Spam_Classification.ipynb

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        "<a href=\"https://colab.research.google.com/github/tzsoulcap/Spam-Classification-with-TFIDF-BOW/blob/master/Spam_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "## EDA"
      ],
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import re"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Research/Datasets/spam text.csv\")"
      ],
      "metadata": {
        "id": "bs6tmzq0a-m-"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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          "data": {
            "text/plain": [
              "  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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              "      <th>Category</th>\n",
              "      <th>Message</th>\n",
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              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
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        "df.info()"
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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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        "df.isna().sum()"
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              "Category    0\n",
              "Message     0\n",
              "dtype: int64"
            ]
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          "execution_count": 17
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1b7e23ab10>"
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            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleansing"
      ],
      "metadata": {
        "id": "ABJ7IK1hdmQU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### import data"
      ],
      "metadata": {
        "id": "z9kmPwQ61N44"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "JPgccjRB1RSM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Research/Datasets/spam text.csv\")"
      ],
      "metadata": {
        "id": "VML2114t1Qfh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### import lib"
      ],
      "metadata": {
        "id": "I3kvlvwmz-PM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "\n",
        "import re"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9hxCh71gjjUd",
        "outputId": "fb709230-fa55-4b75-ce53-a9e310ab8f46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create Function"
      ],
      "metadata": {
        "id": "o9alDywy0A-b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stop = stopwords.words('english')\n",
        "stemmer = PorterStemmer()\n",
        "lemmatizer = WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "X9sm3b1cpaXN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contraction_mapping = {\n",
        "    \"ain't\": \"is not\", \n",
        "    \"aren't\": \"are not\",\n",
        "    \"can't\": \"cannot\", \n",
        "    \"'cause\": \"because\", \n",
        "    \"could've\": \"could have\", \n",
        "    \"couldn't\": \"could not\", \n",
        "    \"didn't\": \"did not\",  \n",
        "    \"doesn't\": \"does not\", \n",
        "    \"don't\": \"do not\", \n",
        "    \"hadn't\": \"had not\", \n",
        "    \"hasn't\": \"has not\", \n",
        "    \"haven't\": \"have not\", \n",
        "    \"he'd\": \"he would\",\n",
        "    \"he'll\": \"he will\", \n",
        "    \"he's\": \"he is\", \n",
        "    \"how'd\": \"how did\", \n",
        "    \"how'd'y\": \"how do you\", \n",
        "    \"how'll\": \"how will\", \n",
        "    \"how's\": \"how is\",  \n",
        "    \"I'd\": \"I would\", \n",
        "    \"I'd've\": \"I would have\", \n",
        "    \"I'll\": \"I will\", \n",
        "    \"I'll've\": \"I will have\",\n",
        "    \"I'm\": \"I am\", \n",
        "    \"I've\": \"I have\", \n",
        "    \"i'd\": \"i would\", \n",
        "    \"i'd've\": \"i would have\", \n",
        "    \"i'll\": \"i will\",  \n",
        "    \"i'll've\": \"i will have\",\n",
        "    \"i'm\": \"i am\", \n",
        "    \"i've\": \"i have\", \n",
        "    \"isn't\": \"is not\", \n",
        "    \"it'd\": \"it would\", \n",
        "    \"it'd've\": \"it would have\", \n",
        "    \"it'll\": \"it will\", \"it'll've\": \n",
        "    \"it will have\",\"it's\": \"it is\", \n",
        "    \"let's\": \"let us\", \n",
        "    \"ma'am\": \"madam\", \n",
        "    \"mayn't\": \"may not\", \n",
        "    \"might've\": \"might have\",\n",
        "    \"mightn't\": \"might not\",\n",
        "    \"mightn't've\": \"might not have\", \n",
        "    \"must've\": \"must have\", \n",
        "    \"mustn't\": \"must not\", \n",
        "    \"mustn't've\": \"must not have\", \n",
        "    \"needn't\": \"need not\", \n",
        "    \"needn't've\": \"need not have\",\n",
        "    \"o'clock\": \"of the clock\", \n",
        "    \"oughtn't\": \"ought not\", \n",
        "    \"oughtn't've\": \"ought not have\", \n",
        "    \"shan't\": \"shall not\", \n",
        "    \"sha'n't\": \"shall not\", \n",
        "    \"shan't've\": \"shall not have\", \n",
        "    \"she'd\": \"she would\", \n",
        "    \"she'd've\": \"she would have\", \n",
        "    \"she'll\": \"she will\", \n",
        "    \"she'll've\": \"she will have\", \n",
        "    \"she's\": \"she is\", \n",
        "    \"should've\": \"should have\", \n",
        "    \"shouldn't\": \"should not\", \n",
        "    \"shouldn't've\": \"should not have\", \n",
        "    \"so've\": \"so have\",\n",
        "    \"so's\": \"so as\", \n",
        "    \"this's\": \"this is\",\n",
        "    \"that'd\": \"that would\", \n",
        "    \"that'd've\": \"that would have\", \n",
        "    \"that's\": \"that is\", \n",
        "    \"there'd\": \"there would\", \n",
        "    \"there'd've\": \"there would have\", \n",
        "    \"there's\": \"there is\", \n",
