Cids2017 Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "da53702c",
   "metadata": {},
   "source": [
    "# IDS Model Based on ANN 'MLPClassifier'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "71e7740d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn as sk\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3126d08e",
   "metadata": {},
   "source": [
    "# Dataset Importation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "958f3738",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "7ff78649",
   "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>Destination Port</th>\n",
       "      <th>Flow Duration</th>\n",
       "      <th>Total Fwd Packets</th>\n",
       "      <th>Total Backward Packets</th>\n",
       "      <th>Total Length of Fwd Packets</th>\n",
       "      <th>Total Length of Bwd Packets</th>\n",
       "      <th>Fwd Packet Length Max</th>\n",
       "      <th>Fwd Packet Length Min</th>\n",
       "      <th>Fwd Packet Length Mean</th>\n",
       "      <th>Fwd Packet Length Std</th>\n",
       "      <th>...</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>Active Mean</th>\n",
       "      <th>Active Std</th>\n",
       "      <th>Active Max</th>\n",
       "      <th>Active Min</th>\n",
       "      <th>Idle Mean</th>\n",
       "      <th>Idle Std</th>\n",
       "      <th>Idle Max</th>\n",
       "      <th>Idle Min</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>389</td>\n",
       "      <td>113095465</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>9668</td>\n",
       "      <td>10012</td>\n",
       "      <td>403</td>\n",
       "      <td>0</td>\n",
       "      <td>201.416667</td>\n",
       "      <td>203.548293</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>203985.500</td>\n",
       "      <td>5.758373e+05</td>\n",
       "      <td>1629110</td>\n",
       "      <td>379</td>\n",
       "      <td>13800000.0</td>\n",
       "      <td>4.277541e+06</td>\n",
       "      <td>16500000</td>\n",
       "      <td>6737603</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>389</td>\n",
       "      <td>113473706</td>\n",
       "      <td>68</td>\n",
       "      <td>40</td>\n",
       "      <td>11364</td>\n",
       "      <td>12718</td>\n",
       "      <td>403</td>\n",
       "      <td>0</td>\n",
       "      <td>167.117647</td>\n",
       "      <td>171.919413</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>178326.875</td>\n",
       "      <td>5.034269e+05</td>\n",
       "      <td>1424245</td>\n",
       "      <td>325</td>\n",
       "      <td>13800000.0</td>\n",
       "      <td>4.229413e+06</td>\n",
       "      <td>16500000</td>\n",
       "      <td>6945512</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>119945515</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6909777.333</td>\n",
       "      <td>1.170000e+07</td>\n",
       "      <td>20400000</td>\n",
       "      <td>6</td>\n",
       "      <td>24400000.0</td>\n",
       "      <td>2.430000e+07</td>\n",
       "      <td>60100000</td>\n",
       "      <td>5702188</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>443</td>\n",
       "      <td>60261928</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2330</td>\n",
