sms_spam_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SMS Spam Detection using Naive Bayes "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal of this notebook is to test Multinomial Naive-Bayes classifier in combination with a plethora of features and see how it perform on the given dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/pantelis/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Ignoring unnecessory warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  \n",
    "# Specialized container datatypes\n",
    "import collections\n",
    "# For data vizualization \n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "# For large and multi-dimensional arrays\n",
    "import numpy as np\n",
    "# For data manipulation and analysis\n",
    "import pandas as pd\n",
    "# Natural language processing library\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer \n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.util import ngrams\n",
    "# For basic cleaning and data preprocessing \n",
    "import re\n",
    "import string \n",
    "# Machine learning libary\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.metrics import precision_recall_fscore_support as score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "# For wordcloud generating \n",
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the data using pandas' read_csv method and let's look at the dataset info to see if everything is alright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5572 entries, 0 to 5571\n",
      "Data columns (total 5 columns):\n",
      "v1            5572 non-null object\n",
      "v2            5572 non-null object\n",
      "Unnamed: 2    50 non-null object\n",
      "Unnamed: 3    12 non-null object\n",
      "Unnamed: 4    6 non-null object\n",
      "dtypes: object(5)\n",
      "memory usage: 217.7+ KB\n"
     ]
    }
   ],
   "source": [
    "DATAPATH = 'input/spam.csv'\n",
    "df = pd.read_csv(DATAPATH)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['v1', 'v2', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Shape of our data\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look on the first 5 rows of the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     v1                                                 v2 Unnamed: 2  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...        NaN   \n",
       "1   ham                      Ok lar... Joking wif u oni...        NaN   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...        NaN   \n",
       "3   ham  U dun say so early hor... U c already then say...        NaN   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...        NaN   \n",
       "\n",
       "  Unnamed: 3 Unnamed: 4  \n",
       "0        NaN        NaN  \n",
       "1        NaN        NaN  \n",
       "2        NaN        NaN  \n",
       "3        NaN        NaN  \n",
       "4        NaN        NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now drop \"unnamed\" columns and rename v1 and v2 to \"label\" and \"message\" respectively.  Also drop any row which has any NaN value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Unnamed: 2','Unnamed: 3','Unnamed: 4'], axis=1, inplace=True)\n",
    "df.rename(columns = {'v1':'label','v2':'message'}, inplace=True)\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the first 5 rows of the dataset again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ham</td>\n",
       "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message\n",
       "0   ham  Go until jurong point, crazy.. Available only ...\n",
       "1   ham                      Ok lar... Joking wif u oni...\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "3   ham  U dun say so early hor... U c already then say...\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target count for data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['label'].value_counts().plot(kind = 'barh', color = ['blue','red'], figsize = (8, 6))\n",
    "plt.title('Horizontal Bar Chart for Data Distribution', fontsize = 20)\n",
    "plt.ylabel('Spam vs Ham')\n",
    "plt.xlabel('Number of messages')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vizualize data distribution using pie chart\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['label'].value_counts().plot(kind = 'pie', colors = ['blue','red'], explode = [0, 0.1], figsize = (6, 6), autopct = '%1.1f%%', shadow = True)\n",
    "plt.title('Pie Chart for Data Distribution', fontsize = 20)\n",
    "plt.ylabel('Spam vs Ham')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's crystal clear that 'ham' sms are 6.5 times more than the 'spam' ones. \n",
    "- We have to be very careful when we gonna split our data set into train and test set or when we gonna use cross-validation. Otherwise we have a chance of our training model being skewed towards normal messages. That's will happen if the sample we choose to train our model consists majorly of 'ham' sms. In this case, it's very propable to end up predicting everything as 'ham'.\n",
    "- Also precision is very important as we don't want to predict any 'ham' sms as 'spam'. Actually, we don't mind if we miss any odd 'spam' sms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look into the data grouped by into labels 'ham' or 'spam'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\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>label</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>653</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",
       "label                                                                       \n",
       "ham      4825   4516                             Sorry, I'll call later   30\n",
       "spam      747    653  Please call our customer service representativ...    4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('label').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how a ham and a spam sms looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>ham</td>\n",