        "    \"here's\": \"here is\",\n",
        "    \"they'd\": \"they would\", \n",
        "    \"they'd've\": \"they would have\", \n",
        "    \"they'll\": \"they will\", \n",
        "    \"they'll've\": \"they will have\", \n",
        "    \"they're\": \"they are\", \n",
        "    \"they've\": \"they have\", \n",
        "    \"to've\": \"to have\", \n",
        "    \"wasn't\": \"was not\", \n",
        "    \"we'd\": \"we would\", \n",
        "    \"we'd've\": \"we would have\", \n",
        "    \"we'll\": \"we will\", \n",
        "    \"we'll've\": \"we will have\", \n",
        "    \"we're\": \"we are\", \n",
        "    \"we've\": \"we have\", \n",
        "    \"weren't\": \"were not\", \n",
        "    \"what'll\": \"what will\", \n",
        "    \"what'll've\": \"what will have\", \n",
        "    \"what're\": \"what are\",  \n",
        "    \"what's\": \"what is\", \n",
        "    \"what've\": \"what have\", \n",
        "    \"when's\": \"when is\", \n",
        "    \"when've\": \"when have\", \n",
        "    \"where'd\": \"where did\", \n",
        "    \"where's\": \"where is\", \n",
        "    \"where've\": \"where have\", \n",
        "    \"who'll\": \"who will\", \n",
        "    \"who'll've\": \"who will have\", \n",
        "    \"who's\": \"who is\", \n",
        "    \"who've\": \"who have\", \n",
        "    \"why's\": \"why is\", \n",
        "    \"why've\": \"why have\", \n",
        "    \"will've\": \"will have\", \n",
        "    \"won't\": \"will not\", \n",
        "    \"won't've\": \"will not have\", \n",
        "    \"would've\": \"would have\", \n",
        "    \"wouldn't\": \"would not\", \n",
        "    \"wouldn't've\": \"would not have\", \n",
        "    \"y'all\": \"you all\", \n",
        "    \"y'all'd\": \"you all would\",\n",
        "    \"y'all'd've\": \"you all would have\",\n",
        "    \"y'all're\": \"you all are\",\n",
        "    \"y'all've\": \"you all have\",\n",
        "    \"you'd\": \"you would\", \n",
        "    \"you'd've\": \"you would have\", \n",
        "    \"you'll\": \"you will\", \n",
        "    \"you'll've\": \"you will have\", \n",
        "    \"you're\": \"you are\", \n",
        "    \"you've\": \"you have\" }"
      ],
      "metadata": {
        "id": "MGBG0IKPG8BZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def NormalizeText(text):\n",
        "    return text.lower()\n",
        "\n",
        "def RemovePunct(text):\n",
        "    return re.sub(r\"(@\\[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)|^rt|http.+?|(www\\.\\w+\\.\\w+)|(\\d+)\", \"\", text)\n",
        "\n",
        "def RemoveStopword(text):\n",
        "    return \" \".join([i for i in word_tokenize(text) if i not in stop])\n",
        "\n",
        "def Lemmatizetion(text):\n",
        "    return \" \".join([lemmatizer.lemmatize(i) for i in word_tokenize(text)])\n",
        "\n",
        "def Contraction_mapping(text):\n",
        "    return \" \".join([contraction_mapping[word] if word in contraction_mapping else word for word in text.split(' ')])\n",
        "\n",
        "def MapWord(text):\n",
        "    mapword = {\n",
        "        'ur': 'you are',\n",
        "        'u': 'you',\n",
        "        'r': 'are',\n",
        "        'c': 'see',\n",
        "        'txt': 'text',\n",
        "        'wkly': 'weekly',\n",
        "    }\n",
        "    return \" \".join([mapword[word] if word in mapword else word for word in text.split(' ')])\n",
        "\n",
        "    \n",
        "def CleanText(text):\n",
        "    text = NormalizeText(text)\n",
        "    text = Contraction_mapping(text)\n",
        "    text = MapWord(text)\n",
        "    text = RemovePunct(text)\n",
        "    text = RemoveStopword(text)\n",
        "    text = Lemmatizetion(text)\n",
        "    text = MapWord(text)\n",
        "    text = RemoveStopword(text)\n",
        "    return text"
      ],
      "metadata": {
        "id": "5bcaBuQhorvL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "re.sub(\"ur\", \"you are\", \"ur understand\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "k6abwk2WaJlh",
        "outputId": "2cf0fb44-eb55-41b1-c61d-74c9ea6c7e6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'you are understand'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Message'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SA3alaDVsIVP",
        "outputId": "0eb68893-c77c-4c5e-c5d9-f8f775db0c41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Go until jurong point, crazy.. Available only ...\n",
              "1                        Ok lar... Joking wif u oni...\n",
              "2    Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3    U dun say so early hor... U c already then say...\n",
              "4    Nah I don't think he goes to usf, he lives aro...\n",
              "Name: Message, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Message'] = df['Message'].map(CleanText)"
      ],
      "metadata": {
        "id": "4GfKcKzkrs6L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "knlCvuZssPw8",
        "outputId": "95a5d3cd-ce49-4834-9602-1f1f4682a231"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Category                                            Message\n",
              "0         ham  go jurong point crazy available bugis n great ...\n",
              "1         ham                              ok lar joking wif oni\n",
              "2        spam  free entry weekly comp win fa cup final tkts s...\n",
              "3         ham                  dun say early hor see already say\n",
              "4         ham                nah think go usf life around though\n",
              "...       ...                                                ...\n",
              "5567     spam  nd time tried contact pound prize claim easy c...\n",
              "5568      ham                          b going esplanade fr home\n",
