Spam_Detection.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.4"
    },
    "colab": {
      "name": "Spam Detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Og07qwNQiiv8"
      },
      "source": [
        "import nltk \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kc1SibFPiiwT"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wd1fvTO0iiwZ"
      },
      "source": [
        "messages = pd.read_csv('/content/SMSSpamCollection',sep = '\\t', names = ['label', 'message'])"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kr21YwrEiiwg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "0ad49227-ad5e-41bd-ff2f-f586d82f08e4"
      },
      "source": [
        "messages.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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",
              "    </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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label                                            message\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61DVso4eiiwm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "9a64fc64-f6e0-4c05-a6d7-d86da36a9f53"
      },
      "source": [
        "messages.groupby('label').describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\">message</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ham</th>\n",
              "      <td>4825</td>\n",
              "      <td>4516</td>\n",
              "      <td>Sorry, I'll call later</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spam</th>\n",
              "      <td>747</td>\n",
              "      <td>653</td>\n",
              "      <td>Please call our customer service representativ...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "      message                                                               \n",
              "        count unique                                                top freq\n",
              "label                                                                       \n",
              "ham      4825   4516                             Sorry, I'll call later   30\n",
              "spam      747    653  Please call our customer service representativ...    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Co-vvdM6iiwr"
      },
      "source": [
        "messages['length'] = messages['message'].apply(len)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqWURmVriiww",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "1eef2791-fb57-4dce-d089-dec4ddeadee4"
      },
      "source": [
        "messages.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
              "    </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",
              "      <td>111</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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "  label                                            message  length\n",
              "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
              "1   ham                      Ok lar... Joking wif u oni...      29\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
              "3   ham  U dun say so early hor... U c already then say...      49\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8uBYFt0iiiw0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93d1f128-83b2-4cc2-a1c9-420e9511302f"
      },
      "source": [
        "messages.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 3 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   label    5572 non-null   object\n",
            " 1   message  5572 non-null   object\n",
            " 2   length   5572 non-null   int64 \n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 130.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xy0EIa6liiw6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "9ae21b20-ae02-4d93-b915-0b4549cfb1b5"
      },
      "source": [
        "plt.figure(figsize=(10, 5))\n",
        "sns.countplot(x=\"label\", data=messages)\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-n9FqOkiiw_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "ee6bc33d-f4f8-475f-b49f-4d2f6a627574"
      },
      "source": [
        "plt.figure(figsize=(14,4))\n",
        "sns.distplot(messages.length.values,70)\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yetI83-iixK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "outputId": "64c21afb-69d1-4c3d-e59b-5b73e57d951d"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "plt.scatter(messages.length.values, range(messages.shape[0]))\n",
        "plt.xlabel('length', fontsize=12)\n",
