/fr1Vb9+fb3++uuKiIjQq6++qgYNGqhHjx6aMmWKXnjhBeXl5dm317RpU02YMEExMTFKSkqSt7e3qlatqkcffVQxMTGaOHGiTp48qa+//rrQPMnJybJYLPYlIiLimo4LAAAAAIDSVuzCPiIiQlu2bNG5c+e0fPlydenSRZJ06tQpeZfwM95btGjh8DotLU2tW7eWyWSyt8XHx+vs2bP68ccf7W1NmjSx/+zu7q4qVaqocePG9rZq1apJkjIzMwvdb1JSkrKysuxLRkZGiRwPAAAAAAAlrdh3xU9MTFS/fv3k7++vyMhIJSQkSPpjiv6fi+eS4Ofnd03v8/T0dHhtMpkc2vK/GPjzKP+fmc1mmc3ma9o3AAAAAABlqdiF/dChQ9WqVStlZGTotttuk5vbH4P+derUueZr7IuqYcOGWrx4sQzDsBfnmzZtUkBAgGrWrFmq+wYAAAAAoDy6psfdxcXFqWfPnvL397e3de/eXfHx8SUWrDBDhw5VRkaGhg8frm+//VafffaZJk2apFGjRtm/YAAAAAAAoDIp0oj9qFGjirzBF1988ZrDXM0NN9yg//3vfxo7dqyaNm2qkJAQDR48WBMmTCi1fQIAAAAAUJ6ZDMMwrtapQ4cODq937dqlS5cuqX79+pKk7777Tu7u7mrRooXWrFlTOkmdyGq1ymKxKCsrS4GBgc6OAwAAAACo4IpThxZpxH7t2rX2n1988UUFBARowYIFCg4OlvTHHfEHDRpkf749AAAAAAAoG0Uasf+zG264QStWrNCNN97o0L5v3z516dJFP//8c4kGLA8YsQcAAAAAlKUSH7H/68Z//fXXAu2//vqrzpw5U9zNuZQtG7Zc8yP4XJm5doyzIwAASkDLyGrOjgAAAEpBsW8l37NnTw0aNEhLlizRjz/+qB9//FGLFy/W4MGDde+995ZGRgAAAAAAcBnFLuzfeOMN3X777erbt68iIyMVGRmpvn37qlu3bnr99ddLI2OJS0lJUVBQkLNjAAAAAABw3Yo1FT83N1c7duzQ888/r5kzZ+rIkSOSpOjo6Eo5RR0AAAAAAGcrVmHv7u6uLl26KC0tTbVr11aTJk1KKxcAAAAAACiCYk/Fb9Sokb7//vvSyFKo5cuXq23btgoKClKVKlV055132mcKpKeny2QyacmSJerQoYN8fX3VtGlTbdmyxWEbKSkpqlWrlnx9fdWzZ0+dPHmyzPIDAAAAAFCail3YT506VWPGjNHSpUt1/PhxWa1Wh6WknTt3TqNGjdKOHTu0evVqubm5qWfPnsrLy7P3efrppzVmzBjt2bNH9erVU58+fXTp0iVJ0rZt2zR48GA98cQT2rNnjzp06KCpU6decZ82m63UjwsAAAAAgJJQ7OfYu7n933cBJpPJ/rNhGDKZTMrNzS25dIX47bffFBoaqm+++Ub+/v6qXbu2/vWvf2nw4MGSpAMHDujGG29UWlqaGjRooL59+yorK0tffPGFfRsPPvigli9frtOnTxe6j8mTJ2vKlCkF2pcvXV4p7yXA4+4AoGLgcXcAALiOUn2O/dq1a6852LU4dOiQJk6cqG3btum3336zj9QfO3ZMsbGxkuRwrX/16tUlSZmZmWrQoIHS0tLUs2dPh222bt1ay5cvv+w+k5KSNGrUKPtrq9WqiIiIEjsmAAAAAABKSrEL+/bt25dGjsu66667FBkZqXnz5qlGjRrKy8tTo0aNlJ2dbe/j6elp/zl/FsGfp+oXl9lsltlsvvbQAAAAAACUkWIX9pJ0+vRpvf3220pLS5Mk3XjjjXr44YdlsVhKNNzJkyd18OBBzZs3T7feeqskaePGjcXaRsOGDbVt2zaHtq1bt5ZYRgAAAAAAnKnYN8/bsWOHoqOjNXv2bP3+++/6/fff9eKLLyo6Olq7du0q0XDBwcGqUqWK3nrrLR0+fFhr1qxxmCJfFCNGjNDy5cs1a9YsHTp0SK+++uoVp+EDAAAAAOBKil3Yjxw5UnfffbfS09O1ZMkSLVmyREePHtWdd96pxMTEkg3n5qZFixZp586datSokUaOHKmZM2cWaxu33HKL5s2bpzlz5qhp06ZasWKFJkyYUKI5AQAAAABwlmLfFd/Hx0e7d+9WgwYNHNoPHDiguLg4nT9/vkQDlgfFuRshAAAAAADXqzh1aLFH7AMDA3Xs2LEC7RkZGQoICCju5gAAAAAAwHUodmH/wAMPaPDgwfroo4+UkZGhjIwMLVq0SI888oj69OlTGhkBAAAAAMBlFPmu+EePHlXt2rU1a9YsmUwm/e1vf9OlS5dkGIa8vLw0ZMgQTZs2rTSzOt2vr83QRW9vZ8cocWEjuecAAAAAALiqIhf20dHRioyMVIcOHdShQwcdPnxYp0+ftq/z9fUtrYwAAAAAAOAyilzYr1mzRqmpqUpNTdWHH36o7Oxs1alTRx07dlTHjh2VkJCgatWqlWZWAAAAAADwF0W+xj4hIUGTJ09WamqqTp06pZUrV6pPnz5KS0vTgAEDVKNGDd14442lmfWabd++XfHx8fLz81NYWJjuv/9+Xbp0ydmxAAAAAAC4bkUesf8zb29vdezYUW3btlWHDh20bNkyvfnmm/r2229LOl+JeOCBB1SvXj3t2LFDeXl5Sk1NdXYkAAAAAABKRLEK++zsbG3dulVr165Vamqqtm3bpoiICLVr106vvvqq2rdvX1o5r4ubm5vuvfdeNWzYUJLK7cwCAAAAAACKq8iFfceOHbVt2zbVrl1b7du31+OPP64PPvhA1atXL818JeKee+7R1KlT1aVLF0VFRV21v81mk81ms7+2Wq2lmA4AAAAAgGtX5GvsN2zYoCpVqqhjx47q1KmTbrvtNpco6hcsWKCUlBQNHTpU7du314EDB+zrXnjhBTVq1KjAe5KTk2WxWOxLREREWUYGAAAAAKDIilzYnz59Wm+99ZZ8fX01ffp01ahRQ40bN9YTTzyh//znP/r1119LM+c1ycvL0/jx4/Xcc89p/Pjxmjhxotq1a6etW7dKkr755hvdeuutBd6XlJSkrKws+5KRkVHW0QEAAAAAKJIiT8X38/NTt27d1K1bN0nSmTNntHHjRq1du1YzZsxQv379FBMTo3379pVa2OLKzMzUiRMn1Lx5c0nS4MGDdebMGXXu3Fn/+te/tHjxYq1evbrA+8xms8xmc1nHBQAAAACg2K7prvjSH4V+SEiIQkJCFBwcLA8PD6WlpZVktusWHBwsHx8frV+/Xq1bt5YkJSYm6syZM+rTp4/uvvtutWrVyskpAQAAAAC4dkUu7PPy8rRjxw6lpqZq7dq12rRpk86dO6cbbrhBHTp00GuvvaYOHTqUZtZiM5vNevLJJzVlyhT5+vqqW7duOnHihPbs2SM/Pz9t2LBBBw8eVP369Z0dFQAAAACAa1Lkwj4oKEjnzp1TeHi4OnTooNmzZyshIUHR0dGlme+6Pf/884qKitKrr76qcePGqWrVqrrvvvuUnp6uhx56SN27d9fWrVtVtWpVZ0cFAAAAAKDYTIZhGEXp+Oabb6pDhw6qV69eaWcqd6xWqywWi7KyshQYGOjsOAAAAACACq44dWiRR+wff/zx6w4GAAAAAABKVpEfdwcAAAAAAMqfa74rfmX062szdNHb29kxSkzYyAnOjgAAAAAAuE6M2AMAAAAA4MJcurBPSUlRUFDQVfuZTCZ9+umnpZ4HAAAAAICy5tKF/QMPPKDvvvvO/nry5Mlq1qyZ8wIBAAAAAFDGXPoaex8fH/n4+Dg7BgAAAAAATlPuRuyXLl2qoKAg5ebmSpL27Nkjk8mk8ePH2/s88sgjeuihhxym4qekpGjKlCnau3evTCaTTCaTUlJS7O/57bff1LNnT/n6+iomJkaff/55WR4WAAAAAAClotwV9rfeeqvOnDmj3bt3S5LWrVunqlWrKjU11d5n3bp1SkhIcHjfAw88oNGjR+vGG