       "      <td>4221</td>\n",
       "      <td>1093</td>\n",
       "      <td>0</td>\n",
       "      <td>258.888889</td>\n",
       "      <td>409.702161</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>269</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>102</td>\n",
       "      <td>322</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BENIGN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Destination Port   Flow Duration   Total Fwd Packets  \\\n",
       "0                389       113095465                  48   \n",
       "1                389       113473706                  68   \n",
       "2                  0       119945515                 150   \n",
       "3                443        60261928                   9   \n",
       "4                 53             269                   2   \n",
       "\n",
       "    Total Backward Packets  Total Length of Fwd Packets  \\\n",
       "0                       24                         9668   \n",
       "1                       40                        11364   \n",
       "2                        0                            0   \n",
       "3                        7                         2330   \n",
       "4                        2                          102   \n",
       "\n",
       "    Total Length of Bwd Packets   Fwd Packet Length Max  \\\n",
       "0                         10012                     403   \n",
       "1                         12718                     403   \n",
       "2                             0                       0   \n",
       "3                          4221                    1093   \n",
       "4                           322                      51   \n",
       "\n",
       "    Fwd Packet Length Min   Fwd Packet Length Mean   Fwd Packet Length Std  \\\n",
       "0                       0               201.416667              203.548293   \n",
       "1                       0               167.117647              171.919413   \n",
       "2                       0                 0.000000                0.000000   \n",
       "3                       0               258.888889              409.702161   \n",
       "4                      51                51.000000                0.000000   \n",
       "\n",
       "   ...   min_seg_size_forward  Active Mean    Active Std   Active Max  \\\n",
       "0  ...                     32   203985.500  5.758373e+05      1629110   \n",
       "1  ...                     32   178326.875  5.034269e+05      1424245   \n",
       "2  ...                      0  6909777.333  1.170000e+07     20400000   \n",
       "3  ...                     20        0.000  0.000000e+00            0   \n",
       "4  ...                     32        0.000  0.000000e+00            0   \n",
       "\n",
       "    Active Min   Idle Mean      Idle Std   Idle Max   Idle Min   Label  \n",
       "0          379  13800000.0  4.277541e+06   16500000    6737603  BENIGN  \n",
       "1          325  13800000.0  4.229413e+06   16500000    6945512  BENIGN  \n",
       "2            6  24400000.0  2.430000e+07   60100000    5702188  BENIGN  \n",
       "3            0         0.0  0.000000e+00          0          0  BENIGN  \n",
