spam-filter-using-naive-bayes-classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "38fc7b0f579e283e26411cd01392d2015f5d59d3"
   },
   "source": [
    "# Spam Filter using Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "3cd0ba00276179eb630b14d809c2bfbdcdc7f5c7",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:39:20.305911Z",
     "iopub.status.busy": "2021-11-24T06:39:20.305370Z",
     "iopub.status.idle": "2021-11-24T06:39:20.320069Z",
     "shell.execute_reply": "2021-11-24T06:39:20.318710Z",
     "shell.execute_reply.started": "2021-11-24T06:39:20.305796Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['spam.csv']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print(os.listdir(\"../input\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "52bd1fe4c789af10c8f6bcebe9e3c8fd3c101781"
   },
   "source": [
    "**Import libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "11ce4eaf13356cd47e341aea85670f36a13164d8",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:39:21.011281Z",
     "iopub.status.busy": "2021-11-24T06:39:21.010872Z",
     "iopub.status.idle": "2021-11-24T06:39:21.845560Z",
     "shell.execute_reply": "2021-11-24T06:39:21.844022Z",
     "shell.execute_reply.started": "2021-11-24T06:39:21.011211Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6213264582afb6541e4ae286710ac0051a859685"
   },
   "source": [
    "**Read csv file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "8163f26bff8891be58ce9cbf544d8a67f71974ec",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:39:21.888138Z",
     "iopub.status.busy": "2021-11-24T06:39:21.887751Z",
     "iopub.status.idle": "2021-11-24T06:39:21.956296Z",
     "shell.execute_reply": "2021-11-24T06:39:21.955287Z",
     "shell.execute_reply.started": "2021-11-24T06:39:21.888082Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('../input/spam.csv', encoding='latin-1')[['v1', 'v2']]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing after renaming the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T06:39:22.585337Z",
     "iopub.status.busy": "2021-11-24T06:39:22.585024Z",
     "iopub.status.idle": "2021-11-24T06:39:22.616393Z",
     "shell.execute_reply": "2021-11-24T06:39:22.615381Z",
     "shell.execute_reply.started": "2021-11-24T06:39:22.585287Z"
    }
   },
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['label', 'message']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "431a0a9e236da04a37713645f200e580124bdae4"
   },
   "source": [
    "View the lable statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "95fa040f71997610cba921618485ae2d465e1188",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:39:23.465996Z",
     "iopub.status.busy": "2021-11-24T06:39:23.464316Z",
     "iopub.status.idle": "2021-11-24T06:39:23.527769Z",
     "shell.execute_reply": "2021-11-24T06:39:23.527061Z",
     "shell.execute_reply.started": "2021-11-24T06:39:23.464855Z"
    }
   },
   "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 ...  freq\n",
       "label         ...      \n",
       "ham      4825 ...    30\n",
       "spam      747 ...     4\n",
       "\n",
       "[2 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('label').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the counts of ham and spam present in label column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "79bbf66e99ac1a0c4c71dcdd7854060037682c06",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:39:24.395941Z",
     "iopub.status.busy": "2021-11-24T06:39:24.394754Z",
     "iopub.status.idle": "2021-11-24T06:39:24.630223Z",
     "shell.execute_reply": "2021-11-24T06:39:24.629104Z",
     "shell.execute_reply.started": "2021-11-24T06:39:24.395316Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faf5cd62390>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "60d48951ca5086115e08ede667a162ee9b92073a"
   },
   "source": [
    "\n",
    "**Steps**\n",
