textClassfication.ipynb

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    "colab": {
      "name": "textClassfication.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP7ViS7s1GYgyxbcr/uJJNP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AhmadAlabrash/Deep-Learning/blob/main/textClassfication.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "79gIga2i_DB5"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('smsspamcollection.tsv', sep='\\t')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "skHlIEMNEGNZ",
        "outputId": "68fbfb84-ef7c-4138-ac2e-2ae82274c6a9"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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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              "  <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",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      "cell_type": "code",
      "source": [
        "df['length'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7V9h8rTIEMDN",
        "outputId": "cc379e1a-70e2-41f3-da55-10e1e15f5293"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    5572.000000\n",
              "mean       80.489950\n",
              "std        59.942907\n",
              "min         2.000000\n",
              "25%        36.000000\n",
              "50%        62.000000\n",
              "75%       122.000000\n",
              "max       910.000000\n",
              "Name: length, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "plt.xscale('log')\n",
        "bins = 1.15**(np.arange(0,50))\n",
        "plt.hist(df[df['label']=='ham']['length'],bins=bins,alpha=0.8)\n",
        "plt.hist(df[df['label']=='spam']['length'],bins=bins,alpha=0.8)\n",
        "plt.legend(('ham','spam'))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "41TfLyZKEXvD",
        "outputId": "2f4a0fc4-9e84-4f56-b30d-1ba4dad31463"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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/R8SERhUrSarOkOGemT8EflXl/hYCd2Tm65n5ApWHZM+roz5J0jDUM+Z+XkQ8UwzbHFK0zQRe7LdOX9EmSRpBww33ZcAfAXOBzcDVte4gIpZERHdEdG/dunWYZUiSBjKscM/MlzJzd2b+FriJ3w29bAJm9Vu1vWgbaB/LM7MrM7va2tqGU4YkaRDDCveImNFv9nRgz5k0q4GzI2JSRMwGDgV+XF+JkqRaHTjUChFxO3AcMC0i+oDLgeMiYi6QQC/wJYDMXB8RdwIbgF3A0szc3ZzSJUmDGTLcM/OcAZpX7Gf9K4Er6ylKklQfr1CVpBIy3CWphAx3SSohw12SSshwl6QSMtwlqYQMd0kqIcNdkkrIcJekEjLcJamEDHdJKiHDXZJKyHCXpBIy3CWphAx3SSohw12SSshwl6QSGjLcI+LmiNgSEc/2a3tHRDwUEc8Vvw8p2iMiro+Inoh4JiL+tJnFS5IGVs2R+y3Agn3aLgEezsxDgYeLeYCTqDwU+1BgCbCsMWVKkmoxZLhn5g+BX+3TvBBYWUyvBE7r135rVjwBTImIGY0qVpJUneGOuU/PzM3F9C+B6cX0TODFfuv1FW2SpBFU9xeqmZlA1rpdRCyJiO6I6N66dWu9ZUiS+hluuL+0Z7il+L2laN8EzOq3XnvR9nsyc3lmdmVmV1tb2zDLkCQNZLjhvhpYVEwvAu7t1/654qyZo4Gd/YZvJEkj5MChVoiI24HjgGkR0QdcDnwLuDMizgV+DpxVrP4gcDLQA7wKfL4JNUuShjBkuGfmOYMs+ugA6yawtN6iJEn18QpVSSohw12SSshwl6QSMtwlqYQMd0kqIcNdkkrIcJekEjLcJamEDHdJKiHDXZJKyHCXpBIa8t4yksaPa3Zc0OoS1CAeuUtSCRnuklRChrsklZDhLkklZLhLUgnVdbZMRPQCLwO7gV2Z2RUR7wBWAR1AL3BWZm6vr0xJUi0aceT+HzNzbmZ2FfOXAA9n5qHAw8W8JGkENWNYZiGwspheCZzWhNeQJO1HveGewP+JiKcjYknRNj0zNxfTvwSm1/kakqQa1XuF6rGZuSki/hB4KCL+tf/CzMyIyIE2LD4MlgC8613vqrMMSVJ/dYV7Zm4qfm+JiL8H5gEvRcSMzNwcETOALYNsuxxYDtDV1TXgB4BUi1P+9rFWlzAmjMQtBgbri/vOP7bpr62KYQ/LRMTbIuKgPdPACcCzwGpgUbHaIuDeeouUJNWmniP36cDfR8Se/fxdZn4/Ip4C7oyIc4GfA2fVX6YkqRbDDvfMfB44coD2bcBH6ylKklQfr1CVpBIy3CWphAx3SSohw12SSshwl6QSMtwlqYQMd0kqIcNdkkqo3huHSSPOe8iML96nZngMd0kjZn8fzIZ1YxnukkYF/0XWWIa7Wso3tNQchrs0Rgx2H/avTbmupvU1PhjuksYkv2jdP8NdGuM8QtdADPcSGo1HNI6tSyPLcNewPgxG4wfIaFTrOLmaazz9f