sonar data_set complete classification project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0986</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>Rock</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>Rock</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>Rock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>Rock</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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",
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       "      <th>203</th>\n",
       "      <td>0.0187</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.0393</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.2028</td>\n",
       "      <td>0.1694</td>\n",
       "      <td>0.2328</td>\n",
       "      <td>0.2684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>Mine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0298</td>\n",
       "      <td>0.0564</td>\n",
       "      <td>0.0760</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>0.1018</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.2154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>Mine</td>\n",
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       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0292</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.1171</td>\n",
       "      <td>0.1257</td>\n",
       "      <td>0.1178</td>\n",
       "      <td>0.1258</td>\n",
       "      <td>0.2529</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>Mine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.0303</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.1354</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>0.1123</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>Mine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.0272</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0338</td>\n",
       "      <td>0.0655</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.1843</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>Mine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 61 columns</p>\n",
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      "text/plain": [
       "         0       1       2       3       4       5       6       7       8   \\\n",
       "0    0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
       "1    0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n",
       "2    0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n",
       "3    0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n",
       "4    0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "203  0.0187  0.0346  0.0168  0.0177  0.0393  0.1630  0.2028  0.1694  0.2328   \n",
       "204  0.0323  0.0101  0.0298  0.0564  0.0760  0.0958  0.0990  0.1018  0.1030   \n",
       "205  0.0522  0.0437  0.0180  0.0292  0.0351  0.1171  0.1257  0.1178  0.1258   \n",
       "206  0.0303  0.0353  0.0490  0.0608  0.0167  0.1354  0.1465  0.1123  0.1945   \n",
       "207  0.0260  0.0363  0.0136  0.0272  0.0214  0.0338  0.0655  0.1400  0.1843   \n",
       "\n",
       "         9   ...      51      52      53      54      55      56      57  \\\n",
       "0    0.2111  ...  0.0027  0.0065  0.0159  0.0072  0.0167  0.0180  0.0084   \n",
       "1    0.2872  ...  0.0084  0.0089  0.0048  0.0094  0.0191  0.0140  0.0049   \n",
       "2    0.6194  ...  0.0232  0.0166  0.0095  0.0180  0.0244  0.0316  0.0164   \n",
       "3    0.1264  ...  0.0121  0.0036  0.0150  0.0085  0.0073  0.0050  0.0044   \n",
       "4    0.4459  ...  0.0031  0.0054  0.0105  0.0110  0.0015  0.0072  0.0048   \n",
       "..      ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "203  0.2684  ...  0.0116  0.0098  0.0199  0.0033  0.0101  0.0065  0.0115   \n",
       "204  0.2154  ...  0.0061  0.0093  0.0135  0.0063  0.0063  0.0034  0.0032   \n",
       "205  0.2529  ...  0.0160  0.0029  0.0051  0.0062  0.0089  0.0140  0.0138   \n",
       "206  0.2354  ...  0.0086  0.0046  0.0126  0.0036  0.0035  0.0034  0.0079   \n",
       "207  0.2354  ...  0.0146  0.0129  0.0047  0.0039  0.0061  0.0040  0.0036   \n",
       "\n",
       "         58      59    60  \n",
       "0    0.0090  0.0032  Rock  \n",
       "1    0.0052  0.0044  Rock  \n",
       "2    0.0095  0.0078  Rock  \n",
       "3    0.0040  0.0117  Rock  \n",
       "4    0.0107  0.0094  Rock  \n",
       "..      ...     ...   ...  \n",
       "203  0.0193  0.0157  Mine  \n",
       "204  0.0062  0.0067  Mine  \n",
       "205  0.0077  0.0031  Mine  \n",
       "206  0.0036  0.0048  Mine  \n",
       "207  0.0061  0.0115  Mine  \n",
       "\n",
       "[208 rows x 61 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar=pd.read_csv('sonar.csv',names=range(0,61),header=0)\n",
    "sonar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>208.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.029164</td>\n",
       "      <td>0.038437</td>\n",
       "      <td>0.043832</td>\n",
       "      <td>0.053892</td>\n",
       "      <td>0.075202</td>\n",
       "      <td>0.104570</td>\n",
       "      <td>0.121747</td>\n",
       "      <td>0.134799</td>\n",
       "      <td>0.178003</td>\n",
       "      <td>0.208259</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016069</td>\n",
       "      <td>0.013420</td>\n",
       "      <td>0.010709</td>\n",
       "      <td>0.010941</td>\n",
       "      <td>0.009290</td>\n",
       "      <td>0.008222</td>\n",
       "      <td>0.007820</td>\n",
       "      <td>0.007949</td>\n",
       "      <td>0.007941</td>\n",
       "      <td>0.006507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.022991</td>\n",
       "      <td>0.032960</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>0.046528</td>\n",
       "      <td>0.055552</td>\n",
       "      <td>0.059105</td>\n",
       "      <td>0.061788</td>\n",
       "      <td>0.085152</td>\n",
       "      <td>0.118387</td>\n",
       "      <td>0.134416</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012008</td>\n",
       "      <td>0.009634</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.007301</td>\n",
       "      <td>0.007088</td>\n",
       "      <td>0.005736</td>\n",
       "      <td>0.005785</td>\n",
       "      <td>0.006470</td>\n",
       "      <td>0.006181</td>\n",
       "      <td>0.005031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.005800</td>\n",
       "      <td>0.006700</td>\n",
       "      <td>0.010200</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>0.007500</td>\n",
       "      <td>0.011300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000800</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0.000600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.013350</td>\n",
       "      <td>0.016450</td>\n",
       "      <td>0.018950</td>\n",
       "      <td>0.024375</td>\n",
       "      <td>0.038050</td>\n",
       "      <td>0.067025</td>\n",
       "      <td>0.080900</td>\n",
       "      <td>0.080425</td>\n",
       "      <td>0.097025</td>\n",
       "      <td>0.111275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008425</td>\n",
       "      <td>0.007275</td>\n",
       "      <td>0.005075</td>\n",
       "      <td>0.005375</td>\n",
       "      <td>0.004150</td>\n",
       "      <td>0.004400</td>\n",
       "      <td>0.003700</td>\n",
       "      <td>0.003600</td>\n",
       "      <td>0.003675</td>\n",
       "      <td>0.003100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.022800</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.034300</td>\n",
       "      <td>0.044050</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.092150</td>\n",
       "      <td>0.106950</td>\n",
       "      <td>0.112100</td>\n",
       "      <td>0.152250</td>\n",
       "      <td>0.182400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013900</td>\n",
       "      <td>0.011400</td>\n",
       "      <td>0.009550</td>\n",
       "      <td>0.009300</td>\n",
       "      <td>0.007500</td>\n",
       "      <td>0.006850</td>\n",
       "      <td>0.005950</td>\n",
       "      <td>0.005800</td>\n",
       "      <td>0.006400</td>\n",
       "      <td>0.005300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.035550</td>\n",
       "      <td>0.047950</td>\n",
       "      <td>0.057950</td>\n",
       "      <td>0.064500</td>\n",
       "      <td>0.100275</td>\n",
       "      <td>0.134125</td>\n",
       "      <td>0.154000</td>\n",
       "      <td>0.169600</td>\n",
       "      <td>0.233425</td>\n",
       "      <td>0.268700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020825</td>\n",
       "      <td>0.016725</td>\n",
       "      <td>0.014900</td>\n",
       "      <td>0.014500</td>\n",
       "      <td>0.012100</td>\n",
       "      <td>0.010575</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>0.010350</td>\n",
       "      <td>0.010325</td>\n",
       "      <td>0.008525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.137100</td>\n",
       "      <td>0.233900</td>\n",
       "      <td>0.305900</td>\n",
       "      <td>0.426400</td>\n",
       "      <td>0.401000</td>\n",
       "      <td>0.382300</td>\n",
       "      <td>0.372900</td>\n",
       "      <td>0.459000</td>\n",
       "      <td>0.682800</td>\n",
       "      <td>0.710600</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100400</td>\n",
       "      <td>0.070900</td>\n",
       "      <td>0.039000</td>\n",
       "      <td>0.035200</td>\n",
       "      <td>0.044700</td>\n",
       "      <td>0.039400</td>\n",
       "      <td>0.035500</td>\n",
       "      <td>0.044000</td>\n",
       "      <td>0.036400</td>\n",
