Project_MultinomialNB_YouTube.ipynb

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      "source": [
        "# Project: Multinomial Naïve Bayes Classifier - the YouTube Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "053831be",
      "metadata": {
        "id": "053831be"
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      "source": [
        "### Introducing the database"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "329b3cb7",
      "metadata": {
        "id": "329b3cb7"
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      "source": [
        "The database is taken from http://archive.ics.uci.edu/dataset/380/youtube+spam+collection"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "### Importing the necessary libraries"
      ]
    },
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      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import glob\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB, ComplementNB\n",
        "from sklearn.metrics import classification_report, ConfusionMatrixDisplay"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ec59c4c3",
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      "source": [
        "### Reading the database"
      ]
    },
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              "['Youtube01.csv',\n",
              " 'Youtube02.csv',\n",
              " 'Youtube05.csv',\n",
              " 'Youtube04.csv',\n",
              " 'Youtube03.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# List CSV files in the Colab environment\n",
        "files = glob.glob('Youtube*.csv')\n",
        "files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "ff0d4f00",
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        "all_df = [] # Create an empty list to store the DataFrames\n",
        "\n",
        "# Read the CSV file, drop specified columns, and append the resulting DataFrame to 'all_df'\n",
        "for i in files:\n",
        "  all_df.append(pd.read_csv(i).drop(['COMMENT_ID', 'AUTHOR', 'DATE'], axis=1))"
      ]
    },
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              "                                               CONTENT  CLASS\n",
              "0    Huh, anyway check out this you[tube] channel: ...      1\n",
              "1    Hey guys check out my new channel and our firs...      1\n",
              "2               just for test I have to say murdev.com      1\n",
              "3              watch?v=vtaRGgvGtWQ   Check this out .﻿      1\n",
              "4    Hey, check out my new website!! This site is a...      1\n",
              "..                                                 ...    ...\n",
              "344  How can this have 2 billion views when there's...      0\n",
              "345         I don't now why I'm watching this in 2014﻿      0\n",
              "346  subscribe to me for call of duty vids and give...      1\n",
              "347  hi guys please my android photo editor downloa...      1\n",
              "348  The first billion viewed this because they tho...      0\n",
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              "                                                CONTENT  CLASS\n",
              "0     Huh, anyway check out this you[tube] channel: ...      1\n",
              "1     Hey guys check out my new channel and our firs...      1\n",
              "2                just for test I have to say murdev.com      1\n",
              "3               watch?v=vtaRGgvGtWQ   Check this out .﻿      1\n",
              "4     Hey, check out my new website!! This site is a...      1\n",
              "...                                                 ...    ...\n",
              "1950                   Like this comment for no reason﻿      1\n",
              "1951                                    love this song﻿      0\n",
              "1952  this song is awesome. these guys are the best....      1\n",
              "1953   HOW MANY THUMBS UP FOR LOUIS SAVING THE DAY!?!?﻿      1\n",
              "1954                                           NICE :3﻿      0\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b0a36ecd-8a20-4a66-aaae-18d68f79509a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b0a36ecd-8a20-4a66-aaae-18d68f79509a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9c7f76fa-eaaa-453d-8b14-8d73425416ef\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9c7f76fa-eaaa-453d-8b14-8d73425416ef')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9c7f76fa-eaaa-453d-8b14-8d73425416ef button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Concatenate all DataFrames in 'all_df' vertically, ignoring the original index, and assign the result to 'data'\n",
        "data = pd.concat(all_df, axis=0, ignore_index=True)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "bc3989fc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bc3989fc",
        "outputId": "5f72a693-aaec-4179-da99-0fec5b70bbb5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CONTENT    0\n",
              "CLASS      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Calculate the sum of null values for each column in the data\n",
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "7b5a7bbc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7b5a7bbc",
        "outputId": "6a4f1894-ae86-472a-c2e7-7e8591a626de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1004\n",
              "0     951\n",
              "Name: CLASS, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Count the occurrences of each unique value in the 'CLASS' column of the data\n",
        "data['CLASS'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CLASS 1 corresponds to spam and CLASS 0 to ham comments. The dataset is balanced which is a good thing for our Naive Baye's classifier that we will use."
