Reviews_Classification_Naive_Bayes.ipynb

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      "source": [
        "import pandas as pd\n",
        "data = pd.read_csv(\"Hamspam.csv\",encoding = \"ISO-8859-1\")"
      ]
    },
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              "   type                                               text\n",
              "0   ham  Hope you are having a good week. Just checking in\n",
              "1   ham                            K..give back my thanks.\n",
              "2   ham        Am also doing in cbe only. But have to pay.\n",
              "3  spam  complimentary 4 STAR Ibiza Holiday or å£10,000...\n",
              "4  spam  okmail: Dear Dave this is your final notice to..."
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              "      <td>ham</td>\n",
              "      <td>Hope you are having a good week. Just checking in</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>K..give back my thanks.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ham</td>\n",
              "      <td>Am also doing in cbe only. But have to pay.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>spam</td>\n",
              "      <td>complimentary 4 STAR Ibiza Holiday or å£10,000...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>spam</td>\n",
              "      <td>okmail: Dear Dave this is your final notice to...</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "06b8c345-0d25-41c1-ac61-888de602ac74"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type    0\n",
              "text    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5559 entries, 0 to 5558\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   type    5559 non-null   object\n",
            " 1   text    5559 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 87.0+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xBeEZW0-1i7M"
      },
      "source": [
        "# Data Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "PJVQspbv1i7R"
      },
      "outputs": [],
      "source": [
        "import re #regular expression\n",
        "import string\n",
        "\n",
        "def clean_text(text):\n",
        "    '''Make text lowercase, remove text in square brackets, remove punctuation and remove words containing numbers.'''\n",
        "    text = text.lower()\n",
        "    text = re.sub('\\[.*?\\]', '', text)\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\n",
        "    text = re.sub(\"[0-9\" \"]+\",\" \",text)\n",
        "    text = re.sub('[‘’“”…]', '', text)\n",
        "    return text\n",
        "\n",
        "clean = lambda x: clean_text(x)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HAj6-iIU1i7V",
        "outputId": "4fb06b64-8e35-4712-acd9-98613ccf71e0"
      },
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        hope you are having a good week just checking in\n",
              "1                                    kgive back my thanks\n",
              "2               am also doing in cbe only but have to pay\n",
              "3       complimentary  star ibiza holiday or å£ cash n...\n",
              "4       okmail dear dave this is your final notice to ...\n",
              "                              ...                        \n",
              "5554    you are a great role model you are giving so m...\n",
              "5555    awesome i remember the last time we got somebo...\n",
              "5556    if you dont your prize will go to another cust...\n",
              "5557    sms ac jsco energy is high but u may not know ...\n",
              "5558                      shall call now dear having food\n",
              "Name: text, Length: 5559, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data['text'] = data.text.apply(clean)\n",
        "data.text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hZ--QAz1i7X",
        "outputId": "7344cc88-974e-4446-ff73-5f28ab7bd995"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "to      2243\n",
              "i       2228\n",
              "you     2118\n",
              "a       1437\n",
              "the     1323\n",
              "u       1298\n",
              "and      965\n",
              "is       892\n",
              "in       886\n",
              "me       789\n",
              "my       753\n",
              "for      709\n",
              "your     659\n",
              "of       619\n",
              "it       616\n",
              "call     577\n",
              "have     575\n",
              "on       534\n",
              "that     509\n",
              "are      490\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "#Word frequency\n",
        "freq = pd.Series(' '.join(data['text']).split()).value_counts()[:20] # for top 20\n",
        "freq"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vD8-Dn0I1i7Z",
        "outputId": "42f55bf0-73fd-4d95-f41b-9d7c4dca823c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "#removing stopwords\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "stop = stopwords.words('english')\n",
        "data['text'] = data['text'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDU-DzIO1i7b",
