Sms Spam Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd9efb93-97e9-4729-9498-81715f7a8983",
   "metadata": {},
   "source": [
    "# Section I Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ba5f1b-2af7-4828-9fab-ef2db02a5dfa",
   "metadata": {},
   "source": [
    "## Importing necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dcea4668-9e03-410d-b911-082eafaf5e04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries for this task.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import string\n",
    "import seaborn as sns\n",
    "import re\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10bb3b62-af92-4acf-bec4-f22ef7fce891",
   "metadata": {},
   "source": [
    "## Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36ee3fef-717d-4885-9885-973460d4c1fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>sms</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": [
       "  class                                                sms\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 = pd.read_csv('E:\\\\4.2\\\\442\\\\Spam Data Task\\\\Jupyter\\\\spam.csv', encoding='latin-1')\n",
    "df = df.drop(['Unnamed: 2','Unnamed: 3','Unnamed: 4'],axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e0ec77b-1fe0-4411-99f9-cb7708a5921d",
   "metadata": {},
   "source": [
    "## General idea about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3154198a-f810-4eb3-a12a-28efa66e113f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">sms</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>class</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": [
       "        sms                                                               \n",
       "      count unique                                                top freq\n",
       "class                                                                     \n",
       "ham    4825   4516                             Sorry, I'll call later   30\n",
       "spam    747    653  Please call our customer service representativ...    4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.groupby('class').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc9f808c-e62b-4b66-ac7a-aff5aae697e9",
   "metadata": {},
   "source": [
    "The above shows spam vs ham messages we in the dataset. We have 747 spam messages and 4825 ham messages.\n",
    "653 of these spam messages and 4516 of these ham messages are unique."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b64d03-84c9-4b7b-ab49-d2e70fc8f6d7",
   "metadata": {},
   "source": [
    "## More information about columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "465d3f07-d2b0-4f7b-a0d7-4b139121fb1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5572 entries, 0 to 5571\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   class   5572 non-null   object\n",
      " 1   sms     5572 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 87.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9060ccf4-8e13-4628-b7d5-a7a73442a41b",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>sms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5572</td>\n",
       "      <td>5572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>ham</td>\n",
       "      <td>Sorry, I'll call later</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>4825</td>\n",
       "      <td>30</td>\n",
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      "text/plain": [
       "       class                     sms\n",
       "count   5572                    5572\n",
       "unique     2                    5169\n",
       "top      ham  Sorry, I'll call later\n",
       "freq    4825                      30"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5c0aa374-d179-4c9f-a126-97e9fbb2bca9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5572, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Previewing data shape.\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "366cbed3-bd69-417d-b99d-461073a3a374",
   "metadata": {},
   "source": [
    "## Previewing ham data head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2a003f1d-8943-4bd4-a2d0-80fb2e144e44",
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>sms</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>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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ham</td>\n",
       "      <td>Even my brother is not like to speak with me. ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  class                                                sms\n",
       "0   ham  Go until jurong point, crazy.. Available only ...\n",
       "1   ham                      Ok lar... Joking wif u oni...\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",
       "6   ham  Even my brother is not like to speak with me. ..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ham = df[df['class']=='ham']\n",
    "ham.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75a3eae3-dd8b-4763-9fc5-b4aba92d9e8e",
   "metadata": {},
   "source": [
    "## Previewing spam data head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "82c973be-0a64-43fd-9909-c4207b0d25b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>sms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\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>5</th>\n",
       "      <td>spam</td>\n",
       "      <td>FreeMsg Hey there darling it's been 3 week's n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>spam</td>\n",
       "      <td>WINNER!! As a valued network customer you have...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>spam</td>\n",
