Network_Attacks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "08f17aca",
   "metadata": {},
   "source": [
    "<center><h1>Network Attacks<center>"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7486c444",
   "metadata": {},
   "source": [
    "Deepthi Sudharsan\n",
    "Sarathi \n",
    "Asmitha\n",
    "Roshan Tushar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d4cac95",
   "metadata": {},
   "source": [
    "### The content will be as follows\n",
    "\n",
    "1. [EDA](#chapter1)\n",
    "2. [Data Processing](#chapter2)\n",
    "3. [Model training](#chapter3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "254b3bcc",
   "metadata": {},
   "source": [
    "### IMPORTING NECESSARY PACKAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc0e975b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt #plotting\n",
    "import seaborn as sns #Visualisation\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\") # ignore warnings\n",
    "import scikitplot as skplt\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb277b7c",
   "metadata": {},
   "source": [
    "## EDA<a class=\"anchor\" id=\"chapter1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2c7a9ba1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>protocoltype</th>\n",
       "      <th>service</th>\n",
       "      <th>flag</th>\n",
       "      <th>srcbytes</th>\n",
       "      <th>dstbytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrongfragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>...</th>\n",
       "      <th>dsthostsrvcount</th>\n",
       "      <th>dsthostsamesrvrate</th>\n",
       "      <th>dsthostdiffsrvrate</th>\n",
       "      <th>dsthostsamesrcportrate</th>\n",
       "      <th>dsthostsrvdiffhostrate</th>\n",
       "      <th>dsthostserrorrate</th>\n",
       "      <th>dsthostsrvserrorrate</th>\n",
       "      <th>dsthostrerrorrate</th>\n",
       "      <th>dsthostsrvrerror_rate</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>181</td>\n",
       "      <td>5450</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>239</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>235</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>219</td>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>tcp</td>\n",
       "      <td>http</td>\n",
       "      <td>SF</td>\n",
       "      <td>217</td>\n",
       "      <td>2032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration protocoltype service flag  srcbytes  dstbytes  land  \\\n",
       "0         0          tcp    http   SF       181      5450     0   \n",
       "1         0          tcp    http   SF       239       486     0   \n",
       "2         0          tcp    http   SF       235      1337     0   \n",
       "3         0          tcp    http   SF       219      1337     0   \n",
       "4         0          tcp    http   SF       217      2032     0   \n",
       "\n",
       "   wrongfragment  urgent  hot  ...  dsthostsrvcount  dsthostsamesrvrate  \\\n",
       "0              0       0    0  ...                9                 1.0   \n",
       "1              0       0    0  ...               19                 1.0   \n",
       "2              0       0    0  ...               29                 1.0   \n",
       "3              0       0    0  ...               39                 1.0   \n",
       "4              0       0    0  ...               49                 1.0   \n",
       "\n",
       "   dsthostdiffsrvrate  dsthostsamesrcportrate  dsthostsrvdiffhostrate  \\\n",
       "0                 0.0                    0.11                     0.0   \n",
       "1                 0.0                    0.05                     0.0   \n",
       "2                 0.0                    0.03                     0.0   \n",
       "3                 0.0                    0.03                     0.0   \n",
       "4                 0.0                    0.02                     0.0   \n",
       "\n",
       "   dsthostserrorrate  dsthostsrvserrorrate  dsthostrerrorrate  \\\n",
       "0                0.0                   0.0                0.0   \n",
       "1                0.0                   0.0                0.0   \n",
       "2                0.0                   0.0                0.0   \n",
       "3                0.0                   0.0                0.0   \n",
       "4                0.0                   0.0                0.0   \n",
       "\n",
       "   dsthostsrvrerror_rate   labels  \n",
       "0                    0.0  normal.  \n",
       "1                    0.0  normal.  \n",
       "2                    0.0  normal.  \n",
       "3                    0.0  normal.  \n",
       "4                    0.0  normal.  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Columns creation and reading of dataset\n",
    "columns = [\"duration\", \"protocoltype\", \"service\", \"flag\", \"srcbytes\", \"dstbytes\", \"land\", \"wrongfragment\",\n",
    "           \"urgent\", \"hot\", \"numfailedlogins\", \"loggedin\", \"numcompromised\", \"rootshell\", \"suattempted\",\n",
    "           \"numroot\", \"numfilecreations\", \"numshells\", \"numaccessfiles\", \"numoutboundcmds\", \"ishostlogin\",\n",
    "           \"isguestlogin\", \"count\", \"srvcount\", \"serrorrate\", \"srvserrorrate\", \"rerrorrate\", \"srvrerrorrate\",\n",
    "           \"samesrvrate\", \"diffsrvrate\", \"srvdiffhostrate\", \"dsthostcount\", \"dsthostsrvcount\",\n",
    "           \"dsthostsamesrvrate\", \"dsthostdiffsrvrate\", \"dsthostsamesrcportrate\", \"dsthostsrvdiffhostrate\",\n",
    "           \"dsthostserrorrate\", \"dsthostsrvserrorrate\", \"dsthostrerrorrate\", \"dsthostsrvrerror_rate\", \"labels\"]\n",
    "\n",
    "data = pd.read_csv(\"kddcup.data_10_percent\", names=columns) # Reading CSV file with Header\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c689bf48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200000, 43)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data set size\n",
    "data = data.sample(n = 200000)\n",
    "data.reset_index(inplace=True)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bf36fb00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking whether the dataset is having null values\n",
    "[col for col in data.columns if data[col].isnull().sum() > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a840daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>duration</th>\n",
       "      <th>srcbytes</th>\n",
       "      <th>dstbytes</th>\n",
       "      <th>land</th>\n",
       "      <th>wrongfragment</th>\n",
       "      <th>urgent</th>\n",
       "      <th>hot</th>\n",
       "      <th>numfailedlogins</th>\n",
       "      <th>loggedin</th>\n",
       "      <th>...</th>\n",
       "      <th>dsthostcount</th>\n",
       "      <th>dsthostsrvcount</th>\n",