       "      <td>Jus ans me lar. U'll noe later.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     label                          message\n",
       "1229   ham  Jus ans me lar. U'll noe later."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pick at random a ham sms \n",
    "df.loc[df['label'] == 'ham'].sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2895</th>\n",
       "      <td>spam</td>\n",
       "      <td>pdate_Now - Double mins and 1000 txts on Orang...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     label                                            message\n",
       "2895  spam  pdate_Now - Double mins and 1000 txts on Orang..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pick at random a spam sms \n",
    "df.loc[df['label'] == 'spam'].sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see 30 most frequent occuring words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Most Frequently Occuring Words - Top 30')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = CountVectorizer()\n",
    "words = cv.fit_transform(df.message)\n",
    "\n",
    "sum_words = words.sum(axis=0)\n",
    "\n",
    "words_freq = [(word, sum_words[0, i]) for word, i in cv.vocabulary_.items()]\n",
    "words_freq = sorted(words_freq, key = lambda x: x[1], reverse = True)\n",
    "\n",
    "frequency = pd.DataFrame(words_freq, columns=['word', 'freq'])\n",
    "\n",
    "frequency.head(30).plot(x='word', y='freq', kind='bar', figsize=(15, 7), color = 'orange')\n",
    "plt.title(\"Most Frequently Occuring Words - Top 30\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's time to generate wordclouds for both 'spam' and 'ham' sms to have a rough estimate of the words that has the highest frequency in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First define a function to find all words (excluding numbers and stopwords) of each category\n",
    "def getWords(label):\n",
    "    temp_words = ' '.join(list(df.loc[df['label'] == label]['message'])) \n",
    "    lst_words = []\n",
    "    words = [word.lower() for word in word_tokenize(temp_words) \n",
    "             if word.lower() not in stopwords.words(\"english\") and word.lower().isalpha()]\n",
    "    lst_words = lst_words + words\n",
    "    return lst_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get both spam and ham words\n",
    "spam_words = getWords('spam')\n",
    "ham_words = getWords('ham')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_wordcloud(words):\n",
    "    # exclude stop words \n",
    "    wordcloud = WordCloud(max_words=1000,width=840, height=540).generate(words)\n",
    "    plt.imshow(wordcloud)\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate wordcloud for 'spam' sms\n",
    "generate_wordcloud(' '.join(spam_words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate wordcloud for 'ham' sms\n",
    "generate_wordcloud(' '.join(ham_words))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see words like 'free', 'text', 'call', 'reply', 'mobile' and 'now' appear very often in 'spam' sms. Respectively the most frequent words in 'ham' sms are 'ok', 'will', 'now', 'got', 'gt', 'lt' and etc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get more precise and find out what are the 20 most frequent words in each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>word</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>call</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>free</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>txt</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>u</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ur</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mobile</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>text</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>stop</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>claim</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>reply</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>prize</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>get</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>new</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>send</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>nokia</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>cash</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>urgent</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>win</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>contact</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>service</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       word  frequency\n",
       "0      call        346\n",
       "1      free        217\n",
       "2       txt        156\n",
       "3         u        144\n",
       "4        ur        144\n",
       "5    mobile        123\n",
       "6      text        121\n",
       "7      stop        114\n",
       "8     claim        113\n",
       "9     reply        104\n",
       "10    prize         92\n",
       "11      get         84\n",
       "12      new         69\n",
       "13     send         68\n",
       "14    nokia         65\n",
       "15     cash         62\n",
       "16   urgent         62\n",
       "17      win         60\n",
       "18  contact         56\n",
       "19  service         55"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 20 most frequent spam words\n",
    "Counter = collections.Counter(spam_words)\n",
    "most_occur_spam_words = Counter.most_common(20)\n",
    "df_most_occur_spam_words = pd.DataFrame(most_occur_spam_words, columns=['word','frequency'])\n",