       "      <td>Had your mobile 11 months or more? U R entitle...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>spam</td>\n",
       "      <td>SIX chances to win CASH! From 100 to 20,000 po...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   class                                                sms\n",
       "2   spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
       "5   spam  FreeMsg Hey there darling it's been 3 week's n...\n",
       "8   spam  WINNER!! As a valued network customer you have...\n",
       "9   spam  Had your mobile 11 months or more? U R entitle...\n",
       "11  spam  SIX chances to win CASH! From 100 to 20,000 po..."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam = df[df['class']=='spam']\n",
    "spam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e030bf6-d918-43ed-b13c-586263cf56e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((4825, 2), (747, 2))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Previewing ham and spam shape.\n",
    "ham.shape, spam.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d5328d2-ecff-467f-ba75-395f6c58bbe3",
   "metadata": {},
   "source": [
    "# Step 2:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "721dce76-b566-4283-b87f-d9e006586007",
   "metadata": {},
   "source": [
    "## Extracting features from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d729eec-9743-4930-b1c7-995147ad42e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>sms</th>\n",
       "      <th>Length</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>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ham</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>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>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>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  class                                                sms  Length\n",
       "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
       "1   ham                      Ok lar... Joking wif u oni...      29\n",
       "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
       "3   ham  U dun say so early hor... U c already then say...      49\n",
       "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Length\"] = df[\"sms\"].apply(len)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ac9d1ab-92d0-42da-b759-e504343b150f",
   "metadata": {},
   "source": [
    "### Length of the longest message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "304a10f4-6836-40b3-8db7-b88f1fbd1ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "910"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Length\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0966af91-3516-4e5d-bd75-1681630f237a",
   "metadata": {},
   "source": [
    "### Longest message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5c6490d7-a158-4dd4-8fc4-df14b68a3e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"For me the love should start with attraction.i should feel that I need her every time around me.she should be the first thing which comes in my thoughts.I would start the day and end it with her.she should be there every time I dream.love will be then when my every breath has her name.my life should happen around her.my life will be named to her.I would cry for her.will give all my happiness and take all her sorrows.I will be ready to fight with anyone for her.I will be in love when I will be doing the craziest things for her.love will be when I don't have to proove anyone that my girl is the most beautiful lady on the whole planet.I will always be singing praises for her.love will be when I start up making chicken curry and end up makiing sambar.life will be the most beautiful then.will get every morning and thank god for the day because she is with me.I would like to say a lot..will tell later..\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Length\"]==910][\"sms\"].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd54d0b5-7e46-4a83-8e32-3f33d9cd737b",
   "metadata": {},
   "source": [
    "### Length of the shortest message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf35ba55-fc97-4d66-a4ba-e35fa6f8b8d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "910"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Length\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6841ba8e-c573-4a81-b4a4-6c6027263739",
   "metadata": {},
   "source": [
    "### Shortest message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3bf9dc6b-220b-44a0-96f6-d0b4cb5b5d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ok'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Length\"]==2][\"sms\"].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "305f2efa-8c9f-4735-9372-a003ab5619b0",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6f476b-ba08-4b09-9ed7-320ab4a30cc9",
   "metadata": {},
   "source": [
    "### Displot - distribution of length of messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f3232ed8-1274-4b7b-b5c5-4e05d0d24bef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21e1c0d1880>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df[\"Length\"], bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5952ab7-1d1d-4c92-bf1d-d352f16aba00",
   "metadata": {},
   "source": [
    "### Barplot - distribution of length of messages by class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "18aa1cfa-d9d4-4832-ada4-44fd0bf591f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='class', ylabel='Length'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.barplot(x='class', y='Length', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c653c1-bd85-49dd-b0ea-43b9124b376c",
