Drug.ipynb

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        "<a href=\"https://colab.research.google.com/github/Ignis130913/Drug_prediction/blob/main/Drug.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "8Cdczx4jJzO9"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug=pd.read_csv('/content/drug.csv')"
      ],
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        "id": "fXKS2MPZKGBA"
      },
      "execution_count": 3,
      "outputs": []
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        "drug.columns"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K', 'Drug'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug\n",
        "drug.columns=[\"age\",\"sex\",\"bp\",\"c\",\"na_k\",\"drug\"]"
      ],
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        "id": "cUjgmKQ4KPNV"
      },
      "execution_count": 5,
      "outputs": []
    },
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      "source": [
        "demo_drug=drug[[\"age\",\"sex\",\"bp\",\"c\",\"na_k\",\"drug\"]]\n",
        "demo_drug"
      ],
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        "id": "1fjnAB7IvYEF",
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      "execution_count": 61,
      "outputs": [
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              "     age  sex  bp  c    na_k  drug\n",
              "0     23    0   0  0  25.355     0\n",
              "1     47    1   1  0  13.093     3\n",
              "2     47    1   1  0  10.114     3\n",
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              "4     61    0   1  0  18.043     0\n",
              "..   ...  ...  .. ..     ...   ...\n",
              "195   56    0   1  0  11.567     3\n",
              "196   16    1   1  0  12.006     3\n",
              "197   52    1   2  0   9.894     4\n",
              "198   23    1   2  1  14.020     4\n",
              "199   40    0   1  1  11.349     4\n",
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    {
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      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "label = le.fit_transform(drug['sex'])\n",
        "label\n",
        "drug.drop(\"sex\", axis=1, inplace=True)\n",
        "drug[\"sex\"] = label\n",
        "drug"
      ],
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        "id": "CZt_Kh_AsGXM",
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      "execution_count": 6,
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              "     age      bp       c    na_k   drug  sex\n",
              "0     23    HIGH    HIGH  25.355  DrugY    0\n",
              "1     47     LOW    HIGH  13.093  drugC    1\n",
              "2     47     LOW    HIGH  10.114  drugC    1\n",
              "3     28  NORMAL    HIGH   7.798  drugX    0\n",
              "4     61     LOW    HIGH  18.043  DrugY    0\n",
              "..   ...     ...     ...     ...    ...  ...\n",
              "195   56     LOW    HIGH  11.567  drugC    0\n",
              "196   16     LOW    HIGH  12.006  drugC    1\n",
              "197   52  NORMAL    HIGH   9.894  drugX    1\n",
              "198   23  NORMAL  NORMAL  14.020  drugX    1\n",
              "199   40     LOW  NORMAL  11.349  drugX    0\n",
              "\n",
              "[200 rows x 6 columns]"
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de62c786-6d70-4582-b939-b3f2a0550d71')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-dfa46927-8796-47a7-98c5-20a8304e290c\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dfa46927-8796-47a7-98c5-20a8304e290c')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-dfa46927-8796-47a7-98c5-20a8304e290c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-de62c786-6d70-4582-b939-b3f2a0550d71 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-de62c786-6d70-4582-b939-b3f2a0550d71');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label = le.fit_transform(drug['bp'])\n",
        "label\n",
        "drug.drop(\"bp\", axis=1, inplace=True)\n",
        "drug[\"bp\"] = label\n",
        "drug"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jeGb31c5smPR",
        "outputId": "77903aee-4752-4e69-ed55-d9e8c1b6431d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age       c    na_k   drug  sex  bp\n",
              "0     23    HIGH  25.355  DrugY    0   0\n",
              "1     47    HIGH  13.093  drugC    1   1\n",
              "2     47    HIGH  10.114  drugC    1   1\n",
              "3     28    HIGH   7.798  drugX    0   2\n",
              "4     61    HIGH  18.043  DrugY    0   1\n",
              "..   ...     ...     ...    ...  ...  ..\n",
              "195   56    HIGH  11.567  drugC    0   1\n",
              "196   16    HIGH  12.006  drugC    1   1\n",
              "197   52    HIGH   9.894  drugX    1   2\n",
              "198   23  NORMAL  14.020  drugX    1   2\n",
              "199   40  NORMAL  11.349  drugX    0   1\n",
              "\n",
              "[200 rows x 6 columns]"
            ],
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              "      <th>na_k</th>\n",
              "      <th>drug</th>\n",
              "      <th>sex</th>\n",
              "      <th>bp</th>\n",
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              "      <td>13.093</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>DrugY</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>11.567</td>\n",
              "      <td>drugC</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>12.006</td>\n",
              "      <td>drugC</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>9.894</td>\n",
              "      <td>drugX</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>14.020</td>\n",
              "      <td>drugX</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>11.349</td>\n",
              "      <td>drugX</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed446e65-5d5a-47d3-9cb4-383344899007')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4397c44a-fd72-48b2-9eff-5fd5c4192c41 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed446e65-5d5a-47d3-9cb4-383344899007 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-ed446e65-5d5a-47d3-9cb4-383344899007');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label = le.fit_transform(drug['c'])\n",
