epilepsy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Epileptic Seizure Recognition.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Unnamed</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>...</th>\n",
       "      <th>X170</th>\n",
       "      <th>X171</th>\n",
       "      <th>X172</th>\n",
       "      <th>X173</th>\n",
       "      <th>X174</th>\n",
       "      <th>X175</th>\n",
       "      <th>X176</th>\n",
       "      <th>X177</th>\n",
       "      <th>X178</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X21.V1.791</td>\n",
       "      <td>135</td>\n",
       "      <td>190</td>\n",
       "      <td>229</td>\n",
       "      <td>223</td>\n",
       "      <td>192</td>\n",
       "      <td>125</td>\n",
       "      <td>55</td>\n",
       "      <td>-9</td>\n",
       "      <td>-33</td>\n",
       "      <td>...</td>\n",
       "      <td>-17</td>\n",
       "      <td>-15</td>\n",
       "      <td>-31</td>\n",
       "      <td>-77</td>\n",
       "      <td>-103</td>\n",
       "      <td>-127</td>\n",
       "      <td>-116</td>\n",
       "      <td>-83</td>\n",
       "      <td>-51</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>X15.V1.924</td>\n",
       "      <td>386</td>\n",
       "      <td>382</td>\n",
       "      <td>356</td>\n",
       "      <td>331</td>\n",
       "      <td>320</td>\n",
       "      <td>315</td>\n",
       "      <td>307</td>\n",
       "      <td>272</td>\n",
       "      <td>244</td>\n",
       "      <td>...</td>\n",
       "      <td>164</td>\n",
       "      <td>150</td>\n",
       "      <td>146</td>\n",
       "      <td>152</td>\n",
       "      <td>157</td>\n",
       "      <td>156</td>\n",
       "      <td>154</td>\n",
       "      <td>143</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>X8.V1.1</td>\n",
       "      <td>-32</td>\n",
       "      <td>-39</td>\n",
       "      <td>-47</td>\n",
       "      <td>-37</td>\n",
       "      <td>-32</td>\n",
       "      <td>-36</td>\n",
       "      <td>-57</td>\n",
       "      <td>-73</td>\n",
       "      <td>-85</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>64</td>\n",
       "      <td>48</td>\n",
       "      <td>19</td>\n",
       "      <td>-12</td>\n",
       "      <td>-30</td>\n",
       "      <td>-35</td>\n",
       "      <td>-35</td>\n",
       "      <td>-36</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>X16.V1.60</td>\n",
       "      <td>-105</td>\n",
       "      <td>-101</td>\n",
       "      <td>-96</td>\n",
       "      <td>-92</td>\n",
       "      <td>-89</td>\n",
       "      <td>-95</td>\n",
       "      <td>-102</td>\n",
       "      <td>-100</td>\n",
       "      <td>-87</td>\n",
       "      <td>...</td>\n",
       "      <td>-82</td>\n",
       "      <td>-81</td>\n",
       "      <td>-80</td>\n",
       "      <td>-77</td>\n",
       "      <td>-85</td>\n",
       "      <td>-77</td>\n",
       "      <td>-72</td>\n",
       "      <td>-69</td>\n",
       "      <td>-65</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>X20.V1.54</td>\n",
       "      <td>-9</td>\n",
       "      <td>-65</td>\n",
       "      <td>-98</td>\n",
       "      <td>-102</td>\n",
       "      <td>-78</td>\n",
       "      <td>-48</td>\n",
       "      <td>-16</td>\n",
       "      <td>0</td>\n",
       "      <td>-21</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>-12</td>\n",
       "      <td>-32</td>\n",
       "      <td>-41</td>\n",
       "      <td>-65</td>\n",
       "      <td>-83</td>\n",
       "      <td>-89</td>\n",
       "      <td>-73</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11495</th>\n",
       "      <td>X22.V1.114</td>\n",
       "      <td>-22</td>\n",
       "      <td>-22</td>\n",
       "      <td>-23</td>\n",
       "      <td>-26</td>\n",
       "      <td>-36</td>\n",
       "      <td>-42</td>\n",
       "      <td>-45</td>\n",
       "      <td>-42</td>\n",
       "      <td>-45</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-18</td>\n",
       "      <td>-37</td>\n",
       "      <td>-47</td>\n",
       "      <td>-48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11496</th>\n",
       "      <td>X19.V1.354</td>\n",
       "      <td>-47</td>\n",
       "      <td>-11</td>\n",
       "      <td>28</td>\n",
       "      <td>77</td>\n",
       "      <td>141</td>\n",
       "      <td>211</td>\n",
       "      <td>246</td>\n",
       "      <td>240</td>\n",
       "      <td>193</td>\n",
       "      <td>...</td>\n",
       "      <td>-65</td>\n",
       "      <td>-33</td>\n",
       "      <td>-7</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>117</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11497</th>\n",
       "      <td>X8.V1.28</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>-13</td>\n",
       "      <td>-16</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>-9</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>-65</td>\n",
       "      <td>-48</td>\n",
       "      <td>-61</td>\n",
       "      <td>-62</td>\n",
       "      <td>-67</td>\n",
       "      <td>-30</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11498</th>\n",
       "      <td>X10.V1.932</td>\n",
       "      <td>-40</td>\n",
       "      <td>-25</td>\n",
       "      <td>-9</td>\n",
       "      <td>-12</td>\n",
       "      <td>-2</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>...</td>\n",
       "      <td>121</td>\n",
       "      <td>135</td>\n",
       "      <td>148</td>\n",
       "      <td>143</td>\n",
       "      <td>116</td>\n",
       "      <td>86</td>\n",
       "      <td>68</td>\n",
