Email_Spam_Classifier_With_NLTK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YPgYoUY4HOMZ"
   },
   "source": [
    "# Email CLassification Project using NLTK\n",
    "This notebook was created by Hamas ur Rehman, Machine Learning and AI course student from Skill4U NIC Peshawar\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y9yKvfx3Hh0p"
   },
   "source": [
    "## Details about the DataSet\n",
    "\n",
    "This dataset was taken from Kaggle\n",
    "[https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset](https://www.kaggle.com/datasets/nitishabharathi/email-spam-dataset)\n",
    "\n",
    "This dataset contains 3 csv files each file contains 2 columns.\n",
    "\n",
    "- The first column is the body of the email\n",
    "- The second column contains our labels \n",
    "  - 0 for **Not Spam**\n",
    "  - 1 for **Spam**\n",
    "\n",
    "### Total values of the dataset\n",
    "The combined values of all 3 files is  `18650`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4V_VXFeBHQl8",
    "outputId": "ea0beb27-2aed-41b0-cd0c-be40d33cd2da"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\hamas\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\hamas\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     C:\\Users\\hamas\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\hamas\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# importing necessary files\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "import re\n",
    "import nltk\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('omw-1.4')\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# importing all of our models \n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "U3Vmi49kIOAS"
   },
   "outputs": [],
   "source": [
    "data1 = pd.read_csv('dataset/completeSpamAssassin.csv')\n",
    "data2 = pd.read_csv('dataset/enronSpamSubset.csv')\n",
    "data3 = pd.read_csv('dataset/lingSpam.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.drop('Unnamed: 0',inplace=True,axis=1)\n",
    "data2.drop(['Unnamed: 0','Unnamed: 0.1'],inplace=True,axis=1)\n",
    "data3.drop('Unnamed: 0',inplace=True,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Shuffle data3\n",
    "data3 = data3.sample(frac = 1)\n",
    "# resetting the index \n",
    "data3.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "testing_data = data3.loc[1500:]\n",
    "data3 = data3.drop(np.array(range(1500,2604)),axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    1266\n",
       "1     235\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =(3,3))\n",
    "data3.Label.value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"])\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.show()\n",
    "data3.Label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will consider the 3rd csv file as our testing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BALANCING DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the data shown in the graph below tells us that the data is not balanced we will first try to fix this issue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BEFORE BALANCED DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    4150\n",
       "1    1896\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =(3,3))\n",
    "data1.Label.value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"])\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.show()\n",
    "data1.Label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_data = data1.drop(data1[data1.Label == 1].index)\n",
    "data1.drop(data1[data1.Label == 0].index , inplace = True)\n",
    "temp_data = temp_data.sample(frac = 1)\n",
    "temp_data.reset_index(drop=True, inplace=True)\n",
    "temp_data = temp_data.drop(np.array(range(0, 2254)),axis = 0)\n",
    "data1 = pd.concat([data1,temp_data], axis=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### AFTER BALANCED DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1    1896\n",
       "0    1896\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig = plt.figure(figsize =(3,3))\n",
    "data1.Label.value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"])\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.show()\n",
    "data1.Label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RZVKponzI8Wi"
   },
   "source": [
    "## Combining our datasets into one\n",
    "We are combinning them so it is easy for us to clean and work with our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "_SOc3GKhJPzl",
    "outputId": "2d41ee07-1553-42ee-e3b3-da5f128c8023"
   },
   "outputs": [],
   "source": [
    "df = pd.concat([data1, data2,data3], axis=0)\n",
    "df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shuffling our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sample(frac = 1)\n",
    "# resetting the index \n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Total spam vs ham Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0    8162\n",
       "1    7131\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,3))\n",