      ],
      "metadata": {
        "id": "L5X0pgGlG3cR"
      },
      "id": "L5X0pgGlG3cR"
    },
    {
      "cell_type": "markdown",
      "id": "3167043b",
      "metadata": {
        "id": "3167043b"
      },
      "source": [
        "### Defining the inputs and the target. Creating the train-test split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "6e7d4ad1",
      "metadata": {
        "id": "6e7d4ad1"
      },
      "outputs": [],
      "source": [
        "# Assign the 'CONTENT' column of the data to the variable inputs, and the 'CLASS' column to the variable target.\n",
        "inputs = data['CONTENT']\n",
        "target = data['CLASS']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "5bfcd1ea",
      "metadata": {
        "id": "5bfcd1ea"
      },
      "outputs": [],
      "source": [
        "# Split the inputs and target data into training and testing sets.\n",
        "X_train, X_test, y_train, y_test = train_test_split(inputs, target, test_size=0.2, random_state=42, stratify=target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "71f87086",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "71f87086",
        "outputId": "19aead71-7791-40ef-b355-b794e59c4d12"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.513427\n",
              "0    0.486573\n",
              "Name: CLASS, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Calculate the normalized value counts of each unique value in the y_train Series\n",
        "y_train.value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "3afe150d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3afe150d",
        "outputId": "95c98377-c2e1-42d4-d488-476870cc608c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.514066\n",
              "0    0.485934\n",
              "Name: CLASS, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Calculate the normalized value counts of each unique value in the y_test Series\n",
        "y_test.value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "14e8a85e",
      "metadata": {
        "id": "14e8a85e"
      },
      "source": [
        "### Tokenizing the YouTube comments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "7b8d139d",
      "metadata": {
        "id": "7b8d139d"
      },
      "outputs": [],
      "source": [
        "# Create an instance of the CountVectorizer class\n",
        "vectorizer= CountVectorizer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "297e9317",
      "metadata": {
        "id": "297e9317"
      },
      "outputs": [],
      "source": [
        "# Fit the vectorizer to the training data and transforms the training data into a matrix of token counts.\n",
        "# Then transform the test data into a matrix of token counts using the same vocabulary learned from the training data.\n",
        "X_train_transf = vectorizer.fit_transform(X_train)\n",
        "X_test_transf = vectorizer.transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "619d169e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "619d169e",
        "outputId": "43cf4fb8-9960-4d80-b52e-a800265dde41"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Convert the sparse matrix X_train_transf into a dense array representation.\n",
        "X_train_transf.toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "b568b208",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b568b208",
        "outputId": "3264c3d3-3fb8-4f46-e8b6-471eb6cbe755"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1564, 3907)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "X_train_transf.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "01926f32",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01926f32",
        "outputId": "5b7627cc-491c-4223-bfbd-4e925d86a51c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(391, 3907)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "X_test_transf.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a11cdb00",
      "metadata": {
        "id": "a11cdb00"
      },
      "source": [
        "### Performing the classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "3ce116cd",
      "metadata": {
        "id": "3ce116cd"
      },
      "outputs": [],
      "source": [
        "# Create an instance of the MultinomialNB class\n",
        "clf = MultinomialNB()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "4f6bd4a7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "4f6bd4a7",
        "outputId": "bd585cde-bdaa-4065-e343-f43d4ac0f7e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#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>MultinomialNB()</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\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "# Train the clf classifier using the training data X_train_transf and the corresponding labels y_train.\n",
        "clf.fit(X_train_transf, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b0d523f",
      "metadata": {
        "id": "0b0d523f"
      },
      "source": [
        "### Performing the evaluation on the test dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "47a3d7bf",
      "metadata": {
        "id": "47a3d7bf"
      },
      "outputs": [],
      "source": [