       "      <td>59</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>X16.V1.210</td>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>57</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>62</td>\n",
       "      <td>54</td>\n",
       "      <td>43</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>-59</td>\n",
       "      <td>-25</td>\n",
       "      <td>-4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11500 rows × 180 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed   X1   X2   X3   X4   X5   X6   X7   X8   X9  ...  X170   \n",
       "0      X21.V1.791  135  190  229  223  192  125   55   -9  -33  ...   -17  \\\n",
       "1      X15.V1.924  386  382  356  331  320  315  307  272  244  ...   164   \n",
       "2         X8.V1.1  -32  -39  -47  -37  -32  -36  -57  -73  -85  ...    57   \n",
       "3       X16.V1.60 -105 -101  -96  -92  -89  -95 -102 -100  -87  ...   -82   \n",
       "4       X20.V1.54   -9  -65  -98 -102  -78  -48  -16    0  -21  ...     4   \n",
       "...           ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   \n",
       "11495  X22.V1.114  -22  -22  -23  -26  -36  -42  -45  -42  -45  ...    15   \n",
       "11496  X19.V1.354  -47  -11   28   77  141  211  246  240  193  ...   -65   \n",
       "11497    X8.V1.28   14    6  -13  -16   10   26   27   -9    4  ...   -65   \n",
       "11498  X10.V1.932  -40  -25   -9  -12   -2   12    7   19   22  ...   121   \n",
       "11499  X16.V1.210   29   41   57   72   74   62   54   43   31  ...   -59   \n",
       "\n",
       "       X171  X172  X173  X174  X175  X176  X177  X178  y  \n",
       "0       -15   -31   -77  -103  -127  -116   -83   -51  4  \n",
       "1       150   146   152   157   156   154   143   129  1  \n",
       "2        64    48    19   -12   -30   -35   -35   -36  5  \n",
       "3       -81   -80   -77   -85   -77   -72   -69   -65  5  \n",
       "4         2   -12   -32   -41   -65   -83   -89   -73  5  \n",
       "...     ...   ...   ...   ...   ...   ...   ...   ... ..  \n",
       "11495    16    12     5    -1   -18   -37   -47   -48  2  \n",
       "11496   -33    -7    14    27    48    77   117   170  1  \n",
       "11497   -48   -61   -62   -67   -30    -2    -1    -8  5  \n",
       "11498   135   148   143   116    86    68    59    55  3  \n",
       "11499   -25    -4     2     5     4    -2     2    20  4  \n",
       "\n",
       "[11500 rows x 180 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11500, 180)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11500 entries, 0 to 11499\n",
      "Columns: 180 entries, Unnamed to y\n",
      "dtypes: int64(179), object(1)\n",
      "memory usage: 15.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Unnamed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>X21.V1.791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>X15.V1.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>X8.V1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>X16.V1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>X20.V1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11495</th>\n",
       "      <td>X22.V1.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11496</th>\n",
       "      <td>X19.V1.354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11497</th>\n",
       "      <td>X8.V1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11498</th>\n",
       "      <td>X10.V1.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>X16.V1.210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11500 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed\n",
       "0      X21.V1.791\n",
       "1      X15.V1.924\n",
       "2         X8.V1.1\n",
       "3       X16.V1.60\n",
       "4       X20.V1.54\n",
       "...           ...\n",
       "11495  X22.V1.114\n",
       "11496  X19.V1.354\n",
       "11497    X8.V1.28\n",
       "11498  X10.V1.932\n",
       "11499  X16.V1.210\n",
       "\n",
       "[11500 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.select_dtypes(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X170</th>\n",
       "      <th>X171</th>\n",
       "      <th>X172</th>\n",
       "      <th>X173</th>\n",
       "      <th>X174</th>\n",
       "      <th>X175</th>\n",
       "      <th>X176</th>\n",
       "      <th>X177</th>\n",
       "      <th>X178</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.00000</td>\n",
       "      <td>11500.00000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "      <td>11500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-11.581391</td>\n",
       "      <td>-10.911565</td>\n",
       "      <td>-10.187130</td>\n",
       "      <td>-9.143043</td>\n",
       "      <td>-8.009739</td>\n",
       "      <td>-7.003478</td>\n",
       "      <td>-6.502087</td>\n",
       "      <td>-6.68713</td>\n",
       "      <td>-6.55800</td>\n",
       "      <td>-6.168435</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.145739</td>\n",
       "      <td>-11.630348</td>\n",
       "      <td>-12.943478</td>\n",
       "      <td>-13.668870</td>\n",
       "      <td>-13.363304</td>\n",
       "      <td>-13.045043</td>\n",
       "      <td>-12.705130</td>\n",
       "      <td>-12.426000</td>\n",
       "      <td>-12.195652</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>165.626284</td>\n",
       "      <td>166.059609</td>\n",
       "      <td>163.524317</td>\n",
       "      <td>161.269041</td>\n",
       "      <td>160.998007</td>\n",
       "      <td>161.328725</td>\n",
       "      <td>161.467837</td>\n",
       "      <td>162.11912</td>\n",
       "      <td>162.03336</td>\n",
       "      <td>160.436352</td>\n",
       "      <td>...</td>\n",
       "      <td>164.652883</td>\n",
       "      <td>166.149790</td>\n",
       "      <td>168.554058</td>\n",