    "1. Clean and Normalize text\n",
    "2. Convert text into vectors (BoW) we use TfIdf\n",
    "3. Train and test Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "519a4d4c06557a8623252baaff13054162cf7ab5"
   },
   "source": [
    "**Cleaning steps**\n",
    "<br>\n",
    "It will be done in following steps:\n",
    "<br>\n",
    "1. Remove punctuations\n",
    "2. Remove all stopwords\n",
    "3. Apply [stemming](https://en.wikipedia.org/wiki/Stemming) (get the stem of the word). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6969645b7c1eae007f7c533cb01f66e7524637bf"
   },
   "source": [
    "** Write a method to return normailzed text in form of tokens (lemmas)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "500971b5d102765bb5e6537943dd3e3d7ce9af02",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:52:34.578054Z",
     "iopub.status.busy": "2021-11-24T06:52:34.577527Z",
     "iopub.status.idle": "2021-11-24T06:52:34.584974Z",
     "shell.execute_reply": "2021-11-24T06:52:34.583605Z",
     "shell.execute_reply.started": "2021-11-24T06:52:34.578004Z"
    }
   },
   "outputs": [],
   "source": [
    "import string\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import PorterStemmer as Stemmer\n",
    "def pre_process(text):\n",
    "    text = text.lower()\n",
    "    text = ''.join([t for t in text if t not in string.punctuation])\n",
    "    text = [t for t in text.split() if t not in stopwords.words('english')] # All words other tha stopwrods and punctuations in lowercase\n",
    "    st = Stemmer()\n",
    "    text = [st.stem(t) for t in text]\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "21d1a8ec02208c20664d67770a94fc0261920f1a",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:52:41.855572Z",
     "iopub.status.busy": "2021-11-24T06:52:41.855208Z",
     "iopub.status.idle": "2021-11-24T06:52:41.866136Z",
     "shell.execute_reply": "2021-11-24T06:52:41.864939Z",
     "shell.execute_reply.started": "2021-11-24T06:52:41.855506Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['holiday', 'lad', 'mount', 'play', 'well']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre_process('It\\'s holiday lads :D. Mount is playing very well!!!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "659631e15f988f3f7282eaea14799243a7e25b5f",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:52:46.763167Z",
     "iopub.status.busy": "2021-11-24T06:52:46.762846Z",
     "iopub.status.idle": "2021-11-24T06:52:46.853618Z",
     "shell.execute_reply": "2021-11-24T06:52:46.852709Z",
     "shell.execute_reply.started": "2021-11-24T06:52:46.763118Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     [go, jurong, point, crazi, avail, bugi, n, gre...\n",
       "1                          [ok, lar, joke, wif, u, oni]\n",
       "2     [free, entri, 2, wkli, comp, win, fa, cup, fin...\n",
       "3         [u, dun, say, earli, hor, u, c, alreadi, say]\n",
       "4     [nah, dont, think, goe, usf, live, around, tho...\n",
       "5     [freemsg, hey, darl, 3, week, word, back, id, ...\n",
       "6     [even, brother, like, speak, treat, like, aid,...\n",
       "7     [per, request, mell, mell, oru, minnaminungint...\n",
       "8     [winner, valu, network, custom, select, receiv...\n",
       "9     [mobil, 11, month, u, r, entitl, updat, latest...\n",
       "10    [im, gonna, home, soon, dont, want, talk, stuf...\n",
       "11    [six, chanc, win, cash, 100, 20000, pound, txt...\n",
       "12    [urgent, 1, week, free, membership, å£100000, ...\n",
       "13    [ive, search, right, word, thank, breather, pr...\n",
       "14                                       [date, sunday]\n",
       "15    [xxxmobilemovieclub, use, credit, click, wap, ...\n",
       "16                                     [oh, kim, watch]\n",
       "17    [eh, u, rememb, 2, spell, name, ye, v, naughti...\n",
       "18    [fine, thatåõ, way, u, feel, thatåõ, way, gota...\n",
