spamemailDetection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cfd20240",
   "metadata": {},
   "source": [
    "# Email Spam Model Using Naive Bayes Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a41f0b85",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c25eecf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import ComplementNB\n",
    "from sklearn.naive_bayes import CategoricalNB\n",
    "from sklearn.naive_bayes import BernoulliNB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e078dd70",
   "metadata": {},
   "source": [
    "# Data Importation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9b7e652",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('emails.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7983550e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Email No.</th>\n",
       "      <th>the</th>\n",
       "      <th>to</th>\n",
       "      <th>ect</th>\n",
       "      <th>and</th>\n",
       "      <th>for</th>\n",
       "      <th>of</th>\n",
       "      <th>a</th>\n",
       "      <th>you</th>\n",
       "      <th>hou</th>\n",
       "      <th>...</th>\n",
       "      <th>connevey</th>\n",
       "      <th>jay</th>\n",
       "      <th>valued</th>\n",
       "      <th>lay</th>\n",
       "      <th>infrastructure</th>\n",
       "      <th>military</th>\n",
       "      <th>allowing</th>\n",
       "      <th>ff</th>\n",
       "      <th>dry</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Email 1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Email 2</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Email 3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Email 4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Email 5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3002 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Email No.  the  to  ect  and  for  of    a  you  hou  ...  connevey  jay  \\\n",
       "0   Email 1    0   0    1    0    0   0    2    0    0  ...         0    0   \n",
       "1   Email 2    8  13   24    6    6   2  102    1   27  ...         0    0   \n",
       "2   Email 3    0   0    1    0    0   0    8    0    0  ...         0    0   \n",
       "3   Email 4    0   5   22    0    5   1   51    2   10  ...         0    0   \n",
       "4   Email 5    7   6   17    1    5   2   57    0    9  ...         0    0   \n",
       "\n",
       "   valued  lay  infrastructure  military  allowing  ff  dry  Prediction  \n",
       "0       0    0               0         0         0   0    0           0  \n",
       "1       0    0               0         0         0   1    0           0  \n",
       "2       0    0               0         0         0   0    0           0  \n",
       "3       0    0               0         0         0   0    0           0  \n",
       "4       0    0               0         0         0   1    0           0  \n",
       "\n",