   "metadata": {},
   "source": [
    "### Histogram - distribution of length of messages by class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d0a828fe-837e-49ef-9052-45f23d3b07be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'ham'}>,\n",
       "       <AxesSubplot:title={'center':'spam'}>], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column='Length',by='class', bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1389968-60f5-4c8f-92be-431fb492456c",
   "metadata": {},
   "source": [
    "# Section II Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9cd167e5-6463-45a4-802b-0ee168ad70d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus = []\n",
    "ps = PorterStemmer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a219fe10-7b53-4218-aff9-36e7a7cd997a",
   "metadata": {},
   "source": [
    "## Normalization; Tokenization; Removing stop words; Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a507585a-78fd-4ab9-ab74-ad2a8d3c45aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\t\t MESSAGE  0\n",
      "\n",
      " After Application of Regular Expression - Message  0  becomes :-\n",
      "  Go until jurong point  crazy   Available only in bugis n great world la e buffet    Cine there got amore wat   \n",
      "\n",
      " After Lower Case Conversion - Message  0  becomes :-\n",
      "  go until jurong point  crazy   available only in bugis n great world la e buffet    cine there got amore wat   \n",
      "\n",
      " After Splitting - Message  0  becomes :  ['go', 'until', 'jurong', 'point', 'crazy', 'available', 'only', 'in', 'bugis', 'n', 'great', 'world', 'la', 'e', 'buffet', 'cine', 'there', 'got', 'amore', 'wat']\n",
      "\n",
      " After Stemming - Message  0 becomes :-\n",
      "  ['go', 'jurong', 'point', 'crazi', 'avail', 'bugi', 'n', 'great', 'world', 'la', 'e', 'buffet', 'cine', 'got', 'amor', 'wat']\n",
      "\n",
      " Final Prepared - Message  0  becomes :-\n",
      "  go jurong point crazi avail bugi n great world la e buffet cine got amor wat \n",
      "\n",
      "\n",
      "\t\t\t\t MESSAGE  1\n",
      "\n",
      " After Application of Regular Expression - Message  1  becomes :-\n",
      "  Ok lar    Joking wif u oni   \n",
      "\n",
      " After Lower Case Conversion - Message  1  becomes :-\n",
      "  ok lar    joking wif u oni   \n",
      "\n",
      " After Splitting - Message  1  becomes :  ['ok', 'lar', 'joking', 'wif', 'u', 'oni']\n",
      "\n",
      " After Stemming - Message  1 becomes :-\n",
      "  ['ok', 'lar', 'joke', 'wif', 'u', 'oni']\n",
      "\n",
      " Final Prepared - Message  1  becomes :-\n",
      "  ok lar joke wif u oni \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Processing the sms messages.\n",
    "for i in range(0, 5572):\n",
    "    \n",
    "    # Applying Regular Expression\n",
    "    '''\n",
    "    Replace email addresses with 'emailaddr'\n",
    "    Replace URLs with 'httpaddr'\n",
    "    Replace money symbols with 'moneysymb'\n",
    "    Replace phone numbers with 'phonenumbr'\n",
    "    Replace numbers with 'numbr'\n",
    "    '''\n",
    "    msg = df['sms'][i]\n",
    "    msg = re.sub('\\b[\\w\\-.]+?@\\w+?\\.\\w{2,4}\\b', 'emailaddr', df['sms'][i])\n",
    "    msg = re.sub('(http[s]?\\S+)|(\\w+\\.[A-Za-z]{2,4}\\S*)', 'httpaddr', df['sms'][i])\n",
    "    msg = re.sub('£|\\$', 'moneysymb', df['sms'][i])\n",
    "    msg = re.sub('\\b(\\+\\d{1,2}\\s)?\\d?[\\-(.]?\\d{3}\\)?[\\s.-]?\\d{3}[\\s.-]?\\d{4}\\b', 'phonenumbr', df['sms'][i])\n",
    "    msg = re.sub('\\d+(\\.\\d+)?', 'numbr', df['sms'][i])\n",
    "    \n",
    "    ''' Remove all punctuations '''\n",
    "    msg = re.sub('[^\\w\\d\\s]', ' ', df['sms'][i])\n",
    "    \n",
    "    if i<2:\n",
    "        print(\"\\t\\t\\t\\t MESSAGE \", i)\n",
    "    \n",
    "    if i<2:\n",
    "        print(\"\\n After Application of Regular Expression - Message \", i, \" becomes :-\\n \", msg)\n",
    "    \n",
    "    # Converting each word to lower case\n",
    "    msg = msg.lower()    \n",
    "    if i<2:\n",
    "        print(\"\\n After Lower Case Conversion - Message \", i, \" becomes :-\\n \", msg)\n",
    "        \n",
    "    # Splitting words to tokenize\n",
    "    msg = msg.split()    \n",
    "    if i<2:\n",
    "        print(\"\\n After Splitting - Message \", i, \" becomes : \", msg)\n",
    "    \n",
    "    # Stemming with porterstemmer handling stop words\n",
    "    msg = [ps.stem(word) for word in msg if not word in set(stopwords.words('english'))]\n",
    "    if i<2:\n",
    "        print(\"\\n After Stemming - Message \", i, \"becomes :-\\n \", msg)\n",
    "    \n",
    "    # Preparing messages with remaining tokens\n",
    "    msg = ' '.join(msg)\n",
    "    if i<2:\n",
    "        print(\"\\n Final Prepared - Message \", i, \" becomes :-\\n \", msg, \"\\n\\n\")\n",
    "    \n",
    "    # Preparing wordvector corpus\n",
    "    corpus.append(msg)   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "429971e8-5ea1-4b81-81ac-6b6d381d2cf8",
   "metadata": {},
   "source": [
    "## Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9e02dba1-780f-4b2a-9408-4db667b63463",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n",
    "x = cv.fit_transform(corpus).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58e75ec-35c7-4129-b1cc-8e085653384b",
   "metadata": {},
   "source": [