              "5569      ham                         pity mood soany suggestion\n",
              "5570      ham  guy bitching acted like would interested buyin...\n",
              "5571      ham                                     rofl true name\n",
              "\n",
              "[5572 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ef0da9c-2580-4f45-b0d3-eda71eed1e15\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>go jurong point crazy available bugis n great ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>ok lar joking wif oni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>free entry weekly comp win fa cup final tkts s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>dun say early hor see already say</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>nah think go usf life around though</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5567</th>\n",
              "      <td>spam</td>\n",
              "      <td>nd time tried contact pound prize claim easy c...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5568</th>\n",
              "      <td>ham</td>\n",
              "      <td>b going esplanade fr home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5569</th>\n",
              "      <td>ham</td>\n",
              "      <td>pity mood soany suggestion</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5570</th>\n",
              "      <td>ham</td>\n",
              "      <td>guy bitching acted like would interested buyin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>ham</td>\n",
              "      <td>rofl true name</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5572 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ef0da9c-2580-4f45-b0d3-eda71eed1e15')\"\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 0 24 24\"\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9ef0da9c-2580-4f45-b0d3-eda71eed1e15 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-9ef0da9c-2580-4f45-b0d3-eda71eed1e15');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df['Message']:\n",
        "    if 'u' in i.split(' '):\n",
        "        print(i)\n",
        "        break"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BlY2kn93YhaZ",
        "outputId": "987e9b6c-c4c0-48fd-9cc9-05098015aa65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "wait still clear sure sarcastic x want live u\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MapWord('wait still clear sure sarcastic x want live u')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "Am3DXxjUcL-0",
        "outputId": "ab3f7fe2-b46c-4180-fe3d-9674dbfca2d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'wait still clear sure sarcastic x want live you'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### spam words"
      ],
      "metadata": {
        "id": "uh2XwHiVABH9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "spam_df = df[df['Category']=='spam']\n",
        "spam_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hSqIU82pxXJI",
        "outputId": "5f61e079-f411-4e73-985f-22566671be3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Category                                            Message\n",
              "2      spam  free entry weekly comp win fa cup final tkts s...\n",
              "5      spam  freemsg hey darling week word back would like ...\n",
              "8      spam  winner valued network customer selected receiv...\n",
              "9      spam  mobile month entitled update latest colour mob...\n",
              "11     spam  six chance win cash pound text csh send cost p..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13ce2a32-e7a6-4f18-911a-61a4a3fc7e14\">\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",
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              "      <th>Message</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>free entry weekly comp win fa cup final tkts s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>spam</td>\n",
              "      <td>freemsg hey darling week word back would like ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>spam</td>\n",
              "      <td>winner valued network customer selected receiv...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>spam</td>\n",
              "      <td>mobile month entitled update latest colour mob...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>spam</td>\n",
              "      <td>six chance win cash pound text csh send cost p...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13ce2a32-e7a6-4f18-911a-61a4a3fc7e14')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-13ce2a32-e7a6-4f18-911a-61a4a3fc7e14');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spam_word2count = {}\n",
        "for data in spam_df['Message']:\n",
        "    words = nltk.word_tokenize(data)\n",
        "    for word in words:\n",
        "        if word not in spam_word2count.keys():\n",
        "            spam_word2count[word] = 1\n",
        "        else:\n",
        "            spam_word2count[word] += 1"
      ],
      "metadata": {
        "id": "IdNv7CGk2YIb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spam_word2count"
      ],
      "metadata": {
        "id": "GnnfEosd2fEM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "spam_df['Message']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwWT42vy4VLA",
        "outputId": "7b2c4524-6183-4beb-ec51-da99fcbe8109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2       free entry weekly comp win fa cup final tkts s...\n",
              "5       freemsg hey darling week word back would like ...\n",
              "8       winner valued network customer selected receiv...\n",
              "9       mobile month entitled update latest colour mob...\n",
              "11      six chance win cash pound text csh send cost p...\n",
              "                              ...                        \n",
              "5537    want explicit sex sec ring cost pmin gsex pobo...\n",