2u4j7Dx9D+X1Ar+K7GiaeEeEQuA64AJwPcy81vNeq0ya+VFH75JWqMMY+j+i6X1mhLuETEB+G/Ax4E+4KmIWJ2ZG5rxes1WlqPtkQjrsn8glCF4Vb2x/N5v1pH7PKCneIg2EXEHsBAYk+Gu5hnOEV6jzvdu5fnh4/VDYn9/dyuP6ms9KGnkQUyzPigiMxu/04gzgAWZ+ZfF/GeB+Zl5Xr91lgBLitk/Bn46wK4OBnYO0TYN+LcGlV6rgeobqf1Uu81Q6+1v+WDLqukXGJ99Y7/sn++ZwduG0y/vzsy2AZdkZsN/gDOojLPvmf8scMMw9rN8qDaguxl/w3DrG6n9VLvNUOvtb/lgy6rpl/HaN/bL6OyXsdA3je6XZt3PfRMwq998e9FWq/uqbGuVRtUynP1Uu81Q6+1v+WDLRnu/QOv6xn7ZP98z1b9OXZo1LHMg8H+Bj1IJ9aeAT2fm+ia8VndmdjV6v6qffTM62S+jU6P7pSlfqGbmrog4D/jfVE6FvLkZwV5Y3qT9qn72zehkv4xODe2Xphy5S5Jay2eoSlIJGe6SVEKGuySVUOnCPSLeFhErI+KmiPiLVtejioh4T0SsiIi7Wl2L3iwiTiveL6si4oRW16OKiJgTEd+NiLsi4iu1bj8mwj0ibo6ILRHx7D7tCyLipxHRExGXFM2fBO7KzC8Cp454seNILf2Smc9n5rmtqXT8qbFv/qF4v3wZ+PNW1Dte1NgvGzPzy8BZwIdqfa0xEe7ALcCC/g39bk52EnAYcE5EHEblgqkXi9V2j2CN49EtVN8vGlm3UHvfXFYsV/PcQg39EhGnAg8AD9b6QmMi3DPzh8Cv9mnee3OyzPx/wJ6bk/VRCXgYI3/fWFVjv2gE1dI3UfFt4B8zc81I1zqe1PqeyczVmXkSUPMQ81gOv5n87ggdKqE+E7gH+FRELGP0XXo9HgzYLxExNSK+CxwVEZe2prRxb7D3zPnAx4AzIuLLrShsnBvsPXNcRFwfETcyjCP30j2JKTN/DXy+1XXozTJzG5UxXY0ymXk9cH2r69CbZeajwKPD3X4sH7k36uZkaiz7ZfSyb0anpvTLWA73p4BDI2J2RLwFOBtY3eKaZL+MZvbN6NSUfhkT4R4RtwOPA38cEX0RcW5m7gL23JxsI3BnE29OpgHYL6OXfTM6jWS/eOMwSSqhMXHkLkmqjeEuSSVkuEtSCRnuklRChrsklZDhLkklZLhLUgkZ7pJUQoa7JJXQ/wc40ROJQvkPuAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxtZJnIFRdCH",
        "outputId": "a32ec90d-efce-49fc-8c89-ce5732ad44c2"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label      0\n",
              "message    0\n",
              "length     0\n",
              "punct      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:,1]\n",
        "y = df.iloc[:,0]\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B2_94sMSEh8Q",
        "outputId": "80e56c28-0105-4c2e-ca91-5265bfccc98f"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Go until jurong point, crazy.. Available only ...\n",
              "1                           Ok lar... Joking wif u oni...\n",
              "2       Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3       U dun say so early hor... U c already then say...\n",
              "4       Nah I don't think he goes to usf, he lives aro...\n",
              "                              ...                        \n",
              "5567    This is the 2nd time we have tried 2 contact u...\n",
              "5568                 Will ü b going to esplanade fr home?\n",
              "5569    Pity, * was in mood for that. So...any other s...\n",
              "5570    The guy did some bitching but I acted like i'd...\n",
              "5571                           Rofl. Its true to its name\n",
              "Name: message, Length: 5572, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "vectorizer = TfidfVectorizer()\n",
        "\n",
        "X_tf = vectorizer.fit_transform(X)\n",
        " # remember to use the original X_train set\n",
        "X_tf.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5faf8a05-6ab5-4bf8-e747-d6fc2c97c9d4",
        "id": "r8EEjJ4xacnh"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 8713)"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_tf, y, test_size=0.33, random_state=42)\n",
        "\n",
        "print('Training Data Shape:', X_train.shape)\n",
        "print('Testing Data Shape: ', X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1xCLkwjF6kR",
        "outputId": "6b08cc72-e90e-4fee-cd4c-063373016d45"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Shape: (3733, 8713)\n",
