Project- Sonar Rock Vs Mine Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf849e10",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the Dependencies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8120594b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Collection and  Data Processing\n",
    "#Loading dataset to pandas dataframe\n",
    "sonar_data = pd.read_csv('sonar_data.csv', header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5185af27",
   "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",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</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",
       "      <th>60</th>\n",
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       "      <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>R</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>R</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>R</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>R</td>\n",
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       "    <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>R</td>\n",
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       "  </tbody>\n",
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       "<p>5 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",
       "       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",
       "       58      59  60  \n",
       "0  0.0090  0.0032   R  \n",
       "1  0.0052  0.0044   R  \n",
       "2  0.0095  0.0078   R  \n",
       "3  0.0040  0.0117   R  \n",
       "4  0.0107  0.0094   R  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5eb98f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208, 61)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of rows and columns\n",
    "sonar_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee6d5130",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>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",
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       "      <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",
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       "    <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",
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       "<p>8 rows × 60 columns</p>\n",
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      ],
      "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar_data.describe() #describe --> statistical measures of the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1ec4affd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60\n",
       "M    111\n",
       "R     97\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar_data[60].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b81a2d3b",
   "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>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",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.034989</td>\n",
       "      <td>0.045544</td>\n",
       "      <td>0.050720</td>\n",
       "      <td>0.064768</td>\n",
       "      <td>0.086715</td>\n",
       "      <td>0.111864</td>\n",
       "      <td>0.128359</td>\n",
       "      <td>0.149832</td>\n",
       "      <td>0.213492</td>\n",
       "      <td>0.251022</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019352</td>\n",
       "      <td>0.016014</td>\n",
       "      <td>0.011643</td>\n",
       "      <td>0.012185</td>\n",
       "      <td>0.009923</td>\n",
       "      <td>0.008914</td>\n",
       "      <td>0.007825</td>\n",
       "      <td>0.009060</td>\n",
       "      <td>0.008695</td>\n",
       "      <td>0.006930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>0.022498</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.035951</td>\n",
       "      <td>0.041447</td>\n",
       "      <td>0.062028</td>\n",
       "      <td>0.096224</td>\n",
       "      <td>0.114180</td>\n",
       "      <td>0.117596</td>\n",
       "      <td>0.137392</td>\n",
       "      <td>0.159325</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>0.010453</td>\n",
       "      <td>0.009640</td>\n",
       "      <td>0.009518</td>\n",
       "      <td>0.008567</td>\n",
       "      <td>0.007430</td>\n",
       "      <td>0.007814</td>\n",
       "      <td>0.006677</td>\n",
       "      <td>0.007078</td>\n",
       "      <td>0.006024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6   \\\n",
       "60                                                                         \n",
       "M   0.034989  0.045544  0.050720  0.064768  0.086715  0.111864  0.128359   \n",
       "R   0.022498  0.030303  0.035951  0.041447  0.062028  0.096224  0.114180   \n",
       "\n",
       "          7         8         9   ...        50        51        52        53  \\\n",
       "60                                ...                                           \n",
       "M   0.149832  0.213492  0.251022  ...  0.019352  0.016014  0.011643  0.012185   \n",
       "R   0.117596  0.137392  0.159325  ...  0.012311  0.010453  0.009640  0.009518   \n",
       "\n",
       "          54        55        56        57        58        59  \n",
       "60                                                              \n",
       "M   0.009923  0.008914  0.007825  0.009060  0.008695  0.006930  \n",
       "R   0.008567  0.007430  0.007814  0.006677  0.007078  0.006024  \n",
       "\n",
       "[2 rows x 60 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# M-->Mine, R-->Rock\n",
    "sonar_data.groupby(60).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fe09d5b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seprating data and labels\n",
    "X=sonar_data.drop(columns=60, axis=1)\n",
    "Y=sonar_data[60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f424a30a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         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]\n",
      "0      R\n",
      "1      R\n",
      "2      R\n",
      "3      R\n",
      "4      R\n",
      "      ..\n",
      "203    M\n",
      "204    M\n",
      "205    M\n",
      "206    M\n",
      "207    M\n",
      "Name: 60, Length: 208, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ff0679a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Training and test data\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.1, stratify=Y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7ad1a1c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(208, 60) (187, 60) (21, 60)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "986ea834",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training the model by Logistic Regression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ed4cc3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the Logistic Regression model with trainning data\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "826faa0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model Evaluation\n",
    "#accuracy on training data\n",
    "X_train_prediction = model.predict(X_train)\n",
    "training_data_accuracy=accuracy_score(X_train_prediction, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "80b4e02d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data:  0.8342245989304813\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on training data: ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a5f30d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy on test data\n",
    "X_test_prediction = model.predict(X_test)\n",
    "test_data_accuracy=accuracy_score(X_test_prediction, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d4d56688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test data:  0.7619047619047619\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy on test data: ', test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b320b1e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M']\n",
      "The object is a Mine\n"
     ]
    }
   ],
   "source": [
    "#Predictive System\n",
    "input_data=(0.0116,0.0179,0.0449,0.1096,0.1913,0.0924,0.0761,0.1092,0.0757,0.1006,0.2500,0.3988,0.3809,0.4753,0.6165,0.6464,0.8024,0.9208,0.9832,0.9634,0.8646,0.8325,0.8276,0.8007,0.6102,0.4853,0.4355,0.4307,0.4399,0.3833,0.3032,0.3035,0.3197,0.2292,0.2131,0.2347,0.3201,0.4455,0.3655,0.2715,0.1747,0.1781,0.2199,0.1056,0.0573,0.0307,0.0237,0.0470,0.0102,0.0057,0.0031,0.0163,0.0099,0.0084,0.0270,0.0277,0.0097,0.0054,0.0148,0.0092)\n",
    "#changing input data to numpy array\n",
    "input_data_as_numpy_array = np.asarray(input_data)\n",
    "\n",
    "#reshape the numpy array as we are predicting it for one instance\n",
    "input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
    "\n",
    "prediction = model.predict(input_data_reshaped)\n",
    "print(prediction)\n",
    "\n",
    "if(prediction[0]=='R'):\n",
    "    print(\"The obejct is a Rock\")\n",
    "else:\n",
    "    print(\"The object is a Mine\")"
   ]
  }
 ],
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