Spam_ham_classification.ipynb

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      "name": "Spam ham classification.ipynb",
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      "authorship_tag": "ABX9TyObbz7ASLtWPs82MZMHAIBj",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Aravindh020/Randomforest/blob/master/Spam_ham_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "0xzV8FkIrb7p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('spam.tsv', sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tASjaat9rYWy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OSRZYC8lv4ua",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import spacy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMbiEdDqv5NU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from spacy import displacy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqbfHtZYyO74",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "nlp = spacy.load('en_core_web_sm')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qm8LZWr-rjqk",
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          "base_uri": "https://localhost:8080/",
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        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>111</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>29</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>155</td>\n",
              "      <td>6</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",
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              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
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              "      <td>61</td>\n",
              "      <td>2</td>\n",
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            ],
            "text/plain": [
              "  label                                            message  length  punct\n",
              "0   ham  Go until jurong point, crazy.. Available only ...     111      9\n",
              "1   ham                      Ok lar... Joking wif u oni...      29      6\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155      6\n",
              "3   ham  U dun say so early hor... U c already then say...      49      6\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro...      61      2"
            ]
          },
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          "execution_count": 7
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      "metadata": {
        "id": "dj4jDs6njRJr",
        "colab_type": "code",
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      },
      "source": [
        "import string\n",
        "punct = string.punctuation\n",
        "punct\n",
        "def text_data_cleaning(sentance):\n",
        "  doc1= nlp(doc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "df['label'].isnull().sum()"
      ],
      "execution_count": 9,
      "outputs": [
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "df['label'].value_counts()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ham     4825\n",
              "spam     747\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
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        "colab": {
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        }
      },
      "source": [
        "ham = df[df['label']=='ham']\n",
        "ham.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>ham</td>\n",
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              "      <td>29</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>49</td>\n",
              "      <td>6</td>\n",
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              "      <th>4</th>\n",
              "      <td>ham</td>\n",
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              "      <td>ham</td>\n",
              "      <td>Even my brother is not like to speak with me. ...</td>\n",
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            "text/plain": [
              "  label                                            message  length  punct\n",
              "0   ham  Go until jurong point, crazy.. Available only ...     111      9\n",
              "1   ham                      Ok lar... Joking wif u oni...      29      6\n",
              "3   ham  U dun say so early hor... U c already then say...      49      6\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro...      61      2\n",
              "6   ham  Even my brother is not like to speak with me. ...      77      2"
            ]
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        }
      },
      "source": [
        "spam = df[df['label']=='spam']\n",
        "spam.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>spam</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>155</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>spam</td>\n",
              "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
              "      <td>147</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>spam</td>\n",
              "      <td>WINNER!! As a valued network customer you have...</td>\n",
              "      <td>157</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>spam</td>\n",
              "      <td>Had your mobile 11 months or more? U R entitle...</td>\n",
              "      <td>154</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>spam</td>\n",
              "      <td>SIX chances to win CASH! From 100 to 20,000 po...</td>\n",
              "      <td>136</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
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              "   label                                            message  length  punct\n",
              "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...     155      6\n",
              "5   spam  FreeMsg Hey there darling it's been 3 week's n...     147      8\n",
              "8   spam  WINNER!! As a valued network customer you have...     157      6\n",
              "9   spam  Had your mobile 11 months or more? U R entitle...     154      2\n",
              "11  spam  SIX chances to win CASH! From 100 to 20,000 po...     136      8"
            ]
          },
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          "execution_count": 12
        }
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "ham.shape, spam.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4825, 4), (747, 4))"
            ]
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          "execution_count": 13
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      },
      "source": [
        "spam.shape[1]"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
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          "metadata": {
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          "execution_count": 14
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      "metadata": {
        "id": "ijtvOcMVr_VE",
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      },
      "source": [
        "ham = ham.sample(spam.shape[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ig9bKUtsEaV",
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        }
      },
      "source": [
        "data = ham.append(spam, ignore_index=True)\n",
        "data.tail()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>1489</th>\n",
              "      <td>spam</td>\n",
              "      <td>Want explicit SEX in 30 secs? Ring 02073162414...</td>\n",
              "      <td>90</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1490</th>\n",
              "      <td>spam</td>\n",
              "      <td>ASKED 3MOBILE IF 0870 CHATLINES INCLU IN FREE ...</td>\n",
              "      <td>158</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1491</th>\n",
              "      <td>spam</td>\n",
              "      <td>Had your contract mobile 11 Mnths? Latest Moto...</td>\n",
              "      <td>160</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1492</th>\n",
              "      <td>spam</td>\n",
              "      <td>REMINDER FROM O2: To get 2.50 pounds free call...</td>\n",
              "      <td>147</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1493</th>\n",
              "      <td>spam</td>\n",
              "      <td>This is the 2nd time we have tried 2 contact u...</td>\n",
              "      <td>160</td>\n",