    "df_most_occur_spam_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2153c358>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_most_occur_spam_words.plot(x='word', y='frequency', kind='bar', figsize=(15, 7), color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>word</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>u</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gt</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lt</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>get</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>go</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>got</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ur</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ok</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>know</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>like</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>call</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>good</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>come</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>time</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>love</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>day</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>going</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>one</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>want</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>home</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     word  frequency\n",
       "0       u        973\n",
       "1      gt        318\n",
       "2      lt        316\n",
       "3     get        301\n",
       "4      go        246\n",
       "5     got        242\n",
       "6      ur        237\n",
       "7      ok        235\n",
       "8    know        234\n",
       "9    like        231\n",
       "10   call        230\n",
       "11   good        227\n",
       "12   come        225\n",
       "13   time        195\n",
       "14   love        180\n",
       "15    day        176\n",
       "16  going        168\n",
       "17    one        167\n",
       "18   want        163\n",
       "19   home        158"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 20 most frequent ham words\n",
    "Counter = collections.Counter(ham_words)\n",
    "most_occur_ham_words = Counter.most_common(20)\n",
    "df_most_occur_ham_words = pd.DataFrame(most_occur_ham_words, columns=['word','frequency'])\n",
    "df_most_occur_ham_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22c98a58>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_most_occur_ham_words.plot(x='word', y='frequency', kind='bar', figsize=(15, 7), color = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many cases bigrams is also an important tool in data analysis. So let's see if there some words which occur more frequently than the other ones for each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>bigram</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(please, call)</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(po, box)</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(guaranteed, call)</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(prize, guaranteed)</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(call, landline)</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(selected, receive)</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(contact, u)</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(send, stop)</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(every, week)</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(await, collection)</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(call, claim)</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(urgent, mobile)</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(call, land)</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(land, line)</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(customer, service)</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(chance, win)</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(free, entry)</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(claim, call)</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(private, account)</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(account, statement)</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  bigram  frequency\n",
       "0         (please, call)         45\n",
       "1              (po, box)         24\n",
       "2     (guaranteed, call)         23\n",
       "3    (prize, guaranteed)         22\n",
       "4       (call, landline)         22\n",
       "5    (selected, receive)         19\n",
       "6           (contact, u)         19\n",
       "7           (send, stop)         19\n",
       "8          (every, week)         19\n",
       "9    (await, collection)         19\n",
       "10         (call, claim)         18\n",
       "11      (urgent, mobile)         18\n",
       "12          (call, land)         18\n",
       "13          (land, line)         18\n",
       "14   (customer, service)         17\n",
       "15         (chance, win)         17\n",
       "16         (free, entry)         16\n",
       "17         (claim, call)         16\n",
       "18    (private, account)         16\n",
       "19  (account, statement)         16"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 20 bigrams in spam_words\n",
    "spam_bigrams = ngrams(spam_words, 2)\n",
    "spam_bigrams_freq = collections.Counter(spam_bigrams)\n",
    "most_freq_spam_bigrams = spam_bigrams_freq.most_common(20)\n",
    "df_most_freq_spam_bigrams = pd.DataFrame(most_freq_spam_bigrams, columns=['bigram','frequency'])\n",