        "plt.ylabel('count', fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u1demZsZw+RFgJXGuahCBOBFR3DBo88hpJ2KSCYC3vek8vPDTcqyzhu9dceHMx3aZFgA4cWoKoxOlVF286wPOlSM7cezk7LmpXgOspNaHJTUQJyJyi0GbR3G/kHVCAfy/Z4/GPvjcdeDIzMfWxXr9tv2zgpjJciURjVo77TEWRIDVbtaSiIjCwY0IHqX1Qhb3gA2YG5gM9hfR2zP395C4t37w0mMsiAJ8zlUlIoonBm0e8UIWHbvAJIlLe156jAURYPmxm5OIiPzH5VGPBvuLWLd1/0wheL1C3kClOo0Tp9Pb/iMqToFJEpf2vASaQRXgsz6MiCh+GLT5YN3aZbbtBtatXTbTbPf2TXsjPMN0aTbMPYmtH7wGmgywiIiygcujPmi1nMQLqr96e7odX9Mwl/ZGJ0quxma1whoyIiJyQ1STUHLeuYGBAR0bG4v6NLBoeHvUp5AqxYj7cDX25wNqgVanAWKz3aOd7iwlIqLkEZFxVR2wu43Lo5RIYXTpb8bvBrROS5x2zZuT0MKEiIj8x6CNEqsxSAozI+Vl80A755nE6QRERBSMUGvaRCQnIhMi8nXz88Ui8qSIPCMim0Skxzx+lvn5M+bti+oe41Pm8YMisibM87fjpq4pyGHbWWcFSV56nXWi0/5o7Z5nEluYEBFRMMLeiPBxAN+v+/wPAdynqm8GcAzAR83jHwVwzDx+n3k/iMhbAHwAwDIAVwP4MxGZXcEdIrcX4PXb9kdzghlgBUleep21YheYd7p5oN3zDKJ5LhERJVNoQZuIXATgGgBfND8XAKsBPG7e5WEAg+bH15qfw7z9Heb9rwXwmKqeUtXnATwD4PJwvoO5ml2A6y/0jbMhyR/1QVInGSm3WVK7wBxAR7tU2z1P7iwlIiJLmDVtnwPwSQCvNz8/H8Ckqk6Znx8CYF3xigBeBgBVnRKR4+b9iwD21D1m/deEzulCa13YGwO6tDO6BJXp8HYj1wdJ7fY6c1vg3yww3z28uu26snbPM6jmuURElDyhZNpE5L0AXlHV8ZCe7zYRGRORsSNHjrT+gg45XWhzIpkL2ACgOyehPl994NJuRsrtMqXfNWWdZM4G+4vYPbwaz49c01GgSERE6RDW8uhKAGtF5AUAj6G2LHo/gIKIWNm+iwBY61MlAAsAwLz9XAA/rT9u8zUzVPUBVR1Q1YG+vj7/vxuT3QVYAFRT3vvOSbkyHdpz5WR2gNhuU91mWdL6ZVK/a8o415OIiDoVStCmqp9S1YtUdRFqGwl2quotAHYBeL95t1sBfM38eKv5Oczbd2qtC/BWAB8wd5cuBnAJgH8J43uwM9hfxPWXFVEfPmQzXAvfzVcsmHOsnYxUs6CrfjNJUAPZh9YswfxCHj+cLM/UQBIRETUT9Rir3wXwCRF5BrWatQfN4w8CON88/gkAwwCgqvsBbAbwNIC/A/AxVY10HXLXgSMM1EK28k3n4Z7B5R1//ehECSdOTTneXr9MGkRmLOz2JERElA6hN9dV1W8D+Lb58XOw2f2pqq8BuMHh6+8FcG9wZ9geu6JyCtYNAws7/lq78VN26pdP/R7Izoa5RETUCU5E8GB0ogQBl0Td8uu1Gnp8H4DOxjjZBUx2/OiD5jT5gA1ziYioEwzaPNi446BjEJLrElRDbH8Rd3kj59uO2kpVccfmvQBmB25uxkO5CYyMnHjug9aspUi7bT+IiIiA6GvaEq1ZAMAX9ox5vQY2XLccXT52BFGdvWHAbZ2Yq8DIh1i72RJoWA1z3TQPJiKi5GBs4UGzPm1hNpmNu9cq0xh78Sj8fknqNwy47bu2amnrFjCVafU8/qrZEmgYbT+42YGIKH24POrB0Jolc4ra/VwGTItypYpHn3w5kMe2giO3dWK7Drhrtuy1vqzVEmjj5gYrK9bp1IPGpeGTp6e42YGIKGWYafPAKWPS2PiVgms4bAVBbpvgut3t67W+rJ0lUK9ZMbuvd5p3y80ORETJxUybR3btIG7ftDeis4nWvF4DqsBk2T5g8JsVBDn1XbMLknIiLQNIP+rL2pkZ6rUFiNsdsQA3OxARJRmDtgAUHZbG0q63pxtDa5aEFrSWK1Ws27ofJ05PoVKdHYjN6zVw9/uWzQl6mgVsAvg6kN1tfzevLUDc3i+IzQ5ERBQeLo8GwG5pLAusIvt2FPIGvCwmT5YrcwI2oBZAWudSv4vSaem6WMhHNpDd63xTp/sV8gZnnBIRpQgzbQE5q7srcxsS5hfybe9OXDb/9dj97FHfz8XKPo1OlDD0+L6ZwM4u0xZ1BsppQ4vbc3L6+nVr52YaiYgouRi0+cztmKS0MXKCRefncUebS6O7nz0KkVrfNT9Z2af12/bbZuK6zOf0czm0U+3UvwXx9URElAwM2nzWTlF4mlSnteOMWRAbS61+bE67KKcVeGHkGv+fuENe55t6/Xo30ySIiChaDNp8lsUNCAB8b5zrldt+bNR85BYDNyKi+OBGBJ8lvUdbIW9EfQq+sGranL6fXiP5P/p+jalyO02CiIiilfwrV0xYF9CgmsiGpe/1PY635UQggK8zRL3IGznHoMyqaXvvigttb69UNdEjnfwcU+W15QgREYWDQZtHoxMlXLr+m7h9095ULI3+4JUTjrdVVTG/kEd3TKK26y8rYt3aZTByc8/nxKkp3DX6FLaM2wcxfswXjZKf2TGvLUeIiCgcrGnzIIs7ReMUmM4EZDbJzclyBY/secnuphlJyCQ5bRDwMzvmteUIERGFg0GbB1ndKRoX1iB6pyXpVgvVcc8kNdsg0GogfTvYMoSIKBkYtHmQhExN2nmpIbTagsRVsyVQv7NjXluGEBFR8FjT5kHcMzVZ0Gq3brNb494WpNkS6GB/ERuuW84xVUREGcJMmwdDa5bMGpFE4cobOVx/WRFbxku2y9TW7V/e85Lt18c9U9pqCZTZMSKibGGmzasMx2tR7iG1Mkv3DC6fyTgBZzJv9bcXE7o7cmjNEuSN3KxjSd8g4FdvOSKiLGKmzYONOw6iErdRACGK8jvfPbx65uNWGaek7o5M2wYBTl4gIvKGQZsHcV9eS7OVIztdBzBJDn7CWAINa+5os40VSXgviIiixqDNA6eaIwpeu1ka1n/ZCzP7lbTJC2EFs0REbrGmzQO7miOjS2DToJ8CUK5UcefmfayP8iDMuaNJmrzg55gwIiK/MGjzwK7twsYbVuDnz43fRSitqqqZuqj6XcgfZvYrSRsrwgxmiYjc4vKoR3bLbnds2hvR2WRb2uujgljK9HOyQitJqi1M2lIuEWUDg7YA9PbkcOI0x1tFIc0X1SAK+cPeWZuU2sIwg1kiIrcYtPmgsWCZAVvwciK2I6zSfFENIvuTpOxXmJLaJoaI0o1Bm0d2S1ZZJwDONrpQrkwH8vhFM7DI2kU1qOxPUrJfYWIwS0RxxKDNI7slq6xTILCADagFxlm8qGYxUI0Sg1kiihsGbR6luYYqrqxRVVm7qLoJVNlbjIgovRi0eeS0ZJU3unBqahoZnnIVGLtatqxoFqhyTBQRUbq57tMmIr/jcPwT/p1O8qxa2jdncHreyOH6yy7CWd05268hb+b1GrEfOh7FYHT2FiMiSrd2mut+2uH4XX6cSBKNTpSwZbw0a3C6ALj+siJ2HTjCWrcAGDnBq69NzepUf/umvbh0/TdjE7xF1U3faRMMl/CJiNKh5fKoiKw2P8yJyCpgVmLpjQB+5uIxzgbwDwDOMp/zcVW9W0QWA3gMwPkAxgH8mqqeFpGzAHwJwGUAfgrgJlV9wXysTwH4KIAqgN9W1R1uvtEg2GU2FMCuA0e4izQgU1WF3eLoZLkSm6XAKAajj06UIIDta5PmNihRYw0hEYXJTU3bg+bfZwN4qO64AvgRgN9y8RinAKxW1VdFxADwTyLyfwB8AsB9qvqYiPw5asHYF8y/j6nqm0XkAwD+EMBNIvIWAB8AsAzAfAB/LyL/UVUjSWk5ZTBKk2XHCyh50+w1jctEhCi66W/ccdD2tRGAu0sDwhpCIgpby+VRVV2sqosBPGJ9bP55o6q+TVW3ungMVdVXzU8N848CWA3gcfP4wwAGzY+vNT+Hefs7RETM44+p6ilVfR7AMwAud/et+s8pg5ETYcAWkTgsBUYxGN3p+1Y4BxBR1N2lCWsIiShsrnePquqHrI9FpKvhtpZNuUQkh9oS6JsBfB7AswAmVXXKvMshANbVpQjgZfOxp0TkOGpLqEUAe+oetv5r6p/rNgC3AcDChQtdfHedceqbxVq26Mwv5CNfsoqin5rTLuaiQ6DYLEsEZKv/Xac4n5SIwtbO7tG3isg/i8gJABXzz5T5d0uqWlXVSwFchFp2bGkH5+uKqj6gqgOqOtDX1xfU02Cwv4gN1y2fuTDmRFCuVCGN20ljJieCS37unKhPw3d5I4dVS/si2QRQr/7nQlALnDZctzzQwGdozRLkjdm7lZsFik5ZovXb9kf++iVFFBlVIsq2dvq0PQxgG4BfB3Cy0ydU1UkR2QXglwAURKTbzLZdBMC6MpQALABwSES6AZyL2oYE67il/msiYV2I67MWcW8jVlXFD145EfVp+KJYyM/KCEWxCcBO2I1/250Q4ZQNOnZy7u9gcakVjBtOqCCisLUTtF0M4PdV2w9JRKQPQMUM2PIA3oXa5oJdAN6P2g7SWwF8zfySrebn/2zevlNVVUS2AvgbEflj1DYiXALgX9o9H79xlFU0zunJYffw6lnH7ti01/a+QS5ZRb0ca2knUHRaTnXCJb+5sjhKjYii1U7Q9lUAVwHopMXGhQAeNuvaugBsVtWvi8jTAB4TkXsATODMTtUHAfy1iDwD4ChqO0ahqvtFZDOAp1Fbmv1YVDtH6/GCFo0Tp6sYnSjNukgGNVTdSRg7CIMICp2yRGd1d2GyPDfbxiU/e1kbpdaOuPwyQ5Qm7QRtZwP4qoj8E2qtPmbUb1Kwo6rfA9Bvc/w52Oz+VNXXANzg8Fj3ArjX/WkHr92sBfln/bb9sy4EYS9ZBb0cG1RQ6JQlAsAlP/KM7VCIgtFO0Pa0+Yca2AUKFI7GGqywl6yC3kEYZFDYLEvEDAl5EZfaUqK0aaflx/ogTyTJrP+EPrF5LwfEx0CYS1ZBL8dG0VaCS37kFduhEAWjnZYfq53+BHmCSTHYX2TAFoFC3ujo6/xqLNtuq412sa0EJRF/bomC0c7y6IMNn/cB6EGtwe0bfTsjojasW7us7a/xs94m6OVYtpWgJOLPLVEw2lkeXVz/ubkT9C64GBifFYW8YbvzjsLhdrea3/U2QS4nsq0EJRF/bomCIR20XTvzxbXGt4dU9ef9OyV/DQwM6NjYWKDPYQUL3EEaPhHgvhsvBWC/69FuEsHi4e2Ow9WfH7kmwLMlIiJqTkTGVXXA7rZ2lkftvAtAy7mjaTY6UcLQ4/tQqbKgLQqqtWDtbKPLdfYs7F5ulEzsM0ZEceM6aBORl4FZCYpe1Hq3/abfJ5Uk67ftZ8AWsXKl6thuxW63GuttqBX2GSOiOGon0/bBhs9PAPg3Vf13H88ncexmNVJ82GXPwqi3YZYm2dhnjIjiqJ2NCP8XAESkC8AFAH6sqpleGqV4a5Y9C3LzALM0ycc+Y0QUR+30aXu9iHwJQBlACUBZRB4WkXMDO7sE6DVcv4QUgpwIBECxkLfdhBCGZlkaSgb2GSOiOGon4vhfAM4BsBxA3vy7F8CfBHBeidHTnWt9pxg4pycZ5+nVtCqeH7kGu4dXR5bVYpYm+YJumkxE1Il2atquBvBGVT1pfv5vIvIRAM/6f1rJcTwhfdlOnE7XXNQuge0EijhkQsLcncrauWCwzxgRxVE7QdtrqE1BeLHu2BsAnPL1jBLG6QKdNCK19hlJkRNBrguzdu4aOcGJU1NYPLzd1UU2qIAnrN2p7dTOMbhrH2ewElHctLM8+kUAT4jIb4jIu0XkNwDsAPCXwZxaMqxa2hf1KfgjQQEbAFSmFZWq4pyeHATAvF4DUGCyXIHiTADjNFPUCnhKk2VX92/HYH8RG65bXjsn01nd/tc+uq2dC/J7JSKi8LRzJbkXwAYA7wfwWfPvP1LVPwjixJJi14EjUZ+CLwq9nQ1ej9qJ01XccuVC9PZ0o9KwXsBF0YcAAB+mSURBVNqs+D+MzQKvVc5srp4sV3wPlNzWznFjBBFROrSzPHo/gMdU9Z3WARF5m4h8TlVv9//UkiEtxeXHTlYgSFzCDQDw6JMvY9phbbc0WcboRGnOMpfTknanS92Ny48nT08F3ufLbe0cN0ZQ2nC5n7KqnUzbzQAah3iOA/hV/04neeJQ+O6XJAZsAFBVbfo+2GW4ciK293U63ozd8qNT02U/AyW3OxzZvoLShMv9lGXtBG0KoLFvRK7Nx0idoTVLYOTav9CTf3IitgGMpVyp4s7N+7B4eDtWjuzE6EQJVYfMnNPxZuyWH534GShZtXPFQr5pbzq2r+jM6EQJK0d2zvq5oehxuZ+yrJ3l0X8E8Aci8klVnTYnI6wzj2eWdYFcv23/THYlqcuMQeo1ulCp6py6s1yXQKcVXkZr3HzFgpn34fZNe23vYwVj1m/l83oN22xYsYOgym32LIhAyc0OR7avaJ/dztw7Nu3F2ItHcc/gck+Py/fBGy73U5a1E7R9HMDXARwWkRcBLARwGMD7gjixJGm8KJ6bN/CzU1Oo2jUSy6hKVXHT5Qvw9X2HMVnX2256WjsOcHMiuPmKBTMX0cH+IjbuONiyLq1cqeKs7i7kjZwvbTmcassKeQPnnNXt6wW604s+21e0xy6bowAe2fMSBi4+r6PXkuPN/BFmH0SiuHG9tKmqhwC8FcC1ADYCGARwmXk80+4afQq3b9o7U2MxWa5AVTMzhcCNyrRi+/cOY93aZbOW6joN2ESAz964Yk7Wo9kyab3j5YqrpUU3nJYf161dht3Dq32b0MBanvA4ZW0U6HgZjst6/uByP2VZO5k2mAPi95h/CLUL6Zf3vDTn+LQCPd1duPe/LMcdm/ZyuRS1Hap3bN7rSxNfVdhmKRqznl0itnVq8wt537JPYS0/NrvoM1Pjr2ZNsztdhuOynj+43E9Z1lbQRnM1+y352MkKNu44yICtjp9TF9wELK8/uxsnTk/NmpwQVW2ZV7zoh2dozRLHX7Y6XYbjsp5/uNxPWZXpnZ9+aHXBTMOIqzhrfP0blxAnyxVAaxMTvC6DRo2tO8Iz2F/ELVcuROO+cC8BP5f1iMgrZto8araM0mt0oVyZZqYtQI0By/pt++csIVamFb093Zj49FVhnprvwpppSjX3DC7HwMXn+bYMx2U9IvJKNElTwjswMDCgY2ONPYH9MzpRwtDf7pvTyqJLgP9wtjFrpyT5K2/kcP1lRew6cGRm167T6y0Anh+5JtwTDABbRhARpZuIjKvqgN1tzLR5ZF0w123dPxMwzOs1cPf7luEOh55h5F2xkMeqpX3YMl6ayTw1C5DTsoTIWh4iouxi0OaRlfk4Xq6g2JD5cNMzjNpXLOSxe3g1Vo7sdD2JYNXSPqwc2ckMFRERJRY3InjQqm+W255h1J5F59eyZm53TfYaXdgyXmJ/M6IQcQwYkf9Y0+bBypGdtpk0KxMEnMnEMePmr7y5ycMNp7Fi9e8TxQNr9tKhcfoDUKtBTerObaIwNatpY6bNA6dMT2myPPMb5sYdBzG0Zsmc1gFpljdyyBvB/mg5BWxdgjmTKJx+LWF/s3jhxIf04PQHomAwaPPAqbhdgFkXnixNRLD6oG247hcjef6cCHq63f1Yp2VzQty5XSbjhT492AiaKBjciOCBXd8su6W4rARsQO01sZa32lnC9EtlWnHsZOs2K0nqb5bkJcN2hqTzQp8enP5AFAxm2jwY7C/OGTqepQDNzh2b9s5kGcMO2FrJiSRuKkLSlwzbyZ5x4kN6cPoDUTBCybSJyAIAXwJwAWqJpwdU9X4ROQ/AJgCLALwA4EZVPSYiAuB+AO8BcBLAh1X1u+Zj3QrgLvOh71HVh8P4Hpw09s1y2pyQFXEIWgt5A6emplNRBJ30IfGtsmf1WcRz8waMnAQ+J9aS5Axm3HH6A1EwwloenQJwp6p+V0ReD2BcRJ4A8GEA31LVEREZBjAM4HcBvBvAJeafKwB8AcAVZpB3N4AB1OKDcRHZqqrHQvo+WrJbMqXgNC5H540c1q1dBiAdF4ykLxk2WyZrXDqdLFdgdAnm9RqYPFkJ9H1rZ9mWOsNG0ET+CyVoU9XDAA6bH/9MRL4PoAjgWgC/Yt7tYQDfRi1ouxbAl7TWj2SPiBRE5ELzvk+o6lEAMAO/qwE8Gsb34Yb1n9T6bftd1VZRZwp5A3vvvqpptiQNF4yk1wY1m5dql0UMa05s0jOYRJRNoW9EEJFFAPoBPAngAjOgA4AfobZ8CtQCupfrvuyQeczpeOy8FrN6rjQxumQmm5b23+aTPiS+2TKZ05i3MLKISc9gElE2hRq0icjrAGwBcLuq/nutdK1GVVVEfCmJEpHbANwGAAsXLvTjIR3ZZXrsfosn/9x0+YJUB2r10lAb5BRYR5lFTHoGk4iyKbSgTUQM1AK2R1T1K+bhH4vIhap62Fz+fMU8XgKwoO7LLzKPlXBmOdU6/u3G51LVBwA8ANQmIvj4bcziVBfDgC1Yjzz5EgYuPi9RgYsXac0mRplFTHoGk4iyKZSWH+Zu0AcBfF9V/7jupq0AbjU/vhXA1+qOf0hqrgRw3FxG3QHgKhGZJyLzAFxlHouEU11MTrI0/yB8qkhU2wuyZ9cyJ6wdvlE+NxFRp0KZPSoibwfwjwCeAmAVe/0eanVtmwEsBPAiai0/jppB3p+itsngJICPqOqY+Vi/bn4tANyrqn/V7LmDnD26eHi7Y4sLp3mX5B+n2aFs5UBE5B3/L41Gs9mjHBjvQbOB8Vnu1RamF0aumfW53aBqIyc4p6cbx8vBtpFII/6nTZRNdv+XJrXfZdJwYHxAmnX9LrKgOXB2y9C2bSSqislyJZETBaKU9GkMRNQ5zgKOJwZtHjSrixlaswRGF2vbglS1yRK7adnA/3jc4X/aRNnFtjjxxIHxHjnt7LOOrdu6H5NlNtkNgl0206mVQ6P6/3i4BGiP/2kTZRfb4sQTM20BGuwvYu/dV+FzN10a9amk0qqlfXOO2S1Z27H+4+ESoDMOcCfKrmblPxQdBm0h4HJSMLaMl+YEV41L1vN6jTnL1PX/8bSzBDg6UcLKkZ1YPLwdK0d2pj6w43/aRNnFtjjxxOVRH7RaXuNyUjCcZkU2Llk3e3/cLgFmccB4GqYxEFHn0trYO8nY8sMju23RAJA3uvBaZRrzC3lMnjyNE6c5JSEoAnQcUDRr21LfA87t/YiIiLxgy48AOc0ZLVemZ2qkGLAFy0stmtslQBblExFR1Bi0ecSLdnx00o7Cbd0Gi/KJiChqrGnzqNBr4NhJtvSIi06CaDd1GxwwTkREUWPQ5sHoRAmvvjYV9WmErkuA6ZiWQs4v5APpu8aifPazo+DxZ4yoOQZtHmzccRCVuEYvGZQ3cli1tC+wXZ5Z3kmVxd2zFC7+jBG1xpo2D7JazxbHODUngusvK2LXgSOeRy9lrR+bGxxpRUHjzxhRa8y0eeB2ZBIFr6qKLeMl2528gPsAm7/t2+Pu2dm4jOc//owRtcZMmwduRyaR/3Iic445BWyA+12e/G3fHnfPnsHRZ8HgzxhRawzaPLDaRczrNaI+lczoyQleGLkG1TaaQrezy5O/7dvjSKszGNgHgz9jRK1xedQHr1Wmoz6FzDhdVYxOlJATcRW45UTampfntOSd9d/2uXv2DAb2weDPGFFrDNo8cpqIQMG5fdNe1/edVm3rP332Y3OW5d2z9RjYB4c/Y0TNcXnUI25EiLd2L6RuJyRQdnEZj4iiwkybR26X6Sgaq5b2Nb3daRdgVoM07opsjct4RBQV0ZQHHAMDAzo2NhbY4y8a3h7YY5N3xUIeu4dX297W2N7DUsgbWLd2WeYuwnavhwC45cqFuGdweXQnRkSUISIyrqoDdrdxedSjIutYIlXIG03fg2bF4U71iJPlSiZbONi9HgrgkT0vZe61ICKKIwZtHrWqY7HqouKua27bs9gTAOvWLsPu4dWOr3GzmrZmAV0WWzg4vR4KZO61ICKKIwZtASoW8nh+5BrH5bk4ieNoqlbe9qbzAAArR3aiNFlGY9zZqji81SaFrLVw6DTAJSKicDBo86hZBuLEqamZ+ZXkv6cP/2ymMz1QywhZrFmkzerSWk20yFoLh6E1S+YEvpasvRZERHHEoM2jZi0/JsuVmTE35L9jJyuOPfKsWaTNarGaTbTIYguHwf4ibrlyYdsZSyIiCgeDNo/sZmBSPLipSxvsL2Li01fhczddyt5sAO4ZXI77+FoQEcUS+7R5lLUebYLZy5BRMXKCc3q6MVmuNL2f21qsLPdma8TXgogonphp8ygJO0P9UizkYxGwAUClqjg1VYWRa57pZC0WERGlBYM2j1oVs6eF1aQ2TkFquTINKGZq0liLRUREacagzSO7WZVpk+uSmZ2wJ05Ntcxuhakyrejt6cYLI9ewFouIiFKNY6wCkKbRVnY1bEaXoKtLcGpqOopTmkMAPD9yTdSnQURE5FmzMVbciBCAQt5oWSCfBHkjZ9tSozKtserG26xujQPQiYjIq7hcS7g8GoB1a5fBSOJcqDo5EcceaO3oNYL9EWtWt2YNQC9Nlmf65WVxpigREXUuTtcSBm0BGOwvYuMNKxLdw82vViYnK/4voeZEXNWt2Q1Az+JMUSIi6lycriWhLI+KyEMA3gvgFVX9BfPYeQA2AVgE4AUAN6rqMRERAPcDeA+AkwA+rKrfNb/mVgB3mQ97j6o+HMb5d2Kwv4ixF4/iy3teivpUOtIlsVoBnWVa1VUNm1OPNi9zNOOSIicionAEcS3pVFiZtv8N4OqGY8MAvqWqlwD4lvk5ALwbwCXmn9sAfAGYCfLuBnAFgMsB3C0i8wI/8w6MTpSwcmRnYgM2IL4BG+C+95rT/Trt3RanFDkREYXD72uJF6EEbar6DwCONhy+FoCVKXsYwGDd8S9pzR4ABRG5EMAaAE+o6lFVPQbgCcwNBCNXf2GnYLjtvWbXQ69V7zYr4F48vB0rR3bOCsjilCInIqJwdHItCUqUu0cvUNXD5sc/AnCB+XERwMt19ztkHnM6HgvWshmDtfiwli3dLmdaAbcVmFmZtLEXj2LXgSOO720UKXIiIgpHu9eSIMWi5Yeqqoj4tiAnIrehtrSKhQsX+vWwjhov9hSsjTsOuv7H0s4cTadM2iN7Xmo6voujsoiI0i0uM5mj3D36Y3PZE+bfr5jHSwAW1N3vIvOY0/E5VPUBVR1Q1YG+vj7fT7yR3cWeghNUNtMpY9YsYOOoLCIiCkuUQdtWALeaH98K4Gt1xz8kNVcCOG4uo+4AcJWIzDM3IFxlHotcq+Wx5Db+iK8giv/bzZjlREIZldWszo6IiLIjlKBNRB4F8M8AlojIIRH5KIARAO8SkR8AeKf5OQB8A8BzAJ4B8JcAfhMAVPUogD8A8B3zz2fMY5FrdrEvFvK476ZLQzybbAii+N+u2LRZwD2tGkrAxh2rREQEhFTTpqo3O9z0Dpv7KoCPOTzOQwAe8vHUfDG0Zsmcmra8kZuVhbl9096oTi+Vgij+byw2LfQaeK1SRdmhQfD8Qj7wvm3NdqzGob6iU+x3R0TUvlhsREi6VjtLspIVCbMhb1DF/1ax6ZnNJfYBW97IYdXSPtvdptbj+CFOTR394rRLF/DvdSMiSiMGbT5x2lliXaCy4OzuLlSqWhsoH7BVS4PdYNJsc0nRDMrDyILNL+RtN14kecdqWrOHRERB4+xRH9kVjGdpZ+nJynQoARsAbP/e4dZ38sApkyUAdg+vxmB/MZQsWJyaOvoljdlDIqIwMNPmE6cln6wEbGE7drIy55ifdVJuMlxhZMHi1NTRL2nMHhIRhYFBm0+clnziPHg9Tfyuk3LaXDK0Zsms6ReC2X3cgsiCxaWpo1+avbZEROSMQZtPnJZ2GLAFo5A3Zn3utk7KbTbOKcMFYFbAocBM4FZMWBYsqh2cacweEhGFgUGbT5yWfCgYJ05VsHh4+8wF302dVLvZOLsM18qRnXOCQytg2z282su3FKqod3CmLXtIRBQGbkTwiV3BOAWnMo1ZzWYLvYbt/errpJpl49xKSxG9H68FERGFi0GbTwb7i7j+siJyUuuhnxNBr8GXNwzlShWqaLnL0o+Ay6lYPmlF9GkJPomIsoRRhU9GJ0rYMl5CVWtFbFVVVKosaAvL8XIFG65bjmIhD0FtubJxLqgfAVdaWnCkJfgkIsoS1rT5xG65KayeZVQLNlrVSfmxazEtRfTcwUlElDwM2nzCZaXwdAGoHy7lNtjwK+BKQxF9WoJPIqIsYdDmE+4eDccHr1wIAHj0yZdRVUVOBNdf5j6ISnrA5WebjqS/FkREWSOq6V7CGxgY0LGxscCfp7GFAgAYOcFUVZHuVzh6Rk5wTk83jpcrqc4Y2f2M5Y3cnNo9IiJKLhEZV9UBu9u4EcEng/3FWYXw83oNQMGALQSVqmKyXJnVAmR0ohT1afmObTqIiLKNy6M+ql9uWjmy03Y+JgXPbhJCGrht0xHVpAMiIgoWM20BYX1btNK4McRNmw5rCbU0WU595pGIKGsYtAVgdKIEifokMi6N/cbc9IjjEioRUXpxeTQAG3ccZC1bA2uoeljPFad+Y34tV7pp08FJB0RE6cWgLQC8QM5WLOSxamkfvrznpVCeT9He0HM3QVWngZffg9lbtelwaj2TxswjEVHWcHk0ALxAzlaaLIcWsAG1INEtNzVgXurEwl6utFtCFQCrlvYF8nxERBQeBm0BGFqzBEaOVW1RaGcU0+hECXdu3tcyqPISeIW9XDnYX8T1lxVn1VQqgC3jJW5GICJKOAZtARjsL+KcHq48h6VL4Dgk3omVPas6NJeuD6qaBV6jEyWsHNmJxcPbsXJk55zAKIrB7LsOHJlTP8jNCEREycfIIiDHy+zRFpb/cLaBvXdf1dbX2GXP6tUHVU51YoVeo2W9WhSD2bkZgYgonRi0+WB0ooT12/bPaqYrYW6XDMi8XgOvvjaFynS8v5FOAuRmAUxjUOUUeKnCcdnUCtqiGMzOzQhEROnEoM2ju0afsi2yT/pI15wIJk9WcG7ewGTMs4ZWMHLX6FOzBsnffMUC3DO43PFr7AKbnMicJVanwOuOTXttH7sxIAx7MHsU2T0iIgoegzYPRidKeKTFrsiciGPdVJxZ5zxZroTaY61dVjDSGDxXVWc+twvcnAIbp5o4u8Br446DscxoRZHdIyKi4IkmMKBox8DAgI6NjQXy2CtHdrYcVyVwzuqkQZQBnQB425vOwws/LTu+vjkRPLvhPba3eW1629iDDWge+BEREbUiIuOqOmB7G4O2zi0e3t4yYCmawcDtDktp5I2boLEYYKaJw9mJiMhPzYI2Lo960CqDZnTJzEWcQVsw3PzK4XUKQTNh16sREVF2sU+bB3bd5y2FvIGNN6zgBT0m2KcsOVr1viMiyipm2jywK/hetbQPuw4cwQ8nyzNBAgO3YLRbTxd2nzIunbbP71mtRERpwqDNo/rlMacLztiLR6M8xVQSAW65YiG2jJeaNsmtN7+QDy2QYvDRmWYjw/i6EVHWcXnUR04XnDCHpWeG1lp5bLhuOeb1Gi3vbuQEq5b2dTz4vV1hD4pPC05zICJyxqDNR7ywhMfqhTbYX0Svizmv5/R0Y9eBI6EFUmEFH2mr/4piVisRUVIwaPMRLyzhWbW0byZgcdMDb7JccbxfEMF2GMGHtQQbRuYwLHabezjNgYioJpFBm4hcLSIHReQZERmO+nwszXaTkr+2f+/wTMDiVRDBdhjBRxqXYAf7i9hw3XIUC3kIaj322KyYiKgmcRsRRCQH4PMA3gXgEIDviMhWVX062jM7U2B+5+Z9iRxdZcfoEtx0+YLY1eUdO+nfPNRVS/t8eyxLGKOk0lr/xd53RET2Ehe0AbgcwDOq+hwAiMhjAK4FEHnQBpy5WDeON0oaa/yWFWg8suel2M4fbVTIGxBxH9jtOnAkkPMIOvhwau7MZXoionRKYtBWBPBy3eeHAFxRfwcRuQ3AbQCwcOHC8M7MVJ9lSeLM0WIhj93Dq2cdi1vAVsgbmCzPDcrqz91tvVtSM1NOQ+9Z/0VElE6JrGlrRVUfUNUBVR3o6/N/6cuNwf4idg+vxuduujSyOrezutt/e50u+sUW2Zu8Ed6PUiFvYN3aZS1rxtzWGCY1M8X6LyKibElipq0EYEHd5xeZx2Kpsbap0Gvg1demUJkOLnclAG65ciHuGVyO0YkS1m/b33SpsNfoQrky3bTuyi6rY8kbOWy4bjkA++kQpckyugTw41vOGzmsW7vMVc2Ym9c+6Zkp1n8REWWHaMIK5kWkG8C/AXgHasHadwD8qqrut7v/wMCAjo2NhXiGrVld+UuTZeREUFWdqcOaPFnBuXUfW8HP9u8dngm8CnkD711x4cy4rEKvAVXgeLniGHjZPWexzeJ4r4/hNI1gdKKEdVv3zyx3Oo2navd82zkHIiKiOBCRcVUdsL0taUEbAIjIewB8DkAOwEOqeq/TfeMYtBERERHZaRa0JXF5FKr6DQDfiPo8iIiIiMKSyo0IRERERGnDoI2IiIgoARi0ERERESUAgzYiIiKiBGDQRkRERJQADNqIiIiIEoBBGxEREVECJLK5bjtE5AiAFwN+mjcA+EnAz0HB4HuXbHz/ko3vX7Lx/QvGxapqOzg99UFbGERkzKl7McUb37tk4/uXbHz/ko3vX/i4PEpERESUAAzaiIiIiBKAQZs/Hoj6BKhjfO+Sje9fsvH9Sza+fyFjTRsRERFRAjDTRkRERJQADNo8EJGrReSgiDwjIsNRnw/NJSILRGSXiDwtIvtF5OPm8fNE5AkR+YH59zzzuIjIn5jv6fdE5K3RfgckIjkRmRCRr5ufLxaRJ833aJOI9JjHzzI/f8a8fVGU502AiBRE5HEROSAi3xeRX+K/veQQkTvM/zf/VUQeFZGz+e8vWgzaOiQiOQCfB/BuAG8BcLOIvCXasyIbUwDuVNW3ALgSwMfM92kYwLdU9RIA3zI/B2rv5yXmn9sAfCH8U6YGHwfw/brP/xDAfar6ZgDHAHzUPP5RAMfM4/eZ96No3Q/g71R1KYAVqL2P/LeXACJSBPDbAAZU9RcA5AB8APz3FykGbZ27HMAzqvqcqp4G8BiAayM+J2qgqodV9bvmxz9D7aJRRO29eti828MABs2PrwXwJa3ZA6AgIheGfNpkEpGLAFwD4Ivm5wJgNYDHzbs0vnfWe/o4gHeY96cIiMi5AP4zgAcBQFVPq+ok+G8vSboB5EWkG0AvgMPgv79IMWjrXBHAy3WfHzKPUUyZ6fp+AE8CuEBVD5s3/QjABebHfF/j5XMAPglg2vz8fACTqjplfl7//sy8d+btx837UzQWAzgC4K/M5e0visg54L+9RFDVEoD/CeAl1IK14wDGwX9/kWLQRpkgIq8DsAXA7ar67/W3aW0LNbdRx4yIvBfAK6o6HvW5UEe6AbwVwBdUtR/ACZxZCgXAf3txZtYaXota8D0fwDkAro70pIhBmwclAAvqPr/IPEYxIyIGagHbI6r6FfPwj62lF/PvV8zjfF/jYyWAtSLyAmrlB6tRq5EqmMs1wOz3Z+a9M28/F8BPwzxhmuUQgEOq+qT5+eOoBXH8t5cM7wTwvKoeUdUKgK+g9m+S//4ixKCtc98BcIm5k6YHtQLNrRGfEzUwayoeBPB9Vf3jupu2ArjV/PhWAF+rO/4hcyfblQCO1y3lUIhU9VOqepGqLkLt39dOVb0FwC4A7zfv1vjeWe/p+837M4sTEVX9EYCXRWSJeegdAJ4G/+0lxUsArhSRXvP/Uev947+/CLG5rgci8h7Uam5yAB5S1XsjPiVqICJvB/CPAJ7Cmbqo30Otrm0zgIUAXgRwo6oeNf9z+lPUlgFOAviIqo6FfuI0i4j8CoDfUdX3isgbUcu8nQdgAsAHVfWUiJwN4K9Rq1s8CuADqvpcVOdMgIhcitomkh4AzwH4CGrJAv7bSwARWQ/gJtR24U8A+K+o1a7x319EGLQRERERJQCXR4mIiIgSgEEbERERUQIwaCMiIiJKAAZtRERERAnAoI2IiIgoARi0EVGmiMgLIvLOkJ9zkYhoXVNSIqK2MWgjIvJZFIEhEaUfgzYiIiKiBGDQRkSZJCJdIjIsIs+KyE9FZLOInGfeZi1n3ioiL4nIT0Tk9+u+Ni8iD4vIMRH5voh8UkQOmbf9NWrd/reJyKsi8sm6p73F7vGIiNxg0EZEWfVbAAYB/DKA+QCOAfh8w33eDmAJanMXPy0i/8k8fjeARQDeCOBdAD5ofYGq/hpqcxvfp6qvU9U/cvF4REQtMWgjoqz6DQC/r6qHVPUUgHUA3t+wWWC9qpZVdR+AfQBWmMdvBPA/VPWYqh4C8Ccun9Pp8YiIWuJOJiLKqosBfFVEpuuOVQFcUPf5j+o+PgngdebH8wG8XHdb/cfNOD0eEVFLzLQRUVa9DODdqlqo+3O2qpZcfO1hABfVfb6g4Xb17SyJiEwM2ogoq/4cwL0icjEAiEifiFzr8ms3A/iUiMwTkSKA/95w+49Rq3cjIvINgzYiyqr7AWwF8E0R+RmAPQCucPm1nwFwCMDzAP4ewOMATtXdvgHAXSIyKSK/498pE1GWiSqz+EREXojIfwPwAVX95ajPhYjSi5k2IqI2iciFIrLS7PW2BMCdAL4a9XkRUbpx9ygRUft6APwFgMUAJgE8BuDPIj0jIko9Lo8SERERJQCXR4mIiIgSgEEbERERUQIwaCMiIiJKAAZtRERERAnAoI2IiIgoARi0ERERESXA/wfHZuFq4viv1AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60y3XqeMiixR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "1c7fdeb0-c680-46ef-e971-1a420ef8c1ac"