        "outputId": "42616f8d-1594-4077-8dc8-57c4921aec8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "u       1298\n",
              "call     577\n",
              "im       462\n",
              "ur       389\n",
              "get      384\n",
              "å£       293\n",
              "dont     287\n",
              "go       282\n",
              "ok       278\n",
              "free     275\n",
              "know     257\n",
              "like     244\n",
              "ill      239\n",
              "got      239\n",
              "good     236\n",
              "come     228\n",
              "time     214\n",
              "day      203\n",
              "love     197\n",
              "send     191\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "#word frequency after removal of stopwords\n",
        "freq_Sw = pd.Series(' '.join(data['text']).split()).value_counts()[:20] # for top 20\n",
        "freq_Sw"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IeceYm0A1i7p",
        "outputId": "a73978ae-a815-4eb3-938a-56947b3461f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ],
      "source": [
        "# count vectoriser tells the frequency of a word.\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import numpy as np\n",
        "vectorizer = CountVectorizer(min_df = 1, max_df = 0.9)\n",
        "X = vectorizer.fit_transform(data[\"text\"])\n",
        "word_freq_df = pd.DataFrame({'term': vectorizer.get_feature_names(), 'occurrences':np.asarray(X.sum(axis=0)).ravel().tolist()})\n",
        "word_freq_df['frequency'] = word_freq_df['occurrences']/np.sum(word_freq_df['occurrences'])\n",
        "#print(word_freq_df.sort('occurrences',ascending = False).head())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "word_freq_df"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Koi88ovF_Yfq",
        "outputId": "0060160c-46fc-44e1-d75f-9d60b18fe920"
      },
      "execution_count": 24,
      "outputs": [
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          "data": {
            "text/plain": [
              "             term  occurrences  frequency\n",
              "0              aa            1   0.000022\n",
              "1             aah            3   0.000065\n",
              "2          aaniye            1   0.000022\n",
              "3     aaooooright            1   0.000022\n",
              "4       aathilove            3   0.000065\n",
              "...           ...          ...        ...\n",
              "8138         ûªve            2   0.000043\n",
              "8139           ûï            9   0.000196\n",
              "8140      ûïharry            1   0.000022\n",
              "8141        ûïits            1   0.000022\n",
              "8142           ûò            2   0.000043\n",
              "\n",
              "[8143 rows x 3 columns]"
            ],
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              "      <th></th>\n",
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              "      <th>0</th>\n",
              "      <td>aa</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000022</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>aah</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>aaniye</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>aaooooright</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>aathilove</td>\n",
              "      <td>3</td>\n",
              "      <td>0.000065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8138</th>\n",
              "      <td>ûªve</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8139</th>\n",
              "      <td>ûï</td>\n",
              "      <td>9</td>\n",
              "      <td>0.000196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8140</th>\n",
              "      <td>ûïharry</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8141</th>\n",
              "      <td>ûïits</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8142</th>\n",
              "      <td>ûò</td>\n",
              "      <td>2</td>\n",
              "      <td>0.000043</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8143 rows × 3 columns</p>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0GMeolk1i7t",
        "outputId": "2789b799-53f6-4dab-d8b8-cc79e1c39e32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ],
      "source": [
        "#TFIDF - Term frequency inverse Document Frequencyt\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "vectorizer = TfidfVectorizer(stop_words='english', max_features= 1000, max_df = 0.5, smooth_idf=True) #keep top 1000 words\n",
        "doc_vec = vectorizer.fit_transform(data[\"text\"])\n",
        "names_features = vectorizer.get_feature_names()\n",
        "dense = doc_vec.todense()\n",
        "denselist = dense.tolist()\n",
        "df = pd.DataFrame(denselist, columns = names_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "IVmCJHhv1i7w",
        "outputId": "931bf1db-2227-45d2-c952-dc27ec208b38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-3e0e629b-a2d0-4586-aeae-c9a1894b6ea1 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-3e0e629b-a2d0-4586-aeae-c9a1894b6ea1');\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": 40