        "# Use the trained classifier clf to make predictions on the test data X_test_transf.\n",
        "y_test_pred= clf.predict(X_test_transf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "344760ab",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "344760ab",
        "outputId": "8f223e38-eafb-4f2c-99b0-93c2f8003d1f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a confusion matrix display based on the predicted labels y_test_pred and the true labels y_test.\n",
        "ConfusionMatrixDisplay.from_predictions(y_test, y_test_pred, labels=clf.classes_, cmap='magma')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "25db1187",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25db1187",
        "outputId": "d47e4357-63bb-4d4d-810f-bc8d8f2bb75f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         Ham       0.95      0.84      0.89       190\n",
            "        Spam       0.87      0.96      0.91       201\n",
            "\n",
            "    accuracy                           0.90       391\n",
            "   macro avg       0.91      0.90      0.90       391\n",
            "weighted avg       0.91      0.90      0.90       391\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Print a classification report based on the predicted labels y_test_pred and the true labels y_test.\n",
        "print(classification_report(y_test, y_test_pred, target_names=['Ham', 'Spam']))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "820ec641",
      "metadata": {
        "id": "820ec641"
      },
      "source": [
        "### Creating probability-distribution figures"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "8b8f7e88",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "8b8f7e88",
        "outputId": "a008f50c-1320-41df-f8ea-584e94e62b58"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a scatter plot to visualize the predicted probabilities of comments belonging to different classes.\n",
        "spam_proba = clf.predict_proba(X_test_transf).round(3)[:,1];\n",
        "\n",
        "df_scatter = pd.DataFrame()\n",
        "\n",
        "df_scatter['True class'] = y_test\n",
        "df_scatter['Predicted class'] = y_test_pred\n",
        "df_scatter['Predicted probability (spam)'] = spam_proba\n",
        "\n",
        "df_scatter = df_scatter.reset_index(drop = True)\n",
        "\n",
        "palette_0 = sns.color_palette(['#000000'])\n",
        "palette_1 = sns.color_palette(['#FF0000'])\n",
        "\n",
        "df_scatter_0 = df_scatter[df_scatter['True class'] == 0].reset_index(drop = True)\n",
        "df_scatter_1 = df_scatter[df_scatter['True class'] == 1].reset_index(drop = True)\n",
        "\n",
        "sns.set()\n",
        "\n",
        "fig, (ax1,ax2) = plt.subplots(2,1, figsize=(12,5))\n",
        "fig.tight_layout(pad = 3)\n",
        "\n",
        "sns.scatterplot(x = 'Predicted probability (spam)',\n",
        "                y = np.zeros(df_scatter_0.shape[0]),\n",
        "                data = df_scatter_0,\n",
        "                hue = 'True class',\n",
        "                s = 50,\n",
        "                markers = ['o'],\n",
        "                palette = palette_0,\n",
        "                style = 'True class',\n",
        "                legend = False,\n",
        "                ax = ax1).set(yticklabels=[])\n",
        "\n",
        "ax1.set_title('Probability distribution of comments belonging to the true \\'ham\\' class')\n",
        "ax1.vlines(0.5, -1, 1, linestyles = 'dashed', colors = 'red');\n",
        "\n",
        "\n",
        "sns.scatterplot(x = 'Predicted probability (spam)',\n",
        "                y = np.zeros(df_scatter_1.shape[0]),\n",
        "                hue = 'True class',\n",
        "                data = df_scatter_1,\n",
        "                s = 50,\n",
        "                palette = palette_1,\n",
        "                markers = ['X'],\n",
        "                style = 'True class',\n",
        "                legend = False,\n",
        "                ax = ax2).set(yticklabels=[])\n",
        "\n",
        "ax2.set_title('Probability distribution of comments belonging to the true \\'spam\\' class')\n",
        "\n",
        "ax2.vlines(0.5, -1, 1, linestyles = 'dashed', colors = 'red');"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3d6fb120",
      "metadata": {
        "id": "3d6fb120"
      },
      "source": [
        "### Making predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "7d261220",
      "metadata": {
        "id": "7d261220"
      },
      "outputs": [],
      "source": [
        "predict_data = vectorizer.transform(['This song is amazing!',\n",
        "                                     'You can win 1m dollars right now, just click here!!!'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "861be544",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "861be544",
        "outputId": "97040391-545e-4974-ab28-79d74afff566"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "clf.predict(predict_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating better model 2"
      ],
      "metadata": {
        "id": "HPzFvunCYTYb"
      },
      "id": "HPzFvunCYTYb"