       "[5 rows x 3002 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "40a4efdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5172"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21a4133d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "572a75aa",
   "metadata": {},
   "source": [
    "# Data Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5173b331",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal and spam\n"
     ]
    },
    {
     "data": {
      "image/png": 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UODmc4iSc4fD33ozVotQuOPlfdbXm7AN0dNS2Uq3V30WjrsJ5oKN2ldodXcMpQkpKw6lUy5S8a+sqnE66CUq1QcWEcx9H7Sq1u75Q6gZdhbOHo3aV2l0lnxJOw6lUy1RMOMt6FjFVliomnLrmVFFTMeF0cpa/Um1Q/uEM5+B0fZG3Uq1V8jPaXIREg6miqORjXbkIis4kpqKo5OGsKnWDaDiL5jBZuOCpdtfrnvAiaEBysLqkbboIp3Zri+Ts+MSPY2IOcl1HOYph1pS+TVU2vhKfpZfhFU/JZ7oueTiDTHIzsLXU7Za7GA31vVg1wHUdZazk/7Ou1pxrHLVbto6NzZsvwh6u6yhj5b/mDJV2y7oCfCM+aYXrGsrcmlI3qGvOMjEs9mYX1zWUuUWlblDXnGWgmq1b9iKnoxkW10elbtBVOFc5arcsnRh7c54IHVzXUeYqZs1Z8k+hcjY6PnmN6xoqQMWE831H7Zal42Nv6VU+xVcx4XzPUbtlpwO1G7ux4WDXdVSAitnm1DVngXw5NmueCO1c11Hm1pHO5UrdqMttTj1LqABGxSevd11DBSh5lxYchTPIJLcCgYu2y80xsfl6FUrxOdmB6fLE97kO2y4Lndm4tjObdHuz+Oa4aNRlOGc5bLssnBGf/o5I6S8CrkCvumjUZThnOmy7LIyMTdnouoYKYICpLhp2Gc7pDtsuC4fH3u3tuoYKMJ90zskZbc7CGWSSK9DjnbutO2tXdWCLztZWfE66tOB+JAQn3YVyMDz++gIRHY+pBKa4ath1OF923H5knRWfusV1DRWiYtecf8NucKtWGiTv93VdQwVYRjrnbNPLaTiDTPITHB1DirJerFpWI1v3d11HBXDWpQX3a06AF1wXEDUj4lN0R1ppTHDZuIYzgobHX2twXUMFaACecFmAD+Gcgo4p1CoDZFE/1zVUgL+Tzn3ssgDn4QxPgn/SdR1R0U8+WVwt9fu6rqMCPOS6AOfhDP3FdQFR8fX4q4HrGirAZuCvrovwJZwTcXTNXNR8LTbNl79ZOXuWdG6t6yK8+EMHmaQBHnFdRxT0l6V6CKX4nHdpwZNwhv7sugDfDZCPPoiL6eW6jjK3GnjedRHgUTiDTPIt4A3XdfhsVPzVxa5rqACPk855cWqkN+EM3e66AJ+dHptR7bqGCuBFlxb8C+d4YKnrIvxkzH7yqV4iVlxzSOcmuS6ikVfhDDLJOuAe13X46DB5f2FMTA/XdZS5X7suIJ9X4QzdC2xyXYRvRsUnOT1bpQJ8gO25ecO7cAaZ5Ep0z+3nnBr/R3sX7S7KNXDKgxsYePd6DrlnPbe/XgvAnE/qGfLABgb9bj3DH9nI2lp75d+Uj7Zy6O/Wc/R/r2fhKnsK8JrNhtP/sgFjvL468FbSuXrXReTzLpyhXwJe7DHzQYyG+j6sdDKlfFUMbj2tPfOu6Mzrl3bi7hl1vL28nnHPbiLzlRrmfrczXz+4ilum2NDe+toWnjynAzd/uT2/m2H/hDdOrOU/htYg4u3ADcuA37suYkdehjPIJAPgv13X4YujZf47InR10XbvLjGO6G1H3+xSIwzsGWPJWsM7KxoY1s8+/q/7V/HkPDuAf3UcNm2FjXWG6ji8t6qBJesaOClR5aL8lvol6Zx3m1JehjN0E6BDPwKj45OXu64BIFjTwKyP6zm2b5wv7R3nmXdsIB9/u45Fa20X9tqhNVz27GZum7aFK49px0//vpkbT6lxWfauLMbu5/COt+EMR0m403UdPjg5Pruz6xrWbzGMfmwjt321PXvUCL8f0Z67Z2zhyPvXs64W2sVtl3Vwrzivj+vEKxd24v3VDfTpEsMA33xiI+f/dRPL1nt3KepNpHO1rotoirfhDP0KKPnsTj6pYmtdT8dTytfV22CeN6iaUQPteRAH7xXnxbGdmHlZZ8YMqqJ/9+23J40x3DSplp8Nq+HnE2v5+ck1nH9oNXdM82pXwvt4uK3ZyOtwBpnkauAG13W4NDT21jwROrpq3xjDpc9sZuBeca4esq17+ukGuwZsMIabJm3hO0dtPwvhg3PqSB5YRfcOwsY6iIm9bayzz1/w1CamL3G+c/QK0rk610U0x+ut9NAdwAXAYa4LcWFUfPJql+1PWVTPn9+sY9DeMQbfa2cbvPkrNby7soG7Z9j/61EDq7h48LYzCzfWGR6cU8eL59vPlKuPa8foxzbRLg6PjO4AwJvL6und2ene24dI57weIkc8P/YEQCKVPQ47ALW3++KL5R81l83ZU9aX1QfT2lrDpc9s4vGznXUIVgADSedWuCqgJSIRToBEKnsfcJnrOkqpPbWb5tVcHBPB692dETSWdM770Te83ubcQQr41HURpXRKbPZ8DWbBvRCFYEKEwhnuHPq+6zpKaVR8ckXvqS6CDcB3XRfRUpEJJ0CQST4GPOi6jlI5NjZPp5QvrJ+RzgWui2ipSIUzdCUVMHVgJzat66JTyhfSdCJ2MX/kwhlkkuuBc4GtrmspptNjM+aLROJQVxTksDuBvDs9aWciF06AIJOcDlzvuo5i+npcp5QvkAZgDOncAteFtFYkwxnK4MkoacVwRGzBPq5rKBPXks5F8v8ksuEMMkn7iQjzXNdSaN1Yt7ojtQe5rqMMPEQ659XQI60R2XACBJlkDhgOrHJdSyEl49MWiET7b+OBmcA410W0ReT/AYJM8j3gG5TRDqKR8SleXsIUIZ8AI0nnNrsupC0iH06AIJN8hTI6QWGQvP8F1zVE2BZgNOlc5AfgLotwAgSZ5L3AL1zX0VZ7s3p5e6nr77qOCPse6dxU10UUQtmEEyDIJK8j4uPenhWf+q7rGiLKAN8nnXvAdSGFUlbhDF1JhE/xOys+1fkVyBFkgO+Szt3lupBCKrtwhtMJXkJEpxQcKB/t57qGiGkALiWdu891IYVWduGEz46BjiViM2bvK58uqZb6fq7riJB64ELSuT+4LqQYyjKcAEEmWY8d3uS3rmtpqZExnVK+FbYC50fl2szdUbbhBNvFDTLJq4FrXdfSEsn4NNclREUd8C3SuUddF1JMZR3ORkEmmcGeLeL1zpYDZIlOKb9rm4BvkM496bqQYquIcAIEmeQDwEhgreNSmnSALP6wShp6u67Dcx8BQ0nnnnFdSClUTDgBgkzyOeAYPDxZflT81Y9c1+C5ScBRpHP/cF1IqVRUOAGCTPIdbEC96hZ9NTZdL6xu3j3AqaRzXswZUyqRGRqzGBKpbAp7yp/jDylj3q85f0VMTE+3dXhnHXBZue/4aU7FrTnzhTuKvgwELusYJB8s1GB+zizgiEoNJlR4OAGCTHIicCjg7JzMUfHJOqX8Nga4CxhCOrfQdTEuVXS3dkeJVPZM7KS9vUrZ7qs1V03vKyuOKWWbnppDK68qEZGfYgd8q8eeyne5MaYsDhhX/JozX7g39xBKeNqf0NDwBVY4mVLeI+uAfweObGUwhwBnAkcYYw4FTgUWFafE0tM9hDsIMslVwNhEKnsvdvLew4vZ3lGy4B0RnM6/6dijwNWkc7vTte8NrDDG1AIYY1YAiEgAjAdOCZc71xizUESGA9cB7YCVwHnGmGUikga+GL7fQcDVwHHAGcASYLgxpuRTBeqasxlBJjkFOAr4DnZWqqIYHZ9cUfO/5JmPPTwyZjeDCfAisK+ILBCRe0TkpLzn1hpjjsFuv94WPvYqcJwx5nDsh8JP8pbvDySBEdie0yvGmEHYM5KSu1lfm2g4dyLIJBuCTPI+7KfpHUDBx/Y5JT6rU6Hf03MbgZ8Ch5HOvdyWNzLGrAeOxM4+txwYLyIXhU8/kvd1SHi/L/A3EZkL/Bi7CdPo+XDtOBeIA41zd84FEm2pc3dpOFsgyCRXB5nkD7CfrncCBRk4qoqtdXuzplKmXFiJnaW8H+nczaRzBZl/3hhTb4yZYIy5Hnuh/ejGp/IXC7/eCdwVrhEvB9rnLdPYNW4A6sy2PaUNONr803C2QpBJLgkyyauwIb2DNob0+Ng/54vQuSDF+SsArgL2I527vpAT1orIABE5MO+hwcCH4f1v5n19LbzfFbsNCXBhoeooFt0htBuCTHIp8INEKpsBvoe94qXVh19GxyevLHRtHpkF/Bp4nHSuWFcDdQbuFJFu2Os7F2K7uGcCNSIyDbsCGhMunwYeF5ElwOvYnUDe0uOcBZBIZauBUdigDmvp62bWXD67h6wbXKy6HHkJuIV07iVXBYR7a49q3HsbVRrOAkuksodgt2fOAZqd76SGLZvn11yEyHbbPVE1G3gCu5Z0PmGQhlPtVCKVjWHXomdjd1JsF9TTYzNm39fut4MdlFYoM7GBfKLST7MrFg1nCYRBPRHb9T0NOPi+6t9MOD3+xslOC2u96WwL5Aeuiyl3Gk4HEqls32y7a084JPbhqdjQ+nj6Xj3wJjA1vE0qhykOokTD6YN0157AUOwB9f7AAeHX7iWqoA47OsQc7PbjP4AZpHMbStS+aoKG02fprt3ZFtTGr/2BHtjzQ5u7NdqKPXNmWXj7JO9+/mMLCnVSgCocDWc5SndtB1QDG0nn9A8cURpOpTylp+8p5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5SkNp1Ke0nAq5an/B5c6+ooVcrEjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Normal and spam\")\n",
    "count_Class = pd.value_counts(dataset['Prediction'], sort=True)\n",
    "count_Class.plot(kind = 'pie',labels=['normal','Spam'], autopct='%1.0f%%,')\n",
    "plt.title('Normal vs Spam')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c46937",
   "metadata": {},
   "source": [
    "# Columns Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "264993f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y= dataset.iloc[:,1:-1].values,dataset.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b616f2",
   "metadata": {},
   "source": [
    "# Data Normalisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da384e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "64dcdd51",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=MinMaxScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "241b5cf0",
   "metadata": {},
   "source": [