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Je4KEh11i7x"
      },
      "source": [
        "# N-gram"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "TM9Z2RM_1i7y"
      },
      "outputs": [],
      "source": [
        "#Bi-gram\n",
        "def get_top_n2_words(corpus, n=None):\n",
        "    vec1 = CountVectorizer(ngram_range=(2,2),  #for tri-gram, put ngram_range=(3,3)\n",
        "            max_features=2000).fit(corpus)\n",
        "    bag_of_words = vec1.transform(corpus)\n",
        "    sum_words = bag_of_words.sum(axis=0) \n",
        "    words_freq = [(word, sum_words[0, idx]) for word, idx in     \n",
        "                  vec1.vocabulary_.items()]\n",
        "    words_freq =sorted(words_freq, key = lambda x: x[1], \n",
        "                reverse=True)\n",
        "    return words_freq[:n]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SdHp2-TT1i70",
        "outputId": "07b15e26-70d9-4c79-dfd5-ba3412ecaea6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Bi-gram  Freq\n",
              "0  please call    53\n",
              "1   call later    50\n",
              "2     ill call    48\n",
              "3     let know    40\n",
              "4    sorry ill    39"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0beadb22-1456-41f9-bb59-3b435bc29d77\">\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>Bi-gram</th>\n",
              "      <th>Freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>please call</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>call later</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ill call</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>let know</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sorry ill</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0beadb22-1456-41f9-bb59-3b435bc29d77')\"\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",
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              "\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-0beadb22-1456-41f9-bb59-3b435bc29d77 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-0beadb22-1456-41f9-bb59-3b435bc29d77');\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": 42
        }
      ],
      "source": [
        "top2_words = get_top_n2_words(data[\"text\"], n=200) #top 200\n",
        "top2_df = pd.DataFrame(top2_words)\n",
        "top2_df.columns=[\"Bi-gram\", \"Freq\"]\n",
        "top2_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        },
        "id": "vke2sX7d1i71",
        "outputId": "69d8bffd-278c-4b7c-cf1e-09b7754ecb54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'please call'),\n",
              " Text(0, 0, 'call later'),\n",
              " Text(0, 0, 'ill call'),\n",
              " Text(0, 0, 'let know'),\n",
              " Text(0, 0, 'sorry ill'),\n",
              " Text(0, 0, 'dont know'),\n",
              " Text(0, 0, 'good morning'),\n",
              " Text(0, 0, 'po box'),\n",
              " Text(0, 0, 'call landline'),\n",
              " Text(0, 0, 'take care'),\n",
              " Text(0, 0, 'im going'),\n",
              " Text(0, 0, 'guaranteed call'),\n",
              " Text(0, 0, 'new year'),\n",
              " Text(0, 0, 'prize guaranteed'),\n",
              " Text(0, 0, 'ok lor'),\n",
              " Text(0, 0, 'pls send'),\n",
              " Text(0, 0, 'every week'),\n",
              " Text(0, 0, 'send stop'),\n",
              " Text(0, 0, 'good night'),\n",
              " Text(0, 0, 'im gonna')]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Bi-gram plot\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "top20_bigram = top2_df.iloc[0:20,:]\n",
        "fig = plt.figure(figsize = (10, 5))\n",
        "plot=sns.barplot(x=top20_bigram[\"Bi-gram\"],y=top20_bigram[\"Freq\"])\n",
        "plot.set_xticklabels(rotation=45,labels = top20_bigram[\"Bi-gram\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "K_R3Hjn_1i72"
      },
      "outputs": [],
      "source": [
        "#Tri-gram\n",
        "def get_top_n3_words(corpus, n=None):\n",
        "    vec1 = CountVectorizer(ngram_range=(3,3), \n",
        "           max_features=2000).fit(corpus)\n",
        "    bag_of_words = vec1.transform(corpus)\n",
        "    sum_words = bag_of_words.sum(axis=0) \n",
        "    words_freq = [(word, sum_words[0, idx]) for word, idx in     \n",
        "                  vec1.vocabulary_.items()]\n",
        "    words_freq =sorted(words_freq, key = lambda x: x[1], \n",
        "                reverse=True)\n",
        "    return words_freq[:n]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "K6nktKnL1i74"
      },
      "outputs": [],
      "source": [
        "top3_words = get_top_n3_words(data[\"text\"], n=200)\n",
        "top3_df = pd.DataFrame(top3_words)\n",
        "top3_df.columns=[\"Tri-gram\", \"Freq\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "SlidesBw1i75",
        "outputId": "a7b7539f-0920-43fc-ca26-d53c1bc03e4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Tri-gram  Freq\n",