              "5540    asked mobile chatlines inclu free min india cu...\n",
              "5547    contract mobile mnths latest motorola nokia et...\n",
              "5566    reminder get pound free call credit detail gre...\n",
              "5567    nd time tried contact pound prize claim easy c...\n",
              "Name: Message, Length: 747, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spamword_freq = pd.DataFrame({\n",
        "    'word': spam_word2count.keys(),\n",
        "    'freq': spam_word2count.values()\n",
        "}).sort_values('freq', ascending=False).reset_index().drop(['index'], axis=1)\n",
        "spamword_freq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "VsMO3KRo24Vj",
        "outputId": "0c87440b-3e8b-4a50-b7e8-d76f54cf843c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   word  freq\n",
              "0                  call   359\n",
              "1                  text   289\n",
              "2                  free   219\n",
              "3                mobile   136\n",
              "4                  stop   118\n",
              "...                 ...   ...\n",
              "1972                 yo     1\n",
              "1973             yohere     1\n",
              "1974               open     1\n",
              "1975  skillgamewinaweek     1\n",
              "1976              house     1\n",
              "\n",
              "[1977 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-be9b0fd8-2c2b-40c8-b870-06272144aaae\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>word</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>call</td>\n",
              "      <td>359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>text</td>\n",
              "      <td>289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>free</td>\n",
              "      <td>219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mobile</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>stop</td>\n",
              "      <td>118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1972</th>\n",
              "      <td>yo</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973</th>\n",
              "      <td>yohere</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974</th>\n",
              "      <td>open</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975</th>\n",
              "      <td>skillgamewinaweek</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1976</th>\n",
              "      <td>house</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1977 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be9b0fd8-2c2b-40c8-b870-06272144aaae')\"\n",
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              "  </svg>\n",
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              "\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-be9b0fd8-2c2b-40c8-b870-06272144aaae');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spamword_freq[:20].pivot_table(\n",
        "    values='freq',\n",
        "    # columns='year',\n",
        "    index='word'\n",
        ").sort_values('freq', ascending=True).plot(kind='barh', figsize=(9, 5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "T4H51rm7_Q1P",
        "outputId": "573cdeb5-5b0a-4b3f-b4e7-5ba91c5e0a7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f598f2a3510>"
            ]
          },
          "metadata": {},
          "execution_count": 128
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ham words"
      ],
      "metadata": {
        "id": "0mdSG9gQAKCw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ham_df = df[df['Category']=='ham']\n",
        "ham_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Eb9t1DrYAJ2o",
        "outputId": "fdfd1444-c4ff-4426-e6ef-06ea5871b7a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Category                                            Message\n",
              "0      ham  go jurong point crazy available bugis n great ...\n",
              "1      ham                              ok lar joking wif oni\n",
              "3      ham                  dun say early hor see already say\n",
              "4      ham                nah think go usf life around though\n",
              "6      ham      even brother like speak treat like aid patent"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d11593d-4b8f-42a6-bb76-50ac111de15f\">\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",
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              "\n",
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              "</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>go jurong point crazy available bugis n great ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>ok lar joking wif oni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>dun say early hor see already say</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>nah think go usf life around though</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ham</td>\n",
              "      <td>even brother like speak treat like aid patent</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d11593d-4b8f-42a6-bb76-50ac111de15f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-4d11593d-4b8f-42a6-bb76-50ac111de15f 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-4d11593d-4b8f-42a6-bb76-50ac111de15f');\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": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ham_word2count = {}\n",
        "for data in ham_df['Message']:\n",
        "    words = nltk.word_tokenize(data)\n",
        "    for word in words:\n",
        "        if word not in ham_word2count.keys():\n",
        "            ham_word2count[word] = 1\n",
        "        else:\n",
        "            ham_word2count[word] += 1"
      ],
      "metadata": {
        "id": "gZ4SLKugAPwa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ham_word2count"
      ],