       "      <td>168.556486</td>\n",
       "      <td>167.257290</td>\n",
       "      <td>164.241019</td>\n",
       "      <td>162.895832</td>\n",
       "      <td>162.886311</td>\n",
       "      <td>164.852015</td>\n",
       "      <td>1.414275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1839.000000</td>\n",
       "      <td>-1838.000000</td>\n",
       "      <td>-1835.000000</td>\n",
       "      <td>-1845.000000</td>\n",
       "      <td>-1791.000000</td>\n",
       "      <td>-1757.000000</td>\n",
       "      <td>-1832.000000</td>\n",
       "      <td>-1778.00000</td>\n",
       "      <td>-1840.00000</td>\n",
       "      <td>-1867.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1867.000000</td>\n",
       "      <td>-1865.000000</td>\n",
       "      <td>-1642.000000</td>\n",
       "      <td>-1723.000000</td>\n",
       "      <td>-1866.000000</td>\n",
       "      <td>-1863.000000</td>\n",
       "      <td>-1781.000000</td>\n",
       "      <td>-1727.000000</td>\n",
       "      <td>-1829.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-54.000000</td>\n",
       "      <td>-55.000000</td>\n",
       "      <td>-54.000000</td>\n",
       "      <td>-54.000000</td>\n",
       "      <td>-54.000000</td>\n",
       "      <td>-54.000000</td>\n",
       "      <td>-54.000000</td>\n",
       "      <td>-55.00000</td>\n",
       "      <td>-55.00000</td>\n",
       "      <td>-54.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-55.000000</td>\n",
       "      <td>-56.000000</td>\n",
       "      <td>-56.000000</td>\n",
       "      <td>-56.000000</td>\n",
       "      <td>-55.000000</td>\n",
       "      <td>-56.000000</td>\n",
       "      <td>-55.000000</td>\n",
       "      <td>-55.000000</td>\n",
       "      <td>-55.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.000000</td>\n",
       "      <td>-8.00000</td>\n",
       "      <td>-7.00000</td>\n",
       "      <td>-7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>36.00000</td>\n",
       "      <td>36.00000</td>\n",
       "      <td>35.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1726.000000</td>\n",
       "      <td>1713.000000</td>\n",
       "      <td>1697.000000</td>\n",
       "      <td>1612.000000</td>\n",
       "      <td>1518.000000</td>\n",
       "      <td>1816.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.00000</td>\n",
       "      <td>2047.00000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1777.000000</td>\n",
       "      <td>1472.000000</td>\n",
       "      <td>1319.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1733.000000</td>\n",
       "      <td>1958.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>2047.000000</td>\n",
       "      <td>1915.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 179 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 X1            X2            X3            X4            X5   \n",
       "count  11500.000000  11500.000000  11500.000000  11500.000000  11500.000000  \\\n",
       "mean     -11.581391    -10.911565    -10.187130     -9.143043     -8.009739   \n",
       "std      165.626284    166.059609    163.524317    161.269041    160.998007   \n",
       "min    -1839.000000  -1838.000000  -1835.000000  -1845.000000  -1791.000000   \n",
       "25%      -54.000000    -55.000000    -54.000000    -54.000000    -54.000000   \n",
       "50%       -8.000000     -8.000000     -7.000000     -8.000000     -8.000000   \n",
       "75%       34.000000     35.000000     36.000000     36.000000     35.000000   \n",
       "max     1726.000000   1713.000000   1697.000000   1612.000000   1518.000000   \n",
       "\n",
       "                 X6            X7           X8           X9           X10   \n",
       "count  11500.000000  11500.000000  11500.00000  11500.00000  11500.000000  \\\n",
       "mean      -7.003478     -6.502087     -6.68713     -6.55800     -6.168435   \n",
       "std      161.328725    161.467837    162.11912    162.03336    160.436352   \n",
       "min    -1757.000000  -1832.000000  -1778.00000  -1840.00000  -1867.000000   \n",
       "25%      -54.000000    -54.000000    -55.00000    -55.00000    -54.000000   \n",
       "50%       -8.000000     -8.000000     -8.00000     -7.00000     -7.000000   \n",
       "75%       36.000000     35.000000     36.00000     36.00000     35.250000   \n",
       "max     1816.000000   2047.000000   2047.00000   2047.00000   2047.000000   \n",
       "\n",
       "       ...          X170          X171          X172          X173   \n",
       "count  ...  11500.000000  11500.000000  11500.000000  11500.000000  \\\n",
       "mean   ...    -10.145739    -11.630348    -12.943478    -13.668870   \n",
       "std    ...    164.652883    166.149790    168.554058    168.556486   \n",
       "min    ...  -1867.000000  -1865.000000  -1642.000000  -1723.000000   \n",
       "25%    ...    -55.000000    -56.000000    -56.000000    -56.000000   \n",
       "50%    ...     -9.000000    -10.000000    -10.000000    -10.000000   \n",
       "75%    ...     34.000000     34.000000     33.000000     33.000000   \n",
       "max    ...   1777.000000   1472.000000   1319.000000   1436.000000   \n",
       "\n",
       "               X174          X175          X176          X177          X178   \n",
       "count  11500.000000  11500.000000  11500.000000  11500.000000  11500.000000  \\\n",
       "mean     -13.363304    -13.045043    -12.705130    -12.426000    -12.195652   \n",
       "std      167.257290    164.241019    162.895832    162.886311    164.852015   \n",
       "min    -1866.000000  -1863.000000  -1781.000000  -1727.000000  -1829.000000   \n",
       "25%      -55.000000    -56.000000    -55.000000    -55.000000    -55.000000   \n",
