Rock_vs_Mine_Prediction.ipynb

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      "source": [
        "Importing the Dependencies"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Collection and Data Processing"
      ],
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        "id": "kl_np2jIYkHr"
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      "source": [
        "#loading the dataset to a pandas dataframe\n",
        "sonar_data = pd.read_csv('/content/Copy of sonar data.csv', header=None)"
      ],
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        "id": "UQ7gXQU9YeNs"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "sonar_data.head()"
      ],
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "#number of rows and columns\n",
        "sonar_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eemlws1kZpzT",
        "outputId": "73f6efd4-fc9e-4ca6-dbee-c3d9c3edf4bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(208, 61)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data.describe() #describe --> statistical measures of the data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "NI-FDcCuaVud",
        "outputId": "47860f48-1aa8-4265-a4c6-5c2d37d24d22"
      },
      "execution_count": null,
      "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-240aa885-7a28-4c25-b45d-06759f3b56d8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "    </tr>\n",
              "  </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",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-240aa885-7a28-4c25-b45d-06759f3b56d8')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-240aa885-7a28-4c25-b45d-06759f3b56d8 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-240aa885-7a28-4c25-b45d-06759f3b56d8');\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-c8261a87-78ec-4f59-b331-c4dd959ca0bd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c8261a87-78ec-4f59-b331-c4dd959ca0bd')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-c8261a87-78ec-4f59-b331-c4dd959ca0bd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data[60].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8gDbs_47jTO0",
        "outputId": "8d8fceb3-479e-4cee-c361-16452155337a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M    111\n",
              "R     97\n",
              "Name: 60, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "M --> Mine\n",
        "R --> Rock"
      ],
      "metadata": {
        "id": "2Vmjk0mQoVTG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_data.groupby(60).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "Ragep_HtobVK",
        "outputId": "b523a14e-a436-406c-8a12-43eeaa6fcd0b"
      },
      "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>58</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",
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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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              "      <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",
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              "      <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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              "\n",
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              "    [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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e21e1353-b3d5-4252-9b8c-43b7a1c6a02f 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-e21e1353-b3d5-4252-9b8c-43b7a1c6a02f');\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",
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              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\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-58880512-4e15-4fcb-a580-76264e148d4c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#separating data and Labels\n",
        "X = sonar_data.drop(columns=60, axis=1)\n",
        "Y = sonar_data[60]"
      ],
      "metadata": {
        "id": "eS3fc5S4ow4Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hG_OPHwxpVCl",
        "outputId": "2cf36acb-5138-4477-f5a2-1e9362382875"
      },
      "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": "P5_asxQSpgsV"
      }
    },
    {
      "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": "qkRkpbpQpY0q"
      },
      "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": "4epTfoBdqe_f",
        "outputId": "e29122a8-9ef5-42b0-9c66-b37c3ff013bc"
      },
      "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": "A1IMJRf0roFX",
        "outputId": "da727550-e94a-4288-a3d4-055a9c122d1d"
      },
      "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": [
        "Model Training --> Logistic Regression"
      ],
      "metadata": {
        "id": "6h6OOKvfqsZc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "OOxKR8AOql37"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the logistic regression model with taining data\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "id": "0uWg9-QDq2BK",
        "outputId": "e7bfa9b3-4a3d-4e1e-9ef0-4e25f1d2ab1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation"
      ],
      "metadata": {
        "id": "FyriLcuhsFGc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy on training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"
      ],
      "metadata": {
        "id": "nrcuOfu4sASs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on training data :', training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4kqD3xKtgu8",
        "outputId": "e02d033e-9421-4f9a-db7c-4ec2bc067864"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data : 0.8342245989304813\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy on test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "HNCHvi_mttaV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy on test data :', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BUF6sQdBuJ4e",
        "outputId": "289a6343-74eb-46dc-e54d-38821f735a84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on test data : 0.7619047619047619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making a Predicting system"
      ],
      "metadata": {
        "id": "1ft_GCPQunwY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (0.0261,0.0266,0.0223,0.0749,0.1364,0.1513,0.1316,0.1654,0.1864,0.2013,0.2890,0.3650,0.3510,0.3495,0.4325,0.5398,0.6237,0.6876,0.7329,0.8107,0.8396,0.8632,0.8747,0.9607,0.9716,0.9121,0.8576,0.8798,0.7720,0.5711,0.4264,0.2860,0.3114,0.2066,0.1165,0.0185,0.1302,0.2480,0.1637,0.1103,0.2144,0.2033,0.1887,0.1370,0.1376,0.0307,0.0373,0.0606,0.0399,0.0169,0.0135,0.0222,0.0175,0.0127,0.0022,0.0124,0.0054,0.0021,0.0028,0.0023)\n",
        "\n",
        "#changing the input_data to a numpy array\n",
        "input_data_as_numpy_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the np array as we are predicting for one instance\n",
        "input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)\n",
        "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": "6bMGFuMXuVQ8",
        "outputId": "78170a76-f22b-4f42-ac9c-14606060c2c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['M']\n",
            "the object is a Mine\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vpsE5AfFwjZp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}