      "metadata": {
        "id": "Gut0ADS-Aa1-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ham_df['Message']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVhMFUkiAaLh",
        "outputId": "24946764-7c42-46d5-ad3e-64f4a212b42b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       go jurong point crazy available bugis n great ...\n",
              "1                                   ok lar joking wif oni\n",
              "3                       dun say early hor see already say\n",
              "4                     nah think go usf life around though\n",
              "6           even brother like speak treat like aid patent\n",
              "                              ...                        \n",
              "5565                                              huh lei\n",
              "5568                            b going esplanade fr home\n",
              "5569                           pity mood soany suggestion\n",
              "5570    guy bitching acted like would interested buyin...\n",
              "5571                                       rofl true name\n",
              "Name: Message, Length: 4825, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hamword_freq = pd.DataFrame({\n",
        "    'word': ham_word2count.keys(),\n",
        "    'freq': ham_word2count.values()\n",
        "}).sort_values('freq', ascending=False).reset_index().drop(['index'], axis=1)\n",
        "hamword_freq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "BtC7AbtIAsNx",
        "outputId": "536c8bab-bf0a-4cda-f55f-bd6eeba73520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             word  freq\n",
              "0             get   318\n",
              "1              go   278\n",
              "2            ltgt   276\n",
              "3              ok   273\n",
              "4             got   245\n",
              "...           ...   ...\n",
              "6791    community     1\n",
              "6792     outreach     1\n",
              "6793           lb     1\n",
              "6794  brilliantly     1\n",
              "6795     bitching     1\n",
              "\n",
              "[6796 rows x 2 columns]"
            ],
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              "      <td>bitching</td>\n",
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              "<p>6796 rows × 2 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-043f11eb-f663-4727-9ebe-d79b78864b90')\"\n",
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              "  </svg>\n",
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              "\n",
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              "    }\n",
              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-043f11eb-f663-4727-9ebe-d79b78864b90');\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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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hamword_freq[:20].pivot_table(\n",
        "    values='freq',\n",
        "    # columns='year',\n",
        "    index='word'\n",
        ").sort_values('freq', ascending=True).plot(kind='barh', figsize=(9, 5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "_wzZcx2TA9_B",
        "outputId": "9996515d-ce29-4f30-f958-9ef4cea5f96d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f598f195590>"
            ]
          },
          "metadata": {},
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spamword_freq[:20].pivot_table(\n",
        "    values='freq',\n",
        "    # columns='year',\n",
        "    index='word'\n",
        ").sort_values('freq', ascending=True).plot(kind='barh', figsize=(9, 5))"
      ],
      "metadata": {
        "id": "7G7mUPtiBJCr",
        "outputId": "665f5af4-8743-46f2-854d-6ec6b0eb8857",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f598f194a50>"
            ]
          },
          "metadata": {},
          "execution_count": 134
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word2count = {}\n",
        "for data in df['Message']:\n",
        "    words = nltk.word_tokenize(data)\n",
        "    for word in words:\n",
        "        if word not in word2count.keys():\n",
        "            word2count[word] = 1\n",
        "        else:\n",
        "            word2count[word] += 1"
      ],
      "metadata": {
        "id": "RO5KjU00okZ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_freq = pd.DataFrame({\n",
        "    'word': word2count.keys(),\n",
        "    'freq': word2count.values()\n",
        "}).sort_values('freq', ascending=False).reset_index().drop(['index'], axis=1)\n",
        "word_freq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "RIQI4cGXoxxf",
        "outputId": "db5fba63-d824-4acd-d9f3-2470840321f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          word  freq\n",
              "0         call   602\n",
              "1          get   401\n",
              "2         text   379\n",
              "3           go   311\n",
              "4         free   278\n",
              "...        ...   ...\n",
              "7882        lb     1\n",
              "7883  outreach     1\n",
              "7884      soil     1\n",
              "7885   african     1\n",
              "7886  bitching     1\n",
              "\n",
              "[7887 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8abe87db-7dc1-4fac-9924-79d3f05b70fd\">\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>word</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>call</td>\n",
              "      <td>602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>get</td>\n",
              "      <td>401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>text</td>\n",
              "      <td>379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>go</td>\n",
              "      <td>311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>free</td>\n",
