Email_Spam_Detection.ipynb

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        "import string\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB, GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import *\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer"
      ]
    },
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      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/spam.csv')\n",
        "df.head()"
      ],
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              "  Category                                            Message\n",
              "0      ham  Go until jurong point, crazy.. Available only ...\n",
              "1      ham                      Ok lar... Joking wif u oni...\n",
              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      ham  U dun say so early hor... U c already then say...\n",
              "4      ham  Nah I don't think he goes to usf, he lives aro..."
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5572,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"spam\",\n          \"ham\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5157,\n        \"samples\": [\n          \"Also sir, i sent you an email about how to log into the usc payment portal. I.ll send you another message that should explain how things are back home. Have a great weekend.\",\n          \"Are you free now?can i call now?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "**Exploratory data analysis (EDA):**"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Category  5572 non-null   object\n",
            " 1   Message   5572 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 87.2+ KB\n"
          ]
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              "       Category                 Message\n",
              "count      5572                    5572\n",
              "unique        2                    5157\n",
              "top         ham  Sorry, I'll call later\n",
              "freq       4825                      30"
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              "\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-2fab2c78-bff4-41e8-a7a3-3413a4966134 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"4825\",\n          \"5572\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5157,\n          \"30\",\n          \"5572\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rjvl_mQ7bXU4",
        "outputId": "545a81af-0863-443b-8e99-8a2047283b80"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category\n",
              "ham     4825\n",
              "spam     747\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Some Emails\n",
        "print(df['Message'][0])\n",
        "print(df['Message'][2])\n",
        "print(df['Message'][5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WL4Uz1CHHRke",
        "outputId": "7131afc8-24a5-4a19-9cf5-79202e14a26d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
            "Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\n",
            "FreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50 to rcv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Preprocessing:**"
      ],
      "metadata": {
        "id": "LDutJVplcnir"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lower casing messages\n",
        "df['Message'] = df['Message'].str.lower()\n",
        "\n",
        "# Removing '#' tag\n",
        "df['Message'] = df['Message'].str.replace('#','')\n",
        "\n",
        "# Removing '@'\n",
        "df['Message'] = df['Message'].str.replace('@','')\n",
        "\n",
        "# Removing URLs\n",
        "df['Message'] = df['Message'].str.replace(r'^https?:\\/\\/.*[\\r\\n]*','')\n",
        "\n",
        "# Removing Punctuations\n",
        "df['Message'] = df['Message'].str.translate(str.maketrans('', '', string.punctuation))"
      ],
      "metadata": {
        "id": "0RL11yKHd3Wo"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a new column by encoding category columns\n",
        "df['spam'] = df['Category'].apply(lambda x: 1 if x=='spam' else 0)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SK7uA6f5fD04",
