Rock vs Mine Prediction.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "8a9a3b26-691f-42a6-9e52-6f7677cab786",
   "metadata": {},
   "source": [
    "# ML Use Case 1. Rock_vs_Mine_Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60b53cc2-7eb3-4e1a-8b95-2ea203169a0a",
   "metadata": {},
   "source": [
    "### Use cae of Logistic Regression\n",
    "Logistic regression is a statistical method used for binary classification tasks in machine learning. It models the probability that an instance belongs to a particular class based on the values of independent variables. Despite its name, it's a classification algorithm rather than a regression algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca7c7ce2-2bb8-48f9-984b-584108dd7472",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "markdown",
   "id": "18219bfd-4571-4f01-ae7b-8092c9d19cf0",
   "metadata": {},
   "source": [
    "### Data Collection and Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5d18e017-e5fa-4da2-8d7d-5265a45d80e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the dataset to a pandas Dataframe\n",
    "sonar_data = pd.read_csv(r'sonar data.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "22930631-5ed6-4c0a-b8a8-9b179a0b6d39",
   "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",
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       "      <th>7</th>\n",
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
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       "      <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",
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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",
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       "      <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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       "<p>5 rows × 61 columns</p>\n",
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       "       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]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d46ce41b-d1d3-4111-8e41-8268b70e6887",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208, 61)"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns\n",
    "sonar_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f03f908a-9b9e-48a2-a3ff-0f16aed69add",
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   "outputs": [
    {
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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",
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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",
       "</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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar_data.describe()  #describe --> statistical measures of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1697c0f-c814-415f-8cd0-49256f14e796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60\n",
       "M    111\n",
       "R     97\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar_data[60].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5784cf1d-c309-472f-b733-58879443e6f1",
   "metadata": {},
   "source": [
    "M --> Mine\n",
    "\n",
    "R --> Rock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0ad3df0d-2cb2-4785-a348-7ad973ef2581",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonar_data.groupby(60).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a23df55-8739-49b1-82fd-b1c03f38a49e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# separating data and Labels\n",
    "X = sonar_data.drop(columns=60, axis=1)\n",
    "Y = sonar_data[60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2cd6643-8311-47e3-9c42-823dc10cea3a",
   "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": "markdown",
   "id": "d40997e8-5625-40b2-ab54-04052607538a",
   "metadata": {},
   "source": [
    "### Training and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c6e8213-155f-421e-a57d-7f65322c3cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 12,
   "id": "684b1f42-2f2c-41ed-bd69-99f268e4fc20",
   "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": 13,
   "id": "abc3966a-74c1-49cb-ab8c-0d71724c0f47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0       1       2       3       4       5       6       7       8   \\\n",
      "115  0.0414  0.0436  0.0447  0.0844  0.0419  0.1215  0.2002  0.1516  0.0818   \n",
      "38   0.0123  0.0022  0.0196  0.0206  0.0180  0.0492  0.0033  0.0398  0.0791   \n",
      "56   0.0152  0.0102  0.0113  0.0263  0.0097  0.0391  0.0857  0.0915  0.0949   \n",
      "123  0.0270  0.0163  0.0341  0.0247  0.0822  0.1256  0.1323  0.1584  0.2017   \n",
      "18   0.0270  0.0092  0.0145  0.0278  0.0412  0.0757  0.1026  0.1138  0.0794   \n",
      "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "140  0.0412  0.1135  0.0518  0.0232  0.0646  0.1124  0.1787  0.2407  0.2682   \n",
      "5    0.0286  0.0453  0.0277  0.0174  0.0384  0.0990  0.1201  0.1833  0.2105   \n",
      "154  0.0117  0.0069  0.0279  0.0583  0.0915  0.1267  0.1577  0.1927  0.2361   \n",
      "131  0.1150  0.1163  0.0866  0.0358  0.0232  0.1267  0.2417  0.2661  0.4346   \n",
      "203  0.0187  0.0346  0.0168  0.0177  0.0393  0.1630  0.2028  0.1694  0.2328   \n",
      "\n",
      "         9   ...      50      51      52      53      54      55      56  \\\n",
      "115  0.1975  ...  0.0222  0.0045  0.0136  0.0113  0.0053  0.0165  0.0141   \n",
      "38   0.0475  ...  0.0149  0.0125  0.0134  0.0026  0.0038  0.0018  0.0113   \n",
      "56   0.1504  ...  0.0048  0.0049  0.0041  0.0036  0.0013  0.0046  0.0037   \n",
      "123  0.2122  ...  0.0197  0.0189  0.0204  0.0085  0.0043  0.0092  0.0138   \n",
      "18   0.1520  ...  0.0045  0.0084  0.0010  0.0018  0.0068  0.0039  0.0120   \n",