    "df.Label.value_counts().plot(kind=\"bar\", color=[\"salmon\", \"lightblue\"])\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.show()\n",
    "df.Label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZdUFim-5Js8N",
    "outputId": "b82b4bf8-6194-4d2d-c461-0a69bba4fe41"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15293 entries, 0 to 15292\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Body    15292 non-null  object\n",
      " 1   Label   15293 non-null  int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 239.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I_t8JX4MJyaP",
    "outputId": "cc869021-fae5-49b2-bdba-b760ce28601d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Body     1\n",
       "Label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2ejzYiA_J084"
   },
   "source": [
    "so we see that there is only one missing value in the body we can remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fEh8DtuZKO9c",
    "outputId": "fcafce80-97b5-42b7-ac7c-75cf85450093"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 15292 entries, 0 to 15292\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Body    15292 non-null  object\n",
      " 1   Label   15292 non-null  int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 358.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IIFtYBgwKUSt"
   },
   "source": [
    "## Removing links from the dataset body column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "id": "1pXrQdOtK0ci",
    "outputId": "c58ffa06-2129-47e4-9cbe-465ab5e47ce4"
   },
   "outputs": [],
   "source": [
    "no_link = [re.sub(r'http\\S+', '', i) for i in df[\"Body\"]]\n",
    "no_link_test = [re.sub(r'http\\S+', '', i) for i in testing_data[\"Body\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EfhGpEaFK3xW"
   },
   "source": [
    "## Removing unncessary symbols from our body column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "id": "MhLqIaTHLDE5",
    "outputId": "7ecea52f-3eb1-494b-b226-b365c4632469"
   },
   "outputs": [],
   "source": [
    "clean = [re.sub('[^a-zA-Z0-9 ]', '', i) for i in no_link]\n",
    "clean_test = [re.sub('[^a-zA-Z0-9 ]', '', i) for i in no_link_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h90ntS3aLFyd"
   },
   "source": [
    "## Changing all the text into lower case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 70
    },
    "id": "7HhekpAHLRIa",
    "outputId": "ba8c709b-5099-460d-c164-9188ce0a9222"
   },
   "outputs": [],
   "source": [
    "lower = [i.lower() for i in clean]\n",
    "lower_test = [i.lower() for i in clean_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gs0wjmpfLStH"
   },
   "source": [
    "## Performing word Tokenization using NLTK library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wF4RBrClLoA_",
    "outputId": "77f204d9-4d72-4283-9f4f-523b66c94aa1"
   },
   "outputs": [],
   "source": [
    "tokens = [nltk.word_tokenize(w) for w in lower]\n",
    "tokens_test = [nltk.word_tokenize(w) for w in lower_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UtV4YTPiLrxO"
   },
   "source": [
    "## Using Lemmatization to remove different forms of the same words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "zVXpc8OdMRgH"
   },
   "outputs": [],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "lemma = WordNetLemmatizer()\n",
    "lemmatized = [[lemma.lemmatize(w) for w in text] for text in tokens]\n",
    "lemma_test = WordNetLemmatizer()\n",
    "lemmatized_test = [[lemma_test.lemmatize(w) for w in text] for text in tokens_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "om7EEw4GMav3"
   },
   "source": [
    "## Removing Stopwords from our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n8ka5QBQMz2W",
    "outputId": "ce72ef1f-db07-4f97-cf85-c3f603410aaa"
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "without_stopwords = [[w for w in text if w not in stopwords.words('english')] for text in lemmatized]\n",
    "without_stopwords_test = [[w for w in text if w not in stopwords.words('english')] for text in lemmatized_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3X3KxtbMM06-"
   },
   "source": [
    "## Vectorizing our data By bag of words method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aj5ZmCSuNZXy",
    "outputId": "73d26ca1-fc7f-4bf9-a7e3-dd8745b5c009"
   },
   "outputs": [
    {
     "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]], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(max_features=20000)\n",
    "X = vectorizer.fit_transform([' '.join(text) for text in without_stopwords]).toarray()\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NfXrchRgQF5x",
    "outputId": "a12640be-5694-45a7-c8f0-42261b826921"
   },
   "outputs": [
    {
     "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]], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer_test = CountVectorizer(max_features=20000)\n",
    "TESTDATA = vectorizer_test.fit_transform([' '.join(text) for text in without_stopwords_test]).toarray()\n",