        "label\n",
        "drug.drop(\"c\", axis=1, inplace=True)\n",
        "drug[\"c\"] = label\n",
        "drug"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jBrc_cy8tD76",
        "outputId": "39e4d9bc-e0a2-4efb-f76d-412208d82e26"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age    na_k   drug  sex  bp  c\n",
              "0     23  25.355  DrugY    0   0  0\n",
              "1     47  13.093  drugC    1   1  0\n",
              "2     47  10.114  drugC    1   1  0\n",
              "3     28   7.798  drugX    0   2  0\n",
              "4     61  18.043  DrugY    0   1  0\n",
              "..   ...     ...    ...  ...  .. ..\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp=drug[\"na_k\"]\n",
        "del drug[\"na_k\"]\n",
        "drug[\"na_k\"]=temp"
      ],
      "metadata": {
        "id": "VuWocyGFupJ-"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = le.fit_transform(drug['drug'])\n",
        "label\n",
        "drug.drop(\"drug\", axis=1, inplace=True)\n",
        "drug[\"drug\"] = label\n",
        "drug"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "qpU6IF4XtGk5",
        "outputId": "be5812c2-0ca2-49f3-843c-29f31cb5e747"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  bp  c    na_k  drug\n",
              "0     23    0   0  0  25.355     0\n",
              "1     47    1   1  0  13.093     3\n",
              "2     47    1   1  0  10.114     3\n",
              "3     28    0   2  0   7.798     4\n",
              "4     61    0   1  0  18.043     0\n",
              "..   ...  ...  .. ..     ...   ...\n",
              "195   56    0   1  0  11.567     3\n",
              "196   16    1   1  0  12.006     3\n",
              "197   52    1   2  0   9.894     4\n",
              "198   23    1   2  1  14.020     4\n",
              "199   40    0   1  1  11.349     4\n",
              "\n",
              "[200 rows x 6 columns]"
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              "  .colab-df-quickchart:hover {\n",
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              "    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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-b4c8333d-ecfb-4687-a84f-2df40f2aec79 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e76d4877-e146-45aa-9d50-9861f013f561 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-e76d4877-e146-45aa-9d50-9861f013f561');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug[\"drug\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDHs4ZQeKdS0",
        "outputId": "f00df81f-e417-4f7a-d9fb-10a9e5f01f24"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    91\n",
              "4    54\n",
              "1    23\n",
              "3    16\n",
              "2    16\n",
              "Name: drug, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IE2C6a1bKtrg",
        "outputId": "698e7ca5-9031-4d10-a961-673e7c693bfe"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age     0\n",
              "sex     0\n",
              "bp      0\n",
              "c       0\n",
              "na_k    0\n",
              "drug    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SsBfUay2K6QI",
        "outputId": "293adfa9-9eb2-4117-a747-5ed8e1beed03"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age       int64\n",
              "sex       int64\n",
              "bp        int64\n",
              "c         int64\n",
              "na_k    float64\n",
              "drug      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug.groupby(drug[\"age\"]).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YAY2I_6hLRNn",
        "outputId": "f9d3d8c5-6d30-4421-a033-0cef4fff0152"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sex  bp  c     na_k  drug\n",
              "age                           \n",
              "15     2   2  2   43.015     4\n",
              "16     2   1  2   46.529     3\n",
              "17     1   2  1   10.832     4\n",
              "18     0   2  2   70.214     4\n",
              "19     0   0  1   39.282     1\n",
              "20     1   3  3   67.868     9\n",
              "21     0   0  1   28.632     0\n",
              "22     3   5  2   79.823    11\n",
              "23     6   9  1  115.480    12\n",
              "24     2   4  2   64.323     5\n",
              "25     1   2  0   19.011     0\n",
              "26     1   2  3   66.537     4\n",
              "28     1  10  1  119.148    11\n",
              "29     1   0  0   42.306     1\n",
              "30     0   2  0   10.443     4\n",
              "31     4   0  3   70.533     2\n",
              "32     1   4  4   73.740    13\n",
              "33     0   1  0   33.486     0\n",
              "34     2   3  2   73.281     4\n",
              "35     2   3  2   29.909     9\n",
              "36     1   3  2   54.865     5\n",
              "37     2   3  4   73.880     9\n",
              "38     1   2  2   59.496     1\n",
              "39     3   8  4   89.202     9\n",
              "40     1   3  1   49.278     8\n",
              "41     2   4  3   67.837     3\n",
              "42     2   2  2   83.086     1\n",
              "43     4   4  2   61.575     5\n",
              "45     3   3  2   49.192     9\n",
              "46     1   2  1   41.971     4\n",
              "47     5   8  3  126.237    17\n",
              "48     1   1  1   25.482     1\n",
              "49     5   7  5   75.774    17\n",
              "50     2   8  3   65.489     9\n",
              "51     2   3  2   66.238     6\n",
              "52     2   3  1   42.816     4\n",
              "53     2   3  1   49.591     6\n",
              "54     1   2  0   24.658     0\n",
              "55     1   2  1   18.238     6\n",
              "56     2   4  0   60.943     7\n",
              "57     1   5  3   69.182     6\n",
              "58     1   2  1  117.538     2\n",
              "59     1   3  0   38.263     9\n",
              "60     4   4  2   61.120    10\n",
              "61     1   4  1   60.301     8\n",
              "62     2   3  1   43.777     0\n",
              "63     1   2  0   25.917     0\n",