      },
      "source": [
        "messages[messages['length']==910]['message'].iloc[0]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"For me the love should start with attraction.i should feel that I need her every time around me.she should be the first thing which comes in my thoughts.I would start the day and end it with her.she should be there every time I dream.love will be then when my every breath has her name.my life should happen around her.my life will be named to her.I would cry for her.will give all my happiness and take all her sorrows.I will be ready to fight with anyone for her.I will be in love when I will be doing the craziest things for her.love will be when I don't have to proove anyone that my girl is the most beautiful lady on the whole planet.I will always be singing praises for her.love will be when I start up making chicken curry and end up makiing sambar.life will be the most beautiful then.will get every morning and thank god for the day because she is with me.I would like to say a lot..will tell later..\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yb7Tiv90iixV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "d6e7b8e6-b85a-4b87-84c8-4bb13c9d6ef3"
      },
      "source": [
        "sns.set_style(\"darkgrid\")\n",
        "sns.set(rc = {'figure.figsize' : (18,6)})\n",
        "messages.hist(column = 'length', by = 'label', bins = 70, edgecolor = 'k')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7faa7d579400>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7faa7d524a90>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7DQzsEGIiixn"
      },
      "source": [
        "Here we see that :\n",
        "Ham message is centered around 1-100\n",
        "spam message is certered around 150"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0TUguzaiixo"
      },
      "source": [
        "import string"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kaN5SYlziixw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1ae730eb-06df-4016-c118-30bfcc5b08dc"
      },
      "source": [
        "string.punctuation"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WaSi_y6iix4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b925399a-cb25-41f0-f166-a237e6831eb7"
      },
      "source": [
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ZnV0jWmiix-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a7df6dc-511f-48b1-c7b0-f1db78372b1c"
      },
      "source": [
        "stopwords.words('english')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i',\n",
              " 'me',\n",
              " 'my',\n",
              " 'myself',\n",
              " 'we',\n",
              " 'our',\n",
              " 'ours',\n",
              " 'ourselves',\n",
              " 'you',\n",
              " \"you're\",\n",
              " \"you've\",\n",
              " \"you'll\",\n",
              " \"you'd\",\n",
              " 'your',\n",
              " 'yours',\n",
              " 'yourself',\n",
              " 'yourselves',\n",
              " 'he',\n",
              " 'him',\n",
              " 'his',\n",
              " 'himself',\n",
              " 'she',\n",
              " \"she's\",\n",
              " 'her',\n",
              " 'hers',\n",
              " 'herself',\n",
              " 'it',\n",
              " \"it's\",\n",
              " 'its',\n",
              " 'itself',\n",
              " 'they',\n",
              " 'them',\n",
              " 'their',\n",
              " 'theirs',\n",
              " 'themselves',\n",
              " 'what',\n",
              " 'which',\n",
              " 'who',\n",
              " 'whom',\n",
              " 'this',\n",
              " 'that',\n",
              " \"that'll\",\n",
              " 'these',\n",
              " 'those',\n",
              " 'am',\n",
              " 'is',\n",
              " 'are',\n",
              " 'was',\n",
              " 'were',\n",
              " 'be',\n",
              " 'been',\n",
              " 'being',\n",
              " 'have',\n",
              " 'has',\n",
              " 'had',\n",
              " 'having',\n",
              " 'do',\n",
              " 'does',\n",
              " 'did',\n",
              " 'doing',\n",
              " 'a',\n",
              " 'an',\n",
              " 'the',\n",
              " 'and',\n",
              " 'but',\n",
              " 'if',\n",
              " 'or',\n",
              " 'because',\n",
              " 'as',\n",
              " 'until',\n",
              " 'while',\n",
              " 'of',\n",
              " 'at',\n",
              " 'by',\n",
              " 'for',\n",
              " 'with',\n",
              " 'about',\n",
              " 'against',\n",
              " 'between',\n",
              " 'into',\n",
              " 'through',\n",
              " 'during',\n",
              " 'before',\n",
              " 'after',\n",
              " 'above',\n",