       "      <td>0.043900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1           2           3           4           5   \\\n",
       "count  208.000000  208.000000  208.000000  208.000000  208.000000  208.000000   \n",
       "mean     0.029164    0.038437    0.043832    0.053892    0.075202    0.104570   \n",
       "std      0.022991    0.032960    0.038428    0.046528    0.055552    0.059105   \n",
       "min      0.001500    0.000600    0.001500    0.005800    0.006700    0.010200   \n",
       "25%      0.013350    0.016450    0.018950    0.024375    0.038050    0.067025   \n",
       "50%      0.022800    0.030800    0.034300    0.044050    0.062500    0.092150   \n",
       "75%      0.035550    0.047950    0.057950    0.064500    0.100275    0.134125   \n",
       "max      0.137100    0.233900    0.305900    0.426400    0.401000    0.382300   \n",
       "\n",
       "               6           7           8           9   ...          50  \\\n",
       "count  208.000000  208.000000  208.000000  208.000000  ...  208.000000   \n",
       "mean     0.121747    0.134799    0.178003    0.208259  ...    0.016069   \n",
       "std      0.061788    0.085152    0.118387    0.134416  ...    0.012008   \n",
       "min      0.003300    0.005500    0.007500    0.011300  ...    0.000000   \n",
       "25%      0.080900    0.080425    0.097025    0.111275  ...    0.008425   \n",
       "50%      0.106950    0.112100    0.152250    0.182400  ...    0.013900   \n",
       "75%      0.154000    0.169600    0.233425    0.268700  ...    0.020825   \n",
       "max      0.372900    0.459000    0.682800    0.710600  ...    0.100400   \n",
       "\n",
       "               51          52          53          54          55          56  \\\n",
       "count  208.000000  208.000000  208.000000  208.000000  208.000000  208.000000   \n",
       "mean     0.013420    0.010709    0.010941    0.009290    0.008222    0.007820   \n",
       "std      0.009634    0.007060    0.007301    0.007088    0.005736    0.005785   \n",
       "min      0.000800    0.000500    0.001000    0.000600    0.000400    0.000300   \n",
       "25%      0.007275    0.005075    0.005375    0.004150    0.004400    0.003700   \n",
       "50%      0.011400    0.009550    0.009300    0.007500    0.006850    0.005950   \n",
       "75%      0.016725    0.014900    0.014500    0.012100    0.010575    0.010425   \n",
       "max      0.070900    0.039000    0.035200    0.044700    0.039400    0.035500   \n",
       "\n",
       "               57          58          59  \n",
       "count  208.000000  208.000000  208.000000  \n",
       "mean     0.007949    0.007941    0.006507  \n",
       "std      0.006470    0.006181    0.005031  \n",
       "min      0.000300    0.000100    0.000600  \n",
       "25%      0.003600    0.003675    0.003100  \n",
       "50%      0.005800    0.006400    0.005300  \n",
       "75%      0.010350    0.010325    0.008525  \n",
       "max      0.044000    0.036400    0.043900  \n",
       "\n",
       "[8 rows x 60 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208, 61)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "     ..\n",
       "56    0\n",
       "57    0\n",
       "58    0\n",
       "59    0\n",
       "60    0\n",
       "Length: 61, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar.isnull().sum()\n",
    "# we can observe there is no null value present in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0986</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</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>203</th>\n",
       "      <td>0.0187</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.0393</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.2028</td>\n",
       "      <td>0.1694</td>\n",
       "      <td>0.2328</td>\n",
       "      <td>0.2684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>0.0157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0298</td>\n",
       "      <td>0.0564</td>\n",
       "      <td>0.0760</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>0.1018</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.2154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0292</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.1171</td>\n",
       "      <td>0.1257</td>\n",
       "      <td>0.1178</td>\n",
       "      <td>0.1258</td>\n",
       "      <td>0.2529</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.0303</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.1354</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>0.1123</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.0272</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0338</td>\n",
       "      <td>0.0655</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.1843</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0181</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0       1       2       3       4       5       6       7       8   \\\n",
       "0    0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
       "1    0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n",
       "2    0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n",
       "3    0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n",
       "4    0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "203  0.0187  0.0346  0.0168  0.0177  0.0393  0.1630  0.2028  0.1694  0.2328   \n",
       "204  0.0323  0.0101  0.0298  0.0564  0.0760  0.0958  0.0990  0.1018  0.1030   \n",
       "205  0.0522  0.0437  0.0180  0.0292  0.0351  0.1171  0.1257  0.1178  0.1258   \n",
       "206  0.0303  0.0353  0.0490  0.0608  0.0167  0.1354  0.1465  0.1123  0.1945   \n",
       "207  0.0260  0.0363  0.0136  0.0272  0.0214  0.0338  0.0655  0.1400  0.1843   \n",
       "\n",
       "         9   ...      50      51      52      53      54      55      56  \\\n",
       "0    0.2111  ...  0.0232  0.0027  0.0065  0.0159  0.0072  0.0167  0.0180   \n",
       "1    0.2872  ...  0.0125  0.0084  0.0089  0.0048  0.0094  0.0191  0.0140   \n",
       "2    0.6194  ...  0.0033  0.0232  0.0166  0.0095  0.0180  0.0244  0.0316   \n",
       "3    0.1264  ...  0.0241  0.0121  0.0036  0.0150  0.0085  0.0073  0.0050   \n",
       "4    0.4459  ...  0.0156  0.0031  0.0054  0.0105  0.0110  0.0015  0.0072   \n",
       "..      ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "203  0.2684  ...  0.0203  0.0116  0.0098  0.0199  0.0033  0.0101  0.0065   \n",
       "204  0.2154  ...  0.0051  0.0061  0.0093  0.0135  0.0063  0.0063  0.0034   \n",
       "205  0.2529  ...  0.0155  0.0160  0.0029  0.0051  0.0062  0.0089  0.0140   \n",
       "206  0.2354  ...  0.0042  0.0086  0.0046  0.0126  0.0036  0.0035  0.0034   \n",
       "207  0.2354  ...  0.0181  0.0146  0.0129  0.0047  0.0039  0.0061  0.0040   \n",
       "\n",
       "         57      58      59  \n",
       "0    0.0084  0.0090  0.0032  \n",
       "1    0.0049  0.0052  0.0044  \n",
       "2    0.0164  0.0095  0.0078  \n",
       "3    0.0044  0.0040  0.0117  \n",
       "4    0.0048  0.0107  0.0094  \n",
       "..      ...     ...     ...  \n",
       "203  0.0115  0.0193  0.0157  \n",
       "204  0.0032  0.0062  0.0067  \n",
       "205  0.0138  0.0077  0.0031  \n",
       "206  0.0079  0.0036  0.0048  \n",
       "207  0.0036  0.0061  0.0115  \n",
       "\n",
       "[208 rows x 60 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets seperate the input and output data\n",
    "x=sonar.iloc[:,:-1]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.576093</td>\n",
       "      <td>0.319393</td>\n",
       "      <td>-0.387291</td>\n",
       "      <td>-0.378009</td>\n",
       "      <td>-0.243780</td>\n",
       "      <td>-0.156243</td>\n",
       "      <td>-0.427736</td>\n",
       "      <td>0.074832</td>\n",
       "      <td>-0.024347</td>\n",
       "      <td>0.318054</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.970667</td>\n",
       "      <td>1.018039</td>\n",
       "      <td>0.834156</td>\n",
       "      <td>0.107423</td>\n",
       "      <td>0.286070</td>\n",
       "      <td>0.178684</td>\n",
       "      <td>-0.260332</td>\n",
       "      <td>0.385600</td>\n",
       "      <td>-0.004030</td>\n",
       "      <td>-0.072529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.325887</td>\n",
       "      <td>0.874209</td>\n",
       "      <td>0.246015</td>\n",
       "      <td>0.863031</td>\n",
       "      <td>0.069110</td>\n",
       "      <td>-0.300833</td>\n",
       "      <td>-0.412807</td>\n",
       "      <td>0.075321</td>\n",
       "      <td>-0.010855</td>\n",
       "      <td>-0.438559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.852035</td>\n",
       "      <td>0.690863</td>\n",
       "      <td>-0.013390</td>\n",
       "      <td>-0.110257</td>\n",
       "      <td>-0.554050</td>\n",
       "      <td>0.397968</td>\n",
       "      <td>0.492569</td>\n",
       "      <td>-0.220460</td>\n",
       "      <td>0.537003</td>\n",
       "      <td>-0.436795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.064756</td>\n",
       "      <td>0.222968</td>\n",
       "      <td>0.638601</td>\n",
       "      <td>0.140432</td>\n",
       "      <td>0.135854</td>\n",
       "      <td>0.052881</td>\n",
       "      <td>-0.386558</td>\n",
       "      <td>0.066549</td>\n",
       "      <td>-0.117006</td>\n",
       "      <td>-0.419277</td>\n",
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       "      <th>...</th>\n",
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       "      <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>203</th>\n",
       "      <td>0.041781</td>\n",
       "      <td>-1.031644</td>\n",
       "      <td>0.265669</td>\n",