      "..      ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
      "140  0.2058  ...  0.0798  0.0376  0.0143  0.0272  0.0127  0.0166  0.0095   \n",
      "5    0.3039  ...  0.0104  0.0045  0.0014  0.0038  0.0013  0.0089  0.0057   \n",
      "154  0.2169  ...  0.0039  0.0053  0.0029  0.0020  0.0013  0.0029  0.0020   \n",
      "131  0.5378  ...  0.0228  0.0099  0.0065  0.0085  0.0166  0.0110  0.0190   \n",
      "203  0.2684  ...  0.0203  0.0116  0.0098  0.0199  0.0033  0.0101  0.0065   \n",
      "\n",
      "         57      58      59  \n",
      "115  0.0077  0.0246  0.0198  \n",
      "38   0.0058  0.0047  0.0071  \n",
      "56   0.0011  0.0034  0.0033  \n",
      "123  0.0094  0.0105  0.0093  \n",
      "18   0.0132  0.0070  0.0088  \n",
      "..      ...     ...     ...  \n",
      "140  0.0225  0.0098  0.0085  \n",
      "5    0.0027  0.0051  0.0062  \n",
      "154  0.0062  0.0026  0.0052  \n",
      "131  0.0141  0.0068  0.0086  \n",
      "203  0.0115  0.0193  0.0157  \n",
      "\n",
      "[187 rows x 60 columns]\n",
      "115    M\n",
      "38     R\n",
      "56     R\n",
      "123    M\n",
      "18     R\n",
      "      ..\n",
      "140    M\n",
      "5      R\n",
      "154    M\n",
      "131    M\n",
      "203    M\n",
      "Name: 60, Length: 187, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X_train)\n",
    "print(Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5016cb73-f135-46fd-bd4d-702403099a2b",
   "metadata": {},
   "source": [
    "# Model Training --> Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e677bdcc-72f6-46f7-90ea-15f59624dfca",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2a24a0b7-a6b4-420c-8d80-090fa537b16d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the Logistic Regression model with training data\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23af4ac6-ccec-4e14-9857-7769ee1620bd",
   "metadata": {},
   "source": [
    "#  Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4f8afdbb-6951-43c9-8bb3-364332310228",
   "metadata": {},
   "outputs": [],
   "source": [
    "#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": 17,
   "id": "5d5a5aaa-bdc5-4e0b-998d-ebe6dd6ff432",
   "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": 18,
   "id": "a1e365e6-8e2d-4130-a785-b72b6bf2ace4",
   "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": 19,
   "id": "4bb11ad4-d05b-4745-8681-a34a785444fa",
   "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": 20,
   "id": "616ac5da-b26a-469f-91a8-7d136aa16240",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'R', 'R', 'M', 'M', 'M', 'M', 'M', 'R', 'M', 'R', 'M', 'R',\n",
       "       'M', 'R', 'M', 'M', 'M', 'R', 'R', 'R'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1467abfc-5c6d-48a1-b8cf-bcb98dec3e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113    M\n",
       "23     R\n",
       "45     R\n",
       "81     R\n",
       "82     R\n",
       "109    M\n",
       "176    M\n",
       "134    M\n",
       "96     R\n",
       "98     M\n",
       "57     R\n",
       "169    M\n",
       "13     R\n",
       "204    M\n",
       "10     R\n",
       "161    M\n",
       "7      R\n",
       "172    M\n",
       "68     R\n",
       "102    M\n",
       "106    M\n",
       "Name: 60, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf89d59-ef39-4c66-bf00-6a6c63bd4411",
   "metadata": {},
   "source": [
    "### ~ 76% match on test Data :)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "514968ed-48ea-4b25-b395-015207d5c171",
   "metadata": {},
   "source": [
    "# Making a Predictive System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae94b079-a26c-42ed-a1d4-bc1a734b3ec3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M']\n",
      "The object is a mine\n"
     ]
    }
   ],
   "source": [
    "input_data2 = (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",
    "\n",
    "# changing the input_data to a numpy array\n",
    "input_data_as_numpy_array = np.asarray(input_data)\n",
    "\n",
    "# reshape the np array as we are predicting 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 object is a Rock')\n",
    "else:\n",
    "  print('The object is a mine')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce3095d-6d2f-4a55-9982-ad90d0077c87",
   "metadata": {},
   "source": [
    "#### Testing 2nd Instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5eb16066-3eeb-4b43-8a92-7014f3ea1fea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['R']\n",
      "The object is a Rock\n"
     ]
    }
   ],
   "source": [
    "input_data2 = (0.0235,0.0291,0.0749,0.0519,0.0227,0.0834,0.0677,0.2002,0.2876,0.3674,0.2974,0.0837,0.1912,0.5040,0.6352,0.6804,0.7505,0.6595,0.4509,0.2964,0.4019,0.6794,0.8297,1.0000,0.8240,0.7115,0.7726,0.6124,0.4936,0.5648,0.4906,0.1820,0.1811,0.1107,0.4603,0.6650,0.6423,0.2166,0.1951,0.4947,0.4925,0.4041,0.2402,0.1392,0.1779,0.1946,0.1723,0.1522,0.0929,0.0179,0.0242,0.0083,0.0037,0.0095,0.0105,0.0030,0.0132,0.0068,0.0108,0.0090)\n",
    "\n",
    "# changing the input_data to a numpy array\n",
    "input_data_as_numpy_array2 = np.asarray(input_data2)\n",
    "\n",
    "# reshape the np array as we are predicting for one instance\n",
    "input_data_reshaped2 = input_data_as_numpy_array2.reshape(1,-1)\n",
    "\n",
    "prediction2 = model.predict(input_data_reshaped2)\n",
    "print(prediction2)\n",
    "\n",
    "if (prediction2[0]=='R'):\n",
    "  print('The object is a Rock')\n",
    "else:\n",
    "  print('The object is a mine')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "394db1a3-9b3b-4e24-8514-8cc23ea9e0b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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