       "4            0         0.0  0.000000e+00          0          0  BENIGN  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4a72a22c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([' Destination Port', ' Flow Duration', ' Total Fwd Packets',\n",
       "       ' Total Backward Packets', 'Total Length of Fwd Packets',\n",
       "       ' Total Length of Bwd Packets', ' Fwd Packet Length Max',\n",
       "       ' Fwd Packet Length Min', ' Fwd Packet Length Mean',\n",
       "       ' Fwd Packet Length Std', 'Bwd Packet Length Max',\n",
       "       ' Bwd Packet Length Min', ' Bwd Packet Length Mean',\n",
       "       ' Bwd Packet Length Std', 'Flow Bytes/s', ' Flow Packets/s',\n",
       "       ' Flow IAT Mean', ' Flow IAT Std', ' Flow IAT Max', ' Flow IAT Min',\n",
       "       'Fwd IAT Total', ' Fwd IAT Mean', ' Fwd IAT Std', ' Fwd IAT Max',\n",
       "       ' Fwd IAT Min', 'Bwd IAT Total', ' Bwd IAT Mean', ' Bwd IAT Std',\n",
       "       ' Bwd IAT Max', ' Bwd IAT Min', 'Fwd PSH Flags', ' Bwd PSH Flags',\n",
       "       ' Fwd URG Flags', ' Bwd URG Flags', ' Fwd Header Length',\n",
       "       ' Bwd Header Length', 'Fwd Packets/s', ' Bwd Packets/s',\n",
       "       ' Min Packet Length', ' Max Packet Length', ' Packet Length Mean',\n",
       "       ' Packet Length Std', ' Packet Length Variance', 'FIN Flag Count',\n",
       "       ' SYN Flag Count', ' RST Flag Count', ' PSH Flag Count',\n",
       "       ' ACK Flag Count', ' URG Flag Count', ' CWE Flag Count',\n",
       "       ' ECE Flag Count', ' Down/Up Ratio', ' Average Packet Size',\n",
       "       ' Avg Fwd Segment Size', ' Avg Bwd Segment Size',\n",
       "       ' Fwd Header Length.1', 'Fwd Avg Bytes/Bulk', ' Fwd Avg Packets/Bulk',\n",
       "       ' Fwd Avg Bulk Rate', ' Bwd Avg Bytes/Bulk', ' Bwd Avg Packets/Bulk',\n",
       "       'Bwd Avg Bulk Rate', 'Subflow Fwd Packets', ' Subflow Fwd Bytes',\n",
       "       ' Subflow Bwd Packets', ' Subflow Bwd Bytes', 'Init_Win_bytes_forward',\n",
       "       ' Init_Win_bytes_backward', ' act_data_pkt_fwd',\n",
       "       ' min_seg_size_forward', 'Active Mean', ' Active Std', ' Active Max',\n",
       "       ' Active Min', 'Idle Mean', ' Idle Std', ' Idle Max', ' Idle Min',\n",
       "       ' Label'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns=dataset.columns\n",
    "columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631aebde",
   "metadata": {},
   "source": [
    "# Columns Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "204e58cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y=dataset[[' Destination Port', ' Flow Duration', ' Total Fwd Packets',\n",
    "       ' Total Backward Packets', 'Total Length of Fwd Packets',\n",
    "       ' Total Length of Bwd Packets', ' Fwd Packet Length Max',\n",
    "       ' Fwd Packet Length Min', ' Fwd Packet Length Mean',\n",
    "       ' Fwd Packet Length Std', 'Bwd Packet Length Max',]],dataset[columns[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "38c0372a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf89bec",
   "metadata": {},
   "source": [
    "# Normalisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa72d369",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalisation\n",