              "64     2   3  2   54.434     4\n",
              "65     2   1  4   91.982     6\n",
              "66     1   2  1   24.454     4\n",
              "67     3   7  3   56.996     8\n",
              "68     2   3  2   58.539     7\n",
              "69     2   4  1   36.998     8\n",
              "70     2   2  0   44.305     4\n",
              "72     3   3  2   47.398     9\n",
              "73     0   2  0   37.569     0\n",
              "74     3   2  2   57.884     6"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-22040aef-7f56-4ef8-ab3b-f669aade266f\">\n",
              "    <div 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>sex</th>\n",
              "      <th>bp</th>\n",
              "      <th>c</th>\n",
              "      <th>na_k</th>\n",
              "      <th>drug</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>43.015</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>46.529</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>10.832</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>70.214</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>39.282</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>67.868</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>28.632</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>79.823</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>115.480</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>64.323</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>19.011</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>66.537</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>119.148</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42.306</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>10.443</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>70.533</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>73.740</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>33.486</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>73.281</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>29.909</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>54.865</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>73.880</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>59.496</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>89.202</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>49.278</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>67.837</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>83.086</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>61.575</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>49.192</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>41.971</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>126.237</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>25.482</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>75.774</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>65.489</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>66.238</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>42.816</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>49.591</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24.658</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>18.238</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>60.943</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>69.182</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>117.538</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>38.263</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>61.120</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>60.301</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>43.777</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>25.917</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>54.434</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>91.982</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>24.454</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>56.996</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>58.539</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>36.998</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>44.305</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>47.398</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>37.569</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>57.884</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22040aef-7f56-4ef8-ab3b-f669aade266f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-f9b7df78-e866-497e-be6a-d107c74a26be\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9b7df78-e866-497e-be6a-d107c74a26be')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f9b7df78-e866-497e-be6a-d107c74a26be button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-22040aef-7f56-4ef8-ab3b-f669aade266f 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-22040aef-7f56-4ef8-ab3b-f669aade266f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.catplot(data=demo_drug,x=\"drug\",y=\"age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "QZ-naIpZLrDl",
        "outputId": "f0e44d1b-3849-4a03-9de5-00bb58588cd3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7dbd5ed64670>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=demo_drug,x=\"age\",hue=\"drug\",multiple=\"stack\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "eDM3Yb2RM9_N",
        "outputId": "60e5462d-f7ad-40fd-8fcc-a3d44abb79fa"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "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": [
        "sns.barplot(data=demo_drug,x=\"drug\",y=\"na_k\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "phYH6wTENR5G",
        "outputId": "009e875d-6c4d-4148-e1b3-dd90f07b3c9c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='drug', ylabel='na_k'>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "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": [
        "demo_drug[\"na_k\"].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "ntrzULy0OGlu",
        "outputId": "7504d8d4-ddbb-4e60-f1e4-b83006b815b6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "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": [
        "drug.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olq0XjnpOTlu",