    "# Data Spliting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "99ef5798",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.15, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da4c459e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(776, 3000)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31dfe879",
   "metadata": {},
   "source": [
    "# Data Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "30664583",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "gnbm= gnb.fit(X_train, y_train)\n",
    "y_pred=gnbm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b5d8ceb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mltn = MultinomialNB()\n",
    "mltnm = mltn.fit(X_train, y_train)\n",
    "y_predm=mltnm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "805b0866",
   "metadata": {},
   "outputs": [],
   "source": [
    "brn = BernoulliNB()\n",
    "brnm= brn.fit(X_train, y_train)\n",
    "y_predb =brnm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "685f6bc3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c8f64faf",
   "metadata": {},
   "source": [
    "# Performance Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e6d42cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce45ba2d",
   "metadata": {},
   "source": [
    "# Confusion Matrix "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02212f48",
   "metadata": {},
   "source": [
    "GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5b1dab95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GaussianNB')"
      ]
     },
     "execution_count": 28,
     "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": [
    "plot_confusion_matrix(gnbm,X_test,y_test)\n",
    "plt.title('GaussianNB')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9fecf88",
   "metadata": {},
   "source": [
    "MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "287b47a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MultinomialNB')"
      ]
     },
     "execution_count": 29,
     "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": [
    "plot_confusion_matrix(mltnm,X_test,y_test)\n",
    "plt.title('MultinomialNB')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c59ddda",
   "metadata": {},
   "source": [
    "BernoulliNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aab5bbfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'BernoulliNB')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATgAAAEWCAYAAADy2YssAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcNklEQVR4nO3debwcVZ338c83C1kI2UkMSYAIEQgoARFhcBAQJehI0Ec0jEtk8EEdtseNxXkGXJ4wqMO4ISqbREUwKAxBnABGI+ADCUlYEwjJEEwuCQlZyE6We3/zR9WFJt7u20Vup7vqft+86tXVp0+d+vW93F9O1ak6pYjAzKyIutQ7ADOzWnGCM7PCcoIzs8JygjOzwnKCM7PCcoIzs8JygrOakRSSDkzXb5L0/9L1v5e0oL7RWWfgBJdDkp6XtEXSRklrJd0taWS946pWRDwQEQe1vk+/zwpJe5aUfUbSjJL3IWlT+p1XSbpFUv/dG7nljRNcfn0wIvoAw4AVwA+zNiCpW4dH9cZ1Ay5sp87h6Xd+MzAA+Fqtg7J8c4LLuYh4BfgNMAZAUg9J/y5pSdor+omkXulnJ0hqknSxpBeBn0n6mqQpkn4uaYOkeZKOam1f0iGSZkh6Of3stJLPZkj6TMn7T0t6sL2YW+PYqfg7wJer6ZVFxHpgaut3NivHCS7nJPUGPgY8nBZ9C3gLMBY4EBgOXFayyZuAgcB+wDlp2WnArUB/ksRxddp2d+Au4F5gCHA+cLOkg+h4s4EZwJfbqyhpAHA6r31nszY5weXXf0p6GVgPvBf4jiQB/xv4QkSsiYgNwBXAhJLtWoDLI2JrRGxJyx6MiN9HRDPwC+DwtPwYoA9wZURsi4g/Ar8DzqzRd7oMOF/S3mU+n5t+51XAvsBPaxSHFYQTXH6dHhH9gR7AecCfgZFAb2BOekj5MjANKE0YL6WHtaVeLFnfDPRMz8/tAyyNiJaSz/9K0ivscBHxFEkCvaRMlSPT79wT+DHwgKSetYjFisEJLuciojkibgeaSXpcW4BDI6J/uvRLT8y/ukmG5pcBIyWV/n+yL/BCur6JJKG2elP2b/A3LifphZZNohGxHbgeGAUc1gH7tIJygss5JcaTjCrOA64DvitpSPr5cEmnvMHmZ5IksYskdZd0AvBBkvN1AI8BH5bUO73e7ew3/EVSEbEI+DVwQbk6kroCZ5Ek8+d2dZ9WXE5w+XWXpI0k5+AmARMjYh5wMbAIeFjSeuAPwBsaFIiIbSQDEKeSnPe6BvhURDyTVvkusI3kMpXJwM1v/Ou8zjeAPdsofzz9zmuBicCHImJNB+3TCkie8NLMiso9ODMrLCc4MyssJzgzKywnODMrrEa62ZrBA7vG/iO71zsMy+DZJ3q3X8kaxitsYlts1a60ccqJe8bqNc1V1Z3zxNZ7ImLcruxvVzRUgtt/ZHdm3ZObWX8MOGWfsfUOwTKYGdN3uY3Va5qZdc++VdXtOmzh4F3e4S5oqARnZo0vgBZa2q3XCHwOzswyCYLt0VzV0p50stMnJT0maXZaNlDSfZIWpq8DSupfKmmRpAXV3KHjBGdmmbVU+V+VToyIsRHROg/hJcD0iBgNTE/fI2kMycw4hwLjgGvS2/bKcoIzs0yCoDmqW96g8SS3/pG+nl5Sfms61ddiklsSj67UkBOcmWXWQlS1AIMlzS5ZztmpqQDulTSn5LOhEbEcIH0dkpYPB5aWbNtEO1N3eZDBzDIJoLn6WbdWlRx6tuW4iFiWzn5zn6RnKtRt6/KWioG4B2dmmWXowVUUEcvS15XAHSSHnCskDQNIX1em1ZtIJnVtNYJkzsKynODMLJMAtkdUtVQiaU9Je7WuA+8DniJ5LsjEtNpE4M50fSowIX2w0ihgNDCr0j58iGpmmQSR5RC1kqHAHcmjROgG/Coipkl6BJgi6WxgCXAGQETMkzQFmA/sAM5NnyNSlhOcmWUT0NwB+S0inuO1BxyVlq8G3lNmm0kkE7xWxQnOzDJJ7mTIByc4M8tINLc5oNl4nODMLJNkkMEJzswKKLkOzgnOzAqqxT04Mysi9+DMrLAC0ZyTewSc4MwsMx+imlkhBWJbVJyGrWE4wZlZJsmFvj5ENbOC8iCDmRVShGgO9+DMrKBa3IMzsyJKBhnykTryEaWZNQwPMphZoTX7OjgzKyLfyWBmhdbiUVQzK6LkZnsnODMroEBs961aZlZEEfhCXzMrKvlCXzMrpsA9ODMrMA8ymFkhBfKEl2ZWTMljA/OROvIRpZk1ED/42cwKKvCdDGZWYO7BmVkhRcg9ODMrpmSQwbdqmVkh+ZkMZlZQySCDz8GZWUH5TgYzKyTfyWBmheaHzphZIUXA9hYnODMroOQQNR8JLh9RmllDaU7vR21vqYakrpIelfS79P1ASfdJWpi+Diipe6mkRZIWSDqlvbbdg+sAnzp6DL36NNOlC3TtFlw97Vnuv6sfv7jqTSxd2JMf/P5Z3nL4FgCeebQ33//KSCAZbv/kl17kuFPX1TF6mzxzPls2dqWlBZp3iPNPfQt79d/BV3/yV4aO2MaKpj2Y9Nn92LjOfy5Qk8tELgSeBvqm7y8BpkfElZIuSd9fLGkMMAE4FNgH+IOkt0REc7mGa/obkzQO+D7QFbg+Iq6s5f7q6du3LaLfoNd+zvsf/AqXXf88P7h45Ovq7X/QFq6etoCu3WD1im58/uSDOOa96+jqv526uuiMA1i/5rVfwkfPW8mjD/ZhytVD+eh5K/jYeSu5YdI+dYywkXTcIaqkEcAHgEnAF9Pi8cAJ6fpkYAZwcVp+a0RsBRZLWgQcDTxUrv2aHaJK6gr8CDgVGAOcmWbgTmHf0VsZeeDWvynv2TteTWbbt3ZB+Rht73SOPWU9f5gyEIA/TBnIsePW1zmixtKSPpehvQUYLGl2yXLOTk19D7gIaCkpGxoRywHS1yFp+XBgaUm9prSsrFr2G44GFkXEcwCSbiXJwPNruM/6UPDVMw8AwQc+uZr3f2J1xerPzO3NVV8cycqmPbjoh0vce6u3EFfc8hwE3P2LQfzXzYMYMHg7a1Z2B2DNyu70H7SjzkE2jmQUtep7UVdFxFFtfSDpH4CVETFH0glVtNVWdyAqbVDLP622su07d66UZvRzAPYdns+/9O/euZBBb9rBy6u6ccmEAxh54Cu89ZhNZesffORmrpuxgCULe/CdC/flHSeuZ4+eFX9PVkNfGH8ga1Z0p9+g7Vx563MsXdSj3iE1tA680Pc44DRJ7wd6An0l/RJYIWlYRCyXNAxYmdZvAkrP+YwAllXaQS1HUavKthFxbUQcFRFH7T0oHzMU7GzQm5J/3fsP3sFx49bxzKO9q9pu39Fb6dm7hecX9KxleNaONSuSntq61d35y7R+HHzEZtau6s7AIdsBGDhkOy+vzuc/vrWS4RC1rIi4NCJGRMT+JIMHf4yITwBTgYlptYnAnen6VGCCpB6SRgGjgVmV9lHLBJc52+bRK5u7sHljl1fX5/x5L/Y/+JWy9V9csgfN6dHOiqbuNP13T4aO2LY7QrU29OjVTK89m19df/u7N/D8Mz15+N6+nPzRNQCc/NE1PHRP30rNdCqto6jVLG/QlcB7JS0E3pu+JyLmAVNITnNNA86tNIIKtT1EfQQYnWbaF0gy9D/WcH91sfalbnz97FEANO+AEz/0Mu84cQN/+a9+XPN/h7