Email_Spam_Filter.ipynb

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    "colab": {
      "name": "Email Spam Filter.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKvFE0qCvEIg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "1497fbea-7f7d-46a7-9255-a2f08574c61e"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import sklearn\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "import gensim\n",
        "from nltk.classify.scikitlearn import SklearnClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix\n",
        "from sklearn.ensemble import VotingClassifier"
      ],
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGg06-af3Ud8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_table('SMSSpamCollection', header=None, encoding='utf-8')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hFGTFrz8TDC",
        "colab_type": "code",
        "outputId": "a58759fc-5471-46b9-951d-c707ce5546da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "data"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5567</th>\n",
              "      <td>spam</td>\n",
              "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5568</th>\n",
              "      <td>ham</td>\n",
              "      <td>Will ü b going to esplanade fr home?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5569</th>\n",
              "      <td>ham</td>\n",
              "      <td>Pity, * was in mood for that. So...any other s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5570</th>\n",
              "      <td>ham</td>\n",
              "      <td>The guy did some bitching but I acted like i'd...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5571</th>\n",
              "      <td>ham</td>\n",
              "      <td>Rofl. Its true to its name</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5572 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         0                                                  1\n",
              "0      ham  Go until jurong point, crazy.. Available only ...\n",
              "1      ham                      Ok lar... Joking wif u oni...\n",
              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      ham  U dun say so early hor... U c already then say...\n",
              "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
              "...    ...                                                ...\n",
              "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
              "5568   ham               Will ü b going to esplanade fr home?\n",
              "5569   ham  Pity, * was in mood for that. So...any other s...\n",
              "5570   ham  The guy did some bitching but I acted like i'd...\n",
              "5571   ham                         Rofl. Its true to its name\n",
              "\n",
              "[5572 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFzAIPk_8iHh",
        "colab_type": "code",
        "outputId": "ac6f5ac1-df09-402a-9216-0111ad030fb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        }
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "stream",
          "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   0       5572 non-null   object\n",
            " 1   1       5572 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 87.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WG0FOawB8tus",
        "colab_type": "code",
        "outputId": "f2857318-b56d-44f4-c869-589f823e9621",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#Check the number of ham and spam messages\n",
        "no_of_ham=0\n",
        "no_of_spam=0\n",
        "for i in range(len(data)):\n",
        "  if(data[0][i] == 'ham'):\n",
        "    no_of_ham+=1\n",
        "  else:\n",
        "    no_of_spam+=1\n",
        "print('ham', no_of_ham)\n",
        "print('spam', no_of_spam)"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ham 4825\n",
            "spam 747\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RaUKzLVz9aS4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "291fa04c-6f3e-43d8-f138-c7443becfad5"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# convert ham to 0 and spam to 1\n",
        "encoder = LabelEncoder()\n",
        "Y = encoder.fit_transform(data[0])\n",
        "\n",
        "Y"
      ],
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvXum1AcFXxH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "dc9424c5-abaf-4164-80ae-67ad4c38ae9a"
      },
      "source": [
        "messages = data[1]\n",
        "messages"
      ],
      "execution_count": 233,
      "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",
              "                              ...                        \n",
              "5567    This is the 2nd time we have tried 2 contact u...\n",
              "5568                 Will ü b going to esplanade fr home?\n",
              "5569    Pity, * was in mood for that. So...any other s...\n",
              "5570    The guy did some bitching but I acted like i'd...\n",
              "5571                           Rofl. Its true to its name\n",
              "Name: 1, Length: 5572, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAKFadPZFUC6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocess_regex(text):\n",
        "  \"\"\"Replace some fixed patterns with single word. \"\"\"\n",
        "\n",
        "  # Replacing the email addresses with string 'email'\n",
        "  text = text.str.replace('^([a-zA-Z0-9_\\-\\.]+)@([a-zA-Z0-9_\\-\\.]+)\\.([a-zA-Z]{2,5})$', 'email')\n",
        "\n",
        "  # Replacing the URLs with 'url'\n",
        "  text = text.str.replace('^http\\://[a-zA-Z0-9\\-\\.]+\\.[a-zA-Z]{2,3}(/\\S*)?$', 'url')\n",
        "      \n",
        "  # Replacing the phone numbers with 'phone'\n",