    "TESTDATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xrD66UZiNhqQ"
   },
   "source": [
    "## Splitting our data to train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sz0r7CnnPuM0",
    "outputId": "90a09778-b515-4ba5-cd26-8f95c64a086d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13762, 20000)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.array(df['Label'])\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,y,random_state=42,test_size=0.1)\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "_SraEQnCQv8z"
   },
   "outputs": [],
   "source": [
    "y_TESTDATA = np.array(testing_data['Label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "bDVpoUu0P3AA"
   },
   "outputs": [],
   "source": [
    "# Put models in a dictionary\n",
    "models = {\"Multinomial NB model\": MultinomialNB(),          \n",
    "          \"Random Forest\": RandomForestClassifier(),\n",
    "          \"SVC\": SVC(),\n",
    "          \"Gaussian NB model\" : GaussianNB(),\n",
    "          \"LogisticRegression\": LogisticRegression(), \n",
    "          \"KNeighborsClassifier\":KNeighborsClassifier(),\n",
    "          \"DecisionTreeClassifier\":DecisionTreeClassifier()\n",
    "         }\n",
    "\n",
    "# Create function to fit and score models\n",
    "def fit_and_score(models, x_train, x_test, y_train, y_test, TESTDATA,y_TESTDATA):\n",
    "    # Random seed for reproducible results\n",
    "    np.random.seed(42)\n",
    "    # Make a list to keep model scores\n",
    "    model_scores_validation = {}\n",
    "    model_scores_test = {}\n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(x_train, y_train)\n",
    "        # Evaluate the model and append its score to model_scores\n",
    "        model_scores_validation[name] = model.score(x_test, y_test) *100\n",
    "        model_scores_test[name] = model.score(TESTDATA, y_TESTDATA) *100\n",
    "        \n",
    "    return model_scores_validation,model_scores_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OMj7Ke4QP6SK",
    "outputId": "fbef37aa-2eee-4e8d-9885-d28378b5b9fa"
   },
   "outputs": [],
   "source": [
    "model_scores_validation,model_scores_test = fit_and_score(models,x_train,x_test,y_train,y_test,TESTDATA,y_TESTDATA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Multinomial NB model': 47.511312217194565,\n",
       " 'Random Forest': 48.77828054298642,\n",
       " 'SVC': 51.49321266968325,\n",
       " 'Gaussian NB model': 55.38461538461539,\n",
       " 'LogisticRegression': 48.05429864253394,\n",
       " 'KNeighborsClassifier': 26.5158371040724,\n",
       " 'DecisionTreeClassifier': 28.687782805429862}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_scores_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1IANopIaSyvW"
   },
   "source": [
    "## Compairing the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "id": "KDqijjT_TBfA",
    "outputId": "59a20c27-f9af-4133-ccfa-14d31293812b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 200x200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2,2))\n",
    "model_compare = pd.DataFrame(model_scores_test, index=['accuracy'])\n",
    "model_compare.T.plot.bar()\n",
    "plt.tick_params(labelsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the best model for further trainning and tunning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning a model with GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GridSearchCV - tries every single combination of hyperparameters and saves the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1000 candidates, totalling 5000 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=GaussianNB(),\n",
       "             param_grid={&#x27;var_smoothing&#x27;: array([1.00000000e+00, 9.79469667e-01, 9.59360829e-01, 9.39664831e-01,\n",
       "       9.20373200e-01, 9.01477631e-01, 8.82969996e-01, 8.64842328e-01,\n",
       "       8.47086827e-01, 8.29695852e-01, 8.12661920e-01, 7.95977700e-01,\n",
       "       7.79636013e-01, 7.63629826e-01, 7.47952252e-01, 7.32596543e-01,\n",
       "       7.17556092e-01, 7.02824426e-01, 6.88...\n",
       "       1.61141428e-09, 1.57833141e-09, 1.54592774e-09, 1.51418933e-09,\n",
       "       1.48310251e-09, 1.45265393e-09, 1.42283046e-09, 1.39361927e-09,\n",
       "       1.36500781e-09, 1.33698374e-09, 1.30953502e-09, 1.28264983e-09,\n",
       "       1.25631660e-09, 1.23052400e-09, 1.20526094e-09, 1.18051653e-09,\n",
       "       1.15628013e-09, 1.13254132e-09, 1.10928986e-09, 1.08651577e-09,\n",
       "       1.06420924e-09, 1.04236067e-09, 1.02096066e-09, 1.00000000e-09])},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=GaussianNB(),\n",
       "             param_grid={&#x27;var_smoothing&#x27;: array([1.00000000e+00, 9.79469667e-01, 9.59360829e-01, 9.39664831e-01,\n",
       "       9.20373200e-01, 9.01477631e-01, 8.82969996e-01, 8.64842328e-01,\n",
       "       8.47086827e-01, 8.29695852e-01, 8.12661920e-01, 7.95977700e-01,\n",
       "       7.79636013e-01, 7.63629826e-01, 7.47952252e-01, 7.32596543e-01,\n",