    "mms=MinMaxScaler()\n",
    "x_scaled=mms.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cdf3c2aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8153ee4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import  MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4141b987",
   "metadata": {},
   "source": [
    "# Data Spliting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7d243cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x_scaled ,  y ,  test_size=0.25 , random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a4c5cbef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42592, 11)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "34f35864",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7afdfa52",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=[]\n",
    "y=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e733abc8",
   "metadata": {},
   "source": [
    "Activation Function:Logistic sigmoid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f96e4f5",
   "metadata": {},
   "source": [
    "# Training With One Layer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3a7ca92e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score=0.9865937265214124\n"
     ]
    }
   ],
   "source": [
    "# avec une seule couche\n",
    "clf1 = MLPClassifier(hidden_layer_sizes=(8, ), activation='logistic', alpha=1e-4,\n",
    "                    solver='lbfgs', tol=1e-4, random_state=1,\n",
    "                    learning_rate_init=.1, verbose=False, max_iter=5000)\n",
    "\n",
    "clf1.fit(x_train, y_train)\n",
    "y_pred1 = clf1.predict(x_test)\n",
    "print('score='+str(accuracy_score(y_test,y_pred1))) \n",
    "x.append('One Neural')\n",
    "y.append(accuracy_score(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f05863",
   "metadata": {},
   "source": [
    "# Training With Two Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e91f5ea1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score=0.9867345980465815\n"
     ]
    }
   ],
   "source": [
    "# avec deux couches\n",
    "clf2 = MLPClassifier(hidden_layer_sizes=(8, 16), activation='logistic', alpha=1e-4,\n",
    "                    solver='lbfgs', tol=1e-4, random_state=1,\n",
    "                    learning_rate_init=.1, verbose=False)\n",
    "\n",
    "clf2.fit(x_train, y_train)\n",
    "y_pred2 = clf2.predict(x_test)\n",
    "print('score='+str(accuracy_score(y_test,y_pred2)))\n",
    "x.append('Two Neurals')\n",
    "y.append(accuracy_score(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "494bf61d",
   "metadata": {},
   "source": [
    "# Training With Three Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "88a5cd93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score=0.9867345980465815\n"
     ]
    }
   ],
   "source": [
    "# avec trois couche\n",
    "clf3 = MLPClassifier(hidden_layer_sizes=(8, 16, 30), activation='logistic', alpha=1e-4,\n",
    "                    solver='lbfgs', tol=1e-4, random_state=1,\n",
    "                    learning_rate_init=.1, verbose=False, max_iter=5000)\n",
    "\n",
    "clf3.fit(x_train, y_train)\n",
    "y_pred3 = clf3.predict(x_test)\n",
    "print('score='+str(accuracy_score(y_test,y_pred3)))\n",
    "x.append('Three Neurals')\n",