2/U8ePHdfz4cT3wwAP29VOmTFHv3r319ddf64477lC/fv30+++/F5rBZrPJarU6LAAAAAAAlEflrrC3WCxq1qyZvZBPTU3VyJEjtXv3bp09e1Y//fSTDh8+rPbt2zu8z8fHR/7+/vLw8FB4eLjCw8MdpukPHDhQffr0Ud26dfXPf/5TZ8+e1VdffVVohuTkZFksFvsSERFRascLAAAAAMD1KHeFvSS1b99eqampMgxDGzZs0L333quGDRtq48aNWrdunWrUqKGYmJhibbNJkyb2n/38/BQYGKjMzMxC+yYlJSkrK8u+ZGRkXNfxAAAAAABQWsrlzfMSEhL0zjvvaO/evfL09FSDBg2UkJCg1NRUnTp1qsBofVF4eno6vDaZTMrLyyu0r9lsltlsvqbsAAAAAACUpXI5Yp9/nf3s2bPtRXx+YZ+amlrg+vp8Xl5e9pvuAQAAAABQGZTLwj44OFhNmjTRwoUL7UV8u3bttGvXLn333XeXHbGPiorS0aNHtWfPHv3222+y2WxlmBoAAAAAgLJXLgt76Y/r7HNzc+2FfUhIiGJjYxUeHq769esX+p777rtP3bp1U4cOHRQaGqoPP/ywDBMDAAAAAFD2TIZhGM4OUd5ZrVZZLBZlZWUpMDDQ2XEAAAAAABVccerQcjtiDwAAAAAAro7CHgAAAAAAF1YuH3dXXv362gxd9PZ2dowSEzZygrMjAAAAAACuEyP2AAAAAAC4MAp7AAAAAABcGIU9AAAAAAAurFIX9jk5Oc6OAAAAAADAdalQhX1UVJReeuklh7ZmzZpp8uTJkiSTyaS5c+fq7rvvlp+fn55//vmyDwkAAAAAQAmqdHfFnzx5sqZNm6aXXnpJHh6FH77NZpPNZrO/tlqtZRUPAAAAAIBiqXSFfd++fTVo0KAr9klOTtaUKVPKKBEAAAAAANeuQk3FL4q4uLir9klKSlJWVpZ9ycjIKINkAAAAAAAUX4UasXdzc5NhGA5tf71Bnp+f31W3YzabZTabSzQbAAAAAACloUKN2IeGhur48eP211arVUePHnViIgAAAAAASleFKuw7duyo9957Txs2bNA333yjAQMGyN3d3dmxAAAAAAAoNRVqKn5SUpKOHj2qO++8UxaLRc899xwj9gAAAACACs1k/PWidBRgtVplsViUlZWlwMBAZ8cBAAAAAFRwxalDK9RUfAAAAAAAKhsKewAAAAAAXFiFusa+tG3ZsKVIj8srr8y1Y5wdAcD/1zKymrMjAAAAoIJgxB4AAAAAABdWKQv7TZs2qXHjxvL09FSPHj2cHQcAAAAAgGtWKafijxo1Ss2aNdOyZcvk7+/v7DgAAAAAAFyzSjlif+TIEXXs2FE1a9ZUUFCQs+MAAAAAAHDNKmRhb7PZNGLECIWFhcnb21tt27bV9u3blZ6eLpPJpJMnT+rhhx+WyWRSSkpKoe+3Wq0OCwAAAAAA5VGFLOzHjRunxYsXa8GCBdq1a5fq1q2rrl27KiAgQMePH1dgYKBeeuklHT9+XA888ECB9ycnJ8tisdiXiIgIJxwFAAAAAABXV+EK+3Pnzmnu3LmaOXOmbr/9dsXGxmrevHny8fHRO++8o/DwcJlMJlksFoWHh8vHx6fANpKSkpSVlWVfMjIynHAkAAAAAABcXYW7ed6RI0eUk5Oj+Ph4e5unp6datWqltLS0Im3DbDbLbDaXVkQAAAAAAEpMhRuxBwAAAACgMqlwhX10dLS8vLy0adMme1tOTo62b9+u2NhYJyYDAAAAAKDkVbip+H5+fhoyZIjGjh2rkJAQ1apVSzNmzND58+c1ePBgZ8cDAAAAAKBEVbjCXpKmTZumvLw89e/fX2fOnFFcXJy+/PJLBQcHOzsaAAAAAAAlymQYhuHsEOWd1WqVxWJRVlaWAgMDnR0HAAAAAFDBFacOrXDX2AMAAAAAUJlQ2AMAAAAA4MIq5DX2peXX12boore3s2MUS9jICc6OAAAAAAAoRYzYAwAAAADgwpxa2A8cOFAmk6nAcvjwYYd1np6eql27tsaNG6eLFy86bKOw95tMJi1atEiSlJqaetk+J06ccMZhAwAAAABQYpw+Fb9bt26aP3++Q1toaKjDupycHO3cuVMDBgyQyWTS9OnTHfrPnz9f3bp1c2gLCgpyeH3w4MECdxIMCwsroaMAAAAAAMA5nF7Ym81mhYeHX3VdRESEOnfurJUrVxYo7IOCgi67jXxhYWEFin0AAAAAAFyd0wv7otq3b582b96syMjIUt+XzWaTzWazv7ZaraW+TwAAAAAAroXTb563dOlS+fv725devXoVWOft7a3GjRsrMzNTY8eOLbCNPn36OGzD399fx44dc+hTs2ZNh/U33njjZTMlJyfLYrHYl4iIiJI7YAAAAAAASpDTR+w7dOiguXPn2l/7+fkVWHfu3DnNnj1bHh4euu+++wpsY/bs2ercubNDW40aNRxeb9iwQQEBAfbXnp6el82UlJSkUaNG2V9brVaKewAAAABAueT0wt7Pz09169a96rp33nlHTZs21dtvv63Bgwc79AsPD7/sNvLVrl27yNfYm81mmc3mIvUFAAAAAMCZnD4Vv6jc3Nz0j3/8QxMmTNCFCxecHQcAAAAAgHLBZQp7SerVq5fc3d312muvObSfPn1aJ06ccFjOnTvn0CczM7NAn5ycnLKMDwAAAABAiXOpwt7Dw0NPPPGEZsyY4VC4Dxo0SNWrV3dYXnnlFYf31q9fv0CfnTt3lvUhAAAAAABQokyGYRjODlHeWa1WWSwWHf7n0wrw9nZ2nGIJGznB2REAAAAAAMWUX4dmZWUpMDDwin2dfvM8VxI6bNxVP1AAAAAAAMqSS03FBwAAAAAAjijsAQAAAABwYUzFL4adxzLlH8Cj9gAAAAC4tpaR1ZwdASWIEXsAAAAAAFxYpS3sBw4cqB49ejg7BgAAAAAA16XSFvYAAAAAAFQEFPYAAAAAALgwbp5XCJvNJpvNZn9ttVqdmAYAAAAAgMtjxL4QycnJslgs9iUiIsLZkQAAAAAAKBSFfSGSkpKUlZVlXzIyMpwdCQAAAACAQjEVvxBms1lms9nZMQAAAAAAuCpG7AEAAAAAcGEU9gAAAAAAuDAKewAAAAAAXFilvcY+JSXF2REAAAAAALhulbawvxYtaoUpMDDQ2TEAAAAAALBjKj4AAAAAAC6Mwh4AAAAAABfGVPxi2HksU/4BF5wdAwCA69IyspqzIwAAgBJUaUfsExISlJiY6OwYAAAAAABcl0o7Yr9kyRJ5eno6OwYAAAAAANel0hb2ISEhzo4AAAAAAMB1Yyo+AAAAAAAurNKO2F+JzWaTzWazv7ZarU5MAwAAAADA5VXaEfsrSU5OlsVisS8RERHOjgQAAAAAQKEo7AuRlJSkrKws+5KRkeHsSAAAAAAAFIqp+IUwm80ym83OjgEAAAAAwFUxYg8AAAAAgAujsAcAAAAAwIVR2AMAAAAA4MIq7TX2qampzo4AAAAAAMB1q7SF/bVoUStMgYGBzo4BAAAAAIAdU/EBAAAAAHBhFPYAAAAAALgwpuIXw85jmfIPuODsGACASqBlZDVnRwAAAC6iQo7YJyQkKDEx0dkxAAAAAAAodRVyxH7JkiXy9PR0dgwAAAAAAEpdhSzsQ0JCnB0BAAAAAIAyUeGn4kdFRemf//ynHn74YQUEBKhWrVp66623nBsQAAAAAIASUiEL+7964YUXFBcXp927d2vo0KEaMmSIDh48eNn+NptNVqvVYQEAAAAAoDyqFIX9HXfcoaFDh6pu3bp66qmnVLVqVa1du/ay/ZOTk2WxWOxLREREGaYFAAAAAKDoKkVh36RJE/vPJpNJ4eHhyszMvGz/pKQkZWVl2ZeMjIyyiAkAAAAAQLFVyJvn/dVf75BvMpmUl5d32f5ms1lms7m0YwEAAAAAcN0qxYg9AAAAAAAVFYU9AAAAAAAujMIeAAAAAAAXViGvsU9NTbX/nJ6eXmD9nj17yiwLAAAAAAClqUIW9qWlRa0wBQYGOjsGAAAAAAB2TMUHAAAAAMCFUdgDAAAAAODCmIpfDDuPZco/4IKzYwBApdIyspqzIwAAAJRrjNgDAAAAAODCKOwBAAAAAHBhFPYAAAAAALiwSlHYR0VF6aWXXnJoa9asmSZPnuyUPAAAAAAAlBRunlcIm80mm81mf221Wp2YBgAAAACAy6sUI/bFlZycLIvFYl8iIiKcHQkAAAAAgEJR2BciKSlJWVlZ9iUjI8PZkQAAAAAAKFSlmIrv5uYmwzAc2nJyci7b32w2y2w2l3YsAAAAAACuW6UYsQ8NDdXx48ftr61Wq44ePerERAAAAAAAlIxKUdh37NhR7733njZs2KBvvvlGAwYMkLu7u7NjAQAAAABw3SrFVPykpCQdPXpUd955pywWi5577jlG7AEAAAAAFYLJ+OvF5yjAarXKYrEoKytLgYGBzo4DAAAAAKjgilOHVoqp+AAAAAAAVFQU9gAAAAAAuDAKewAAAAAAXFiluHleSdl5LFP+ARecHQMAKryWkdWcHQEAAMBlMGIPAAAAAIALo7AHAAAAAMCFUdgDAAAAAODCXK6wT0hI0PDhw5WYmKjg4GBVq1ZN8+bN07lz5zRo0CAFBASobt26WrZsmQzDUN26dTVr1iyHbezZs0cmk0mHDx920lEAAAAAAFAyXK6wl6QFCxaoatWq+uqrrzR8+HANGTJEvXr1Ups2bbRr1y516dJF/fv314ULF/Twww9r/vz5Du+fP3++2rVrp7p16xa6fZvNJqvV6rAAAAAAAFAemQzDMJwdojgSEhKUm5urDRs2SJJyc3NlsVh077336t1335UknThxQtWrV9eWLVtUq1Yt1apVS5s3b1arVq2Uk5OjGjVqaNasWRowYECh+5g8ebKmTJlSoH3NN4fkHxBQegcHAJDEXfEBAACsVqssFouysrIUGBh4xb4uOWLfpEkT+8/u7u6qUqWKGjdubG+rVu2PPwgzMzNVo0YNde/eXe+8844k6b///a9sNpt69ep12e0nJSUpKyvLvmRkZJTSkQAAAAAAcH1csrD39PR0eG0ymRzaTCaTJCkvL0+S9Mgjj2jRokW6cOGC5s+frwceeEC+vr6X3b7ZbFZgYKDDAgAAAABAeeTh7ABl4Y477pCfn5/mzp2r5cuXa/369c6OBAAAAABAiXDJEfvicnd318CBA5WUlKSYmBi1bt3a2ZEAAAAAACgRlaKwl6TBgwcrOztbgwYNcnYUAAAAAABKjMtNxU9NTS3Qlp6eXqDtrzf7/+mnn+Tp6am//e1v17zvFrXCuN4eAAAAAFCuuFxhX1w2m02//vqrJk+erF69etnvmA8AAAAAQEVQ4afif/jhh4qMjNTp06c1Y8YMZ8cBAAAAAKBEmYy/zllHAVarVRaLRVlZWUzFBwAAAACUuuLUoRV+Kn5J2nksU/4BF5wdAwBKRctILlUCAABwRRV+Kj4AAAAAABUZhT0AAAAAAC6Mwh4AAAAAABfmcoV9QkKChg8frsTERAUHB6tatWqaN2+ezp07p0GDBikgIEB169bVsmXL7O/Zt2+fbr/9dvn7+6tatWrq37+/fvvtNyceBQAAAAAAJcPlCntJWrBggapWraqvvvpKw4cP15AhQ9SrVy+1adNGu3btUpcuXdS/f3+dP39ep0+fVseOHdW8eXPt2LFDy5cv1y+//KLevXtfdvs2m01Wq9VhAQAAAACgPHK5x90lJCQoNzdXGzZskCTl5ubKYrHo3nvv1bvvvitJOnHihKpXr64tW7Zo1apV2rBhg7788kv7Nn788UdFRETo4MGDqlevXoF9TJ48WVOmTCnQvuabQ/IPCCilIwMA5+Ku+AAAAOVHcR5355Ij9k2aNLH/7O7uripVqqhx48b2tmrV/vjjNDMzU3v37tXatWvl7+9vXxo0aCBJOnLkSKHbT0pKUlZWln3JyMgoxaMBAAAAAODaueRz7D09PR1em0wmhzaTySRJysvL09mzZ3XXXXdp+vTpBbZTvXr1QrdvNptlNptLMDEAAAAAuKa8vDxlZ2c7O0aF5OXlJTe36x9vd8nCvjhuuukmLV68WFFRUfLwqPCHCwAAAAAlJjs7W0ePHlVeXp6zo1RIbm5uql27try8vK5rOxW+0h02bJjmzZunPn36aNy4cQoJCdHhw4e1aNEi/etf/5K7u7uzIwIAAABAuWMYho4fPy53d3dFRESUyMgy/k9eXp5+/vlnHT9+XLVq1bLPPL8WFb6wr1GjhjZt2qSnnnpKXbp0kc1mU2RkpLp168YvJgAAAABcxqVLl3T+/HnVqFFDvr6+zo5TIYWGhurnn3/WpUuXClxyXhwud1d8ZyjO3QgBAAAAoCK4ePGijh49qqioKPn4+Dg7ToV04cIFpaenq3bt2vL29nZYV+Hvig8AAAAAKBvXM0UcV1ZSny2FPQAAAAAALozCHgAAAAAAF1bhb55XknYey5R/wAVnxwBQBC0jqzk7AgAAQIWUOXtqme4vbOSEYr/n119/1cSJE/XFF1/ol19+UXBwsJo2baqJEycqPj6+FFI6l8sU9qmpqerQoYNOnTqloKAgZ8cBAAAAAJRT9913n7Kzs7VgwQLVqVNHv/zyi1avXq2TJ086O1qpKLdT8RMSEpSYmFji242KitJLL71U4tsFAAAAADjf6dOntWHDBk2fPl0dOnRQZGSkWrVqpaSkJN19992S/rhp3dy5c3X77bfLx8dHderU0X/+8x+H7Tz11FOqV6+efH19VadOHT3zzDPKycmxr588ebKaNWumd955R7Vq1ZK/v7+GDh2q3NxczZgxQ+Hh4QoLC9Pzzz9f6sdcbgt7AAAAAACKy9/fX/7+/vr0009ls9ku2++ZZ57Rfffdp71796pfv3568MEHlZaWZl8fEBCglJQUHThwQHPmzNG8efM0e/Zsh20cOXJEy5Yt0/Lly/Xhhx/q7bffVvfu3fXjjz9q3bp1mj59uiZMmKBt27aV2vFK5bSwHzhwoNatW6c5c+bIZDLJZDIpPT1dkrRz507FxcXJ19dXbdq00cGDB+3vO3LkiO655x5Vq1ZN/v7+atmypVatWmVfn5CQoB9++EEjR460bxcAAAAAUHF4eHgoJSVFCxYsUFBQkOLj4/WPf/xDX3/9tUO/Xr166ZFHHlG9evX03HPPKS4uTq+88op9/YQJE9SmTRtFRUXprrvu0pgxY/Txxx87bCMvL0/vvPOOYmNjddddd6lDhw46ePCgXnrpJdWvX1+DBg1S/fr1tXbt2lI95nJZ2M+ZM0etW7fWo48+quPHj+v48eOKiIiQJD399NN64YUXtGPHDnl4eOjhhx+2v+/s2bO64447tHr1au3evVvdunXTXXfdpWPHjkmSlixZopo1a+rZZ5+1b7cwNptNVqvVYQEAAAAAuIb77rtPP//8sz7//HN169ZNqampuummm5SSkmLv07p1a4f3tG7d2mHE/qOPPlJ8fLzCw8Pl7++vCRMm2GvLfFFRUQoICLC/rlatmmJjY+Xm5ubQlpmZWcJH6KhcFvYWi0VeXl7y9fVVeHi4wsPD5e7uLkl6/vnn1b59e8XGxmr8+PHavHmzLl68KElq2rSpHn/8cTVq1EgxMTF67rnnFB0drc8//1ySFBISInd3dwUEBNi3W5jk5GRZLBb7kv+lAgAAAADANXh7e+u2227TM888o82bN2vgwIGaNGlSkd67ZcsW9evXT3fccYeWLl2q3bt36+mnn1Z2drZDP09PT4fXJpOp0La8vLzrO5irKJeF/ZU0adLE/nP16tUlyf7tx9mzZzVmzBg1bNhQQUFB8vf3V1paWoFvVa4mKSlJWVlZ9iUjI6PkDgAAAAAAUOZiY2N17tw5++utW7c6rN+6dasaNmwoSdq8ebMiIyP19NNPKy4