       "50%      -10.000000     -9.000000     -9.000000     -9.000000     -9.000000   \n",
       "75%       34.000000     34.000000     34.000000     34.000000     34.000000   \n",
       "max     1733.000000   1958.000000   2047.000000   2047.000000   1915.000000   \n",
       "\n",
       "                  y  \n",
       "count  11500.000000  \n",
       "mean       3.000000  \n",
       "std        1.414275  \n",
       "min        1.000000  \n",
       "25%        2.000000  \n",
       "50%        3.000000  \n",
       "75%        4.000000  \n",
       "max        5.000000  \n",
       "\n",
       "[8 rows x 179 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = df['y'].value_counts()\n",
    "plt.figure(figsize=(7,7))\n",
    "values.plot(kind='pie',fontsize=17, autopct='%.2f')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20, 10)\n",
    "df.loc[:,::25].plot()\n",
    "plt.title(\"Visual representation different channels when stacked aganist each other\")\n",
    "plt.xlabel(\"total number of values of x\")\n",
    "plt.ylabel(\"range of values of y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "4    2300\n",
       "1    2300\n",
       "5    2300\n",
       "2    2300\n",
       "3    2300\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "0    9200\n",
       "1    2300\n",
       "Name: count, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['y'] = df['y'].replace([2,3,4,5],0)\n",
    "df.y.value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6),dpi=100)\n",
    "sns.despine(left=True)\n",
    "sns.scatterplot(x='X1', y='X2', hue = 'y', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.drop(['Unnamed'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X7</th>\n",
       "      <th>X8</th>\n",
       "      <th>X9</th>\n",
       "      <th>X10</th>\n",
       "      <th>...</th>\n",
       "      <th>X170</th>\n",
       "      <th>X171</th>\n",
       "      <th>X172</th>\n",
       "      <th>X173</th>\n",
       "      <th>X174</th>\n",
       "      <th>X175</th>\n",
       "      <th>X176</th>\n",
       "      <th>X177</th>\n",
       "      <th>X178</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135</td>\n",
       "      <td>190</td>\n",
       "      <td>229</td>\n",
       "      <td>223</td>\n",
       "      <td>192</td>\n",
       "      <td>125</td>\n",
       "      <td>55</td>\n",
       "      <td>-9</td>\n",
       "      <td>-33</td>\n",
       "      <td>-38</td>\n",
       "      <td>...</td>\n",
       "      <td>-17</td>\n",
       "      <td>-15</td>\n",
       "      <td>-31</td>\n",
       "      <td>-77</td>\n",
       "      <td>-103</td>\n",
       "      <td>-127</td>\n",
       "      <td>-116</td>\n",
       "      <td>-83</td>\n",
       "      <td>-51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>386</td>\n",
       "      <td>382</td>\n",
       "      <td>356</td>\n",
       "      <td>331</td>\n",
       "      <td>320</td>\n",
       "      <td>315</td>\n",
       "      <td>307</td>\n",
       "      <td>272</td>\n",
       "      <td>244</td>\n",
       "      <td>232</td>\n",
       "      <td>...</td>\n",
       "      <td>164</td>\n",
       "      <td>150</td>\n",
       "      <td>146</td>\n",
       "      <td>152</td>\n",
       "      <td>157</td>\n",
       "      <td>156</td>\n",
       "      <td>154</td>\n",
       "      <td>143</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-32</td>\n",
       "      <td>-39</td>\n",
       "      <td>-47</td>\n",
       "      <td>-37</td>\n",
       "      <td>-32</td>\n",
       "      <td>-36</td>\n",
       "      <td>-57</td>\n",
       "      <td>-73</td>\n",
       "      <td>-85</td>\n",
       "      <td>-94</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>64</td>\n",
       "      <td>48</td>\n",
       "      <td>19</td>\n",
       "      <td>-12</td>\n",
       "      <td>-30</td>\n",
       "      <td>-35</td>\n",
       "      <td>-35</td>\n",
       "      <td>-36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-105</td>\n",
       "      <td>-101</td>\n",
       "      <td>-96</td>\n",
       "      <td>-92</td>\n",
       "      <td>-89</td>\n",
       "      <td>-95</td>\n",
       "      <td>-102</td>\n",
       "      <td>-100</td>\n",
       "      <td>-87</td>\n",
       "      <td>-79</td>\n",
       "      <td>...</td>\n",
       "      <td>-82</td>\n",
       "      <td>-81</td>\n",
       "      <td>-80</td>\n",
       "      <td>-77</td>\n",
       "      <td>-85</td>\n",
       "      <td>-77</td>\n",
       "      <td>-72</td>\n",
       "      <td>-69</td>\n",
       "      <td>-65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-9</td>\n",
       "      <td>-65</td>\n",
       "      <td>-98</td>\n",
       "      <td>-102</td>\n",
       "      <td>-78</td>\n",
       "      <td>-48</td>\n",
       "      <td>-16</td>\n",
       "      <td>0</td>\n",
       "      <td>-21</td>\n",
       "      <td>-59</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>-12</td>\n",
       "      <td>-32</td>\n",
       "      <td>-41</td>\n",
       "      <td>-65</td>\n",
       "      <td>-83</td>\n",
       "      <td>-89</td>\n",
       "      <td>-73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11495</th>\n",
       "      <td>-22</td>\n",
       "      <td>-22</td>\n",
       "      <td>-23</td>\n",
       "      <td>-26</td>\n",
       "      <td>-36</td>\n",
       "      <td>-42</td>\n",
       "      <td>-45</td>\n",
       "      <td>-42</td>\n",
       "      <td>-45</td>\n",
       "      <td>-49</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>-18</td>\n",
       "      <td>-37</td>\n",
       "      <td>-47</td>\n",