    "df_most_freq_spam_bigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22a21240>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_most_freq_spam_bigrams.plot(x='bigram', y='frequency', kind='bar', figsize=(15, 7), color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>bigram</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(lt, gt)</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(gon, na)</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(call, later)</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(let, know)</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(sorry, call)</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(r, u)</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(u, r)</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(good, morning)</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(take, care)</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(u, wan)</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(wan, na)</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(lt, decimal)</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(decimal, gt)</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(new, year)</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(u, get)</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(pls, send)</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(ok, lor)</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(gt, lt)</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(u, still)</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(good, night)</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             bigram  frequency\n",
       "0          (lt, gt)        276\n",
       "1         (gon, na)         58\n",
       "2     (call, later)         50\n",
       "3       (let, know)         39\n",
       "4     (sorry, call)         38\n",
       "5            (r, u)         37\n",
       "6            (u, r)         35\n",
       "7   (good, morning)         30\n",
       "8      (take, care)         29\n",
       "9          (u, wan)         29\n",
       "10        (wan, na)         28\n",
       "11    (lt, decimal)         23\n",
       "12    (decimal, gt)         23\n",
       "13      (new, year)         23\n",
       "14         (u, get)         23\n",
       "15      (pls, send)         22\n",
       "16        (ok, lor)         22\n",
       "17         (gt, lt)         21\n",
       "18       (u, still)         19\n",
       "19    (good, night)         19"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 20 bigrams in ham_words\n",
    "ham_bigrams = ngrams(ham_words, 2)\n",
    "ham_bigrams_freq = collections.Counter(ham_bigrams)\n",
    "most_freq_ham_bigrams = ham_bigrams_freq.most_common(20)\n",
    "df_most_freq_ham_bigrams = pd.DataFrame(most_freq_ham_bigrams, columns=['bigram','frequency'])\n",
    "df_most_freq_ham_bigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a22eb5780>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_most_freq_ham_bigrams.plot(x='bigram', y='frequency', kind='bar', figsize=(15, 7), color = 'blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now define our text precessing function. It will remove any punctuation and stopwords. Also it will convert all letters to lowercase and perform stemming aswell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_text(text):\n",
    "    # remove all punctuation\n",
    "    text = re.sub(r'[^\\w\\d\\s]', ' ', text)\n",
    "    # collapse all white spaces\n",
    "    text = re.sub(r'\\s+', ' ', text)\n",
    "    # convert to lower case\n",
    "    text = re.sub(r'^\\s+|\\s+?$', '', text.lower())\n",
    "    # remove stop words and perform stemming\n",
    "    stop_words = nltk.corpus.stopwords.words('english')\n",
    "    lemmatizer = WordNetLemmatizer() \n",
    "    return ' '.join(\n",
    "        lemmatizer.lemmatize(term) \n",
    "        for term in text.split()\n",
    "        if term not in set(stop_words)\n",
    "    )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>processed_text</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>go jurong point crazy available bugis n great ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</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",
       "      <td>free entry 2 wkly comp win fa cup final tkts 2...</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>u dun say early hor u c already 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",
       "      <td>nah think go usf life around though</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...   \n",
       "1   ham                      Ok lar... Joking wif u oni...   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3   ham  U dun say so early hor... U c already then say...   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...   \n",
       "\n",
       "                                      processed_text  \n",
       "0  go jurong point crazy available bugis n great ...  \n",
       "1                            ok lar joking wif u oni  \n",
       "2  free entry 2 wkly comp win fa cup final tkts 2...  \n",
       "3                u dun say early hor u c already say  \n",
       "4                nah think go usf life around though  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['processed_text'] = df.message.apply(lambda row : preprocess_text(row))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have seen from the EDA step, both 1-gram and 2-gram  play a significant role when it comes to classify a message as spam or ham. So our role is to transform these 1-gram and 2-gram into a feature vector. To do that, we gonna use TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "[5 rows x 39255 columns]"
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     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_vec = TfidfVectorizer(ngram_range=(1, 2)).fit_transform(df.processed_text)\n",
    "tfidf_data = pd.DataFrame(tfidf_vec.toarray())\n",
    "tfidf_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now a data with 38084 columns where each column represent a different 1/2-gram contained in the current message"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Message length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>message</th>\n",
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       "    </tr>\n",
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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>go jurong point crazy available bugis n great ...</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>ok lar joking wif u oni</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>spam</td>\n",
       "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