       "      <th>dsthostsamesrvrate</th>\n",
       "      <th>dsthostdiffsrvrate</th>\n",
       "      <th>dsthostsamesrcportrate</th>\n",
       "      <th>dsthostsrvdiffhostrate</th>\n",
       "      <th>dsthostserrorrate</th>\n",
       "      <th>dsthostsrvserrorrate</th>\n",
       "      <th>dsthostrerrorrate</th>\n",
       "      <th>dsthostsrvrerror_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>2.000000e+05</td>\n",
       "      <td>2.000000e+05</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>200000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>246851.578525</td>\n",
       "      <td>47.635025</td>\n",
       "      <td>5.223014e+03</td>\n",
       "      <td>8.484594e+02</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.006075</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.036790</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.149675</td>\n",
       "      <td>...</td>\n",
       "      <td>232.359160</td>\n",
       "      <td>188.530065</td>\n",
       "      <td>0.753206</td>\n",
       "      <td>0.031132</td>\n",
       "      <td>0.600785</td>\n",
       "      <td>0.006715</td>\n",
       "      <td>0.176866</td>\n",
       "      <td>0.176633</td>\n",
       "      <td>0.058460</td>\n",
       "      <td>0.057674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>142648.262260</td>\n",
       "      <td>697.518592</td>\n",
       "      <td>1.551722e+06</td>\n",
       "      <td>3.261604e+04</td>\n",
       "      <td>0.006324</td>\n",
       "      <td>0.130684</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>0.817863</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>0.356753</td>\n",
       "      <td>...</td>\n",
       "      <td>64.870862</td>\n",
       "      <td>106.084232</td>\n",
       "      <td>0.410957</td>\n",
       "      <td>0.109984</td>\n",
       "      <td>0.481542</td>\n",
       "      <td>0.042345</td>\n",
       "      <td>0.380689</td>\n",
       "      <td>0.381091</td>\n",
       "      <td>0.231177</td>\n",
       "      <td>0.230638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>123154.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>246914.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>370257.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.032000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>494015.000000</td>\n",
       "      <td>40806.000000</td>\n",
       "      <td>6.933756e+08</td>\n",
       "      <td>5.153460e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               index       duration      srcbytes      dstbytes  \\\n",
       "count  200000.000000  200000.000000  2.000000e+05  2.000000e+05   \n",
       "mean   246851.578525      47.635025  5.223014e+03  8.484594e+02   \n",
       "std    142648.262260     697.518592  1.551722e+06  3.261604e+04   \n",
       "min         1.000000       0.000000  0.000000e+00  0.000000e+00   \n",
       "25%    123154.500000       0.000000  4.400000e+01  0.000000e+00   \n",
       "50%    246914.000000       0.000000  5.200000e+02  0.000000e+00   \n",
       "75%    370257.500000       0.000000  1.032000e+03  0.000000e+00   \n",
       "max    494015.000000   40806.000000  6.933756e+08  5.153460e+06   \n",
       "\n",
       "                land  wrongfragment         urgent            hot  \\\n",
       "count  200000.000000  200000.000000  200000.000000  200000.000000   \n",
       "mean        0.000040       0.006075       0.000015       0.036790   \n",
       "std         0.006324       0.130684       0.005000       0.817863   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         0.000000       0.000000       0.000000       0.000000   \n",
       "50%         0.000000       0.000000       0.000000       0.000000   \n",
       "75%         0.000000       0.000000       0.000000       0.000000   \n",
       "max         1.000000       3.000000       2.000000      30.000000   \n",
       "\n",
       "       numfailedlogins       loggedin  ...   dsthostcount  dsthostsrvcount  \\\n",
       "count    200000.000000  200000.000000  ...  200000.000000    200000.000000   \n",
       "mean          0.000105       0.149675  ...     232.359160       188.530065   \n",
       "std           0.012845       0.356753  ...      64.870862       106.084232   \n",
       "min           0.000000       0.000000  ...       0.000000         0.000000   \n",
       "25%           0.000000       0.000000  ...     255.000000        44.000000   \n",
       "50%           0.000000       0.000000  ...     255.000000       255.000000   \n",
       "75%           0.000000       0.000000  ...     255.000000       255.000000   \n",
       "max           4.000000       1.000000  ...     255.000000       255.000000   \n",
       "\n",
       "       dsthostsamesrvrate  dsthostdiffsrvrate  dsthostsamesrcportrate  \\\n",
       "count       200000.000000       200000.000000           200000.000000   \n",
       "mean             0.753206            0.031132                0.600785   \n",
       "std              0.410957            0.109984                0.481542   \n",
       "min              0.000000            0.000000                0.000000   \n",
       "25%              0.400000            0.000000                0.000000   \n",
       "50%              1.000000            0.000000                1.000000   \n",
       "75%              1.000000            0.040000                1.000000   \n",
       "max              1.000000            1.000000                1.000000   \n",
       "\n",
       "       dsthostsrvdiffhostrate  dsthostserrorrate  dsthostsrvserrorrate  \\\n",
       "count           200000.000000      200000.000000         200000.000000   \n",
       "mean                 0.006715           0.176866              0.176633   \n",
       "std                  0.042345           0.380689              0.381091   \n",
       "min                  0.000000           0.000000              0.000000   \n",
       "25%                  0.000000           0.000000              0.000000   \n",
       "50%                  0.000000           0.000000              0.000000   \n",
       "75%                  0.000000           0.000000              0.000000   \n",
       "max                  1.000000           1.000000              1.000000   \n",
       "\n",
       "       dsthostrerrorrate  dsthostsrvrerror_rate  \n",
       "count      200000.000000          200000.000000  \n",
       "mean            0.058460               0.057674  \n",
       "std             0.231177               0.230638  \n",
       "min             0.000000               0.000000  \n",
       "25%             0.000000               0.000000  \n",
       "50%             0.000000               0.000000  \n",
       "75%             0.000000               0.000000  \n",