        "outputId": "7ee5f2db-9504-4106-ba90-458d08a58f71"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Category                                            Message  spam\n",
              "0      ham  go until jurong point crazy available only in ...     0\n",
              "1      ham                            ok lar joking wif u oni     0\n",
              "2     spam  free entry in 2 a wkly comp to win fa cup fina...     1\n",
              "3      ham        u dun say so early hor u c already then say     0\n",
              "4      ham  nah i dont think he goes to usf he lives aroun...     0"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Message</th>\n",
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              "      <td>0</td>\n",
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              "      <td>ok lar joking wif u oni</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>free entry in 2 a wkly comp to win fa cup fina...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>u dun say so early hor u c already then say</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>nah i dont think he goes to usf he lives aroun...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0fe42ce9-1abc-4ce1-a3b0-d9765117e9f3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0fe42ce9-1abc-4ce1-a3b0-d9765117e9f3 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-0fe42ce9-1abc-4ce1-a3b0-d9765117e9f3');\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",
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              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee9a9acd-2825-40bb-ac12-32d46a606967')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "     width=\"24px\">\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ee9a9acd-2825-40bb-ac12-32d46a606967 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5572,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"spam\",\n          \"ham\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Message\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5131,\n        \"samples\": [\n          \"u still havent got urself a jacket ah\",\n          \"hi wkend ok but journey terrible wk not good as have huge back log of marking to do\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"spam\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Models Training**"
      ],
      "metadata": {
        "id": "aH2PilD8ic-K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert text data to numerical data\n",
        "cv = CountVectorizer()\n",
        "\n",
        "# Fitting CountVectorizer on X\n",
        "X = cv.fit_transform(df['Message']).toarray()\n",
        "\n",
        "Y = df['spam']"
      ],
      "metadata": {
        "id": "kb7KvdJfjAZg"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Train Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=0)"
      ],
      "metadata": {
        "id": "ZhB-xgpmkMQ3"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model 1 : Decision Tree Classifier**"
      ],
      "metadata": {
        "id": "JeymDrtaY7Bm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DTC = DecisionTreeClassifier(max_depth=5)\n",
        "DTC.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "9DW_mzyyVDv9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "d536ca0d-2198-4250-91a2-760e10098c16"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5)"
            ],
            "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>DecisionTreeClassifier(max_depth=5)</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DTC_pred = DTC.predict(X_test)\n",
        "DTC_accuracy = accuracy_score(y_test, DTC_pred)\n",
        "DTC_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDsFYaMmVNeg",
        "outputId": "59c6b630-e681-458e-fbc0-3dc1f2f2289e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.944377990430622"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DTC_confusion = confusion_matrix(y_test, DTC_pred)\n",
        "display = ConfusionMatrixDisplay(confusion_matrix = DTC_confusion, display_labels = [0, 1])\n",
