Rohit_Project(Rock,mine_prediction).ipynb

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      "source": [
        "Importing Libraries"
      ],
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        "id": "RqpjIZwXId_f"
      }
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "OTKpRwNKIdyv"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
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      "source": [
        "Data Collection and Processing"
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      }
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    {
      "cell_type": "code",
      "source": [
        "#loafing the dataset \n",
        "sonar_data = pd.read_csv('/content/Copy of sonar data.csv', header = None)"
      ],
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        "id": "t0sme7eaIdwR"
      },
      "execution_count": 6,
      "outputs": []
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      "source": [
        "sonar_data.head()"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#for number of rows & columns\n",
        "sonar_data.shape"
      ],
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        },
        "id": "Gvwc8cADIdq5",
        "outputId": "72af7436-7e32-4dc3-feee-380783fe4728"
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              "(208, 61)"
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          "metadata": {},
          "execution_count": 9
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    {
      "cell_type": "code",
      "source": [
        "#for srtatistical data\n",
        "sonar_data.describe() #gives us knowledge about the data for ex: mean,min and consider 50% of data is less than 0.022800 in first column"
      ],
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              "               0           1           2           3           4           5   \\\n",
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              "      <td>208.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>208.000000</td>\n",
              "    </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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfaf0e73-feb0-4d7f-b6cf-21bad7e140f2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      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-cfaf0e73-feb0-4d7f-b6cf-21bad7e140f2 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-cfaf0e73-feb0-4d7f-b6cf-21bad7e140f2');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data[60].value_counts() #in the data M is for 'Mines' and R is fro 'Rock'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNdD_m1HIdkT",
        "outputId": "d752cb6a-3118-402f-d2b4-150f33012297"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M    111\n",
              "R     97\n",
              "Name: 60, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "M--> Mine\n",
        "R-->ROck"
      ],
      "metadata": {
        "id": "t1pSNJXzMu_H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data.groupby(60).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "1ypKDLW0Idhm",
        "outputId": "6d92927e-3fb4-42ec-de60-910f30a1c89b"
      },
      "execution_count": 13,
      "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>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",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <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",
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              "    <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",
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              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 60 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39f5b593-5b15-41d7-b2a2-4bdb166cf8e2')\"\n",
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              "      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-39f5b593-5b15-41d7-b2a2-4bdb166cf8e2 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-39f5b593-5b15-41d7-b2a2-4bdb166cf8e2');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#separating data and labels\n",
        "all_data = sonar_data.drop(columns=60, axis=1)\n",
        "last_col = sonar_data[60]\n"
      ],
      "metadata": {
        "id": "dneE18kOIde4"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(all_data)\n",
        "print(last_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5nI0CqMKIdbv",
        "outputId": "84e59992-c488-4432-da34-31dfd9dc148c"
      },
      "execution_count": 15,
      "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",
            "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": [
        "Test Data"
      ],
      "metadata": {
        "id": "jjbsNH3qNbp-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(all_data, last_col, test_size=0.1, stratify=last_col, random_state=1)"
      ],
      "metadata": {
        "id": "KSOswICnIdWF"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(all_data.shape, x_train.shape, x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zivzRafGIdTl",
        "outputId": "15af6786-4ee3-4d3b-8832-fcd0024a4fca"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(208, 60) (187, 60) (21, 60)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "using Logistic Regression"
      ],
      "metadata": {
        "id": "bXyI1sCZOpcV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()\n"
      ],
      "metadata": {
        "id": "eA24WJJuOkDH"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zo0Ih9lVOy4H",
        "outputId": "bc8e771c-e43a-4e04-d734-5d977f31c923"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation"
      ],
      "metadata": {
        "id": "yMHQH4nRPIan"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy for train dtaa\n",
        "x_train_prediction = model.predict(x_train)\n",
        "training_data_acc = accuracy_score(x_train_prediction, y_train)"
      ],
      "metadata": {
        "id": "pSmDS-PQO55q"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy score: \", training_data_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LpmnH8gGPvFd",
        "outputId": "4cbda0aa-a8af-4a4e-c5f7-09ebdd310907"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score:  0.8342245989304813\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy for test data\n",
        "x_test_prediction = model.predict(x_test)\n",
        "test_data_acc = accuracy_score(x_test_prediction, y_test)"
      ],
      "metadata": {
        "id": "DkK9QZqQQDde"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy score for test data: \", test_data_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mum1XJz-QWOL",
        "outputId": "635e9d44-6f8f-4212-876e-9c12e763cfd8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score for test data:  0.7619047619047619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here I have just input one value from mine to check if the object is a rock or mine"
      ],
      "metadata": {
        "id": "CFlBwlzvTMTp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (0.0114,0.0222,0.0269,0.0384,0.1217,0.2062,0.1489,0.0929,0.1350,0.1799,0.2486,0.2973,0.3672,0.4394,0.5258,0.6755,0.7402,0.8284,0.9033,0.9584,1.0000,0.9982,0.8899,0.7493,0.6367,0.6744,0.7207,0.6821,0.5512,0.4789,0.3924,0.2533,0.1089,0.1390,0.2551,0.3301,0.2818,0.2142,0.2266,0.2142,0.2354,0.2871,0.2596,0.1925,0.1256,0.1003,0.0951,0.1210,0.0728,0.0174,0.0213,0.0269,0.0152,0.0257,0.0097,0.0041,0.0050,0.0145,0.0103,0.0025)\n",
        "\n",
        "#changing the input_data to numpy array\n",
        "input_data_as_numpy = np.asarray(input_data)\n",
        "\n",
        "\n",
        "#as we are inputting one instance\n",
        "input_data_new = input_data_as_numpy.reshape(1,-1)\n",
        "\n",
        "prediction = model.predict(input_data_new)\n",
        "print(prediction)\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": "44wQRnQVQfgf",
        "outputId": "a60cf24f-6f13-4e39-c119-8cad7474a6cf"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['M']\n",
            "The object is a Mine\n"
          ]
        }
      ]
    }
  ]
}