       "       7.17556092e-01, 7.02824426e-01, 6.88...\n",
       "       1.61141428e-09, 1.57833141e-09, 1.54592774e-09, 1.51418933e-09,\n",
       "       1.48310251e-09, 1.45265393e-09, 1.42283046e-09, 1.39361927e-09,\n",
       "       1.36500781e-09, 1.33698374e-09, 1.30953502e-09, 1.28264983e-09,\n",
       "       1.25631660e-09, 1.23052400e-09, 1.20526094e-09, 1.18051653e-09,\n",
       "       1.15628013e-09, 1.13254132e-09, 1.10928986e-09, 1.08651577e-09,\n",
       "       1.06420924e-09, 1.04236067e-09, 1.02096066e-09, 1.00000000e-09])},\n",
       "             scoring=&#x27;accuracy&#x27;, verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=GaussianNB(),\n",
       "             param_grid={'var_smoothing': array([1.00000000e+00, 9.79469667e-01, 9.59360829e-01, 9.39664831e-01,\n",
       "       9.20373200e-01, 9.01477631e-01, 8.82969996e-01, 8.64842328e-01,\n",
       "       8.47086827e-01, 8.29695852e-01, 8.12661920e-01, 7.95977700e-01,\n",
       "       7.79636013e-01, 7.63629826e-01, 7.47952252e-01, 7.32596543e-01,\n",
       "       7.17556092e-01, 7.02824426e-01, 6.88...\n",
       "       1.61141428e-09, 1.57833141e-09, 1.54592774e-09, 1.51418933e-09,\n",
       "       1.48310251e-09, 1.45265393e-09, 1.42283046e-09, 1.39361927e-09,\n",
       "       1.36500781e-09, 1.33698374e-09, 1.30953502e-09, 1.28264983e-09,\n",
       "       1.25631660e-09, 1.23052400e-09, 1.20526094e-09, 1.18051653e-09,\n",
       "       1.15628013e-09, 1.13254132e-09, 1.10928986e-09, 1.08651577e-09,\n",
       "       1.06420924e-09, 1.04236067e-09, 1.02096066e-09, 1.00000000e-09])},\n",
       "             scoring='accuracy', verbose=1)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "nb_classifier = GaussianNB()\n",
    "\n",
    "params_NB = {'var_smoothing': np.logspace(0,-9, num=1000)}\n",
    "gs_NB = GridSearchCV(estimator=nb_classifier, \n",
    "                 param_grid=params_NB, \n",
    "                 cv=5,   \n",
    "                 verbose=1, \n",
    "                 scoring='accuracy') \n",
    "gs_NB.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'var_smoothing': 5.479472336900293e-07}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PRINTING OUT THE BEST PARAMETERS\n",
    "gs_NB.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "l0ap_f3OTJVs",
    "outputId": "4c1f6c40-2474-4f39-e56a-2dd88dae3d92"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our Best Model Score on the Validation Data :  90.07 % \n"
     ]
    }
   ],
   "source": [
    "model = GaussianNB(var_smoothing = 5.479472336900293e-07)\n",
    "model.fit(x_train,y_train)\n",
    "score = model.score(x_test,y_test)\n",
    "print(f\"Our Best Model Score on the Validation Data : {score*100: .2f} % \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "id": "Uw_MvLKjIhmw"
   },
   "outputs": [],
   "source": [
    "y_preds = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "2UEaGWI9Iiq7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import ROC curve function from metrics module\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "# Plot ROC curve and calculate AUC metric\n",
    "RocCurveDisplay.from_estimator(model,x_test,y_test)\n",
    "plt.tick_params(labelsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "id": "4Muga9D-Ilx1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot=True, # Annotate the boxes\n",
    "                     cbar=False)\n",
    "plt.xlabel(\"true label\")\n",
    "plt.ylabel(\"predicted label\")\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "0QV-0x_cInlc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.86      0.90       838\n",
      "           1       0.85      0.95      0.90       692\n",
      "\n",
      "    accuracy                           0.90      1530\n",
      "   macro avg       0.90      0.90      0.90      1530\n",
      "weighted avg       0.91      0.90      0.90      1530\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "# Show classification report\n",
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "id": "D1gKghv_Iwgu"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['spam_classifier_with_NLTK.joblib']"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump,load\n",
    "dump(model, filename= 'spam_classifier_with_NLTK.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "id": "6Apea8RiIy0f"
   },
   "outputs": [],
   "source": [
    "loadmodel = load('spam_classifier_with_NLTK.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loadmodel.predict(x_test[0].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting one value using our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import re\n",
    "import nltk\n",
    "nltk.download('punkt');\n",
    "nltk.download('wordnet');\n",
    "nltk.download('omw-1.4');\n",
    "nltk.download('stopwords');\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "from joblib import load\n",
    "from IPython.display import clear_output\n",
    "\n",
    "clear_output()\n",
    "def predict_email(email_input):\n",