    "y.append(accuracy_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7297c755",
   "metadata": {},
   "source": [
    "# performance evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b6a9ff",
   "metadata": {},
   "source": [
    "# Accuracy Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8c99fd9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x,y)\n",
    "plt.xlabel('Number of neurals')\n",
    "plt.ylabel('Accuracy')\n",
    "for i in range(len(y)):\n",
    "    plt.hlines(y[i],0,x[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9377ab50",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e4afc3a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c3d93e",
   "metadata": {},
   "source": [
    "One Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f1942fdc",
   "metadata": {},
   "outputs": [
    {
     "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(clf1,x_test,y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8fb52c",
   "metadata": {},
   "source": [
    "Two layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c71033d6",
   "metadata": {},
   "outputs": [
    {
     "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(clf2,x_test,y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "adf8bae7",
   "metadata": {},
   "outputs": [
    {
     "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(clf3,x_test,y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "884e4fea",
   "metadata": {},
   "source": [
    "# Testing in Other Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696069dd",
   "metadata": {},
   "source": [
    "# Data importation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "935ca14b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset1=pd.read_csv('Friday-WorkingHours-Morning.pcap_ISCX.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "dcd4d32b",
   "metadata": {},
   "outputs": [],
   "source": [
    "xt,yt=dataset1[[' Destination Port', ' Flow Duration', ' Total Fwd Packets',\n",
    "       ' Total Backward Packets', 'Total Length of Fwd Packets',\n",
    "       ' Total Length of Bwd Packets', ' Fwd Packet Length Max',\n",
    "       ' Fwd Packet Length Min', ' Fwd Packet Length Mean',\n",
    "       ' Fwd Packet Length Std', 'Bwd Packet Length Max',]],dataset1[columns[-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a7fcf31",
   "metadata": {},
   "source": [
    "# Normalisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "fcb1983d",
   "metadata": {},
   "outputs": [],
   "source": [
    "xt_scaled=mms.fit_transform(xt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eacb853",
   "metadata": {},
   "source": [
    "# Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b5b32fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with one Layer: 98.94782576832274\n",
      "Accuracy with Two Layers: 98.9708584380709\n",
      "Accuracy with Three Layers: 98.9708584380709\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy with one Layer:',accuracy_score(clf1.predict(xt),yt)*100)\n",
    "print('Accuracy with Two Layers:',accuracy_score(clf2.predict(xt),yt)*100)\n",