       "      <td>0.021516</td>\n",
       "      <td>-0.015439</td>\n",
       "      <td>0.097304</td>\n",
       "      <td>0.003784</td>\n",
       "      <td>0.177926</td>\n",
       "      <td>-0.027908</td>\n",
       "      <td>-0.264179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.219149</td>\n",
       "      <td>-1.153807</td>\n",
       "      <td>0.203619</td>\n",
       "      <td>0.045836</td>\n",
       "      <td>0.183272</td>\n",
       "      <td>0.012770</td>\n",
       "      <td>0.078507</td>\n",
       "      <td>0.264831</td>\n",
       "      <td>0.070749</td>\n",
       "      <td>-0.303296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.297733</td>\n",
       "      <td>-1.151860</td>\n",
       "      <td>0.250096</td>\n",
       "      <td>-0.057702</td>\n",
       "      <td>0.163566</td>\n",
       "      <td>0.015275</td>\n",
       "      <td>0.045792</td>\n",
       "      <td>0.225904</td>\n",
       "      <td>0.072130</td>\n",
       "      <td>-0.313044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>-0.045761</td>\n",
       "      <td>-0.995970</td>\n",
       "      <td>0.199677</td>\n",
       "      <td>-0.066516</td>\n",
       "      <td>-0.147227</td>\n",
       "      <td>-0.084600</td>\n",
       "      <td>0.031038</td>\n",
       "      <td>0.067444</td>\n",
       "      <td>0.025771</td>\n",
       "      <td>-0.171120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>-0.051115</td>\n",
       "      <td>-0.912526</td>\n",
       "      <td>0.008910</td>\n",
       "      <td>-0.044387</td>\n",
       "      <td>-0.027915</td>\n",
       "      <td>-0.029949</td>\n",
       "      <td>0.033107</td>\n",
       "      <td>-0.020868</td>\n",
       "      <td>-0.118992</td>\n",
       "      <td>-0.144134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "0    0.576093  0.319393 -0.387291 -0.378009 -0.243780 -0.156243 -0.427736   \n",
       "1   -0.970667  1.018039  0.834156  0.107423  0.286070  0.178684 -0.260332   \n",
       "2   -0.325887  0.874209  0.246015  0.863031  0.069110 -0.300833 -0.412807   \n",
       "3    0.852035  0.690863 -0.013390 -0.110257 -0.554050  0.397968  0.492569   \n",
       "4   -0.064756  0.222968  0.638601  0.140432  0.135854  0.052881 -0.386558   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "203  0.041781 -1.031644  0.265669  0.021516 -0.015439  0.097304  0.003784   \n",
       "204  0.219149 -1.153807  0.203619  0.045836  0.183272  0.012770  0.078507   \n",
       "205  0.297733 -1.151860  0.250096 -0.057702  0.163566  0.015275  0.045792   \n",
       "206 -0.045761 -0.995970  0.199677 -0.066516 -0.147227 -0.084600  0.031038   \n",
       "207 -0.051115 -0.912526  0.008910 -0.044387 -0.027915 -0.029949  0.033107   \n",
       "\n",
       "            7         8         9  \n",
       "0    0.074832 -0.024347  0.318054  \n",
       "1    0.385600 -0.004030 -0.072529  \n",
       "2    0.075321 -0.010855 -0.438559  \n",
       "3   -0.220460  0.537003 -0.436795  \n",
       "4    0.066549 -0.117006 -0.419277  \n",
       "..        ...       ...       ...  \n",
       "203  0.177926 -0.027908 -0.264179  \n",
       "204  0.264831  0.070749 -0.303296  \n",
       "205  0.225904  0.072130 -0.313044  \n",
       "206  0.067444  0.025771 -0.171120  \n",
       "207 -0.020868 -0.118992 -0.144134  \n",
       "\n",
       "[208 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as we can see the column is big so we can reduce it to 10 with PCA\n",
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(n_components=10)\n",
    "xpca=pca.fit_transform(x)\n",
    "x=pd.DataFrame(xpca)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Rock\n",
       "1      Rock\n",
       "2      Rock\n",
       "3      Rock\n",
       "4      Rock\n",
       "       ... \n",
       "203    Mine\n",
       "204    Mine\n",
       "205    Mine\n",
       "206    Mine\n",
       "207    Mine\n",
       "Name: 60, Length: 208, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seperating output data y\n",
    "y=sonar.iloc[:,-1]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      208\n",
       "unique       2\n",
       "top       Mine\n",
       "freq       111\n",
       "Name: 60, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.772485</td>\n",
       "      <td>0.536374</td>\n",
       "      <td>-1.003884</td>\n",
       "      <td>-1.127681</td>\n",
       "      <td>-0.813348</td>\n",
       "      <td>-0.561474</td>\n",
       "      <td>-1.580997</td>\n",
       "      <td>0.349679</td>\n",
       "      <td>-0.123615</td>\n",
       "      <td>1.739490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.301573</td>\n",
       "      <td>1.709649</td>\n",
       "      <td>2.162189</td>\n",
       "      <td>0.320467</td>\n",
       "      <td>0.954444</td>\n",
       "      <td>0.642118</td>\n",
       "      <td>-0.962237</td>\n",
       "      <td>1.801847</td>\n",
       "      <td>-0.020459</td>\n",
       "      <td>-0.396672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.436983</td>\n",
       "      <td>1.468107</td>\n",
       "      <td>0.637688</td>\n",
       "      <td>2.574604</td>\n",
       "      <td>0.230577</td>\n",
       "      <td>-1.081070</td>\n",
       "      <td>-1.525816</td>\n",
       "      <td>0.351962</td>\n",
       "      <td>-0.055114</td>\n",
       "      <td>-2.398554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.142499</td>\n",
       "      <td>1.160203</td>\n",
       "      <td>-0.034708</td>\n",
       "      <td>-0.328919</td>\n",
       "      <td>-1.848530</td>\n",
       "      <td>1.430135</td>\n",
       "      <td>1.820632</td>\n",
       "      <td>-1.030172</td>\n",
       "      <td>2.726452</td>\n",
       "      <td>-2.388903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.086831</td>\n",
       "      <td>0.374442</td>\n",
       "      <td>1.655298</td>\n",
       "      <td>0.418939</td>\n",
       "      <td>0.453263</td>\n",
       "      <td>0.190032</td>\n",
       "      <td>-1.428795</td>\n",
       "      <td>0.310971</td>\n",
       "      <td>-0.594057</td>\n",
       "      <td>-2.293097</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.056025</td>\n",
       "      <td>-1.732496</td>\n",
       "      <td>0.688633</td>\n",
       "      <td>0.064186</td>\n",
       "      <td>-0.051512</td>\n",
       "      <td>0.349671</td>\n",
       "      <td>0.013986</td>\n",
       "      <td>0.831421</td>\n",
       "      <td>-0.141691</td>\n",
       "      <td>-1.444840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.293858</td>\n",
       "      <td>-1.937652</td>\n",
       "      <td>0.527795</td>\n",
       "      <td>0.136739</td>\n",
       "      <td>0.611466</td>\n",
       "      <td>0.045891</td>\n",
       "      <td>0.290176</td>\n",
       "      <td>1.237511</td>\n",
       "      <td>0.359205</td>\n",
       "      <td>-1.658774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.399232</td>\n",
       "      <td>-1.934382</td>\n",
       "      <td>0.648265</td>\n",
       "      <td>-0.172138</td>\n",
       "      <td>0.545721</td>\n",
       "      <td>0.054893</td>\n",
       "      <td>0.169254</td>\n",
       "      <td>1.055611</td>\n",
       "      <td>0.366215</td>\n",
       "      <td>-1.712088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>-0.061361</td>\n",
       "      <td>-1.672586</td>\n",
       "      <td>0.517577</td>\n",
       "      <td>-0.198430</td>\n",
       "      <td>-0.491206</td>\n",
       "      <td>-0.304020</td>\n",
       "      <td>0.114722</td>\n",
       "      <td>0.315155</td>\n",
       "      <td>0.130843</td>\n",
       "      <td>-0.935882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>-0.068540</td>\n",
       "      <td>-1.532456</td>\n",
       "      <td>0.023094</td>\n",
       "      <td>-0.132416</td>\n",
       "      <td>-0.093137</td>\n",
       "      <td>-0.107626</td>\n",
       "      <td>0.122371</td>\n",
       "      <td>-0.097515</td>\n",
       "      <td>-0.604141</td>\n",
       "      <td>-0.788294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "0    0.772485  0.536374 -1.003884 -1.127681 -0.813348 -0.561474 -1.580997   \n",
       "1   -1.301573  1.709649  2.162189  0.320467  0.954444  0.642118 -0.962237   \n",
       "2   -0.436983  1.468107  0.637688  2.574604  0.230577 -1.081070 -1.525816   \n",
       "3    1.142499  1.160203 -0.034708 -0.328919 -1.848530  1.430135  1.820632   \n",
       "4   -0.086831  0.374442  1.655298  0.418939  0.453263  0.190032 -1.428795   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "203  0.056025 -1.732496  0.688633  0.064186 -0.051512  0.349671  0.013986   \n",
       "204  0.293858 -1.937652  0.527795  0.136739  0.611466  0.045891  0.290176   \n",
       "205  0.399232 -1.934382  0.648265 -0.172138  0.545721  0.054893  0.169254   \n",
       "206 -0.061361 -1.672586  0.517577 -0.198430 -0.491206 -0.304020  0.114722   \n",
       "207 -0.068540 -1.532456  0.023094 -0.132416 -0.093137 -0.107626  0.122371   \n",
       "\n",
       "            7         8         9  \n",
       "0    0.349679 -0.123615  1.739490  \n",
       "1    1.801847 -0.020459 -0.396672  \n",
       "2    0.351962 -0.055114 -2.398554  \n",
       "3   -1.030172  2.726452 -2.388903  \n",
       "4    0.310971 -0.594057 -2.293097  \n",
       "..        ...       ...       ...  \n",
       "203  0.831421 -0.141691 -1.444840  \n",
       "204  1.237511  0.359205 -1.658774  \n",
       "205  1.055611  0.366215 -1.712088  \n",
       "206  0.315155  0.130843 -0.935882  \n",
       "207 -0.097515 -0.604141 -0.788294  \n",
       "\n",