              "0           ill call later    42\n",
              "1           sorry ill call    38\n",
              "2    prize guaranteed call    21\n",
              "3           call land line    18\n",
              "4           happy new year    18\n",
              "..                     ...   ...\n",
              "195           said mind go     5\n",
              "196        mind go bedroom     5\n",
              "197      go bedroom minute     5\n",
              "198      bedroom minute ok     5\n",
              "199          minute ok sed     5\n",
              "\n",
              "[200 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7ca485ae-e1c3-427d-8184-453e8779ef0f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Tri-gram</th>\n",
              "      <th>Freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ill call later</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sorry ill call</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>prize guaranteed call</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>call land line</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>happy new year</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>said mind go</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>mind go bedroom</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>go bedroom minute</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>bedroom minute ok</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>minute ok sed</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ca485ae-e1c3-427d-8184-453e8779ef0f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      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-7ca485ae-e1c3-427d-8184-453e8779ef0f 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-7ca485ae-e1c3-427d-8184-453e8779ef0f');\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": 46
        }
      ],
      "source": [
        "top3_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "jsWWsoKr1i76",
        "outputId": "cb0792dd-35ca-4c49-e76a-02042ff3f759"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0, 'ill call later'),\n",
              " Text(0, 0, 'sorry ill call'),\n",
              " Text(0, 0, 'prize guaranteed call'),\n",
              " Text(0, 0, 'call land line'),\n",
              " Text(0, 0, 'happy new year'),\n",
              " Text(0, 0, 'private account statement'),\n",
              " Text(0, 0, 'guaranteed call land'),\n",
              " Text(0, 0, 'call identifier code'),\n",
              " Text(0, 0, 'land line claim'),\n",
              " Text(0, 0, 'draw shows prize'),\n",
              " Text(0, 0, 'shows prize guaranteed'),\n",
              " Text(0, 0, 'identifier code expires'),\n",
              " Text(0, 0, 'pls send message'),\n",
              " Text(0, 0, 'account statement shows'),\n",
              " Text(0, 0, 'statement shows unredeemed'),\n",
              " Text(0, 0, 'camcorder reply call'),\n",
              " Text(0, 0, 'cant pick phone'),\n",
              " Text(0, 0, 'pick phone right'),\n",
              " Text(0, 0, 'phone right pls'),\n",
              " Text(0, 0, 'right pls send')]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Tri-gram plot\n",
        "import seaborn as sns\n",
        "top20_trigram = top3_df.iloc[0:20,:]\n",
        "fig = plt.figure(figsize = (10, 5))\n",
        "plot=sns.barplot(x=top20_trigram[\"Tri-gram\"],y=top20_trigram[\"Freq\"])\n",
        "plot.set_xticklabels(rotation=45,labels = top20_trigram[\"Tri-gram\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kVZOBuXE1i77"
      },
      "source": [
        "# WordCloud"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "3BfHllnR1i79"
      },
      "outputs": [],
      "source": [
        "string_Total = \" \".join(data[\"text\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "qqiGMAC41i7-",
        "outputId": "28e09384-c11c-4861-eef7-fd01dd24995b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f367045fc10>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#wordcloud for entire corpus\n",
        "from wordcloud import WordCloud\n",
        "wordcloud_stw = WordCloud(\n",
        "                background_color= 'black',\n",
        "                width = 1800,\n",
        "                height = 1500\n",
        "                ).generate(string_Total)\n",
        "plt.imshow(wordcloud_stw)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZfN2Qdjb1i7_"
      },
      "source": [
        "# Applying naive bayes for classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UjvFhZNe1i8A",
        "outputId": "b345ebcf-9192-4e22-8f3b-15619d1f2f3f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   type                                               text\n",
              "0   ham                            hope good week checking\n",
              "1   ham                                  kgive back thanks\n",
              "2   ham                                       also cbe pay\n",
              "3  spam  complimentary star ibiza holiday å£ cash needs...\n",
              "4  spam  okmail dear dave final notice collect tenerife..."