              "      <td>278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7882</th>\n",
              "      <td>lb</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7883</th>\n",
              "      <td>outreach</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7884</th>\n",
              "      <td>soil</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7885</th>\n",
              "      <td>african</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7886</th>\n",
              "      <td>bitching</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7887 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8abe87db-7dc1-4fac-9924-79d3f05b70fd')\"\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 0 24 24\"\n",
              "       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-8abe87db-7dc1-4fac-9924-79d3f05b70fd 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-8abe87db-7dc1-4fac-9924-79d3f05b70fd');\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": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split data"
      ],
      "metadata": {
        "id": "tnWwUrsVtLV8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = df['Message']\n",
        "label = df['Category']"
      ],
      "metadata": {
        "id": "jEScvx5Rpfxq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(text, label, train_size=0.8, random_state=42)"
      ],
      "metadata": {
        "id": "8ZlSik5StOii"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Extraction"
      ],
      "metadata": {
        "id": "dQpfUXHAo9rn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Bag of words"
      ],
      "metadata": {
        "id": "tIWscPYspDm9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "vectorizer = CountVectorizer(analyzer = \"word\",\n",
        "                             tokenizer = None,\n",
        "                             preprocessor = None,\n",
        "                             stop_words = None,\n",
        "                             max_features = 3000) \n",
        "\n",
        "X_train = vectorizer.fit_transform(X_train).toarray()\n",
        "X_test = vectorizer.fit_transform(X_test).toarray()"
      ],
      "metadata": {
        "id": "6f5oB63Jp2GS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RLlwwOER8weh",
        "outputId": "9c7e273a-458c-4eaa-cb49-f7b827e5fc1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word2count = {}\n",
        "wordcount = {}\n",
        "for data in X_train:\n",
        "    words = nltk.word_tokenize(data)\n",
        "    for word in words:\n",
        "        wordcount[word] = 0\n",
        "        if word not in word2count.keys():\n",
        "            word2count[word] = 1\n",
        "        else:\n",
        "            word2count[word] += 1"
      ],
      "metadata": {
        "id": "0U8S_6j5g2dS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy"
      ],
      "metadata": {
        "id": "X2ZtdMBNvIT5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def TransformBOW(X_train):\n",
        "    bag = []\n",
        "    # n = 0\n",
        "    for data in X_train:\n",
        "        blank_wordcount = wordcount.copy()\n",
        "        # print(\"FBlank:\\t\", blank_wordcount)\n",
        "        words = nltk.word_tokenize(data)\n",
        "        # print(\"Words:\\t\", words)\n",
        "        for word in words:\n",
        "            if word in blank_wordcount.keys():\n",
        "                blank_wordcount[word] += 1\n",
        "        # print(\"Blank:\\t\", blank_wordcount)\n",
        "        # print(list(blank_wordcount.values()))\n",
        "        # print()\n",
        "        # n += 1\n",
        "        # if n>3:\n",
        "        #     break\n",
        "        bag.append(list(blank_wordcount.values()))\n",
        "    return numpy.asarray(bag)"
      ],
      "metadata": {
        "id": "qbpfQC3wm7E2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = TransformBOW(X_train)\n",
        "x_test = TransformBOW(X_test)"
      ],
      "metadata": {
        "id": "FZDsUW0rofO4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_freq = pd.DataFrame({\n",
        "    'word': word2count.keys(),\n",
        "    'freq': word2count.values()\n",
        "}).sort_values('freq', ascending=False).reset_index().drop(['index'], axis=1)\n",
        "word_freq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "gHVWvaojhhGY",
        "outputId": "5f726153-c807-4312-e8a8-cb9b8c3e958c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           word  freq\n",
              "0          call   487\n",
              "1           get   327\n",
              "2          text   301\n",
              "3            go   248\n",
              "4          free   234\n",
              "...         ...   ...\n",
              "6934    costmax     1\n",
              "6935   parentsi     1\n",
              "6936     tensed     1\n",
              "6937  rencontre     1\n",
              "6938     wlcome     1\n",
              "\n",
              "[6939 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8145f045-88e6-4c7a-83e1-639fed0c767a\">\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>word</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>call</td>\n",
              "      <td>487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>get</td>\n",
              "      <td>327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>text</td>\n",
              "      <td>301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>go</td>\n",
              "      <td>248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>free</td>\n",
              "      <td>234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6934</th>\n",
              "      <td>costmax</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6935</th>\n",
              "      <td>parentsi</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6936</th>\n",