       "[208 rows x 10 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Scale the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scale=StandardScaler()\n",
    "x=scale.fit_transform(x)\n",
    "x=pd.DataFrame(x)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert the target output to Binary for regression purpose\n",
    "le=LabelEncoder()\n",
    "y=le.fit_transform(y.astype(str))\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(208, 10)\n",
      "(205, 10)\n"
     ]
    }
   ],
   "source": [
    "# lets check the outliers present in the data sets\n",
    "from scipy.stats import zscore\n",
    "z_score=abs(zscore(x))\n",
    "print(x.shape)\n",
    "df_x=x.loc[(z_score<3).all(axis=1)]\n",
    "print(df_x.shape)\n",
    "# we can see there were outliers present in the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.192347\n",
       "1    0.418013\n",
       "2    0.198325\n",
       "3    0.465196\n",
       "4   -0.085946\n",
       "5    0.256839\n",
       "6    0.281034\n",
       "7   -0.040029\n",
       "8   -0.222618\n",
       "9   -0.429555\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets check the skewness off data\n",
    "df_x.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_x=x\n",
    "# we can observe there is no skewed data present in the dataset all are in prescribed limit so no need to convert it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets apply cassification to check accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.20, random_state=42,stratify=y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN=KNeighborsClassifier(n_neighbors=6)\n",
    "SVC=SVC()\n",
    "LR=LogisticRegression()\n",
    "DT=DecisionTreeClassifier(random_state=6)\n",
    "GNB=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "models= []\n",
    "models.append(('KNeighborsClassifier',KNN))\n",
    "models.append(('SVC',SVC))\n",
    "models.append(('LogisticRegression',LR))\n",
    "models.append(('DecisionTreeClassifier',DT))\n",
    "models.append(('GaussianNB',GNB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,roc_curve,auc,confusion_matrix,classification_report\n",
    "from sklearn.model_selection import cross_val_score,cross_val_predict\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************** KNeighborsClassifier ***************************\n",
      "\n",
      "\n",
      "KNeighborsClassifier(n_neighbors=6)\n",
      "\n",
      "\n",
      "accuracy_score= 0.9047619047619048\n",
      "\n",
      "\n",
      "cross_val_score 0.6152380952380953\n",
      "\n",
      "\n",
      "roc_auc_score =  0.8870192307692308\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.96      0.93        26\n",
      "           1       0.93      0.81      0.87        16\n",
      "\n",
      "    accuracy                           0.90        42\n",
      "   macro avg       0.91      0.89      0.90        42\n",
      "weighted avg       0.91      0.90      0.90        42\n",
      "\n",
      "\n",
      "\n",
      "[[25  1]\n",
      " [ 3 13]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "****************************** SVC ***************************\n",
      "\n",
      "\n",
      "SVC()\n",
      "\n",
      "\n",
      "accuracy_score= 0.9047619047619048\n",
      "\n",
      "\n",
      "cross_val_score 0.6488095238095237\n",
      "\n",
      "\n",
      "roc_auc_score =  0.9110576923076923\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.88      0.92        26\n",
      "           1       0.83      0.94      0.88        16\n",
      "\n",
      "    accuracy                           0.90        42\n",
      "   macro avg       0.90      0.91      0.90        42\n",
      "weighted avg       0.91      0.90      0.91        42\n",
      "\n",
      "\n",
      "\n",
      "[[23  3]\n",
      " [ 1 15]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "****************************** LogisticRegression ***************************\n",
      "\n",
      "\n",
      "LogisticRegression()\n",
      "\n",
      "\n",
      "accuracy_score= 0.7619047619047619\n",
      "\n",
      "\n",
      "cross_val_score 0.6971428571428572\n",
      "\n",
      "\n",
      "roc_auc_score =  0.7836538461538463\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.69      0.78        26\n",
      "           1       0.64      0.88      0.74        16\n",
      "\n",
      "    accuracy                           0.76        42\n",
      "   macro avg       0.77      0.78      0.76        42\n",
      "weighted avg       0.80      0.76      0.77        42\n",
      "\n",
      "\n",
      "\n",
      "[[18  8]\n",
      " [ 2 14]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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5ElYIQ5gkScozhBXCECZJknJcJ6wYhjBJkpRnCCuEIUySJOW5TFghDGGSJCnH6chiGMIkSVKeIawQhjBJkpTndGQhDGGSJCnH6chi9Cm7AEmS1M00dXJbgYi4KiJmRcRTLfZdEBHPRsQTEXF7RAzv8s/RzRnCJElSTmpKndo64Bpgz1b7/g/YMqW0FfAv4PSu/RTdnyFMkiTldfFIWErpPmBuq313p5QaKg8fANbpmuJ7DnvCJElSTiq+Mf9I4KbC37VkjoRJkqS8To6ERcSkiJjSYpvU0beKiDOABuC6Lv4U3Z4jYZIkKaezI2EppcnA5M6+T0QcDnwC2D2l1OsuyTSESZKkvAKmIyNiT+BUYJeU0qLqv2P3YwiTJEk5Xd0TFhE3ALsCoyNiOnA22dWQA4D/iwiAB1JKR3XtO3dvhjBJkpTT1SEspfSZNnZf2bXv0vMYwiRJUk4JV0f2SoYwSZKUl6LsCnoFQ5gkScpxJKwYhjBJkpSTmhwJK4IhTJIk5TgSVgxDmCRJykn2hBXCECZJknIcCSuGIUySJOXYE1YMQ5gkScrpfXdxLIchTJIk5TgSVgxDmCRJyjGEFcMQJkmScpyOLIYhTJIk5TgSVgxDmCRJynGdsGIYwiRJUo7rhBXDECZJknKaHAkrhCFMkiTlOB1ZDEOYJEnKsTG/GIYwSZKU4xIVxTCESZKkHEfCimEIkyRJOTbmF8MQJkmScmzML4YhTJIk5dgTVow+ZRcgSZK6l6YUndpWJCKuiohZEfFUi30jI+L/ImJa5d8jqvqhuiFDmCRJykkpOrV1wDXAnq32nQb8IaU0AfhD5XGvYgiTJEk5KXVuW/H50n3A3Fa79wV+Vvn6Z8B+XfoheoAe1xM2aOwHyi5B6pXe+tZHyi5BUkEKujpyjZTSTICU0syIWL2IN+1OHAmTJEk5nZ2OjIhJETGlxTap7M/QE/S4kTBJklRdnR0JSylNBiZ38m3+ExFrVUbB1gJmdfL1PZ4jYZIkKSd1cltJdwCHV74+HPj1yp+qZ3IkTJIk5XR1T1hE3ADsCoyOiOnA2cB5wM0R8TngZeCALn3THsAQJkmScrp6xfyU0mfaObR7l75RD2MIkyRJOU1lF9BLGMIkSVJOwntHFsEQJkmScpq8d2QhDGGSJCmnyZGwQhjCJElSjtORxTCESZKkHBvzi2EIkyRJOY6EFcMQJkmSchwJK4YhTJIk5RjCimEIkyRJOU5HFsMQJkmScprMYIUwhEmSpBzXCSuGIUySJOW4YH4xDGGSJCnHxvxiGMIkSVJOUzgdWQRDmCRJynE6shiGMEmSlON0ZDEMYZIkKcclKophCJMkSTkuUVEMQ5gkScqxJ6wYhjBJkpTjdGQxDGGSJCnHxvxiGMIkSVKO05HF6FN2AZIkqXtpis5tHRERJ0TE0xHxVETcEBEDq/spuj9DmCRJymnq5LYiEbE2cBwwMaW0JdAXOKjrK+9ZnI6UJEk5VeoJ6wcMioh6YDAwozpv03M4EiZJknJSdG5b4flSehX4HvAyMBN4M6V0d3U/RfdnCJMkSTmdnY6MiEkRMaXFNqnl+SJiBLAvsD4wFhgSEYcW9Xm6K6cjJUlSTmenI1NKk4HJy3nKHsALKaXXASLiNmBn4NqVq3DV4EiYJEnKSZ3cOuBl4L0RMTgiAtgdmNrVdfc0joRJkqScrl4xP6X0YETcCjwCNACPsvyRs17BECZJknKqcXVkSuls4OwqnLrHMoRJkqQcb1tUDEOYJEnK8bZFxTCESZKknK7uCVPbDGGSJCnH6chiGMIkSVKO05HFMIRJkqScJmNYIQxhkiQpx+nIYhjCJElSjuNgxTCESZKkHEfCimEIkyRJOS5RUQxDmCRJyrExvxiGMEmSlGMEK4YhTJIk5dgTVgxDmCRJynE6shiGMEmSlGMEK4YhTJIk5TgdWQxDmCRJynE6shiGMEmSlGMEK4YhTJIk5TgdWQxDmCRJykmOhRXCECZJknIcCSuGIUySJOXYmF8MQ5gkScoxghWjT9kFaNW3zjpjuefuW3jyiXt5/LE/cuwxnyu7JGmVVrPXkQw65mIGHvmtdxzrt+OeDD71ahhUW0Jl6imaSJ3atHIMYaq6hoYGTj7lG7xnq115///bmy996bNsttmEssuSVlkNT/6Fxbd8/x37Y7WR9B2/BU1vzi6hKvUkTZ3cOiIihkfErRHxbERMjYj3dX3lPYshTFX32muzePSxpwBYsGAhzz47jbXHrllyVdKqq2n6v+DtBe/Y33/3g6j7080lVKSeJnXynw66GPhdSmlTYGtgatU+QA/RbUJYRBxRdg2qvnHj1mGbrbfkwX88WnYpUq/Sd6NtSPPnkV5/pexS1AN09UhYRAwFPghcCZBSqkspzev6ynuWbhPCgG+0dyAiJkXElIiY0tS0sMia1IWGDBnMzTddwYknnc38+e/8W7qkKulXQ7/3fYL6+28vuxL1EJ0dCWv5e7qyTWp1yg2A14GrI+LRiPhpRAwp4aN1K4VeHRkRT7R3CFijvdellCYDkwH61axtB2AP1K9fP2656QpuuOF2fvWru8ouR+pVYvjq9Bk2hoFHfjN7vNoIBn72HBb//Juw8K2Sq1N31Nl1wlr+nm5HP2A74NiU0oMRcTFwGnDWSpa4Sih6iYo1gI8Cb7TaH8DfCq5FBbpi8oVMffY5Lrp4eT+jkqohzZ7O2z8+funjgUddwOKffaPNvjEJoCl1+XjHdGB6SunByuNbyUJYr1b0dORvgdqU0kuttheBewuuRQV5/847cNih+/OhD+3MlIfuZspDd7PXnruVXZa0yqrZ+4sMPOxMYuSaDDz6Qvpu9YGyS1Ivl1J6DXglIjap7NodeKbEkrqFQkfCUkrtLhCVUjq4yFpUnL/+7SH61axddhlSr1H3m58s9/jiy08uqBL1VFXq+zkWuC4iaoDngV5/QZ4r5kuSpJxqLMCaUnoMmNjlJ+7BDGGSJCmnE2t/6V0whEmSpJzOXh2plWMIkyRJOd4PshiGMEmSlON0ZDEMYZIkKcfpyGIYwiRJUk7q+sVa1QZDmCRJyrEnrBiGMEmSlON0ZDEMYZIkKcfG/GIYwiRJUo7TkcUwhEmSpBwb84thCJMkSTn2hBXDECZJknLsCSuGIUySJOXYE1YMQ5gkScqxJ6wYhjBJkpTjSFgxDGGSJCnHnrBiGMIkSVJOk9ORhTCESZKkHCNYMQxhkiQpx56wYhjCJElSjiGsGIYwSZKU4xIVxTCESZKkHEfCitGn7AIkSVL3kjr5T0dFRN+IeDQiflvF8nsMR8IkSVJOFacjjwemAkOr9QY9iSNhkiQpp4nUqa0jImId4OPAT6tafA9iCJMkSTkppU5tETEpIqa02Ca1cdqLgFOApoI/TrfldKQkScrpbGN+SmkyMLm94xHxCWBWSunhiNj13VW36jCESZKknCrcO/L9wD4R8TFgIDA0Iq5NKR3a1W/UkzgdKUmScppS6tS2Iiml01NK66SUxgMHAX/s7QEMHAmTJEmtVGEkTG0whEmSpJyOjG6trJTSvcC9VXuDHsQQJkmSchwJK4YhTJIk5VRzJEzLGMIkSVKOI2HFMIRJkqQcR8KKYQiTJEk5joQVwxAmSZJyUvLOQkUwhEmSpJzO3rZIK8cQJkmScpI9YYUwhEmSpBxHwophCJMkSTmOhBXDECZJknJcoqIYhjBJkpTjEhXFMIRJkqQcpyOLYQiTJEk5NuYXwxAmSZJyHAkrhiFMkiTl2JhfDEOYJEnKcSSsGIYwSZKUY09YMQxhkiQpx5GwYhjCJElSjj1hxTCESZKkHBdrLYYhTJIk5TgSVgxDmCRJyrEnrBh9yi5AkiR1L6mT/6xIRKwbEX+KiKkR8XREHF/Ax+j2HAmTJEk5VRgJawC+mlJ6JCJWAx6OiP9LKT3T1W/UkxjCJElSTleHsJTSTGBm5ev5ETEVWBvo1SEsnPdVkSJiUkppctl1SL2NP3uqpoiYBExqsWtye/+9RcR44D5gy5TSW9WvrvsyhKlQETElpTSx7Dqk3safPXUHEVEL/Bn4n5TSbWXXUzYb8yVJUtVFRH/gl8B1BrCMIUySJFVVRARwJTA1pfT9suvpLgxhKpo9KVI5/NlTmd4PHAbsFhGPVbaPlV1U2ewJkyRJKoEjYZIkSSUwhEmSJJXAEKZCRMSeEfHPiHguIk4rux6pt4iIqyJiVkQ8VXYtkvIMYaq6iOgLXALsBWwOfCYiNi+3KqnXuAbYs+wiJL2TIUxF2BF4LqX0fEqpDrgR2LfkmqReIaV0HzC37DokvZMhTEVYG3ilxePplX2SJPVahjAVIdrY59ookqRezRCmIkwH1m3xeB1gRkm1SJLULRjCVISHgAkRsX5E1AAHAXeUXJMkSaUyhKnqUkoNwDHA74GpwM0ppafLrUrqHSLiBuDvwCYRMT0iPld2TZIy3rZIkiSpBI6ESZIklcAQJkmSVAJDmCRJUgkMYZIkSSUwhEmSJJXAECZJklQCQ5gkSVIJDGGSJEklMIRJkiSVwBAmSZJUAkOYJElSCQxhkiRJJTCESZIklcAQJkmSVAJDmCRJUgkMYZIkSSUwhEmSJJXAECZJklQCQ5gkSVIJDGGSShURl0fEWSvxuvUiYkFE9K1GXd3Fyn5/JHV/kVIquwZJPUhEvAh8PqV0T09434jYFfgjsAhIwAzgvJTS1V1doyR1hiNhknqDGSmlWmAocAJwRURs0tVvEhH9uvqcklZdhjBJ71pEDIiIiyJiRmW7KCIGtDh+SkTMrBz7fESkiNiocuyaiDi38vXoiPhtRMyLiLkRcX9E9ImIXwDrAb+pTEGeEhHjK+fpV3ntyIi4uvIeb0TEr1rXmTJ3AnOBrSqv6xMRp0XEvyNiTkTcHBEjW9T+3xHxUuXYWRHxYkTsUTl2TkTcGhHXRsRbwGcjYlhEXFn5vK9GxLnNU6YRsVFE/Dki3oyI2RFxU2V/RMQPImJW5dgTEbFl6+9P5fEXIuK5yvfnjogY2+