       "      <td>-48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11496</th>\n",
       "      <td>-47</td>\n",
       "      <td>-11</td>\n",
       "      <td>28</td>\n",
       "      <td>77</td>\n",
       "      <td>141</td>\n",
       "      <td>211</td>\n",
       "      <td>246</td>\n",
       "      <td>240</td>\n",
       "      <td>193</td>\n",
       "      <td>136</td>\n",
       "      <td>...</td>\n",
       "      <td>-65</td>\n",
       "      <td>-33</td>\n",
       "      <td>-7</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>117</td>\n",
       "      <td>170</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11497</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>-13</td>\n",
       "      <td>-16</td>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>-9</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>-65</td>\n",
       "      <td>-48</td>\n",
       "      <td>-61</td>\n",
       "      <td>-62</td>\n",
       "      <td>-67</td>\n",
       "      <td>-30</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11498</th>\n",
       "      <td>-40</td>\n",
       "      <td>-25</td>\n",
       "      <td>-9</td>\n",
       "      <td>-12</td>\n",
       "      <td>-2</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>29</td>\n",
       "      <td>...</td>\n",
       "      <td>121</td>\n",
       "      <td>135</td>\n",
       "      <td>148</td>\n",
       "      <td>143</td>\n",
       "      <td>116</td>\n",
       "      <td>86</td>\n",
       "      <td>68</td>\n",
       "      <td>59</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11499</th>\n",
       "      <td>29</td>\n",
       "      <td>41</td>\n",
       "      <td>57</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>62</td>\n",
       "      <td>54</td>\n",
       "      <td>43</td>\n",
       "      <td>31</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>-59</td>\n",
       "      <td>-25</td>\n",
       "      <td>-4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11500 rows × 179 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        X1   X2   X3   X4   X5   X6   X7   X8   X9  X10  ...  X170  X171   \n",
       "0      135  190  229  223  192  125   55   -9  -33  -38  ...   -17   -15  \\\n",
       "1      386  382  356  331  320  315  307  272  244  232  ...   164   150   \n",
       "2      -32  -39  -47  -37  -32  -36  -57  -73  -85  -94  ...    57    64   \n",
       "3     -105 -101  -96  -92  -89  -95 -102 -100  -87  -79  ...   -82   -81   \n",
       "4       -9  -65  -98 -102  -78  -48  -16    0  -21  -59  ...     4     2   \n",
       "...    ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   \n",
       "11495  -22  -22  -23  -26  -36  -42  -45  -42  -45  -49  ...    15    16   \n",
       "11496  -47  -11   28   77  141  211  246  240  193  136  ...   -65   -33   \n",
       "11497   14    6  -13  -16   10   26   27   -9    4   14  ...   -65   -48   \n",
       "11498  -40  -25   -9  -12   -2   12    7   19   22   29  ...   121   135   \n",
       "11499   29   41   57   72   74   62   54   43   31   23  ...   -59   -25   \n",
       "\n",
       "       X172  X173  X174  X175  X176  X177  X178  y  \n",
       "0       -31   -77  -103  -127  -116   -83   -51  0  \n",
       "1       146   152   157   156   154   143   129  1  \n",
       "2        48    19   -12   -30   -35   -35   -36  0  \n",
       "3       -80   -77   -85   -77   -72   -69   -65  0  \n",
       "4       -12   -32   -41   -65   -83   -89   -73  0  \n",
       "...     ...   ...   ...   ...   ...   ...   ... ..  \n",
       "11495    12     5    -1   -18   -37   -47   -48  0  \n",
       "11496    -7    14    27    48    77   117   170  1  \n",
       "11497   -61   -62   -67   -30    -2    -1    -8  0  \n",
       "11498   148   143   116    86    68    59    55  0  \n",
       "11499    -4     2     5     4    -2     2    20  0  \n",
       "\n",
       "[11500 rows x 179 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1.iloc[:,0:178].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df1['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report , accuracy_score , confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix is \n",
      " [[1811   24]\n",
      " [  24  441]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1835\n",
      "           1       0.95      0.95      0.95       465\n",
      "\n",
      "    accuracy                           0.98      2300\n",
      "   macro avg       0.97      0.97      0.97      2300\n",
      "weighted avg       0.98      0.98      0.98      2300\n",
      "\n",
      "Accuracy score is  0.9791304347826087\n"
     ]
    }
   ],
   "source": [
    "classifier_rf = RandomForestClassifier(random_state=21, n_jobs=-1, max_depth=80, n_estimators=100,max_features=10)\n",
    "classifier_rf.fit(X_train,y_train)\n",
    "y_pred = classifier_rf.predict(X_test)\n",
    "result = confusion_matrix(y_test, y_pred)\n",
    "print(\"Confusion matrix is \\n\",result)\n",
    "result1 =classification_report(y_pred, y_test)\n",
    "print(\"Classification report is\\n\",result1)\n",
    "accuracy_rf = accuracy_score(y_pred, y_test)\n",
    "print(\"Accuracy score is \",accuracy_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix is \n",
      " [[1834    1]\n",
      " [ 100  365]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.95      0.97      1934\n",
      "           1       0.78      1.00      0.88       366\n",
      "\n",
      "    accuracy                           0.96      2300\n",
      "   macro avg       0.89      0.97      0.93      2300\n",
      "weighted avg       0.97      0.96      0.96      2300\n",
      "\n",
      "Accuracy score is  0.9560869565217391\n",
      "Confusion matrix is \n",