    "# Section III Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "86b3a1db-2ca2-4733-9b08-c118e6cbc538",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ham     4825\n",
      "spam     747\n",
      "Name: class, dtype: int64\n",
      "ham\n",
      "ham\n"
     ]
    }
   ],
   "source": [
    "y = df['class']\n",
    "print (y.value_counts())\n",
    "print(y[0])\n",
    "print(y[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ab9d775-3306-4797-8991-8d8505cf82dc",
   "metadata": {},
   "source": [
    "## Encoding class labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "41fcfebc-31be-4a9b-ada7-7672cacd11f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "print(y[0])\n",
    "print(y[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3418a5-0650-47a3-a40b-6f9fcffe294d",
   "metadata": {},
   "source": [
    "## Splitting the dataset in training and testing sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "083c5bda-1c21-488c-bbcc-c9f22fe6384a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e89e881-64ed-4230-a540-ef0063297839",
   "metadata": {},
   "source": [
    "## Applying Guassian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "17fd0824-d0ff-4ca1-9ac8-48cf2ba01b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_classifier = GaussianNB()\n",
    "bayes_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcdae474-40f3-4206-b92f-a9dde6c51d82",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c4953e90-6cdd-47af-b671-29f399cacacb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = bayes_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce30ef71-7fee-40b9-85eb-20678de0bc23",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9bca59b0-b1c0-4162-a7c9-37e52d6a9553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[824, 125],\n",
       "       [ 19, 147]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83bbc91d-2d0b-430c-ba1f-fed15e99a068",
   "metadata": {},
   "source": [
    "### Heatmap for the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8c1f1961-5a7e-4652-8227-1078a8f1a341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm/np.sum(cm), annot=True, fmt='.2%', cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1c93f11-bcf2-43d2-b6e0-84187aa408b1",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9416016a-09d0-4f20-b76c-9b202bc54c36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.87      0.92       949\n",
      "           1       0.54      0.89      0.67       166\n",
      "\n",
      "    accuracy                           0.87      1115\n",
      "   macro avg       0.76      0.88      0.80      1115\n",
      "weighted avg       0.91      0.87      0.88      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (classification_report(y_test, bayes_classifier.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908099e6-11d9-4a3b-8333-e1a2bdcdb27b",
   "metadata": {},
   "source": [
    "## Accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "50a686f7-b640-4ef7-8d43-f3d94c714aee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.87085 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy : %0.5f \\n\\n\" % accuracy_score(y_test, bayes_classifier.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d6c9e3d-5ca6-409d-97f1-219a1187d317",
   "metadata": {},
   "source": [
    "## F-Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c50e3536-cc30-4bcd-a8e6-7ad9c6e3c5f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Measure: 0.671\n"
     ]
    }
   ],
   "source": [
    "actual = y_test.tolist()\n",
    "predicted = y_pred\n",
    "f_measure = f1_score(actual, predicted, average = 'binary', sample_weight=None, zero_division='warn')\n",
    "print('F-Measure: %.3f' % f_measure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92b54c44-5034-45bc-988a-6d833fe4facd",
   "metadata": {},
   "source": [
    "## Applying Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "97c873ff-8c5a-428d-95e7-09c782a86856",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=50)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(random_state=50)\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d09cc0-bbf9-4c7b-9813-d31ccd9766fe",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b4b9714a-fad0-4ee9-93e2-572726c31b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab2ed66b-1450-4b7a-82ee-868723bdcd89",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "65ca2a3f-1c8f-4ae4-a0f8-bf209b6a7d19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[943,   6],\n",
       "       [ 29, 137]], dtype=int64)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_matrix = confusion_matrix(y_test, y_pred_dt)\n",
    "cf_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31b06ba-2d38-4416-bd4e-db12191425fb",
   "metadata": {},
   "source": [
    "### Heatmap for the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6e210935-6209-4863-a5a0-73bba3b60a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, fmt='.2%', cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71f18944-3769-4aa6-b15b-c610c1104a4c",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d089893f-70e4-4442-90e0-bca9ddb79b78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98       949\n",
      "           1       0.96      0.83      0.89       166\n",
      "\n",
      "    accuracy                           0.97      1115\n",
      "   macro avg       0.96      0.91      0.93      1115\n",
      "weighted avg       0.97      0.97      0.97      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (classification_report(y_test, dt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc8cfd3-be07-4fd7-93d0-18662d013a1d",
   "metadata": {},
   "source": [