              " 'below',\n",
              " 'to',\n",
              " 'from',\n",
              " 'up',\n",
              " 'down',\n",
              " 'in',\n",
              " 'out',\n",
              " 'on',\n",
              " 'off',\n",
              " 'over',\n",
              " 'under',\n",
              " 'again',\n",
              " 'further',\n",
              " 'then',\n",
              " 'once',\n",
              " 'here',\n",
              " 'there',\n",
              " 'when',\n",
              " 'where',\n",
              " 'why',\n",
              " 'how',\n",
              " 'all',\n",
              " 'any',\n",
              " 'both',\n",
              " 'each',\n",
              " 'few',\n",
              " 'more',\n",
              " 'most',\n",
              " 'other',\n",
              " 'some',\n",
              " 'such',\n",
              " 'no',\n",
              " 'nor',\n",
              " 'not',\n",
              " 'only',\n",
              " 'own',\n",
              " 'same',\n",
              " 'so',\n",
              " 'than',\n",
              " 'too',\n",
              " 'very',\n",
              " 's',\n",
              " 't',\n",
              " 'can',\n",
              " 'will',\n",
              " 'just',\n",
              " 'don',\n",
              " \"don't\",\n",
              " 'should',\n",
              " \"should've\",\n",
              " 'now',\n",
              " 'd',\n",
              " 'll',\n",
              " 'm',\n",
              " 'o',\n",
              " 're',\n",
              " 've',\n",
              " 'y',\n",
              " 'ain',\n",
              " 'aren',\n",
              " \"aren't\",\n",
              " 'couldn',\n",
              " \"couldn't\",\n",
              " 'didn',\n",
              " \"didn't\",\n",
              " 'doesn',\n",
              " \"doesn't\",\n",
              " 'hadn',\n",
              " \"hadn't\",\n",
              " 'hasn',\n",
              " \"hasn't\",\n",
              " 'haven',\n",
              " \"haven't\",\n",
              " 'isn',\n",
              " \"isn't\",\n",
              " 'ma',\n",
              " 'mightn',\n",
              " \"mightn't\",\n",
              " 'mustn',\n",
              " \"mustn't\",\n",
              " 'needn',\n",
              " \"needn't\",\n",
              " 'shan',\n",
              " \"shan't\",\n",
              " 'shouldn',\n",
              " \"shouldn't\",\n",
              " 'wasn',\n",
              " \"wasn't\",\n",
              " 'weren',\n",
              " \"weren't\",\n",
              " 'won',\n",
              " \"won't\",\n",
              " 'wouldn',\n",
              " \"wouldn't\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Npt7ldH4nD9T"
      },
      "source": [
        "\n",
        "**Data pre-processing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4ALNBXkiiyL"
      },
      "source": [
        "def text_processing(mess):\n",
        "    nopunc = [char for char in mess if char not in string.punctuation]\n",
        "    \n",
        "    nopunc = ''.join(nopunc)\n",
        "    \n",
        "    return [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtzo_HwuiiyZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f57a7128-fcfa-4cb0-dd15-b40842b36391"
      },
      "source": [
        "messages['message'].head().apply(text_processing)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [Go, jurong, point, crazy, Available, bugis, n...\n",
              "1                       [Ok, lar, Joking, wif, u, oni]\n",
              "2    [Free, entry, 2, wkly, comp, win, FA, Cup, fin...\n",
              "3        [U, dun, say, early, hor, U, c, already, say]\n",
              "4    [Nah, dont, think, goes, usf, lives, around, t...\n",
              "Name: message, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCIHQVxRiiyj"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhM-FSoPiiyo"
      },
      "source": [
        "bow_transformer = CountVectorizer(analyzer=text_processing).fit(messages['message'])"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tySslKlfiiyv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6547b5cb-0755-4d36-9408-3d22653e4fbc"
      },
      "source": [
        "print(len(bow_transformer.vocabulary_))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11425\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGW6DQYciiy5"
      },
      "source": [
        "mess4 = messages['message'][3]"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MdkJk1jmiiy9"
      },
      "source": [
        "#mess4\n",
        "bow4 = bow_transformer.transform([mess4])"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WrWB7gaqiizG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7961bc91-56a7-4571-b964-244269ac0abc"
      },
      "source": [
        "bow4.shape"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 11425)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lD_bx8J7iizL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c21a9b5-29b3-42ea-f07e-86c23ac1177a"
      },
      "source": [
        "print(bow4)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  (0, 4068)\t2\n",
            "  (0, 4629)\t1\n",
            "  (0, 5261)\t1\n",
            "  (0, 6204)\t1\n",
            "  (0, 6222)\t1\n",
            "  (0, 7186)\t1\n",