uTjExMfrhhx/KNG9xuMzj7vL9+duP/Gvk87/9GDNmjFauXKlZs2apbt268vHx0f3331/gW5WrMZvNMpvNJRcaAAAAAFAmTp48qV69eunhhx9WkyZNFBAQoB07dmjGjBm655577P3+/e9/Ky4uTm3bttXChQv11Vdf6e2335YkxcTE6NixY1q0aJFatmypL774Qp988omzDumqym1h7+Xlpdzc3GK9Z9OmTRo4cKB69uwp6Y8R/Pyb7l3PdgEAAAAAfwgbOcHZEa7I399fN998s2bPnq0jR44oJydHERERevTRR/WPf/zD3m/KlClatGiRhg4dqurVq+vDDz9UbGysJOnuu+/WyJEj9cQTT8hms6l79+565plnNHnyZCcd1ZWV28I+KipK27ZtU3p6uvz9/Yt0TUJMTIyWLFmiu+66SyaTSc8880yB90VFRWn9+vV68MEHZTabVbVq1dI6BAAAAABAGTObzUpOTlZycvIV+9WoUUMrVqy47PoZM2ZoxowZDm2JiYn2nydPnlyg0P/zzfnypaamXi3ydSu319iPGTNG7u7uio2NVWhoaJGuk3/xxRcVHBysNm3a6K677lLXrl110003OfR59tlnlZ6erujoaIWGhpZWfAAAAAAAyoTJMAzD2SHKO6vVKovFoqysLAUGBjo7DgAAAACUuosXL+ro0aOqXbu2vL29nR2nRJlMJn3yySfq0aOHU3Nc6TMuTh1abqfiAwAAAABQGira+Ha5nYoPAAAAAACujhH7Yth5LFP+ARecHQOosFpGVnN2BAAAAMDlMGIPAAAAAIALqxSFfUJCgsNjCQAAAAAAqCgqxVT8JUuWyNPTU9Ifz7FPTEyk0AcAAAAAVAiVorAPCQlxdgQAAAAAAEpFpZqKn5CQoB9++EEjR46UyWSSyWRydjQAAAAAAK5LpRixz7dkyRI1bdpUjz32mB599NHL9rPZbLLZbPbXVqu1LOIBAAAAQLm3/YdfynR/1/LkpIEDB+r06dP69NNPHdpTU1PVoUMHnTp1SkFBQSUTsByoFCP2+UJCQuTu7q6AgACFh4crPDy80H7JycmyWCz2JSIiooyTAgAAAABQNJWqsC+qpKQkZWVl2ZeMjAxnRwIAAAAAlKCTJ0+qT58+uuGGG+Tr66vGjRvrww8/dOiTkJCg4cOHKzExUcHBwapWrZrmzZunc+fOadCgQQoICFDdunW1bNkyJx3FHyjsC2E2mxUYGOiwAAAAAAAqjosXL6pFixb64osvtG/fPj322GPq37+/vvrqK4d+CxYsUNWqVfXVV19p+PDhGjJkiHr16qU2bdpo165d6tKli/r376/z58876UgqYWHv5eWl3NxcZ8cAAAAAAJSipUuXyt/f32G5/fbb7etvuOEGjRkzRs2aNVOdOnU0fPhwdevWTR9//LHDdpo2baoJEyYoJiZGSUlJ8vb2VtWqVfXoo48qJiZGEydO1MmTJ/X111+X9SHaVaqb50l/PMd+/fr1evDBB2U2m1W1alVnRwIAAAAAlLAOHTpo7ty5Dm3btm3TQw89JEnKzc3VP//5T3388cf66aeflJ2dLZvNJl9fX4f3NGnSxP6zu7u7qlSposaNG9vbqlX74+Z+mZmZpXUoV1XpCvtnn31Wjz/+uKKjo2Wz2WQYhrMjAQAAAABKmJ+fn+rWrevQ9uOPP9p/njlzpubMmaOXXnpJjRs3lp+fnxITE5Wdne3wHk9PT4fXJpPJoS3/Mep5eXklfQhFVikK+9TUVPvPt9xyi/bu3XtN22lRK4zr7QEAAACgAti0aZPuuece+wh+Xl6evvvuO8XGxjo5WfFVumvsAQAAAACIiYnRypUrtXnzZqWlpenxxx/XL7/84uxY16RSjNgDAAAAAEpGy8hqzo5QIiZMmKDvv/9eXbt2la+vrx577DH16NFDWVlZzo5WbCaDi8yvymq1ymKxaM03h+QfEODsOIBTVJT/gAMAAKBoLl68qKNHj6p27dry9vZ2dpwK6UqfcX4dmpWVddVLwpmKDwAAAACAC6OwBwAAAADAhVWqwj49PV0mk0l79uxxdhQAAAAAAEpEpSrsAQAAAACoaCpcYb98+XK1bdtWQUFBqlKliu68804dOXJEklS7dm1JUvPmzWUymZSQkODEpAAAAABQ/nG/9dJTUp9thSvsz507p1GjRmnHjh1avXq13Nzc1LNnT+Xl5emrr76SJK1atUrHjx/XkiVLCt2GzWaT1Wp1WAAAAACgMnF3d5ckZWdnOzlJxZX/2eZ/1teqwj3H/r777nN4/c477yg0NFQHDhxQaGioJKlKlSoKDw+/7DaSk5M1ZcqUUs0JAAAAAOWZh4eHfH199euvv8rT01NubhVuXNip8vLy9Ouvv8rX11ceHtdXmle4wv7QoUOaOHGitm3bpt9++015eXmSpGPHjik2NrZI20hKStKoUaPsr61WqyIiIkolLwAAAACURyaTSdWrV9fRo0f1ww8/ODtOheTm5qZatWrJZDJd13YqXGF/1113KTIyUvPmzVONGjWUl5enRo0aFWv6iNlsltlsLsWUAAAAAFD+eXl5KSYmhun4pcTLy6tEZkJUqML+5MmTOnjwoObNm6dbb71VkrRx40b7ei8vL0lSbm6uU/IBAAAAgKtxc3OTt7e3s2PgCipUYR8cHKwqVarorbfeUvXq1XXs2DGNHz/evj4sLEw+Pj5avny5atasKW9vb1ksFicmBgAAAADg+lSoux+4ublp0aJF2rlzpxo1aqSRI0dq5syZ9vUeHh56+eWX9eabb6pGjRq65557nJgWAAAAAIDrZzJ4KOFVWa1WWSwWZWVlKTAw0NlxAAAAAAAVXHHq0Ao1Fb+05H/3wfPsAQAAAABlIb/+LMpYPIV9EZw8eVKSeOQdAAAAAKBMnTlz5qr3hqOwL4KQkBBJ0rFjx7jZXgVntVoVERGhjIwMLruowDjPlQPnuXLgPFcOnOfKgfNceXCui8YwDJ05c0Y1atS4al8K+yLIf66gxWLhF6+SCAwM5FxXApznyoHzXDlwnisHznPlwHmuPDjXV1fUgeUKdVd8AAAAAAAqGwp7AAAAAABcGIV9EZjNZk2aNElms9nZUVDKONeVA+e5cuA8Vw6c58qB81w5cJ4rD851yeM59gAAAAAAuDBG7AEAAAAAcGEU9gAAAAAAuDAKewAAAAAAXBiFPQAAAAAALozCvghee+01RUVFydvbWzfffLO++uorZ0dCESUnJ6tly5YKCAhQWFiYevTooYMHDzr0uXjxooYNG6YqVarI399f9913n3755ReHPseOHVP37t3l6+ursLAwjR07VpcuXSrLQ0ExTJs2TSaTSYmJifY2znPF8dNPP+mhhx5SlSpV5OPjo8aNG2vHjh329YZhaOLEiapevbp8fHzUuXNnHTp0yGEbv//+u/r166fAwEAFBQVp8ODBOnv2bFkfCi4jNzdXzzzzjGrXri0fHx9FR0frueee05/v98t5dj3r16/XXXfdpRo1ashkMunTTz91WF9S5/Trr7/WrbfeKm9vb0VERGjGjBmlfWj4kyud55ycHD311FNq3Lix/Pz8VKNGDf3tb3/Tzz//7LANzrNruNq/6T/7+9//LpPJpJdeesmhnXNdcijsr+Kjjz7SqFGjNGnSJO3atUtNmzZV165dlZmZ6exoKIJ169Zp2LBh2rp1q1auXKmcnBx16dJF586ds/cZOXKk/vvf/+rf//631q1bp59//ln33nuvfX1ubq66d++u7Oxsbd68WQsWLFBKSoomTpzojEPCVWzfvl1vvvmmmjRp4tDOea4YTp06pfj4eHl6emrZsmU6cOCAXnjhBQUHB9v7zJgxQy+//LLeeOMNbdu2TX5+furatasuXrxo79OvXz/t379fK1eu1NKlS7V+/Xo99thjzjgkFGL69OmaO3euXn31VaWlpWn69OmaMWOGXnnlFXsfzrPrOXfunJo2barXXnut0PUlcU6tVqu6dOmiyMhI7dy5UzNnztTkyZP11ltvlfrx4Q9XOs/nz5/Xrl279Mwzz2jXrl1asmSJDh48qLvvvtuhH+fZNVzt33S+Tz75RFu3blWNGjUKrONclyADV9SqVStj2LBh9te5ublGjRo1jOTkZCemwrXKzMw0JBnr1q0zDMMwTp8+bXh6ehr//ve/7X3S0tIMScaWLVsMwzCM//3vf4abm5tx4sQJe5+5c+cagYGBhs1mK9sDwBWdOXPGiImJMVauXGm0b9/eePLJJw3D4DxXJE899ZTRtm3by67Py8szwsPDjZkzZ9rbTp8+bZjNZuPDDz80DMMwDhw4YEgytm/fbu+zbNkyw2QyGT/99FPphUeRde/e3Xj44Ycd2u69916jX79+hmFwnisCScYnn3xif11S5/T11183goODHf67/dRTTxn169cv5SNCYf56ngvz1VdfGZKMH374wTAMzrOruty5/vHHH40bbrjB2LdvnxEZGWnMnj3bvo5zXbIYsb+C7Oxs7dy5U507d7a3ubm5qXPnztqyZYsTk+FaZWVlSZJCQkIkSTt37lROTo7DOW7QoIFq1aplP8dbtmxR48aNVa1aNXufrl27ymq1av/+/WWYHlczbNgwde/e3eF8SpzniuTzzz9XXFycevXqpbCwMDVv3lzz5s2zrz969KhOnDjhcK4tFotuvvlmh3MdFBSkuLg4e5/OnTvLzc1N27ZtK7uDwWW1adNGq1ev1nfffSdJ2rt3rzZu3Kjbb79dEue5Iiqpc7plyxa1a9dOXl5e9j5du3bVwYMHderUqTI6GhRHVlaWTCaTgoKCJHGeK5K8vDz1799fY8eO1Y033lhgPee6ZFHYX8Fvv/2m3Nxchz/0JalatWo6ceKEk1LhWuXl5SkxMVHx8fFq1KiRJOnEiRPy8vKy/88k35/P8YkTJwr9Hchfh/Jh0aJF2rVrl5KTkwus4zxXHN9//73mzp2rmJgYffnllxoyZIhGjBihBQsWSPq/c3Wl/26fOHFCYWFhDus9PDwUEhLCuS4nxo8frwcffFANGjSQp6enmjdvrsTERPXr108S57kiKqlzyn/LXcvFixf11FNPqU+fPgoMDJTEea5Ipk+fLg8PD40YMaLQ9ZzrkuXh7ABAWRk2bJj27dunjRs3OjsKSlhGRoaefPJJrVy5Ut7e3s6Og1KUl5enuLg4/fOf/5QkNW/eXPv27dMbb7yhAQMGODkdSsrHH3+shQsX6oMPPtCNN96oPXv2KDExUTVq1OA8AxVETk6OevfuLcMwNHfuXGfHQQnbuXOn5syZo127dslkMjk7TqXAiP0VVK1aVe7u7gXunP3LL78oPDzcSalwLZ544gktXbpUa9euVc2aNe3t4eHhys7O1unTpx36//kch4eHF/o7kL8Ozrdz505lZmbqpptukoeHhzw8PLRu3Tq9/PLL8vDwULVq1TjPFUT16tUVGxvr0NawYUMdO3ZM0v+dqyv9dzs8PLzADVAvXbqk33//nXNdTowdO9Y+at+4cWP1799fI0eOtM/I4TxXPCV1TvlvuWvIL+p/+OEHrVy50j5aL3GeK4oNGzYoMzNTtWrVsv9t9sMPP2j06NGKioqSxLkuaRT2V+Dl5aUWLVpo9erV9ra8vDytXr1arVu3dmIyFJVhGHriiSf0ySefaM2aNapdu7bD+hYtWsjT09PhHB88eFDHjh2zn+PWrVvrm2++cfgPT/7/hP5aYMA5OnXqpG+++UZ79uyxL3FxcerXr5/9Z85zxRAfH1/gkZXfffedIiMjJUm1a9dWeHi4w7m2Wq3atm2bw7k+ffq0du7cae+zZs0a5eXl6eabby6Do8DVnD9/Xm5ujn+iuLu7Ky8vTxLnuSIqqXPaunVrrV+/Xjk5OfY+K1euVP369R2engHnyS/qDx06pFWrVqlKlSoO6znPFUP//v319ddfO/xtVqNGDY0dO1ZffvmlJM51iXP23fvKu0WLFhlms9lISUkxDhw4YDz22GNGUFCQw52zUX4NGTLEsFgsRmpqqnH8+HH7cv78eXufv//970atWrWMNWvWGDt27DBat25ttG7d2r7+0qVLRqNGjYwuXboYe/bsMZYvX26EhoYaSUlJzjgkFNGf74pvGJzniuKrr74yPDw8jOeff944dOiQsXDhQsPX19d4//337X2mTZtmBAUFGZ999pnx9ddfG/fcc49Ru3Zt48KFC/Y+3bp1M5o3b25s27bN2LhxoxETE2P06dPHGYeEQgwYMMC44YYbjKVLlxpHjx41lixZYlStWtUYN26cvQ/n2fWcOXPG2L17t7F7925DkvHiiy8au3fvtt8NvSTO6enTp41q1aoZ/fv3N/bt22csWrTI8PX1Nd58880yP97K6krnOTs727j77ruNmjVrGnv27HH42+zPdz3nPLuGq/2b/qu/3hXfMDjXJYnCvgheeeUVo1atWoaXl5fRqlUrY+vWrc6OhCKSVOgyf/58e58LFy4YQ4cONYKDgw1fX1+jZ8+exvHjxx22k56ebtx+++2Gj4+PUbVqVWP06NFGTk5OGR8NiuOvhT3nueL473//azRq1Mgwm81GgwYNjLfeesthfV5envHMM88Y1apVM8xms9GpUyfj4MGDDn1Onjxp9OnTx/D39zcCAwONQYMGGWfOnCnLw8AVWK1W48knnzRq1apleHt7G3Xq1DGefvpphz/8Oc+uZ+3atYX+P3nAgAGGYZTcOd27d6/Rtm1bw2w2GzfccIMxbdq0sjpEGFc+z0ePHr3s32Zr1661b4Pz7Bqu9m/6rwor7DnXJcdkGIZRFjMDAAAAAABAyeMaewAAAAAAXBiFPQAAAAAALozCHgAAAAAAF0ZhDwAAAACAC6OwBwAAAADAhVHYAwAAAADgwijsAQAAAABwYRT2AAAAAAC4MAp7AABw3RISEpSYmOjsGAAAVEoU9gAAuLg33nhDAQEBunTpkr3t7Nmz8vT0VEJCgkPf1NRUmUwmHTlypIxTStnZ2ZoxY4aaNm0qX19fVa1aVfHx8Zo/f75ycnLKNAtfRAAAKhIPZwcAAADXp0OHDjp79qx27NihW265RZK0YcMGhYeHa9u2bbp48aK8vb0lSWvXrlWtWrUUHR1d7P0YhqHc3Fx5eBT/z4fs7Gx17dpVe/fu1XPPPaf4+HgFBgZq69atmjVrlpo3b65mzZoVe7sAAIARewAAXF79+vVVvXp1paam2ttSU1N1zz33qHbt2tq6datDe4cOHSRJNptNI0aMUFhYmLy9vdW2bVtt377doa/JZNKyZcvUokULmc1mbdy4UefOndPf/vY3+fv7q3r16nrhhReumvGll17S+vXrtXr1ag0bNkzNmjVTnTp11LdvX23btk0xMTFFypSSkqKgoCCHbX/66acymUz215MnT1azZs303nvvKSoqShaLRQ8++KDOnDkjSRo4cKDWrVunOXPmyGQyyWQyKT09vcifNwAA5Q2FPQAAFUCHDh20du1a++u1a9cqISFB7du3t7dfuHBB27Ztsxf248aN0+LFi7VgwQLt2rVLdevWVdeuXfX77787bHv8+PGaNm2a0tLS1KRJE40dO1br1q3TZ599phUrVig1NVW7du26Yr6FCxeqc+fOat68eYF1np6e8vPzK1amqzly5Ig+/fRTLV26VEuXLtW6des0bdo0SdKcOXPUunVrPfroozp+/LiOHz+uiIiIYm0fAIDyhMIeAIAKoEOHDtq0aZMuXbqkM2fOaPfu3Wrfvr3atWtnH8nfsmWLbDabOnTooHPnzmnu3LmaOXOmbr/9dsXGxmrevHny8fHR22+/7bDtZ599Vrfddpuio6Pl5eWlt99+W7NmzVKnTp3UuHFjLViwwOH6/sIcOnRIDRo0uGKf4mS6mry8PKWkpKhRo0a69dZb1b9/f61evVqSZLFY5OXlJV9fX4WHhys8PFzu7u7F2j4AAOUJhT0AABVAQkKCzp07p+3bt2vDhg2qV6+eQkND1b59e/t19qmpqapTp45q1aqlI0eOKCcnR/Hx8fZteHp6qlWrVkpLS3PYdlxcnP3nI0eOKDs7WzfffLO9LSQkRPXr179iPsMwrnoMxcl0NVFRUQoICLC/rl69ujIzM4u1DQAAXAU3zwMAoAKoW7euatasqbVr1+rUqVNq3769JKlGjRqKiIjQ5s2btXbtWnXs2LHY286fJn896tWrp2+//fa6t+Pm5lbgS4LC7qjv6enp8NpkMikvL++69w8AQHnEiD0AABVEhw4dlJqaqtTUVIfH3LVr107Lli3TV199Zb++Pn9a/aZNm+z9cnJytH37dsXGxl52H9HR0fL09NS2bdvsbadOndJ33313xWx9+/bVqlWrtHv37gLrcnJydO7cuSJlCg0N1ZkzZ3Tu3Dl7nz179lxx34Xx8vJSbm5usd8HAEB5RGEPAEAF0aFDB23cuFF79uyxj9hLUvv27fXmm28qOzvbXtj7+flpyJAhGjt2rJYvX64DBw7o0Ucf1fnz5zV48ODL7sPf31+DBw/W2LFjtWbNGu3bt08DBw6Um9uV/6RITExUfHy8OnXqpNdee0179+7V999/r48//li33HKLDh06VKRMN998s3x9ffWPf/xDR44c0QcffKCUlJRif1ZRUVHatm2b0tPT9dtvvzGaDwBwaUzFBwCggujQoYMuXLigBg0aqFq1avb29u3b68yZM/bH4uWbNm2a8vLy1L9/f505c0ZxcXH68ssvFRwcfMX9zJw5U2fPntVdd92lgIAAjR49WllZWVd8j9ls1sqVKzV79my9+eabGjNmjHx9fdWwYUONGDFCjRo1KlKmkJAQvf/++xo7dqzmzZunTp06afLkyXrssceK9VmNGTNGAwYMUGxsrC5cuKCjR48qKiqqWNsAAKC8MBlFuZsNAAAAAAAol5iKDwAAAACAC6OwBwAAAADAhVHYAwAAAADgwijsAQAAAABwYRT2AAAAAAC4MAp7AAAAAABcGIU9AAAAAAAujMIeAAAAAAAXRmEPAAAAAIALo7AHAAAAAMCFUdgDAAAAAODC/h9N1UQXgsLWrwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1zHQNG_T3yaS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5rXVcW6S3ydv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "A4jW8pvf3yhb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 8: Feature Engineering</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "Feature engineering enhances model performance by transforming raw data into informative features.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Key Actions</span>\n",
        "- Vectorized email messages using `CountVectorizer` to convert text into numerical features.\n",
        "- Calculated message lengths and scaled them using `StandardScaler` for uniformity across features.\n",
        "- Combined text features with message lengths to create a comprehensive feature set for model training."
      ],
      "metadata": {
        "id": "N6EKLXG70Jjs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicates"
      ],
      "metadata": {
        "id": "mRxM8ir_vGyo"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned = df_cleaned.drop_duplicates()  # Remove duplicate rows from the cleaned DataFrame"
      ],
      "metadata": {
        "id": "_2UP7ButvG1Q"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BtmrIzqzvG3d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6KXRNmzCvG5n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for outliers in message length"
      ],
      "metadata": {
        "id": "INcFUv_4vG78"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))  # Set the figure size for the boxplot\n",
        "sns.boxplot(x='label', y='message_length', data=df_cleaned)  # Create a boxplot for message length by label\n",
        "plt.title('Boxplot of Message Length by Label')  # Set the title of the boxplot\n",
        "plt.savefig('/content/message_length_boxplot.pdf')  # Save the boxplot as a PDF\n",
        "plt.show()  # Display the boxplot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "_N7o5cVavG-H",
        "outputId": "f712fc25-dafe-4378-dca5-6ab0243996fe"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "k7seGAimvHAN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w03DumQQvHCa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare data for modeling"
      ],
      "metadata": {
        "id": "HYjSYeEdvHEy"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_cleaned['message']  # Define features (messages)"
      ],
      "metadata": {
        "id": "vLtxxVGPvHHg"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_cleaned['label']  # Define target variable (labels)"
      ],
      "metadata": {
        "id": "TfwH9r0TvHK5"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o45W-XRHxrxw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset"
      ],
      "metadata": {
        "id": "SmZU6pBdxr1D"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  # Split data into training and testing sets\n"
      ],
      "metadata": {
        "id": "i-_QY5bExr4O"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "rBAPDFNcxr7a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4iinArwOxr-6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Vectorize the text"
      ],
      "metadata": {
        "id": "piVxwb4qxsDT"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = CountVectorizer()  # Initialize the CountVectorizer"
      ],
      "metadata": {
        "id": "DbBiVm3ixsFm"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_vec = vectorizer.fit_transform(X_train)  # Fit and transform training messages"
      ],
      "metadata": {
        "id": "cgkIz8igvHMc"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_vec = vectorizer.transform(X_test)  # Transform testing messages"
      ],
      "metadata": {
        "id": "pmci8BTpvHOS"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pz3Myr8cvHQR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UUjr7X5HvHSo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create message lengths for training and testing sets"
      ],
      "metadata": {
        "id": "NsyZRN7GvHUw"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_length = X_train.apply(len).values.reshape(-1, 1)  # Calculate lengths of training messages"
      ],
      "metadata": {
        "id": "B58KBbgmvHXB"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_length = X_test.apply(len).values.reshape(-1, 1)  # Calculate lengths of testing messages"
      ],
      "metadata": {
        "id": "4j1CusLAvHZO"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KSaic1scvHbv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wBxw4UycvHdy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scale the message lengths"
      ],
      "metadata": {
        "id": "nk-5270wvHgJ"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()  # Initialize the StandardScaler"
      ],
      "metadata": {
        "id": "RIJ7qpSovHlc"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_length_scaled = scaler.fit_transform(X_train_length)  # Scale training message lengths"
      ],
      "metadata": {
        "id": "cnU5K2FXvHnt"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_length_scaled = scaler.transform(X_test_length)  # Scale testing message lengths"
      ],
      "metadata": {
        "id": "sqH4UmZdvHps"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FaiUU-dsyH-J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zTgVPxGWyIGq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Combine text vectors with message lengths"
      ],
      "metadata": {
        "id": "hdRfu7OgyIKQ"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_combined = sp.hstack((X_train_vec, X_train_length_scaled))  # Combine training text vectors and lengths"
      ],
      "metadata": {
        "id": "lIH6TpC8yIOR"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_combined = sp.hstack((X_test_vec, X_test_length_scaled))  # Combine testing text vectors and lengths"
      ],
      "metadata": {
        "id": "M-WuThi7yIQv"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1u-cW5PsyIS_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CiEacLLEyIe5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qnr-0JJL0Ycg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 9: Model Training and Evaluation</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "This step involves training the SVM model and evaluating its performance based on the test data.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Key Actions</span>\n",
        "- Split the dataset into training and testing sets for unbiased evaluation.\n",
        "- Trained a linear SVM model on the training data.\n",
        "- Made predictions and calculated accuracy, generating a classification report and confusion matrix for performance assessment."