       "max             1.000000               1.000000  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe() # Statistical information of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11a2058e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Attacks')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analysis of the attack lables\n",
    "grouped_labels = data.groupby(\"labels\")[\"labels\"].count().sort_values(ascending=False)\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (12,8)\n",
    "font = {\"size\"   : 14}\n",
    "plt.rc('font', **font)\n",
    "sns.set_style(\"darkgrid\")\n",
    "plt.xticks(rotation=90)\n",
    "sns.barplot(x=grouped_labels.index, y=grouped_labels.values)\n",
    "plt.title(\"Count of attacks and normal events\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xlabel(\"Attacks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "377d2e20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analysis on the protocol types\n",
    "grouped_labels = data.groupby(\"protocoltype\")[\"protocoltype\"].count().sort_values(ascending=False)\n",
    "#define data\n",
    "data_new =grouped_labels.values\n",
    "labels = grouped_labels.index\n",
    "#define Seaborn color palette to use\n",
    "colors = sns.color_palette('pastel')[0:5]\n",
    "#create pie chart\n",
    "plt.pie(data_new, labels = labels, colors = colors, autopct='%d%%')\n",
    "plt.title(\"Labels by Protocol type\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "507c7104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['smurf' 'neptune' 'normal' 'warezclient' 'portsweep' 'back' 'satan' 'pod'\n",
      " 'ipsweep' 'nmap' 'ftp_write' 'teardrop' 'guess_passwd' 'multihop'\n",
      " 'warezmaster' 'loadmodule' 'buffer_overflow' 'rootkit' 'imap' 'phf'\n",
      " 'land' 'perl']\n"
     ]
    }
   ],
   "source": [
    "# Function to remove dots from attack names\n",
    "def remove_dot(label):\n",
    "    \"\"\" Remove dot from labels \"\"\"\n",
    "    return label.replace(\".\", \"\")\n",
    "data[\"labels\"] = data[\"labels\"].apply(lambda label: remove_dot(label))\n",
    "print(pd.unique(data[\"labels\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d2f0505d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dos' 'normal' 'r2l' 'probe' 'u2r']\n"
     ]
    }
   ],
   "source": [
    "# Grouping attack labels based on the type of attack\n",
    "attack_families = {\n",
    "    \"back\": \"dos\",\n",
    "    \"buffer_overflow\": \"u2r\",\n",
    "    \"ftp_write\": \"r2l\",\n",
    "    \"guess_passwd\": \"r2l\",\n",
    "    \"imap\": \"r2l\",\n",
    "    \"ipsweep\": \"probe\",\n",
    "    \"land\": \"dos\",\n",
    "    \"loadmodule\": \"u2r\",\n",
    "    \"multihop\": \"r2l\",\n",
    "    \"neptune\": \"dos\",\n",
    "    \"nmap\": \"probe\",\n",
    "    \"perl\": \"u2r\",\n",
    "    \"phf\": \"r2l\",\n",
    "    \"pod\": \"dos\",\n",
    "    \"portsweep\": \"probe\",\n",
    "    \"rootkit\": \"u2r\",\n",
    "    \"satan\": \"probe\",\n",
    "    \"smurf\": \"dos\",\n",
    "    \"spy\": \"r2l\",\n",
    "    \"teardrop\": \"dos\",\n",
    "    \"warezclient\": \"r2l\",\n",
    "    \"warezmaster\": \"r2l\",\n",
    "    \"normal\": \"normal\",\n",
    "}\n",
    "\n",
    "# Function to map attack labels to its family\n",
    "def map_attacks_to_families(attack):\n",
    "    \"\"\" Map attack to it's family \"\"\"\n",
    "    return attack_families[attack]\n",
    "\n",
    "data[\"labels\"] = data[\"labels\"].apply(lambda attack: map_attacks_to_families(attack))\n",
    "print(pd.unique(data[\"labels\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "abae44b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Class balance')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analysis of the transformed attack labels\n",
    "sns.countplot(x=\"labels\", data=data)\n",
    "plt.title(\"Class balance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8f0f7b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of maliscious events within the different protocols')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 452.9x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analysis of the attacks on protocols\n",
    "sns.catplot(x=\"protocoltype\", y=\"count\", hue=\"labels\", data=data)\n",
    "plt.title(\"Number of maliscious events within the different protocols\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "606c07ee",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of failed logins within the different protocols for each attacks')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 452.9x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analysis of number of failed logins with respect to protocols\n",
    "sns.catplot(x=\"protocoltype\", y=\"numfailedlogins\", hue=\"labels\", data=data)\n",
    "plt.title(\"Number of failed logins within the different protocols for each attacks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0df6cf6f",
   "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>duration</th>\n",
       "      <th>srcbytes</th>\n",
       "      <th>dstbytes</th>\n",
       "      <th>numroot</th>\n",
       "      <th>numshells</th>\n",
       "      <th>numaccessfiles</th>\n",
       "      <th>protocoltype</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1032</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>0</td>\n",
       "      <td>520</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>2</th>\n",
       "      <td>0</td>\n",
       "      <td>520</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>0</td>\n",
       "      <td>1032</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>0</td>\n",
       "      <td>1032</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  srcbytes  dstbytes  numroot  numshells  numaccessfiles  \\\n",
       "0         0      1032         0        0          0               0   \n",
       "1         0       520         0        0          0               0   \n",
       "2         0       520         0        0          0               0   \n",
       "3         0      1032         0        0          0               0   \n",
       "4         0      1032         0        0          0               0   \n",
       "\n",
       "   protocoltype  labels  \n",
       "0             0       0  \n",
       "1             0       0  \n",
       "2             0       0  \n",
       "3             0       0  \n",
       "4             0       0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['duration', 'srcbytes','dstbytes', 'numroot','numshells','numaccessfiles','protocoltype','labels']\n",
    "data1 = data[cols]\n",
    "data1[\"protocoltype\"] = data1[\"protocoltype\"].astype('category')\n",
    "data1[\"protocoltype\"] = data1[\"protocoltype\"].cat.codes\n",
    "data1[\"labels\"] = data1[\"labels\"].astype('category')\n",