        "outputId": "40db1521-9e26-413c-cf29-d1cb33686f0a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'bp', 'c', 'na_k', 'drug'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data=demo_drug,x=\"drug\",y=\"bp\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "x_YaxTSxOWXj",
        "outputId": "bd2826ed-619a-4c88-caeb-2bad34041e7d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='drug', ylabel='bp'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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": [
        "sns.regplot(data=demo_drug,x=\"age\",y=\"na_k\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "galF530oOlTI",
        "outputId": "a48a1a85-6fd5-4b27-dd5c-4f07c1657096"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='na_k'>"
            ]
          },
          "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": [
        "drug.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4W-i3LStO9HZ",
        "outputId": "7796eb9d-138b-4ea0-cb55-48cab020dc6b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  bp  c    na_k  drug\n",
              "0   23    0   0  0  25.355     0\n",
              "1   47    1   1  0  13.093     3\n",
              "2   47    1   1  0  10.114     3\n",
              "3   28    0   2  0   7.798     4\n",
              "4   61    0   1  0  18.043     0"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-1ebbfd90-4684-4581-a1e0-e68c81d3ec4f\">\n",
              "    <div 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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bp</th>\n",
              "      <th>c</th>\n",
              "      <th>na_k</th>\n",
              "      <th>drug</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.355</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>13.093</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10.114</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>7.798</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18.043</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ebbfd90-4684-4581-a1e0-e68c81d3ec4f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
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              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4b2efda4-286a-4da1-8d68-406983a90f14')\"\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",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4b2efda4-286a-4da1-8d68-406983a90f14 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ebbfd90-4684-4581-a1e0-e68c81d3ec4f 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-1ebbfd90-4684-4581-a1e0-e68c81d3ec4f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=demo_drug,x=\"c\",y=\"age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "tdnL5HoJPFvH",
        "outputId": "4503f62b-31a4-4ee2-ebb0-775e4c66108f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='c', ylabel='age'>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "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": [
        "sns.histplot(data=demo_drug,x=\"c\",y=\"drug\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "ruvJsi85PVCn",
        "outputId": "c4a64530-3906-4c91-9c21-1144488286e3"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='c', ylabel='drug'>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "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": [
        "from sklearn.linear_model import LogisticRegression\n",
        "model=LogisticRegression()"
      ],
      "metadata": {
        "id": "kjLBXJp8Px5O"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drug.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4tL1LEhiQDKE",
        "outputId": "d6e2e2d0-0c9a-468b-f707-716e30c7b859"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=drug[[\"age\",\"sex\",\"bp\",\"na_k\"]].copy()\n",
        "y=drug[\"drug\"]"
      ],
      "metadata": {
        "id": "DfLurm8yQLDn"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ZE8AHHrVQUwO",
        "outputId": "2723a19a-35df-4bbc-db0b-ed5cb43638c3"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  bp    na_k\n",
              "0     23    0   0  25.355\n",
              "1     47    1   1  13.093\n",
              "2     47    1   1  10.114\n",
              "3     28    0   2   7.798\n",
              "4     61    0   1  18.043\n",
              "..   ...  ...  ..     ...\n",
              "195   56    0   1  11.567\n",
              "196   16    1   1  12.006\n",
              "197   52    1   2   9.894\n",
              "198   23    1   2  14.020\n",
              "199   40    0   1  11.349\n",
              "\n",
              "[200 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-beaeeed1-9a27-471d-ae7b-022accbb06f8\">\n",
              "    <div 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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bp</th>\n",
              "      <th>na_k</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13.093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>7.798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>18.043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>11.567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>9.894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>14.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>11.349</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-beaeeed1-9a27-471d-ae7b-022accbb06f8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-6a14f1c5-3f13-4ac0-91eb-020157958ea5\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6a14f1c5-3f13-4ac0-91eb-020157958ea5')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-6a14f1c5-3f13-4ac0-91eb-020157958ea5 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-beaeeed1-9a27-471d-ae7b-022accbb06f8 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-beaeeed1-9a27-471d-ae7b-022accbb06f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(\n",
        "          x, y, test_size = 0.2, random_state = 3)"
      ],
      "metadata": {
        "id": "_s9YrM4GSE4M"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "KMZ-7XalQXW-",
        "outputId": "9cd3600a-ea6b-453a-e321-8e2b7722347d"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  bp    na_k\n",
              "156   31    1   0  11.227\n",
              "115   51    1   0  18.295\n",