      ],
      "metadata": {
        "id": "T_GH7vDj0ZxT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the SVM model"
      ],
      "metadata": {
        "id": "S3aHvSjxvHsI"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svm_model = SVC(kernel='linear')  # Initialize the Support Vector Classifier"
      ],
      "metadata": {
        "id": "knlfqZQRrB49"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svm_model.fit(X_train_combined, y_train)  # Fit the model on the training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "mMWwLBuEq7SV",
        "outputId": "a0739702-1016-4ad8-b0bb-41f136bfe574"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MhARCa8Wq7VW"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YSRh_i3nq7Z8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions"
      ],
      "metadata": {
        "id": "lXyW9zWQq7cu"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svm_model.predict(X_test_combined)  # Predict labels for the testing data"
      ],
      "metadata": {
        "id": "e4eIa0BCq7fk"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BD1S5J2Wq7if"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K5YLGyFnq7kx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model"
      ],
      "metadata": {
        "id": "9z0I3MBWq7nH"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)  # Calculate accuracy of the predictions"
      ],
      "metadata": {
        "id": "F0ZHHjacq7rg"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Accuracy: {accuracy * 100:.2f}%\")  # Print the accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpLUplcGq7t3",
        "outputId": "2f16f3a2-694a-437e-80a5-7b3a00789c30"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 98.74%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D5unp1ztq7v3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nClassification Report:\")  # Print a message before the classification report\n",
        "print(classification_report(y_test, y_pred))  # Print the classification report"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LlEl6uZoq7yA",
        "outputId": "42276757-32ad-4b77-dbab-ea61c825822a"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99       889\n",
            "           1       0.99      0.92      0.95       145\n",
            "\n",
            "    accuracy                           0.99      1034\n",
            "   macro avg       0.99      0.96      0.97      1034\n",
            "weighted avg       0.99      0.99      0.99      1034\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_LiICMSvq74-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8O14ZcdQq77l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix"
      ],
      "metadata": {
        "id": "OteXuMz9q7-E"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conf_matrix = confusion_matrix(y_test, y_pred)  # Generate the confusion matrix"
      ],
      "metadata": {
        "id": "iDJvboDGq8Ef"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(6, 4))  # Set the figure size for the confusion matrix\n",
        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Ham', 'Spam'], yticklabels=['Ham', 'Spam'])  # Create a heatmap for the confusion matrix\n",
        "plt.title('Confusion Matrix')  # Set the title of the confusion matrix\n",
        "plt.ylabel('Actual Label')  # Set the y-axis label\n",
        "plt.xlabel('Predicted Label')  # Set the x-axis label\n",
        "plt.savefig('/content/confusion_matrix.pdf')  # Save the confusion matrix as a PDF\n",
        "plt.show()  # Display the confusion matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "d1LSHfNrq8Gw",
        "outputId": "6095812e-a0f9-47f2-b2a9-ec94b9327a3f"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w5AgWPawq8JM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "avxZ8qBLq8Lk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6QYj5GLR0ghz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Step 10: Save Outputs</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Overview</span>\n",
        "Saving outputs is crucial for documentation and future reference. This step includes saving plots and performance metrics.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Key Actions</span>\n",
        "- Saved visualizations as PDF files for clear presentation.\n",
        "- Exported the classification report and accuracy metrics to a CSV file for further analysis."
      ],
      "metadata": {
        "id": "sL71hmfx0g4j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save classification report and accuracy to CSV"
      ],
      "metadata": {
        "id": "7b9xSe2Uq8QG"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classification_report_dict = classification_report(y_test, y_pred, output_dict=True)  # Generate classification report as a dictionary\n"
      ],
      "metadata": {
        "id": "OseFF8jzq8Sb"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_report = pd.DataFrame(classification_report_dict).transpose()  # Convert the report into a DataFrame"
      ],
      "metadata": {
        "id": "JScWEmj5yvPZ"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_report['accuracy'] = accuracy  # Add accuracy to the DataFrame"
      ],
      "metadata": {
        "id": "1-n9fdGgyvSl"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_report.to_csv('/content/spam_classification_report.csv', index=False)  # Save the report as a CSV file"
      ],
      "metadata": {
        "id": "2FW-t1dLyvZZ"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nClassification report and accuracy saved to CSV file.\")  # Print a message indicating save completion"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ib6bXgZwy2Kk",
        "outputId": "d3a063f2-17fc-4f20-df16-af031fadb0c5"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification report and accuracy saved to CSV file.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "22eT1Wv1y2Nx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JspnRKDKy2Qc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NDXUoCYb0ixX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <span style=\"color: #2E86C1;\">Conclusion</span>\n",
        "\n",
        "## <span style=\"color: #28B463;\">Summary of Findings</span>\n",
        "In this project, we successfully developed a spam classification model using a Support Vector Machine (SVM) algorithm. Through comprehensive exploratory data analysis (EDA), we gained insights into the dataset's characteristics, including the distribution of spam and ham emails. Our feature engineering efforts transformed raw text data into informative numerical features, enhancing model performance.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Model Performance</span>\n",
        "The SVM model demonstrated effective performance, achieving a commendable accuracy rate. The evaluation metrics, including precision, recall, and F1-score, indicated the model's robustness in distinguishing between spam and ham emails. The confusion matrix further illustrated the model's ability to minimize false positives and negatives.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Future Work</span>\n",
        "Future improvements could involve experimenting with advanced algorithms such as Random Forests or deep learning techniques for potentially better performance. Additionally, incorporating more sophisticated feature extraction methods like TF-IDF or word embeddings may enhance the model's ability to understand context and nuances in the text.\n",
        "\n",
        "## <span style=\"color: #28B463;\">Final Thoughts</span>\n",
        "This project not only reinforced the importance of data preprocessing and feature engineering but also highlighted the value of EDA in deriving meaningful insights. The successful implementation of the spam classification model showcases the practical application of machine learning techniques in real-world scenarios."
      ],
      "metadata": {
        "id": "EJ-QVeAj0jA8"
      }
    }
  ]
}