Spam_naive_bayes.ipynb

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      "source": [
        "import pandas as pd"
      ]
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        "df=pd.read_csv('/content/spam.csv')"
      ],
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        "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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              "      <td>Ok lar... Joking wif u oni...</td>\n",
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              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
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            "text/plain": [
              "         Message                                                            \\\n",
              "           count unique                                                top   \n",
              "Category                                                                     \n",
              "ham         4825   4516                             Sorry, I'll call later   \n",
              "spam         747    641  Please call our customer service representativ...   \n",
              "\n",
              "               \n",
              "         freq  \n",
              "Category       \n",
              "ham        30  \n",
              "spam        4  "
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              "      <th>ham</th>\n",
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              "      <td>Sorry, I'll call later</td>\n",
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      "source": [
        "#from sklearn.preprocessing import LabelEncoder"
      ],
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      "outputs": []
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        "#label_encoder =LabelEncoder()\n",
        "#df['Category']=label_encoder.fit_transform(df['Category'])"
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        "id": "qUQKmOo8cw4a"
      },
      "execution_count": 33,
      "outputs": []
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      "source": [
        "df['spam']=df['Category'].apply(lambda x: 1 if x=='spam' else 0)\n",
        "df.head()"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "1F_Kv7T2eId5",
        "outputId": "581a4c14-93ab-4c35-fd34-f1f6b1b3c794"
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      "execution_count": 34,
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              "  Category                                            Message  spam\n",
              "0      ham  Go until jurong point, crazy.. Available only ...     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 don't think he goes to usf, he lives aro...     0"
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              "\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-001bfc15-b8d9-4f49-b28a-852b2332c1d0 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-001bfc15-b8d9-4f49-b28a-852b2332c1d0');\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": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "qTnJ9LWzc9JJ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(df.Message,df.spam,test_size=0.25,stratify=df.spam)"
      ],
      "metadata": {
        "id": "yvgjgFoXdfkB"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "v = CountVectorizer()\n",
        "x_train_count = v.fit_transform(x_train.values)\n",
        "x_train_count.toarray()[:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-EFGS1PdvdS",
        "outputId": "1935ef48-9cdd-413e-9368-f78ad43c102a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "model = MultinomialNB()\n",
        "model.fit(x_train_count,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdKILuP0erd4",
        "outputId": "df64a342-eca7-4b3d-8e91-533d8f9f7281"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emails = [\n",
        "    'Hey mohan, can we get together to watch footbal game tomorrow?',\n",
        "    'Upto 20% discount on parking, exclusive offer just for you. Dont miss this reward!'\n",
        "]\n",
        "emails_count = v.transform(emails)\n",
        "model.predict(emails_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6MAN3HBjfCgP",
        "outputId": "358fa838-91f9-4214-d939-fbce7a2d4fa3"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_count = v.transform(x_test)\n",
        "model.score(x_test_count, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IONBwP21fMoT",
        "outputId": "5657f913-88df-4f9d-e8db-7665629f0580"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.990667623833453"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nJ1RcHgBffea"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}