       "19    [england, v, macedonia, dont, miss, goalsteam,...\n",
       "20                               [serious, spell, name]\n",
       "Name: message, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test with our dataset\n",
    "df['message'][:21].apply(pre_process)"
   ]
  },
  {
   "attachments": {
    "6a549a7a-1244-4e21-bbb9-9a9408450d87.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refer scikitlearn for details on TfIDf\n",
    "\n",
    "\n",
    "![image.png](attachment:6a549a7a-1244-4e21-bbb9-9a9408450d87.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "99201820a778649a32132381142034c46ec69c7b"
   },
   "source": [
    "**Fit and transform SMS corpus**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "0afb400d1c0b2e74bccc09e44b1698616034b003",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:56:19.745443Z",
     "iopub.status.busy": "2021-11-24T06:56:19.744758Z",
     "iopub.status.idle": "2021-11-24T06:56:34.070538Z",
     "shell.execute_reply": "2021-11-24T06:56:34.069675Z",
     "shell.execute_reply.started": "2021-11-24T06:56:19.745370Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf= TfidfVectorizer(analyzer=pre_process)\n",
    "data = tfidf.fit_transform(df['message'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T06:58:33.149023Z",
     "iopub.status.busy": "2021-11-24T06:58:33.148405Z",
     "iopub.status.idle": "2021-11-24T06:58:33.154748Z",
     "shell.execute_reply": "2021-11-24T06:58:33.153911Z",
     "shell.execute_reply.started": "2021-11-24T06:58:33.148956Z"
    }
   },
   "outputs": [],
   "source": [
    "message=df.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "eff9cedfea7508bdafd8a361acbd82a554e16ea4",
    "execution": {
     "iopub.execute_input": "2021-11-24T06:58:33.969260Z",
     "iopub.status.busy": "2021-11-24T06:58:33.968932Z",
     "iopub.status.idle": "2021-11-24T06:58:33.982106Z",
     "shell.execute_reply": "2021-11-24T06:58:33.981203Z",
     "shell.execute_reply.started": "2021-11-24T06:58:33.969198Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 6535)\t1.0\n",
      "  (1, 7741)\t0.18906287739887084\n",
      "  (1, 7708)\t0.14471405235314777\n",
      "  (1, 7276)\t0.12336193745345178\n",
      "  (1, 7099)\t0.2190885570936267\n",
      "  (1, 6959)\t0.11759458460817876\n",
      "  (1, 5856)\t0.16027970945850903\n",
      "  (1, 5815)\t0.2330497030932461\n",
      "  (1, 5768)\t0.2330497030932461\n",
      "  (1, 4592)\t0.15903719770411495\n",
      "  (1, 3091)\t0.11505037200973967\n",
      "  (1, 2969)\t0.16669800498830506\n",
      "  (1, 2868)\t0.4660994061864922\n",
      "  (1, 2748)\t0.3571909758763146\n",
      "  (1, 2246)\t0.20302402339849024\n",
      "  (1, 2076)\t0.19516151371199045\n",
      "  (1, 1180)\t0.16669800498830506\n",
      "  (1, 833)\t0.2190885570936267\n",
      "  (1, 433)\t0.22518719340674634\n",
      "  (1, 420)\t0.22518719340674634\n",
      "  (1, 413)\t0.09987750376879972\n",
      "  (1, 72)\t0.2330497030932461\n"
     ]
    }
   ],
   "source": [
    "print(tfidf.transform(df.iloc[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8b59fb4f20880e31341d09e0590de002693d134c"
   },
   "source": [
    "**Having messages in form of vectors, we are ready to train our classifier. <br>We will use Naive Bayes which is well known classifier while working with text data. \n",
    "<br>Before that we will use pipeline feature of sklearn to create a pipeline of TfidfVectorizer followed by Classifier.**\n",
    "<br>Input will be message passed to first stage TfidfVectorizer which will transform it and pass it to Naive Bayes Classifier to get output label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T07:05:20.760005Z",
     "iopub.status.busy": "2021-11-24T07:05:20.759688Z",
     "iopub.status.idle": "2021-11-24T07:05:20.766249Z",
     "shell.execute_reply": "2021-11-24T07:05:20.764986Z",
     "shell.execute_reply.started": "2021-11-24T07:05:20.759959Z"
