ML_20_Naive Bayes Algorithm.ipynb

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        "#mporting required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report"
      ]
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      "source": [
        "data = pd.read_excel('/content/drive/MyDrive/Data Science Files/spam.xlsx')"
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "display(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd6d4cb3-4500-49b7-bf80-c54339db4572",
      "metadata": {
        "id": "fd6d4cb3-4500-49b7-bf80-c54339db4572"
      },
      "outputs": [],
      "source": [
        "#drop the unwanted columns\n",
        "data.drop(['Unnamed: 2','Unnamed: 3','Unnamed: 4'],axis=1,inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "585f8ac6-322a-4519-85ce-c6cedee1fc90",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "585f8ac6-322a-4519-85ce-c6cedee1fc90",
        "outputId": "e702d200-f852-4026-f432-c152f776d2f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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   v1      5572 non-null   object\n",
            " 1   v2      5572 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 87.2+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "619fe14d-342b-44dc-abb0-30475045f6c4",
      "metadata": {
        "id": "619fe14d-342b-44dc-abb0-30475045f6c4"
      },
      "outputs": [],
      "source": [
        "data['labels'] = data['v1']\n",
        "data.drop('v1' , axis = 1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "10de343e-246e-4f53-9458-03be5d2472c3",
      "metadata": {
        "id": "10de343e-246e-4f53-9458-03be5d2472c3"
      },
      "outputs": [],
      "source": [
        "data['message'] = data['v2']\n",
        "data.drop('v2',axis = 1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8c9c8f1a-0477-4bc1-9aed-657103cbb24a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8c9c8f1a-0477-4bc1-9aed-657103cbb24a",
        "outputId": "b1ad0973-2730-4df7-ed7a-aa96f77b4d5e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     labels                                            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...\n",
              "...     ...                                                ...\n",
              "5567   spam  This is the 2nd time we have tried 2 contact u...\n",
              "5568    ham               Will 蘝 b going to esplanade fr home?\n",
              "5569    ham  Pity, * was in mood for that. So...any other s...\n",
              "5570    ham  The guy did some bitching but I acted like i'd...\n",
              "5571    ham                         Rofl. Its true to its name\n",
              "\n",
              "[5572 rows x 2 columns]"
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              "      <th>4</th>\n",
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              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>5569</th>\n",
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              "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
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              "    <tr>\n",
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              "      <td>The guy did some bitching but I acted like i'd...</td>\n",
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              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>ham</td>\n",
              "      <td>Rofl. Its true to its name</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5572 rows × 2 columns</p>\n",
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              "          const element = document.querySelector('#df-bb1ede71-a1a1-4144-9277-9eb2ad2822b0');\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",
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              "  "
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          "metadata": {},
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        }
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    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fdfbd7de-e0fa-4b88-a725-8c1ea31296b0",
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          "height": 175
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        "outputId": "6a191a42-d6cb-44b9-bf75-298349d92bfb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       labels                 message\n",
              "count    5572                    5572\n",
              "unique      2                    5163\n",
              "top       ham  Sorry, I'll call later\n",
              "freq     4825                      30"
            ],
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              "      <th>unique</th>\n",
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              "      <td>4825</td>\n",
              "      <td>30</td>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e9a8d734-5691-4ece-8789-131b0ad7df1f button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e9a8d734-5691-4ece-8789-131b0ad7df1f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ],
      "source": [
        "data.describe()"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a69bcd89-0f23-4ee0-9fe7-6a908b419ad3",
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        {
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          "data": {