    "    no_link = [re.sub(r'http\\S+', '', i) for i in email_input[\"Body\"]]\n",
    "    clean = [re.sub('[^a-zA-Z0-9 ]', '', i) for i in no_link]\n",
    "    lower = [i.lower() for i in clean]\n",
    "    tokens = [nltk.word_tokenize(w) for w in lower]\n",
    "    lemma = WordNetLemmatizer()\n",
    "    lemmatized = [[lemma.lemmatize(w) for w in text] for text in tokens]\n",
    "    without_stopwords = [[w for w in text if w not in stopwords.words('english')] for text in lemmatized]\n",
    "    vectorizer = CountVectorizer(max_features=20000)\n",
    "    X = vectorizer.fit_transform([' '.join(text) for text in without_stopwords]).toarray()\n",
    "    X = np.pad(X, ((0, 0), (0,  20000 - X.shape[1])))\n",
    "    loadmodel = load('spam_classifier_with_NLTK.joblib')    \n",
    "    if(loadmodel.predict(X) == 0):\n",
    "        print(\"Email is not spam\")\n",
    "        print(loadmodel.predict(X))\n",
    "    else:\n",
    "        print(\"Email is spam !!!\")\n",
    "        print(loadmodel.predict(X))\n",
    "        \n",
    "def check_email():\n",
    "    '''This function is used to predict whether an email is spam or not using the email_spam classifier model.\n",
    "        it doesnt take in a parameter but a input prompt is shown\n",
    "        Just simply pass in the body of the email and you will get a prediciton of whether email classifies as spam or not\n",
    "    '''\n",
    "    data = input(\"Enter body of the email: \")\n",
    "    clear_output(wait = True)\n",
    "    email_input = pd.DataFrame([data], columns=['Body'])\n",
    "    prediction = predict_email(email_input)\n",
    "    \n",
    "def check_email_variable(data):\n",
    "    '''This function is used to predict whether an email is spam or not using the email_spam classifier model.\n",
    "        It only takes in one parameter by passing it into it :\n",
    "        Body.\n",
    "        Just simply pass in the body of the email and you will get a prediciton of whether email classifies as spam or not\n",
    "    '''\n",
    "    email_input = pd.DataFrame([data], columns=['Body'])\n",
    "    prediction = predict_email(email_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have below two emails to test out\n",
    "\n",
    "email1 = \"\"\"'Subject: ijcai95 workshop on context in nlp\\n \\n call for papers ijcai-95 workshop on context in natural language processing august 19 , 20 , or 21 , 1995 montreal , canada correct interpretation of natural language utterances and texts requires linguistic and non-linguistic context . the goal of this workshop is to investigate the nature of context in natural language , its role in natural language processing , and shed some light on this largely unexplored area of great theoretical and practical importance . dialogue and text processing are two application domains where the lack of good theories of context impedes significant progress in applying and developing new technologies . as speech technology matures , it becomes technically feasible to build dialogue systems . however , understanding dialogues , and especially multimodal dialogues , is not possible without some account of the role of context . similarly , with today \\'s text processing technology it is feasible to automatically create knowledge bases from fairly unconstrained texts such as newspaper archives . ignoring context in such texts , however , results in knowledge bases that are not only very incomplete , but also dramatically different from knowledge bases created by humans , based on the same texts . we invite papers from researchers active in the fields of natural language processing , knowledge representation , and other related areas addressing theoretical aspects of context and their implications for designing practical nlp systems . we are interested in reports on implemented nlp systems utilizing contextual information . we are also interested in knowledge representation systems , inference methods , and algorithms that would allow one to computationally handle specific aspects of context . agenda : our workshop will provide answers and insights into how to go about answering a number of questions , including the following : i : role of context in natural language what is context ? what is \" context of the previous utterance / sentence \" \" context of the dialog-so - far / text-so - far \" and what is the relationship between them ? how many different contexts are there ? what makes two contexts different ? what is the relationship between formalization of context and natural language ideas of context ? what is the status of context in a formal representation aiming at truthfully capturing all the characteristics of natural language ? is context an inherent characteristic of natural language that ultimately decides the formal power of natural language ? is natural language minus context a less powerful formal language ? does representing context and truthfully capturing characteristics of natural language require new knowledge