    "## Accuracy of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a2f1e745-13b2-409b-b0cf-110f0c331bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.96861 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy : %0.5f \\n\\n\" % accuracy_score(y_test, dt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965d4a1c-b916-478c-a15b-f13a8bdd5608",
   "metadata": {},
   "source": [
    "## F-Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "13b26a86-4ebf-4a36-b4a1-97e428e045d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Measure: 0.887\n"
     ]
    }
   ],
   "source": [
    "actual = y_test.tolist()\n",
    "predicted = y_pred_dt\n",
    "f_measure = f1_score(actual, predicted, average = 'binary', sample_weight=None, zero_division='warn')\n",
    "print('F-Measure: %.3f' % f_measure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e37f8f9-8833-4df9-a80b-7c9ccecfe967",
   "metadata": {},
   "source": [
    "# Undersampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8c4eacfd-3d44-468e-8dce-4079abe26d76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original dataset shape: (5572,)\n",
      "Resample dataset shape (1494,)\n"
     ]
    }
   ],
   "source": [
    "rus = RandomUnderSampler(random_state=42, replacement=True)# fit predictor and target variable\n",
    "x_rus, y_rus = rus.fit_resample(x, y)\n",
    "print('original dataset shape:', y.shape)\n",
    "print('Resample dataset shape', y_rus.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b17e4314-5777-4640-a787-f979e0d23ab6",
   "metadata": {},
   "source": [
    "## Splitting the dataset in training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "eb44bd69-cce3-4364-9f4a-3d3edbcc7eba",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x_rus, y_rus, test_size=0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d60a5f-9a30-41c6-9a8c-195194c0c0ee",
   "metadata": {},
   "source": [
    "## Applying Guassian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4b6871c5-12c0-441f-a658-f7a9363db803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_classifier = GaussianNB()\n",
    "bayes_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65eee88f-3b2e-40d7-8631-09a3593a0053",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a11425c7-e66f-4585-9bf7-a15907f33d4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = bayes_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d7d028-7955-429d-9b24-46175693faf4",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "92756373-4c8b-46cc-9f58-e827e5e04b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[143,   8],\n",
       "       [  8, 140]], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe9cf10-2610-4904-a4e1-c8d30f334fd7",
   "metadata": {},
   "source": [
    "### Heatmap for the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3065d7d2-18f2-415b-86f6-d25bf204721f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm/np.sum(cm), annot=True, fmt='.2%', cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7c3cd34-668c-4925-93dc-ca96d7c76433",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "80fb19a7-4044-43d1-98dc-beffaad80dba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95       151\n",
      "           1       0.95      0.95      0.95       148\n",
      "\n",
      "    accuracy                           0.95       299\n",
      "   macro avg       0.95      0.95      0.95       299\n",
      "weighted avg       0.95      0.95      0.95       299\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (classification_report(y_test, bayes_classifier.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c99d5313-3b14-4a8e-b45a-7f4c75d28ac8",
   "metadata": {},
   "source": [
    "## Accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b4baa189-b677-4a54-a5c9-81cc62c7979c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.94649 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy : %0.5f \\n\\n\" % accuracy_score(y_test, bayes_classifier.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4d9e81-3584-4334-8f2d-69966b910747",
   "metadata": {},
   "source": [
    "## F-Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e758e676-a279-4c8d-b343-dfd7e4e717c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Measure: 0.946\n"
     ]
    }
   ],
   "source": [
    "actual = y_test.tolist()\n",
    "predicted = y_pred\n",
    "f_measure = f1_score(actual, predicted, average = 'binary', sample_weight=None, zero_division='warn')\n",
    "print('F-Measure: %.3f' % f_measure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e52485da-7ddf-40b5-94fb-78689265a153",
   "metadata": {},
   "source": [
    "## Applying Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "61ef204d-b90f-4416-a0dd-d6063a77ba29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=50)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(random_state=50)\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd2dcf2-d5a9-471a-8a01-b508c93ba4e4",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cd541af3-7ddf-4d52-9d75-2cb1d6ba879b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cdc669d-e585-4ca6-8d19-878790638497",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ebb1772f-0bbd-480e-8ef0-dff93d29a064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[145,   6],\n",
       "       [ 18, 130]], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_matrix = confusion_matrix(y_test, y_pred_dt)\n",
    "cf_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44908f76-5cf0-47ea-b66a-d0fc2c77a625",
   "metadata": {},
   "source": [