            "Testing Data Shape:  (1839, 8713)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.multiclass import OneVsOneClassifier\n",
        "\n",
        "\n",
        "m = OneVsOneClassifier(LogisticRegression())\n",
        "m.fit(X_train,y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0YC4wZBGB-z",
        "outputId": "e6b04088-450f-4799-ea33-95b4b40497b3"
      },
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneVsOneClassifier(estimator=LogisticRegression())"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "m = RandomForestClassifier(n_estimators=1 , max_depth=(5))\n",
        "m.fit(X_train ,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QcPkyHI8KZte",
        "outputId": "ce791e2b-cb1f-45d6-b395-2d87494a6d24"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, n_estimators=1)"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "predictions = m.predict(X_test)\n",
        "print(metrics.classification_report(y_test,predictions)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edJIh2MxL-A0",
        "outputId": "47aa3080-867a-4830-a29a-f34a738f1fc4"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.89      1.00      0.94      1593\n",
            "        spam       0.96      0.22      0.35       246\n",
            "\n",
            "    accuracy                           0.89      1839\n",
            "   macro avg       0.93      0.61      0.65      1839\n",
            "weighted avg       0.90      0.89      0.86      1839\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "m = KNeighborsClassifier(n_neighbors=10)\n",
        "m.fit(X_train ,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8B2ZWS1FMZq-",
        "outputId": "7207676c-7587-48d0-9086-c2532fb1b537"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=10)"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "predictions = m.predict(X_test)\n",
        "print(metrics.classification_report(y_test,predictions)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZihmMy3eMqcm",
        "outputId": "76fd7033-cc7b-4bbf-a5f1-f99084053b0b"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.95      1.00      0.98      1593\n",
            "        spam       1.00      0.67      0.81       246\n",
            "\n",
            "    accuracy                           0.96      1839\n",
            "   macro avg       0.98      0.84      0.89      1839\n",
            "weighted avg       0.96      0.96      0.95      1839\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "m = DecisionTreeClassifier(max_depth=(10))\n",
        "m.fit(X_train ,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n-H6YAtwMxqG",
        "outputId": "aaf400af-a7ba-4015-d56e-90e8df4d3f53"
      },
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=10)"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "predictions = m.predict(X_test)\n",
        "print(metrics.classification_report(y_test,predictions)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4809ff9-d21a-4dbf-bbd0-af9348c74faa",
        "id": "VlxwNcA0NELB"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.97      0.99      0.98      1593\n",
            "        spam       0.93      0.83      0.88       246\n",
            "\n",
            "    accuracy                           0.97      1839\n",
            "   macro avg       0.95      0.91      0.93      1839\n",
            "weighted avg       0.97      0.97      0.97      1839\n",
            "\n"
          ]
        }
      ]
    }
  ]
}