04SpamTextMessageClassification_.ipynb

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      "name": "04SpamTextMessageClassification .ipynb",
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      "cell_type": "markdown",
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        "id": "jQr1qmgdD87F",
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      "source": [
        "# 1) Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYdRUs_9EBhw",
        "colab_type": "text"
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      "source": [
        "## Importing the libraries"
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "IFnTd2rbnj3A",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZyND4uHnlSz",
        "colab_type": "text"
      },
      "source": [
        "## Importing the data set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41yFPUUB4RoF",
        "colab_type": "code",
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      },
      "source": [
        "df = pd.read_csv('spam.tsv',sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwcR2vPy4Zwm",
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        "outputId": "740dbafd-5fa1-4a9e-f558-e06078796ba2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </thead>\n",
              "  <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",
              "      <td>49</td>\n",
              "      <td>6</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",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\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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          "metadata": {
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          "execution_count": 3
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      },
      "source": [
        "# check null values in dataset\n",
        "df.isnull().sum()\n",
        "# no null values in dataset"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label      0\n",
              "message    0\n",
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      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5572.000000</td>\n",
              "      <td>5572.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>80.489950</td>\n",
              "      <td>4.177495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>59.942907</td>\n",
              "      <td>4.623919</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>122.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>910.000000</td>\n",
              "      <td>133.000000</td>\n",
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              "            length        punct\n",
              "count  5572.000000  5572.000000\n",
              "mean     80.489950     4.177495\n",
              "std      59.942907     4.623919\n",
              "min       2.000000     0.000000\n",
              "25%      36.000000     2.000000\n",
              "50%      62.000000     3.000000\n",
              "75%     122.000000     6.000000\n",
              "max     910.000000   133.000000"
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          "execution_count": 5
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        "id": "-bmP0VhW5Fic",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "# check number of ham and spam\n",
        "df['label'].value_counts()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ham     4825\n",
              "spam     747\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdv5gguF5dZS",
        "colab_type": "code",
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      },
      "source": [
        "# 4825 messages are legitimate that is ham\n",
        "# and 747 messages are spam messages"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4c4TXzxnrwz",
        "colab_type": "text"
      },
      "source": [
        "## Balancing the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32dgZuygnuMN",
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        "outputId": "fa59a3bd-b155-4c6b-f2fe-320472f72b6e",
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      },
      "source": [
        "# select ham data\n",
        "ham = df[df['label']=='ham']\n",
        "ham.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <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",
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              "    <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>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",
              "    </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",
              "      <td>61</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ham</td>\n",
              "      <td>Even my brother is not like to speak with me. ...</td>\n",
              "      <td>77</td>\n",
              "      <td>2</td>\n",
              "    </tr>\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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          "metadata": {
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          "execution_count": 7
        }
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "# select spam data\n",
        "spam = df[df['label']=='spam']\n",
        "spam.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\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>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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            ],
            "text/plain": [
              "   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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "qfDWKoDLpuLP",
        "colab_type": "code",
        "outputId": "2a7f3d86-878d-44c4-bd24-bcc7c2a3726d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# check the shape of data\n",
        "ham.shape, spam.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4825, 4), (747, 4))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPHzp0XLqQ00",
        "colab_type": "code",
        "outputId": "4a53dde2-3d4e-4ee1-d1a8-38911ab747d1",
        "colab": {
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      },
      "source": [
        "spam.shape[0] # output = no of samples in spam data"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "747"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pR6s7jl-puPl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# now we have to select 747 samples from ham to balance the data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmtGRtSPpuSj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ham = ham.sample(spam.shape[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2bWABGsLpuVA",
        "colab_type": "code",
        "outputId": "4ed28e12-d7d2-4b39-8fe9-26cdcd6ba7d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "ham.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(747, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H69NYR9Rqixk",
        "colab_type": "code",
        "outputId": "566f907f-0280-4d51-f197-40c980a8fb12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# check the shape of data\n",
        "ham.shape, spam.shape\n",
        "\n",
