project_1_Sonor_Rock_vs_Mine_prediction.ipynb

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      }
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      "cell_type": "code",
      "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"
      ],
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        "id": "AgffDBzlVpI3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "id": "yjDtKYdgWkLc"
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      "source": [
        "#loading the dataset to pandas datafram\n",
        "sonar_data=pd.read_csv('/content/sonar data.csv',header=None)"
      ],
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        "id": "yjFaKpN2WhzR"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "sonar_data.head()"
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              "(208, 61)"
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          "metadata": {},
          "execution_count": 4
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      "cell_type": "code",
      "source": [
        "sonar_data.describe()  #discribe----> statistical measure of the data"
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              "               0           1           2           3           4           5   \\\n",
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              "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",
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              "    <div>\n",
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              "    <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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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      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-8b486919-c7a7-4c28-b0cb-0d7685afb3e9 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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data[60].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCg9QNb4Z7C4",
        "outputId": "307d3df4-a8fb-4d26-dc75-ca7f63bfa1d6"
      },
      "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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "M---> Mine\n"
      ],
      "metadata": {
        "id": "BWpcvqJMctvG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "R---> Rocke"
      ],
      "metadata": {
        "id": "sgakrk6gc-uk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data.groupby(60).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "T-N1AGfiazTz",
        "outputId": "58c78355-f237-49a2-8994-f748c793d0e7"
      },
      "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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              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "  <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",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <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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              "      <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",
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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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              "\n",
              "    .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert {\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-c1f7a4a9-34b7-4e9f-b5f2-d12f8067fb99 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-c1f7a4a9-34b7-4e9f-b5f2-d12f8067fb99');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\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-a780a175-e421-4fa9-ae77-ed36c56659dd 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": [
        "from re import X\n",
        "# separation data labels\n",
        "X=sonar_data.drop(columns=60,axis=1)\n",
        "Y=sonar_data[60]"
      ],
      "metadata": {
        "id": "eSWbB9kgdYWy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2854nC0Oeo0z",
        "outputId": "93d6d7d0-0c59-4c0e-fc2b-ab9f4594f52c"
      },
      "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",
            "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": [
        "Training and test data"
      ],
      "metadata": {
        "id": "OpMf4OPle4xS"
      }
    },
    {
      "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": "3Swi__ometxL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape,X_train.shape,X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KE1nN3qwfSSH",
        "outputId": "b18b23f9-f8e7-435a-f203-083de370015a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(208, 60) (187, 60) (21, 60)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)\n",
        "print(Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RQxfGrLugWUy",
        "outputId": "77791715-eb55-4349-a5a1-a3ca9ed864b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training model ----> Logistic regression\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "4bT3wUxVfm9w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "Ut0rVr8igiTV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the logistic regression model with training data\n",
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "WJixCdM8fXnA",
        "outputId": "d7d020ef-54a0-462d-995b-dc246d845c2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model evaluation"
      ],
      "metadata": {
        "id": "VmbccWrHg92g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy and training data\n",
        "X_train_prediction=model.predict(X_train)\n",
        "training_data_accuracy=accuracy_score(X_train_prediction,Y_train)"
      ],
      "metadata": {
        "id": "kQ_vSJTtgnzC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on training data :',training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vc8rgLo-h2dU",
        "outputId": "0ab0c56e-e08c-4b47-fa1e-65b05c1dc91c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data : 0.8342245989304813\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy and test data\n",
        "X_test_prediction=model.predict(X_test)\n",
        "test_data_accuracy=accuracy_score(X_test_prediction,Y_test)"
      ],
      "metadata": {
        "id": "gJoc6EXeh7fv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on test data :',test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdHAWLouiqdv",
        "outputId": "05c824d1-e666-4921-9c06-1d15771c5dbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test data : 0.7619047619047619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make Predicting model"
      ],
      "metadata": {
        "id": "2_WfcbKCjQBq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inpute_data = (0.0015,0.0186,0.0289,0.0195,0.0515,0.0817,0.1005,0.0124,0.1168,0.1476,0.2118,0.2575,0.2354,0.1334,0.0092,0.1951,0.3685,0.4646,0.5418,0.6260,0.7420,0.8257,0.8609,0.8400,0.8949,0.9945,1.0000,0.9649,0.8747,0.6257,0.2184,0.2945,0.3645,0.5012,0.7843,0.9361,0.8195,0.6207,0.4513,0.3004,0.2674,0.2241,0.3141,0.3693,0.2986,0.2226,0.0849,0.0359,0.0289,0.0122,0.0045,0.0108,0.0075,0.0089,0.0036,0.0029,0.0013,0.0010,0.0032,0.0047)\n",
        "\n",
        "# changing the input data to numpy array\n",
        "input_data_as_numpy_array = np.asarray(inpute_data)\n",
        "\n",
        "# reshape the np array as we are predcting for one instance\n",
        "inpute_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
        "\n",
        "prediction = model.predict(inpute_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": "lU4WpbzBitkK",
        "outputId": "fc93f23a-2686-4522-c99d-1865dd9fed52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['M']\n",
            "The object is a Mine\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-05FuPA7nHRW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}