Rock_vs_Mine_Prediction.ipynb

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      "source": [
        "The goal of this project is to develop a machine learning model that can accurately classify objects detected by sonar signals as either rocks or mines. The model will be trained and evaluated using the SONAR dataset, which contains sonar signal data.\n"
      ],
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        "id": "oMeJixgbo9Dg"
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      "source": [
        "Importing Dependecies"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\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",
      "source": [
        "Data collection and Processing"
      ],
      "metadata": {
        "id": "Xv--W0EiXFRs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading dataset to a pandas DataFrame\n",
        "dataset = pd.read_csv('/content/sonar data.csv', header=None)"
      ],
      "metadata": {
        "id": "z6ybz7NrW8jU"
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      "execution_count": null,
      "outputs": []
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        "dataset.head()"
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      "source": [
        "# size of the dataset (rows, columns)\n",
        "dataset.shape"
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        "id": "AuPuOL4lXiRQ",
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              "(208, 61)"
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          "execution_count": 7
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      "source": [
        "# Dataset Statistics\n",
        "dataset.describe()"
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          "height": 350
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        "id": "ioMfqvNnX-sD",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d983f7a-2fe5-4b9a-b5a4-05d1eb1d6cf0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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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>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>\n",
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              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8535edb5-e826-42f2-9c36-ee48de3bcaec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwmGqy9rYZzm",
        "outputId": "a557ce8e-724d-47d7-d9d1-ccf74dbd61fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[60].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfSpvVn0YurI",
        "outputId": "b12deee2-b5eb-4185-8c14-1edfc855d2a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60\n",
              "M    111\n",
              "R     97\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mine(M) ---> 111\n",
        "\n",
        "Rock(R) ---> 97\n",
        "\n"
      ],
      "metadata": {
        "id": "pLmlbCbCZOJK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# grouping the data according to the mean\n",
        "dataset.groupby(60).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "KgPQy0dSZFIg",
        "outputId": "bdfe7bea-8354-4eca-ce6f-8eb9db61420b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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]"
            ],
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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",
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              "    <tr>\n",
              "      <th>60</th>\n",
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              "    <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",
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              "      <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",
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              "      <td>0.008695</td>\n",
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              "      <th>R</th>\n",
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              "      <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",
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              "<p>2 rows × 60 columns</p>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4f834f7b-4260-4e87-bdc1-44dc8e8e1ddd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4f834f7b-4260-4e87-bdc1-44dc8e8e1ddd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-404eb0ad-04a9-4ff1-91a2-856840a29598\">\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-404eb0ad-04a9-4ff1-91a2-856840a29598 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Seperating data and label\n",
        "X = dataset.drop(columns=60, axis=1)\n",
        "Y = dataset[60]"
      ],
      "metadata": {
        "id": "_crPLXNhavyZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fNdjV9PbXUC",
        "outputId": "01c26f2e-0bca-412e-ee64-c603f4365a37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kX3dfzwFbaFx",
        "outputId": "cecc2ec7-a772-469d-f3c3-cac00e198813"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spliting data into Training and Test data"
      ],
      "metadata": {
        "id": "vcjrQQaRbkbV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train , Y_test = train_test_split(X,Y, test_size=0.1, stratify=Y, random_state=1)"
      ],
      "metadata": {
        "id": "tWgtRDv-bcBT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape, X_test.shape, Y_train.shape , Y_test.shape )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGkrL-PRcvRT",
        "outputId": "8e4cba20-058c-4684-a8d2-22dd1fad85dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(187, 60) (21, 60) (187,) (21,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have 187 train data and 21 test data.\n"
      ],
      "metadata": {
        "id": "UzG-9yrddF09"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Training** with Logistic Regression"
      ],
      "metadata": {
        "id": "EhOChoj8dPfE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "b3qEHzfxc4Ld"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# training the model with training data\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "w3pqAkbLdoxP",
        "outputId": "c210bd77-e1ee-4706-ad80-3108c1350628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "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>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation"
      ],
      "metadata": {
        "id": "FwGvV0qBeKoN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on training data\n",
        "train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(train_prediction, Y_train)"
      ],
      "metadata": {
        "id": "0-xQVg-3d9qb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy of training data: ', training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-eqlfzufPcn",
        "outputId": "a64cbb00-5719-49e5-95fc-395b04c37c02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of training data:  0.8342245989304813\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy on test data\n",
        "test_prediction = model.predict(X_test)\n",
        "testing_data_accuracy = accuracy_score(test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "5ANu-yLRfiOm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy of test data: ', testing_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXu1VTblgCGE",
        "outputId": "00f58b79-2e10-4d36-c479-4bf536be9e97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of test data:  0.7619047619047619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making a Predictive System"
      ],
      "metadata": {
        "id": "sxWe6xFchu2x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction with a Rock data"
      ],
      "metadata": {
        "id": "RDUsqRQFnpfv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (0.0293,0.0644,0.0390,0.0173,0.0476,0.0816,0.0993,0.0315,0.0736,\n",
        "              0.0860,0.0414,0.0472,0.0835,0.0938,0.1466,0.0809,0.1179,0.2179,\n",
        "              0.3326,0.3258,0.2111,0.2302,0.3361,0.4259,0.4609,0.2606,0.0874,\n",
        "              0.2862,0.5606,0.8344,0.8096,0.7250,0.8048,0.9435,1.0000,0.8960,\n",
        "              0.5516,0.3037,0.2338,0.2382,0.3318,0.3821,0.1575,0.2228,0.1582,\n",
        "              0.1433,0.1634,0.1133,0.0567,0.0133,0.0170,0.0035,0.0052,0.0083,\n",
        "              0.0078,0.0075,0.0105,0.0160,0.0095,0.0011)\n",
        "\n",
        "# converting input_data to a numpy array\n",
        "\n",
        "input_data_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the array\n",
        "input_data_reshaped = input_data_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')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z0gg1TBWgNFE",
        "outputId": "494f4bc2-7f4e-42f4-dfa4-05e420a5da7e"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['R']\n",
            "The object is a Rock\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction with a Mine data"
      ],
      "metadata": {
        "id": "rYmYkr5RnioO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mine_input_data = np.array([0.0363,0.0478,0.0298,0.0210,0.1409,0.1916,0.1349,\n",
        "                            0.1613,0.1703,0.1444,0.1989,0.2154,0.2863,0.3570,\n",
        "                            0.3980,0.4359,0.5334,0.6304,0.6995,0.7435,0.8379,\n",
        "                            0.8641,0.9014,0.9432,0.9536,1.0000,0.9547,0.9745,\n",
        "                            0.8962,0.7196,0.5462,0.3156,0.2525,0.1969,0.2189,\n",
        "                            0.1533,0.0711,0.1498,0.1755,0.2276,0.1322,0.1056,\n",
        "                            0.1973,0.1692,0.1881,0.1177,0.0779,0.0495,0.0492,\n",
        "                            0.0194,0.0250,0.0115,0.0190,0.0055,0.0096,0.0050,\n",
        "                            0.0066,0.0114,0.0073,0.0033]).reshape(1,-1)\n",
        "prediction = model.predict(mine_input_data)\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')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPa5fTLulYk_",
        "outputId": "cb8d67cb-2de3-4d5c-dd1c-b7c3fab13ef8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['M']\n",
            "The object is a Mine\n"
          ]
        }
      ]
    }
  ]
}