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an instance of the MultinomialNB class\n",
        "clf2 = ComplementNB()"
      ],
      "metadata": {
        "id": "zNgjoUQ6Ycxj"
      },
      "id": "zNgjoUQ6Ycxj",
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the clf classifier using the training data X_train_transf and the corresponding labels y_train.\n",
        "clf2.fit(X_train_transf, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "OfUwpAYxYhyN",
        "outputId": "97f2ddde-f300-4104-a65a-4141a2e21c40"
      },
      "id": "OfUwpAYxYhyN",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ComplementNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ComplementNB()</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ComplementNB</label><div class=\"sk-toggleable__content\"><pre>ComplementNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained classifier clf to make predictions on the test data X_test_transf.\n",
        "y_test_pred= clf2.predict(X_test_transf)"
      ],
      "metadata": {
        "id": "cxCbEKRwYoBo"
      },
      "id": "cxCbEKRwYoBo",
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.reset_orig()\n",
        "\n",
        "# Create a confusion matrix display based on the predicted labels y_test_pred and the true labels y_test.\n",
        "ConfusionMatrixDisplay.from_predictions(y_test, y_test_pred, labels=clf2.classes_, cmap='magma')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "k_AUpvboYrUF",
        "outputId": "814a2da0-e40a-43c6-f242-2534693978a0"
      },
      "id": "k_AUpvboYrUF",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print a classification report based on the predicted labels y_test_pred and the true labels y_test.\n",
        "print(classification_report(y_test, y_test_pred, target_names=['Ham', 'Spam']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEmUe1lzYuJe",
        "outputId": "143f109e-172b-42c5-b8e2-f1545e3e7dfd"
      },
      "id": "aEmUe1lzYuJe",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         Ham       0.95      0.88      0.92       190\n",
            "        Spam       0.90      0.96      0.93       201\n",
            "\n",
            "    accuracy                           0.92       391\n",
            "   macro avg       0.93      0.92      0.92       391\n",
            "weighted avg       0.93      0.92      0.92       391\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a scatter plot to visualize the predicted probabilities of comments belonging to different classes.\n",
        "spam_proba = clf2.predict_proba(X_test_transf).round(3)[:,1];\n",
        "\n",
        "df_scatter2 = pd.DataFrame()\n",
        "\n",
        "df_scatter2['True class'] = y_test\n",
        "df_scatter2['Predicted class'] = y_test_pred\n",
        "df_scatter2['Predicted probability (spam)'] = spam_proba\n",
        "\n",
        "df_scatter2 = df_scatter2.reset_index(drop = True)\n",
        "\n",
        "palette_0 = sns.color_palette(['#000000'])\n",
        "palette_1 = sns.color_palette(['#FF0000'])\n",
        "\n",
        "df_scatter_0_2 = df_scatter2[df_scatter2['True class'] == 0].reset_index(drop = True)\n",
        "df_scatter_1_2 = df_scatter2[df_scatter2['True class'] == 1].reset_index(drop = True)\n",
        "\n",
        "sns.set()\n",
        "\n",
        "fig, (ax1,ax2) = plt.subplots(2,1, figsize=(12,5))\n",
        "fig.tight_layout(pad = 3)\n",
        "\n",
        "sns.scatterplot(x = 'Predicted probability (spam)',\n",
        "                y = np.zeros(df_scatter_0_2.shape[0]),\n",
        "                data = df_scatter_0_2,\n",
        "                hue = 'True class',\n",
        "                s = 50,\n",
        "                markers = ['o'],\n",
        "                palette = palette_0,\n",
        "                style = 'True class',\n",
        "                legend = False,\n",
        "                ax = ax1).set(yticklabels=[])\n",
        "\n",
        "ax1.set_title('Probability distribution of comments belonging to the true \\'ham\\' class')\n",
        "ax1.vlines(0.5, -1, 1, linestyles = 'dashed', colors = 'red');\n",
        "\n",
        "\n",
        "sns.scatterplot(x = 'Predicted probability (spam)',\n",
        "                y = np.zeros(df_scatter_1_2.shape[0]),\n",
        "                hue = 'True class',\n",
        "                data = df_scatter_1_2,\n",
        "                s = 50,\n",
        "                palette = palette_1,\n",
        "                markers = ['X'],\n",
        "                style = 'True class',\n",
        "                legend = False,\n",
        "                ax = ax2).set(yticklabels=[])\n",
        "\n",
        "ax2.set_title('Probability distribution of comments belonging to the true \\'spam\\' class')\n",
        "\n",
        "ax2.vlines(0.5, -1, 1, linestyles = 'dashed', colors = 'red');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "UoLsGOKsYyGj",
        "outputId": "c53fb854-24b7-4cf4-f178-b50a866a1634"
      },
      "id": "UoLsGOKsYyGj",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating better model 3"
      ],
      "metadata": {
        "id": "hZR1oRerbDxr"
      },