       "      <td>free entry 2 wkly comp win fa cup final tkts 2...</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ham</td>\n",
       "      <td>U dun say so early hor... U c already then say...</td>\n",
       "      <td>u dun say early hor u c already say</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ham</td>\n",
       "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
       "      <td>nah think go usf life around though</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...   \n",
       "1   ham                      Ok lar... Joking wif u oni...   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3   ham  U dun say so early hor... U c already then say...   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...   \n",
       "\n",
       "                                      processed_text  message_length  \n",
       "0  go jurong point crazy available bugis n great ...             111  \n",
       "1                            ok lar joking wif u oni              29  \n",
       "2  free entry 2 wkly comp win fa cup final tkts 2...             155  \n",
       "3                u dun say early hor u c already say              49  \n",
       "4                nah think go usf life around though              61  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['message_length'] = df.message.apply(lambda row : len(row))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df['message_length'], df['label'])\n",
    "plt.title('Distribution of message length')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1a284ffba8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1a2238f2e8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='message_length', by='label', bins=50, figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Punctuation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_punct(text):\n",
    "    count = sum([1 for char in text if char in string.punctuation])\n",
    "    return round(count/(len(text)), 3)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>processed_text</th>\n",
       "      <th>message_length</th>\n",
       "      <th>punct%</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>go jurong point crazy available bugis n great ...</td>\n",
       "      <td>111</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>ok lar joking wif u oni</td>\n",
       "      <td>29</td>\n",
       "      <td>20.7</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>free entry 2 wkly comp win fa cup final tkts 2...</td>\n",
       "      <td>155</td>\n",
       "      <td>3.9</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>u dun say early hor u c already say</td>\n",
       "      <td>49</td>\n",
       "      <td>12.2</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>nah think go usf life around though</td>\n",
       "      <td>61</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...   \n",
       "1   ham                      Ok lar... Joking wif u oni...   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3   ham  U dun say so early hor... U c already then say...   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...   \n",
       "\n",
       "                                      processed_text  message_length  punct%  \n",
       "0  go jurong point crazy available bugis n great ...             111     8.1  \n",
       "1                            ok lar joking wif u oni              29    20.7  \n",
       "2  free entry 2 wkly comp win fa cup final tkts 2...             155     3.9  \n",
       "3                u dun say early hor u c already say              49    12.2  \n",
       "4                nah think go usf life around though              61     3.3  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['punct%'] = df.message.apply(lambda row : count_punct(row))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df['punct%'], df['label'])\n",
    "plt.title('Distribution of punctuation')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1a28d13908>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1a28b3db70>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='punct%', by='label', bins=50, figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Http address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_https(text):\n",
    "    http_regex = re.compile(r'(http[s]?\\S+)|(\\w+\\.[A-Za-z]{2,4}\\S*)')\n",
    "    count = sum([1 for word in text.split() if http_regex.match(word)])\n",
    "    return round(count/(len(text)), 3)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>processed_text</th>\n",
       "      <th>message_length</th>\n",
       "      <th>punct%</th>\n",
       "      <th>httpaddr%</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>go jurong point crazy available bugis n great ...</td>\n",
       "      <td>111</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>ok lar joking wif u oni</td>\n",
       "      <td>29</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.0</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>free entry 2 wkly comp win fa cup final tkts 2...</td>\n",
       "      <td>155</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</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>u dun say early hor u c already say</td>\n",
       "      <td>49</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</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>nah think go usf life around though</td>\n",
       "      <td>61</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...   \n",
       "1   ham                      Ok lar... Joking wif u oni...   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3   ham  U dun say so early hor... U c already then say...   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...   \n",
       "\n",
       "                                      processed_text  message_length  punct%  \\\n",
       "0  go jurong point crazy available bugis n great ...             111     8.1   \n",
       "1                            ok lar joking wif u oni              29    20.7   \n",