    "data1[\"labels\"] = data1[\"labels\"].cat.codes\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "145ffc76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>srcbytes</th>\n",
       "      <th>dstbytes</th>\n",
       "      <th>numroot</th>\n",
       "      <th>numshells</th>\n",
       "      <th>numaccessfiles</th>\n",
       "      <th>protocoltype</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003171</td>\n",
       "      <td>0.008914</td>\n",
       "      <td>0.067436</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.036091</td>\n",
       "      <td>0.164035</td>\n",
       "      <td>0.138577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>srcbytes</th>\n",
       "      <td>0.003171</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>-0.000063</td>\n",
       "      <td>0.002526</td>\n",
       "      <td>0.011679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dstbytes</th>\n",
       "      <td>0.008914</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029440</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0.013369</td>\n",
       "      <td>0.024278</td>\n",
       "      <td>0.065011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numroot</th>\n",
       "      <td>0.067436</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>0.029440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.026375</td>\n",
       "      <td>0.486408</td>\n",
       "      <td>0.005270</td>\n",
       "      <td>0.010868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numshells</th>\n",
       "      <td>0.000300</td>\n",
       "      <td>-0.000011</td>\n",
       "      <td>0.000485</td>\n",
       "      <td>0.026375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024780</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.030470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>numaccessfiles</th>\n",
       "      <td>0.036091</td>\n",
       "      <td>-0.000063</td>\n",
       "      <td>0.013369</td>\n",
       "      <td>0.486408</td>\n",
       "      <td>0.024780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.024413</td>\n",
       "      <td>0.049797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>protocoltype</th>\n",
       "      <td>0.164035</td>\n",
       "      <td>0.002526</td>\n",
       "      <td>0.024278</td>\n",
       "      <td>0.005270</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.024413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.575169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>labels</th>\n",
       "      <td>0.138577</td>\n",
       "      <td>0.011679</td>\n",
       "      <td>0.065011</td>\n",
       "      <td>0.010868</td>\n",
       "      <td>0.030470</td>\n",
       "      <td>0.049797</td>\n",
       "      <td>0.575169</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                duration  srcbytes  dstbytes   numroot  numshells  \\\n",
       "duration        1.000000  0.003171  0.008914  0.067436   0.000300   \n",
       "srcbytes        0.003171  1.000000 -0.000028 -0.000014  -0.000011   \n",
       "dstbytes        0.008914 -0.000028  1.000000  0.029440   0.000485   \n",
       "numroot         0.067436 -0.000014  0.029440  1.000000   0.026375   \n",
       "numshells       0.000300 -0.000011  0.000485  0.026375   1.000000   \n",
       "numaccessfiles  0.036091 -0.000063  0.013369  0.486408   0.024780   \n",
       "protocoltype    0.164035  0.002526  0.024278  0.005270   0.007949   \n",
       "labels          0.138577  0.011679  0.065011  0.010868   0.030470   \n",
       "\n",
       "                numaccessfiles  protocoltype    labels  \n",
       "duration              0.036091      0.164035  0.138577  \n",
       "srcbytes             -0.000063      0.002526  0.011679  \n",
       "dstbytes              0.013369      0.024278  0.065011  \n",
       "numroot               0.486408      0.005270  0.010868  \n",
       "numshells             0.024780      0.007949  0.030470  \n",
       "numaccessfiles        1.000000      0.024413  0.049797  \n",
       "protocoltype          0.024413      1.000000  0.575169  \n",
       "labels                0.049797      0.575169  1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df99181b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data1.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d1d1d88",
   "metadata": {},
   "source": [
    "### Data Processing<a class=\"anchor\" id=\"chapter2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b67efa56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating feature vector and target vector.\n",
    "X = data1.drop(\"labels\", axis=1)\n",
    "y = data1[\"labels\"]\n",
    "\n",
    "# Constant features \n",
    "[col for col in X.columns if X[col].nunique() == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8d2feb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "97cc4c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical columns and split data.\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Encoding target variable\n",
    "lr = LabelEncoder()\n",
    "y = lr.fit_transform(y)\n",
    "# Splitting data into train and test...since our data is implaced we stratify it\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e209ca31",
   "metadata": {},
   "source": [
    "### Model training<a class=\"anchor\" id=\"chapter3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "81bf05c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "Accuracy Score:\n",
      " 0.9831833333333333\n",
      "\n",
      "Random Forest\n",
      "Accuracy Score:\n",
      " 0.9836333333333334\n",
      "\n",
      "Decision Tree\n",
      "Accuracy Score:\n",
      " 0.9775833333333334\n",
      "\n",
      "Kmeans\n",
      "Accuracy Score:\n",
      " 0.7913166666666667\n",
      "\n",
      "Naive Bayes\n",
      "Accuracy Score:\n",
      " 0.8240333333333333\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Define models to train\n",
    "names = [\"KNN\",\"Random Forest\",\"Decision Tree\",\"Kmeans\",\"Naive Bayes\"]\n",
    "\n",
    "# Train the model using the training sets\n",
    "classifiers = [\n",
    "     KNeighborsClassifier(n_neighbors=7),\n",
    "     RandomForestClassifier(max_depth=1000, n_estimators=50,random_state=43),\n",
    "     DecisionTreeClassifier(criterion=\"entropy\", max_depth=3),\n",
    "     KMeans(n_clusters=5),\n",
    "     GaussianNB()\n",
    "]\n",
    "#purpose of zip() is to map the similar index of multiple containers\n",
    "# so that they can be used just using as single entity.\n",
    "models = zip(names, classifiers)\n",
    "acc_score=[]\n",
    "Accuracy = []\n",
    "precision = []\n",
    "recall = []\n",
    "F1score = []\n",
    "Reportlist = []\n",
    "confusion_mat = []\n",
    "for name, classifier in models:\n",
    "    #fit function in ML, is used for training of model using data examples.\n",