            "  (0, 9554)\t2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qGopWcCHiizQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "11c28015-b2bd-48d2-83cd-419303c89c9a"
      },
      "source": [
        "bow_transformer.get_feature_names()[9554]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'say'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZazHeD51iizU"
      },
      "source": [
        "messages_bow =bow_transformer.transform(messages['message'])"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x4ZGPjaTiizc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de553980-9015-44f9-c4cb-7dc5ebac9d9d"
      },
      "source": [
        "messages_bow.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 11425)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pry0qaVYiizj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5d0dc1f-b850-4254-f9ae-d4909e721bc5"
      },
      "source": [
        "messages_bow.nnz"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50548"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbG41226iizs"
      },
      "source": [
        "sparsity = 100.0*messages_bow.nnz/(messages_bow.shape[0]*messages_bow.shape[1])\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlaekpgciizx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3f158ec-6763-4923-fece-9727337e4bc9"
      },
      "source": [
        "sparsity"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07940295412668218"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWT_kLX_iiz7"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer "
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNQa0HaVii0A"
      },
      "source": [
        "tfidf_transformer = TfidfTransformer().fit(messages_bow)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7LMDjhpii0E"
      },
      "source": [
        "tfidf4 = tfidf_transformer.transform(bow4)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TE6ulLhrii0I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "84c91cba-ba93-4248-9b6c-8fa8432f1e0b"
      },
      "source": [
        "print(tfidf4)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  (0, 9554)\t0.5385626262927564\n",
            "  (0, 7186)\t0.4389365653379857\n",
            "  (0, 6222)\t0.3187216892949149\n",
            "  (0, 6204)\t0.29953799723697416\n",
            "  (0, 5261)\t0.29729957405868723\n",
            "  (0, 4629)\t0.26619801906087187\n",
            "  (0, 4068)\t0.40832589933384067\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3A-k7Ri1ii0L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac0d261b-f838-4c24-a252-7a12e0b9f7f7"
      },
      "source": [
        "#checking Inverse document freq. for a one word\n",
        "tfidf_transformer.idf_[bow_transformer.vocabulary_['university']]"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.527076498901426"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bu_FJegDii0P"
      },
      "source": [
        "messages_tfidf = tfidf_transformer.transform(messages_bow)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9glDVHYVii0X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92f063f4-064d-4953-be03-a1c823b2f98b"
      },
      "source": [
        "messages_tfidf.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 11425)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2JfDToOii0j"
      },
      "source": [
        "# Naive bayes "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4zRsupBii0k"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNIKeN8Gii0o"
      },
      "source": [
        "spam_detect_model = MultinomialNB().fit(messages_tfidf, messages['label'])"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPI_056Eii0t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e492f2fe-6ade-413c-e4de-e16edcf1ae1e"
      },
      "source": [
        "spam_detect_model.predict(tfidf4)[0]"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'ham'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlzj6coMii0z"
      },
      "source": [
        "all_pred = spam_detect_model.predict(messages_tfidf)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojabQdHuii05",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2d01b9bb-b1a0-470e-cdc1-df76520507cc"
      },
      "source": [
        "all_pred"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ham', 'ham', 'spam', ..., 'ham', 'ham', 'ham'], dtype='<U4')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MsNmzEzYii1Q"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VScFrjqNii09"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hDnCgk9ii1A"
      },
      "source": [
        "msg_train,msg_test,label_train,label_test = train_test_split(messages['message'],\n",
        "                                                            messages['label'], \n",
        "                                                            test_size = 0.3)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kb62YlZjii1C"
      },
      "source": [
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhjQGDWWotCE"
      },
      "source": [
        "**Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9yxCIcWlii1F"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W9bVFN0Iii1L"
      },
      "source": [
        "pipeline = Pipeline([\n",
        "    ('bow', CountVectorizer(analyzer=text_processing)),\n",
