Rock_vs_Mine_prediction.ipynb

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      "source": [
        "**Problem Statement**\n",
        "\n",
        "This prediction model is a binary classification algorithm trained to distinguish between \"rock\" and \"mine\" objects, making it applicable for military purposes, particularly in conflict zones. By analyzing input data such as sonar readings or visual cues, the model outputs a prediction, aiding in the identification of potential threats underwater or in other environments. Deployed within military systems, it serves to safeguard personnel and assets by swiftly recognizing hazards, thereby informing strategic decisions during wartime operations."
      ],
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      "source": [
        "Importing Dependencies"
      ],
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        "id": "LrovwjPG3AhT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np    #To build numpy arrays\n",
        "import pandas as pd   #To make pandas DataFrame\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "gLelLUSi3AKi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Loading the CSV Sonar dataset into pandas DataFrame for better understanding and usecase"
      ],
      "metadata": {
        "id": "M4gldY3d3kS0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_dataset = pd.read_csv('/content/sonar data.csv',header=None)"
      ],
      "metadata": {
        "id": "8-vzc9Eu3icX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking the first five rows of the dataframe"
      ],
      "metadata": {
        "id": "oluO3kEL34s3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sonar_dataset.head()"
      ],
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              "       0       1       2       3       4       5       6       7       8   \\\n",
              "0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
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              "4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
              "\n",
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              "\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the number of rows and columns of the DataFrame\n",
        "sonar_dataset.shape"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZfaMlY5J3-Lp",
        "outputId": "89f3f035-2007-4e2b-b378-3b8f8aaf9736"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(208, 61)"
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          },
          "metadata": {},
          "execution_count": 6
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      ]
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    {
      "cell_type": "code",
      "source": [
        "#Checking the statistical Description of the Dataframe\n",
        "sonar_dataset.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "gsDTv62q4OpM",
        "outputId": "24130436-3c3e-4f87-ad1b-e233f0629ed7"
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      "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-d96a1166-dc4b-4114-95f1-7a4de352cec3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<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",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-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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              "    30% {\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    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-f7131a7d-ff65-4c9a-ba11-745374628ed9 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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will check if the Sonar dataset is inbalanced or not"
      ],
      "metadata": {
        "id": "URwVqypn4Zgy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#This value_counts() function returns the frequency of parameters in the label\n",
        "sonar_dataset[60].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dA-icsTN4VhE",
        "outputId": "ceb80bbd-837b-438e-b189-98035f60dae3"
      },
      "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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Separating the features from the labels"
      ],
      "metadata": {
        "id": "9CIl58Br43f1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# x will store all the features as we dropped the label column\n",
        "x = sonar_dataset.drop(columns=60,axis = 1)\n",
        "# y will store the actual answer(label)\n",
        "y = sonar_dataset[60]"
      ],
      "metadata": {
        "id": "VNS9Z-4s4l_v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting the features and label into training and testing data"
      ],
      "metadata": {
        "id": "K8r8f7sL5d4k"
      }
    },
    {
      "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": "U2shXNfg5aeV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Training --> Logistic Regression"
      ],
      "metadata": {
        "id": "belGrVJs50ni"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "b7LdAod45yGD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model with training data\n",
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "SiwmannW59I9",
        "outputId": "4eaf5b70-5856-4a6a-c544-cc14e1f4a8d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation"
      ],
      "metadata": {
        "id": "54o6DDCB6JMk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy on the training data\n",
        "training_prediction = model.predict(x_train)\n",
        "training_evaluation = accuracy_score(training_prediction,y_train)"
      ],
      "metadata": {
        "id": "5l5KGWXe6DOK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on training data is :\",training_evaluation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67ZOBdlL6dxn",
        "outputId": "1610459f-1f58-4059-b9d0-6f5df93ccaea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on training data is : 0.8342245989304813\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy on testing data\n",
        "testing_prediction = model.predict(x_test)\n",
        "testing_evaluation = accuracy_score(testing_prediction,y_test)"
      ],
      "metadata": {
        "id": "-kChC8uJ6oAV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy on the testing data is :\",testing_evaluation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcwGceaW68yP",
        "outputId": "7a9505a4-ebd5-4fdc-8658-213861a4e911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy on the testing data is : 0.7619047619047619\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making a prediction system"
      ],
      "metadata": {
        "id": "m8acUM9M7HdX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input=[0.0264,0.0071,0.0342,0.0793,0.1043,0.0783,0.1417,0.1176,0.0453,0.0945,0.1132,0.0840,0.0717,0.1968,0.2633,0.4191,0.5050,0.6711,0.7922,0.8381,0.8759,0.9422,1.0000,0.9931,0.9575,0.8647,0.7215,0.5801,0.4964,0.4886,0.4079,0.2443,0.1768,0.2472,0.3518,0.3762,0.2909,0.2311,0.3168,0.3554,0.3741,0.4443,0.3261,0.1963,0.0864,0.1688,0.1991,0.1217,0.0628,0.0323,0.0253,0.0214,0.0262,0.0177,0.0037,0.0068,0.0121,0.0077,0.0078,0.0066]\n",
        "#We will change the input list into a numpy array so as to make the execution fast"
      ],
      "metadata": {
        "id": "sf3eh9yQ8GpG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_array = np.asarray(input)"
      ],
      "metadata": {
        "id": "iWy-1Kpk9MHB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_array_reshaped = input_array.reshape(1,-1)"
      ],
      "metadata": {
        "id": "9pL3BhWG8iII"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = model.predict(input_array_reshaped)"
      ],
      "metadata": {
        "id": "7D4fsTFD8Zak"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(prediction)\n",
        "if (prediction[0] == 'M'):\n",
        "  print(\"The object is a mine\")\n",
        "else:\n",
        "  print(\"The object is a rock\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Q8kMDWr8fXm",
        "outputId": "85b4152f-9a81-4afd-9cf2-356d53ca82d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['M']\n",
            "The object is a mine\n"
          ]
        }
      ]
    }
  ]
}