            ],
            "text/html": [
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              "    <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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>type</th>\n",
              "      <th>text</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>hope good week checking</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>kgive back thanks</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ham</td>\n",
              "      <td>also cbe pay</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>spam</td>\n",
              "      <td>complimentary star ibiza holiday å£ cash needs...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>spam</td>\n",
              "      <td>okmail dear dave final notice collect tenerife...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ddd1571c-1975-413e-9ca8-8ac226276928 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-ddd1571c-1975-413e-9ca8-8ac226276928');\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": 50
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "3wB943zX1i8C"
      },
      "outputs": [],
      "source": [
        "def split_into_words(i):\n",
        "    return (i.split(\" \"))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "vh4vyDHw1i8D"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "email_train,email_test = train_test_split(data,test_size=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "F-QvzTMe1i8E",
        "outputId": "cacd9953-2a73-4407-9cd7-edc9959447c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      type                                               text\n",
              "690   spam  private account statement shows unredeemed bon...\n",
              "864    ham                    good morning princess great day\n",
              "493    ham      noe shes da car later c lar im wearing shorts\n",
              "4679   ham                                    tirupur call da\n",
              "2732   ham  forgot working today wanna chat things ok drop...\n",
              "...    ...                                                ...\n",
              "2299   ham                                         mm food da\n",
              "2534   ham                                tick tick tick babe\n",
              "1485   ham  please protect ethreats sib never asks sensiti...\n",
              "3208   ham           probably im almost gas get cash tomorrow\n",
              "3312   ham                                   almost see u sec\n",
              "\n",
              "[1668 rows x 2 columns]"
            ],
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              "\n",
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              "      <td>private account statement shows unredeemed bon...</td>\n",
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              "    <tr>\n",
              "      <th>864</th>\n",
              "      <td>ham</td>\n",
              "      <td>good morning princess great day</td>\n",
              "    </tr>\n",
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              "      <th>493</th>\n",
              "      <td>ham</td>\n",
              "      <td>noe shes da car later c lar im wearing shorts</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4679</th>\n",
              "      <td>ham</td>\n",
              "      <td>tirupur call da</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2732</th>\n",
              "      <td>ham</td>\n",
              "      <td>forgot working today wanna chat things ok drop...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2299</th>\n",
              "      <td>ham</td>\n",
              "      <td>mm food da</td>\n",
              "    </tr>\n",
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              "      <th>2534</th>\n",
              "      <td>ham</td>\n",
              "      <td>tick tick tick babe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1485</th>\n",
              "      <td>ham</td>\n",
              "      <td>please protect ethreats sib never asks sensiti...</td>\n",
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              "    <tr>\n",
              "      <th>3208</th>\n",
              "      <td>ham</td>\n",
              "      <td>probably im almost gas get cash tomorrow</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3312</th>\n",
              "      <td>ham</td>\n",
              "      <td>almost see u sec</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1668 rows × 2 columns</p>\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-08bc6641-7054-47a2-91f6-d3c5e0401a5a 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-08bc6641-7054-47a2-91f6-d3c5e0401a5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "email_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "cyad3qpx1i8F"
      },
      "outputs": [],
      "source": [
        "# Preparing email texts into word count matrix format \n",
        "emails_bow = CountVectorizer(analyzer=split_into_words).fit(data.text)\n",
        "\n",
        "# [\"mailing\",\"body\",\"texting\"]\n",
        "# [\"mailing\",\"awesome\",\"good\"]\n",
        "\n",
        "# [\"mailing\",\"body\",\"texting\",\"good\",\"awesome\"]\n",
        "\n",
        "\n",
        "\n",
        "#        \"mailing\" \"body\" \"texting\" \"good\" \"awesome\"\n",
        "#  0          1        1       1        0       0\n",
        " \n",
        "#  1          1        0        0       1       1    \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghy4_gJ51i8j",
        "outputId": "b7ee6395-bc13-40a5-add4-d07c35f822fd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5559, 8175)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "# For all messages\n",
        "all_emails_matrix = emails_bow.transform(data.text)\n",
        "all_emails_matrix.shape "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sa1_pEcc1i8k",
        "outputId": "34d92dee-a488-40fa-dc42-be89473800b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1668, 8175)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "# For training messages\n",