        "    ('tfidf', TfidfTransformer()),\n",
        "    ('classifier', MultinomialNB())\n",
        "])"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgAUExolii1R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d9ce960-fee8-4b8e-90b6-da2e7a5efc3c"
      },
      "source": [
        "pipeline.fit(msg_train, label_train)                     \n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('bow',\n",
              "                 CountVectorizer(analyzer=<function text_processing at 0x7faa7d585158>,\n",
              "                                 binary=False, decode_error='strict',\n",
              "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
              "                                 input='content', lowercase=True, max_df=1.0,\n",
              "                                 max_features=None, min_df=1,\n",
              "                                 ngram_range=(1, 1), preprocessor=None,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
              "                                 tokenizer=None, vocabulary=None)),\n",
              "                ('tfidf',\n",
              "                 TfidfTransformer(norm='l2', smooth_idf=True,\n",
              "                                  sublinear_tf=False, use_idf=True)),\n",
              "                ('classifier',\n",
              "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TEq0cPVwii1Y"
      },
      "source": [
        "# Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgMuwkaZii1Y"
      },
      "source": [
        "predictions = pipeline.predict(msg_test)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwcZ4xxBii1c"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RjEyVLbii1j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c59cba7-4777-4e11-8194-c90be3cc0d80"
      },
      "source": [
        "print(classification_report(label_test, predictions))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.96      1.00      0.98      1447\n",
            "        spam       1.00      0.75      0.85       225\n",
            "\n",
            "    accuracy                           0.97      1672\n",
            "   macro avg       0.98      0.87      0.92      1672\n",
            "weighted avg       0.97      0.97      0.96      1672\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6BV_JnLii1p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "578cd2d2-f9aa-4c70-807c-3698b2b857b3"
      },
      "source": [
        "print(confusion_matrix(label_test, predictions))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1447    0]\n",
            " [  57  168]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fWAG66iii1t"
      },
      "source": [
        "#Lets fit a Random forest classifier\n",
        "pipeline2 = Pipeline([\n",
        "    ('bow', CountVectorizer(analyzer=text_processing)),\n",
        "    ('tfidf', TfidfTransformer()),\n",
        "    ('classifier', RandomForestClassifier())\n",
        "])"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYKBT8Jwii1v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92e31e8a-842d-4fb7-fc71-6156122e60af"
      },
      "source": [
        "pipeline2.fit(msg_train, label_train)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('bow',\n",
              "                 CountVectorizer(analyzer=<function text_processing at 0x7faa7d585158>,\n",
              "                                 binary=False, decode_error='strict',\n",
              "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
              "                                 input='content', lowercase=True, max_df=1.0,\n",
              "                                 max_features=None, min_df=1,\n",
              "                                 ngram_range=(1, 1), preprocessor=None,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+...\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": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7h_6RvJii1y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b0820e8-114d-4208-b9db-3b6bb60c831c"
      },
      "source": [
        "predictions2 = pipeline2.predict(msg_test)\n",
        "print(classification_report(label_test, predictions2))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.98      1.00      0.99      1447\n",
            "        spam       0.99      0.84      0.91       225\n",
            "\n",
            "    accuracy                           0.98      1672\n",
            "   macro avg       0.99      0.92      0.95      1672\n",
            "weighted avg       0.98      0.98      0.98      1672\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fz05NPc3ii13",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cd19b5b1-9425-4b46-ab4c-b0f272034dfb"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(label_test, predictions2))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.98      1.00      0.99      1447\n",
            "        spam       0.99      0.84      0.91       225\n",
            "\n",
            "    accuracy                           0.98      1672\n",
            "   macro avg       0.99      0.92      0.95      1672\n",
            "weighted avg       0.98      0.98      0.98      1672\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}