      " [[1834    1]\n",
      " [ 166  299]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.92      0.96      2000\n",
      "           1       0.64      1.00      0.78       300\n",
      "\n",
      "    accuracy                           0.93      2300\n",
      "   macro avg       0.82      0.96      0.87      2300\n",
      "weighted avg       0.95      0.93      0.93      2300\n",
      "\n",
      "Accuracy score is  0.9273913043478261\n",
      "Confusion matrix is \n",
      " [[1834    1]\n",
      " [ 135  330]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.93      0.96      1969\n",
      "           1       0.71      1.00      0.83       331\n",
      "\n",
      "    accuracy                           0.94      2300\n",
      "   macro avg       0.85      0.96      0.90      2300\n",
      "weighted avg       0.96      0.94      0.94      2300\n",
      "\n",
      "Accuracy score is  0.9408695652173913\n",
      "Confusion matrix is \n",
      " [[1835    0]\n",
      " [ 169  296]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.92      0.96      2004\n",
      "           1       0.64      1.00      0.78       296\n",
      "\n",
      "    accuracy                           0.93      2300\n",
      "   macro avg       0.82      0.96      0.87      2300\n",
      "weighted avg       0.95      0.93      0.93      2300\n",
      "\n",
      "Accuracy score is  0.9265217391304348\n",
      "Confusion matrix is \n",
      " [[1832    3]\n",
      " [ 156  309]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.92      0.96      1988\n",
      "           1       0.66      0.99      0.80       312\n",
      "\n",
      "    accuracy                           0.93      2300\n",
      "   macro avg       0.83      0.96      0.88      2300\n",
      "weighted avg       0.95      0.93      0.94      2300\n",
      "\n",
      "Accuracy score is  0.9308695652173913\n",
      "Confusion matrix is \n",
      " [[1833    2]\n",
      " [ 175  290]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.91      0.95      2008\n",
      "           1       0.62      0.99      0.77       292\n",
      "\n",
      "    accuracy                           0.92      2300\n",
      "   macro avg       0.81      0.95      0.86      2300\n",
      "weighted avg       0.95      0.92      0.93      2300\n",
      "\n",
      "Accuracy score is  0.9230434782608695\n",
      "Confusion matrix is \n",
      " [[1833    2]\n",
      " [ 166  299]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.92      0.96      1999\n",
      "           1       0.64      0.99      0.78       301\n",
      "\n",
      "    accuracy                           0.93      2300\n",
      "   macro avg       0.82      0.96      0.87      2300\n",
      "weighted avg       0.95      0.93      0.93      2300\n",
      "\n",
      "Accuracy score is  0.9269565217391305\n",
      "Confusion matrix is \n",
      " [[1833    2]\n",
      " [ 186  279]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.91      0.95      2019\n",
      "           1       0.60      0.99      0.75       281\n",
      "\n",
      "    accuracy                           0.92      2300\n",
      "   macro avg       0.80      0.95      0.85      2300\n",
      "weighted avg       0.95      0.92      0.93      2300\n",
      "\n",
      "Accuracy score is  0.9182608695652174\n",
      "Confusion matrix is \n",
      " [[1833    2]\n",
      " [ 180  285]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.91      0.95      2013\n",
      "           1       0.61      0.99      0.76       287\n",
      "\n",
      "    accuracy                           0.92      2300\n",
      "   macro avg       0.81      0.95      0.86      2300\n",
      "weighted avg       0.95      0.92      0.93      2300\n",
      "\n",
      "Accuracy score is  0.9208695652173913\n",
      "Accuracy result in array is [0.9560869565217391, 0.9273913043478261, 0.9408695652173913, 0.9265217391304348, 0.9308695652173913, 0.9230434782608695, 0.9269565217391305, 0.9182608695652174, 0.9208695652173913]\n"
     ]
    }
   ],
   "source": [
    "accuracy_result = []\n",
    "for n in range(1,10):\n",
    "  classifier1 = KNeighborsClassifier(n_neighbors= n,n_jobs=-1)\n",
    "  classifier1.fit(X_train,y_train)\n",
    "  y_pred = classifier1.predict(X_test)\n",
    "  result = confusion_matrix(y_test, y_pred)\n",
    "  print(\"Confusion matrix is \\n\",result)\n",
    "  result1 =classification_report(y_pred, y_test)\n",
    "  print(\"Classification report is\\n\",result1)\n",
    "  result2 = accuracy_score(y_pred, y_test)\n",
    "  print(\"Accuracy score is \",result2)\n",
    "  accuracy_result.append(result2)\n",
    "print(\"Accuracy result in array is\",accuracy_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix is \n",
      " [[1814   21]\n",
      " [  24  441]]\n",
      "classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1838\n",
      "           1       0.95      0.95      0.95       462\n",
      "\n",
      "    accuracy                           0.98      2300\n",
      "   macro avg       0.97      0.97      0.97      2300\n",
      "weighted avg       0.98      0.98      0.98      2300\n",
      "\n",
      "Accuracy score is  0.9804347826086957\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report , accuracy_score , confusion_matrix\n",
    "from sklearn.svm import SVC\n",
    "svc = SVC(C = 100, kernel = 'rbf', gamma = 'scale')\n",
    "svc.fit(X_train,y_train)\n",
    "y_pred_svm = svc.predict(X_test)\n",
    "result = confusion_matrix(y_test, y_pred_svm)\n",
    "print(\"Confusion matrix is \\n\",result)\n",
    "result1 = classification_report(y_pred_svm, y_test)\n",
    "print(\"classification report is\\n\",result1)\n",