NudTf+9ZNv5oBDt3DFLc/x1Kw9+fXVo+jWDbp0Cc6/oul1o6+2ew3YeweX3/A8kFzi86c7BjB7Rl8WPN6bf/nJXxk3YQ0rX0guE7HXdPSFvhExg2S0lIhYDbynTL1JJCOuVVFE7c79pMfW3yO5TOTGNLiyjjq8Z8y6Z2SlKtZgTtlnbL1DsAxmxnTWx5pdOoE24OAhcdKNH6mq7u3H/XhOuUGG3aGmJxYi4vfA72u5DzPb/TybiJkVkie8NLNCc4Izs0LyhJdmVmjtXePWKJzgzCyTCNjhCS/NrKh8iGpmheRzcGZWaOEEZ2ZF5UEGMyukCJ+DM7PCEs0eRTWzovI5ODMrJN+LambFFcl5uDxwgjOzzDyKamaFFB5kMLMi8yGqmRWWR1HNrJAinODMrMB8mYiZFZbPwZlZIQWixaOoZlZUOenAOcGZWUYeZDCzQstJF84Jzswyy30PTtIPqZCnI+KCmkRkZg0tgJaWnCc4YPZui8LM8iOAvPfgImJy6XtJe0bEptqHZGaNLi/XwbV7MYukYyXNB55O3x8u6ZqaR2ZmjSuqXOqsmqv1vgecAqwGiIjHgeNrGJOZNTQRUd1Sb1WNokbEUul1wTbXJhwzy4UG6J1Vo5oEt1TS3wEhaQ/gAtLDVTPrhAIiJ6Oo1Ryifg44FxgOvACMTd+bWaelKpf6arcHFxGrgI/vhljMLC9ycohazSjqmyXdJeklSSsl3SnpzbsjODNrUAUaRf0VMAUYBuwD3AbcUsugzKyBtV7oW81SgaSekmZJelzSPElfT8sHSrpP0sL0dUDJNpdKWiRpgaRT2gu1mgSniPhFROxIl1/SELnZzOolorqlHVuBkyLicJJz++MkHQNcAkyPiNHA9PQ9ksYAE4BDgXHANZK6VtpB2QSXZtGBwJ8kXSJpf0n7SboIuLuKn4GZFVWLqlsqiMTG9G33dAlgPNB6J9Vk4PR0fTxwa0RsjYjFwCLg6Er7qDTIMCfdWWuUny2NDfhmxejNrLBU/THcYEml97VfGxHXvtpO0gObAxwI/CgiZkoaGhHLASJiuaQhafXhwMMlbTWlZWVVuhd1VNVfwcw6j2wDCKsi4qiyTUU0A2Ml9QfukHRYhbba6hJWjKSqOxnSnY4BepYE9vNqtjWzoml/ACGriHhZ0gySc2srJA1Le2/DgJVptSZgZMlmI4Blldqt5jKRy4EfpsuJwLeB0zJ/AzMrjg64TETS3mnPDUm9gJOBZ4CpwMS02kTgznR9KjBBUg9Jo4DRwKxK+6imB/cR4HDg0Yg4S9JQ4PoqtjOzomrpkFaGAZPT83BdgCkR8TtJDwFTJJ0NLAHOAIiIeZKmAPOBHcC56SFuWdUkuC0R0SJph6S+JN1FX+hr1ll10ISXEfEEcEQb5auB95TZZhIwqdp9VJPgZqfdyOtIRjs20k630MyKLcMoal1Vcy/qP6erP5E0DeibZl4z66zynuAkHVnps4iYW5uQzMw6RqUe3FUVPgvgpA6OhWef3JNx+1W8MNkazNZTD693CJZBPPhQh7ST+0PUiDhxdwZiZjkRtHsbVqPwg5/NLLu89+DMzMrJ/SGqmVlZOUlw1dyqJUmfkHRZ+n5fSR4JMOvMCjSj7zXAscCZ6fsNwI9qFpGZNTRF9Uu9VXOI+s6IOFLSowARsTZ9fKCZdVYFGkXdnt4MG5DMAEBH3WprZrnUCL2zalRziPoD4A5giKRJwIPAFTWNyswaW07OwVVzL+rNkuaQ3N0v4PSI8JPtzTqrBjm/Vo12E5ykfYHNwF2lZRGxpJaBmVkDK0qCI3mCVuvDZ3oCo4AFJI/uMrNOSDk5C1/NIepbS9+ns4x8tkx1M7OGkflOhoiYK+kdtQjGzHKiKIeokr5Y8rYLcCTwUs0iMrPGVqRBBmCvkvUdJOfkflubcMwsF4qQ4NILfPtExFd2Uzxmlgd5T3CSukXEjkpTl5tZ5yOKMYo6i+R822OSpgK3AZtaP4yI22scm5k1ooKdgxsIrCZ5BkPr9XABOMGZdVYFSHBD0hHUp3gtsbXKydczs5rISQaolOC6An14fWJrlZOvZ2a1UIRD1OUR8Y3dFomZ5UcBElw+ZrQzs90rijGK+p7dFoWZ5Uvee3ARsWZ3BmJm+VGEc3BmZm1zgjOzQmqQ6cir4QRnZpkIH6KaWYE5wZlZcTnBmVlhOcGZWSEVbDYRM7PXy0mCq+bJ9mZmr6OW6paKbUgjJf1J0tOS5km6MC0fKOk+SQvT1wEl21wqaZGkBZJOaS9OJzgzy0xR3dKOHcCXIuIQ4BjgXEljgEuA6RExGpievif9bALJM5nHAdekj1UoywnOzLKJDEulZiKWR8TcdH0D8DQwHBgPTE6rTQZOT9fHA7dGxNaIWAwsAo6utA8nODPLrvoEN1jS7JLlnLaak7Q/cAQwExgaEcshSYLAkLTacGBpyWZNaVlZHmQws0wy3smwKiKOqtie1IfkUaT/JyLWS2Vnass8+a4TnJllppaOGUaV1J0kud1c8iCrFZKGRcRyScOAlWl5EzCyZPMRwLJK7fsQ1cyy6aBzcEq6ajcAT0fEf5R8NBWYmK5PBO4sKZ8gqYekUcBokqf/leUenJll1kEX+h4HfBJ4UtJjadlXgSuBKZLOBpYAZwBExDxJU4D5JCOw50ZEc6UdOMGZWXYdkOAi4kHKPxqhzRnFI2ISMKnafTjBmVlmvlXLzIrLCc7MCqkgT9UyM/sbntHXzIot8pHhnODMLDP34DqpyQ8+zuZNXWlphuZmccEHD2XUIZu54Irn6dm7hRVNe/DtCw9g88aKkyBYDV101v0c+7YlvLyhF2dd9r8A+PRpc/jA8QtYt6EnANfd/g5mPvnaRfNDBm5k8jd/w01Tj+TX97ytLnE3DD9VCyTdCPwDsDIiDqvVfhrRxRMOYv3a7q++/8K3FnPdpJE8ObMv7/voS3zks8v5+VUj6hhh5zbtL6O5Y/oYvvqZP7+u/Df3HVY2eZ074WFmPjWyzc86o7wMMtTyVq2bSOZs6