      "id": "hZR1oRerbDxr"
    },
    {
      "cell_type": "code",
      "source": [
        "# Create an instance of the MultinomialNB class\n",
        "clf3 = MultinomialNB(class_prior=np.array([0.6, 0.4]))"
      ],
      "metadata": {
        "id": "uldNjuYpbImG"
      },
      "id": "uldNjuYpbImG",
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the clf classifier using the training data X_train_transf and the corresponding labels y_train.\n",
        "clf3.fit(X_train_transf, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "tWroVZMJbZ7i",
        "outputId": "b2c6f80d-7ddd-4fb6-dcf4-bb6fb713e862"
      },
      "id": "tWroVZMJbZ7i",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(class_prior=array([0.6, 0.4]))"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB(class_prior=array([0.6, 0.4]))</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(class_prior=array([0.6, 0.4]))</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained classifier clf to make predictions on the test data X_test_transf.\n",
        "y_test_pred= clf3.predict(X_test_transf)"
      ],
      "metadata": {
        "id": "TCA3uum7bdWz"
      },
      "id": "TCA3uum7bdWz",
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.reset_orig()\n",
        "\n",
        "# Create a confusion matrix display based on the predicted labels y_test_pred and the true labels y_test.\n",
        "ConfusionMatrixDisplay.from_predictions(y_test, y_test_pred, labels=clf3.classes_, cmap='magma')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "SR0wzeD0bkBU",
        "outputId": "44e6bcc2-1313-4b3d-f985-17408978c577"
      },
      "id": "SR0wzeD0bkBU",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print a classification report based on the predicted labels y_test_pred and the true labels y_test.\n",
        "print(classification_report(y_test, y_test_pred, target_names=['Ham', 'Spam']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-F14JDubrGp",
        "outputId": "52e0d2b2-c908-4793-e4af-b7b2920f5c61"
      },
      "id": "A-F14JDubrGp",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         Ham       0.95      0.91      0.93       190\n",
            "        Spam       0.91      0.96      0.93       201\n",
            "\n",
            "    accuracy                           0.93       391\n",
            "   macro avg       0.93      0.93      0.93       391\n",
            "weighted avg       0.93      0.93      0.93       391\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a scatter plot to visualize the predicted probabilities of comments belonging to different classes.\n",
        "spam_proba = clf3.predict_proba(X_test_transf).round(3)[:,1];\n",
        "\n",
        "df_scatter3 = pd.DataFrame()\n",
        "\n",
        "df_scatter3['True class'] = y_test\n",
        "df_scatter3['Predicted class'] = y_test_pred\n",
        "df_scatter3['Predicted probability (spam)'] = spam_proba\n",
        "\n",
        "df_scatter3 = df_scatter3.reset_index(drop = True)\n",
        "\n",
        "palette_0 = sns.color_palette(['#000000'])\n",
        "palette_1 = sns.color_palette(['#FF0000'])\n",
        "\n",
        "df_scatter_0_3 = df_scatter3[df_scatter3['True class'] == 0].reset_index(drop = True)\n",
        "df_scatter_1_3 = df_scatter3[df_scatter3['True class'] == 1].reset_index(drop = True)\n",
        "\n",
        "sns.set()\n",
        "\n",
        "fig, (ax1,ax2) = plt.subplots(2,1, figsize=(12,5))\n",
        "fig.tight_layout(pad = 3)\n",
        "\n",
        "sns.scatterplot(x = 'Predicted probability (spam)',\n",
        "                y = np.zeros(df_scatter_0_3.shape[0]),\n",
        "                data = df_scatter_0_3,\n",
        "                hue = 'True class',\n",
        "                s = 50,\n",
        "                markers = ['o'],\n",
        "                palette = palette_0,\n",
        "                style = 'True class',\n",
        "                legend = False,\n",
        "                ax = ax1).set(yticklabels=[])\n",
        "\n",
        "ax1.set_title('Probability distribution of comments belonging to the true \\'ham\\' class')\n",
        "ax1.vlines(0.5, -1, 1, linestyles = 'dashed', colors = 'red');\n",
        "\n",
        "\n",
        "sns.scatterplot(x = 'Predicted probability (spam)',\n",
        "                y = np.zeros(df_scatter_1_3.shape[0]),\n",
        "                hue = 'True class',\n",
        "                data = df_scatter_1_3,\n",
        "                s = 50,\n",
        "                palette = palette_1,\n",
        "                markers = ['X'],\n",
        "                style = 'True class',\n",
        "                legend = False,\n",
        "                ax = ax2).set(yticklabels=[])\n",
        "\n",
        "ax2.set_title('Probability distribution of comments belonging to the true \\'spam\\' class')\n",
        "\n",
        "ax2.vlines(0.5, -1, 1, linestyles = 'dashed', colors = 'red');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "u3O4LvOlbxko",
        "outputId": "6a514db3-b69d-4e7f-fea4-1abd8769fb11"
      },
      "id": "u3O4LvOlbxko",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}