        "  text = text.str.replace('^\\(?[\\d]{3}\\)?[\\s-]?[\\d]{3}[\\s-]?[\\d]{4}$', 'phone')\n",
        "      \n",
        "  # Replacing the numbers with 'number'\n",
        "  text = text.str.replace(r'\\d+(\\.\\d+)?', 'number')\n",
        "\n",
        "  return text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DbthKlQgecYD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages = preprocess_regex(messages)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K-87J_BNIP-P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Data Cleaning round 1\n",
        "import re\n",
        "import string\n",
        "\n",
        "def clean_text_round1(text):\n",
        "    '''Make text lowercase, remove text in square brackets, remove punctuation and remove words containing numbers.'''\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\n",
        "    text = re.sub('\\w*\\f\\w*', '', text)\n",
        "    text = re.sub('\\(.*?\\)', '', text)\n",
        "    text = re.sub('\\[.*]\\)', '', text)\n",
        "    text = text.lower()\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
        "    return text\n",
        "\n",
        "round1 = lambda x: clean_text_round1(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TNhP9K5JeW8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages = messages.apply(clean_text_round1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaoQgW5oJnnH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Apply a second round of cleaning\n",
        "def clean_text_round2(text):\n",
        "    '''Get rid of some additional punctuation and non-sensical text that was missed the first time around.'''\n",
        "    text = re.sub('[‘’“”…]', '', text)\n",
        "    text = re.sub('\\n', '', text)\n",
        "    text = re.sub('\\t', '', text)\n",
        "    return text\n",
        "\n",
        "round2 = lambda x: clean_text_round2(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80-x6-uKJsHb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages = messages.apply(clean_text_round2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnO5vhUIJt_P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "58b57c1d-6b9a-4fbd-f1e1-897d979d554c"
      },
      "source": [
        "messages"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       go until jurong point crazy available only in ...\n",
              "1                                 ok lar joking wif u oni\n",
              "2       free entry in number a wkly comp to win fa cup...\n",
              "3             u dun say so early hor u c already then say\n",
              "4       nah i dont think he goes to usf he lives aroun...\n",
              "                              ...                        \n",
              "5567    this is the numbernd time we have tried number...\n",
              "5568                  will ü b going to esplanade fr home\n",
              "5569    pity  was in mood for that soany other suggest...\n",
              "5570    the guy did some bitching but i acted like id ...\n",
              "5571                            rofl its true to its name\n",
              "Name: 1, Length: 5572, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-H91kkjSNFu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from nltk.corpus import stopwords\n",
        "def remove_stopwords(text):\n",
        "  stop_words = set(stopwords.words('english'))\n",
        "\n",
        "  text = text.apply(lambda x: ' '.join(\n",
        "      term for term in x.split() if term not in stop_words))\n",
        "  \n",
        "  return text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WwVWa6ZITBLo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages = remove_stopwords(messages)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbG0iaAaSaO5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from nltk.corpus import wordnet\n",
        "#lemmatizing using wordnet lemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "messages = messages.apply(lambda x: ' '.join(lemmatizer.lemmatize(term) for term in x.split()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3qqUyoRKoPj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from collections import Counter\n",
        "#Creating Bag of Words for the messages data\n",
        "def count_words(text):\n",
        "  for word in text:\n",
        "    words = word_tokenize(word)\n",
        "    for w in words:\n",
        "        all_words.append(w)\n",
        "  word_counts = Counter(all_words)\n",
        "  return word_counts"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEU57f-xK1er",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "word_counts = count_words(messages)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQrBfdNQP3V7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "601f5200-16a6-4ff8-d3cf-483b98fbde7d"
      },
      "source": [
        "# printing 10 most common words\n",
        "word_counts.most_common(10)"
      ],
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('number', 14118),\n",
              " ('u', 7173),\n",
              " ('call', 3612),\n",
              " ('im', 2820),\n",
              " ('get', 2370),\n",
              " ('ur', 2340),\n",
              " ('go', 1842),\n",
              " ('dont', 1752),\n",
              " ('ok', 1668),\n",
              " ('free', 1650)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaxsuLU1Wpm7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Using the 2000 most common words as features.\n",
        "w_features = list(word_counts.keys())[:2000]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwME1uy3XULJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The find_features function will determine which of the 1500 word features are contained in the review.\n",
        "def find_features(message):\n",