vSGv/kVnpy5FwBzH+jLcaeurXNEndsTzw5jw6YeVdd/1xHPs/ylvXj+hf61CypnOmLCy92hZgkuIu4H1tSq/UYVwBW/fJYf/m4ep56Z3CP812d7ccx7Xwbg+A+sZe9h2+oXoJX1oZPmc8PXfstFZ91Pn95bAei5x3bOPPUJJk89ss7RNZAgGWSoZqmzup+DS+eHOgegJ73rHM2u++KHD2HNyj3oN2g7//bLBSz97178x1dG8fmvLeHjFy7j4fv6s2N72elgrE7unHEIP7/rCALxT6fP5p8/NpNv/+x4zjp9LrfdexhbtnZvv5FOxIMMVYqIa4FrAfp2GZSTH1t5a1buAcC61d35//cM4KCxG/nttcP4l08eBMDwUa9w9Enr6hmitWHt+tf+cb37/oP5twvvBeCQUSt599sX87kzZtGn9zZaQmzb3pU7/nhovUJtDDn5S617giuSHr2a6dIFtmzqSo9ezRx5/Dpu/v5w+g3azrrV3ZGCM89fxt03713vUG0nA/ttZs26JMm968jnWfxC8pyTC771wVfrfPq0OWzZ2r3TJzdf6NtJDRi8ncuuXQRA127Bn+4cxJw/92P8WS/ywU8l5+P+Mm0A904ZXM8wO71/PeePjD1oOf36vMJt3/kVP7vz7Yw9eDkHjlxNBLy4ei+u+vm76h1m44rosAkva01RoxOBkm4BTgAGAyuAyyPihkrb9O0yKI7p7oHXPHnl5MPrHYJl8OiDP2DDuqZdOgm8V/8RccTxF1ZV94G7LprT3pTltVSzHlxEnFmrts2svnyIambFFEBODlGd4Mwsu3zkNyc4M8vOh6hmVlh5GUV1gjOzbDybiJkVVXKhbz4ynBOcmWXXADOFVMMJzswycw/OzIrJ5+DMrLjycy+qE5yZZedDVDMrJD/42cwKzT04MyusfOQ3Jzgzy04t+ThGdYIzs2wCX+hrZsUkwhf6mlmBOcGZWWE5wZlZIfkcnJkVmUdRzaygIjeHqF3qHYCZ5UyQJLhqlnZIulHSSklPlZQNlHSfpIXp64CSzy6VtEjSAkmntNe+E5yZZddS5dK+m4Cdn/Z+CTA9IkYD09P3SBoDTAAOTbe5RlLXSo07wZlZZoqoamlPRNwPrNmpeDwwOV2fDJxeUn5rRGyNiMXAIuDoSu07wZlZdtUfog6WNLtkOaeK1odGxPJkN7EcGJKWDweWltRrSsvK8iCDmWUTAc1Vj6KuioijOmjPaiuaShu4B2dm2XXQIEMZKyQNA0hfV6blTcDIknojgGWVGnKCM7PsapvgpgIT0/WJwJ0l5RMk9ZA0ChgNzKrUkA9RzSybADromQySbgFOIDlX1wRcDlwJTJF0NrAEOAMgIuZJmgLMB3YA50ZEc6X2neDMLKOA6Jg7GSLizDIfvadM/UnApGrbd4Izs2yCLIMMdeUEZ2bZ5eRWLSc4M8vOCc7Miik/N9s7wZlZNgF4uiQzKyz34MysmDLdqlVXTnBmlk1AdNB1cLXmBGdm2XXQnQy15gRnZtn5HJyZFVKER1HNrMDcgzOzYgqiueIkHg3DCc7MsunA6ZJqzQnOzLLzZSJmVkQBhHtwZlZI0XETXtaaE5yZZZaXQQZFAw33SnoJ+Gu946iBwcCqegdhmRT1d7ZfROy9Kw1Imkby86nGqojY+cn1u01DJbiikjS7A58NabuBf2fF4McGmllhOcGZWWE5we0e19Y7AMvMv7MC8Dk4Myss9+DMrLCc4MyssJzgakjSOEkLJC2SdEm947H2SbpR0kpJT9U7Ftt1TnA1Iqkr8CPgVGAMcKakMfWNyqpwE1C3C1OtYznB1c7RwKKIeC4itgG3AuPrHJO1IyLuB9bUOw7rGE5wtTMcWFryviktM7PdxAmudtRGma/JMduNnOBqpwkYWfJ+BLCsTrGYdUpOcLXzCDBa0ihJewATgKl1jsmsU3GCq5GI2AGcB9wDPA1MiYh59Y3K2iPpFuAh4CBJTZLOrndM9sb5Vi0zKyz34MyssJzgzKywnODMrLCc4MyssJzgzKywnOByRFKzpMckPSXpNkm9d6GtmyR9JF2/vtJEAJJOkPR3b2Afz0v6m6cvlSvfqc7GjPv6mqQvZ43Ris0JLl+2RMTYiDgM2AZ8rvTDdAaTzCLiMxExv0KVE4DMCc6s3pzg8usB4MC0d/UnSb8CnpTUVdJ3JD0i6QlJnwVQ4mpJ8yXdDQxpbUjSDElHpevjJM2V9Lik6ZL2J0mkX0h7j38vaW9Jv0338Yik49JtB0m6V9Kjkn5K2/fjvo6k/5Q0R9I8Sefs9NlVaSzTJe2dlh0gaVq6zQOSDu6Qn6YVkp9sn0OSupHMMzctLToaOCwiFqdJYl1EvENSD+Avku4FjgAOAt4KDAXmAzfu1O7ewHXA8WlbAyNijaSfABsj4t/Ter8CvhsRD0ral+RujUOAy4EHI+Ibkj4AvC5hlfFP6T56AY9I+m1ErAb2BOZGxJckXZa2fR7Jw2A+FxELJb0TuAY46Q38GK0TcILLl16SHkvXHwBuIDl0nBURi9Py9wFvaz2/BvQDRgPHA7dERDOwTNIf22j/GOD+1rYioty8aCcDY6RXO2h9Je2V7uPD6bZ3S1pbxXe6QNKH0vWRaayrgRbg12n5L4HbJfVJv+9tJfvuUcU+rJNygsuXLRExtrQg/UPfVFoEnB8R9+xU7/20P12TqqgDyamNYyNiSxuxVH3vn6QTSJLlsRGxWdIMoGeZ6pHu9+WdfwZm5fgcXPHcA3xeUncASW+RtCdwPzAhPUc3DDixjW0fAt4taVS67cC0fAOwV0m9e0kOF0nrjU1X7wc+npadCgxoJ9Z+wNo0uR1M0oNs1QVo7YX+I8mh73pgsaQz0n1I0uHt7MM6MSe44rme5Pza3PTBKT8l6anfASwEngR+DPx55w0j4iWS82a3S3qc1w4R7wI+1DrIAFwAHJUOYszntdHcrwPHS5pLcqi8pJ1YpwHdJD0BfBN4uOSzTcChkuaQnGP7Rlr+ceDsNL55eBp4q8CziZhZYbkHZ2aF5QRnZoXlBGdmheUEZ2aF5QRnZoXlBGdmheUEZ2aF9T/VcqOKc1O3RwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(brnm,X_test,y_test)\n",