    }
   },
   "outputs": [],
   "source": [
    " from sklearn.base import TransformerMixin\n",
    "class DenseTransformer(TransformerMixin):\n",
    "\n",
    "    def fit(self, X, y=None, **fit_params):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None, **fit_params):\n",
    "        return X.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "5685a89bdfa619ae1413437aee6d4024077c73d3",
    "execution": {
     "iopub.execute_input": "2021-11-24T07:10:25.399720Z",
     "iopub.status.busy": "2021-11-24T07:10:25.399266Z",
     "iopub.status.idle": "2021-11-24T07:10:25.404975Z",
     "shell.execute_reply": "2021-11-24T07:10:25.404239Z",
     "shell.execute_reply.started": "2021-11-24T07:10:25.399679Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.naive_bayes import GaussianNB, MultinomialNB\n",
    "spam_filter = Pipeline([\n",
    "    ('vectorizer', TfidfVectorizer(analyzer=pre_process)),\n",
    "    ('to_dense', DenseTransformer()),\n",
    "    ('classifier', GaussianNB())                   \n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8707bbc4eb1ad42849c45793ff7c78e6977de6a1"
   },
   "source": [
    "train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "c946f692d4c604cb5c0b94d14ad049ae54db140c",
    "execution": {
     "iopub.execute_input": "2021-11-24T07:10:26.219578Z",
     "iopub.status.busy": "2021-11-24T07:10:26.219122Z",
     "iopub.status.idle": "2021-11-24T07:10:26.227193Z",
     "shell.execute_reply": "2021-11-24T07:10:26.226288Z",
     "shell.execute_reply.started": "2021-11-24T07:10:26.219526Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x=df['message']\n",
    "y=df['label']\n",
    "x=x.values\n",
    "y=y.values\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.20, random_state = 21) #Pareto principle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "ab41b3101572b0a7172e4936d2abd58995ec78b6"
   },
   "source": [
    "**Train spam_filter**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "577d06464b3350f7c40b583818b75d97875205a0",
    "execution": {
     "iopub.execute_input": "2021-11-24T07:10:27.075118Z",
     "iopub.status.busy": "2021-11-24T07:10:27.074736Z",
     "iopub.status.idle": "2021-11-24T07:10:39.760170Z",
     "shell.execute_reply": "2021-11-24T07:10:39.759174Z",
     "shell.execute_reply.started": "2021-11-24T07:10:27.075014Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('vectorizer', TfidfVectorizer(analyzer=<function pre_process at 0x7faf8c4ac400>,\n",
       "        binary=False, decode_error='strict', dtype=<class 'numpy.float64'>,\n",
       "        encoding='utf-8', input='content', lowercase=True, max_df=1.0,\n",
       "        max_features=None, min_df=1, ngram_range=(1, 1), norm='l...ransformer object at 0x7faf4fee5390>), ('classifier', GaussianNB(priors=None, var_smoothing=1e-09))])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam_filter.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d8278911d15092c3c4b226636bab9c79b4dd310b"
   },
   "source": [
    "**Predict for test cases**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "0591c0c7bb36f033051855728908c2ef0b1fa2f5",
    "execution": {
     "iopub.execute_input": "2021-11-24T07:10:39.761628Z",
     "iopub.status.busy": "2021-11-24T07:10:39.761402Z",
     "iopub.status.idle": "2021-11-24T07:10:43.024963Z",
     "shell.execute_reply": "2021-11-24T07:10:43.023238Z",
     "shell.execute_reply.started": "2021-11-24T07:10:39.761593Z"
    }
   },
   "outputs": [],
   "source": [
    "predictions = spam_filter.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "0b5dd451d1f1cf5cc1d7548720474bd0293b18b9",
    "execution": {
     "iopub.execute_input": "2021-11-24T07:10:43.026961Z",
     "iopub.status.busy": "2021-11-24T07:10:43.026549Z",
     "iopub.status.idle": "2021-11-24T07:10:43.034807Z",
     "shell.execute_reply": "2021-11-24T07:10:43.033736Z",