JYioijImJa5XtwSUREF/0xS+pihjBJXeEM4L3ANsDWwI7AmQARsSdwIrAHsBGwy3LO81VgOjAGWAP4Gll2Ogx4Gdg7pVSbUjq/jdf+AhgMbAGsDvyg9RMqgWsfYDTwXGX3ccB+lbrGAm8Al1SevzlwKXAIsBYwDFi71Wn3BW4FhgPXAT8DGiqfdVvgI8DnK8/9FnA3MAJYB/hRZf9HgA8CG1fO81/AnDbq3w34DnBgpZ6XgBtbPe0TwA5kfw4HAh9tfR5J3YMhTFJXOAT4ZkppVkrpdeAbwGGVYwcCV6eUnk4pLaoca089WbgYl1KqTyndnzrQuBoRawF7AUellN6ovPbPLZ4yNiLmAW8DtwMnppQerRz7InBGSml6SmkJcA6wf2WEbX/gNymlv6SU6oCvk/WVtfT3lNKvUkpNZNOdewFfSSktTCnNIguDB7X4fOOAsSmlxSmlv7TYvxqwKVmv7tSU0sw2PuohwFUppUcqtZ4OvC8ixrd4znkppXkppZeBP5EFY0ndkCFMUlcYSzYq0+ylyr7mY6+0ONby69YuIBuhujsino+I0zr4/usCc1NKb7RzfEZKaThZSPohsFuLY+OA2ytToPOAqUAj2UhcrvZKiGw9QtXy84wD+gMzW5zvJ2QjcwCnAAH8IyKejogjK+f9I/BjshG4/0TE5IgY2sbnyH2fU0oLKvW0HJ17rcXXi4Datr8lkspmCJPUFWaQBZBm61X2Acwkm3prtm57J0kpzU8pfTWltAGwN3BiROzefHg57/8KMDIihi+vyMro0anAeyJivxav3SulNLzFNjCl9Grr2iNiEDCq9Wlb1bEEGN3iXENTSltU3v+1lNIXUkpjyUbgLm3ujUsp/TCltD3ZdOrGwMltfITc9zkihlTqeXV5n1tS92QIk7Qy+kfEwOYNuAE4MyLGRMRosmm7ayvPvRk4IiI2i4jBlWNtiohPVJrXA3iLbESqsXL4P8AGbb2uMnV3F1moGRER/SPig+08tw64sEUdlwP/ExHjKjWMiYh9K8duBfaOiJ0jooZsKrXdRvdKHXcDF0bE0EoP2oYRsUvl3AdERHOoe4MswDVGxA4RsVNE9LUrj9gAACAASURBVAcWAotbfO6Wrif7Xm4T2YUP3wYeTCm92F5NkrovQ5iklXEnWX9V8zYQmAI8ATwJPAKcC5BSuotsCvBPZFONf6+cY0kb550A3AMsqDzv0pTSvZVj3yELevMi4qQ2XnsYWW/Vs8As4CvLqf8qYL2I2Bu4GLiDbAp0PvAAsFOl9qeBY8ma32cC8yvnbqv2Zv8N1ADPkAWtW8n63CBrmH8wIhZU3vP4lNILZNOkV1Se/xLZFOP3Wp84pfQH4Czgl5V6NmRZv5mkHsbFWiUVKiI2A54CBqSUGsqupzMiohaYB0yohCdJWmmOhEmquoj4ZETURMQI4LtkVxz2iAAWEXtHxOBK/9X3yEb6Xiy3KkmrAkOYpCJ8EXgd+DdZr9OXyi2nU/Yla4ifQTZdelBHls2QpBVxOlKSJKkEjoRJkiSVoMfdbHb06NFp/PjxZZchSZK0Qg8//PDslNKYto71uBA2fvx4pkyZUnYZkiRJKxQRL7V3zOlISZKkEhjCJEmSSmAIkyRJKoEhTJIkqQSGMEmSpBIYwiRJkkpgCJMkSSpB1UJYRFwVEbMi4ql2jkdE/DAinouIJyJiu2rVIkmS1N1UcyTsGmDP5Rzfi+xmuBOAScBlVaxFkiSpW6naivkppfsiYvxynrIv8POU3UH8gYgYHhFrpZRmVqsmSZLUOyxpaGTuwjrmLKhj7sJsm7OwjjkLltD/macZ9uxT9DvicP77feNLq7HM2xatDbzS4vH0yr53hLCImEQ2WsZ6661XSHGSJKn7WFzfmAtTcxcuYc6CyteVf89ZuCR7zoI65i9paPM8/WnivsuPZlDDEm7e/4CCP0VemSEs2tiX2npiSmkyMBlg4sSJbT5HkiT1HIvrG1sEqCVLR62aA9bchXXMbjGKtaCdUNWvTzBySA0jh9QwqraGdUYMZ9SQGkYNqWFkbQ2jB/Rho7/czZp33EL9TbcwdFgtfT55B0yYwKSRIwv+1K1qL/G9pwPrtni8DjCjpFokSdK78HZd47IwtXQacEmrkapKwFpQx8K6xjbP079vc6gawKghNYwbNTgLWM37apu/rmHUkAEMHdSPiDbGdRYtgquvhgsvhBdegAkT4PWZMGJj2GmnKn83OqbMEHYHcExE3AjsBLxpP5gkSd3DorqGXD/V7AVL3tFbtWxqsI5F7YSqmr59ciNV648avDRMNYer7OsBjBxSw9CB7YSqznjxRZg4EebMgfe+Nwti++wDffu+u/N2saqFsIi4AdgVGB0R04Gzgf4AKaXLgTuBjwHPAYuAI6pViyRJvVlKiUV1jbkA1Rye3hGwKtODi+ub2jxXTb8+y0aiagewwZjapSFrdIsw1Ryuagd0QajqiOefh8cfh09+EsaNg0MOgQMOgPe/H4p4/5UQ2cWJPcfEiRPTlClTyi5DkqTSpJRYWNe4tJ+qecTqHQ3rLULWkoa2Q9WA5lBVm03vNQeskbU1jK4EqpG1zaFqAENq+hYTqjpqyhS44AK49VYYMQJefRUGDCi7qqUi4uGU0sS2jpU5HSlJkshC1YIlDa2a0Ze06K16Z8CqaydUDezfJwtTtdlI1IQ1apcGqGW9VTWMrjwe3N1CVUc9+ih89avwpz/BsGFw8slw3HHdKoCtiCFMkqQullJi/pKGNkeqcg3rLUJWXWPboWpQ/75Lp/rG1A5gkzWG5pvTa5c1sY+qrWFwzSr8q72+Ht56C0aNgj59YNo0+N734AtfgKFDy66u01bhPylJkrpGSom3Fje8oxn9nb1VWcB6Y2F9u6FqcE3fpcFpjaED2WytlqFqQItG9Wx6cFBN92omL8X8+XDFFXDRRbDLLvCLX8DWW2dXPfbruVGm51YuSdJKSinx5tv1udGoOZWlE9pqWH9jUR31jW33UA+p6bt0qm/ssIFsOXYoo2rb6K2qBK2B/Q1VHfbaa/DDH8Kll8Kbb2YB7OCDlx3vwQEMDGGSpFVAU1M+VM1duCS30GfrJRXeWFhHQ1PboWq1Af0YWRmJWmfEILZeZ/jSANV8ReDSgGWoqq4LLshGvz71qazna8cdy66oS3l1pCSp22lqSsx7uz7XiN686GdbDetvLKqjsb1QNbBfi9A0oLKMwrJ+qlHNSypU9g/oZ6gqzd/+BuefD8ccA3vskY2ELVgAG21UdmUrzasjJUmlamxKzFu0/Ob0pff9q2ztZCqGDuy3dPpv3KjBbDdueNsBa8gARgzpb6jq7pqa4De/ycLX3/4GI0fCpz+dHVtzzXJrqzJDmCSp0xqbEm8sanuhz7kL81cENvdUtReqhg3qv3Skav3RQ9h+3MjclX8tR6pGDK6hpl+fYj+sqmuPPbJlJsaNy/q/jjwShgwpu6pCGMIkSTQ0NvHGovp3XPHX1u1p5ixYwry362mvm2X44P7ZkgpDBrDhmFp2WL+mxQ2VB7S4718NI4bU0L+voapXmTcPfvYzOPpo6N8/C12TJsH++/f4RvvO6l2fVpJ6iYbGJuYuarXQZyVMzV7aW1UZsVpYx5vthKoIGD6o/9KG9Amr17LT+iPfufhnZcRqxOD+9DNUqS2vvJI12U+enPV5bb45fPjDcOihZVdWGkOYJPUA9Y1NvNFiNfXm/qllt6fJP37z7fo2zxMBIwYvu9JvkzVXa7efalRtDcMHGar0Ls2fnzXaX389pAQHHQQnnQTbbFN2ZaUzhElSCeoamnJhqnVzen6F9SW8tbihzfP0aQ5VlQC12VpDW/RSLbuZcnPAGj64hr59euAtatSzpAQvvQTjx2f9Xc8+C1/+MpxwQtb7JcAQJkldYklDYy48vaNhvVVv1fzlhKqRLW6ivPnYoZV+qgG5tapGV1ZcHzaov6FK3UdjI9x2W3al47Rp2RTkaqvB3/+e3WZIOYYwSWrD4vrGXIBquV7V3KXrVlVC1oI65i9pO1T17RO5myZvufawpTdObt7fsrdq2KD+9DFUqadZtAiuuQYuvBCefx4mTMiCWE1NdtwA1iZDmKReYXF9Y4sAlb+hci5gVbYF7YSqfpVQ1Tzlt86I4S2u/HtnqBo60FClXuDpp7Ppxp12ym6ovc8+0Nf12VbEECapR3q7rnFZ/1TrxT9brrBeuR/gwrrGNs/Tv28sbUwfVVn8c9nI1YAWvVXZlODQQf2IMFSpl3v+efj+97MrPX70I9hhB3jkkazZ3p+PDjOESeoWFtU1tLvQ5+wFLa7+qwSut+vbDlU1ffvkRqrWHzV4aZhaNv23rGF96EBDldRhDz+c3c/xlluyka7PfS5rwo+Abbctu7oexxAmqcullFhU15hrSG851dd6hfU5C5ewuL6pzXPV9OuTu3HyBmNql4as0S3CVHO4qh1gqJKq4oc/hOOPh6FDsyUmjj8exo4tu6oezRAmaYVSSiysa2TugjpmV6b3lrea+pyFdSxpaDtUDWgOVZXFPTdqDlW12QrrI1v1Vg2p6WuokspQXw833QSbbJJNN+69N9TVZavbDx1adnWrBEOY1AullFiwpKFVM/qSpQuBttWwXtdOqBrYv8/ShT1H1dYwYY3ad66mPqRm6RWBgw1VUvc2fz789Kfwgx9kS0x86UtZCFt//WwETF3GECatAlJKzF/SsKw5PTdStaxhvWWfVV1j26FqUP++S6f6xtQOYJM1huab02uXNbGPqq1hcI3/G5FWGRdcAN/+dnZ/xw9+EC67DPbaq+yqVln+31PqhlJKvPV2Q/tX/7XRsF7f2PbdlAfX9F0anNYcNjBb/LPFaurLGtWz6cFBNV5WLvUq06bBBhtkjfb19bD77nDyydlyE6oqQ5hUovrGJi6/999Mm7Ugd0XgG4vaD1VDavouneobO2wgW44dyqjaZSusL+2tqgStgf0NVZLa8Pe/Zwuq/vrXWe/XAQfA6ae7xESBDGFSic7/3bNccf8LrDdycGXhz0Fsvc7w3O1pcgHLUCXp3Whqgt/+Ngtff/0rjBgBZ54Ju+ySHTeAFcoQJpXkd0/N5Ir7X+Cw947jW/ttWXY5klZlzWt5pZQ119fVwcUXw5FHQm1t2dX1WoYwqQQvzl7Iybc8wVbrDOPMT2xWdjmSVlXz5sFPfgLXXZdNPw4ZAnfdBePGQT8jQNm8o6ZUsMX1jXzpukfo0ye45ODtGNDP6UVJXWz69GzEa7314LTTYK21YPbs7NiGGxrAugn/FKSCnXPH00yd+RZXfXYi644cXHY5klY1zz0Hm22WTT3+139lVzpus03ZVakNhjCpQLdMeYUbH3qFL39oQ3bbdI2yy5G0KkgJ7r0Xnn02W1h1o43gu9+FT30Kxo8vuzoth9ORUkGmznyLs379FO/bYBQn7LFx2eVI6ukaG7Mbae+4I+y2Wxa86uqyYyeeaADrAQxhUgHmL67n6OseYejA/lz8mW3o19cfPUnvwr33Zvd0PPDArPn+8sth6lSoqSm7MnWC05FSlaWUOPWXT/Dy3EVc//mdWH21gWWXJKknmj0bFizIRrjGjoUxY7L1vvbdN1vtXj2Ofx2Xquzqv77InU++xikf3YSdNhhVdjmSepoXXoBjj82udDzxxGzfxhtnS0586lMGsB7MkTCpih5+6Q2+fedU9thsDSZ9cIOyy5HUkzz6aNbndcstWdA69NBs2QmtMgxhUpXMWbCEY65/hLWGD+TCA7YmvB2IpBVJlXvGRsBtt2ULq550Ehx3HKy9drm1qcs5HSlVQWNT4is3PcachXVcdsj2DBvcv+ySJHVn9fVw7bXZel533JHtO+kkePnlbDTMALZKMoRJVfCjP07j/mmzOWfvLdhy7WFllyOpu1qwAC66KFvF/rDDoKFh2RWOw4Zlm1ZZVQ1hEbFnRPwzIp6LiNPaOD4sIn4TEY9HxNMRcUQ165GKcP+017n4D9P41LZr85kd1y27HEnd2a67wgknwPrrw29/C08+CXvtVXZVKkjVQlhE9AUuAfYCNgc+ExGbt3ral4FnUkpbA7sCF0aEi5yox5r55tscf+NjTFi9lnM/uaV9YJLy/vWvLHQtXpw9PvdceOAB+POf4eMfhz5OUPUm1fzT3hF4LqX0fEqpDrgR2LfVcxKwWmS/qWqBuUBDFWuSqqa+sYkvX/cIS+obuezQ7Rlc43Uvkiqal5PYdFO47DL4xz+y/XvuCTvtVG5tKk01Q9jawCstHk+v7Gvpx8BmwAzgSeD4lFJTFWuSqua8u57lkZfncd6nt2LDMbVllyOpO5g3Dz7wAdh552yV+zPOgJdegg9+sOzK1A1UM4S1NQ+TWj3+KPAYMBbYBvhxRAx9x4kiJkXElIiY8vrrr3d9pdK7dNeTM7nyLy/w2Z3Hs/fWY8suR1KZlizJphgha6xfYw24+OLsSsdvfSt7LFHdEDYdaNmVvA7ZiFdLRwC3pcxzwAvApq1PlFKanFKamFKaOGbMmKoVLK2MF2Yv5ORbn2CbdYfztY9tVnY5ksoyb162nMT668Puu8Pcudl6X7femq3zVesIufKqGcIeAiZExPqVZvuDgDtaPedlYHeAiFgD2AR4voo1SV3q7bpGvnTtw/TrG1xyyHbU9LOpVup1XnsNTj45u63QaafBllvCr34FI0aUXZm6uap1DqeUGiLiGOD3QF/gqpTS0xFxVOX45cC3gGsi4kmy6ctTU0qzq1WT1NW+/uunePa1+Vx9xA6sPXxQ2eVIKlJDA/TrB3PmZGt9HXBAFsa23bbsytRDVPXyrZTSncCdrfZd3uLrGcBHqlmDVC03P/QKtzw8nWN324gPbbJ62eVIKkJK2XISF1wAQ4fCDTfAFlvAq6/C6v5/QJ3j3Im0Ep6Z8RZn/fopdt5wFF/ZY+Oyy5FUbY2NWW/XTjvBhz4EDz0EW2+97F6PBjCtBEOY1ElvLa7n6OseZvjg/vzwM9vSt48LskqrvO9+N5tunDcPLr88W2bitNOyxntpJbmapNQJKSVOueUJXnnjbW6c9F5G1w4ouyRJ1TBnDlxyCbz//dmVjkcckS20uu++0Ldv2dVpFeFImNQJV/7lBX739Guctuem7DB+ZNnlSOpqL7wAxx6bXel49tnZAqsAa62VrXhvAFMXciRM6qApL87lvLue5SObr8HnP7B+2eVI6mpf/Wp2lWPfvnDIIXDSSVnTvVQljoRJHTB7wRKOuf5R1h4xiAsO2Nobc0urgpTgnnugri57vPHGWRB74QW4+moDmKrOkTBpBRqbEl+58THmLqrj9qN3Ztig/mWXJOndqK+Hm2/Olpl4/HH4+c/hsMPgi18suzL1Mo6ESStw8R+m8ZfnZvOtfbdgi7HDyi5H0spqaMimGzfaCA49NAtjV10FBx5YdmXqpRwJk5bj3n/O4kd/nMant1uHAyeuu+IXSOp+Fi+GgQOzXq9rroFx47IrHz/2MejjWITKYwiT2vHqvLc54abH2GSN1Th3vy3tA5N6mn/9Cy68EG6/HaZNg2HDstXuhzmire7BvwJIbahraOLL1z1CfWPi0kO2Y1CNl6VLPcYDD2TLSWy6KfzsZ/DJT8KSJdkxA5i6EUfCpDZ8566pPPbKPC45eDs2GFNbdjmSOuqf/4T3vQ9GjICvfS1b82uNNcquSmqTIUxq5X+fmMnVf32RI94/no9vtVbZ5UhaniVL4Prrs2UlvvlN2GQTuOUW2HNPqPUvUOrenI6UWvj36ws45dbH2Xa94Zy+12ZllyOpPW++CeefD+uvD0ceCb/7XXb1I8D++xvA1CMYwqSKt+saOfraR6jp14dLDt6Omn7+eEjd0m9+A+uuC6eemi2oevfd8OCD0M/JHfUs/hcrkd2Y+4xfPcm/Zs3nmiN2ZOzwQWWXJKmlp56CpibYaivYemvYe+/stkLbblt2ZdJK86/6EnDTQ69w2yOvctxuE9hl4zFllyMJstsK/fnP8PGPw3veA2eeme1fbz247joDmHo8Q5h6vadefZOv3/E0H5gwmuN2n1B2OZIA7roL3vte2HVXeOihrOn+6qvLrkrqUk5Hqld78+16jr7uEUYOruGi/9qGvn1ckFUqzdtvQ//+WW/X44/D3Llw2WVw+OEwyBYBrXocCVOvlVLipFseZ8a8t7nkkG0ZVTug7JKk3mnOnGyka9y47MbaACecAM8+C0cdZQDTKsuRMPVaV9z/PP/3zH848+Obsf24kWWXI/U+L7wAP/gBXHklLFqU3ctxo42yYwP8S5FWfYYw9UoPvTiX7/7un+y5xZp87v+tX3Y5Uu+TEuy3H0ydCgcfnF3puOWWZVclFapD05ERMSgiNql2MVIRXp+/hC9f9wjrjhjE+Qds5Y25pSKklK3n9clPwvz5EAE//Sk8/zxcc40BTL3SCkNYROwNPAb8rvJ4m4i4o9qFSdXQ2JQ4/sZHefPtei49ZHuGDuxfdknSqq2+Prut0Lbbwkc/mi2q+uyz2bEddoB11im3PqlEHRkJOwfYEZgHkFJ6DBhfvZKk6rnonn/xt3/P4Vv7bcnmY4eWXY60aps7N+vxOuQQqKuDq67K+sB22KHsyqRuoSM9YQ0ppTedslFP96d/zuJHf3yOAyeuw4ET1y27HGnVNGsW/PWv2bTjyJHw6U/DbrtlTfd9vCBfaqkjIeypiDgY6BsRE4DjgL9Vtyypa01/YxEn3PQYm665Gt/c194TqctNmwYXXpj1d0XAzJkwfDh8//tlVyZ1Wx35a8mxwBbAEuB64E3g+GoWJXWlJQ2NfPn6R2lsTFx26PYM7N+37JKkVce0adlo1yabZAHs8MOzhVaHDy+7Mqnb68hI2MdTSmcAZzTviIgDgFuqVpXUhb79v1N5/JV5XH7odqw/ekjZ5Ug9X1MTzJuXTTf27Qv33Qdf+xoceyyssUbZ1Uk9RkdGwk7v4D6p27nj8Rn87O8v8fn/tz57brlW2eVIPduSJdn9G7fcMhvxAthgA3j1VTj3XAOY1EntjoRFxF7Ax4C1I+KHLQ4NBRqqXZj0bj03awGn/fIJth83glP32rTscqSe68034Sc/gYsuynq9tt4aPvOZZcdrasqrTerBljcdOQOYAuwDPNxi/3zghGoWJb1bi+oaOPq6hxnYvy8/Pnhb+vf1qixppf3oR3DWWbD77lnf14c/nDXfS3pX2g1hKaXHgccj4vqUUn2BNUnvSkqJM25/immzFvDzI3dkrWHe/FfqlKefhu99D/bZJ1tq4uijsyUmttuu7MqkVUpHGvPHR8R3gM2Bgc07U0obVK0q6V244R+vcPujr3LCHhvzgQljyi5H6hlSgvvvh/PPh//9Xxg8GLbZJjs2cmS2SepSHQlhVwNnAz8APgQcATgOrW7pyelvcs4dT/PBjcdw7G4blV2O1HMcemh2e6ExY+Cb38xGv0aNKrsqaZXWkUaZQSmlPwCRUnoppXQOsFt1y5I6781F9Rx9/cOMqq3hov/ahj59/LuC1K6334bJk2HRouzxfvvBZZfBSy9l/V8GMKnqOjIStjgi+gDTIuIY4FVg9eqWJXVOU1Piq7c8xsx5i7n5qPcxcohXa0ltmjMHLr00a7Z//XWorYWDD4YDDii7MqnX6chI2FeAwWS3K9oeOBQ4vJpFSZ01+f7nuWfqLM74+GZst96IssuRup+6Ojj+eFhvPfj617ObaN97b36pCUmFWm4Ii4i+wIEppQUppekppSNSSp9OKT3QkZNHxJ4R8c+IeC4iTmvnObtGxGMR8XRE/HklPoN6uQeen8MFv/8nH3/PWnx25/FllyN1LzNmZP+uqcluJ3TAAfDkk1nz/S67uNSEVKLlTkemlBojYvuIiJRS6syJKwHuEuDDwHTgoYi4I6X0TIvnDAcuBfZMKb0cEU5zqlNmzV/MsTc8yriRgznv0+8h/IUiZVc63nNPdqXjX/6S9Xmtvjr84Q/ZbYYkdQsd6Ql7FPh1RNwCLGzemVK6bQWv2xF4LqX0PEBE3AjsCzzT4jkHA7ellF6unHNWJ2pXL9fQ2MRxNzzK/MX1/OJzO7LawP5llySVq74ebrkFLrgAHnsM1lwTzjkHBlXWyjOASd1KR0LYSGAO+SsiE7CiELY28EqLx9OBnVo9Z2Ogf0TcC6wGXJxS+nnrE0XEJGASwHrrrdeBktUb/OCef/HA83P53gFbs+maQ8suRyrfCy9kS01ssglceSUccggMGFB2VZLascIQllI6YiXP3da8UOspzX5kzf67A4OAv0fEAymlf7WqYTIwGWDixImdmhbVqumPz/6HS/70bw7aYV32336dssuRyjFrVnaV42uvwRVXwMYbwwMPwMSJ0MdbdUndXTV/SqcD67Z4vA7Z/ShbP+d3KaWFKaXZwH3A1lWsSauAV+Yu4oSbHmfztYZyzj5blF2OVLxp0+Coo7IrHf/nf+CNN6CxMTu2444GMKmHqOZP6kPAhIhYPyJqgIOAO1o959fAByKiX0QMJpuunFrFmtTDLWlo5MvXP0JTSlx26HYM7G+Pi3qZ66/PphuvuQYOPxymToVbb7XfS+qBOtITtlJSSg2VxV1/D/QFrkopPR0RR1WOX55SmhoRvwOeAJqAn6aUnqpWTer5zv3tVJ6Y/iY/OWx7xo0aUnY5UvU1NcGdd8KwYfCBD8Duu8Ppp8Oxx2aN95J6rFjRyhMRsQbwbWBsSmmviNgceF9K6coiCmxt4sSJacqUKWW8tUr268de5fgbH2PSBzfgax/brOxypOqqq8tGvS64AJ55BvbfP7vyUVKPEhEPp5QmtnWsI9OR15CNZo2tPP4X2Sr6UmGm/Wc+p9/2JDuMH8HJH92k7HKk6rr6alh/fTjiCOjXD669NgtkklYpHQlho1NKN5NNF5JSagAaq1qV1MLCJQ186bpHGFzTlx8fvB39+9p0rFXQq6/C4sXZ1wsWwGabwe9/n633dcgh0N918KRVTUd+my2MiFFUlpeIiPcCb1a1KqkipcTptz3J868v4OKDtmWNoQPLLknqWs88A0cemY18/byyTOIxx2Qr3n/kI95WSFqFdaQx/6tkVzVuGBF/BcYA+1e1Kqni2gdf5o7HZ3DSRzbm/RuNLrscqWuklN1O6Pzz4be/zVa0/+IX4cMfzo4bvKReoSOLtT4cEbsAm5AtwPrPlFJ91StTr/fE9Hl86zfPsOsmYzh6143KLkfqWieeCC++CN/4Bhx9NIz2LxlSb7PCEBbx/9u77/ioqvz/468PIYTeEYFQonQpkW5by4JiBRUBxd5FZFUs7P7UXXVd7FgQsWFDCII0FcQCKqJ8IYQOgkgNICCdUJOc3x9n1IgBBsjMTTLv5+ORRzL33pl5b+7ifHLuuZ9jc4DhwHDn3M+RjyQCW3ft444haVQuXYz+XZMpUkQjA1KA7d4N774Lr73mLzNWqgTDhkH16lCyZNDpRCQg4cwJuwTIBD40sxlmdp+ZaQFHiZjsbEefD+ewYcceXunRggqligUdSeTobN4M//0v1K4Nd9zh73T85Re/r25dFWAiMe6wRZhzbqVz7mnnXEvgKqAZsDziySRmDfr2Z776cQMPXdiYk2tVCDqOyNHZuNEvK/Tww9C6NUyeDNOnw0laaktEvLA65ptZHaAr0A3fnuKByEWSWPbDz5t4duJiLmpWjWtPqR10HJEjM3s2TJ0Kd94JVarAY4/5yfZNmwadTETyoXDmhP0fEA+MAK5wzi2LeCqJSRu27+GuYbOoU7kUT17eDNMdYlIQOAdffeXvdPziCyhfHq65BsqW9ZPvRUQOIpyRsOuccz9GPInEtMysbHoNm0XG3kyG3tKW0gkRW9ZUJO/MmuV7fM2e7ddxfPJJ32qibNmgk4lIAXDQTzozu9o5NwS4wMwuOHC/c+75iCaTmPLs50uYvnwzz3dtTv2qZYKOI3JwGRmwYYNvrlq1ql9g+623fFf7hISg04lIAXKo4YZSoe+5fSIeetVvkSPwxcL1DPrmZ65qW4vLWiQGHUckdxs2wIAB8Mor0Lw5TJrkW0zMjL+/ngAAIABJREFUnq3mqiJyVA5ahDnnXgv9+KVzbmrOfWZ2WkRTScxYtWkXfT6cTZMaZXnkosZBxxH5q6VL4bnn4J13YO9e6NQJ7r//j/0qwETkKIXTJ+zlMLeJHJE9+7PoOXQmAK/2aEnx+LiAE4nk4EID/mPHwuDBfrL9okUwejScemqw2USkUDjUnLBTgFOBKmaW8xafsoA+LeWYPf7JQuav2c4b17aiZkU1rZR8IDsbxo+HZ57xE+6vu85PtO/Rw0+8FxHJQ4caCSsGlMYXamVyfG1HC3jLMRo9K50P/m8Vt515Ah0aVw06jsS6ffv85camTeHii2H5coiP9/tKl1YBJiIRcag5Yd8A35jZO865lVHMJIXckvU7+Neo+bRJqsj95zYIOo6In+f12We+CHv/fejW7Y8iTEQkQg51OfIF59zdwAAz+8vdkM65SyKaTAqlnXszuX3ITEolFGXAlSdTNC6caYkieWztWn+n44MPQrly0KcP3H03nHuuJtqLSNQcqkXF+6Hvz0YjiBR+zjn+OWoeK37NYMjNbTmubPGgI0msWbgQnn0WhgyBrCxo29aPgrVvH3QyEYlBh7ocOTP0/ZvftplZBaCmc25uFLJJIfP+tJV8PGct95/XgFNPrBx0HIkle/fCFVfAxx9DiRJ+sv299/qGqyIiATnstSAz+9rMyppZRWAO8LaZqVu+HJHZq7fy+CcLOafhcdxx5olBx5FYkJUFqan+54QEP8H+0Udh1Sp4+WUVYCISuHAW6CvnnNtuZjcDbzvn/m1mGgmTsG3J2MedH6RxXJniPN+1OUWKaM6NRNDu3fDee77B6vLlsGwZ1KwJQ4cGnUxE5E/CmRVd1MyqAV2BTyKcRwqZ7GzHPR/OZuOOvbx6dQvKlywWdCQprLZuhSeegDp14Pbb/YT7oUP90kIiIvlQOEXYY8BE4Gfn3AwzOwH4KbKxpLAY+PVSvl68kYcvbkyzxPJBx5HCKCvLf9+2Df7zH2jZEiZPhunT/TywOPWWFpH86bCXI51zI4AROR4vAy6PZCgpHL5f+ivPf7GES5pX5+q2tYKOI4XN7Nm+s/22bfDJJ1C7NqxYATVqBJ1MRCQs4UzMTzSz0Wa2wczWm9lHZpYYjXBScK3fvofeKbM4oUpp+l3WFFPvJckLzsGXX/p+XiefDOPGQYMGf4yGqQATkQIknMuRbwPjgOpADeDj0DaRXO3PyqbX0DQy9mbxao8WlEoI5/4PkTC88QZ06ADz5kG/frB6tZ+Ar0uOIlIAhfPpWMU5l7PoesfM7o5UICn4np24mBkrtvBi92TqVS0TdBwpyDIyYPBg307ioouga1dfcF19tW87ISJSgIUzEvarmV1tZnGhr6uBTZEOJgXTxAW/8Nq3y7i6XS06JevSkBylDRvgkUegVi3o3RvGjvXby5eHm25SASYihUI4RdiN+PYUv4S+uoS2ifzJyk0Z3DdiDs0Sy/HwRY2DjiMF1ZNP+kn2jz8OZ5wB333nL0OKiBQy4dwduQrQYt1ySHv2Z3HHkDSKmPHKVS1IKKo5OnIEpk+HRo2gTBlITPSXG/v0gYYNg04mIhIx4dwdeYKZfWxmG0N3SI4N9QoT+d2jHy9g4brtPN+1OTUrlgw6jhQEzsH48XDWWX4h7cGD/farr/YjXyrARKSQC+dy5FDgQ6Aa/g7JEcCwSIaSgmXkzHSGTV9Nz7NO5O+NqgYdR/I75+Ddd6FZM7jwQvj5Z3j+ebhRsxxEJLaEc3ekOefez/F4iJn1ilQgKVh+/GU7D42ZR7sTKnJvh/pBx5H8bP9+iI8HMz/qZebXeOze3W8XEYkx4RRhk82sL5ACOKAb8KmZVQRwzm2OYD7Jx3buzaTnkDTKFI/npStPpmhcOAOrEnPWroUXX4S334Y5c6BaNRg1CipW9IWYiEiMCqcI6xb6ftsB22/EF2WaHxaDnHM8+NFcVmzKYOgt7TiuTPGgI0l+s3AhPPssDBniO9p36QJ79/p9lSoFm01EJB8I5+7IpKN9cTPrCLwIxAFvOueePMhxrYFpQDfn3MijfT+Jnne/X8Gnc9fxYMeGtDtBH6hygPXr/ZyvYsXg1lvh3nvhBP29JiKSU8TWkzGzOOAVoAOQDswws3HOuYW5HPcUMDFSWSRvpa3awhPjF9G+0XHc9jd9sAp+pGvsWEhNhf/9D6pWhaFD4ZxzoHLloNOJiORLkZzE0wZY6pxb5pzbh59T1imX4+4CPgI2RDCL5JHNGfvo9UEaVcsW57krkilSRHN6YtqePfD6677H1+WXw/DhsHOn39e1qwowEZFDiGQRVgNYneNxemjb78ysBnApMCiCOSSPZGc77h4+m1937uPVHi0pV1J3tMW0KVN8Z/vbboOyZX0BtngxlC4ddDIRkQIhnGatFlo78pHQ41pm1iaM185tiMQd8PgF4EHnXNZhMtxqZqlmlrpx48Yw3loiYcDkpXy7ZCP/vqQxTRPLBR1HgrBqFcya5X9u3BhOOQUmTYIZM/zIV9GIzXAQESl0whkJGwicAlwZerwDP9frcNKBmjkeJwJrDzimFZBiZivwa1IONLPOB76Qc+5151wr51yrKlWqhPHWkte+++lX+n+5hEtPrsFVbWoFHUeibc4c38n+hBOgV6hNYKVKMGYMnH22Wk2IiByFcIqwts65O4E9AM65LUCxMJ43A6hnZklmVgzoDozLeYBzLsk5V8c5VwcYCfR0zo05kv8BEnnrtu2md8os6h1XmicubYLpAzd2fP89nHceJCf7iff/+AekpASdSkSkUAjn2sH+0B2MDsDMqgDZh3uScy4z1Fl/Ir5FxWDn3AIzuz20X/PACoD9Wdn0GjqLPfuzGNijJSWL6XJToZeZCdnZvr3E3Ll+FOx//4Pbb4cKFYJOJyJSaJhzB07TOuAAsx74hq0tgHfxlw0fcs6NiHy8v2rVqpVLTU0N4q1j0hOfLuSNKct56cqTuaR59aDjSCRlZPiu9s89B/ffDz17+uaqzkFxNeMVETkaZjbTOdcqt33hNGv9wMxmAn/HT7bv7JxblMcZJR/6bP463piynOtOqa0CrDDbuBEGDPBfmzfDqadC/dA6oAkJwWYTESnEDluEmVktYBfwcc5tzrlVkQwmwVrxawb3j5hL88Ry/OvCRkHHkUjq1g0mT4ZOnfwI2GmnBZ1IRCQmhDPB51P8fDADigNJwGLgpAjmkgDt2Z/FHR+kUaSI8UqPFiQUjQs6kuSl6dOhf3946SWoUgWeeQZKlYKGDYNOJiISU8K5HNk052Mza8FfF/OWQuTfYxewaN123r6+NYkVSgYdR/KCczBhAjz9NHzzDZQr5yfct28PLVsGnU5EJCYd8a1uzrm00ILbUgh9mLqa4amr6XV2Xc5ueFzQcSQv7N4N7dr5Ox0TE/3E+1tugTJlgk4mIhLTwpkTdm+Oh0Xwd0mqbX0htHDtdh4eM59TT6zEPR3qBx1HjsX27b6TfefOUKIEnHsu3Hefn/9VLJw2fyIiEmnhjITl/HM5Ez9H7KPIxJGgbN+zn54fzKRciXhe7H4ycVqYu2BauxZefBEGDYIdO2DlSqhZ08/7EhGRfOWQRVioSWtp59z9UcojAXDO8eDIuazesptht7SjShm1JShw1qyBRx6B99+HrCzo0sXf6Viz5uGfKyIigThoEWZmRUNd71tEM5BE3+CpK5gw/xf+dUFD2iRVDDqOhMs5f9mxXDm/cPbo0XDrrXDvvX6NRxERydcONRI2HT//a7aZjQNGABm/7XTOjYpwNomCmSu30G/8Is5tXJVbztAHd4GQne3XcXzmGYiLgylToGpVPxpWokTQ6UREJEzhzAmrCGwCzuGPfmEOUBFWwG3auZdeQ9OoXr4Ez1zRXAtz53d79vjLjc8+C0uWQFIS9Onji7IiRVSAiYgUMIcqwo4L3Rk5nz+Kr98cesFJyfeysh13D5/Npox9jLrjVMqViA86khzO22/79RxbtoThw+Gyy/xlSBERKZAO9V/wOKA0fy6+fqMirIB7edJPTPnpV/pd1pQmNcoFHUdys2oVvPACnHwyXHMNXHstNGgAZ58NGrUUESnwDlWErXPOPRa1JBI13y7ZyItf/cRlLWrQvbXunst35s71872GDfOP+/b130uVgnPOCS6XiIjkqUMVYfpTuxBau3U3/0iZRf3jyvDfzk00Dyy/ufdev65jqVLQuzfcfTfUqhV0KhERiYBDFWF/j1oKiYp9mdn0GprGvsxsBl7dgpLFNJ8ocJmZ8NFH0KEDVKzoR7qqVIHbb4cKFYJOJyIiEVTkYDucc5ujGUQi78kJP5K2aitPdWnGiVVKBx0ntmVkwIABUL8+dO8OQ4b47RddBP/8pwowEZEYcNAiTAqX8fPWMXjqcq4/tQ4XNasedJzY5Rw8+ijUrg133QXHH++brPbqFXQyERGJMl2PigHLNu7kgZFzSa5Znn9d0CjoOLFp40Z/mdEMZsyA006DBx7w30VEJCapCCvkdu/LoucHacTHGa/0aEGxohr8jKoZM/ydjmPHwo8/+garo0dDvPqyiYjEOn0iF3KPjJ3P4vU76N8tmRrl1VE9KpyDCRN8P682beDzz/1dj6VD8/BUgImICBoJK9Q+nLGaETPT6f33epzV4Lig48SOX36BTp38eo7PPQe33AJlygSdSkRE8hkVYYXUgrXbeHjsfE6vW5l//L1e0HEKt+3b4Y03YPZsv7ZjtWowaZIfBStWLOh0IiKST+lyZCG0fc9+en6QRvmS8bzQPZm4ImrIGhHr1vlu9rVqwX33QXo67Nrl951+ugowERE5JI2EFTLOOe4fMYf0LbsZfms7KpdOCDpS4TRxIlxyiW+2evnlcP/90Lp10KlERKQAURFWyLz13XImLljPQxc2olWdikHHKVymToW9e31X+1NOgTvu8L2+Tjwx6GQiIlIA6XJkITJjxWb6TfiR806qyk2nJwUdp3DIzoYxY+DUU/0lxkcf9dvLloUXXlABJiIiR01FWCHx68699BqaRmKFEjxzRXMtzJ0XxoyBRo3g0kv9HY8vvwzjxwedSkRECgldjiwEsrId/0iZxdZd+xnVszVli6sP1VHbssX38SpdGnbu9N9TUvy8r6L65yIiInlHI2GFwItf/cTUpZt4vFMTTqpeLug4BdOqVb6haq1a8OqrfttVV0FqKnTrpgJMRETynD5ZCrivF2/g5Uk/cUXLRLq2rhl0nIJn7ly/rFBKiu90f+WV0LGj31dEf6OIiEjkqAgrwNZs3c09w2fToGoZHuvUJOg4BVOfPvDDD/4ux7vv9iNhIiIiUaA/9QuofZnZ3PlBGvuzHK9e3ZISxeKCjpT/ZWbChx/69hKrV/ttr77qf37+eRVgIiISVSrCCqj/jV/E7NVbebpLM5Iqlwo6Tv62axe88grUr+/nd23e7LvbA9StCxUqBJtPRERiki5HFkCfzF3LO9+v4MbTkrigabWg4+Rvu3b5Xl6//ALt2vkFtS+5BOI0cigiIsFSEVbA/LxxJw+OnEuLWuXpe37DoOPkTz//7Pt53XUXlCwJDzzglxQ67TRQ/zQREcknVIQVILv2ZXLHkJkkxMcx4KoWFCuqq8l/kprq73QcOdK3lLj8cqheHe65J+hkIiIif6FP8QLCOcdDo+fz04advNAtmerlSwQdKf9YutSv59i6tV9Y+/77YflyX4CJiIjkUxEtwsyso5ktNrOlZtY3l/09zGxu6Ot7M2seyTwFWcqM1YyatYZ//L0ef6tfJeg4wdu/H1as8D9XqgTr1sGzz/qmq08+qQJMRETyvYhdjjSzOOAVoAOQDswws3HOuYU5DlsOnOmc22Jm5wOvA20jlamgmr9mG/8et4Az6lXmrnPqBR0nWNu3wxtv+MWzq1SBmTP93Y0LF2q+l4iIFCiRnBPWBljqnFsGYGYpQCfg9yLMOfd9juOnAYkRzFMgbdu9n54fpFGpVDFe6JZMXJEYLTTWrYOXXvJ9vbZtg7PO8pcdf6MCTERECphIXo6sAazO8Tg9tO1gbgImRDBPgeOc474Rc1i7dTcDrmpBpdIJQUeKPuf89wkT4Omn4dxzYfp0mDwZLrhAxZeIiBRYkSzCcvt0dLkeaHY2vgh78CD7bzWzVDNL3bhxYx5GzN/emLKMLxau518XNKJl7RhrKDp1KnTuDC+/7B/36AGLF/uO961bB5tNREQkD0SyCEsHcq4onQisPfAgM2sGvAl0cs5tyu2FnHOvO+daOedaVakSG5PSpy/fzFOfLeb8Jsdzw2l1go4THdnZMGaM7+d1+ukwZcofI10JCb67vYiISCERySJsBlDPzJLMrBjQHRiX8wAzqwWMAq5xzi2JYJYCZeOOvfQamkatiiV5ukszLFYuud1wA1x6Kaxd60fAVq3yDVdFREQKoYhNzHfOZZpZL2AiEAcMds4tMLPbQ/sHAY8AlYCBoUIj0znXKlKZCoKsbEfvYbPYtns/797YhjLF44OOFDlbt8KgQXDddVCtGtx8s5/ndfnlvtmqiIhIIRbRTzrn3Hhg/AHbBuX4+Wbg5khmKGj6f7GEH5Zt4pkuzWhUrWzQcSJj9WrfYuL112HnTqhc2RdgZ5wRdDIREZGo0XBDPjLpx/UMmLyUbq1qckWrmod/QkGTnQ033QRDhvi7Hrt3h/vug+TkoJOJiIhEnZYtyifSt+zinuFzaFStLI92OinoOHnHOZg71/9cpIifaN+rl19ke8gQFWAiIhKzNBKWD+zNzOLOobPIzna82qMFxePjgo507DIzYdQov6B2aqrvaN+oEQweHHQyERGRfEEjYfnA/z5dxJzVW3nmiubUqVwq6DjHZvduGDgQGjSAbt18d/vXXoOkpKCTiYiI5CsaCQvYuDlrefeHldxyRhIdmxwfdJyj55y/1JiR4ed5NWvmF9S+5BKIKwQjeyIiInlMRViAlm7YQd+P5tKqdgUe6Ngw6DhHZ9kyeP55f7nxq6/8nY4LFkCdOlpSSERE5BB0OTIgu/ZlcseQNErExzHgqhbExxWwU5Ga6i831qvnW00kJcGePX5fUpIKMBERkcPQSFgAnHP8a9Q8lm7cyfs3tuX4csWDjnRkRo+Gyy6DsmXh/vuhd2+oXj3oVCIiIgWKirAADJ2+ijGz13Jvh/qcXq9y0HEOb/9+SEmBEiWgSxfo2BH694cbb/SFmIiIiByxAnYNrOCbl76NR8ct5G/1q9Dr7Hy+IPWOHX6+14knwrXXwnvv+e0lSsDdd6sAExEROQYqwqJo26793PHBTCqXLsYL3ZIpUiQfz5t6802oWRP69IETToBPP4WxY4NOJSIiUmjocmSUZGc77v1wNuu372H4badQsVSxoCP91eLF/u7GSpX89w4d/JyvNm2CTiYiIlLoaCQsSl77dhlf/biB/3dBI1rUqhB0nD/7/nvo3Nl3tH/5Zb+tc2cYMUIFmIiISISoCIuCacs28ezni7mwWTWuO7VO0HH+MG4cnHaa/5oyBR56CHr2DDqViIhITNDlyAjbsGMPdw2bRe1KJXnq8mZY0P2zsrL+6GA/eDCsWQMvveTvdCxVwJdMEhERKUBUhEVQZlY2vYfNYsee/Qy5qS2lEwL8dW/dCoMGwYAB8PXXULcuvPEGVKgARfV/AxERkWjTp28EPf/FEqYt28zzXZvT4PgywYRYvRpeeMF3td+500+2/62zfZUqwWQSERERFWGR8tWi9Qz8+meubFOTy1okBhNi505o3Bh27/ZLDN13H5x8cjBZRERE5E9UhEXA6s27uGf4bE6qXpZ/X3xS9N7YOfjmG5gwAZ56CkqX9v2+2rWD2rWjl0NEREQOS3dH5rG9mVn0/CANBwzs0YLi8XGRf9OsrD/aSZx9NrzzDqxf7/d166YCTEREJB9SEZbHHv9kIfPWbOO5K5pTu1IU7jacNw/q14euXWHbNnjtNVi5EqpWjfx7i4iIyFHT5cg8NGbWGoZMW8VtfzuBc086PnJv9OuvvtBq2RKSkqBePXjmGejU6Y/2EyIiIpKvqQjLIz+t38E/R82jTZ2K3Hdeg8i8yfLlfkHtt97ylxgXLvTzvj77LDLvJyIiIhGjy5F5IGNvJnd8kEaphDhevupk4uPy+Nc6fz507+57e732mv/5o48g6MavIiIictQ0EnaMnHP8c9Q8lm3cyZCb21K1bPG8emHYvx+KFfNF2IQJvsVE795Qo0bevIeIiIgERiNhx2jI/61i3Jy19Dm3AaeeWPnYX3D/fnj/fWjeHJ591m/r0gVWrfJtJ1SAiYiIFAoqwo7BnNVbefzjhZzdoAp3nHnisb3Yjh3Qvz+ceCJce61vO1G/vt9XtCiUK3fsgUVERCTf0OXIo7R11z56fpBGlTIJ9O+WTJEixzg/68YbYeRIOPNMePVVOP98KKIaWUREpLDSp/xRyM523PvhHDbs2MMrPVpQvmSxI3+RxYvh1lthxQr/+KGHYNo0v7j2hReqABMRESnkNBJ2FF795mcm/biBxzqdRHLN8kf25O+/9z29xo71k+7bt4c6dfwcMBEREYkZKsKO0Pc//8pzny/m4ubVuabdESwHlJUFHTrA5MlQoYIf+erVC447LnJhRUQkJu3fv5/09HT27NkTdJSYUbx4cRITE4mPjw/7OSrCjsD67XvoPWwWSZVL0e+yptjh+nTt3QtffAEXXeQ72Z9yCnTu7Od/lS4dndAiIhJz0tPTKVOmDHXq1Dn8Z5UcM+ccmzZtIj09naSkpLCfpyIsTJlZ2dw1bBYZe7MYeks7Sicc4le3datvqvrii7BuHcydC02bwhNPRC+wiIjErD179qgAiyIzo1KlSmzcuPGInqfZ32F69vMlTF++mX6XNaV+1TK5H7R5s2+oWqsW9O0LTZrA55/77yIiIlGkAiy6jub3rSIsDF8sXM+gb36mR9tadD45l2apu3b570WLwttvw8UXQ1qaL8A6dNDyQiIiEpNGjx6NmfHjjz/+vu3rr7/moosu+tNx119/PSNHjgT8fLa+fftSr149mjRpQps2bZgwYcIxZ+nXrx9169alQYMGTJw4MddjunXrRnJyMsnJydSpU4fk5OTfM1133XU0bdqURo0a0a9fv2POA7oceVirNu2iz4ezaVqjHA9f1PiPHc7BN9/A009DejrMmQNly8LKlZrvJSIiAgwbNozTTz+dlJQU/vOf/4T1nIcffph169Yxf/58EhISWL9+Pd98880x5Vi4cCEpKSksWLCAtWvX0r59e5YsWUJcXNyfjhs+fPjvP/fp04dyoUbpI0aMYO/evcybN49du3bRuHFjrrzySurUqXNMuTQSdgh79mfRc+hMAAb2aEHx+Dh/l+OIEdC2LZx9NsycCd26+eWGQAWYiIgIsHPnTqZOncpbb71FSkpKWM/ZtWsXb7zxBi+//DIJCQkAVK1ala5dux5TlrFjx9K9e3cSEhJISkqibt26TJ8+/aDHO+f48MMPufLKKwF/qTEjI4PMzEx2795NsWLFKFu27DFlAo2EHdJjnyxk/prtvHltK2pWLOk3jhwJ3btD3bowaJBfYqhEiWCDioiIHMSjHy9g4drtefqajauX5d8Xn3TIY8aMGUPHjh2pX78+FStWJC0tjRYtWhzyOUuXLqVWrVphFTj33HMPkydP/sv27t2707dv3z9tW7NmDe3atfv9cWJiImvWrDnoa0+ZMoWqVatSr149ALp06cLYsWOpVq0au3bton///lSsWPGwGQ8nokWYmXUEXgTigDedc08esN9C+y8AdgHXO+fSIpkpXKPS0hn6f6u4p0VF2o98DY4/3ne4v+wyGD3az/s6YBhTREREvGHDhnH33XcDvjAaNmwYLVq0OOgE9iOd2N6/f/+wj3XOHdH7DRs27PdRMIDp06cTFxfH2rVr2bJlC2eccQbt27fnhBNOOKLMB4pYEWZmccArQAcgHZhhZuOccwtzHHY+UC/01RZ4NfQ9UIt/2cHAwV8yaMGnnPfyeD/x/pZbfBEWH+97fYmIiBQAhxuxioRNmzYxadIk5s+fj5mRlZWFmfH0009TqVIltmzZ8qfjN2/eTOXKlalbty6rVq1ix44dlClzkE4EIUcyEpaYmMjq1at/f5yenk716tVzfd3MzExGjRrFzJkzf982dOhQOnbsSHx8PMcddxynnXYaqampx1yERXJOWBtgqXNumXNuH5ACdDrgmE7Ae86bBpQ3s2oRzHRYO/dm8u1NfZg48CbOmzoW69YNFiyA118PMpaIiEiBMXLkSK699lpWrlzJihUrWL16NUlJSXz33XfUq1ePtWvXsmjRIgBWrlzJnDlzSE5OpmTJktx000307t2bffv2AbBu3TqGDBnyl/fo378/s2fP/svXgQUYwCWXXEJKSgp79+5l+fLl/PTTT7Rp0ybX7F9++SUNGzYkMTHx9221atVi0qRJOOfIyMhg2rRpNGzY8Jh/T5EswmoAq3M8Tg9tO9JjMLNbzSzVzFKPtBHakVq+MYO04+ryy809seXLYfBgaNz48E8UERERwF/Ou/TSS/+07fLLL2fo0KEkJCQwZMgQbrjhBpKTk+nSpQtvvvnm73ci/ve//6VKlSo0btyYJk2a0LlzZ6pUqXJMeU466SS6du1K48aN6dixI6+88srvd0befPPNpKam/n5sSkrKny5FAtx5553s3LmTJk2a0Lp1a2644QaaNWt2TJkALLfrpHnBzK4AznPO3Rx6fA3Qxjl3V45jPgX6Oee+Cz3+CnjAOTczt9cEaNWqlcv5y4qEXfsyKVlM9yyIiEjBtGjRIho1ahR0jJiT2+/dzGY651rldnwkR8LSgZo5HicCa4/imKhTASYiIiKRFskibAZQz8ySzKwY0B0Yd8Ax44BrzWsHbHPOrYtgJhEREZF8IWJDPs65TDPrBUzEt6gY7JxbYGa3h/YPAsbj21OxJMs3AAAIvUlEQVQsxbeouCFSeURERETyk4hed3POjccXWjm3DcrxswPujGQGERGRWOSc0yLeUXQ0c+y1bJGIiEghU7x4cTZt2nRUhYEcOeccmzZtonjx4kf0PM1AFxERKWQSExNJT08n0m2d5A/Fixf/U2+xcKgIExERKWTi4+NJSkoKOoYchi5HioiIiARARZiIiIhIAFSEiYiIiAQgYssWRYqZbQRWRuGtKgO/RuF9JHw6J/mPzkn+pPOS/+ic5E/ROC+1nXO5Ln5Z4IqwaDGz1IOt9STB0DnJf3RO8iedl/xH5yR/Cvq86HKkiIiISABUhImIiIgEQEXYwb0edAD5C52T/EfnJH/Secl/dE7yp0DPi+aEiYiIiARAI2EiIiIiAYjpIszMOprZYjNbamZ9c9lvZvZSaP9cM2sRRM5YE8Z56RE6H3PN7Hszax5EzlhyuHOS47jWZpZlZl2imS9WhXNezOwsM5ttZgvM7JtoZ4w1Yfz3q5yZfWxmc0Ln5IYgcsYSMxtsZhvMbP5B9gf2WR+zRZiZxQGvAOcDjYErzazxAYedD9QLfd0KvBrVkDEozPOyHDjTOdcMeBzNtYioMM/Jb8c9BUyMbsLYFM55MbPywEDgEufcScAVUQ8aQ8L8t3InsNA51xw4C3jOzIpFNWjseQfoeIj9gX3Wx2wRBrQBljrnljnn9gEpQKcDjukEvOe8aUB5M6sW7aAx5rDnxTn3vXNuS+jhNODIlq2XIxXOvxWAu4CPgA3RDBfDwjkvVwGjnHOrAJxzOjeRFc45cUAZMzOgNLAZyIxuzNjinPsW/3s+mMA+62O5CKsBrM7xOD207UiPkbx1pL/zm4AJEU0khz0nZlYDuBQYFMVcsS6cfyv1gQpm9rWZzTSza6OWLjaFc04GAI2AtcA84B/OuezoxJODCOyzvmg03iSfsly2HXiraDjHSN4K+3duZmfji7DTI5pIwjknLwAPOuey/B/4EgXhnJeiQEvg70AJ4Aczm+acWxLpcDEqnHNyHjAbOAc4EfjCzKY457ZHOpwcVGCf9bFchKUDNXM8TsT/ZXKkx0jeCut3bmbNgDeB851zm6KULVaFc05aASmhAqwycIGZZTrnxkQnYkwK979hvzrnMoAMM/sWaA6oCIuMcM7JDcCTzveHWmpmy4GGwPToRJRcBPZZH8uXI2cA9cwsKTQpsjsw7oBjxgHXhu6caAdsc86ti3bQGHPY82JmtYBRwDX6iz4qDntOnHNJzrk6zrk6wEigpwqwiAvnv2FjgTPMrKiZlQTaAouinDOWhHNOVuFHJjGzqkADYFlUU8qBAvusj9mRMOdcppn1wt/JFQcMds4tMLPbQ/sHAeOBC4ClwC78XzASQWGel0eASsDA0MhLphbGjZwwz4lEWTjnxTm3yMw+A+YC2cCbzrlcb9OXYxfmv5XHgXfMbB7+MtiDzrlfAwsdA8xsGP5O1Mpmlg78G4iH4D/r1TFfREREJACxfDlSREREJDAqwkREREQCoCJMREREJAAqwkREREQCoCJMREREJAAqwkQkIswsy8xm5/iqc4hjd0Yv2cGZWXUzGxn6OdnMLsix7xIz6xvFLHXM7KpovZ+IRJ9aVIhIRJjZTudc6bw+NlrM7HqglXOuVwTfo6hzLtfFm83sLOA+59xFkXp/EQmWRsJEJCrMrLSZfWVmaWY2z8w65XJMNTP7NjRyNt/MzghtP9fMfgg9d4SZ/aVgCy1S/YKZfR96bpvQ9opmNsbM5prZtNCSV5jZmTlG6WaZWZnQ6NP8ULfzx4Buof3dzOx6MxtgZuXMbIWZFQm9TkkzW21m8WZ2opl9Flose4qZNcwl53/M7HUz+xx4L/SeU0L/29LM7NTQoU/iu93PNrN7zCzOzJ4xsxmh/y235dGpEZGAxGzHfBGJuBJmNjv083LgCuBS59x2M6sMTDOzce7Pw/FXAROdc0+YWRxQMnTsQ0B751yGmT0I3Isvkg5Uyjl3qpn9DRgMNAEeBWY55zqb2TnAe0AycB9wp3Nuaqio2/Pbizjn9pnZI+QYCQuNjOGc22Zmc4AzgcnAxaHM+83sdeB259xPZtYWGIhfqPlALYHTnXO7Q8sJdXDO7TGzesAw/FqcfckxEmZmt+KXU2ltZgnAVDP73Dm3PIxzISL5kIowEYmU3c655N8emFk88L9QgZQN1ACqAr/keM4MYHDo2DHOudlmdibQGF90ABQDfjjIew4DcM59a2Zlzaw8cDpweWj7JDOrZGblgKnA82b2ATDKOZceev1wDAe64Yuw7vgltEoDpwIjcrxOwkGeP845tzv0czwwwMySgSyg/kGecy7QzMy6hB6XA+rhC1wRKYBUhIlItPQAqgAtQ6NGK4DiOQ8IFU9/Ay4E3jezZ4AtwBfOuSvDeI8DJ7k6/Pp8fznOOfekmX2KXzNumpm1J8do2GGMA/qZWUX8qNYkoBSwNWfheQgZOX6+B1gPNMdPETlYBgPucs5NDDOjiORzmhMmItFSDtgQKsDOBmofeICZ1Q4d8wbwFtACmAacZmZ1Q8eUNLODjRZ1Cx1zOv7S3TbgW3wB+Ntk919Dl0RPdM7Nc849BaQCB87f2gGUye1NnHM7genAi8Anzrks59x2YLmZXRF6LzOz5mH+XtY557KBa/ALP+f2/hOBO0KjhJhZfTMrFcbri0g+pZEwEYmWD4CPzSwVmA38mMsxZwH3m9l+YCdwrXNuY2g+1rDQXCjwc8SW5PL8LWb2PVAWuDG07T/A22Y2F9gFXBfafneoGMwCFgITgGo5Xmsy0Dc0r61fLu81HBgRyvybHsCrZvYQ/jJjCjAnl+fmNBD4KFS8TeaPUbK5QGZo/tk7+IKvDpBm/nrnRqDzYV5bRPIxtagQkULBzL7GT2RPDTqLiEg4dDlSREREJAAaCRMREREJgEbCRERERAKgIkxEREQkACrCRERERAKgIkxEREQkACrCRERERAKgIkxEREQkAP8fwbnP0iXoEfUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "****************************** DecisionTreeClassifier ***************************\n",
      "\n",
      "\n",
      "DecisionTreeClassifier(random_state=6)\n",
      "\n",
      "\n",
      "accuracy_score= 0.7142857142857143\n",
      "\n",
      "\n",
      "cross_val_score 0.569047619047619\n",
      "\n",
      "\n",
      "roc_auc_score =  0.7211538461538461\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.69      0.75        26\n",
      "           1       0.60      0.75      0.67        16\n",
      "\n",
      "    accuracy                           0.71        42\n",
      "   macro avg       0.71      0.72      0.71        42\n",
      "weighted avg       0.74      0.71      0.72        42\n",
      "\n",
      "\n",
      "\n",
      "[[18  8]\n",
      " [ 4 12]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "****************************** GaussianNB ***************************\n",
      "\n",
      "\n",
      "GaussianNB()\n",
      "\n",
      "\n",
      "accuracy_score= 0.7380952380952381\n",
      "\n",
      "\n",
      "cross_val_score 0.6104761904761905\n",
      "\n",
      "\n",
      "roc_auc_score =  0.7403846153846154\n",
      "\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.73      0.78        26\n",
      "           1       0.63      0.75      0.69        16\n",
      "\n",
      "    accuracy                           0.74        42\n",
      "   macro avg       0.73      0.74      0.73        42\n",
      "weighted avg       0.75      0.74      0.74        42\n",
      "\n",
      "\n",
      "\n",
      "[[19  7]\n",
      " [ 4 12]]\n",
      "\n",
      "\n",
      "AxesSubplot(0.125,0.808774;0.62x0.0712264)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Model=[]\n",
    "score=[]\n",
    "cvs=[]\n",
    "rocscore=[]\n",
    "for name,model in models:\n",
    "    print('******************************',name,'***************************')\n",
    "    print('\\n')\n",
    "    Model.append(name)\n",
    "    model.fit(x_train,y_train)\n",
    "    print(model)\n",
    "    pre=model.predict(x_test)\n",
    "    print('\\n')\n",
    "    AS=accuracy_score(y_test,pre)\n",
    "    print('accuracy_score=',AS)\n",
    "    score.append(AS*100)\n",
    "    print('\\n')\n",
    "    sc=cross_val_score(model,x,y,cv=10,scoring='accuracy').mean()\n",
    "    print('cross_val_score',sc)\n",
    "    cvs.append(sc*100)\n",
    "    print('\\n')\n",
    "    false_positive_rate,true_positive_rate,thresholds=roc_curve(y_test,pre)\n",
    "    roc_auc= auc(false_positive_rate,true_positive_rate)\n",
    "    print('roc_auc_score = ',roc_auc)\n",
    "    rocscore.append(roc_auc*100)\n",
    "    print('\\n')\n",
    "    print('classification_report',classification_report(y_test,pre))\n",
    "    print('\\n')\n",
    "    cm=confusion_matrix(y_test,pre)\n",
    "    print(cm)\n",
    "    print('\\n')\n",
    "    plt.figure(figsize=(10,50))\n",
    "    plt.subplot(911)\n",
    "    plt.title(name)\n",
    "    print(sns.heatmap(cm,annot=True))\n",
    "    plt.subplot(912)\n",
    "    plt.title(name)\n",
    "    plt.plot(false_positive_rate,true_positive_rate, label='AUC = %0.2f'% roc_auc)\n",