        "# size of ham and spam data is same, now this is the balenced data"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((747, 4), (747, 4))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XU2Fpktuovw9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# append spam data into ham data\n",
        "data = ham.append(spam,ignore_index=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spP6_dfwo5FH",
        "colab_type": "code",
        "outputId": "f2ca9a78-8651-426c-a425-3c1f3d949c90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "data.head()\n",
        "# at the starting we have all the ham data"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <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>Hey we can go jazz power yoga hip hop kb and y...</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Got fujitsu, ibm, hp, toshiba... Got a lot of ...</td>\n",
              "      <td>65</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ham</td>\n",
              "      <td>I'm really not up to it still tonight babe</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok Chinese food on its way. When I get fat you...</td>\n",
              "      <td>69</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ham</td>\n",
              "      <td>Keep ur problems in ur heart, b'coz nobody wil...</td>\n",
              "      <td>350</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label                                            message  length  punct\n",
              "0   ham  Hey we can go jazz power yoga hip hop kb and y...      54      0\n",
              "1   ham  Got fujitsu, ibm, hp, toshiba... Got a lot of ...      65      9\n",
              "2   ham         I'm really not up to it still tonight babe      42      1\n",
              "3   ham  Ok Chinese food on its way. When I get fat you...      69      3\n",
              "4   ham  Keep ur problems in ur heart, b'coz nobody wil...     350     24"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sARYSjpHpAOz",
        "colab_type": "code",
        "outputId": "753ab852-0d3f-4663-9187-743783215c9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "data.tail()\n",
        "# at the end we have all the spam data"
      ],
      "execution_count": 15,
      "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>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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDWwfq3lpcbb",
        "colab_type": "code",
        "outputId": "0c3fa604-ac6b-41e1-ebc9-02cfec624c1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "data.shape # final shape of the data"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1494, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zTKeUUOfreRq",
        "colab_type": "text"
      },
      "source": [
        "## Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uSAJ_QdSrod-",
        "colab_type": "code",
        "outputId": "b9cdd2ec-ee22-4265-a008-6db15386ae3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# plot histogram of length for ham messages\n",
        "plt.hist(data[data['label']=='ham']['length'], bins=100, alpha=0.7)\n",
        "plt.show()\n",
        "# from the histogram we can say that, the number of charactors in ham messages are less than 100"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2hQNVFrrrmh",
        "colab_type": "code",
        "outputId": "3f681cd5-200f-4625-e851-8bc93687e52d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# plot histogram of length for ham and spam both\n",
        "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",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I6StJMUkrrpL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# It looks like there's a small range of values where a message is more likely to be spam than ham"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AV4KjSZSrrrv",
        "colab_type": "code",
        "outputId": "0ca015df-28ee-4a42-e3b3-e411fcec2483",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# plot histogram of punct for ham and spam both\n",
        "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",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VmwsgcSv_iX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# here we are not getting more information"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L-M00N8qwS78",
        "colab_type": "text"
      },
      "source": [
        "## Split the data into train & test sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TIkmRrmwxokK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBzVzjxawUl9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(data['message'],data['label'],test_size=0.3,\n",
        "                                                    random_state=0, shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBNgxP9UyZp7",
        "colab_type": "code",
        "outputId": "9e3402ed-116d-4e84-c08e-2ad13c35fb2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "x_train"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1438      You have 1 new message. Please call 08715205273\n",
              "431     Dear,regret i cudnt pick call.drove down frm c...\n",
              "194      Lol no ouch but wish i'd stayed out a bit longer\n",
              "240     Dunno lei... I might b eatin wif my frens... I...\n",
              "1309    Had your mobile 11 months or more? U R entitle...\n",
              "                              ...                        \n",
              "763     Your free ringtone is waiting to be collected....\n",
              "835     XCLUSIVE@CLUBSAISAI 2MOROW 28/5 SOIREE SPECIAL...\n",
              "1216    Dorothy@kiefer.com (Bank of Granite issues Str...\n",
              "559     Babe! How goes that day ? What are you up to ?...\n",
              "684                      Why didn't u call on your lunch?\n",
              "Name: message, Length: 1045, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YebTJrLvygsW",
        "colab_type": "code",
        "outputId": "00c9b360-72ed-4248-a433-71cdbc551a32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "y_train"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1438    spam\n",
              "431      ham\n",
              "194      ham\n",
              "240      ham\n",
              "1309    spam\n",
              "        ... \n",
              "763     spam\n",
              "835     spam\n",
              "1216    spam\n",
              "559      ham\n",
              "684      ham\n",
              "Name: label, Length: 1045, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5wIb9NA0qHh",
        "colab_type": "text"
      },
      "source": [
        "# 2) Building the Model (Random Forest)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9j5uFWQA0wgV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "# there will be lot of repeated processes for training and testing the dataset separately,\n",
        "# to avoid that we are using pipeline\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "# we are importing TfidfVectorizer to utilize bag of words model in sklearn\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYmQwWx70dX0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier = Pipeline([('tfidf', TfidfVectorizer()), ('classifier',RandomForestClassifier(n_estimators=100))])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3AIcUKm2nkF",