    "    #Fit function adjusts weights according to data values so that better accuracy can be achieved.\n",
    "    classifier.fit(X_train, y_train)\n",
    "    #predict() function enables us to predict the labels of the data values on the basis of the trained model.\n",
    "    predicted = classifier.predict(X_test)\n",
    "    print(name)\n",
    "    # examining the output \n",
    "    Report = classification_report(y_test, predicted, output_dict=True)\n",
    "#     skplt.metrics.plot_confusion_matrix(y_test,predicted)\n",
    "    Report_ = classification_report(y_test, predicted)\n",
    "    Reportlist.append(Report_)\n",
    "    confusion_mat.append((y_test,predicted))\n",
    "    acc_score.append(accuracy_score(y_test, predicted))\n",
    "    print(\"Accuracy Score:\\n\",accuracy_score(y_test, predicted)) #tp+tn/tp+tn+fp+fn\n",
    "    Accuracy.append(Report['accuracy'])\n",
    "    precision.append(Report['macro avg']['precision']) \n",
    "    recall.append(Report['macro avg']['recall'])\n",
    "    F1score.append(Report['macro avg']['f1-score'])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c8a63b",
   "metadata": {},
   "source": [
    "### KNN Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a3f78850",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      " 0.9831833333333333\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99     47478\n",
      "           1       1.00      0.95      0.97     11859\n",
      "           2       0.98      0.36      0.52       512\n",
      "           3       0.98      0.90      0.93       144\n",
      "           4       0.00      0.00      0.00         7\n",
      "\n",
      "    accuracy                           0.98     60000\n",
      "   macro avg       0.79      0.64      0.68     60000\n",
      "weighted avg       0.98      0.98      0.98     60000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score:\\n\",Accuracy[0])\n",
    "print(\"Classification report:\\n\",Reportlist[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "086c6bb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Confusion Matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oUIZFj7PPY+MP31Vs9z5nCPm5OXuVadbicMC5Nc7PzeH6W+/mpWkTST/4kIoyjQ86iG3FRRzStBl7du9m9qvP89ybC0MaezBJSUlcO/RK3p7zFi//c1ZYYwlm8JDz2fDtt+EOw5PioiLS09MrthMTEyktLSUpKbKa6W1CVA9U9Q0ROTYUx67s0EObcuKJbUhOTuZEEX788QcSk5L4080j2FVSwtqvvuT2kX+ie3YPfv75J77QrwEYOOBsMjt3ocOZHUMdYo1eeW4Si+bO4YnnZtGwYQpNDkplx/ZtFe/v2LaN1DTnH8W/Viym/ZmdOSg1vbrD1Ztp02fw888P061LBv/+7EuaRMEtZqRLTUujuLi4Ytvv90dc8iPKandeRHU679Q5iwXz5xEIBPhp40aOOOJ3/Ovfa5i74COen/kKbdqexLhHn+DgQw6hUUojGjZsSEpKCgenH8zWLVvCGvv0SeNZ/fFKnnphNgcf2hSA087IIG/ZIvx+Pz9v/B5/wF/x3qrlS+jUvVc4Q+blF2fyyMNOe2Tjxo1JSEggMTExrDHFik6duzDvg/cByMvNpV27U8Ic0W+VL4vp5RUtIuxXTO30O2cAy3OW0r1LBn6/n8cmTKzyH2SXrK58tGghZ3XtREJCAp06d6FHr/D0pAIUbv4Pzz41DjnpNG69+kIAep0zmPMvvYbTOnTi2gv7EPD7GfXXRyo+8903/0f/wReHK2QABg4ewvXXDqXXWd3Ys2cPjzz6BCkpIW3mjRsDBw1m0cIFZHftTCAQYMq06eEOqUpRlNs88ZX3lNY19xb4VVXNDFa2zB8IlJSGJIw69/n3W8MdQq3YwugGIDkREnwHNqH9lxuLApc/m++pbP49Z+UDHQ7kfPUhZDVAVf0WCJr8jDHRI9ZqgFF9C2yMqT8+ny/mRoJYAjTGeBZNHRxeWAI0xngWY/nPEqAxxjurARpj4lMMPghtCdAY44mP2JsOyxKgMcYzuwU2xsSpuhnmJiKJwFRAgDJgKE4F83kgAHwO3KiqfhG5DhgGlAIPqOq7ItIIeBFoARQDV6pqgYhkAhPcsvNV9b5gsUT1WGBjTD2quwlRzwVQ1S7AvcBj7muMqnZ1zsRAETkcuBnoAvQFxopIQ+AGYI1b9gVgjHvcycAlQBaQISK/DxaIJUBjjCe1mQyhoKCgmYh8XOl1fflxVHU2UL59DLAJOANY4u77AOgFdASWq+ouVd0KrANOxUlwcyuXFZE0oKGqrlfVADAP6BnsO9ktsDHGM693wM2bN9+sqtWOBVbVUhGZAQwGLgAGuIkLnNvadCANqDz4vqr9lfcV7VO2VbA4rQZojPEsIcHn6eWFql4JnIjTHlh5BuNUYAtOQksNsj9Y2Zq/j6dIjTFxz+eDBJ/P06smInK5iNzpbu4A/MDHIpLt7usHLANWAV1FJEVE0oG2OB0ky4H+lcuqahGwW0Rai4gPp81wWbDvZLfAxhjP6ugpmDeB6SKyFGgA/An4CpgqIsnuz7NUtUxEnsRJZAnA3apaIiLPADNEJAfYjdPxATAceAlIxOkFzgv6fUI1H2Bt2HyAoWPzARqom/kA//c/2wI3zfrCU9l5IzLiez5AY0zsibGBIJGRAH1Ez4W1GpWJVzYfoDEmrh3gXXTEqTYBVn5wcV+qOiU04RhjIlmMVQBrrAG2rLcojDERr3wkSCypNgFWHkgsIr2A44A84H/rIS5jTASKsfwXvA1QRP4OHInzEOJu4E7gDyGOyxgTgYI95BxtvIwEyVLVK4BtqjoDpyZojIkzPl/dDoWLBF56gZNEJAUIuPN4lYU4JmNMhIqxCqCnBPg4kA80x2kDfDykERljIlTwcb7RJmgCVNXXRWQh0Br4RlULQx+WMSYSxVb689AGKCIdgIXAbOAdETkl1EEZYyJPbSZEjRZeOkGeBC5X1SNx5uafFNqQjDERyec8CO3lFS28JMCdqvolgKquwXkUxhgTh+KmF7jSULg9IjIJWIozR39RdZ8xxsSuuBoJwq9D4Va6fwrOPPyrQxmQMSZyRVHlzhOvQ+Fa4szc6gOOqIe4jDGRxhdfNUAARORZoBPQBGfhkq+BzBDHZYyJMD7i8DEYnDHAJ+Oss3kSUBLSiIwxEcpHYoK3V7TwkgCL3fU6m6jqZiA5xDF5VlZWxrDrrqZH9yx69+jO1+vXV7x3+6hbmTplcsX2o488TEaH0+ndozvvv/duOML9jVV5efTpmQ3A+nXr6NE9i57ZXbn5xhvw+/3hDY694/t09Wq6dcmkR/cshl17dUV8Tz7xOF07Z9C1cwYP3n9fDUcLj8rfIZL5/X5uGjGc7lmd6NMzm/Xr1oU7pCrF43OA+SIyCtgoIq8S5LZZRBqIyEwRWSYiq0TkvDqJtArvvfsOAIuW5HDPX+7jjttGUlBQwMBz+1e8B/D5mjW89uorLMlZyTvvz+P++/7Cjh07QhWWJ4+OH8eIYddSUuJUqO+47c/89W8P8OHiZQQCAd55e05ExffgA/dx15h7WbQkh127dvHB++/xzddf889XXmLxshUsyVnJwgXzWfPZZ2GNu7J9v0Mke3vObEpKSliSs5L7H3yI0bePDHdIv+H0Ant7RYugCVBV7wIm40yD9RIwIMhHLgMKVbUrzpqdEw80yOqcN3AQTz/jTE793YYNtDisBdu3bePue/7CHy65rKKcrv2Krt27k5KSQkpKCscffwKfrwnvP9RWrVrz6utvVmx/8kk+Xbt1B6DP2f346MOF4QoN+G187dufzi///S+BQIBt24pp0KABRx51FHPem0tiYiIJCQns2bOHlJSUMEa9t32/QyRbsTyH3n3PBiAjM5P8/I/DHFEV6mhd4EhSbQIUkbEi8nd3PsC7gL/hdIb8KcgxXwfuqbQd0gUvk5KSuO7qqxh5680MHnIBxx53HB07ZuxV5uR2p5CzbBnFxcUUFhaSm7uC7du3hzKsoAYPOZ8GDRpUbAcCgYpbh9TUVLZuDe/ym/vG1/r4Exh56820P6UtmzZtolv3bBo0aECzZs0IBAKMvn0U7dufzgknnhjGqPe273eIZMVFRaSnp1dsJyYmUloaeWvFxloNsKbb2bX7c0BV3QYgIqnALGDM/hynNqY+9zz3//wQ3bMy+eTTL2jSpMle77dp25bhI25k0Ln9ad36eM48M4OmTZuFOqxaSUj49XdRcXEx6QcfHL5gqnDbn29h4UfLOOnkk5k86WlG3zaSJ556mpKSEoZddzWpB6UyYaKNktxfqWlpFBcXV2z7/X6SkiJvzbJoat/zoqbnAGfs70FF5CjgLWCSqr68v8cJ5uUXZ/Ljjz9w2x130rhxYxISEkhMTPxNuYKCAgo3b+bDxcvYunUr5/bvy8nt2oUqrP3Svv3pLF2ymG7ds5k/9wO6ZZ8V7pD2csihh5KalgZAyyOOYOXK5QQCAS4cMpDuZ/Vg1G13hDnC6Napcxfef/cdLrjwf8jLzaVdu8ibc8QHJMZLAtxfInIYMB/4o6p+WNfHr2zg4CEMu/Zqevfozp49exg3/vEq26CaNWvGN998TVanjiQnJ/P3h8ZVmSjD6aFxjzJi+HXs3r2bNm3aMuT8C8Id0l4mTZ7GFZdeTFJSEsnJyUyaPJW358xm2dIl7Nq1i/lzPwDgbw+MJbNTpzBHG30GDhrMooULyO7amUAgwJRp08Md0m9F2UQHXvgCgUCdHlBEJgAXsfctdD9V3VndZ/z+QGBXlMwzHWu3ACY+JCdCwgEu6vv9lp2Bx5du8FT2sfPa5AMdDuR89cHLSJDfAQ/jzAg9C/hMVfOqK6+qtwC31FmExpgIEV3P+Hnh5TnAKcBzOA9ALwUmhDQiY0zEisf5AFNUdREQUFXFhsIZE5di8UFoL50gu0SkL5AoIplYAjQmPvkgKZqymwdeaoDXA0OBZsAo4IaQRmSMiUhxWQNU1R+Ai+shFmNMhIumYW5eeOkF/gkI4PwCOBT4WlXbhjowY0zkibH856kGWD41PiJyDPDXUAZkjIlMPqKrh9cLL22AFVR1A9AmRLEYYyKZj5ibENXLLfArOLfA4CyUtCmkERljIlIs1gC9PAbzT+AX9+cSIAInKjPG1IcDHE0XcbwkwFGqmhXySIwxEa8uaoAi0gBndNmxQEPgAeBL4Hmcu83PgRtV1S8i1wHDcOYVfUBV3xWRRsCLQAugGLhSVQvc55QnuGXnV17Zstrv4yHe/4rILSJytoj0EZE+tfu6xphYUH4LXAdD4aqaNf4xYIy7zwcMFJHDgZuBLkBfYKyINMR5FnmNW/YFfp1zdDJwCZAFZIjI74MF4qUGWAi0d1/gZOj5Hj5njIkltVjwqKCgoFlWVlbl5rIpqjrF/fl1nIlVypUCZwBL3O0PgD5AGbBcVXfhjEhbB5yKk+DGVSp7j4ikAQ1VdT2AiMwDegKf1BRntQlQRP6pqhep6tCg39YYExcSPT430rx5882qWuV0WNXMGj/eXX0SnNvadCANqLw2RFX7K+8r2qdsq2Bx1vR1mgf7sDEmfji3wHWzKJI7a/xHwEx31vjK68CmAltwElpqkP3Bytaoplvg1u6CSL/hrhRnjIkzddQJUtWs8f8WkWxVXYzTLvgRsAp4UERScDpL2uJ0kCwH+rvv9wOWqWqRiOwWkdbA1zhthkE7QWpKgDsA3Y/vZ4yJRXU30cFdwCE4bXflK0jeAjwpIsnAV8AsVS0TkSeBZTh3q3eraomIPAPMEJEcYDdOxwfAcJylexNxeoGrnbi54itVNyW+iHykqvWyMo8/QGB3lEyJb0w0qosp8f+zbVfg9U9/8lT2xi7HRv2U+Pn1FoUxJirEzWQIqjqqPgMxxkQ2H5AUY2PhIm/lZWNMxIqbGqAxxuzFF4cTohpjDPw6JX4ssQRojPGsVhOIRgFLgMYYj7yN8ogmlgCNMZ6UD4WLJZYAjTGexVb6swRojKmFGKsAWgI0xnjkw/N8gNHCEqAxxhMf1gtsjIlj1glijIlbdgtsjIlLdgtsjIlj3hdFihaWAI0xnsVW+ou9Gm2F//znPxx/3FHo2rXhDqVaq/Ly6NMzu2J7zuy3uPLyS6r/QJj5/X5uGjGc7lmd6NMzm/Xr1oU7pL1Uvp6frl5Nz+yu9OmZzbn9+7Jp0yYAxj/yMBlntKfXWd14/713wxjt3iL92oKT/BJ9Pk+vaBGSGqCIJAJTAcFZ23No+Xqd9WHPnj38ccQwGjVqVF+nrLVHx4/jlRdn0rhJEwBG3noLCxfM49TT2oc3sBq8PWc2JSUlLMlZSV5uLqNvH8nrb84Jd1jAb6/nqD/fwmNPPMVp7dszbco/ePSRh7niyqG89srLLF3hLBVxVtfOZJ/Vg8aNG4czdCCyr22FulsTJGKEqgZ4LoCqdgHuxVn1vd6Mvn0U110/nJYtj6jP09ZKq1atefX1Nyu2Mzt15smJz4QxouBWLM+hd9+zAcjIzCQ//+Mgn6g/+17PF156ldPatwegtLSUlJQU1q79iq7ds0lJSSElJYXWJ5zAms8+C1PEe4vka1uZz+N/0SIkCVBVZwPXu5vHAJtCcZ6qzJzxPM2bN6d3n771dcr9MnjI+TRo0KBi+8L/uSjiG5iLi4pIT0+v2E5MTKS0tDSMEf1q3+vZsmVLAFauWMHkZyZy0y230q7dKSxftpTi4mIKCwvJXbmCHTu2hyvkvUTytS1XPh+gl1e0CFkniKqWisgMYDBwQajOs68Zzz+Hz+dj0YcL+ezT1Vwz9ApmvfU2hx9+eH2FELNS09IoLi6u2Pb7/SQlRW4/2uuv/ZNxYx/krTnv0bx5c5o3b87wEX9k4IB+tG59PGd2zKBp02bhDhOInmubEEW1Oy9C2gmiqlcCJwJTRaRJKM9VbuFHS1mwaAnzP1zMqae159npL1jyqyOdOndh3gfvA5CXm0u7dqeEOaLqvfLSi0yeNJF5Hy7muFatACgoKGBz4WYWLclh/OMT+OH77zm5XbswR+qIlmtrNUAPRORy4EhVHYuzwLofpzPERLGBgwazaOECsrt2JhAIMGXa9HCHVKWysjJG3nozRx11NBdfOASArt26M+bev/Lt11/TJfNMkpOTGfvwIyQmJoY5Wkc0XNtYnA+w2oXRD4Rb25sOHA40AB5S1Wq7tGxhdGNCqy4WRi/auSfwrw1bPZXt2aZZ1C+Mvt9UdTvwP6E4tjEmXKKrh9eLyGtlNcZEpihr3/PCEqAxxhMfWA3QGBO/EmIr/1kCNMZ4F2u9wJYAjTGe+Ii92WAsARpjPLMaoDEmbsVW+rMEaIypjRjLgJYAjTGe+Hx2C2yMiWOxlf4sARpjaiPGMqAlQGOMR3U7FlhEMoCHVTVbRI4HngcCwOfAjarqF5HrgGFAKfCAqr4rIo2AF4EWQDFwpaoWiEgmMMEtO19V7wsWQ8wuimSMqXt1NR+giNwOTANS3F2PAWNUtStOPXOgiBwO3Ax0AfoCY0WkIXADsMYt+wIwxj3GZOASIAvIEJHfB4vDEqAxxjOfx5cH64EhlbbPAJa4P38A9AI6AstVdZeqbgXWAafiJLi5lcuKSBrQUFXXq2oAmAf0DBaE3QIbYzxx1gTxlt4KCgqaZWVlVV7ZaYqqTinfUNU3ROTYyod3Exc4t7XpQBpQeQLCqvZX3le0T9lWweK0BGiM8czrUzDNmzffrKq1mRDVX+nnVGALTkJLDbI/WNka2S2wMcazOrwF3te/RSTb/bkfsAxYBXQVkRQRSQfa4nSQLAf6Vy6rqkXAbhFpLSI+nDbDZcFOajVAY4w3oZ0NYSTO4mnJwFfALFUtE5EncRJZAnC3qpaIyDPADBHJAXbjdHwADAdeAhJxeoHzgp00JGuC1JatCWJMaNXFmiA7dpcF1m3a6ansqUcdFL9rghhjYlOMjYSzBGiM8cbpBQ53FHXLEqAxxjNbE8QYE7esBmiMiVsxlv8sARpjaiHGMqAlQGOMJzYhqjEmrsVW+rMEaIypjRjLgJYAjTEe1e2EqJHAEqAxxrMYawK0BGiM8S7G8p8lQGOMN7WZEDVaWAI0xngWY/nPEqAxxrsYy3+xNyO03+/nphHD6Z7ViT49s1m/bl24QwrqkYfH0j2rE507nsHzzz0b7nCqFY3XdlVeHn16Zoc7jKCi4tp6nQ46irJkyBKgiLQQke9FpE2ozlGVt+fMpqSkhCU5K7n/wYcYffvI+jx9rS1dspjclSv4aOly5i9awg8/fB/ukKoVbdf20fHjGDHsWkpKSsIdSlDRcm19Hv+LFiFJgCLSAPgH4G362Dq0YnkOvfueDUBGZib5+R8H+UR4LZg/j5PbncJFFwzm/EHn0q//gHCHVK1ou7atWrXm1dffDHcYnkTLta2rdYEjRajaAMfjLFJ8Z4iOX63ioiLS09MrthMTEyktLSUpKTKbOws3b+a77zbw5px3+fabb7hgyHl8+vnaiOxti7ZrO3jI+Wz49ttwh+FJNFxbH5AQef9bHpA6rwGKyFVAgarOq+tje5GalkZxcXHFtt/vj6j/ifZ1aNOm9OrTl+TkZE4UIaVhCgUFBeEOq0rRdm2jSfRc29hqBAzFLfDVQG8RWQy0B14QkcNDcJ4qderchXkfvA9AXm4u7dqdUl+n3i+du2SxYN5cAoEAGzduZPuO7TRt2jTcYVUp2q5tNImWa2u3wEGoarfyn90kOFxVf67r81Rn4KDBLFq4gOyunQkEAkyZNr2+Tr1f+p8zgJxlS8nq1JGA388TTz5NYmJiuMOqUrRd22gSLdc2inKbJyFdFrNSAlxbUzlbFtOY0KqLZTF3l/oDhdtLPZVtmZ5sy2KqanYoj2+MqT/O7W1s1QEjsZXVGBOhYiv9WQI0xtRCjFUALQEaY7yKrlEeXlgCNMZ4F1v5zxKgMca7GMt/lgCNMd44Q+FiKwVaAjTGeBZj+S/25gM0xhivrAZojPEmysb5emEJ0BjjmT0GY4yJS86qcOGOom5ZAjTGeGYJ0BgTt+wW2BgTt6wGaIyJW3WR/0QkAZgEnAbsAq5V1bCsA2rPARpjvKubJUEGASmq2gkYDTwammCDswRojPHE5/OR4PEVRBYwF0BVcwnjzNERcQuc4GNzShIbwh2HMTHsmAM9QIKPeSlJNPNS9rvvvkvp3bt35cWNp6jqFPfnNGBrpffKRCRJVb3Nt1+HIiIBAs3DHYAxJqizvRY8+uijUdXq3i4CUittJ4Qj+YHdAhtj6t9yoD+AiGQCa8IVSKTUAI0x8eMtnLXDV+B0mQwNVyAhXRbTGGMimd0CG2PiliVAY0zcsgRojIlbMdcJEknDbLwSkQzgYVXNDncsNRGRBsBzwLFAQ+ABVX07rEHVQEQSgamAAGXAUFVdH96oaiYiLYB8oLeqrg13PLEuFmuAg4iQYTZeiMjtwDQgJdyxeHAZUKiqXYF+wMQwxxPMuQCq2gW4F3gsvOHUzP0F8w9gZ7hjiRexmAAjZpiNR+uBIeEOwqPXgXsqbYfl4VWvVHU2cL27eQywKXzReDIemAxsDHcg8SIWE2CVw2zCFUwwqvoGsCfccXihqttUtVhEUoFZwJhwxxSMqpaKyAzgKZyYI5KIXAUUqOq8cMcST2IxAUbMMJtYJCJHAR8BM1X15XDH44WqXgmcCEwVkSbhjqcaV+M8HLwYaA+8ICKHhzWiOBCxNaMDsByn7ee1cA+ziTUichgwH/ijqn4Y7niCEZHLgSNVdSywA/DjdIZEHFXtVv6zmwSHq+rP4YsoPsRiAoyYYTYx6C7gEOAeESlvC+ynqpHaaP8mMF1ElgINgD+pakmYYzIRxIbCGWPiViy2ARpjjCeWAI0xccsSoDEmblkCNMbELUuAxpi4FYuPwcQ0EckGXgO+BAJAI+AlVX1qP471ELAWWA2cp6p/q6bcYCBPVYMO0RKRs4GLVfWqfWIerqoXV/OZq4A2qjraw/E9lzUmGEuA0WlReTIRkYaAishMVd2yPwdT1dU4SbA6twDDsTGqJsZYAox+qTijG0rdEQQFOA8rn4MzLdgJOE0dY1R1sYicjzOGtwBIBtZWrqGJyDXADUAiMAf4F78OzcoChgGX4NQ+X1XVJ0WkLc40Wdvd1y/VBSsif8SZ/KEBzpjt8okgOonIhzhjuf+qqu+JSHfgQff7rXfPbUydsTbA6NRDRBaLyCLgJeAmVd3mvveyqvbCGVu62R1iNRB42n1/HNAL6IszPKyCOxfdaKArcAaQDizBqR1eARwPXIQz404WMEhEBLgfuNc974rqgnbnamwK9HKn1GoAnOm+vd2N6xxgYqW5/IaoanfgR+Cq2l0mY2pmNcDotKi69jSgfDHWU4Cu7mSrAEnuWN4iVS0EcIcLVtYK+LzS0LZb3XLl77fDmVaqfBzwIThJ8WRglbtvOdC2ysBU/SKyG3hFRLYBR+IkQYAcVQ0A/xGRrUAzoCXOmG5w2jrn49QEjakTVgOMPX73z7XAK+4s0/1w5vL7BUgXkfKF6M/c57PrgTZuuyIiMktEfuceMwEnuX4BnOUe93mcySbWAp2qOWYFETkVGKSqFwE3ucf0Vf6cOwPKQcBm4AdgoHuuB3FmoTGmzlgCjF3/wElmS3BuSzeo6m6cySHmichCnDbACqpaADwMLBGRlcAnqvqj+/kXgO9xan85IvIxTvvij8AI4C63DS+D6q0DtrufXQD8BBzhvtfIvaV/GximqmU4nS/vuTXVEcDnB3RFjNmHTYZgjIlbVgM0xsQtS4DGmLhlCdAYE7csARpj4pYlQGNM3LIEaIyJW5YAjTFx6/8BQxLoKVcrHrMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "skplt.metrics.plot_confusion_matrix(confusion_mat[0][0],confusion_mat[0][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb7c9f0e",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c3e35008",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      " 0.9836333333333334\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99     47478\n",
      "           1       1.00      0.95      0.97     11859\n",
      "           2       0.99      0.37      0.54       512\n",
      "           3       1.00      0.95      0.98       144\n",
      "           4       0.80      0.57      0.67         7\n",
      "\n",
      "    accuracy                           0.98     60000\n",
      "   macro avg       0.95      0.77      0.83     60000\n",
      "weighted avg       0.98      0.98      0.98     60000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score:\\n\",Accuracy[1])\n",