    "plt.title('BernoulliNB')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b08c87bf",
   "metadata": {},
   "source": [
    "# Accuracy Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a48c4db7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "labels=['gaussien','Multinomial','Bernoulli']\n",
    "xl=[accuracy_score(y_pred,y_test),accuracy_score(y_predm,y_test),accuracy_score(y_predcm,y_test)]\n",
    "data1=pd.DataFrame({\n",
    "    'accuracy':xl,\n",
    "    'label':labels\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2521ec13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "16fd9898",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='label', ylabel='count'>,\n",
       " 0.923969    1\n",
       " 0.943299    1\n",
       " 0.940722    1\n",
       " Name: accuracy, dtype: int64)"
      ]
     },
     "execution_count": 23,
     "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": [
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(x='label',data=data1),data1['accuracy'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65858b93",
   "metadata": {},
   "source": [
    "# Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "047728f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "xl1=[recall_score(y_pred,y_test),recall_score(y_predm,y_test),recall_score(y_predcm,y_test)]\n",
    "data2=pd.DataFrame({\n",
    "    'recall':xl1,\n",
    "    'label':labels\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4404fe05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='label', ylabel='count'>,\n",
       " 0.849372    1\n",
       " 0.850622    1\n",
       " 0.877451    1\n",
       " Name: recall, dtype: int64)"
      ]
     },
     "execution_count": 27,
     "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": [
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(x='label',data=data2),data2['recall'].value_counts()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e369c1",
   "metadata": {},
   "source": [
    "# F Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "591caeaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "xl3=[f1_score(y_pred,y_test),f1_score(y_predm,y_test),f1_score(y_predcm,y_test)]\n",
    "data3=pd.DataFrame({\n",
    "    'f_score':xl3,\n",
    "    'label':labels\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ca85c2b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='label', ylabel='count'>,\n",
       " 0.898230    1\n",
       " 0.858513    1\n",
       " 0.903084    1\n",
       " Name: f_score, dtype: int64)"
      ]
     },
     "execution_count": 33,
     "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": [
    "plt.figure(figsize=(5,5))\n",
    "sns.countplot(x='label',data=data3),data3['f_score'].value_counts()\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e80ec268",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}