representation or automated reasoning systems ? what is the relationship between context , and the semantics and pragmatics of natural language ? is context different from possible worlds and situations ? what is the relationship between domain ontologies and contexts ? ii : context-dependent interpretation of natural language in which way does context affect interpretation of natural language utterances and texts ? which aspects of context or which contexts result in refined , more general , and different interpretations of natural language ? which aspects of context are explicit and which are implicit in natural language utterances and texts ? which phenomena and inferences observed in natural language are context-independent and which ones always depend on context ? iii : computability does handling context increase or decrease computational complexity of natural language processing ? how to automatically identify context-provided constraints resulting in conveying and understanding additional or different aspects of information ? how to represent those extra constraints provided by context and how to automatically compute context-dependent interpretation of natural language ? should the final interpretation of natural language be decontextualized when stored in a knowledge base ? how can information obtained in one context be utilized in another , possibly unanticipated context ? workshop format : we will hold four sessions : i : role of context in natural language ii : context - dependent interpretation of natural language iii : computability iv : general discussion sessions i , ii , and iii will be mildly structured : first , the committee will present a brief overview of possible answers to the specific questions included in the agenda , and discuss their own answers . this presentation will be mixed with questions from the participants . second , the participants whose papers were accepted will briefly comment on their own answers to these questions . these presentations will also be mixed with questions from the participants . session iv , a general discussion , will give each participant a chance to make a statement about any context-related issue , make a comment , raise a question , argue for or against some answers etc . pre-worshop activities : a pre-workshop mailing list will be established ; please , indicate whether you want to be included ; in order to facilitate interaction and focus the discussion , two months before the workshop , we will provide all the participants with specific examples and data illustrating various aspects of context . authors + titles + abstracts of the accepted papers , but not the papers themselves , will be available on-line to everybody . the papers will only be available to the workshop participants . we hope that this will encourage people to make strong claims even if the support for them is not quite there , report on partial , ongoing , promising research , be frank in evaluating existing approaches and their own accomplishments , openly comment on limitations , in short , say all those ( very ) informative things that are sometimes difficult , if not impossible , to communicate in \" official \" publications . shared data , references , papers and the mailing list are to allow the participants to : - sort out as many as possible things before the workshop - help focusing the workshop discussion on the hardest and most controversial issues - raise objections and bring up controversial claims early on in order to prepare well thought of answers and constructive critique participants : a limited number , 30 or so , active participants will be selected on the basis of submitted papers . a small number , 5 or so , of no-paper - attendance-only participants will also be considered ; such persons should submit a one page research summary and a list of relevant publications . attendees are required to register for the main ijcai-95 conference . schedule / deadlines : very soon mosaic home page for the workshop set 3 . 15 . 95 papers received 4 . 03 . 95 selected papers accepted participants chosen 4 . 05 . 95 acceptance notifications sent to authors sent to ijcai selected papers to be included in the working notes list of confirmed participants request for equipment / room 4 . 15 . 95 mosaic home page for the workshop updated e-mail discussion begins 5 . 01 . 95 distributed to the participants final list of specific issues to be discussed at the workshop references to the existing work on context examples and data illustrating various aspects of context 7 . 15 . 95 final list of participants sent to ijcai 8 . 19 . 95 workshop takes place 12 . 01 . 