Untitled6.ipynb

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        "import numpy as np\n",
        "import pandas as pd"
      ]
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        "df= pd.read_csv('/content/Placement.csv')"
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              "   Student_ID  CGPA   IQ  Placement\n",
              "0           1   6.8  123          1\n",
              "1           2   5.9  106          0\n",
              "2           3   5.3  121          0\n",
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              "   CGPA   IQ  Placement\n",
              "0   6.8  123          1\n",
              "1   5.9  106          0\n",
              "2   5.3  121          0\n",
              "3   7.4  132          1\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58810afc-2159-409d-a142-e5bf5d404dbe')\"\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-58810afc-2159-409d-a142-e5bf5d404dbe 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",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"CGPA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1436336737775692,\n        \"min\": 3.3,\n        \"max\": 8.5,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          6.3,\n          4.4,\n          5.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IQ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 37,\n        \"max\": 233,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          91,\n          123,\n          64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Placement\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "qOg2H-H0sH9q"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['CGPA'],df['IQ'], c= df['Placement'])\n",
        "# we will use logistic regression to solve this problem"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "QXkBi1PwsMMm",
        "outputId": "288dc72f-e7ab-4c53-ca1d-43330441b73d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7ba888d62350>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x= df.iloc[:,0:2]\n",
        "y=df.iloc[:,-1]"
      ],
      "metadata": {
        "id": "G8bDbyaDtNUl"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Su7ILE5ctXSQ",
        "outputId": "68ab8c08-be8a-4e4d-edc8-f16bbdebfd06"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    CGPA   IQ\n",
              "0    6.8  123\n",
              "1    5.9  106\n",
              "2    5.3  121\n",
              "3    7.4  132\n",
              "4    5.8  142\n",
              "..   ...  ...\n",
              "95   4.3  200\n",
              "96   4.4   42\n",
              "97   6.7  182\n",
              "98   6.3  103\n",
              "99   6.2  113\n",
              "\n",
              "[100 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2a7f417c-f9e4-4a17-91a2-35d90f602e19\" 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>CGPA</th>\n",
              "      <th>IQ</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.8</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.9</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.3</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.4</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.8</td>\n",
              "      <td>142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>4.3</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>4.4</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>6.7</td>\n",
              "      <td>182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>6.3</td>\n",
              "      <td>103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>6.2</td>\n",
              "      <td>113</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 2 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-2a7f417c-f9e4-4a17-91a2-35d90f602e19')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-2a7f417c-f9e4-4a17-91a2-35d90f602e19 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-2a7f417c-f9e4-4a17-91a2-35d90f602e19');\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-b736c818-6f29-46ef-b2d0-161ae2dc58ad\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b736c818-6f29-46ef-b2d0-161ae2dc58ad')\"\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-b736c818-6f29-46ef-b2d0-161ae2dc58ad button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_163cceb1-4756-4a90-81be-bf087dbaa182\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_163cceb1-4756-4a90-81be-bf087dbaa182 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x",
              "summary": "{\n  \"name\": \"x\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"CGPA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1436336737775692,\n        \"min\": 3.3,\n        \"max\": 8.5,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          6.3,\n          4.4,\n          5.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IQ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 37,\n        \"max\": 233,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          91,\n          123,\n          64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.1)"
      ],
      "metadata": {
        "id": "EDAHXwgWwZIq"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ZY-atX6_w_tI",
        "outputId": "1a809eec-997c-4b5d-aef1-66bbe76e1160"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    CGPA   IQ\n",
              "53   8.3  168\n",
              "73   4.9   61\n",
              "0    6.8  123\n",
              "47   5.2  161\n",
              "6    5.7  143\n",
              "..   ...  ...\n",
              "50   3.5  233\n",
              "65   8.1  166\n",
              "97   6.7  182\n",
              "62   6.0  102\n",
              "17   3.3  183\n",
              "\n",
              "[90 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8edab06f-2933-472c-a3f0-d189930493e7\" 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>CGPA</th>\n",
              "      <th>IQ</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>8.3</td>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>4.9</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.8</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>5.2</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5.7</td>\n",
              "      <td>143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>3.5</td>\n",
              "      <td>233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>8.1</td>\n",
              "      <td>166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>6.7</td>\n",
              "      <td>182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>6.0</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>3.3</td>\n",
              "      <td>183</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90 rows × 2 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-8edab06f-2933-472c-a3f0-d189930493e7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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-8edab06f-2933-472c-a3f0-d189930493e7 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-8edab06f-2933-472c-a3f0-d189930493e7');\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-ae60a525-99d7-40c1-9795-fbc1460dea7e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ae60a525-99d7-40c1-9795-fbc1460dea7e')\"\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-ae60a525-99d7-40c1-9795-fbc1460dea7e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8682b5f6-ed80-4048-8056-f646ceaae4c1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8682b5f6-ed80-4048-8056-f646ceaae4c1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_train",
              "summary": "{\n  \"name\": \"x_train\",\n  \"rows\": 90,\n  \"fields\": [\n    {\n      \"column\": \"CGPA\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1375883991113653,\n        \"min\": 3.3,\n        \"max\": 8.5,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          6.2,\n          6.4,\n          5.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"IQ\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 37,\n        \"max\": 233,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          200,\n          155,\n          168\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()"
      ],
      "metadata": {
        "id": "m2OezWJpxcQE"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train=scaler.fit_transform(x_train)\n",
        "# x_train"
      ],
      "metadata": {
        "id": "fWcGgWB_xp-H"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test=scaler.transform(x_test)\n",
        "# x_test"
      ],
      "metadata": {
        "id": "TUiUhCz2x-65"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "t_SnHCdZyNTk"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf=LogisticRegression()"
      ],
      "metadata": {
        "id": "aPXUijbxyUu8"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Xe348xQmyZsM",
        "outputId": "f1e98942-ba78-45a6-fb2e-f713ffd218b0"
      },
      "execution_count": 38,
      "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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(x_test)"
      ],
      "metadata": {
        "id": "R_I53l1Oypp6"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0l4MFd-yuDz",
        "outputId": "f90651a4-6782-4e80-cd90-c03c67cd0ce2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "64    1\n",
              "18    0\n",
              "60    1\n",
              "93    1\n",
              "96    0\n",
              "71    1\n",
              "42    1\n",
              "57    1\n",
              "26    1\n",
              "34    0\n",
              "Name: Placement, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n"
      ],
      "metadata": {
        "id": "cYubQCjEy5oA"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1bhb_iu1eet",
        "outputId": "83400f54-3c6d-4699-f3a3-b5ab746fb9c9"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.plotting import plot_decision_regions"
      ],
      "metadata": {
        "id": "UBorFGjY2ATQ"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_regions(x_train,y_train.values,clf=clf,legend=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "TeA9Fbjf1uD1",
        "outputId": "2edb4d40-6887-49c3-d525-74e7a40be7cd"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}