    "## Heatmap for the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "509d246e-5267-442c-9bec-769c5fc4359a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, fmt='.2%', cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c669e4-553c-4c53-8eb4-5bcfc87675ef",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7603d6dc-055d-48f3-a907-b69d6ddf3b5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.96      0.92       151\n",
      "           1       0.96      0.88      0.92       148\n",
      "\n",
      "    accuracy                           0.92       299\n",
      "   macro avg       0.92      0.92      0.92       299\n",
      "weighted avg       0.92      0.92      0.92       299\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (classification_report(y_test, dt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b867c139-88f0-47f3-9b79-2a664f8c3269",
   "metadata": {},
   "source": [
    "## Accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cc7014f2-6eaf-48a5-bff9-208193b1c7bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.91973 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy : %0.5f \\n\\n\" % accuracy_score(y_test, dt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03c77f76-d232-471a-bf2e-0cbdece850dc",
   "metadata": {},
   "source": [
    "## F-Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ad50a42b-ccd6-4ee5-a3cb-3d32e4ad4c8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Measure: 0.915\n"
     ]
    }
   ],
   "source": [
    "actual = y_test.tolist()\n",
    "predicted = y_pred_dt\n",
    "f_measure = f1_score(actual, predicted, average = 'binary', sample_weight=None, zero_division='warn')\n",
    "print('F-Measure: %.3f' % f_measure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8801e92e-310f-4546-8df5-782fc60c2a68",
   "metadata": {},
   "source": [
    "# Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2ebf97e3-ba4c-4175-ac29-1e0f471a052e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original dataset shape: (5572,)\n",
      "Resample dataset shape (9650,)\n"
     ]
    }
   ],
   "source": [
    "rus = RandomOverSampler(random_state=42)# fit predictor and target variable\n",
    "x_rus, y_rus = rus.fit_resample(x, y)\n",
    "print('original dataset shape:', y.shape)\n",
    "print('Resample dataset shape', y_rus.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee419d20-ef3c-45dd-b7c6-71085c29d42b",
   "metadata": {},
   "source": [
    "## Splitting the dataset in training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "92c0d9ad-fa33-4d81-b060-18391957c8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x_rus, y_rus, test_size=0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9161f0c5-fe09-40ed-9afa-ba078573a8b3",
   "metadata": {},
   "source": [
    "## Applying Guassian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "edf864de-4dea-4d58-bb61-d45956c4dba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_classifier = GaussianNB()\n",
    "bayes_classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "175fb9a8-0248-4ea7-ae74-d011ce885e8a",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4d575c64-31c7-487a-929b-cf81adcc7ebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = bayes_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f61ad4-ef65-4cd8-87fe-70e2c10c4b84",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "73f1e8b5-a692-4fea-aae1-b7e09624f227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[789, 147],\n",
       "       [  0, 994]], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f51a38d-1b64-40e7-81f9-ab13a7bc4f05",
   "metadata": {},
   "source": [
    "### Heatmap for the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1ffac032-ad52-49bb-98a8-f9fa78c4d0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cm/np.sum(cm), annot=True, fmt='.2%', cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e779c4b-d8ac-4ac9-b160-28a5b947caf7",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f3c98f1e-575f-4d0c-95f5-3fd407cd0c55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.84      0.91       936\n",
      "           1       0.87      1.00      0.93       994\n",
      "\n",
      "    accuracy                           0.92      1930\n",
      "   macro avg       0.94      0.92      0.92      1930\n",
      "weighted avg       0.93      0.92      0.92      1930\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (classification_report(y_test, bayes_classifier.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec20bc7-a1c4-40ea-a5d1-0ed116e9606a",
   "metadata": {},
   "source": [
    "## Accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8c051942-c1ae-4550-b94d-b2a3ef3f1349",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.92383 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy : %0.5f \\n\\n\" % accuracy_score(y_test, bayes_classifier.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a77eb966-a798-4719-8b4d-c26836ffdbf7",
   "metadata": {},
   "source": [
    "## F-Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a2a003c4-bf7d-4ade-b3fd-afa7a623ac99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Measure: 0.931\n"
     ]
    }
   ],
   "source": [
    "actual = y_test.tolist()\n",
    "predicted = y_pred\n",
    "f_measure = f1_score(actual, predicted, average = 'binary', sample_weight=None, zero_division='warn')\n",
    "print('F-Measure: %.3f' % f_measure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "078c61c7-0097-4911-8e26-6054e2779564",
   "metadata": {},
   "source": [