95 written review of the workshop ready paper format : same as ijcai-95 : 12 pt article latex style 15 pages maximum , including title , abstract , figures , and references the first page must include : title author \\'s name ( s ) affiliation complete mailing address e-mail address phone / fax number ( s ) abstract of 200 or so words keywords submissions : electronic submissions are strongly preferred : direct : anonymous ftp to ftp . cs . wayne . edu ~ pub / context directory place the postscript or ascii version of your paper , your author + title + abstract ascii file , your references ( bibtex format preferred ) file , and a file with sample data and / or specific examples please , name your files with the name of the first author . for example , john block might place there the following : block . ps a . ps version of his paper block . text an ascii version of his paper if . ps not possible block-a . text an ascii file with authors names , title , and abstract block-r . bbl a bibliography file with references block-e . text an ascii file with sample data and / or specific examples email : lucja @ cs . wayne . edu as the last resort , four hard copies of the paper can be snail mailed to lucja iwanska department of computer science wayne state university detroit , mi 48202 , usa ( 313 ) 577-1667 ( phone ) ( 313 ) 577-2478 ( secretary ) ( 313 ) 577-6868 ( fax ) info about the workshop and the abstracts of the accepted papers is available via a mosaic home page at http : / / www . cs . wayne . edu / context primary contact : should you have any questions or need additional information , please contact lucja iwanska department of computer science wayne state university detroit , mi 48202 lucja @ cs . wayne . edu ( 313 ) 577-1667 ( phone ) ( 313 ) 577-2478 ( secretary ) ( 313 ) 577-6868 ( fax ) program committee : harry c . bunt , kub - university ( netherlands ) keith devlin , saint mary \\'s college ( usa ) r . v . guha , microelectronics and computer technology corporation ( usa ) lucja iwanska , wayne state university ( usa ) karen jensen , microsoft corporation ( usa ) john mccarthy , stanford university ( usa ) john f . sowa , suny at binghampton ( usa ) wlodek zadrozny , ibm tj watson research center ( usa )\\n'\"\"\"\n",
    "email2 = \"\"\"'Subject: your free info .\\n \\n dear nlpeople , - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - we were recently given your email address as someone who may be interested in exciting new opportunities . if this is an error , and you are no longer interested , please hit reply , and type remove in the subject line , and you will be removed immediately from our database . - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - please , please do yourself the biggest favor of your life and keep this letter until you have the time to read it carefully . you already took the time to go through your mail to decide what to keep ( and what to throw away ) , and this just may be what you \\' ve been looking for all your life . do n\\'t risk losing what could be your best chance at real security and happiness by assuming anything at this point . please read this entire letter very carefully before you make any rash judgments . we promise that you \\' ll thank us for it later ! how \\' s life been treating you ? * do you have enough money in the bank to support yourself and your family for a year ( or more ) if you were to lose your job ? * could you survive a major unexpected expense without going deeper in debt or filing bankruptcy ? * can you afford a nice vacation every year without floating a loan ? * do you have a realistic and affordable way to earn a second cash income in excess of $ 200 , 000 per year ? if you would like to answer yes to any or all of these questions , then you need respond with the subject \" big money \" , because we \\' re going to give you a large dose of genuine honesty and explain how you can easily and substantially improve your financial outlook . thank you and god bless opportunity shop online\\n'\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Email 1 test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Email is spam !!!\n",
      "[1]\n"
     ]
    }
   ],
   "source": [
    "check_email_variable(email1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Email 2 test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Email is not spam\n",
      "[0]\n"
     ]
    }
   ],
   "source": [
    "check_email_variable(email2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Testing email using input prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Email is not spam\n",
      "[0]\n"
     ]
    }
   ],
   "source": [
    "# This email was passed\n",
    "'''\n",
    "'Subject: MANAGE YOUR BLOOD PRESSURE + MELT AWAY EXCESS POUNDS FAST/n/n*Regulates Blood Pressure\\n*Balances Blood Sugar Levels\\n*Lowers Bad Cholestoral\\n\\n ORDER YOURS NOW!'\n",
    "'''\n",
    "check_email()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Email is spam !!!\n",
      "[1]\n"
     ]
    }
   ],
   "source": [
    "# This email was passed\n",
    "'''\n",
    "'Subject: RE: Python lesson \\n\\nKeith  Just a follow up. I would be grateful if you find out how much it would cost to learn to speak python as I have recently brought a pet snake.   Regards  Jake\n",
    "'''\n",
    "check_email()"
   ]
  }
 ],
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