       "2  free entry 2 wkly comp win fa cup final tkts 2...             155     3.9   \n",
       "3                u dun say early hor u c already say              49    12.2   \n",
       "4                nah think go usf life around though              61     3.3   \n",
       "\n",
       "   httpaddr%  \n",
       "0        0.0  \n",
       "1        0.0  \n",
       "2        0.0  \n",
       "3        0.0  \n",
       "4        0.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['httpaddr%'] = df.message.apply(lambda row : count_https(row))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df['httpaddr%'], df['label'])\n",
    "plt.title('Distribution of https')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1a28818d30>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1a286f18d0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='httpaddr%', by='label', bins=50, figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Money symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_money_symb(text):\n",
    "    money_symb = ['£','$','€']\n",
    "    count = sum([1 for char in text if char in money_symb])\n",
    "    return round(count/(len(text)), 3)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>processed_text</th>\n",
       "      <th>message_length</th>\n",
       "      <th>punct%</th>\n",
       "      <th>httpaddr%</th>\n",
       "      <th>money_symb%</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>go jurong point crazy available bugis n great ...</td>\n",
       "      <td>111</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>ok lar joking wif u oni</td>\n",
       "      <td>29</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>free entry 2 wkly comp win fa cup final tkts 2...</td>\n",
       "      <td>155</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>u dun say early hor u c already say</td>\n",
       "      <td>49</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>nah think go usf life around though</td>\n",
       "      <td>61</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...   \n",
       "1   ham                      Ok lar... Joking wif u oni...   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3   ham  U dun say so early hor... U c already then say...   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...   \n",
       "\n",
       "                                      processed_text  message_length  punct%  \\\n",
       "0  go jurong point crazy available bugis n great ...             111     8.1   \n",
       "1                            ok lar joking wif u oni              29    20.7   \n",
       "2  free entry 2 wkly comp win fa cup final tkts 2...             155     3.9   \n",
       "3                u dun say early hor u c already say              49    12.2   \n",
       "4                nah think go usf life around though              61     3.3   \n",
       "\n",
       "   httpaddr%  money_symb%  \n",
       "0        0.0          0.0  \n",
       "1        0.0          0.0  \n",
       "2        0.0          0.0  \n",
       "3        0.0          0.0  \n",
       "4        0.0          0.0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['money_symb%'] = df.message.apply(lambda row : count_money_symb(row))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df['money_symb%'], df['label'])\n",
    "plt.title('Distribution of money symbols')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1a28bda240>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1a22071748>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='money_symb%', by='label', bins=50, figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Phone number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_phone_numbers(text):\n",
    "    phone_regex = re.compile(r'\\b(\\+\\d{1,2}\\s)?\\d?[\\-(.]?\\d{3}\\)?[\\s.-]?\\d{3}[\\s.-]?\\d{4}\\b')\n",
    "    count = sum([1 for word in text.split() if phone_regex.match(word)])\n",
    "    return round(count/(len(text)), 3)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>processed_text</th>\n",
       "      <th>message_length</th>\n",
       "      <th>punct%</th>\n",
       "      <th>httpaddr%</th>\n",
       "      <th>money_symb%</th>\n",
       "      <th>phone_numb%</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>go jurong point crazy available bugis n great ...</td>\n",
       "      <td>111</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>ok lar joking wif u oni</td>\n",
       "      <td>29</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>free entry 2 wkly comp win fa cup final tkts 2...</td>\n",
       "      <td>155</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>u dun say early hor u c already say</td>\n",
       "      <td>49</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>nah think go usf life around though</td>\n",
       "      <td>61</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...   \n",
       "1   ham                      Ok lar... Joking wif u oni...   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3   ham  U dun say so early hor... U c already then say...   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...   \n",
       "\n",
       "                                      processed_text  message_length  punct%  \\\n",
       "0  go jurong point crazy available bugis n great ...             111     8.1   \n",
       "1                            ok lar joking wif u oni              29    20.7   \n",
       "2  free entry 2 wkly comp win fa cup final tkts 2...             155     3.9   \n",
       "3                u dun say early hor u c already say              49    12.2   \n",
       "4                nah think go usf life around though              61     3.3   \n",
       "\n",
       "   httpaddr%  money_symb%  phone_numb%  \n",
       "0        0.0          0.0          0.0  \n",
       "1        0.0          0.0          0.0  \n",
       "2        0.0          0.0          0.0  \n",
