Email_spam.ipynb

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  "metadata": {
    "colab": {
      "name": "Email_spam.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMnzBsYm6r1e9El4/CRAAYW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Chandradithya8/Machine_learning-Projects/blob/master/Email_spam.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZkstZxWbpSQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "8b4e0c77-0012-4905-c183-21a8d7ca716e"
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv(\"/content/spam.csv.txt\")\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</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": [
              "  Category                                            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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2R0iGdYbkjah",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        },
        "outputId": "d070a30a-9889-490d-d093-9aca8c136a59"
      },
      "source": [
        "df.groupby('Category').describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <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>Category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <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>641</td>\n",
              "      <td>Please call our customer service representativ...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Message                                                               \n",
              "           count unique                                                top freq\n",
              "Category                                                                       \n",
              "ham         4825   4516                             Sorry, I'll call later   30\n",
              "spam         747    641  Please call our customer service representativ...    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5VC8TQBk1G0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "d10a708c-dc71-403f-b5c1-21ce1b7f1cf7"
      },
      "source": [
        "df['spam']=df['Category'].apply(lambda x: 1 if x=='spam' else 0)\n",
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>Category</th>\n",
              "      <th>Message</th>\n",
              "      <th>spam</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>0</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>1</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>0</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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Category                                            Message  spam\n",
              "0      ham  Go until jurong point, crazy.. Available only ...     0\n",
              "1      ham                      Ok lar... Joking wif u oni...     0\n",
              "2     spam  Free entry in 2 a wkly comp to win FA Cup fina...     1\n",
              "3      ham  U dun say so early hor... U c already then say...     0\n",
              "4      ham  Nah I don't think he goes to usf, he lives aro...     0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uNauMXRSlQG7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x=df['Message']\n",
        "y=df['spam']"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "joWGSeq9lYM3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import  train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mK2HVlJslof9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "v=CountVectorizer()\n",
        "x_train_count=v.fit_transform(x_train)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpWkhPbrmFt9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "model=MultinomialNB()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6U9kJE4m_kz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1080b5b1-ca52-4ef1-f56c-ca50bd0ea680"
      },
      "source": [
        "model.fit(x_train_count,y_train)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6szSUflnGJD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3b49df1c-f95d-483f-8761-024bc0a568c5"
      },
      "source": [
        "x_test_count=v.transform(x_test)\n",
        "model.score(x_test_count,y_test)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9899497487437185"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71DPxoWsndki",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e4406a24-ffc7-4e44-ba13-d6576c034aea"
      },
      "source": [
        "emails=['Hey mohan, can we get together to watch footbal game tomorrow?',\n",
        "    'Upto 20% discount on parking, exclusive offer just for you. Dont miss this reward!']\n",
        "emails_count=v.transform(emails)\n",
        "model.predict(emails_count)    "
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vq1AmB5qoJf0",
        "colab_type": "text"
      },
      "source": [
        "# Sklearn Pipelearn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRj9oXEaoHIy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "cf=Pipeline([('count',CountVectorizer()),('NB',MultinomialNB())])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmxqA-vlpiQj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "edd4ea28-1f09-4721-ef68-84af1479aeef"
      },
      "source": [
        "cf.fit(x_train,y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('count',\n",
              "                 CountVectorizer(analyzer='word', binary=False,\n",
              "                                 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",
              "                ('NB',\n",
              "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ZvbsLo8poWV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6e9bb1c9-9c8a-4cca-afda-6d933a3425d8"
      },
      "source": [
        "cf.score(x_test,y_test)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9899497487437185"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bg1NdVOGqOks",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7526e1f0-b24a-4146-d8e3-f7dadf5952d9"
      },
      "source": [
        "emails=['Hey mohan, can we get together to watch footbal game tomorrow?',\n",
        "    'Upto 20% discount on parking, exclusive offer just for you. Dont miss this reward!']\n",
        "cf.predict(emails)    "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    }
  ]
}