        "train_emails_matrix = emails_bow.transform(email_train.text)\n",
        "train_emails_matrix.shape # (3891,8175)\n",
        "\n",
        "# For testing messages\n",
        "test_emails_matrix = emails_bow.transform(email_test.text)\n",
        "test_emails_matrix.shape # (1668,8175)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "N4bbkZs51i8l"
      },
      "outputs": [],
      "source": [
        "####### Without TFIDF matrices ########################\n",
        "# Preparing a naive bayes model on training data set \n",
        "\n",
        "from sklearn.naive_bayes import MultinomialNB as MB\n",
        "from sklearn.naive_bayes import GaussianNB as GB\n",
        "\n",
        "# Multinomial Naive Bayes\n",
        "classifier_mb = MB()\n",
        "classifier_mb.fit(train_emails_matrix,email_train.type)\n",
        "train_pred_m = classifier_mb.predict(train_emails_matrix)\n",
        "accuracy_train_m = np.mean(train_pred_m==email_train.type) # 98%\n",
        "\n",
        "test_pred_m = classifier_mb.predict(test_emails_matrix)\n",
        "accuracy_test_m = np.mean(test_pred_m==email_test.type) # 96%\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_train_m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dGV7dedFq1L",
        "outputId": "2ccde1a1-2df5-4fb1-a405-2ace2f884898"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9902338730403495"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_test_m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5pUiBYMFqqw",
        "outputId": "48202081-c59b-47e2-c75f-9d1d7199c977"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9706235011990407"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "R7fwwKra1i8l"
      },
      "outputs": [],
      "source": [
        "# Gaussian Naive Bayes \n",
        "classifier_gb = GB()\n",
        "classifier_gb.fit(train_emails_matrix.toarray(),email_train.type.values) # we need to convert tfidf into array format which is compatible for gaussian naive bayes\n",
        "train_pred_g = classifier_gb.predict(train_emails_matrix.toarray())\n",
        "accuracy_train_g = np.mean(train_pred_g==email_train.type) # 95%\n",
        "\n",
        "test_pred_g = classifier_gb.predict(test_emails_matrix.toarray())\n",
        "accuracy_test_g = np.mean(test_pred_g==email_test.type) # 8%"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_train_g"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTqCxAwLFuV0",
        "outputId": "81c635ed-773c-40a0-a810-732ec19587b2"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9457722950398355"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_test_g"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qg3_bkQZFv3d",
        "outputId": "51a269ac-6a2b-4c75-88b7-1f3645557449"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9028776978417267"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZGVTFL8-1i8m"
      },
      "source": [
        "# Using TFIDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hO0DudQc1i8n",
        "outputId": "6e8b1fe7-ad65-4784-d322-59ad4a9166ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1668, 8175)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "\n",
        "# Learning Term weighting and normalizing on entire emails\n",
        "tfidf_transformer = TfidfTransformer().fit(all_emails_matrix)\n",
        "\n",
        "# Preparing TFIDF for train emails\n",
        "train_tfidf = tfidf_transformer.transform(train_emails_matrix)\n",
        "\n",
        "train_tfidf.shape # (3891, 6661)\n",
        "\n",
        "# Preparing TFIDF for test emails\n",
        "test_tfidf = tfidf_transformer.transform(test_emails_matrix)\n",
        "\n",
        "test_tfidf.shape #  (1668, 6661)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "pJTbapnQ1i8o"
      },
      "outputs": [],
      "source": [
        "# Preparing a naive bayes model on training data set \n",
        "\n",
        "from sklearn.naive_bayes import MultinomialNB as MB\n",
        "from sklearn.naive_bayes import GaussianNB as GB\n",
        "\n",
        "# Multinomial Naive Bayes\n",
        "classifier_mb = MB()\n",
        "classifier_mb.fit(train_tfidf,email_train.type)\n",
        "train_pred_m = classifier_mb.predict(train_tfidf)\n",
        "accuracy_train_m = np.mean(train_pred_m==email_train.type) # 96%\n",
        "\n",
        "test_pred_m = classifier_mb.predict(test_tfidf)\n",
        "accuracy_test_m = np.mean(test_pred_m==email_test.type) # 96%\n",
        "\n",
        " \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2AQ1djh1i8p",
        "outputId": "adf068c2-3b8c-44f0-8d09-0cd6f548c81e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9722436391673092"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "accuracy_train_m"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_test_m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NUC97A_F3ei",
        "outputId": "fe5a00f9-bc3a-4c8c-d979-55801baf0f7a"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9580335731414868"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "uOCmv2ah1i8q"
      },
      "outputs": [],
      "source": [
        "# Gaussian Naive Bayes \n",
        "classifier_gb = GB()\n",
        "classifier_gb.fit(train_tfidf.toarray(),email_train.type.values) # we need to convert tfidf into array format which is compatible for gaussian naive bayes\n",
        "train_pred_g = classifier_gb.predict(train_tfidf.toarray())\n",
        "accuracy_train_g = np.mean(train_pred_g==email_train.type) # 95%\n",
        "test_pred_g = classifier_gb.predict(test_tfidf.toarray())\n",
        "accuracy_test_g = np.mean(test_pred_g==email_test.type) # 88%\n",
        "\n",
        "# inplace of tfidf we can also use train_emails_matrix and test_emails_matrix instead of term inverse document frequency matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_train_g"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wbqASAk_F6kL",
        "outputId": "cf9df68a-3a0e-4c59-9863-d2dc3874079b"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9457722950398355"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
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    },
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