              "      <td>8</td>\n",
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            "text/plain": [
              "     label                                            message  length  punct\n",
              "1489  spam  Want explicit SEX in 30 secs? Ring 02073162414...      90      3\n",
              "1490  spam  ASKED 3MOBILE IF 0870 CHATLINES INCLU IN FREE ...     158      5\n",
              "1491  spam  Had your contract mobile 11 Mnths? Latest Moto...     160      8\n",
              "1492  spam  REMINDER FROM O2: To get 2.50 pounds free call...     147      3\n",
              "1493  spam  This is the 2nd time we have tried 2 contact u...     160      8"
            ]
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          "execution_count": 16
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      },
      "source": [
        "plt.hist(data[data['label']=='ham']['length'], bins = 100, alpha = 0.7)\n",
        "plt.hist(data[data['label']=='spam']['length'], bins = 100, alpha = 0.7)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IeCoifhARd5X2SeyLCyLiuxHxn6Uv/r60XxYRj5W/+YGyiICIeFXZP1KObxln/YMWEedF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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lw-3H2iYsKqI",
        "colab_type": "code",
        "outputId": "657356ab-7d4f-4153-a943-1ce2354fabec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.hist(data[data['label']=='ham']['punct'], bins = 100, alpha = 0.7)\n",
        "plt.hist(data[data['label']=='spam']['punct'], bins = 100, alpha = 0.7)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5MLADeHqFnk/noruuP/Oq4GxH932f44wRviR8vlcWw68aLnvX3K9cFvOq5NB73wyM9spj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\n5S5JDbLcJalBlrskNej/A5wOE9IvjBQzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eVtW8tDDsbmC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VDe5S2kLsiJd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxiM5AEqsmbV",
        "colab_type": "code",
        "outputId": "2fcf61d2-6d24-4c87-b4a7-ff98e37063d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 21,
      "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>label</th>\n",
              "      <th>message</th>\n",
              "      <th>length</th>\n",
              "      <th>punct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Why didn't u call on your lunch?</td>\n",
              "      <td>32</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>No, its true..k,Do u knw dis no. &amp;lt;#&amp;gt; ?</td>\n",
              "      <td>44</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ham</td>\n",
              "      <td>and  picking them up from various points</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>Just sent you an email – to an address with in...</td>\n",
              "      <td>72</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Pick you up bout 7.30ish? What time are  and t...</td>\n",
              "      <td>56</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label                                            message  length  punct\n",
              "0   ham                   Why didn't u call on your lunch?      32      2\n",
              "1   ham       No, its true..k,Do u knw dis no. &lt;#&gt; ?      44     11\n",
              "2   ham           and  picking them up from various points      41      0\n",
              "3   ham  Just sent you an email – to an address with in...      72      2\n",
              "4   ham  Pick you up bout 7.30ish? What time are  and t...      56      3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asywe9bwsp75",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data['message'], data['label'], test_size = 0.3, train_size=0.7, shuffle = True, stratify=data['label'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aRuZb70sss5b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vectorizer = TfidfVectorizer()\n",
        "X_train_vect = vectorizer.fit_transform(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rTkKccLswtK",
        "colab_type": "code",
        "outputId": "d7936385-3ed7-4b24-b2ef-90fdbeb02792",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "X_train_vect.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1045, 3785)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjY9GCGcszaI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = Pipeline([('tfidf', TfidfVectorizer()), ('clf', RandomForestClassifier(n_estimators=100, n_jobs=-1))])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXD-o9lcs74I",
        "colab_type": "code",
        "outputId": "a3548d7d-ef98-4e36-a90d-0b0381a5f0a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        }
      },
      "source": [
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('tfidf',\n",
              "                 TfidfVectorizer(analyzer='word', binary=False,\n",
              "                                 decode_error='strict',\n",
              "                                 dtype=<class 'numpy.float64'>,\n",
              "                                 encoding='utf-8', input='content',\n",
              "                                 lowercase=True, max_df=1.0, max_features=None,\n",
              "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
              "                                 preprocessor=None, smooth_idf=True,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 sublinear_tf=False,\n",
              "                                 token_pattern='...\n",
              "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                        class_weight=None, criterion='gini',\n",
              "                                        max_depth=None, max_features='auto',\n",
              "                                        max_leaf_nodes=None, max_samples=None,\n",
              "                                        min_impurity_decrease=0.0,\n",
              "                                        min_impurity_split=None,\n",
              "                                        min_samples_leaf=1, min_samples_split=2,\n",
              "                                        min_weight_fraction_leaf=0.0,\n",
              "                                        n_estimators=100, n_jobs=-1,\n",
              "                                        oob_score=False, random_state=None,\n",
              "                                        verbose=0, warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Drc4w9WIs-dE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = clf.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhnwFBr_tCkm",
        "colab_type": "code",
        "outputId": "c6b810c8-938b-4e6f-aee9-b888ee7e0d5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[221,   3],\n",
              "       [ 13, 212]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPfmKlmntFCH",
        "colab_type": "code",
        "outputId": "9e987e99-3cc4-4877-89c3-9b3ad53a1e26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.94      0.99      0.97       224\n",
            "        spam       0.99      0.94      0.96       225\n",
            "\n",
            "    accuracy                           0.96       449\n",
            "   macro avg       0.97      0.96      0.96       449\n",
            "weighted avg       0.97      0.96      0.96       449\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2IMqKqStHe6",
        "colab_type": "code",
        "outputId": "3269aa52-0bb9-45ab-bc91-7a192b4b6753",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9643652561247216"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uw9TmKtPtKmN",
        "colab_type": "code",
        "outputId": "5f32a507-0cae-4a6c-cbc6-b72ffde386ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "clf.predict([\"SIX chances to win CASH! From 100 to 20,000 pounds\"])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['spam'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8ADvek_tNa2",
        "colab_type": "code",
        "outputId": "5e89c772-cf28-41d9-91c3-8300ab66aa17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "clf.predict([\"I'm gonna be home soon and i don't want to talk about this stuff anymore tonight\"])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ham'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOTYhSlzFwoU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}