    "print(\"Classification report:\\n\",Reportlist[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e156c258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Confusion Matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lZ5RZ1vwmLT2dvLy84u1gMBizyc8XfFa788LX6bxtu0wWLVxAKBTi++++48gjj+KD/65n/qJ3mDZjFie0OLE4+e3YsYM9e/bwh6OPjm7Qrqnjx7JuzXs8/uxrHHp4vXLLnnJ6a1YtXQTAe8sWc+oZbcstbxxt27Vnwby3AFidnU3LlidHOSJ/K1oWM57aAH3967DXOeeycsVyOrVvTTAY5OFxT5CcnFxq2U3/+4wmTY6t3gDLsH3bVp5+/AHkxFO5YcBFAHQ7py8X/P3KUsv3v3okd940jDn/ns6hh9XjjkcmV2e4vtW7T1+WLF5EVod2hEIhJk2ZGu2QfM9Huc2TQFFPaVVzb4FfUNU24coWBkOh/IKIhFHlPv56R7RDqBBbGN0A1EyGpEDlJrTf8F1u6NKn13oqu/a2zmuBMypzvuoQsRqgqn4JhE1+xhj/iLcaoK9vgY0x1ScQCMTdSBBLgMYYz/zUweGFJUBjjGdxlv8sARpjvLMaoDEmMcXhg9CWAI0xngSIv+mwLAEaYzyzW2BjTIKqmmFuIpIMTAYEKAT641QwpwEh4GPgalUNisggYAhQANylqnNFpDYwE2gI5AGXq2qOiLQBxrllF6rqv8LF4uuxwMaYalR1E6KeB6Cq7YHbgYfd1xhV7eCcid4i0ggYDrQHegL3ikgt4CpgvVv2WWCMe9yJwMVAJtBaRE4LF4glQGOMJxWZDCEnJ6e+iKwp8RpcdBxVfQ0o2m4C/AicDixz980DugFnAitVdY+q7gA2AafgJLj5JcuKSDpQS1U3q2oIWAB0Dfed7BbYGOOZ1zvgBg0abFPVMscCq2qBiEwH+gIXAue6iQuc29oMIB0oOfi+tP0l9+XuV7ZpuDitBmiM8SwpKeDp5YWqXg4cj9MeWHIG4zTgF5yElhZmf7iy5X8fT5EaYxJeIABJgYCnV3lE5FIR+Ye7+SsQBNaISJa7rxfwLvA+0EFEUkUkA2iB00GyEji7ZFlVzQX2ikgzEQngtBm+G+472S2wMcazKnoK5hVgqogsB2oA1wOfApNFpKb782xVLRSRx3ASWRJwq6rmi8gEYLqIrAD24nR8AAwFngOScXqBV4f9PpGaD7AibD7AyLH5AA1UzXyAn23dGbp29ieeyi4Y1jqx5wM0xsSfOBsIEhsJMIB/LqzfalSxUMOviHgbaRBPbD5AY0xCq+RddMwpMwGWfHBxf6o6KTLhGGNiWZxVAMutATautiiMMTGvaCRIPCkzAZYcSCwi3YDjgNXAZ9UQlzEmBsVZ/gvfBigi9wB/wHkIcS/wD+BvEY7LGBODwj3k7DdeRoJkquplwE5VnY5TEzTGJJhAoGqHwsUCL73AKSKSCoTcebwKIxyTMSZGxVkF0FMCfARYCzTAaQN8JKIRGWNiVPhxvn4TNgGq6ksishhoBnyhqtsjH5YxJhbFV/rz0AYoImcAi4HXgDdE5ORIB2WMiT0VmRDVL7x0gjwGXKqqf8CZm398ZEMyxsSkgPMgtJeXX3hJgLtVdQOAqq7HeRTGGJOAEqYXuMRQuH0iMh5YjjNHf25ZnzHGxK+EGgnCb0Ph3nP/FJx5+NdFMiBjTOzyUeXOE69D4RrjzNwaAI6shriMMbEmkFg1QABE5GmgLVAXZ+GSz4E2EY7LGBNjAiTgYzA4Y4BPwlln80QgP6IRGWNiVIDkJG8vv/CSAPPc9Trrquo2oGaEY/KssLCQIYMG0KVTJt27dOLzzZuL3xs18gYmT5pYvP3Qg/fT+ow/0b1LJ956c240wi3T1q1baX7c0ejGjdEO5Xfef381Pbt1BuDDdevolNmWrlkdGDJoAMFgEIjt6xoMBrl22FA6ZbalR9csNm/aFO2QyuSXWOPtOUAvQ+HWishI4DsReSHcZ0SkBvAMcCxQC7hLVV+vbKCleXPuGwAsWbaC5cuWcvNNIxj/1GQGDricTf/7jOtvFAA+Xr+eF1+YxbKV2QB07tierM5dqFOnTiTCqpB9+/ZxzbAh1K5dO3zhavTw2AeY9dxM6tStC8A9d93BP269jbN6nU3/yy5h3ltv0qTJsTF7XQFen/Ma+fn5LFvxHquzsxk9agQvvTIn2mGVyg+xOr3A0Y6iaoWtAarqLcBEnGmwngPODfORS4DtqtoBZ83OJyobZFnO792HJyc4k1N/tWULDY9oyK6dO7n1tv/jbxdfUlxON35Kh06dSE1NJTU1lebN/8jH6z+KVFgVMnrUSAYNHkrjxrHVt9S0aTNmvfhy8faprVrx808/EQqFyMvLo0aNGjF9XQFWrVxB955nAdC6TRvWrl0T5YjK5otYq2hd4FhSZgIUkXtF5B53PsBbgDtwOkOuD3PMl4DbSmxHdMHLlJQUBg24ghE3DKdvvws59rjjOPPM1r8rc1LLk1nx7rvk5eWxfft2srNXsWvXrkiG5cmM6dNo0KAB3Xv0jHYoB+jT7wJq1KhRvN28+R8ZeeN1/OnkE9m69Uc6dsqK2etaJC83l4yMjOLt5ORkCgpic/1Vv8QaCHh7+UV5t7MH1SClqjsBRCQNmA2MOZjjVMTkZ6Zx5w/30SmzDf/58BPqurdtRU5o0YKhw66mz3ln06xZc/7859bUq1c/0mGFNX3aMwQCAZa8vZiPPlzHlf0vY/arr9OoUaNoh3aAm0Zcz6IlyznxpJOYOOFJRo8awaOPPRmT17VIWno6eXl5xdvBYJCUlNhcB8wvsfqpfc+L8p4DnH6wBxWRo4FXgfGq+vzBHiec52fO4Ntvv+Gmm/9BnTp1SEpKIjk5+YByOTk5bN+2jbeXvsuOHTs47+yenNSyZaTC8mzxO8uLf+7RNYvHn5wYk8kP4LDDDic9PR2Axo2PJHvVqpi9rkXatmvPW3Pf4MKL/h+rs7Np2TJ25/HwQ6wBIDlREuDBEpEjgIXANar6dlUfv6TeffsxZOAAunfpxL59+3hg7COkpqYeUK5+/fp88cXnZLY9k5o1a3LPfQ+UmihN2cY/NZnLLvkbKSkp1KxZkycnTIr569q7T1+WLF5EVod2hEIhJk2ZGu2QyuSLWH020YEXgapeOFtExgF/4fe30L1UdXdZnwkGQ6E9Ppln2m+3ALYwugGomQxJlVzU9+tfdoceWb7FU9mHzz9hLXBGZc5XHbyMBDkKuB9nRujZwEequrqs8qp6HXBdlUVojIkR/nrGzwsvD0JPwnmurybOjDDjIhqRMSZmJeJ8gKmqugQIqapiQ+GMSUhFD0InymMwRfaISE8gWUTaYAnQmMQUgBQ/ZTcPvNQABwP9gfrASOCqiEZkjIlJCVkDVNVvgL9WQyzGmBjnp2FuXnjpBf4eCOH8Ajgc+FxVW0Q6MGNM7Imz/OepBlg0NT4i0gT4ZyQDMsbEpgD+6uH1wksbYDFV3QKcEKFYjDGxLEDcTYjq5RZ4Fs4tMDgLJf0Y0YiMMTEpHmuAXh6D+Tfws/tzPhCDE5UZY6pDJUfTxRwvCXCkqmZGPBJjTMyrihpgabPGAxuAaTh3mx8DV6tqUEQGAUNw5hW9S1XnikhtYCbQEMgDLlfVHPc55XFu2YUlV7Ys8/t4iPcnEblORM4SkR4i0qNiX9cYEw+KboGrYChcabPGPwyMcfcFgN4i0ggYDrQHegL3ikgtnGeR17tln+W3OUcnAhcDmUBrETktXCBeaoDbgVbuC5wMvdDD54wx8aQCCx7l5OTUz8zMLNlcNklVJ7k/v4QzsUqRAuB0YJm7PQ/oARQCK1V1D86ItE3AKTgJ7oESZW8TkXSglqpuBhCRBUBX4D/lxVlmAhSRf6vqX1S1f9hva4xJCMkenxtp0KDBNlUtdTqsMmaNH+uuPgnObW0GkA7sKPHR0vaX3Je7X9mm4eIs7+s0CPdhY0zicG6Bq2ZRJHfW+HeAGe6s8cESb6cBv+AktLQw+8OVLVd5t8DN3AWRDuCuFGeMSTBV1AlS2qzx/xWRLFVditMu+A7wPnC3iKTidJa0wOkgWQmc7b7fC3hXVXNFZK+INAM+x2kzDNsJUl4C/BXQg/h+xph4VHUTHdwCHIbTdle0guR1wGMiUhP4FJitqoUi8hjwLs7d6q2qmi8iE4DpIrIC2IvT8QEwFGfp3mScXuAyJ24u/kplTZkuIu+oaueD/ooVEAwR2uuTKfGN8aOqmBJ/6849oZc+/N5T2avbH+v7KfHXVlsUxhhfSJjJEFR1ZHUGYoyJbQEgJc7GwsXeysvGmJiVMDVAY4z5nUACTohqjDHw25T48cQSoDHGswpNIOoDlgCNMR55G+XhJ5YAjTGeFA2FiyeWAI0xnsVX+rMEaIypgDirAFoCNMZ4FMDzfIB+YQnQGONJAOsFNsYkMOsEMcYkLLsFNsYkJLsFNsYkMO+LIvmFJUBjjGfxlf7ir0ZbbOvWrTQ/7mh048Zoh1KuwsJChgwcQOeO7enWuSOfb94c7ZDKFAwGuXbYUDpltqVH1yw2b9oU7ZB+5/3Vq+nRNQuATzdsoEunTDp3bM911wyjsLCQD9eto0fXrOLXoYeksnDB/OgG7Yr1awtO8ksOBDy9/CIiNUARSQYmA4Kztmf/ovU6q8O+ffu4ZtgQateuXV2nPGhvzn0DgHeWr2T5sqXcfNONvPTKnChHVbrX57xGfn4+y1a8x+rsbEaPGhEzsT409gFmzZxBnbp1Abj9tlu44657yOzQkUEDrmDuG6/Tu09fFr69FICXZ79E4yOPpEfPs6IY9W9i+doWq7o1QWJGpGqA5wGoanvgdpxV36vN6FEjGTR4KI0bH1mdpz0o5/fuw5MTnfWiv9qyhYYNj4hyRGVbtXIF3d2E0bpNG9auXRPmE9WnadNmvPDSK8XbL7z4MpkdOrJ3715+/PGH313XXbt2cdcd/8dDjzwWjVBLFcvXtqSAx//8IiIJUFVfAwa7m02AHyNxntLMmD6NBg0a0L1Hz+o6ZaWlpKQwsP/l3Hj9tfS94MJoh1OmvNxcMjIyireTk5MpKCiIYkS/6dvvAmrUqFG8nZyczJYtWzjt1JPYvm0bx4sUvzftmafpd8FF1K9fPxqhliqWr22RovkAvbz8ImJtgKpaICLTgcdxVn+vFtOnPcPbixfRo2sWH324jiv7X8YPP/xQXac/aFOmTuejDZ8xbOggdu3aFe1wSpWWnk5eXl7xdjAYJCUldvvRmjRpwsef/o+Bg4dy88gbi/e/MOs5rhgwMIqRHcgv1zaJgKeXX0S0E0RVLweOByaLSN1InqvI4neWs2jJMha+vZRTTm3F01OfpVGjRtVx6oPy/MwZPHj/vQDUqVOHpKQkkpOToxxV6dq2a8+CeW8BsDo7m5YtT45yRGW7sO/5bPrf/wA4JC2NpCTnf/UdO3awd88ejj766GiGdwC/XNt4qwFGqhPkUuAPqnovzgLrQZzOELOf3n37MXhgf7p17si+fft48KFHSU1NjXZYperdpy9LFi8iq0M7QqEQk6ZMjXZIZRpx02gGXXkFNWvWpE6dOox/agoA//vsM4459tjoBlcKP1zbeJwPsMyF0SvDre1NBRoBNYD7VLXMLi1bGN2YyKqKhdFzd+8LfbBlh6eyXU+o7/uF0Q+aqu4C/l8kjm2MiRZ/9fB6EXutrMaY2OSz9j0vLAEaYzwJgNUAjTGJKym+8p8lQGOMd/HWC2wJ0BjjSYD4mw3GEqAxxjOrARpjElZ8pT9LgMaYioizDGgJ0BjjSSBgt8DGmAQWX+nPEqAxpiLiLANaAjTGeFS1Y4FFpDVwv6pmiUhzYBoQAj4GrlbVoIgMAoYABcBdqjpXRGoDM4GGQB5wuarmiEgbYJxbdqGq/itcDHG7KJIxpupV1XyAIjIKmAIUzf32MDBGVTvg1DN7i0gjYDjQHugJ3CsitYCrgPVu2WeBMe4xJgIXA5lAaxE5LVwclgCNMZ4FPL482Az0K7F9OrDM/Xke0A04E1ipqntUdQewCTgFJ8HNL1lWRNKBWqq6WVVDwAKga7gg7BbYGOOJsyaIt/SWk5NTPzMzs+TKTpNUdVLRhqq+LCLHljy8m7jAua3NANKBkhMQlra/5L7c/co2DRenJUBjjGden4Jp0KDBNlWtyISowRI/pwG/4CS0tDD7w5Utl90CG2M8q8Jb4P39V0Sy3J97Ae8C7wMdRCRVRDKAFjgdJCuBs0uWVdVcYK+INBORAE6b4bvhTmo1QGOMN5GdDWEEzuJpNYFPgdmqWigij+EksiTgVlXNF5EJwHQRWQHsxen4ABgKPAck4/QCrw530oisCVJRtiaIMZFVFWuC/Lq3MLTpx92eyp5y9CGJuyaIMSY+xdlIOEuAxhhvnF7gaEdRtSwBGmM8szVBjDEJy2qAxpiEFWf5zxKgMaYC4iwDWgI0xnhiE6IaYxJafKU/S4DGmIqIswxoCdAY41HVTogaCywBGmM8i7MmQEuAxhjv4iz/WQI0xnhTkQlR/cISoDHGszjLf5YAjTHexVn+i78ZoYPBINcOG0qnzLb06JrF5k2boh1SuQoLCxkycACdO7anW+eOfL55c7RDKpPfrq3f4gXYunUrzY87Gt24MdqhHMjrdNA+ypIRS4Ai0lBEvhaREyJ1jtK8Puc18vPzWbbiPe68+z5GjxpRnaevsDfnvgHAO8tXcvs/7+Dmm26MckRl89u19Vu8+/bt45phQ6hdu3a0QylTwON/fhGRBCgiNYCnAG/Tx1ahVStX0L3nWQC0btOGtWvXhPlEdJ3fuw9PTnQWy/pqyxYaNjwiyhGVzW/X1m/xjh41kkGDh9K48ZHRDqVMVbUucKyIVA1wLM4ixd9F6PhlysvNJSMjo3g7OTmZgoKC6g6jQlJSUhjY/3JuvP5a+l5wYbTDKZPfrq2f4p0xfRoNGjSge4+e0Q6lTAEgKeDt5RdVngBF5AogR1UXVPWxvUhLTycvL694OxgMkpIS+309U6ZO56MNnzFs6CB27doV7XBK5bdr66d4p097hrcXL6JH1yw++nAdV/a/jB9++CHaYZUivhoBI1EDHAB0F5GlQCvgWRFpFIHzlKptu/YsmPcWAKuzs2nZ8uTqOvVBeX7mDB68/14A6tSpQ1JSEsnJyVGOqnR+u7Z+infxO8tZtGQZC99eyimntuLpqc/SqFG1/bPxLN5ugav816Gqdiz62U2CQ1W12n6V9e7TlyWLF5HVoR2hUIhJU6ZW16kPSu++/Rg8sD/dOndk3759PPjQo6SmpkY7rFL57tr6LF4/8FFu8ySiy2KWSIDl9unbspjGRFZVLIu5tyAY2r7LWxtq44yatiymqmZF8vjGmOrj3N7GVx0wNluEjTExKb7SnyVAY0wFxFkF0BKgMcYrf43y8MISoDHGu/jKf5YAjTHexVn+swRojPHGGQoXXynQEqAxxrM4y3/xNx+gMcZ4ZTVAY4w3Phvn64UlQGOMZ/YYjDEmITmrwkU7iqplCdAY45klQGNMwrJbYGNMwrIaoDEmYVVF/hORJGA8cCqwBxioqlFZs9SeAzTGeFc1S4L0AVJVtS0wGngoMsGGZwnQGONJIBAgyeMrjExgPoCqZhPFmaNj4hY4KcC21BS2RDsOY+JYk8oeICnAgtQU6nsp+9VXX6V279695ELMk1R1kvtzOrCjxHuFIpKiqtW+ZmlMJECgQbQDMMaEdZbXgscccwyqWtbbuUBaie2kaCQ/sFtgY0z1WwmcDSAibYD10QokVmqAxpjE8SrO2uGrcLpM+kcrkIgui2mMMbHMboGNMQnLEqAxJmFZAjTGJKy46wSJpWE2XolIa+B+Vc2KdizlEZEawDPAsUAt4C5VfT2qQZVDRJKByYAAhUB/Vd0c3ajKJyINgbVAd1XdGO144l081gD7ECPDbLwQkVHAFCA12rF4cAmwXVU7AL2AJ6IcTzjnAahqe+B24OHohlM+9xfMU8DuaMeSKOIxAcbMMBuPNgP9oh2ERy8Bt5XYjsrDq16p6mvAYHezCfBj9KLxZCwwEfgu2oEkinhMgKUOs4lWMOGo6svAvmjH4YWq7lTVPBFJA2YDY6IdUziqWiAi04HHcWKOSSJyBZCjqguiHUsiiccEGDPDbOKRiBwNvAPMUNXnox2PF6p6OXA8MFlE6kY7njIMwHk4eCnQCnhWRBpFNaIEELM1o0pYidP282K0h9nEGxE5AlgIXKOqb0c7nnBE5FLgD6p6L/ArEMTpDIk5qtqx6Gc3CQ5V1R+iF1FiiMcEGDPDbOLQLcBhwG0iUtQW2EtVY7XR/hVgqogsB2oA16tqfpRjMjHEhsIZYxJWPLYBGmOMJ5YAjTEJyxKgMSZhWQI0xiQsS4DGmIQVj4/BxDURyQJeBDYAIaA28JyqPn4Qx7oP2AisA85X1TvKKNcXWK2qYYdoichZwF9V9Yr9Yh6qqn8t4zNXACeo6mgPx/dc1phwLAH605KiZCIitQAVkRmq+svBHExV1+EkwbJcBwzFxqiaOGMJ0P/ScEY3FLgjCHJwHlY+B2dasD/iNHWMUdWlInIBzhjeHKAmsLFkDU1ErgSuApKBOcAH/DY0KxMYAlyMU/t8QVUfE5EWONNk7XJfP5cVrIhcgzP5Qw2cMdtFE0G0FZG3ccZy/1NV3xSRTsDd7vfb7J7bmCpjbYD+1EVElorIEuA54FpV3em+97yqdsMZW7rNHWLVG3jSff8BoBvQE2d4WDF3LrrRQAfgdCADWIZTO7wMaA78BWfGnUygj4gIcCdwu3veVWUF7c7VWA/o5k6pVQP4s/v2Ljeuc4AnSszl109VOwHfAldU7DIZUz6rAfrTkrLa04CixVhPBjq4k60CpLhjeXNVdTuAO1ywpKbAxyWGtt3glit6vyXOtFJF44APw0mKJwHvu/tWAi1KDUw1KCJ7gVkishP4A04SBFihqiFgq4jsAOoDjXHGdIPT1rkQpyZoTJWwGmD8Cbp/bgRmubNM98KZy+9nIENEihai//N+n90MnOC2KyIis0XkKPeYSTjJ9ROgs3vcaTiTTWwE2pZxzGIicgrQR1X/AlzrHjNQ8nPuDCiHANuAb4De7rnuxpmFxpgqYwkwfj2Fk8yW4dyWblHVvTiTQywQkcU4bYDFVDUHuB9YJiLvAf9R1W/dzz8LfI1T+1shImtw2he/BYYBt7hteK0p2yZgl/vZRcD3wJHue7XdW/rXgSGqWojT+fKmW1MdBnxcqStizH5sMgRjTMKyGqAxJmFZAjTGJCxLgMaYhGUJ0BiTsCwBGmMSliVAY0zCsgRojElY/x/dEs2szOOCVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "skplt.metrics.plot_confusion_matrix(confusion_mat[1][0],confusion_mat[1][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0780c1",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5601ac6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      " 0.9775833333333334\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      1.00      0.99     47478\n",
      "           1       0.99      0.93      0.96     11859\n",
      "           2       0.47      0.27      0.34       512\n",
      "           3       0.00      0.00      0.00       144\n",
      "           4       0.00      0.00      0.00         7\n",
      "\n",
      "    accuracy                           0.98     60000\n",
      "   macro avg       0.49      0.44      0.46     60000\n",
      "weighted avg       0.97      0.98      0.98     60000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score:\\n\",Accuracy[2])\n",
    "print(\"Classification report:\\n\",Reportlist[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "7d95a4a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Confusion Matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "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": [
    "%matplotlib inline\n",
    "skplt.metrics.plot_confusion_matrix(confusion_mat[2][0],confusion_mat[2][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3d976f",
   "metadata": {},
   "source": [
    "### Kmeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e9e67c70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      " 0.7913166666666667\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      1.00      0.88     47478\n",
      "           1       0.00      0.00      0.00     11859\n",
      "           2       0.00      0.00      0.00       512\n",
      "           3       0.17      0.01      0.01       144\n",
      "           4       0.00      0.00      0.00         7\n",
      "\n",
      "    accuracy                           0.79     60000\n",
      "   macro avg       0.19      0.20      0.18     60000\n",
      "weighted avg       0.63      0.79      0.70     60000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score:\\n\",Accuracy[3])\n",
    "print(\"Classification report:\\n\",Reportlist[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8e8457f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Confusion Matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "execution_count": 56,
     "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": [
    "%matplotlib inline\n",
    "skplt.metrics.plot_confusion_matrix(confusion_mat[3][0],confusion_mat[3][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2b9e63",
   "metadata": {},
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f764be80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:\n",
      " 0.8240333333333333\n",
      "Classification report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.99      0.90     47478\n",
      "           1       0.89      0.18      0.31     11859\n",
      "           2       0.81      0.03      0.05       512\n",
      "           3       0.13      0.12      0.13       144\n",
      "           4       0.00      0.14      0.00         7\n",
      "\n",
      "    accuracy                           0.82     60000\n",
      "   macro avg       0.53      0.29      0.28     60000\n",
      "weighted avg       0.84      0.82      0.78     60000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy Score:\\n\",Accuracy[4])\n",
    "print(\"Classification report:\\n\",Reportlist[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8a8bab92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Confusion Matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "execution_count": 58,
     "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": [
    "%matplotlib inline\n",
    "skplt.metrics.plot_confusion_matrix(confusion_mat[4][0],confusion_mat[4][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8277e052",
   "metadata": {},
   "source": [
    "### Comparison of Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a7caf545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a=np.arange(3)\n",
    "w = 0.15\n",
    "fig,ax=plt.subplots(figsize=(12,7),edgecolor='k')\n",
    "\n",
    "p1=ax.bar(a,[Accuracy[0],recall[0],F1score[0],],w,color='b')\n",
    "p2=ax.bar(a-w,[Accuracy[1],recall[1],F1score[1]],w,color='g')\n",
    "p3=ax.bar(a-2*w,[Accuracy[2],recall[2],F1score[2]],w,color='y')\n",
    "p4=ax.bar(a-3*w,[Accuracy[3],recall[3],F1score[3]],w,color='r')\n",
    "p5=ax.bar(a-4*w,[Accuracy[4],recall[4],F1score[4]],w,color='k')\n",
    "\n",
    "ax.set_xticks(a)\n",
    "ax.set_xticklabels(('Accuracy','Recall','F1-Score'))\n",
    "ax.set_title('Comparision Between Classifiers')\n",
    "\n",
    "ax.legend((p1[0],p2[0],p3[0],p4[0],p5[0]),(\"KNN\",\"Random Forest\",\"Decision Tree\",\"Kmeans\",\"Naive Bayes\"))\n",
    "plt.xlabel('Classifiers')\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "175a4aa3",
   "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
}