              "155   49    1   1  10.537\n",
              "15    16    0   0  15.516\n",
              "61    24    1   0   9.475\n",
              "..   ...  ...  ..     ...\n",
              "0     23    0   0  25.355\n",
              "184   18    0   0  37.188\n",
              "131   52    1   1  32.922\n",
              "152   55    1   2   7.261\n",
              "106   22    1   2  11.953\n",
              "\n",
              "[160 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-1e630707-79fd-4745-aa6f-7b70a913e293\">\n",
              "    <div 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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bp</th>\n",
              "      <th>na_k</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11.227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>51</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>18.295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>9.475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>32.922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>55</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>7.261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>11.953</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>160 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e630707-79fd-4745-aa6f-7b70a913e293')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-c5d06f89-c25d-4847-a23d-322796518f08\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5d06f89-c25d-4847-a23d-322796518f08')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-c5d06f89-c25d-4847-a23d-322796518f08 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1e630707-79fd-4745-aa6f-7b70a913e293 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-1e630707-79fd-4745-aa6f-7b70a913e293');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "o-dB-S3Sxr_e",
        "outputId": "a211801b-9138-403b-9eaf-2423afaafdba"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction=model.predict(x_test)\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(accuracy_score(y_test,prediction))\n",
        "print(mean_absolute_error(y_test,prediction))"
      ],
      "metadata": {
        "id": "17d0-mImyC0v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71f6b178-f62f-475a-fa76-8a6a98357d2c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8\n",
            "0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "hEXfhtuQuM_j",
        "outputId": "f2b1ea70-55ea-4de3-e95d-05fff9bd0004"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  bp    na_k\n",
              "156   31    1   0  11.227\n",
              "115   51    1   0  18.295\n",
              "155   49    1   1  10.537\n",
              "15    16    0   0  15.516\n",
              "61    24    1   0   9.475\n",
              "..   ...  ...  ..     ...\n",
              "0     23    0   0  25.355\n",
              "184   18    0   0  37.188\n",
              "131   52    1   1  32.922\n",
              "152   55    1   2   7.261\n",
              "106   22    1   2  11.953\n",
              "\n",
              "[160 rows x 4 columns]"
            ],
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              "\n",
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bp</th>\n",
              "      <th>na_k</th>\n",
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              "      <th>156</th>\n",
              "      <td>31</td>\n",
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              "      <td>11.227</td>\n",
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              "      <th>115</th>\n",
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              "      <td>15.516</td>\n",
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              "      <th>61</th>\n",
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              "      <td>25.355</td>\n",
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              "      <th>184</th>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>32.922</td>\n",
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              "      <th>152</th>\n",
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              "      <td>7.261</td>\n",
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              "      <td>11.953</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>160 rows × 4 columns</p>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </g>\n",
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              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e27f4cd2-1559-4ae1-92ba-620f7163df3b button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1f53315c-b230-4750-9515-b632dce95481 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-1f53315c-b230-4750-9515-b632dce95481');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drug[\"drug\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5giqwCMvF1K",
        "outputId": "bed5c46c-93b8-462f-b781-912c71059898"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    91\n",
              "4    54\n",
              "1    23\n",
              "3    16\n",
              "2    16\n",
              "Name: drug, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input=np.array([50,1,2,15.55]).reshape(1,4)\n",
        "p=model.predict(input)\n",
        "if p==0: print(\"drug_0\")\n",
        "elif p==1: print(\"drug_1\")\n",
        "elif p==2: print(\"drug_2\")\n",
        "elif p==3: print(\"drug_3\")\n",
        "else: print(\"drug_4\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaI8B_5KuURk",
        "outputId": "29618226-3799-431b-e215-7d3b1fa8fddd"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drug_4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input=np.array([18,0,0,5.55]).reshape(1,4)\n",
        "p=model.predict(input)\n",
        "if p==0: print(\"drug_0\")\n",
        "elif p==1: print(\"drug_1\")\n",
        "elif p==2: print(\"drug_2\")\n",
        "elif p==3: print(\"drug_3\")\n",
        "else: print(\"drug_4\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUV70hBMvyah",
        "outputId": "f7cb5df2-b100-48c8-b81f-7e6c665a3837"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drug_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}