        "display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "mb2EeakLVNFt",
        "outputId": "e63ce52f-78bc-4692-c050-3aeb2da79b98"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model 2 : Naive Bayes (Gaussian)**"
      ],
      "metadata": {
        "id": "ikzSu8punBQY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "NB = GaussianNB()\n",
        "NB.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "ghEam9-4UAjL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "d439ec49-9200-4f15-8771-9a9a46c6c3e4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NB_pred = NB.predict(X_test)\n",
        "NB_accuracy = accuracy_score(y_test, NB_pred)\n",
        "NB_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MSUrrOt6YLDg",
        "outputId": "2e618ecf-0d0a-4046-e17b-490f9e637bba"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.902511961722488"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NB_confusion = confusion_matrix(y_test, NB_pred)\n",
        "display = ConfusionMatrixDisplay(confusion_matrix = NB_confusion, display_labels = [0, 1])\n",
        "\n",
        "display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Iy3MtpPZYTy-",
        "outputId": "15a20f84-c2b9-42c3-87fd-facd9184d0d6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model 3 :  Naive Bayes Multinomial**"
      ],
      "metadata": {
        "id": "BIT2RSP3Z3gP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MNB = MultinomialNB()\n",
        "MNB.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "8CCmVt7EkdDY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "ed78defd-656f-4736-a955-b7c2c92cd8ff"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MNB_pred = MNB.predict(X_test)\n",
        "MNB_accuracy = accuracy_score(y_test, MNB_pred)\n",
        "MNB_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_SySUoJFlVB-",
        "outputId": "8ac5ade3-f94a-40ed-d5e3-cf1247e9687f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9808612440191388"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MNB_confusion = confusion_matrix(y_test, MNB_pred)\n",
        "display = ConfusionMatrixDisplay(confusion_matrix = MNB_confusion, display_labels = [0, 1])\n",
        "\n",
        "display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "-wK7nGaomdUk",
        "outputId": "5c399fe4-e777-4648-82f6-f05e0b880b04"
      },
      "execution_count": 22,
      "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": [
        "# Creating data containg models and their accuracies\n",
        "data = pd.DataFrame({\n",
        "    'Model': ['Decision Tree Classifier', 'Naive Bayes (Gaussian)', 'Naive Bayes Multinomial'],\n",
        "    'Accuracy': [DTC_accuracy, NB_accuracy, MNB_accuracy]\n",
        "})\n",
        "\n",
        "# Bar chart for comparing model accuracies\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=\"Model\", y=\"Accuracy\", data=data)\n",
        "plt.title(\"Models with thier accuracies\")\n",
        "plt.xlabel(\"Models\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "IYP02Ikua3yc",
        "outputId": "3be2b1f3-18d5-4b2c-a548-6e52921b6be0"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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xY0cdPnxYn3zySZqP73KnVatWevXVVxUVFaWGDRvq+++/16effprqSHm3bt308ccfa8iQIc4vaS5duqTVq1erb9++atOmjdvlZ3R/P/TQQypSpIgaNWqk4OBg7d+/X1OmTFHLli1T3T8BAHAHZMs90wEAd5UbHxmWnpsfGWaMMRcuXDCDBw82xYoVM7lz5zblypUzb775psujk4wx5sqVK2bAgAGmYMGCxs/Pz0RGRpoTJ064fRRWXFyc6devnwkJCTG5c+c2RYoUMQ888ID54IMPnH1ufmTYmTNnTL9+/UzFihWNn5+fyZs3rwkLCzP//ve/M7QPfvjhByPJVKpUyaX9n//8p5FkRo4c6XZ/3PyYqhkzZpjSpUsbT09Pl8eHudt3xqR+pFVaNm3aZGrXrm28vLxc9ln37t2Nn59fqv6jR482N39MuN19nfKYrQULFtyyzhQfffSRKVeunPH29jYVK1Y0s2bNcluTMdcfwVWzZk3j7e1t8ufPb5o2bWpWrVrl0ufzzz83DRs2NL6+viYwMNDUq1fPfPbZZy59JkyYYIoXL268vb1No0aNzLZt29J8ZJi7bbl69aoZOnSoKVq0qPH19TWNGjUymzdvdvtvdPnyZfPSSy+ZUqVKOfdb+/btzeHDh519bnd/v//++6ZJkyamYMGCxtvb25QpU8YMHz7cnD9//la7HQBggcOYv3j3FQAAAAAA4BbXdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsCRXdhdwpyUnJ+u3335TQECAHA5HdpcDAAAAAMiBjDG6cOGCihUrJg+PtI9n/8+F7t9++00hISHZXQYAAAAA4C5w4sQJ/eMf/0jz/f+50B0QECDp+o4JDAzM5moAAAAAADlRfHy8QkJCnBkzLf9zoTvllPLAwEBCNwAAAADgL7nVZcvcSA0AAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALMnW0P3NN98oMjJSxYoVk8Ph0NKlS285z7p161SrVi15e3urbNmymj17tvU6AQAAAAC4Hdkaui9duqTq1avrvffey1D/o0ePqmXLlmrWrJl27dqlQYMGqXfv3lq5cqXlSgEAAAAAyLxc2bnyhx9+WA8//HCG+0+fPl2lSpXShAkTJEmVKlXShg0bNGnSJEVERNgqEwAAAACA25KjrunevHmzwsPDXdoiIiK0efPmbKoIAAAAAIC0ZeuR7syKjY1VcHCwS1twcLDi4+N15coV+fr6pponISFBCQkJzun4+HjrdQIAAAAAIOWwI923Izo6Wnnz5nW+QkJCsrskAAAAAMD/iBwVuosUKaK4uDiXtri4OAUGBro9yi1JI0aM0Pnz552vEydO3IlSAQAAAADIWaeXN2jQQF9++aVL26pVq9SgQYM05/H29pa3t7ft0gAAAAAASCVbj3RfvHhRu3bt0q5duyRdfyTYrl27dPz4cUnXj1J369bN2f/pp5/WkSNH9Nxzz+nAgQOaOnWq/v3vf2vw4MHZUT4AAAAAAOnK1tC9bds21axZUzVr1pQkDRkyRDVr1tSoUaMkSSdPnnQGcEkqVaqUvvjiC61atUrVq1fXhAkT9OGHH/K4MAAAAADA35LDGGOyu4g7KT4+Xnnz5tX58+cVGBiY3eUAAAAAAHKgjGbLHHUjNQAAAAAAchJCNwAAAAAAlhC6AQAAAACwJEc9MgwAAAD4q2oP/zi7SwCQhba/2e3WnbIRR7oBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsCRXdhfwv6b28I+zuwQAWWj7m92yuwQAAAD8jXGkGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAluTK7gIAADlL7eEfZ3cJALLQ9je7ZXcJAHBX40g3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsyfbQ/d577yk0NFQ+Pj4KCwvTli1b0u0/efJkVahQQb6+vgoJCdHgwYN19erVO1QtAAAAAAAZl62he/78+RoyZIhGjx6tHTt2qHr16oqIiNCpU6fc9p87d65eeOEFjR49Wvv379dHH32k+fPn68UXX7zDlQMAAAAAcGvZGronTpyoPn36KCoqSpUrV9b06dOVJ08ezZw5023/TZs2qVGjRurSpYtCQ0P10EMPqXPnzrc8Og4AAAAAQHbIttCdmJio7du3Kzw8/L/FeHgoPDxcmzdvdjtPw4YNtX37dmfIPnLkiL788ku1aNHijtQMAAAAAEBm5MquFZ85c0ZJSUkKDg52aQ8ODtaBAwfcztOlSxedOXNG9957r4wx+vPPP/X000+ne3p5QkKCEhISnNPx8fFZswEAAAAAANxCtt9ILTPWrVuncePGaerUqdqxY4cWL16sL774QmPHjk1znujoaOXNm9f5CgkJuYMVAwAAAAD+l2Xbke5ChQrJ09NTcXFxLu1xcXEqUqSI23lGjhypJ554Qr1795YkVa1aVZcuXdKTTz6pl156SR4eqb9DGDFihIYMGeKcjo+PJ3gDAAAAAO6IbDvS7eXlpdq1aysmJsbZlpycrJiYGDVo0MDtPJcvX04VrD09PSVJxhi383h7eyswMNDlBQAAAADAnZBtR7olaciQIerevbvq1KmjevXqafLkybp06ZKioqIkSd26dVPx4sUVHR0tSYqMjNTEiRNVs2ZNhYWF6dChQxo5cqQiIyOd4RsAAAAAgL+LbA3dnTp10unTpzVq1CjFxsaqRo0aWrFihfPmasePH3c5sv3yyy/L4XDo5Zdf1q+//qqgoCBFRkbqtddey65NAAAAAAAgTdkauiWpf//+6t+/v9v31q1b5zKdK1cujR49WqNHj74DlQEAAAAA8NfkqLuXAwAAAACQkxC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWJLtofu9995TaGiofHx8FBYWpi1btqTb/9y5c+rXr5+KFi0qb29vlS9fXl9++eUdqhYAAAAAgIzLlZ0rnz9/voYMGaLp06crLCxMkydPVkREhA4ePKjChQun6p+YmKgHH3xQhQsX1sKFC1W8eHH9/PPPypcv350vHgAAAACAW8jW0D1x4kT16dNHUVFRkqTp06friy++0MyZM/XCCy+k6j9z5kydPXtWmzZtUu7cuSVJoaGhd7JkAAAAAAAyLNtOL09MTNT27dsVHh7+32I8PBQeHq7Nmze7nefzzz9XgwYN1K9fPwUHB+uee+7RuHHjlJSUdKfKBgAAAAAgw7LtSPeZM2eUlJSk4OBgl/bg4GAdOHDA7TxHjhzRmjVr1LVrV3355Zc6dOiQ+vbtq2vXrmn06NFu50lISFBCQoJzOj4+Pus2AgAAAACAdGT7jdQyIzk5WYULF9YHH3yg2rVrq1OnTnrppZc0ffr0NOeJjo5W3rx5na+QkJA7WDEAAAAA4H9ZtoXuQoUKydPTU3FxcS7tcXFxKlKkiNt5ihYtqvLly8vT09PZVqlSJcXGxioxMdHtPCNGjND58+edrxMnTmTdRgAAAAAAkI5sC91eXl6qXbu2YmJinG3JycmKiYlRgwYN3M7TqFEjHTp0SMnJyc62H3/8UUWLFpWXl5fbeby9vRUYGOjyAgAAAADgTsjW08uHDBmiGTNmaM6cOdq/f7+eeeYZXbp0yXk3827dumnEiBHO/s8884zOnj2rgQMH6scff9QXX3yhcePGqV+/ftm1CQAAAAAApClbHxnWqVMnnT59WqNGjVJsbKxq1KihFStWOG+udvz4cXl4/Pd7gZCQEK1cuVKDBw9WtWrVVLx4cQ0cOFDPP/98dm0CAAAAAABpytbQLUn9+/dX//793b63bt26VG0NGjTQt99+a7kqAAAAAAD+uhx193IAAAAAAHISQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGBJpkN3aGioXn31VR0/ftxGPQAAAAAA3DUyHboHDRqkxYsXq3Tp0nrwwQc1b948JSQk2KgNAAAAAIAc7bZC965du7RlyxZVqlRJzz77rIoWLar+/ftrx44dNmoEAAAAACBHuu1rumvVqqV33nlHv/32m0aPHq0PP/xQdevWVY0aNTRz5kwZY7KyTgAAAAAAcpxctzvjtWvXtGTJEs2aNUurVq1S/fr11atXL/3yyy968cUXtXr1as2dOzcrawUAAAAAIEfJdOjesWOHZs2apc8++0weHh7q1q2bJk2apIoVKzr7PPLII6pbt26WFgoAAAAAQE6T6dBdt25dPfjgg5o2bZratm2r3Llzp+pTqlQpPfbYY1lSIAAAAAAAOVWmQ/eRI0dUsmTJdPv4+flp1qxZt10UAAAAAAB3g0zfSO3UqVP67rvvUrV/99132rZtW5YUBQAAAADA3SDTobtfv346ceJEqvZff/1V/fr1y5KiAAAAAAC4G2Q6dO/bt0+1atVK1V6zZk3t27cvS4oCAAAAAOBukOnQ7e3trbi4uFTtJ0+eVK5ct/0EMgAAAAAA7jqZDt0PPfSQRowYofPnzzvbzp07pxdffFEPPvhglhYHAAAAAEBOlulD02+99ZaaNGmikiVLqmbNmpKkXbt2KTg4WP/617+yvEAAAAAAAHKqTIfu4sWLa8+ePfr000+1e/du+fr6KioqSp07d3b7zG4AAAAAAP5X3dZF2H5+fnryySezuhYAAAAAAO4qt33ns3379un48eNKTEx0aW/duvVfLgoAAAAAgLtBpkP3kSNH9Mgjj+j777+Xw+GQMUaS5HA4JElJSUlZWyEAAAAAADlUpu9ePnDgQJUqVUqnTp1Snjx59MMPP+ibb75RnTp1tG7dOgslAgAAAACQM2X6SPfmzZu1Zs0aFSpUSB4eHvLw8NC9996r6OhoDRgwQDt37rRRJwAAAAAAOU6mj3QnJSUpICBAklSoUCH99ttvkqSSJUvq4MGDWVsdAAAAAAA5WKaPdN9zzz3avXu3SpUqpbCwML3xxhvy8vLSBx98oNKlS9uoEQAAAACAHCnTofvll1/WpUuXJEmvvvqqWrVqpcaNG6tgwYKaP39+lhcIAAAAAEBOlenQHRER4fz/smXL6sCBAzp79qzy58/vvIM5AAAAAADI5DXd165dU65cubR3716X9gIFChC4AQAAAAC4SaZCd+7cuVWiRAmexQ0AAAAAQAZk+u7lL730kl588UWdPXvWRj0AAAAAANw1Mn1N95QpU3To0CEVK1ZMJUuWlJ+fn8v7O3bsyLLiAAAAAADIyTIdutu2bWuhDAAAAAAA7j6ZDt2jR4+2UQcAAAAAAHedTF/TDQAAAAAAMibTR7o9PDzSfTwYdzYHAAAAAOC6TIfuJUuWuExfu3ZNO3fu1Jw5c/TKK69kWWEAAAAAAOR0mQ7dbdq0SdXWvn17ValSRfPnz1evXr2ypDAAAAAAAHK6LLumu379+oqJicmqxQEAAAAAkONlSei+cuWK3nnnHRUvXjwrFgcAAAAAwF0h06eX58+f3+VGasYYXbhwQXny5NEnn3ySpcUBAAAAAJCTZTp0T5o0ySV0e3h4KCgoSGFhYcqfP3+WFgcAAAAAQE6W6dDdo0cPC2UAAAAAAHD3yfQ13bNmzdKCBQtStS9YsEBz5szJkqIAAAAAALgbZDp0R0dHq1ChQqnaCxcurHHjxmVJUQAAAAAA3A0yHbqPHz+uUqVKpWovWbKkjh8/niVFAQAAAABwN8h06C5cuLD27NmTqn337t0qWLBglhQFAAAAAMDdINOhu3PnzhowYIDWrl2rpKQkJSUlac2aNRo4cKAee+wxGzUCAAAAAJAjZfru5WPHjtWxY8f0wAMPKFeu67MnJyerW7duXNMNAAAAAMANMh26vby8NH/+fP3zn//Url275Ovrq6pVq6pkyZI26gMAAAAAIMfKdOhOUa5cOZUrVy4rawEAAAAA4K6S6Wu6H330Ub3++uup2t944w116NAhS4oCAAAAAOBukOnQ/c0336hFixap2h9++GF98803WVIUAAAAAAB3g0yH7osXL8rLyytVe+7cuRUfH58lRQEAAAAAcDfIdOiuWrWq5s+fn6p93rx5qly5cpYUBQAAAADA3SDTN1IbOXKk2rVrp8OHD+v++++XJMXExGju3LlauHBhlhcIAAAAAEBOlenQHRkZqaVLl2rcuHFauHChfH19Vb16da1Zs0YFChSwUSMAAAAAADnSbT0yrGXLlmrZsqUkKT4+Xp999pmGDRum7du3KykpKUsLBAAAAAAgp8r0Nd0pvvnmG3Xv3l3FihXThAkTdP/99+vbb7/NytoAAAAAAMjRMnWkOzY2VrNnz9ZHH32k+Ph4dezYUQkJCVq6dCk3UQMAAAAA4CYZPtIdGRmpChUqaM+ePZo8ebJ+++03vfvuuzZrAwAAAAAgR8vwke7ly5drwIABeuaZZ1SuXDmbNQEAAAAAcFfI8JHuDRs26MKFC6pdu7bCwsI0ZcoUnTlzxmZtAAAAAADkaBkO3fXr19eMGTN08uRJPfXUU5o3b56KFSum5ORkrVq1ShcuXLBZJwAAAAAAOU6m717u5+ennj17asOGDfr+++81dOhQjR8/XoULF1br1q1t1AgAAAAAQI50248Mk6QKFSrojTfe0C+//KLPPvssq2oCAAAAAOCu8JdCdwpPT0+1bdtWn3/+eVYsDgAAAACAu0KWhG4AAAAAAJAaoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAAS/4Wofu9995TaGiofHx8FBYWpi1btmRovnnz5snhcKht27Z2CwQAAAAA4DZke+ieP3++hgwZotGjR2vHjh2qXr26IiIidOrUqXTnO3bsmIYNG6bGjRvfoUoBAAAAAMicbA/dEydOVJ8+fRQVFaXKlStr+vTpypMnj2bOnJnmPElJSeratateeeUVlS5d+g5WCwAAAABAxmVr6E5MTNT27dsVHh7ubPPw8FB4eLg2b96c5nyvvvqqChcurF69et2JMgEAAAAAuC25snPlZ86cUVJSkoKDg13ag4ODdeDAAbfzbNiwQR999JF27dqVoXUkJCQoISHBOR0fH3/b9QIAAAAAkBnZfnp5Zly4cEFPPPGEZsyYoUKFCmVonujoaOXNm9f5CgkJsVwlAAAAAADXZeuR7kKFCsnT01NxcXEu7XFxcSpSpEiq/ocPH9axY8cUGRnpbEtOTpYk5cqVSwcPHlSZMmVc5hkxYoSGDBninI6Pjyd4AwAAAADuiGwN3V5eXqpdu7ZiYmKcj/1KTk5WTEyM+vfvn6p/xYoV9f3337u0vfzyy7pw4YLefvttt2Ha29tb3t7eVuoHAAAAACA92Rq6JWnIkCHq3r276tSpo3r16mny5Mm6dOmSoqKiJEndunVT8eLFFR0dLR8fH91zzz0u8+fLl0+SUrUDAAAAAJDdsj10d+rUSadPn9aoUaMUGxurGjVqaMWKFc6bqx0/flweHjnq0nMAAAAAACT9DUK3JPXv39/t6eSStG7dunTnnT17dtYXBAAAAABAFuAQMgAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWPK3CN3vvfeeQkND5ePjo7CwMG3ZsiXNvjNmzFDjxo2VP39+5c+fX+Hh4en2BwAAAAAgu2R76J4/f76GDBmi0aNHa8eOHapevboiIiJ06tQpt/3XrVunzp07a+3atdq8ebNCQkL00EMP6ddff73DlQMAAAAAkL5sD90TJ05Unz59FBUVpcqVK2v69OnKkyePZs6c6bb/p59+qr59+6pGjRqqWLGiPvzwQyUnJysmJuYOVw4AAAAAQPqyNXQnJiZq+/btCg8Pd7Z5eHgoPDxcmzdvztAyLl++rGvXrqlAgQK2ygQAAAAA4Lbkys6VnzlzRklJSQoODnZpDw4O1oEDBzK0jOeff17FihVzCe43SkhIUEJCgnM6Pj7+9gsGAAAAACATsv308r9i/PjxmjdvnpYsWSIfHx+3faKjo5U3b17nKyQk5A5XCQAAAAD4X5WtobtQoULy9PRUXFycS3tcXJyKFCmS7rxvvfWWxo8fr6+++krVqlVLs9+IESN0/vx55+vEiRNZUjsAAAAAALeSraHby8tLtWvXdrkJWspN0Ro0aJDmfG+88YbGjh2rFStWqE6dOumuw9vbW4GBgS4vAAAAAADuhGy9pluShgwZou7du6tOnTqqV6+eJk+erEuXLikqKkqS1K1bNxUvXlzR0dGSpNdff12jRo3S3LlzFRoaqtjYWEmSv7+//P39s207AAAAAAC4WbaH7k6dOun06dMaNWqUYmNjVaNGDa1YscJ5c7Xjx4/Lw+O/B+SnTZumxMREtW/f3mU5o0eP1pgxY+5k6QAAAAAApCvbQ7ck9e/fX/3793f73rp161ymjx07Zr8gAAAAAACyQI6+ezkAAAAAAH9nhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgCaEbAAAAAABLCN0AAAAAAFhC6AYAAAAAwBJCNwAAAAAAlhC6AQAAAACwhNANAAAAAIAlhG4AAAAAACwhdAMAAAAAYAmhGwAAAAAASwjdAAAAAABYQugGAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACW/C1C93vvvafQ0FD5+PgoLCxMW7ZsSbf/ggULVLFiRfn4+Khq1ar68ssv71ClAAAAAABkXLaH7vnz52vIkCEaPXq0duzYoerVqysiIkKnTp1y23/Tpk3q3LmzevXqpZ07d6pt27Zq27at9u7de4crBwAAAAAgfdkeuidOnKg+ffooKipKlStX1vTp05UnTx7NnDnTbf+3335bzZs31/Dhw1WpUiWNHTtWtWrV0pQpU+5w5QAAAAAApC9bQ3diYqK2b9+u8PBwZ5uHh4fCw8O1efNmt/Ns3rzZpb8kRUREpNkfAAAAAIDskis7V37mzBklJSUpODjYpT04OFgHDhxwO09sbKzb/rGxsW77JyQkKCEhwTl9/vx5SVJ8fPxfKf22JSVcyZb1ArAju8aS7MQ4BtxdGMcA5HTZNY6lrNcYk26/bA3dd0J0dLReeeWVVO0hISHZUA2Au03ed5/O7hIA4C9hHAOQ02X3OHbhwgXlzZs3zfezNXQXKlRInp6eiouLc2mPi4tTkSJF3M5TpEiRTPUfMWKEhgwZ4pxOTk7W2bNnVbBgQTkcjr+4BUBq8fHxCgkJ0YkTJxQYGJjd5QDAbWEsA5DTMY7BNmOMLly4oGLFiqXbL1tDt5eXl2rXrq2YmBi1bdtW0vVQHBMTo/79+7udp0GDBoqJidGgQYOcbatWrVKDBg3c9vf29pa3t7dLW758+bKifCBdgYGBDPAAcjzGMgA5HeMYbErvCHeKbD+9fMiQIerevbvq1KmjevXqafLkybp06ZKioqIkSd26dVPx4sUVHR0tSRo4cKCaNm2qCRMmqGXLlpo3b562bdumDz74IDs3AwAAAACAVLI9dHfq1EmnT5/WqFGjFBsbqxo1amjFihXOm6UdP35cHh7/vcl6w4YNNXfuXL388st68cUXVa5cOS1dulT33HNPdm0CAAAAAABuOcytbrUGIFMSEhIUHR2tESNGpLq0AQByCsYyADkd4xj+LgjdAAAAAABY4nHrLgAAAAAA4HYQugEAAAAAsITQjb+F0NBQTZ48Ocv7/i9Yt26dHA6Hzp07Z31dY8aMUY0aNVK1BQcHy+FwaOnSperRo4fzEYDA3eC+++5zeUwlUhs5cqSefPLJ7C4j02bPnp3ljxE9c+aMChcurF9++SVLlwtkFmPXneXuM9LNjh07JofDoV27dt2RmtKTkXpvlvJZD7fBAGno3r27kWQkmVy5cpnChQub8PBw89FHH5mkpKQsXdepU6fMpUuXsrzv7bhxu929SpYsaW3d7uzYscO0b9/eFC5c2Hh7e5uyZcua3r17m4MHDxpjjFm7dq2RZP744w/rtVy4cMGcOXPGOb1v3z4jySxZssScPHnSXL161Zw7d+6O1AKkJ+X3ODo62qV9yZIlJrN/+n7//XcTHx+fleWlcvO4U6BAARMREWF2795tdb1Z4eTJkyYgIMAcO3YsVfuAAQNMmTJljLe3tylcuLBp2LChmTp1qtUxPDMuX75s4uLisny5Q4cONT179szy5eLux9h1Zxw9etRIMh4eHuaXX35xee+3334znp6eRpI5evRohpc5evRoU716ded09+7dTZs2bVz6/Pnnn+bkyZPm2rVrf6H6rHHzZ7qMSPnMh8zjSDfS1bx5c508eVLHjh3T8uXL1axZMw0cOFCtWrXSn3/+mWXrCQoKUp48ebK87+14++23dfLkSedLkmbNmuWc3rp1q0v/xMREa7UsW7ZM9evXV0JCgj799FPt379fn3zyifLmzauRI0daW29a/P39VbBgQef04cOHJUlt2rRRkSJF5O3trbx58/6lI0fGmCz92cL/Lh8fH73++uv6448//tJyChQooICAgCyqKm0p4+3JkycVExOjXLlyqVWrVtbX+1d9+OGHatiwoUqWLOlsO3LkiGrWrKmvvvpK48aN086dO7V582Y999xzWrZsmVavXp2NFf+Xr6+vChcunOXLjYqK0qeffqqzZ89m+bJx92PsunOKFy+ujz/+2KVtzpw5Kl68uJX1eXp6qkiRIsqVK9uf2pzqMx3sInQjXd7e3ipSpIiKFy+uWrVq6cUXX9T//d//afny5Zo9e7az37lz59S7d28FBQUpMDBQ999/v3bv3u2yrP/85z+qW7eufHx8VKhQIT3yyCPO9248ZdwYozFjxqhEiRLy9vZWsWLFNGDAALd9pevPcm/Tpo38/f0VGBiojh07Ki4uzvl+yukz//rXvxQaGqq8efPqscce04ULF9xuc968eVWkSBHnS5Ly5cvnnK5bt67Gjh2rbt26KTAw0HlK5YYNG9S4cWP5+voqJCREAwYM0KVLl5zLTUhI0LBhw1S8eHH5+fkpLCxM69atS3PfX758WVFRUWrRooU+//xzhYeHq1SpUgoLC9Nbb72l999/3+18v//+uzp37qzixYsrT548qlq1qj777DOXPgsXLlTVqlXl6+urggULKjw83FnrunXrVK9ePfn5+Slfvnxq1KiRfv75Z5d9mfL/kZGRkiQPDw85HA5JSnV6eXJysqKjo1WqVCn5+vqqevXqWrhwofP9lNPjly9frtq1a8vb21sbNmxIc78AGRUeHq4iRYooOjo6zT4Z+X258RTNF198UWFhYamWU716db366qvO6Q8//FCVKlWSj4+PKlasqKlTp96y3pTxtkiRIqpRo4ZeeOEFnThxQqdPn3b2ef7551W+fHnlyZNHpUuX1siRI3Xt2jVJ109b9PDw0LZt21yWO3nyZJUsWVLJycmSpL179+rhhx+Wv7+/goOD9cQTT+jMmTPO/umND+7MmzfPORak6Nu3r3LlyqVt27apY8eOqlSpkkqXLq02bdroiy++cOk/ceJEVa1aVX5+fgoJCVHfvn118eJF5/vuToGcPHmyQkNDndPpjVu7d+9Ws2bNFBAQoMDAQNWuXdu5j24+vfzw4cNq06aNgoOD5e/vr7p166b6giA0NFTjxo1Tz549FRAQoBIlSuiDDz5w6VOlShUVK1ZMS5YsSXO/AWlh7LrO9tglSd27d9esWbNc2mbNmqXu3bu7tLm7FGXp0qXOzz43GzNmjObMmaP/+7//k8PhkMPh0Lp161KdXp7yGSgmJkZ16tRRnjx51LBhQx08eNBledOmTVOZMmXk5eWlChUq6F//+pfL+w6HQ++//75atWqlPHnyqFKlStq8ebMOHTqk++67T35+fmrYsKHzYElKjTeOrVu3btWDDz6oQoUKKW/evGratKl27NiR7v5DxhG6kWn333+/qlevrsWLFzvbOnTooFOnTmn58uXavn27atWqpQceeMD5Lf8XX3yhRx55RC1atNDOnTsVExOjevXquV3+okWLNGnSJL3//vv66aeftHTpUlWtWtVt3+TkZLVp00Znz57V119/rVWrVunIkSPq1KmTS7/Dhw9r6dKlWrZsmZYtW6avv/5a48ePv+198NZbb6l69erauXOnRo4cqcOHD6t58+Z69NFHtWfPHs2fP18bNmxQ//79nfP0799fmzdv1rx587Rnzx516NBBzZs3108//eR2HStXrtSZM2f03HPPuX0/raPJV69eVe3atfXFF19o7969evLJJ/XEE09oy5YtkqSTJ0+qc+fO6tmzp/bv369169apXbt2ziPMbdu2VdOmTbVnzx5t3rxZTz75pNs/KsOGDXP+obrxrICbRUdH6+OPP9b06dP1ww8/aPDgwXr88cf19ddfu/R74YUXNH78eO3fv1/VqlVzuywgMzw9PTVu3Di9++67aV5fe6vfl5t17dpVW7Zscfng8sMPP2jPnj3q0qWLJOnTTz/VqFGj9Nprr2n//v0aN26cRo4cqTlz5mS49osXL+qTTz5R2bJlXY5EBAQEaPbs2dq3b5/efvttzZgxQ5MmTZJ0PQyGh4e7/QDZo0cPeXh46Ny5c7r//vtVs2ZNbdu2TStWrFBcXJw6duwoKf3xwZ2zZ89q3759qlOnjrPt999/11dffaV+/frJz8/P7Xw3jikeHh5655139MMPP2jOnDlas2ZNmuOeO7cat7p27ap//OMf2rp1q7Zv364XXnhBuXPndrusixcvqkWLFoqJidHOnTvVvHlzRUZG6vjx4y79JkyYoDp16mjnzp3q27evnnnmmVQfkuvVq6f169dneDuAFIxd19kcu1K0bt1af/zxh/PL/g0bNuiPP/5I9UViZg0bNkwdO3Z0OQugYcOGafZ/6aWXNGHCBG3btk25cuVSz549ne8tWbJEAwcO1NChQ7V371499dRTioqK0tq1a12WkXJAaNeuXapYsaK6dOmip556SiNGjNC2bdtkjHH5XHqzCxcuqHv37tqwYYO+/fZblStXTi1atEjzIBUyKTvPbcffm7trUVJ06tTJVKpUyRhjzPr1601gYKC5evWqS58yZcqY999/3xhjTIMGDUzXrl3TXFfJkiXNpEmTjDHGTJgwwZQvX94kJibesu9XX31lPD09zfHjx53v//DDD0aS2bJlizHm+jU2efLkcbmuafjw4SYsLCztjb+Bbrp+pWTJkqZt27YufXr16mWefPJJl7b169cbDw8Pc+XKFfPzzz8bT09P8+uvv7r0eeCBB8yIESPcrvf11183kszZs2fTrS8j13S3bNnSDB061BhjzPbt242kVNdfGnP9+i