              "      <td>tensed</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6937</th>\n",
              "      <td>rencontre</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6938</th>\n",
              "      <td>wlcome</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6939 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8145f045-88e6-4c7a-83e1-639fed0c767a')\"\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 0 24 24\"\n",
              "       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-8145f045-88e6-4c7a-83e1-639fed0c767a 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-8145f045-88e6-4c7a-83e1-639fed0c767a');\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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### TF-IDF"
      ],
      "metadata": {
        "id": "wk0ci4qWq8h2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "word2count"
      ],
      "metadata": {
        "id": "P4TLFfkLc62x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# calculating tf-idf values\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "import pandas as pd\n",
        "\n",
        "tfidf = TfidfVectorizer()\n",
        "train_tfidf = tfidf.fit_transform(X_train).todense()\n",
        "# X_test = tfidf.fit_transform(X_test).todense()\n",
        "\n",
        "# tfidf_df = pd.DataFrame(\n",
        "# \tfeatures.todense(),\n",
        "#     # features.toarray(),\n",
        "# \tcolumns = tfidf.get_feature_names_out()\n",
        "# )"
      ],
      "metadata": {
        "id": "A2s74Xl_q_AJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_tf(bow, doc):\n",
        "    tf = []\n",
        "    for i in range(len(doc)):\n",
        "        tf_dict ={}\n",
        "        doc_count = len(word_tokenize(doc[i]))\n",
        "        # print(\"total term\", doc_count)\n",
        "        for word, count in bow[i].items():\n",
        "            tf_dict[word] = count/doc_count\n",
        "        tf.append(tf_dict)\n",
        "    return tf\n",
        "\n",
        "def compute_idf(docs):    \n",
        "    import math\n",
        "    N = len(docs)\n",
        "    idfDict = dict.fromkeys(word2count.keys(),0)\n",
        "    for doc in docs:\n",
        "        for word, val in doc.items():\n",
        "            if (val > 0 and word in idfDict.keys()):\n",
        "                idfDict[word] +=1\n",
        "\n",
        "\n",
        "    for word, val in idfDict.items():\n",
        "        # standard notation\n",
        "        if val!=0:\n",
        "            idfDict[word] = math.log(N / float(val))\n",
        "        else:\n",
        "            idfDict[word] = 0\n",
        "        #sklearn notation\n",
        "        #idfDict[word] = (math.log((N+1) / (val+1))) + 1\n",
        "\n",
        "    return idfDict\n",
        "\n",
        "def compute_tfidf(tf, idf):\n",
        "    tfidf = []\n",
        "    for i in tf:\n",
        "        tfidf_dict = dict.fromkeys(word2count.keys(),0)\n",
        "        for word, tfVal in i.items():\n",
        "            if word in word2count.keys():\n",
        "                tfidf_dict[word] = tfVal * idf[word]\n",
        "\n",
        "        tfidf.append(list(tfidf_dict.values()))\n",
        "    return tfidf\n",
        "\n",
        "def TransformTFIDF(X_train):\n",
        "    tfs = compute_tf(tf_word2count, X_train.to_list())\n",
        "    idfs = compute_idf(tf_word2count)\n",
        "    tfidfs = compute_tfidf(tfs, idfs)\n",
        "    \n",
        "    return numpy.asarray(tfidfs)\n",
        "\n",
        "def TransformTFIDF4Test(X_test):\n",
        "    tfs = compute_tf(BoWforTest(X_test), X_test.to_list())\n",
        "    idfs = compute_idf(BoWforTest(X_test))\n",
        "    tfidfs = compute_tfidf(tfs, idfs)\n",
        "    \n",
        "    return numpy.asarray(tfidfs)\n",
        "\n",
        "def BoWforTest(texts):\n",
        "    bow_tf_word2count = []\n",
        "    for data in texts:\n",
        "        doc_word2count = {}\n",
        "        words = nltk.word_tokenize(data)\n",
        "        for word in words:\n",
        "            if word not in doc_word2count.keys():\n",
        "                doc_word2count[word] = 1\n",
        "            else:\n",
        "                doc_word2count[word] += 1\n",
        "        bow_tf_word2count.append(doc_word2count)\n",
        "    return bow_tf_word2count\n"
      ],
      "metadata": {
        "id": "wSFCG50gDFKk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "TF(m) = term m in doc / total term in doc"
      ],
      "metadata": {
        "id": "Fh99-09W4b6D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "IDF(m) = total number of docs / number of docs having term m"
      ],
      "metadata": {
        "id": "TQgJ4Rey5h_y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tfidfs = compute_tfidf(tfs, idfs)\n",
        "tf_word2count = []\n",
        "word2count = {}\n",
        "wordcount = {}\n",
        "for data in X_train:\n",
        "    doc_word2count = {}\n",
        "    words = nltk.word_tokenize(data)\n",
        "    for word in words:\n",
        "        wordcount[word] = 0\n",
        "        if word not in doc_word2count.keys():\n",
        "            doc_word2count[word] = 1\n",
        "        else:\n",
        "            doc_word2count[word] += 1\n",
        "        if word not in word2count.keys():\n",
        "            word2count[word] = 1\n",
        "        else:\n",
        "            word2count[word] += 1\n",
        "    tf_word2count.append(doc_word2count)\n",
        "x_train = TransformTFIDF(X_train)\n",
        "x_test = TransformTFIDF4Test(X_test)"
      ],
      "metadata": {
        "id": "hH0GHcRIAZ_C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training model"
      ],
      "metadata": {
        "id": "7Ai41LL7vTk8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Naive Bayes"
      ],
      "metadata": {