       "3        0.0          0.0          0.0  \n",
       "4        0.0          0.0          0.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['phone_numb%'] = df.message.apply(lambda row : count_phone_numbers(row))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df['phone_numb%'], df['label'])\n",
    "plt.title('Distribution of phone numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1a21ff0400>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1a28e65358>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='phone_numb%', by='label', bins=50, figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numbers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_numbers(text):\n",
    "    count = sum([1 for word in text.split() if word.isdigit()])\n",
    "    return round(count/(len(text)), 3)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>label</th>\n",
       "      <th>message</th>\n",
       "      <th>processed_text</th>\n",
       "      <th>message_length</th>\n",
       "      <th>punct%</th>\n",
       "      <th>httpaddr%</th>\n",
       "      <th>money_symb%</th>\n",
       "      <th>phone_numb%</th>\n",
       "      <th>numbers%</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>go jurong point crazy available bugis n great ...</td>\n",
       "      <td>111</td>\n",
       "      <td>8.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</td>\n",
       "      <td>Ok lar... Joking wif u oni...</td>\n",
       "      <td>ok lar joking wif u oni</td>\n",
       "      <td>29</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>free entry 2 wkly comp win fa cup final tkts 2...</td>\n",
       "      <td>155</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</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>u dun say early hor u c already say</td>\n",
       "      <td>49</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>nah think go usf life around though</td>\n",
       "      <td>61</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label                                            message  \\\n",
       "0   ham  Go until jurong point, crazy.. Available only ...   \n",
       "1   ham                      Ok lar... Joking wif u oni...   \n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...   \n",
       "3   ham  U dun say so early hor... U c already then say...   \n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...   \n",
       "\n",
       "                                      processed_text  message_length  punct%  \\\n",
       "0  go jurong point crazy available bugis n great ...             111     8.1   \n",
       "1                            ok lar joking wif u oni              29    20.7   \n",
       "2  free entry 2 wkly comp win fa cup final tkts 2...             155     3.9   \n",
       "3                u dun say early hor u c already say              49    12.2   \n",
       "4                nah think go usf life around though              61     3.3   \n",
       "\n",
       "   httpaddr%  money_symb%  phone_numb%  numbers%  \n",
       "0        0.0          0.0          0.0       0.0  \n",
       "1        0.0          0.0          0.0       0.0  \n",
       "2        0.0          0.0          0.0       1.3  \n",
       "3        0.0          0.0          0.0       0.0  \n",
       "4        0.0          0.0          0.0       0.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['numbers%'] = df.message.apply(lambda row : count_numbers(row))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(df['numbers%'], df['label'])\n",
    "plt.title('Distribution of numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x1a9164acc0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x1a916a5fd0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='numbers%', by='label', bins=50, figsize=(12,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see, only phone number and money symbol are trustworthy indicators of a spam sms. So our final data will contain money_symb%, phone_numb% and tfidf_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "   money_symb%  phone_numb%    0    1    2    3    4    5    6    7  ...  \\\n",
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       "\n",
       "[5 rows x 39257 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data = pd.concat([df['money_symb%'], df['phone_numb%'], tfidf_data], axis=1)\n",
    "final_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and evaluating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split data set into train and test set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(final_data, df['label'], test_size=.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit classifier and make predictions on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.99      0.99      0.99       974\n",
      "        spam       0.96      0.94      0.95       141\n",
      "\n",
      "   micro avg       0.99      0.99      0.99      1115\n",
      "   macro avg       0.98      0.97      0.97      1115\n",
      "weighted avg       0.99      0.99      0.99      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = MultinomialNB(alpha=0.2)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.987\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy score: {}\".format(round(accuracy_score(y_test,y_pred),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a confusion matrix to take a peek at what types of mistakes the classifier is making."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>spam</th>\n",
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      "text/plain": [
       "            predicted     \n",
       "                 spam  ham\n",
       "actual spam       969    5\n",
       "       ham          9  132"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    confusion_matrix(y_test, y_pred),\n",
    "    index=[['actual', 'actual'], ['spam', 'ham']],\n",
    "    columns=[['predicted', 'predicted'], ['spam', 'ham']]\n",
    ")"
   ]
  }
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