    "print('Accuracy with Three Layers:',accuracy_score(clf3.predict(xt),yt)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "7d2f79e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix with one Layer:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x28696610d00>"
      ]
     },
     "execution_count": 93,
     "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": [
    "print('Confusion Matrix with one Layer:')\n",
    "plot_confusion_matrix(clf1,xt,yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "47711c0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix with Two Layers:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x286965bd0d0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OIem7wAbgV6loCbBzRCyTtC/wO0l7QtnGlN7GWWlbU3Uqcg/bzAolWvGmYyXpJuA/AiemNAcRsT4ilqX1J4AXgA+S9Y7zaZNhQOmtIAuB4emY3YB+ZCmYjeVl6lTkgG1mhdOClEiLSRoH/AfwTxGxNle+fXrDFZJ2Ibu5+GJELAHWSDow5adPAm5P1e4ASiNAjgWmpS+Au4AjJA1INxuPoMwbsxpzSsTMCqe1nnSUdCMwhizXvZBs5MY5QA/gnjQ675E0IuRQ4HxJG4B64LSIKN2wPJ1sxElPspx3Ke99DXC9pHlkPevxWftjuaQLgMfTfufnjlWRA7aZFUrWe26dgB0RJ5QpvqbCvrcBt1XYNgPYq0z5OuC4CnUmA5OrbiwO2GZWQF31SUcHbDMrnM3NTxedA7aZFUogGvwCAzOzYuiiHWwHbDMrmFa86Vg0DthmVjxdtIvtgG1mheMediOSfkIT32MR8bU2aZGZWRMCaGhwwG5sRru1wsysWgG4h72piLg2/1nSdhHxVts3ycysaV11HHazgxklHSTpWeC59HlvSVe1ecvMzCqJKpetTDWjz/+H7O0IpWkFnyKbBMXMrANU93qwrfHGZFWjRCJiQZq1qqS+bZpjZlaFrbD3XI1qAvYCSQcDIWkb4Guk9IiZWbsLiC46SqSalMhpwBlk7xtbBIxKn83MOoiqXLYuzfawI2IpcGI7tMXMrDpdNCVSzSiRXST9XtIbkl6XdHt6PY6ZWcfwKJGKbgBuAYYAOwG/Bm5sy0aZmVVUenCmmmUrU03AVkRcHxEb0vJLtsrvLjMrirZ8CW9n1tRcIgPT6p8lnQ3cRBaojwf+2A5tMzMrz6NE3uMJsvlEjgdOBf4M3E/2duB/afOWmZlVoKhuafY40uR0b+7pXNlASfdImpt+DshtO0fSPElzJI3Nle8raXbadoXSgyuSeki6OZU/KmlErs6EdI65kiZUc90VA3ZEjIyIXdLPxotvOppZx6j2hmN1KZEpwLhGZWcD90XEbsB96TOS9gDGA3umOldJqk11JgKnALulpXTMk4EVEbErcDlwcTrWQOBc4ABgf+Dc/BdDJVW9GE3SXpL+WdJJpaWaemZmra/KG45V3HSMiAeB5Y2KjwJKk99dCxydK78pItZHxEvAPGB/SUOAvhExPSICuK5RndKxbgUOT73vscA9EbE8IlYA9/DeL473aHYctqRzgTHAHsCdwJHAw6lRZmbtr/obioMl5aeKnhQRk5qps2NELAGIiCWSdkjlQ4FHcvstTGV1ab1xeanOgnSsDZJWAYPy5WXqVFTNo+nHAnsDf4uIf5G0I/CLKuqZmbWNhqr3XBoRo1vprOW67NFE+ebWqaialMjbEdEAbJDUF3gdcA7bzDpG24/Dfi2lOUg/X0/lC4Hhuf2GAYtT+bAy5ZvUkdQN6EeWgql0rCZVE7BnSOoPXE02cuRJ4LEq6pmZtYnWGiVSwR1AadTGBOD2XPn4NPJjJNnNxcdS+mSNpANTfvqkRnVKxzoWmJby3HcBR0gakG42HpHKmlTNXCL/mlZ/JulPZMn1Wc3VMzNrM630UIykG8nu0Q2WtJBs5MZFwC2STgZeAY4DiIhnJN0CPAtsAM6IiNJU06eTjTjpCUxNC8A1wPWS5pH1rMenYy2XdAHweNrv/IhofPPzPZp6cGafprZFxJPNHdzMrDOLiBMqbDq8wv4XAheWKZ8B7FWmfB0p4JfZNhmYXHVjabqHfVkT2wL4VEtOVATPz+rF2GH7dnQzrEX8Lo2uaAvSHYXW1Et4D2vPhpiZVSXoso+mV/WKMDOzTsU9bDOzYnBKxMysKLpowK7mjTOS9AVJ/50+7yxp/7ZvmplZBX7jTEVXAQcBpeEva4CftlmLzMyaUO1DM1tj2qSalMgBEbGPpL8BRMQKSdu0cbvMzCrzKJGK6tKcrwEgaXtaMvWKmVkr2xp7z9WoJiVyBfBbYAdJF5JNrfqDNm2VmVlTumgOu5q5RH4l6QmyRzUFHB0Rz7V5y8zMytlK89PVqOYFBjsDa4Hf58si4pW2bJiZWUUO2BX9kXcn3N4WGAnMIXuvmZlZu1MXvYtWTUrkI/nPaRa/U9usRWZmVlaLn3SMiCcl7dcWjTEzq4pTIuVJ+kbuYw2wD/BGm7XIzKwpvunYpD659Q1kOe3b2qY5ZmZVcMB+r/TATO+I+FY7tcfMrHkO2JuS1C0iNjT1qjAzs/Ymuu4okaaedCy9GX2mpDskfVHS50pLezTOzOw9WnHyJ0kfkjQzt6yWdJak8yQtypV/JlfnHEnzJM2RNDZXvq+k2WnbFekN6qS3rN+cyh+VNGJzL72aHPZAYBnZOxxL47ED+M3mntTMbIu0UkokIuYAo2BjCngR2VQc/wJcHhGX5veXtAfZm8/3BHYC7pX0wfT29InAKcAjwJ3AOLK3p58MrIiIXSWNBy4Gjt+c9jYVsHdII0Se5t1AvfE6N+dkZmatom0i0OHACxHxcuocl3MUcFNErAdekjQP2F/SfKBvREwHkHQdcDRZwD4KOC/VvxW4UpIiosVX0VRKpBbonZY+ufXSYmbWIdpoPuzxwI25z2dKmiVpsqQBqWwosCC3z8JUNjStNy7fpE5EbABWAYNa3Dqa7mEviYjzN+egZmZtqvpgPFjSjNznSRExqfFOaY7/fwLOSUUTgQvSmS4ALgO+zKaZhnxrKpXTzLYWaSpgd80Zws2sc4sWjRJZGhGjq9jvSODJiHgNoPQTQNLVwB/Sx4XA8Fy9YcDiVD6sTHm+zkJJ3YB+wPKqryCnqZTI4ZtzQDOzNtf682GfQC4dImlIbtsxZPfyAO4AxqeRHyOB3YDHImIJsEbSgWl0yEnA7bk6E9L6scC0zclfQxM97IjYrG8AM7O21pqPpkvqBfwfNp3U7keSRpGF/fmlbRHxjKRbgGfJnvw+I40QATgdmAL0JLvZODWVXwNcn25QLifLlW+WFk/+ZGbW4VoxYEfEWhrdBIyILzax/4XAhWXKZwB7lSlfBxy35S11wDazotlKX/9VDQdsMysU4dn6zMwKwwHbzKwoHLDNzArCAdvMrAD8xhkzswJxwDYzK4au+gIDB2wzKxynRMzMisAPzpiZFYgDtplZ5+cnHc3MCkQNXTNiO2CbWbE4h21mVhxOiZiZFYUDtplZMbiHbWZWFA7YZmYF0LK3pm9VHLDNrFC68jjsmo5ugJlZi0VUt1RB0nxJsyXNlDQjlQ2UdI+kuenngNz+50iaJ2mOpLG58n3TceZJukKSUnkPSTen8kcljdjcy3bANrPCUVS3tMBhETEqIkanz2cD90XEbsB96TOS9gDGA3sC44CrJNWmOhOBU4Dd0jIulZ8MrIiIXYHLgYs397qdEimob1z6Mgd8ehUrl3bj1E/vAcAuH17LVy9aQM/t6nltwTZc/NWRrH0z+29p5IfX8rWLFrBd73oaAr76D7tTt76Gbt0bOOP7C/joQW8SDTDlRzvx8J0Dmjq1taHRY1Zz2gWLqa0Jpt44kFuu3LGjm9T5tM+DM0cBY9L6tcD9wH+k8psiYj3wkqR5wP6S5gN9I2I6gKTrgKOBqanOeelYtwJXSlJElX8C5BQuYEuqB2aTpbLqgTMj4q9N7D8CODgibmifFraPu389kDumbM+3/mf+xrKzLnmFq78/lNmP9OGI45dy7Gmvcd2lO1FTG3z7ivlc8rURvPhcL/r030B9nQA44WuvsnJpd04+dE+koE//+g66IqupCc74wSLOGb8LS5d05yd3zuWRu/rxytxtO7ppnU4LbjoOLqU5kkkRManRPgHcLSmAn6ftO0bEEoCIWCJph7TvUOCRXN2FqawurTcuL9VZkI61QdIqYBCwtOqrSIqYEnk7/emyN3AO8MNm9h8BfL7NW9XOnn60D2tW1m5SNuwD65j9SG8A/vZgXz7xmZUA7PvJ1bz0XE9efK4XAGtWdqOhIQvYY49fxk2pFxchVq8o3Hf4VuNDH1vL4vnb8OorPdhQV8P9t/fnoLGrOrpZnZIaqluApRExOrc0DtYAH4+IfYAjgTMkHdrUqcuURRPlTdVpsSIG7Ly+wAoAZS6R9HRK/B+f9rkIOCTdUPh6h7W0Hbw8pycHHZH9D37IP65g+53eAWDYyPVEiAt/OZcrpz7Hcae/CsB2fTcAMOFbS7hy6nN892cv0n9wXcc03hj0vjreWLzNxs9Ll3Rn8BD/Pt4jaNWbjhGxOP18HfgtsD/wmqQhAOnn62n3hcDwXPVhwOJUPqxM+SZ1JHUD+gHLW3rZUMyA3TMF378DvwAuSOWfA0YBewOfBi5J/9BnAw+lXvnljQ8m6RRJMyTNqGN9+1xBG/nxN9/PZye8wZV3PkfP3g1sSGmP2m7BXvu9ycVfHck3j/kQB49bxaiPr6a2FrbfqY5nZ2zHmUd+mOee2I6v/NeiDr6Krktl+mEtz3J2Da1101HSdpL6lNaBI4CngTuACWm3CcDtaf0OYHwa+TGS7ObiYyl9skbSgWl0yEmN6pSOdSwwbXPy11DAHDYpJQIg6SDgOkl7AZ8AboyIerJvxweA/YDVTR0s/Yk0CaCvBhb6f48FL2zLd07cDYChI9dxwOFZb/uNJd2Z9UjvjemOx6f1ZdePvM3Mv/Rh3doa/jK1PwAP/WEA48Yv65C2W9ajLv1VBDB4SB3LXu3egS3qxFrv/9Qdgd+mEXjdgBsi4k+SHgdukXQy8ApwHEBEPCPpFuBZYANwRoo5AKcDU4CeZDcbp6bya4Dr0w3K5WSjTDZLEQP2RhExXdJgYHvK54m6lH6D6li1rDtS8Pl/e5U/XD8YgCce6Mtxp79Gj20bqKsTHz3wTX5z9Q6AeOSefnz0oDd56q99GPWJNbzsG1wdZs7MXgwd+Q47Dl/Psle7M+aolVx0xvs7ulmdTms+OBMRL5L9Vd64fBlweIU6FwIXlimfAexVpnwdKeBvqUIHbEm7A7XAMuBB4FRJ1wIDgUOBb5Hdoe3TYY1sI2df+RIfPWgN/QZu4JePz+b6y4bQc7sGPjvhDQD+MrU/d988CIA3V3XjN1fvwE/++Hci4LE/9+Wxaf0AuOYHO/Ht/32Z0763gFXLunPZNxwgOkpDvfjpd4fygxtepKYW7r5pIC8/7y/Q94josi8w0GamUjpMblgfZF+234mIP6a80Y/I7vQG8P2IuFlSd+BPwGBgSrk8dklfDYwDao9o2wuw1tXgYYhF8mjcx+pYvkV/DffpPyw+dui/VbXvQ7//9hO5h2EKr3A97IiorVAeZD3qbzUqr6PCnzZmVkxddS6RwgVsM+viAuiiKREHbDMrnq4Zrx2wzax4nBIxMyuIrjpKxAHbzIqlfWbr65QcsM2sULIHZ7pmxHbANrPi8TsdzcyKwT1sM7MicA7bzKwouu5cIg7YZlY8TomYmRVAtOidjlsVB2wzKx73sM3MCqJrxmsHbDMrHjV0zZyIA7aZFUvgB2fMzIpARJd9cKamoxtgZtZiEdUtzZA0XNKfJT0n6RlJ/5bKz5O0SNLMtHwmV+ccSfMkzZE0Nle+r6TZadsV6bWFSOoh6eZU/qikEZt72Q7YZlY8rRSwgQ3ANyPiw8CBwBmS9kjbLo+IUWm5EyBtGw/sCYwDrpJUem3hROAUYLe0jEvlJwMrImJX4HLg4s29bAdsMyuWUg67mqW5Q0UsiYgn0/oa4DlgaBNVjgJuioj1EfESMA/YX9IQoG9ETE/vl70OODpX59q0fitweKn33VIO2GZWOGpoqGoBBkuakVtOqXjMLFXxMeDRVHSmpFmSJksakMqGAgty1RamsqFpvXH5JnUiYgOwChi0OdftgG1mBVNlOiRLiSyNiNG5ZVK5I0rqDdwGnBURq8nSGx8ARgFLgMtKu5ZvUMXypuq0mAO2mRVL0Jo5bCR1JwvWv4qI3wBExGsRUR8RDcDVwP5p94XA8