        "id": "6Zg56NVk0sDS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "VQjRiNyhviJR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "BeM28_8cyel7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gnb = GaussianNB()\n",
        "# model = gnb.fit(X_train, y_train, cross_validate)\n",
        "# scores = cross_val_score(gnb, X_train, y_train, cv=10)"
      ],
      "metadata": {
        "id": "BKz3jQwCvWxX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBy0hZ7fxCJp",
        "outputId": "be418e0f-18c1-4554-e56e-588446a90d55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.88565022, 0.86995516, 0.87892377, 0.87892377, 0.87892377,\n",
              "       0.88116592, 0.8632287 , 0.85842697, 0.88764045, 0.87640449])"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = gnb.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "HCJFHuYgxsRZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.score(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fQsoprkdxvmF",
        "outputId": "011e1fc8-997a-42a7-ceb3-0353d70bdad3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9427866277765313"
            ]
          },
          "metadata": {},
          "execution_count": 423
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(numpy.array(x_test))"
      ],
      "metadata": {
        "id": "lFiXJJFB3KVV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = numpy.array(list(map(lambda x: 1 if x=='spam' else 0, pred)))\n",
        "y_true = numpy.array(list(map(lambda x: 1 if x=='spam' else 0, y_test.to_list())))"
      ],
      "metadata": {
        "id": "ba3M993wysvH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_true, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_Meu1Ca1K9o",
        "outputId": "22a61808-669c-4a31-8039-453b3da39936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8825112107623319"
            ]
          },
          "metadata": {},
          "execution_count": 426
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score(y_true, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4ugAEyGyiM0",
        "outputId": "e0e0b5d6-6460-4291-e295-51aabfb20035"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6649616368286445"
            ]
          },
          "metadata": {},
          "execution_count": 427
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Logistic Regression"
      ],
      "metadata": {
        "id": "jJ163Wal0vl8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "XmZSljbi0yAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "lr_model = lr.fit(x_train, y_train)\n",
        "lr_model.score(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFIe1zBU06aU",
        "outputId": "db0442fc-31f7-43ad-d3be-3ef21bf68667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9881085932241418"
            ]
          },
          "metadata": {},
          "execution_count": 428
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_lr = lr_model.predict(x_test)"
      ],
      "metadata": {
        "id": "ciQAhOrG2UNQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = numpy.array(list(map(lambda x: 1 if x=='spam' else 0, pred_lr)))\n",
        "y_true = numpy.array(list(map(lambda x: 1 if x=='spam' else 0, y_test.to_list())))"
      ],
      "metadata": {
        "id": "mAnbfuyU0REb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_true, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1vAQy7df1edJ",
        "outputId": "4406dad0-d308-462f-d3fd-90bdca958531"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.968609865470852"
            ]
          },
          "metadata": {},
          "execution_count": 431
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score(y_true, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yw01tVTV1WKf",
        "outputId": "6eaecdbd-47dd-4c2e-eb9e-84cd516536bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8736462093862817"
            ]
          },
          "metadata": {},
          "execution_count": 432
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_true, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lE7gRS_54cxu",
        "outputId": "55e294f9-42e0-489c-b1de-3b322df8b897"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[966,   0],\n",
              "       [ 19, 130]])"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Random Forest"
      ],
      "metadata": {
        "id": "SBKw0LYS1Vm_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "KFnz1tOC1XYp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=10, max_depth=None)\n",
        "rf_model = rf.fit(x_train, y_train)\n",
        "rf_model.score(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQ58utuM1rhq",
        "outputId": "6522bf6c-cf16-4a48-f4df-0aec5673c49a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9966345075162666"
            ]
          },
          "metadata": {},
          "execution_count": 443
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_rf = rf_model.predict(x_test)"
      ],
      "metadata": {
        "id": "RpZIB2cm2aWF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = numpy.array(list(map(lambda x: 1 if x=='spam' else 0, pred_rf)))\n",
        "y_true = numpy.array(list(map(lambda x: 1 if x=='spam' else 0, y_test.to_list())))"
      ],
      "metadata": {
        "id": "2Xy-Unbi0njP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_true, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13RhriS012Qm",
        "outputId": "ef7741a1-a259-476d-e68e-9cdaafdc6a77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9721973094170404"
            ]
          },
          "metadata": {},
          "execution_count": 446
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score(y_true, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muUsiZX511qq",
        "outputId": "c5b92359-2a33-4585-fef8-b22d457cc849"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8838951310861423"
            ]
          },
          "metadata": {},
          "execution_count": 447
        }
      ]
    }
  ]
}