9JZt26dW6Xc/P1Su6uMbvx5+bq1asmT548ZtOmTS59evXqZTp37uxS/9KlS9PdTiAzbvw5rF+/vvP62oxcF3nj74sxxjRt2tQMHDjQOV29enXz6quvOqdHjBjhMp6UKVPGzJ0712WZY8eONQ0aNEi3Xk9PT+Pn52f8/PyMJFO0aFGzffv2dGt98803Te3atZ3T8+fPN/nz53eOx9u3bzcOh8N5XeLYsWPNQw895LKMEydOGEnm4MGD6Y4P7uzcudNIchmDv/32WyPJLF682KVvwYIFndv33HPPpbnMBQsWmIIFCzqnbx53jDFm0qRJzvtr3GrcCggIMLNnz3b73qxZs0zevHnT2UJjqlSpYt59913ndMmSJc3jjz/unE5OTjaFCxc206ZNc5lv8ODB5r777kt32cDNGLvuzNiVck33zp07zaBBg0xUVJQxxpioqCgzePBg59iWsn53Y8XN/yYZuab7xvUa89/PQKtXr3b2+eKLL4wkc+XKFWOMMQ0bNjR9+vRxWU6HDh1MixYtnNOSzMsvv+yc3rx5s5FkPvroI2fbZ599Znx8fNKs92ZJSUkmICDA/Oc//3FZD9d03x6OdOO2GGOcRxF2796tixcvqmDBgvL393e+jh496vxGddeuXXrggQcytOwOHTroypUrKl26tPr06aMlS5akeY3v/v37FRISopCQEGdb5cqVlS9fPu3fv9/ZFhoa6nJdU9GiRXXq1KlMb3eKG4/qSNf3wezZs122PyIiQsnJyTp69Ki+//57JSUlqXz58i59vv76a5dvnW9kbvHtbFqSkpI0duxYVa1aVQUKFJC/v79WrlzpPFJTvXp1PfDAA6patao6dOigGTNmOK8bK1CggHr06KGIiAhFRkY6r2+/XYcOHdLly5f14IMPumz3xx9/nGq7b96nQFZ5/fXXNWfOHJcxIcWtfl/c6dq1q+bOnSvp+u/pZ599pq5du0qSLl26pMOHD6tXr14uP/P//Oc/0/xdT9GsWTPt2rVLu3bt0pYtWxQREaGHH37YeZq0JM2fP1+NGjVSkSJF5O/vr5dfftml1rZt28rT09N5WvPs2bPVrFkz56nYu3fv1tq1a11qq1ixoqTrZwSlNz64c+XKFUnXr0G9lS1btmjXrl2qUqWKEhISnO2rV6/WAw88oOLFiysgIEBPPPGEfv/9d12+fPmWy5RuPW4NGTJEvXv3Vnh4uMaPH5/uv8PFixc1bNgwVapUSfny5ZO/v7/279+f6ufhxrNxHA6HihQpkupviq+vb4a3AXCHscve2HWjnj17asGCBYqNjdWCBQtcjjLfKTeOKUWLFpUk55iyf/9+NWrUyKV/o0aNUv1c3LiM4OBgSXI5UzQ4OFhXr15VfHy82xri4uLUp08flStXTnnz5lVgYKAuXryY7s8UMo7Qjduyf/9+lSpVStL1DylFixZ1Drgpr4MHD2r48OGSrn/4yKiQkBAdPHhQU6dOla+vr/r27asmTZo4r/25HTefSuhwOJzXCN2Om0+ZvHjxop566imX7d+9e7d++uknlSlTRhcvXpSnp6e2b9/u0mf//v16++233a6jfPnykqQDBw5kqrY333xTb7/9tp5//nmtXbtWu3btUkREhPOGb56enlq1apWWL1+uypUr691331WFChV09OhRSddP59q8ebMaNmyo+fPnq3z58vr2228zu4uc+0W6fnnBjdu9b98+l+u6pdT7FMgqTZo0UUREhEaMGJHqvVv9vrjTuXNnHTx4UDt27NCmTZt04sQJ5yUtKT/zM2bMcPmZ37t37y1/j/z8/FS2bFmVLVtWdevW1YcffqhLly5pxowZkqTNmzera9euatGihZYtW6adO3fqpZdecqnVy8tL3bp106xZs5SYmKi5c+e6fIC8ePGiIiMjU43XP/30k5o0aXLL8eFmhQoVkiSXD7dly5aVw+FIdbp16dKlVbZsWZe/B8eOHVOrVq1UrVo1LVq0SNu3b9d7770n6b83qfTw8Ej1JeTNfw/SG7fGjBmjH374QS1bttSaNWtUuXLlNK+1HjZsmJYsWaJx48Zp/fr12rVrl6pWrZrq5yEjf1POnj2roKAgt+sBMoKxy97YdaOqVauqYsWK6ty5sypVqqR77rknVZ+MjEN/xY1jSspBrcx+TnW3jMwst3v37tq1a5fefvttbdq0Sbt27VLBggWt3jD4f0n23zoPOc6aNWv0/fffa/DgwZKkWrVqKTY2Vrly5XK5sc2NqlWrppiYGEVFRWVoHb6+voqMjFRkZKT69eunihUr6vvvv1etWrVc+lWqVEknTpzQiRMnnEe79+3bp3Pnzqly5cq3v5GZVKtWLe3bt09ly5Z1+37NmjWVlJSkU6dOqXHjxhla5kMPPaRChQrpjTfecPsB8dy5c26v6964caPatGmjxx9/XNL1wfXHH3902R8Oh0ONGjVSo0aNNGrUKJUsWVJLlizRkCFDnPXWrFlTI0aMUIMGDTR37lzVr18/Q3XfqHLlyvL29tbx48fVtGnTTM8PZJXx48erRo0aqlChgkt7Rn5fbvaPf/xDTZs21aeffqorV67owQcfdN4BOzg4WMWKFdORI0ecR5Bul8PhkIeHh/No8qZNm1SyZEm99NJLzj43HklK0bt3b91zzz2aOnWq/vzzT7Vr1875Xq1atbRo0SKFhoameffcW40PNypTpowCAwO1b98+5xeFBQsW1IMPPqgpU6bo2WefTfcLte3btys5OVkTJkyQh8f14wD//ve/XfoEBQUpNjbW5Qwrd8+4TW/cKl++vMqXL6/Bgwerc+fOmjVrlsvNPFNs3LhRPXr0cL538eJFHTt2LM3607N3717dd999tzUvkIKx67qsHrtu1rNnT/Xt21fTpk1z+35QUJAuXLigS5cuOce0Wz1r28vLS0lJSbdc961UqlRJGzdudLm528aNG7P8c+7GjRs1depUtWjRQpJ04sQJlxvV4a8hdCNdCQkJio2NVVJSkuLi4rRixQpFR0erVatW6tatm6Trd9ls0KCB2rZtqzfeeEPly5fXb7/95rx5Wp06dTR69Gg98MADKlOmjB577DH9+eef+vLLL/X888+nWufs2bOVlJSksLAw5cmTR5988ol8fX1dHkeTIjw8XFWrVlXXrl01efJk/fnnn+rbt6+aNm16R09Xfv7551W/fn31799fvXv3lp+fn/bt26dVq1ZpypQpKl++vLp27apu3bppwoQJqlmzpk6fPq2YmBhVq1ZNLVu2TLVMPz8/ffjhh+rQoYNat26tAQMGqGzZsjpz5oz+/e9/6/jx45o3b16q+cqVK6eFCxdq06ZNyp8/vyZOnKi4uDjn4Pzdd98pJiZGDz30kAoXLqzvvvtOp0+fVqVKlXT06FF98MEHat26tYoVK6aDBw/qp59+cv5bZ1ZAQICGDRumwYMHKzk5Wffee6/Onz+vjRs3KjAwMNXdQQFbUsaJd955x6X9Vr8vaenatatGjx6txMRE582AUrzyyisaMGCA8ubNq+bNmyshIUHbtm3TH3/8ke6Hv5TxVrp+5HjKlCnOozsptab83tetW1dffPGF2y/kKlWqpPr16+v5559Xz549XY4s9+vXTzNmzFDnzp313HPPqUCBAjp06JDmzZunDz/8UNu2bUtzfHDHw8ND4eHh2rBhg8tTC6ZOnapGjRqpTp06GjNmjKpVqyYPDw9t3bpVBw4cUO3atSVdPyp+7do1vfvuu4qMjNTGjRs1ffp0l3Xcd999On36tN544w21b99eK1as0PLlyxUYGChJ6Y5bV65c0fDhw9W+fXuVKlVKv/zyi7Zu3apHH33U7faUK1dOixcvVmRkpBwOh0aOHHlbZ0VdvnxZ27dv17hx4zI9L3Ajxq7rsnrsulmfPn3UoUOHNG9Sm/KZ9MUXX9SAAQP03XffuTzFx53Q0FCtXLlSBw8eVMGCBZU3b94M1XKz4cOHq2PHjqpZs6bCw8P1n//8R4sXL87yRy+WK1dO//rXv1SnTh3Fx8dr+PDhmTpTFbeQjdeT42+ue/fuRpKRZHLlymWCgoJMeHi4mTlzpklKSnLpGx8fb5599llTrFgxkzt3bhMSEmK6du3qcnOdRYsWmRo1ahgvLy9TqFAh065dO+d7N94cbcmSJSYsLMwEBgYaPz8/U79+