22-2_SpamClassifier.ipynb

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        "**SPAM CLASSIFIER**"
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        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import re\n",
        "import nltk"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"SMSSpamCollection.txt\",sep='\\t',names=['label','text'])"
      ],
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        "id": "-wQ3VB2HduEW"
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              "     label                                               text\n",
              "0      ham  Go until jurong point, crazy.. Available only ...\n",
              "1      ham                      Ok lar... Joking wif u oni...\n",
              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3      ham  U dun say so early hor... U c already then say...\n",
              "4      ham  Nah I don't think he goes to usf, he lives aro...\n",
              "...    ...                                                ...\n",
              "5567  spam  This is the 2nd time we have tried 2 contact u...\n",
              "5568   ham               Will ü b going to esplanade fr home?\n",
              "5569   ham  Pity, * was in mood for that. So...any other s...\n",
              "5570   ham  The guy did some bitching but I acted like i'd...\n",
              "5571   ham                         Rofl. Its true to its name\n",
              "\n",
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              "ham     4825\n",
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            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "Stopwords=stopwords.words('english')\n",
        "lemitizer=WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "9oJAyvtte9EV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e1030c4-e1c9-480e-c059-36785676db19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cleanData(text):\n",
        "  text=re.sub(\"[^A-Za-z0-9]+\",\" \",text)\n",
        "  text=text.lower()\n",
        "  strr=[]\n",
        "  strr=[lemitizer.lemmatize(word) for word in text.split() if word not in Stopwords]\n",
        "  strr=' '.join(strr)\n",
        "  return strr"
      ],
      "metadata": {
        "id": "mVCQq0x2hxoN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text']=df['text'].apply(cleanData)"
      ],
      "metadata": {
        "id": "hqtThLsoh20k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['text'][3]"
      ],
      "metadata": {
        "id": "dFbnizT8h2xV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "606e82d8-e90f-4b4f-e5a7-962dd6f637f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'u dun say early hor u c already say'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv=CountVectorizer(max_features=2500)\n",
        "X=cv.fit_transform(df['text'])"
      ],
      "metadata": {
        "id": "ol_4CFC3h2n1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X=X.toarray()"
      ],
      "metadata": {
        "id": "Inj5QeI_h2lF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "id": "mrhxIMJth2hd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9733e5e0-e694-4da1-9996-430575de7db9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y=pd.get_dummies(df['label'])\n",
        "Y=Y.iloc[:,1].values"
      ],
      "metadata": {
        "id": "K8lq5tP2h2YN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "8bbbb866-006d-4611-e402-f06fddf6b6d8"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 0, 0], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(X,Y,random_state=42,test_size=0.2,stratify=Y)"
      ],
      "metadata": {
        "id": "XUtnuihMumgn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T_3S6CZvvAy-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}