    "accuracy_Svm = accuracy_score(y_pred_svm, y_test)\n",
    "print(\"Accuracy score is \",accuracy_Svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "144/144 [==============================] - 8s 42ms/step - loss: 2.2143 - accuracy: 0.6482 - val_loss: 0.5054 - val_accuracy: 0.7787\n",
      "Epoch 2/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.5775 - accuracy: 0.7673 - val_loss: 0.4586 - val_accuracy: 0.7713\n",
      "Epoch 3/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.4699 - accuracy: 0.8080 - val_loss: 0.4096 - val_accuracy: 0.8648\n",
      "Epoch 4/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.4331 - accuracy: 0.8371 - val_loss: 0.4127 - val_accuracy: 0.8609\n",
      "Epoch 5/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.3947 - accuracy: 0.8673 - val_loss: 0.3664 - val_accuracy: 0.8957\n",
      "Epoch 6/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.3775 - accuracy: 0.8746 - val_loss: 0.3471 - val_accuracy: 0.8891\n",
      "Epoch 7/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.3400 - accuracy: 0.8907 - val_loss: 0.3195 - val_accuracy: 0.9157\n",
      "Epoch 8/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.3212 - accuracy: 0.9047 - val_loss: 0.3156 - val_accuracy: 0.9196\n",
      "Epoch 9/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.2888 - accuracy: 0.9170 - val_loss: 0.2837 - val_accuracy: 0.9170\n",
      "Epoch 10/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.2513 - accuracy: 0.9279 - val_loss: 0.2624 - val_accuracy: 0.9243\n",
      "Epoch 11/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.2204 - accuracy: 0.9388 - val_loss: 0.2432 - val_accuracy: 0.9496\n",
      "Epoch 12/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.2050 - accuracy: 0.9459 - val_loss: 0.2645 - val_accuracy: 0.9522\n",
      "Epoch 13/50\n",
      "144/144 [==============================] - 6s 41ms/step - loss: 0.1741 - accuracy: 0.9553 - val_loss: 0.2072 - val_accuracy: 0.9591\n",
      "Epoch 14/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.1567 - accuracy: 0.9573 - val_loss: 0.1977 - val_accuracy: 0.9596\n",
      "Epoch 15/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.1443 - accuracy: 0.9612 - val_loss: 0.1463 - val_accuracy: 0.9704\n",
      "Epoch 16/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.1050 - accuracy: 0.9730 - val_loss: 0.1359 - val_accuracy: 0.9691\n",
      "Epoch 17/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0857 - accuracy: 0.9789 - val_loss: 0.1361 - val_accuracy: 0.9752\n",
      "Epoch 18/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0846 - accuracy: 0.9785 - val_loss: 0.1179 - val_accuracy: 0.9730\n",
      "Epoch 19/50\n",
      "144/144 [==============================] - 5s 38ms/step - loss: 0.0682 - accuracy: 0.9820 - val_loss: 0.1242 - val_accuracy: 0.9748\n",
      "Epoch 20/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0701 - accuracy: 0.9829 - val_loss: 0.1321 - val_accuracy: 0.9726\n",
      "Epoch 21/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.0590 - accuracy: 0.9825 - val_loss: 0.1138 - val_accuracy: 0.9735\n",
      "Epoch 22/50\n",
      "144/144 [==============================] - 5s 38ms/step - loss: 0.0681 - accuracy: 0.9801 - val_loss: 0.1167 - val_accuracy: 0.9739\n",
      "Epoch 23/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.0474 - accuracy: 0.9860 - val_loss: 0.0911 - val_accuracy: 0.9804\n",
      "Epoch 24/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0456 - accuracy: 0.9873 - val_loss: 0.1231 - val_accuracy: 0.9757\n",
      "Epoch 25/50\n",
      "144/144 [==============================] - 5s 38ms/step - loss: 0.0524 - accuracy: 0.9859 - val_loss: 0.0943 - val_accuracy: 0.9817\n",
      "Epoch 26/50\n",
      "144/144 [==============================] - 5s 38ms/step - loss: 0.0444 - accuracy: 0.9878 - val_loss: 0.1356 - val_accuracy: 0.9696\n",
      "Epoch 27/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.0348 - accuracy: 0.9907 - val_loss: 0.0857 - val_accuracy: 0.9796\n",
      "Epoch 28/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.0372 - accuracy: 0.9892 - val_loss: 0.0903 - val_accuracy: 0.9809\n",
      "Epoch 29/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0326 - accuracy: 0.9898 - val_loss: 0.0811 - val_accuracy: 0.9783\n",
      "Epoch 30/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0285 - accuracy: 0.9926 - val_loss: 0.0798 - val_accuracy: 0.9809\n",
      "Epoch 31/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0302 - accuracy: 0.9925 - val_loss: 0.0713 - val_accuracy: 0.9839\n",
      "Epoch 32/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0236 - accuracy: 0.9932 - val_loss: 0.0673 - val_accuracy: 0.9852\n",
      "Epoch 33/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0237 - accuracy: 0.9927 - val_loss: 0.0691 - val_accuracy: 0.9852\n",
      "Epoch 34/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.0225 - accuracy: 0.9941 - val_loss: 0.0744 - val_accuracy: 0.9835\n",
      "Epoch 35/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.0277 - accuracy: 0.9927 - val_loss: 0.0623 - val_accuracy: 0.9843\n",
      "Epoch 36/50\n",
      "144/144 [==============================] - 6s 40ms/step - loss: 0.0241 - accuracy: 0.9932 - val_loss: 0.0590 - val_accuracy: 0.9848\n",
      "Epoch 37/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0201 - accuracy: 0.9939 - val_loss: 0.0634 - val_accuracy: 0.9835\n",
      "Epoch 38/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0155 - accuracy: 0.9960 - val_loss: 0.0674 - val_accuracy: 0.9857\n",
      "Epoch 39/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.0203 - accuracy: 0.9939 - val_loss: 0.0861 - val_accuracy: 0.9804\n",