fZcbTNzY1xhjfv75Z9O6dWvj5+dnAgICTIcOHUxsbKzz/VvdhOdW5OZGajeuP8WWLVvMgw8+aPz9/Y2fn5+pVq2aee2115zvJyYmmlGjRpnQ0FCTO3duU7RoUfPII4+YPXv2pLv+rVu3mnbt2pmgoCDj7e1typYta5588knz008/GWNS30jt999/N23atDH+/v6mcOHC5uWXXzbdunVz3sxj3759JiIiwrm88uXLO28SFBsba9q2bWuKFi1qvLy8TMmSJc2oUaOc/96ZvZGaMddvMjR58mRToUIFkzt3bhMUFGQiIiLM119/7bZ+ICukdQMbLy8vl5/ZW/2+GJP6ZkTGGPPHH38Yb29vkydPHnPhwoVU6//000+d413+/PlNkyZNUt1Y7OZ6U8ZbSSYgIMDUrVvXLFy40KXf8OHDTcGCBY2/v7/p1KmTmTRpktsbgX300UcuN5S80Y8//mgeeeQRky9fPuPr62sqVqxoBg0aZJKTk9MdH9Ly5ZdfmuLFi6f6u/Dbb7+Z/v37m1KlSpncuXMbf39/U69ePfPmm2+aS5cuOftNnDjRFC1a1Pj6+pqIiAjz8ccfpxoTpk2bZkJCQoyfn5/p1q2bee2115xjeHrjVkJCgnnsscdMSEiI8fLyMsWKFTP9+/d33qDo5psjHT161DRr1sz4+vqakJAQM2XKlFT//u7+BlSvXt2MHj3aOT137lxToUKFdPcb4A5j150Zu26+odnNbr6RmjHXP/OULVvW+Pr6mlatWpkPPvgg3RupnTp1yvm5UJJZu3ZtmjdSu3G8c7fuqVOnmtKlS5vcuXOb8uXLm48//til3ps/q7rbvpvXdXO9O3bsMHXq1DE+Pj6mXLlyZsGCBanGu5vXg4xzGHObd2sCAAB/S2PHjtWCBQu0Z88e6+syxigsLMx56jak+vXra8CAAc5HMQHImDs5dgF3EjdSAwDgLnHx4kXt3bvXeT31neBwOPTBBx+k+ZSJ/zVnzpxRu3bt+AICyITsGLuAO4kj3QAA3CV69Oihzz77TG3bttXcuXPl6emZ3SUBwC0xduFuR+gGAAAAAMASTi8HAAAAAMASQjcAAAAAAJYQugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAIA0rVu3Tg6HQ+fOncvwPKGhoZo8ebK1mgAAyEkI3QAA5GA9evSQw+HQ008/neq9fv36yeFwqEePHne+MAAAIInQDQBAjhcSEqJ58+bpypUrzrarV69q7ty5KlGiRDZWBgAACN0AAORwtWrVUkhIiBYvXuxsW7x4sUqUKKGaNWs62xISEjRgwAAVLlxYPj4+uvfee7V161aXZX355ZcqX768fH191axZMx07dizV+jZs2KDGjRvL19dXISEhGjBggC5duuS2NmOMxowZoxIlSsjb21vFihXTgAEDsmbDAQDIAQjdAADcBXr27KlZs2Y5p2fOnKmoqCiXPs8995wWLVqkOXPmaMeOHSpbtqwiIiJ09uxZSdKJEyfUrl07RUZGateuXerdu7deeOEFl2UcPnxYzZs316OPPqo9e/Zo/vz52rBhg/r37++2rkWLFmnSpEl6//339dNPP2np0qWqWrVqFm89AAB/X4RuAADuAo8//rg2bNign3/+WT///LM2btyoxx9/3Pn+pUuXNG3aNL355pt6+OGHVblyZc2YMUO+vr766KOPJEnTpk1TmTJlNGHCBFWoUEFdu3ZNdT14dHS0unbtqkGDBqlcuXJq2LCh3nnnHX388ce6evVqqrqOHz+uIkWKKDw8XCVKlFC9evXUp08fq/sCAIC/E0I3AAB3gaCgILVs2VKzZ8/WrFmz1LJlSxUqVMj5/uHDh3Xt2jU1atTI2ZY7d27Vq1dP+/fvlyTt379fYWFhLstt0KCBy/Tu3bs1e/Zs+fv7O18RERFKTk7W0aNHU9XVoUMHXblyRaVLl1afPn20ZMkS/fnnn1m56QAA/K3lyu4CAABA1ujZs6fzNO/33nvPyjouXryop556yu112e5u2hYSEqKDBw9q9erVWrVqlfr27as333xTX3/9tXLnzm2lRgAA/k440g0AwF2iefPmSkxM1LVr1xQREeHyXpkyZeTl5aWNGzc6265du6atW7eqcuXKkqRKlSppy5YtLvN9++23LtO1atXSvn37VLZs2VQvLy8vt3X5+voqMjJS77zzjtatW6fNmzfr+++/z4pNBgDgb48j3QAA3CU8PT2dp4p7enq6vOfn56dnnnlGw4cPV4ECBVSiRAm98cYbunz5snr16iVJevrppzVhwgQNHz5cvXv31vbt2zV79myX5Tz//POqX7+++vfvr969e8vPz0/79u3TqlWrNGXKlFQ1zZ49W0lJSQoLC1OePHn0ySefyNfXVyVLlrSzEwAA+JvhSDcAAHeRwMBABQYGun1v/PjxevTRR/XEE0+oVq1aOnTokFauXKn8+fNLun56+KJFi7R06VJVr15d06dP17hx41yWUa1aNX399df68ccf1bhxY9WsWVOjRo1SsWLF3K4zX758mjFjhho1aqRq1app9erV+s9//qOCBQtm7YYDAPA35TDGmOwuAgAAAACAuxFHugEAAAAAsITQDQAAAACAJYRuAAAAAAAsIXQDAAAAAGAJoRsAAAAAAEsI3QAAAAAAWELoBgAAAADAEkI3AAAAAACWELoBAAAAALCE0A0AAAAAgCWEbgAAAAAALCF0AwAAAABgyf8DJshc1JZpqxYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Performing model\n",
        "data[data['Accuracy']==data['Accuracy'].max()]"
      ],
      "metadata": {
        "id": "80-k0dNpbrC-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "f4114d24-75ee-49dd-d2ac-d0cdf0f12158"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Model  Accuracy\n",
              "2  Naive Bayes Multinomial  0.980861"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9613021d-6518-4355-85a3-f0fcd130b91a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Naive Bayes Multinomial</td>\n",
              "      <td>0.980861</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9613021d-6518-4355-85a3-f0fcd130b91a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <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-9613021d-6518-4355-85a3-f0fcd130b91a 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-9613021d-6518-4355-85a3-f0fcd130b91a');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[data['Accuracy']==data['Accuracy']\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Naive Bayes Multinomial\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.9808612440191388,\n        \"max\": 0.9808612440191388,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9808612440191388\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ykJVUDrYlZkQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}