     "shell.execute_reply.started": "2021-11-24T07:10:43.026889Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of test cases 1115\n",
      "Number of wrong of predictions 140\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "for i in range(len(y_test)):\n",
    "    if y_test[i] != predictions[i]:\n",
    "        count += 1\n",
    "print('Total number of test cases', len(y_test))\n",
    "print('Number of wrong of predictions', count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "17916e6ce16c1eee2f3fc765a87b247e8eb008aa"
   },
   "source": [
    "**Check for wrong predictions that were classified as ham**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "4e826b346995dd748e156f21122606458f6c8c7c",
    "execution": {
     "iopub.execute_input": "2021-11-24T07:10:43.036759Z",
     "iopub.status.busy": "2021-11-24T07:10:43.036372Z",
     "iopub.status.idle": "2021-11-24T07:10:43.051116Z",
     "shell.execute_reply": "2021-11-24T07:10:43.049286Z",
     "shell.execute_reply.started": "2021-11-24T07:10:43.036688Z"
    }
   },
   "outputs": [],
   "source": [
    "#x_test[y_test != predictions]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "9493853ec664ba66d1dd3f4b65d3f222f861276c"
   },
   "source": [
    "**Use classification report to get more details**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "cf9b861cfd6e62d284c87d30e9b840c2271b60b2",
    "execution": {
     "iopub.execute_input": "2021-11-24T07:10:43.053391Z",
     "iopub.status.busy": "2021-11-24T07:10:43.053003Z",
     "iopub.status.idle": "2021-11-24T07:10:43.101501Z",
     "shell.execute_reply": "2021-11-24T07:10:43.100430Z",
     "shell.execute_reply.started": "2021-11-24T07:10:43.053329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.87      0.98      0.92       861\n",
      "        spam       0.91      0.50      0.64       254\n",
      "\n",
      "   micro avg       0.87      0.87      0.87      1115\n",
      "   macro avg       0.89      0.74      0.78      1115\n",
      "weighted avg       0.88      0.87      0.86      1115\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.874439461883408"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(classification_report(predictions, y_test))\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0eb6dde3ac2244ebfe7f1ec9bf015719018d899b"
   },
   "source": [
    "Function to predict whether passed message is ham or spam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "f471837c49484f5fbdcd71b21b649c1dd5cf0a02",
    "execution": {
     "iopub.execute_input": "2021-11-24T07:10:43.102990Z",
     "iopub.status.busy": "2021-11-24T07:10:43.102707Z",
     "iopub.status.idle": "2021-11-24T07:10:43.117508Z",
     "shell.execute_reply": "2021-11-24T07:10:43.116574Z",
     "shell.execute_reply.started": "2021-11-24T07:10:43.102938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'spam'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def detect_spam(s):\n",
    "    return spam_filter.predict([s])[0]\n",
    "detect_spam('Your cash-balance is currently 500 pounds - to maximize your cash-in now, send COLLECT to 83600.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T07:16:55.926648Z",
     "iopub.status.busy": "2021-11-24T07:16:55.926191Z",
     "iopub.status.idle": "2021-11-24T07:16:55.931245Z",
     "shell.execute_reply": "2021-11-24T07:16:55.930284Z",
     "shell.execute_reply.started": "2021-11-24T07:16:55.926605Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-24T07:19:08.479348Z",
     "iopub.status.busy": "2021-11-24T07:19:08.478643Z",
     "iopub.status.idle": "2021-11-24T07:19:08.494250Z",
     "shell.execute_reply": "2021-11-24T07:19:08.492784Z",
     "shell.execute_reply.started": "2021-11-24T07:19:08.479281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[848,  13],\n",
       "       [127, 127]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(predictions,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}