    "    plt.plot([0,1],[0,1],'r--')\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.xlabel('False positive rate')\n",
    "    plt.ylabel('True positive rate')\n",
    "    plt.show()\n",
    "    \n",
    "    print('\\n\\n\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Model</th>\n",
       "      <th>Accuracy_score</th>\n",
       "      <th>Cross_val_score</th>\n",
       "      <th>roc_auc_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>90.476190</td>\n",
       "      <td>61.523810</td>\n",
       "      <td>88.701923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVC</td>\n",
       "      <td>90.476190</td>\n",
       "      <td>64.880952</td>\n",
       "      <td>91.105769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>76.190476</td>\n",
       "      <td>69.714286</td>\n",
       "      <td>78.365385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>71.428571</td>\n",
       "      <td>56.904762</td>\n",
       "      <td>72.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>73.809524</td>\n",
       "      <td>61.047619</td>\n",
       "      <td>74.038462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Model  Accuracy_score  Cross_val_score  roc_auc_score\n",
       "0    KNeighborsClassifier       90.476190        61.523810      88.701923\n",
       "1                     SVC       90.476190        64.880952      91.105769\n",
       "2      LogisticRegression       76.190476        69.714286      78.365385\n",
       "3  DecisionTreeClassifier       71.428571        56.904762      72.115385\n",
       "4              GaussianNB       73.809524        61.047619      74.038462"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result=pd.DataFrame({'Model':Model, 'Accuracy_score': score,'Cross_val_score':cvs,'roc_auc_score':rocscore})\n",
    "result"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#observation\n",
    "we can observe from the above table that kNeighbors and SVc is working well with accuracy score of 90 and Cross validation score of 61 and 65 % respectively\n",
    "\n",
    "*** lets check other algorithm and grid search cv for better result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 1}\n",
      "1\n",
      "0.7886029411764706\n"
     ]
    }
   ],
   "source": [
    "# lets apply grid search cv to get the best parameters\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "neighbors={'n_neighbors':range(1,30)}\n",
    "knr=KNeighborsClassifier()\n",
    "gknr=GridSearchCV(knr,neighbors,cv=10)\n",
    "gknr.fit(x_train,y_train)\n",
    "print(gknr.best_params_)\n",
    "print(gknr.best_estimator_.n_neighbors)\n",
    "print(gknr.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Kneighbors classifier on CV data:  0.9047619047619048\n"
     ]
    }
   ],
   "source": [
    "knr=KNeighborsClassifier(n_neighbors=1)\n",
    "knr.fit(x_train,y_train)\n",
    "pred=knr.predict(x_test)\n",
    "print(\"Accuracy for Kneighbors classifier on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean cross val score for KneighborsClassifier: 0.6009523809523809\n",
      "standard deviation in accuracy score for KneighborsClassifier: 0.16299075554938025\n"
     ]
    }
   ],
   "source": [
    "print('mean cross val score for KneighborsClassifier:',cross_val_score(knr,x,y,cv=10,scoring='accuracy').mean())\n",
    "print('standard deviation in accuracy score for KneighborsClassifier:',cross_val_score(knr,x,y,cv=10,scoring='accuracy').std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we are getting cross val score of almost 60% we can say our model is under fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.84349376114082\n",
      "rbf\n",
      "{'C': 10, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters={'kernel':('linear','rbf'),'C':[1,10]}\n",
    "svc=svm.SVC()\n",
    "clf=GridSearchCV(svc, parameters)\n",
    "clf.fit(x_train,y_train)\n",
    "print(clf.best_score_)\n",
    "print(clf.best_estimator_.kernel)\n",
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for svc classifier on CV data:  0.9047619047619048\n"
     ]
    }
   ],
   "source": [
    "svc=svm.SVC(kernel='rbf',C=10)\n",
    "svc.fit(x_train,y_train)\n",
    "pred=svc.predict(x_test)\n",
    "print(\"Accuracy for svc classifier on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean cross val score for SVC Classifier: 0.6490476190476191\n",
      "standard deviation in accuracy score for SVC Classifier: 0.20379394689027358\n"
     ]
    }
   ],
   "source": [
    "print('mean cross val score for SVC Classifier:',cross_val_score(svc,x,y,cv=10,scoring='accuracy').mean())\n",
    "print('standard deviation in accuracy score for SVC Classifier:',cross_val_score(svc,x,y,cv=10,scoring='accuracy').std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can observe cross val score i improved as compared to KNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini'}\n",
      "0.7106951871657754\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc=DecisionTreeClassifier()\n",
    "grid_param ={\n",
    "    'criterion':['gini','entropy']\n",
    "}\n",
    "\n",
    "gd_sr = GridSearchCV(estimator=dtc,param_grid=grid_param, scoring='accuracy',cv=5)\n",
    "\n",
    "gd_sr.fit(x_train,y_train)\n",
    "\n",
    "best_parameters=gd_sr.best_params_\n",
    "print(best_parameters)\n",
    "best_result= gd_sr.best_score_\n",
    "print(best_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Decision tree classifier on CV data:  0.6904761904761905\n"
     ]
    }
   ],
   "source": [
    "dtc=DecisionTreeClassifier(criterion='gini')\n",
    "dtc.fit(x_train,y_train)\n",
    "pred=dtc.predict(x_test)\n",
    "print(\"Accuracy for Decision tree classifier on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets apply random forest and ada boost algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'gini', 'n_estimators': 10}\n",
      "0.8074866310160427\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rfc=RandomForestClassifier(random_state=42)\n",
    "#randomforestclassifier(100)-----Default\n",
    "parameters={'n_estimators':[10,100,500,1000],'criterion' :['gini', 'entropy']}\n",
    "rfc=GridSearchCV(estimator=rfc,param_grid=parameters,cv=5)\n",
    "rfc.fit(x_train,y_train)\n",
    "print(rfc.best_params_)\n",
    "print(rfc.best_score_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Random Forest on CV data:  0.8095238095238095\n"
     ]
    }
   ],
   "source": [
    "rfc1=RandomForestClassifier(random_state=42,n_estimators=10,criterion='gini')\n",
    "rfc1.fit(x_train,y_train)\n",
    "pred=rfc1.predict(x_test)\n",
    "print(\"Accuracy for Random Forest on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean cross val score for random forest classifier: 0.5196283391405342\n",
      "standard deviation in accuracy score for random forest clasifier: 0.17836310710092634\n"
     ]
    }
   ],
   "source": [
    "print('mean cross val score for random forest classifier:',cross_val_score(rfc1,x,y,cv=5,scoring='accuracy').mean())\n",
    "print('standard deviation in accuracy score for random forest clasifier:',cross_val_score(rfc1,x,y,cv=5,scoring='accuracy').std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy score is low lets apply ada boost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.001, 'n_estimators': 1000}\n",
      "0.740819964349376\n"
     ]
    }
   ],
   "source": [
    "adc=AdaBoostClassifier()\n",
    "parameters={'n_estimators':[500,1000,2000],'learning_rate':[.001,0.01,.1]}\n",
    "adc=GridSearchCV(estimator=adc,param_grid=parameters,cv=5)\n",
    "adc.fit(x_train,y_train)\n",
    "print(adc.best_params_)\n",
    "print(adc.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for ADA on CV data:  0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "adc=AdaBoostClassifier(learning_rate=0.0001,n_estimators=1000)\n",
    "adc.fit(x_train,y_train)\n",
    "pred=adc.predict(x_test)\n",
    "print(\"Accuracy for ADA on CV data: \",accuracy_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#conclusion\n",
    "# we can conclude that our svc model is working quite well with an accuracy score of 90% and Cross validation score of 66%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for svc classifier on CV data:  0.9047619047619048\n",
      "\n",
      "\n",
      "mean cross val score for SVC classifier: 0.6642276422764228\n",
      "\n",
      "\n",
      "standard deviation in accuracy score for SVC clasifier: 0.09554594589040183\n",
      "\n",
      "\n",
      "RMSE is:  0.3086066999241838\n"
     ]
    }
   ],
   "source": [
    "# lets save the Support vector model\n",
    "\n",
    "svc=svm.SVC(kernel='rbf',C=10)\n",
    "svc.fit(x_train,y_train)\n",
    "pred=svc.predict(x_test)\n",
    "print(\"Accuracy for svc classifier on CV data: \",accuracy_score(y_test,pred))\n",
    "print('\\n')\n",
    "print('mean cross val score for SVC classifier:',cross_val_score(svc,x,y,cv=5,scoring='accuracy').mean())\n",
    "print('\\n')\n",
    "print('standard deviation in accuracy score for SVC clasifier:',cross_val_score(svc,x,y,cv=5,scoring='accuracy').std())\n",
    "print('\\n')\n",
    "print('RMSE is: ',np.sqrt(mean_squared_error(y_test,pred)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sonar_dataset.pkl']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets save the model\n",
    "import joblib\n",
    "joblib.dump(svc,'sonar_dataset.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets test the model\n",
    "\n",
    "test_svc=svc.predict(df_x)\n",
    "test_svc"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "we can conclude that our model is working well with an accuracy score of 90% and it give the difference of output between rock and mine"
   ]
  }
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