    "## Applying Decision Tree Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a4b1a874-fd7c-45fa-aaf1-4b42c183ece8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=50)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier(random_state=50)\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad20c5a6-aa51-417e-a0e0-d493355020fb",
   "metadata": {},
   "source": [
    "## Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "652fd3e5-8076-4937-9b25-22c42bcc983f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_dt = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a123783-3b9b-4e2f-8b01-88b0a7ece5d8",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "dcdd95f7-1e97-4593-8cf9-68f5c5e06bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[915,  21],\n",
       "       [  0, 994]], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_matrix = confusion_matrix(y_test, y_pred_dt)\n",
    "cf_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1911c98b-dedc-43ec-af3e-fcafc118ec73",
   "metadata": {},
   "source": [
    "## Heatmap for the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d9183769-f8ee-44cd-95e0-46957df7a591",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD4CAYAAABPLjVeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAX6klEQVR4nO3deXwW5bnG8d+dhLDIIlUTKESlFRdQK8qm1bIIGmQTV1SOgtKINVbbWsXlcBTr7tFjyyJRsVSRRXtsA0SosqigIrRaFBQNVCEoCVYUPLKF3OcPYgwQ8r4xb+YdhuvLZz7NzDzzzDMVrz69Z4m5OyIiEoyUZA9ARORAotAVEQmQQldEJEAKXRGRACl0RUQClFbXJ2h41oN6PEL2smHGjckegoRQ4/pmte2jYYfcuDNny9tjan2+mqrz0BURCZSF+//AK3RFJFpqP1muUwpdEYmWkM90wz06EZGaMot/idmVZZvZSjMrNLORVewfamYbzOyd8mV4rD410xWRaElJTUg3ZpYKjAV6A0XAEjPLd/cVezSd5u65cQ8vIaMTEQkLS4l/qV5noNDdV7v7dmAqMLC2w1Poiki01KC8YGY5Zra00pJTqadWwNpK60Xl2/Z0vpktM7PnzSwr1vBUXhCRaKnBjTR3zwPyanG2GcAUd99mZlcDk4Ce1R2gma6IREvibqStAyrPXFuXb6vg7v92923lq08Ap8TqVKErItGSuJruEqCtmbUxs3RgMJC/26nMWlZaHQC8H6tTlRdEJFoS9PSCu5eaWS4wB0gFJrr7cjMbDSx193zgl2Y2ACgFvgCGxupXoSsi0ZLAlyPcvQAo2GPbqEo/3wLcUpM+FboiEi0peg1YRCQ4IX8NWKErItGiD96IiAQoQTfS6opCV0SiReUFEZEAqbwgIhIgzXRFRAKkma6ISIA00xURCZCeXhARCZBmuiIiAVJNV0QkQJrpiogESDNdEZEAaaYrIhIcS1HoiogExlReEBEJULgzV6ErItGima6ISIAUuiIiAUrRjTQRkQCFe6Kr0BWRaFF5QUQkQApdEZEAKXRFRAKk0BURCZClKHRFRAKjma6ISIAUuiIiQQp35ip0RSRaNNMVEQmQQldEJEBh//ZCuEcnIlJTVoMlVldm2Wa20swKzWxkNe3ONzM3s46x+tRMV0QiJVHlBTNLBcYCvYEiYImZ5bv7ij3aNQGuBxbH069muiISKWYW9xJDZ6DQ3Ve7+3ZgKjCwinZ3AfcDW+MZn0JXRCKlJqFrZjlmtrTSklOpq1bA2krrReXbKp/rZCDL3WfFOz6VF0QkUmryGrC75wF53+s8ZinAw8DQmhynmW4NpKQYb4y7nD+PPg+Al//7Et4cfwVvjr+C1VOuYfod5+7z2CaN0imcPIJHrj2zYtsdQ0/no8lXs+Gv1+/W9pqBHViaN5QXfnc+9dJ2/SM6rX0rHhjRI/EXJQlx56hb6dXtNC4a1L/K/e7OA/f9joF9z+Li8wfw/orlFft+/8hDXDSoPxcN6s/fZhdUbL9t5I1cfP4Axjz6cMW2J/LGM3/ey3V3IRGQwPLCOiCr0nrr8m3fagIcDywws4+BrkB+rJtpCt0ayB10CivX/LtivddvptD1mkl0vWYSi1d8yl8WfrTPY//ritNZ+O7a3bYVvLmKM657Zq+2g3u2o9PVf+TNFevo3bENACMvO5V7J7+RoCuRROs/YBB/GP/4PvcvWvgqaz/5hL/MnMPto0Zz7+/uBOC1VxfwwfsrePa5F5g0eRpPT5rI119/zUcfrqR+/QZM+3M+K5a/x+bNm9mwoYT3lv2THj17BXVZ+6UEhu4SoK2ZtTGzdGAwkP/tTnf/yt0Pdfcj3f1I4E1ggLsvra7TmKFrZsea2c1m9vvy5WYzOy7WcVHT6tDGZHf+EU/NfnevfU0apdPtpMOZ8XrVoduhbSYZzRvx8t8/3m37Wx98xvov/m+v9mZQLy2VRvXrsaN0J5ec2Y6/LfkXGzfHVaeXJDi5YyeaNWu2z/2vzJ9L3/4DMTNO+MlJfL15Exs2lPCvVavocEpH0tLSaNioEW2PPobXF71GWloa27ZtpaysjNLSHaSmpvDY2D9w9S+uC/Cq9k+JCl13LwVygTnA+8B0d19uZqPNbMD3HV+1oWtmN7Prjp0Bb5UvBkyp7pm1KHrwmp7c9sQrlJX5Xvv6n9aWBe98wuZvtu+1zwzuy+nOLXkL4j7X+L++zSuPXkZWRlPeWL6Oy88+nsfy367N8CXJSkqKyWzRsmI9I7MFG0qKaXvMMbyx6DW2bNnCxo0bWfrWYorXf0abH/2Y5s1/wGUXn8cZ3Xqwds0a3Ms4rl37JF7FfiKBz+m6e4G7H+3uP3b3u8u3jXL3/Crado81y4XYN9KuAtq7+47drsnsYWA5cF9VB5XfAcwBSDvuPNJad401jlDr0+VHlHz5DW9/VMwZJ2bttf+iHsfyxxf3ngEDXN2/A3Pe+hfrPv867vNNmbuCKXN3PQp4y2WnMu4v/+DsTm24rHd7ijZs5uYJ8/G9s1/2Q6eedjor3nuPKy+/hObNf8AJPzmJ1JRUAG68+daKdjfkjuC2UXfyZN5jfPjhB3TpehrnXXBRsoYdamF/DThWeaEM+GEV21uW76uSu+e5e0d377i/By7Aqe1b0a/rUXzwpxz+dGt/up90OBNv7gvAIU0b0vGYlry4eFWVx3Zp90NGDOzAB3/K4d6c7lzaqz13XfmzuM7b8gcH0fGYlsx4vZDrL+jEkLtn8OXX2+jR4YiEXZsEIyMjk+L1n1WslxSv57CMTACuyhnBlOf+wri8ibg7hx955G7HLpg/l+Pateebb76hqGgN9z/0P8x9eQ5btmwJ8hL2GykpFveSDLFmujcAc83sI757Xu1w4Ch21ToOCKMmvsaoia8BcMaJWdxwQSeuvH/XY3mDzjiaFxevYtuOnVUeO+y+7x7fG9K7Pacc3YL/nPhqfOcdejp3/WkhAA3T03B3ytxpVL9ebS5HkuBn3Xsyfcpkzu7Tl/eW/ZPGTZpw2GEZ7Ny5k82bN3Hwwc356MOVFH74IV1P/WnFcTt27ODZZybx6JgJrF3zScUsrmxnGaU7dkDDhsm6pNAK+0y32tB199lmdjS73sz49qHgdcASd686ZQ4wF3Y/loem7f7238ltMxne7yR+8cicao+9e3g3Lu5xHI3q16Nw8giemr2Mu59+HYCf/DgDgHcKSwCYNv99lk4YRtGGzTw8/a06uBKpjVtv+jVLly7hyy830qdXN67+xXWUlpYCcMFFgzn9jG4seu1VBvY9iwYNGnDHXfcAUFpayvChQwA46KDG3HXvA6Slffev5XNTn6XfgHNp2LAhbY8+hq1btnDRef05/YxuNGnaNPgL3Q+EPHMxr+PiYMOzHlT1UfayYcaNyR6ChFDj+rWPzGNunhN35qy8/+zAI1pvpIlIpIR9pqvQFZFISdYNsngpdEUkUhS6IiIBUnlBRCRA+/UjYyIi+xuFrohIgEKeuQpdEYkW3UgTEQmQygsiIgEKeeYqdEUkWjTTFREJUMgzV6ErItGima6ISID09IKISIBCPtFV6IpItKi8ICISoJBnrkJXRKJFM10RkQApdEVEAqSnF0REAhTyia5CV0SiReUFEZEAhTxzFboiEi0pIU9dha6IRIpupImIBCjkmavQFZFo0Y00EZEAhTxzSUn2AEREEslq8CdmX2bZZrbSzArNbGQV+0eY2btm9o6ZLTSzdrH6VOiKSKSkWPxLdcwsFRgL9AHaAZdUEarPuvsJ7n4S8ADwcKzxqbwgIpGSwKcXOgOF7r4awMymAgOBFd82cPdNldofBHisThW6IhIpNXlO18xygJxKm/LcPa/851bA2kr7ioAuVfRxLfBrIB3oGeucCl0RiZSa3EgrD9i8mA2r72MsMNbMLgVuB66orr1CV0QiJYGPjK0Dsiqtty7fti9TgfGxOtWNNBGJFLP4lxiWAG3NrI2ZpQODgfzdz2VtK632BT6K1almuiISKakJmum6e6mZ5QJzgFRgorsvN7PRwFJ3zwdyzawXsAPYSIzSAih0RSRiEvlGmrsXAAV7bBtV6efra9qnQldEIkXfXhARCZC+vSAiEqCQZ65CV0SiRTNdEZEApYa8qKvQFZFICXfkKnRFJGL0O9JERAIU8sxV6IpItOhGmohIgEKeuQpdEYkWPb0gIhKgA768sLHgt3V9CtkPNe+Um+whSAhteXtMrfsI+/dqNdMVkUg54Ge6IiJBCnlJV6ErItGiG2kiIgEKeeYqdEUkWkJe0lXoiki06NsLIiIB0iNjIiIBCvlEV6ErItGipxdERAIU8sxV6IpItOhGmohIgEKeuQpdEYkWlRdERAJkIf/VlApdEYmUtJA/qKvQFZFI0acdRUQCpJquiEiAQj7RVeiKSLToOV0RkQClhvxGWsiHJyJSMylY3EssZpZtZivNrNDMRlax/9dmtsLMlpnZXDM7Ivb4REQixCz+pfp+LBUYC/QB2gGXmFm7PZq9DXR09xOB54EHYo1PoSsikZJi8S8xdAYK3X21u28HpgIDKzdw9/nu/k356ptA65jjq/kliYiEV4pZ3IuZ5ZjZ0kpLTqWuWgFrK60XlW/bl6uAF2ONTzfSRCRSavLwgrvnAXm1P6cNAToC3WK1VeiKSKQk8CPm64CsSuuty7ftxsx6AbcB3dx9W6xOVV4QkUhJqcESwxKgrZm1MbN0YDCQX7mBmXUAJgAD3L0knvFppisikZKoby+4e6mZ5QJzgFRgorsvN7PRwFJ3zwceBBoDz5Wfd427D6iuX4WuiERKIt9Hc/cCoGCPbaMq/dyrpn0qdEUkUvQasIhIgMIduQpdEYmYlJB/21GhKyKREvZHshS6IhIp+s0RIiIBCnfkKnRFJGI00xURCVCqQldEJDjhjlyFrohETMgnugpdEYmWeH4NTzIpdEUkUjTTFREJkGmmKyISHD29ICISoJBnrkJXRKJFoSsiEiDVdEVEAhTyLzsqdEUkWvSbI0REAhT28kLYv/cbSotee5UBfc+mX3Zvnnw8b6/927dv57e/uYF+2b25bPCFrFtXVLHvyccn0C+7NwP6ns2iha8B8MUXX3DFkEs4b2A/5s19uaLt9bnXUFJSXPcXJN/bB7PuZMn0W3lz6kgWTr4JgPN6deDvz9/G//3995zc7vAaHQvQvGkjZo7P5d2/jmLm+FwObtIQgHPPPIm/P38bLz95Az9odhAAbVofytP3DavDK9z/pFj8S1LGl5zT7r927tzJPXePZtxjT/BC/ixmF8xkVWHhbm1e+PNzNG3alJmzX2LI5UP5n4cfAmBVYSGzC2bxv/mzGDfhCe753Z3s3LmTFwtmcuHFg5k89TkmPz0JgAXz53Hsce3IyMgM/BqlZrJzHqXr4Ps4/bIHAFi+6lMG/+ZxFv5jVY2PBbhxWG8WvLWSEwaOZsFbK7lx2FkAXDO4G6cPeYAn/ryIi/t0BOCOa/txx7iZdXBV+y+rwZ9kUOjW0HvvLiMr6whaZ2VRLz2d7HP6smD+3N3azJ83jwEDBwHQ+6yzeevNN3B3FsyfS/Y5fUlPT6d16yyyso7gvXeXUS8tja1btrJj+3ZSUlIoLS1l8tOTGHrl8GRcotTSyn8V89EnJd/7+H7dT+SZGYsBeGbGYvr3OBGAsrIy6tdLo1GDdHaU7uSnHX5M8eebWLVmQ0LGHRVm8S/JoNCtoZLiYlq0bFGxnpGZSXHx7iWAkpJiWrRoCUBaWhqNmzThyy83UlxcTGaL747NbJFJSXExffr2Z8H8uVz982EMzxnBtKnP0q//QBo2bBjMRcn35u7MGJfLosk3ceV5P03IsRmHNGH955sAWP/5JjIOaQLAgxNfYtZj13HOz45n+uyljPx5Nvc+PjtxFxMRVoMlGb73jTQzG+buT+1jXw6QAzBm3ASu+nnO9z3NAaFJkyaMGb+rNrzpq6+Y+EQejzw6hjtH3c6mTZu4fOgwfnJShySPUqpy5rBH+HTDVxzWvDEzH8tl5cfrWRRHWaEmx7rv+s95iz9g3mUfAHBpv87MWbictkdkcMPlZ7Jx0zfc+ODzbNm6I2HXtr8K+2vAtZnp3rmvHe6e5+4d3b1j1AI3IzOT9Z+tr1gvKS4mM3P3umtGRibr138GQGlpKV9v3szBBzcnMzOT4vXfHVu8vpiMPY6d8Ng4hueM4MWCWXQ4+RTuuuc+xo8dU4dXJLXx6YavANiw8Wvy5y2jU/sja31syb830+LQpgC0OLQpG77YvNtxDRvU4z/6d+Gx6a9y+4i+DP/Pp3n9ndUM7tOp9hcUBSGf6lYbuma2bB/Lu8ABeYen/fEnsGbNxxQVrWXH9u3MLphFtx49d2vTvUdP8v/6AgAv/W0Onbt0xczo1qMnswtmsX37doqK1rJmzcccf8KJFcd98snHlBSvp1PnLmzdugVLMcyMbdu2BnqNEp9GDdJp3Kh+xc+9Tj2W5as+rfWxs155lyH9uwAwpH8XZi5Yttuxv7q8F+OmvEJpaRkNG9TDccrKymjUID1Rl7ZfC/uNtFjlhUzgbGDjHtsNeL1ORhRyaWlp3HLbKK7JGU5Z2U7OHXQ+Rx3VlrF/eJT27Y+ne88zGXT+Bdw28rf0y+5N02bNeOChRwA46qi2nJXdh0EDziE1NZVbbx9FampqRd9jHn2E3Ot/BUD2Of341S+vZeITj3Nt7i+Tcq1SvYxDmjDt4Z8DkJaayrQXl/LS6+8zoMeJPHzzhRzavDH/+/sRLFu5jgHXjqXlYc0YN+pSBl03fp/HAjz01Es8c/+VXHHuqaz57AuG3DSx4pwtD2tGx+OP4J68FwEYP+UVFj5zE19t/oaLfv14wP8NhFPIqwuYf1swqmqn2ZPAU+6+sIp9z7r7pbFOsLWUfZ9ADljNO+UmewgSQlveHlPryFyy+qu4M6fTj5oFHtHVznTd/apq9sUMXBGRwIV8pqvXgEUkUvTtBRGRAIU7chW6IhI1IU9dvZEmIpGSyEfGzCzbzFaaWaGZjaxi/8/M7B9mVmpmF8QzPoWuiERKor69YGapwFigD9AOuMTM2u3RbA0wFHg23vGpvCAikZLA+2idgUJ3X72rX5sKDARWfNvA3T8u31cWb6ea6YpIpCSwvNAKWFtpvah8W60odEUkUmpSXjCzHDNbWmmp84/FqLwgIpFSk+qCu+cBe//6l13WAVmV1luXb6sVzXRFJFoS95WxJUBbM2tjZunAYCC/tsNT6IpIpCSqpuvupUAuMAd4H5ju7svNbLSZDQAws05mVgRcCEwws+WxxqfygohESiJ/4aS7FwAFe2wbVennJewqO8RNoSsi0RLyN9IUuiISKcn6OHm8FLoiEikh/8iYQldEoiXkmavQFZGICXnqKnRFJFL0EXMRkQCFO3IVuiISNSFPXYWuiESKHhkTEQlQyEu6Cl0RiRaFrohIgFReEBEJkGa6IiIBCnnmKnRFJFo00xURCVS4U1ehKyKRksiPmNcFha6IRIrKCyIiAdIjYyIiQQp35ip0RSRaQp65Cl0RiRbVdEVEAmQhT12FrohESrgjV6ErIhET8omuQldEokWPjImIBEgzXRGRACl0RUQCpPKCiEiANNMVEQlQyDNXoSsiERPy1FXoikikqKYrIhKgsH/EPCXZAxARSSirwRKrK7NsM1tpZoVmNrKK/fXNbFr5/sVmdmSsPhW6IhIpVoM/1fZjlgqMBfoA7YBLzKzdHs2uAja6+1HAI8D9scan0BWRSDGLf4mhM1Do7qvdfTswFRi4R5uBwKTyn58HzrQYnzmr85pug7SQV7UDZGY57p6X7HGEwZa3xyR7CKGhvxeJVZPMMbMcIKfSprxK/yxaAWsr7SsCuuzRRUUbdy81s6+AQ4DP93VOzXSDlRO7iRyA9PciSdw9z907Vlrq/H/8FLoiIlVbB2RVWm9dvq3KNmaWBjQD/l1dpwpdEZGqLQHamlkbM0sHBgP5e7TJB64o//kCYJ67e3Wd6jndYKluJ1XR34sQKq/R5gJzgFRgorsvN7PRwFJ3zweeBJ42s0LgC3YFc7UsRiiLiEgCqbwgIhIgha6ISIAUugGJ9TqhHHjMbKKZlZjZe8keiwRHoRuAOF8nlAPPH4HsZA9CgqXQDUY8rxPKAcbdX2XXHW85gCh0g1HV64StkjQWEUkiha6ISIAUusGI53VCETkAKHSDEc/rhCJyAFDoBsDdS4FvXyd8H5ju7suTOypJNjObArwBHGNmRWZ2VbLHJHVPrwGLiARIM10RkQApdEVEAqTQFREJkEJXRCRACl0RkQApdEVEAqTQFREJ0P8D745rDVh6NZkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, fmt='.2%', cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a887e15c-bc20-4da4-b7bd-307e709e76ed",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e82ed545-a577-4b2d-a279-2f4aa370c381",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99       936\n",
      "           1       0.98      1.00      0.99       994\n",
      "\n",
      "    accuracy                           0.99      1930\n",
      "   macro avg       0.99      0.99      0.99      1930\n",
      "weighted avg       0.99      0.99      0.99      1930\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (classification_report(y_test, dt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144f7127-a6bc-460e-94fa-42a6af5ec0f3",
   "metadata": {},
   "source": [
    "## Accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "21d3e1c9-4f78-465b-9212-78a876b2e359",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.98912 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (\"Accuracy : %0.5f \\n\\n\" % accuracy_score(y_test, dt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f5cd3a-b3ba-4b17-8505-e0d7f900858d",
   "metadata": {},
   "source": [
    "## F-Measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7f52e00c-a9d4-4361-acfc-294dcf5cb5d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F-Measure: 0.990\n"
     ]
    }
   ],
   "source": [
    "actual = y_test.tolist()\n",
    "predicted = y_pred_dt\n",
    "f_measure = f1_score(actual, predicted, average = 'binary', sample_weight=None, zero_division='warn')\n",
    "print('F-Measure: %.3f' % f_measure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14f68040-d805-4ec3-b6a1-4967fd7d74b9",
   "metadata": {},
   "source": [
    "##### Highest Accuracy - Decision Tree Algorithm with Oversampling"
   ]
  }
 ],
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