        "colab_type": "code",
        "outputId": "b3e1ff1c-110f-4c8e-98ef-ffc8926c9e6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        }
      },
      "source": [
        "classifier.fit(x_train, y_train)"
      ],
      "execution_count": 27,
      "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=None,\n",
              "                                        oob_score=False, random_state=None,\n",
              "                                        verbose=0, warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjeEz7hu4eT3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# all the parameters that you can see while training the model are the default parameters"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hNwI_nuo5G5P",
        "colab_type": "text"
      },
      "source": [
        "# 3) Predicting the results (Random Forest)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gHtEV_P8_ZNX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06Yz_GRv5KF6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = classifier.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21nNba8f_Ofu",
        "colab_type": "code",
        "outputId": "24fcdcc6-1adf-4b2c-e88d-6b1f2a149732",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# confusion_matrix\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[226,   1],\n",
              "       [ 18, 204]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uakROEvr_roR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ham and spam\n",
        "# spam and ham"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYipit57_-tJ",
        "colab_type": "code",
        "outputId": "c1a48e66-92a3-4295-b8b6-0ef4ed88b8ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "# classification_report\n",
        "print(classification_report(y_test, y_pred))\n",
        "# we are getting almost 95% accuracy"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.93      1.00      0.96       227\n",
            "        spam       1.00      0.92      0.96       222\n",
            "\n",
            "    accuracy                           0.96       449\n",
            "   macro avg       0.96      0.96      0.96       449\n",
            "weighted avg       0.96      0.96      0.96       449\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYYqmV7NATUD",
        "colab_type": "code",
        "outputId": "62aeb05f-d48d-4a5f-8f81-59723e7e165f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "accuracy_score(y_test, y_pred)\n",
        "# 94.5% accuracy"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9576837416481069"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-M1J9iyAabD",
        "colab_type": "code",
        "outputId": "1373b052-244b-41c2-dbf5-df2cb89a9952",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# Predict a real message\n",
        "classifier.predict(['Hello, You are learning atural Language Processing'])"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ham'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3PKrIbQkA6uP",
        "colab_type": "code",
        "outputId": "406c6386-a19e-4d8a-912f-de96e8421189",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "classifier.predict(['Hope you are doing good and learning new things !'])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ham'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xN_ppPgBCMD",
        "colab_type": "code",
        "outputId": "f445e265-f4db-4228-bf73-4f1fc4062d88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "classifier.predict(['Congratulations, You won a lottery ticket worth $1 Million ! To claim call on 446677'])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['spam'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDie4-eRB9Z9",
        "colab_type": "text"
      },
      "source": [
        "# 4) Building the model (SVM)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SdfZ_1RCBhE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import SVC"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-5Ay8bxCNf_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "classifier = Pipeline([('tfidf', TfidfVectorizer()), ('classifier',SVC(C=100,gamma='auto'))])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfMWxvB-Ce0g",
        "colab_type": "code",
        "outputId": "d8c02940-c903-4984-9f2e-8d4a47c49eb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        }
      },
      "source": [
        "classifier.fit(x_train, y_train)"
      ],
      "execution_count": 38,
      "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='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
              "                                 tokenizer=None, use_idf=True,\n",
              "                                 vocabulary=None)),\n",
              "                ('classifier',\n",
              "                 SVC(C=100, break_ties=False, cache_size=200, class_weight=None,\n",
              "                     coef0=0.0, decision_function_shape='ovr', degree=3,\n",
              "                     gamma='auto', kernel='rbf', max_iter=-1, probability=False,\n",
              "                     random_state=None, shrinking=True, tol=0.001,\n",
              "                     verbose=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JiexU2jsCo-X",
        "colab_type": "text"
      },
      "source": [
        "# 5) Predicting the results (SVM)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gkAYZDwBCwyS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = classifier.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oEhg5Ei4Cw3G",
        "colab_type": "code",
        "outputId": "5e1c4ca1-02bb-4ef5-8d56-a9c81cbd31ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# confusion_matrix\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[222,   5],\n",
              "       [ 16, 206]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16Dkkt0cCw1A",
        "colab_type": "code",
        "outputId": "24b70a09-bcf9-40e5-de86-1b61f8e64c4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9532293986636972"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "54ik4INuDv--",
        "colab_type": "code",
        "outputId": "f1da7e84-6c49-430d-c866-d2f7b83b1fcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# Predict a real message\n",
        "classifier.predict(['Hello, You are learning atural Language Processing'])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ham'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSDMeRGjDxlo",
        "colab_type": "code",
        "outputId": "eb05f2db-e482-4ffa-ea1e-a6775682da29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "classifier.predict(['Hope you are doing good and learning new things !'])"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ham'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oW6jvQzmDzHb",
        "colab_type": "code",
        "outputId": "91a723e2-0c48-4e6a-e321-69056cd7859b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "classifier.predict(['Congratulations, You won a lottery ticket worth $1 Million ! To claim call on 446677'])"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['spam'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    }
  ]
}