Fz1YcDiVD6sTPkmdSR1A/oBy1t+4Q7YZlZErZTDTrnka4DnIuLHufIhud2OAZ5O63cA49PIj5FkNxcfi4glwBpJB6ZjngTcnqszIa0fC0xLee4W8zhsMyucVhyH/XHgi8BsSTNT2XeAEySNIuvPzwdOBYiIZyTdAjxLNsLkjIioT/VOB6YAPYGpaYHsC+F6SfPIetbjN7exDthmVjytFLAj4mHK55jvbKLOhcCFZcpnAHuVKV8HHLcFzdzIAdvMiiUC6rvms+kO2GZWPF300XQHbDMrHgdsM7MCCMDvdDQzK4KAcA7bzKzzC3zT0cysMJzDNjMrCAdsM7MiqH6ekK2NA7aZFUsAfgmvmVlBuIdtZlYEfjTdzKwYAsLjsM3MCsJPOpqZFYRz2GZmBRDhUSJmZoXhHraZWREEUV/f/G5bIQdsMysWT69qZlYgXXRYX01HN8DMrCUCiIaoaqmGpHGS5kiaJ+nstm39lnHANrNiifQCg2qWZkiqBX4KHAnsAZwgaY82voLN5pSImRVOK9503B+YFxEvAki6CTgKeLa1TtCaHLBz1rBi6b31N7/c0e1oA4OBpR3dCGuRrfV39v4tPcAaVtx1b9w6uMrdt5U0I/d5UkRMyn0eCizIfV4IHLClbWwrDtg5EbF9R7ehLUiaERGjO7odVj3/ziqLiHGteDiVO0UrHr9VOYdtZl3ZQmB47vMwYHEHtaVZDthm1pU9DuwmaaSkbYDxwB0d3KaKnBLpGiY1v4t1Mv6dtYOI2CDpTOAuoBaYHBHPdHCzKlJ00WfyzcyKxikRM7OCcMA2MysIB+xOTFK9pJmSnpL0pKSDU/kISW+nbaXlpLRtvqTbcsc4VtKUtP4lSVfmtn1B0ixJz6Rz/EJS/7Tt/vz4VUmjJd3fLhfeRVT6/Tax/whJn2+v9lnn45uOndvbETEKQNJY4IfAJ9O2F0rbyhgtac+mbp5IGgd8HTgyIhalR3QnADsCK9NuO0g6MiKmbvGVWDlN/X7LGQF8HrihzVtmnZJ72MXRF1hR5b6XAt9pZp/vAv8eEYsAIqI+IiZHxJzcPpcA/9niltrm2Pj7VeYSSU9Lmi3p+LTPRcAhqVf+9Q5rqXUY97A7t56SZgLbAkOAT+W2fSBtK/lqRDyU1m8B/lXSrk0ce0/gyWbOPx04RtJhwJqWNNyqUun3+zlgFLA32SPqj0t6EDib7Ev2H9u/qdYZuIfdub0dEaMiYndgHHCdpNKjtC+kbaXloVy9erLe8TnVnETSR1Kv7YVcb67k+7iX3VYq/X4/AdyY/up5DXgA2K8jG2qdgwN2QUTEdLLeVrXznVwPHArsXGH7M8A+6dizUy51KtCz0XmnkfUAD2x5q61ajX6/5ea3MHPALgpJu5M9ibWsmv0jog64HDirwi4/BC6VNCxX1rPCvhcC366upbY5Gv1+HwSOl1QraXuyL97HyNJSfTquldbRnMPu3Hrm8tQCJkREfcqKNM5hT46IKxrVv4YK6YyIuDMFg6lphMhK4GmyR3TL7fvGllyIlVXp9/tb4CDgKbKZ474dEa9KWgZskPQUMCUiLu+QVluH8aPpZmYF4ZSImVlBOGCbmRWEA7aZWUE4YJuZFYQDtplZQThgW9Vys8s9LenXknptwbGmSDo2rf9C0h5N7DumuZnsKtSbL+k9b9euVN5onzdbeK7zJP17S9to1hIO2NYSpUep9wLeAU7Lb0zjuVssIv5vRDzbxC5jgBYHbLOtjQO2ba6HgF1T7/fPkm4AZqen8y6R9Hiaa/tU2DgD3ZWSnpX0R2CH0oHS3Nuj0/q4NDf0U5LukzSC7Ivh66l3f4ik7SXdls7xuKSPp7qDJN0t6W+Sfk4Vj3hL+p2kJ9Kc4Kc02nZZast96SEjJH1A0p9SnYfSE4pm7cJPOlqLSeoGHAn8KRXtD+wVES+loLcqIvaT1AP4i6S7gY8BHwI+Qjbn9rPA5EbH3R64Gjg0HWtgRCyX9DPgzYi4NO13A3B5RDwsaWeypzM/DJwLPBwR50v6B2CTAFzBl9M5epLNindbRCwDtgOejIhvSvrvdOwzyV6Oe1pEzJV0AHAVm86iaNZmHLCtJfKPUj9E9uj7wcBjEfFSKj8C+GgpPw30A3Yjmw/jxoioBxZLmlbm+AcCD5aOFRHLK7Tj08Ae705cSF9JfdI5Ppfq/lFSNfOHf03SMWl9eGrrMqABuDmV/xL4jaTe6Xp/nTt3jyrOYdYqHLCtJTa+IaUkBa638kVkc3Pf1Wi/z5DNi9EUVbEPZKm8gyLi7TJtqXquBUljyIL/QRGxVtkr0LatsHuk865s4k0/Zm3KOWxrbXcBp0vqDiDpg5K2I5uBbnzKcQ8BDitTdzrwSUkjU92BqbzxLHV3k6UnSPuNSqsPAiemsiOBAc20tR+wIgXr3dl0CtkaoPRXwufJUi2rgZckHZfOIUl7N3MOs1bjgG2t7Rdk+eknJT0N/JzsL7nfAnOB2cBEskn5NxERb5DlnX+TZqQrpSR+T/bmm5mSDgG+RvbeylmSnuXd0SrfAw6V9CRZauaVZtr6J6CbpFnABcAjuW1vAXtKeoIsR31+Kj8RODm17xngqCr+TcxahWfrMzMrCPewzcwKwgHbzKwgHLDNzArCAdvMrCAcsM3MCsIB28ysIBywzcwK4v8DL8w8iQ2pLKIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix with Two Layers:')\n",
    "plot_confusion_matrix(clf2,xt,yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e7d544ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix with Three Layers:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x286965a24c0>"
      ]
     },
     "execution_count": 95,
     "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": [
    "print('Confusion Matrix with Three Layers:')\n",
    "plot_confusion_matrix(clf3,xt,yt)"
   ]
  }
 ],
 "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
}