      "Epoch 40/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.0178 - accuracy: 0.9945 - val_loss: 0.0658 - val_accuracy: 0.9826\n",
      "Epoch 41/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0172 - accuracy: 0.9949 - val_loss: 0.0754 - val_accuracy: 0.9848\n",
      "Epoch 42/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.0124 - accuracy: 0.9958 - val_loss: 0.0613 - val_accuracy: 0.9848\n",
      "Epoch 43/50\n",
      "144/144 [==============================] - 5s 38ms/step - loss: 0.0123 - accuracy: 0.9967 - val_loss: 0.0737 - val_accuracy: 0.9809\n",
      "Epoch 44/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0090 - accuracy: 0.9978 - val_loss: 0.0647 - val_accuracy: 0.9852\n",
      "Epoch 45/50\n",
      "144/144 [==============================] - 6s 38ms/step - loss: 0.0152 - accuracy: 0.9952 - val_loss: 0.0684 - val_accuracy: 0.9852\n",
      "Epoch 46/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0147 - accuracy: 0.9950 - val_loss: 0.0576 - val_accuracy: 0.9852\n",
      "Epoch 47/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0212 - accuracy: 0.9952 - val_loss: 0.0729 - val_accuracy: 0.9817\n",
      "Epoch 48/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0094 - accuracy: 0.9977 - val_loss: 0.0780 - val_accuracy: 0.9822\n",
      "Epoch 49/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0120 - accuracy: 0.9967 - val_loss: 0.0661 - val_accuracy: 0.9835\n",
      "Epoch 50/50\n",
      "144/144 [==============================] - 6s 39ms/step - loss: 0.0102 - accuracy: 0.9966 - val_loss: 0.0544 - val_accuracy: 0.9848\n",
      "72/72 [==============================] - 1s 7ms/step\n",
      "Test Accuracy: 98.48%\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(178, 1)))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Conv1D(filters=128, kernel_size=3, activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model with an appropriate learning rate and custom metrics\n",
    "optimizer = Adam(learning_rate=0.0001)\n",
    "model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model with more epochs and larger batch size\n",
    "model.fit(X_train, y_train, epochs=50, batch_size=64, validation_data=(X_test, y_test))\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred_binary = [1 if pred > 0.5 else 0 for pred in y_pred]\n",
    "accuracy = accuracy_score(y_test, y_pred_binary)\n",
    "print(f\"Test Accuracy: {accuracy * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "data_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2) \n",
    "pca.fit(data_scaled)\n",
    "\n",
    "# Transform the data to the lower-dimensional space\n",
    "data_pca = pca.transform(data_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance Ratio: [0.05607905 0.05239361]\n",
      "Total Variance Explained: 0.10847265807153882\n"
     ]
    }
   ],
   "source": [
    "explained_variance = pca.explained_variance_ratio_\n",
    "print(\"Explained Variance Ratio:\", explained_variance)\n",
    "\n",
    "# Total variance explained\n",
    "total_variance_explained = np.sum(explained_variance)\n",
    "print(\"Total Variance Explained:\", total_variance_explained)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pca1 = np.array(data_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.125936  , -1.13545325],\n",
       "       [-1.80890888, -3.70272275],\n",
       "       [ 0.49967325, -0.25481136],\n",
       "       ...,\n",
       "       [-0.29274257, -0.20014894],\n",
       "       [-0.19286325, -1.15373064],\n",
       "       [-0.06093699,  0.30662636]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pca1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test,y_train, y_test = train_test_split(data_pca,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix is \n",
      " [[1786   49]\n",
      " [  96  369]]\n",
      "Classification report is\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.95      0.96      1882\n",
      "           1       0.79      0.88      0.84       418\n",
      "\n",
      "    accuracy                           0.94      2300\n",
      "   macro avg       0.88      0.92      0.90      2300\n",
      "weighted avg       0.94      0.94      0.94      2300\n",
      "\n",
      "Accuracy score is  0.9369565217391305\n"
     ]
    }
   ],
   "source": [
    "classifier_rf = RandomForestClassifier(random_state=42, n_jobs=-1, max_depth=5, n_estimators=100, oob_score=True)\n",
    "classifier_rf.fit(X_train,y_train)\n",
    "y_pred = classifier_rf.predict(X_test)\n",
    "result = confusion_matrix(y_test, y_pred)\n",
    "print(\"Confusion matrix is \\n\",result)\n",
    "result1 =classification_report(y_pred, y_test)\n",
    "print(\"Classification report is\\n\",result1)\n",
    "accuracy_rf = accuracy_score(y_pred, y_test)\n",
    "print(\"Accuracy score is \",accuracy_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred_rf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)\n",
      "Cell \u001b[1;32mIn[33], line 1\u001b[0m\n",
      "\u001b[1;32m----> 1\u001b[0m hybrid_predictions \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39margmax(np\u001b[38;5;241m.\u001b[39mbincount([svm_pred, rf_pred])) \u001b[38;5;28;01mfor\u001b[39;00m svm_pred, rf_pred \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(y_pred,\u001b[43my_pred_rf\u001b[49m)]\n",
      "\n",
      "\u001b[1;31mNameError\u001b[0m: name 'y_pred_rf' is not defined"
     ]
    }
   ],
   "source": [
    "hybrid_predictions = [np.argmax(np.bincount([svm_pred, rf_pred])) for svm_pred, rf_pred in zip(y_pred,y_pred_rf)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(y_test, hybrid_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9756521739130435"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}