            "text/plain": [
              "       message                                                               \n",
              "         count unique                                                top freq\n",
              "labels                                                                       \n",
              "ham       4825   4516                             Sorry, I'll call later   30\n",
              "spam       747    647  Please call our customer service representativ...    4"
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              "      <th>ham</th>\n",
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              "      <th>spam</th>\n",
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              "      <td>4</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c573098b-0ff6-4314-8b07-fa666ba476f7')\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c573098b-0ff6-4314-8b07-fa666ba476f7 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-c573098b-0ff6-4314-8b07-fa666ba476f7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ],
      "source": [
        "data.groupby('labels').describe()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking unique values \n",
        "data.nunique()"
      ],
      "metadata": {
        "id": "d6viC0CXJ8Ow",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05988057-21a7-4feb-b40f-18471ce6438f"
      },
      "id": "d6viC0CXJ8Ow",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "labels        2\n",
              "message    5163\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9f2c0ca4-7761-4685-b2f5-51c07408090e",
      "metadata": {
        "id": "9f2c0ca4-7761-4685-b2f5-51c07408090e"
      },
      "source": [
        "**Let's make a new column to detect how long the text messages are:**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Message text pre processing**"
      ],
      "metadata": {
        "id": "Qn1TObxYcc7y"
      },
      "id": "Qn1TObxYcc7y"
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "    '''Make text lowercase, remove text in square brackets,remove links,remove punctuation\n",
        "    and remove words containing numbers.'''\n",
        "    text = str(text).lower()\n",
        "    text = re.sub('\\[.*?\\]', '', text)\n",
        "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
        "    text = re.sub('<.*?>+', '', text)\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "    text = re.sub('\\n', '', text)\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\n",
        "    return text"
      ],
      "metadata": {
        "id": "DzcZppTXoRvT"
      },
      "id": "DzcZppTXoRvT",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['clean_message'] = data['message'].apply(clean_text)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "L_uTyRZKqNCq",
        "outputId": "f5dcfc04-446b-49f5-87c1-544c5ce13464"
      },
      "id": "L_uTyRZKqNCq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  labels                                            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...   \n",
              "\n",
              "                                       clean_message  \n",
              "0  go until jurong point crazy available only in ...  \n",
              "1                            ok lar joking wif u oni  \n",
              "2  free entry in  a wkly comp to win fa cup final...  \n",
              "3        u dun say so early hor u c already then say  \n",
              "4  nah i dont think he goes to usf he lives aroun...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64958e79-6a87-4e47-92d4-7ae0ca1613ae\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>labels</th>\n",
              "      <th>message</th>\n",
              "      <th>clean_message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>go until jurong point crazy available only in ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>ok lar joking wif u oni</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>free entry in  a wkly comp to win fa cup final...</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>u dun say so early hor u c already then say</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>nah i dont think he goes to usf he lives aroun...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64958e79-6a87-4e47-92d4-7ae0ca1613ae')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-64958e79-6a87-4e47-92d4-7ae0ca1613ae 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-64958e79-6a87-4e47-92d4-7ae0ca1613ae');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Label encoding on target variable**"
      ],
      "metadata": {
        "id": "7cQW_gEhvF-2"
      },
      "id": "7cQW_gEhvF-2"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "le = LabelEncoder()\n",
        "le.fit(data['labels'])\n",
        "\n",
        "data['labels_encoded'] = le.transform(data['labels'])\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-1AqXyiVvFrD",
        "outputId": "e4aefee8-14b3-4331-c236-2dcfed8060eb"
      },
      "id": "-1AqXyiVvFrD",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  labels                                            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...   \n",
              "\n",
              "                                       clean_message  labels_encoded  \n",