        "    words = word_tokenize(message)\n",
        "    features = {}\n",
        "    for word in w_features:\n",
        "        features[word] = (word in words)\n",
        "\n",
        "    return features"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UlhGajmXgAW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features = list(zip(messages, Y))\n",
        "seed = 1\n",
        "np.random.seed = seed\n",
        "np.random.shuffle(features)\n",
        "feature_set=[]\n",
        "for (x,y) in features:\n",
        "  feature_set.append((find_features(x), y))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSZSG6o6X4EB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# spliting feature set into training data and testing data using scikit-learn\n",
        "from sklearn import model_selection\n",
        "train_data, test_data = model_selection.train_test_split(feature_set, test_size = 0.25, random_state=seed)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OFG0I0bJanHI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4b97f643-82ea-4d98-c9ff-93e668b0279d"
      },
      "source": [
        "len(train_data)"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4179"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Foe-c0qUatHy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ff647bba-7fc7-45b1-9c8d-e62e48391415"
      },
      "source": [
        "len(test_data)"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1393"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7DyCYhOa3gy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "a60cc203-d2be-472b-f9ba-5b9201cb2443"
      },
      "source": [
        "# Defining all the models to train\n",
        "model_name = [\"K Nearest Neighbors\", \"Decision Tree\", \"SGD Classifier\", \"Naive Bayes\", \"SVM Linear\"]\n",
        "\n",
        "model_classifier = [ KNeighborsClassifier(), DecisionTreeClassifier(), SGDClassifier(max_iter = 100), MultinomialNB(), SVC(kernel = 'linear')]\n",
        "\n",
        "all_models = list(zip(model_name, model_classifier))\n",
        "\n",
        "for model_name, model_classifier in all_models:\n",
        "    nltk_model = SklearnClassifier(model_classifier)\n",
        "    nltk_model.train(train_data)\n",
        "    accuracy = nltk.classify.accuracy(nltk_model, test_data)*100\n",
        "    print(\"{} Accuracy: {}\".format(model_name, accuracy))"
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "K Nearest Neighbors Accuracy: 94.25699928212491\n",
            "Decision Tree Accuracy: 96.91313711414213\n",
            "SGD Classifier Accuracy: 98.06173725771716\n",
            "Naive Bayes Accuracy: 97.70279971284997\n",
            "SVM Linear Accuracy: 98.49246231155779\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLMdvZOTbwNK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "0606e58b-9772-4328-809a-90f88a4dba46"
      },
      "source": [
        "# Voting classifier to find the best of all models.\n",
        "\n",
        "nltk_ensemble = SklearnClassifier(VotingClassifier(estimators = all_models, voting = 'hard', n_jobs = -1))\n",
        "nltk_ensemble.train(train_data)\n",
        "accuracy = nltk.classify.accuracy(nltk_model, test_data)*100\n",
        "print(\"Voting Classifier Accuracy: {}\".format(accuracy))"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/joblib/externals/loky/process_executor.py:691: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
            "  \"timeout or by a memory leak.\", UserWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Voting Classifier Accuracy: 98.49246231155779\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAmp_joib3Td",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# make class label prediction for testing set\n",
        "txtfeatures, labels = zip(*test_data)\n",
        "prediction = nltk_ensemble.classify_many(txtfeatures)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHaQC_o7dEr9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "ae3107f2-15cf-4ffe-b4a8-cf7d670aee29"
      },
      "source": [
        "# print a classification report\n",
        "print(classification_report(labels, prediction))"
      ],
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      1.00      0.99      1221\n",
            "           1       0.99      0.92      0.95       172\n",
            "\n",
            "    accuracy                           0.99      1393\n",
            "   macro avg       0.99      0.96      0.97      1393\n",
            "weighted avg       0.99      0.99      0.99      1393\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAEseKjwdJOg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        },
        "outputId": "1c83e23f-5425-4ee3-91e1-aba8b3837a6c"
      },
      "source": [
        "# print a confusion matrix\n",
        "confusionmatrix= pd.DataFrame(confusion_matrix(labels, prediction), index = [['actual', 'actual'], ['ham', 'spam']], columns = [['predicted', 'predicted'], ['ham', 'spam']])\n",
        "confusionmatrix"
      ],
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">predicted</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>ham</th>\n",
              "      <th>spam</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">actual</th>\n",
              "      <th>ham</th>\n",
              "      <td>1219</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spam</th>\n",
              "      <td>14</td>\n",
              "      <td>158</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            predicted     \n",
              "                  ham spam\n",
              "actual ham       1219    2\n",
              "       spam        14  158"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eam-QOzTnocd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}