              "0  go until jurong point crazy available only in ...               0  \n",
              "1                            ok lar joking wif u oni               0  \n",
              "2  free entry in  a wkly comp to win fa cup final...               1  \n",
              "3        u dun say so early hor u c already then say               0  \n",
              "4  nah i dont think he goes to usf he lives aroun...               0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4281c273-9ef7-4a2a-a599-83d7df705c85\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>labels</th>\n",
              "      <th>message</th>\n",
              "      <th>clean_message</th>\n",
              "      <th>labels_encoded</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>go until jurong point crazy available only in ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <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>free entry in  a wkly comp to win fa cup final...</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>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 don't think he goes to usf, he lives aro...</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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4281c273-9ef7-4a2a-a599-83d7df705c85')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4281c273-9ef7-4a2a-a599-83d7df705c85 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-4281c273-9ef7-4a2a-a599-83d7df705c85');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**split the data**"
      ],
      "metadata": {
        "id": "scQ1VwQ-vYJA"
      },
      "id": "scQ1VwQ-vYJA"
    },
    {
      "cell_type": "code",
      "source": [
        "x = data.clean_message\n",
        "y = data.labels_encoded\n",
        "\n",
        "print(len(x), len(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aiFKxyC_vTXa",
        "outputId": "cb7d7ba0-8c22-4e28-9e35-285a88d6832c"
      },
      "id": "aiFKxyC_vTXa",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5572 5572\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train test split \n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42)\n",
        "print(len(x_train), len(y_train))\n",
        "print(len(x_test), len(y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xOcKI5HFvTUA",
        "outputId": "fc34dfeb-8335-4be3-b6a0-13fe29f5c48b"
      },
      "id": "xOcKI5HFvTUA",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4179 4179\n",
            "1393 1393\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0dcae590-f48b-47b0-b296-6278f207fe11",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dcae590-f48b-47b0-b296-6278f207fe11",
        "outputId": "89da6b1b-ce9b-48db-a533-a71ab5f493d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.7)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from nltk) (4.64.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk) (1.1.0)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk) (2022.6.2)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk) (7.1.2)\n"
          ]
        }
      ],
      "source": [
        "pip install nltk"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "from nltk.corpus import stopwords\n",
        "import nltk\n",
        "import re\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "bg5BPs9Jb7Di"
      },
      "id": "bg5BPs9Jb7Di",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')\n",
        "stopwords.words('english')[0:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XRn6fhGBLUWQ",
        "outputId": "5cc7d1ef-dc6d-41f0-c121-3b55365b8fca"
      },
      "id": "XRn6fhGBLUWQ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\"]"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Vectorization**"
      ],
      "metadata": {
        "id": "NmgDdvLZrNR4"
      },
      "id": "NmgDdvLZrNR4"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Currently, we have the messages as lists of tokens (also known as lemmas) and now we need to convert each of those messages into a vector the SciKit Learn's algorithm models can work with.\n",
        "\n",
        "Now we'll convert each message, represented as a list of tokens (lemmas) above, into a vector that machine learning models can understand.\n",
        "\n",
        "We'll do that in three steps using the bag-of-words model:\n",
        "\n",
        "1. Count how many times does a word occur in each message (Known as term frequency)\n",
        "\n",
        "2. Weigh the counts, so that frequent tokens get lower weight (inverse document frequency)\n",
        "\n",
        "3.  Normalize the vectors to unit length, to abstract from the original text length (L2 norm)"
      ],
      "metadata": {
        "id": "7F6QRf1QrSIz"
      },
      "id": "7F6QRf1QrSIz"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each vector will have as many dimensions as there are unique words in the SMS corpus. We will first use SciKit Learn's CountVectorizer. This model will convert a collection of text documents to a matrix of token counts.\n",
        "\n",
        "We can imagine this as a 2-Dimensional matrix. Where the 1-dimension is the entire vocabulary (1 row per word) and the other dimension are the actual documents, in this case a column per text message.\n",
        "\n"
      ],
      "metadata": {
        "id": "inyD1gyCrwom"
      },
      "id": "inyD1gyCrwom"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# instantiate the vectorizer\n",
        "vect = CountVectorizer()\n",
        "vect.fit(data['clean_message'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JsiUI0sqjaOz",
        "outputId": "70a95357-e9f5-40a4-c3f4-eac40cb0e14d"
      },
      "id": "JsiUI0sqjaOz",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CountVectorizer()"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the trained to create a document-term matrix from train and test sets\n",
        "x_train_vect = vect.transform(x_train)\n",
        "x_test_vect = vect.transform(x_test)"
      ],
      "metadata": {
        "id": "Dps40ASZvqRi"
      },
      "id": "Dps40ASZvqRi",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **TfidfTransformer**"
      ],
      "metadata": {
        "id": "OdQMbnRDupKB"
      },
      "id": "OdQMbnRDupKB"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "\n",
        "tfidf_transformer = TfidfTransformer()\n",
        "\n",
        "tfidf_transformer.fit(x_train_vect)\n",
        "x_train_tfidf = tfidf_transformer.transform(x_train_vect)\n",
        "\n",
        "x_train_tfidf\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SwKVthEQuppr",
        "outputId": "77c02c3f-8d9a-40c2-c027-5c8087a9314b"
      },
      "id": "SwKVthEQuppr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<4179x8189 sparse matrix of type '<class 'numpy.float64'>'\n",
              "\twith 51701 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Multinomial Naive Bayes model**"
      ],
      "metadata": {
        "id": "tBYy7muwrz9-"
      },
      "id": "tBYy7muwrz9-"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create a Multinomial Naive Bayes model\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "nb = MultinomialNB()\n",
        "\n",
        "# Train the model\n",
        "nb.fit(x_train_dtm, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPUtlpMqszml",
        "outputId": "04fc483b-07a3-46b5-8f63-b87b91ee0cc1"
      },
      "id": "tPUtlpMqszml",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "import sklearn.metrics as metrics\n",
        "\n",
        "pipe = Pipeline([('bow', CountVectorizer()), \n",
        "                 ('tfid', TfidfTransformer()),  \n",
        "                 ('model', MultinomialNB())])"
      ],
      "metadata": {
        "id": "Us9-htviw2oe"
      },
      "id": "Us9-htviw2oe",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the pipeline with the data\n",
        "pipe.fit(x_train, y_train)\n",
        "\n",
        "y_pred_class = pipe.predict(x_test)\n",
        "\n",
        "print(metrics.accuracy_score(y_test, y_pred_class))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19QPMy2xiXAX",
        "outputId": "c78c895a-afab-4bb2-9905-41e4eedfaa00"
      },
      "id": "19QPMy2xiXAX",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9511844938980617\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Classification Report**"
      ],
      "metadata": {
        "id": "2B8RkiVkyNfQ"
      },
      "id": "2B8RkiVkyNfQ"
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred_class))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IiWgCS5_x0mF",
        "outputId": "f17c6c0e-da96-4d50-9f47-f24dc8bfa114"
      },
      "id": "IiWgCS5_x0mF",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.97      1202\n",
            "           1       1.00      0.64      0.78       191\n",
            "\n",
            "    accuracy                           0.95      1393\n",
            "   macro avg       0.97      0.82      0.88      1393\n",
            "weighted avg       0.95      0.95      0.95      1393\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
        "cm = confusion_matrix(y_test, y_pred_class)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMLMLYiWiEyl",
        "outputId": "8bbfa69c-6a19-4ddf-e838-8e2776b80bf5"
      },
      "id": "OMLMLYiWiEyl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1202,    0],\n",
              "       [  68,  123]])"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#defining the size of the canvas\n",
        "plt.rcParams['figure.figsize'] = [15,8]\n",
        "#confusion matrix to DataFrame\n",
        "conf_matrix = pd.DataFrame(data = cm,columns = ['Predicted:0','Predicted:1',], index = ['Actual:0','Actual:1',])\n",
        "#plotting the confusion matrix\n",
        "sns.heatmap(conf_matrix, annot = True, fmt = 'd', cmap = 'BuPu', cbar = False,linewidths = 0.1, annot_kws = {'size':25})\n",
        "plt.xticks(fontsize = 20)\n",
        "plt.yticks(fontsize = 20)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "xakEF966yAAm",
        "outputId": "e6fc4083-8136-4fc3-f3fd-5277b99567ae"
      },
      "id": "xakEF966yAAm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jsMOtSaXyAsd"
      },
      "id": "jsMOtSaXyAsd",
      "execution_count": null,
      "outputs": []
    }
  ],
  "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": "3.8.9"
    },
    "toc-autonumbering": true,
    "toc-showcode": true,
    "toc-showmarkdowntxt": false,
    "toc-showtags": false,
    "colab": {
      "name": "ML_19_K_Nearest_Neighbors.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "IOPh7huyg7wF",
        "_q3BNn_shBOy",
        "pcu-_rzohL8v",
        "9ac0a7fd-eef6-4b65-a0c9-e901f123b0bb"
      ],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}