ML_Final_Project_1_(1).ipynb

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      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns "
      ]
    },
    {
      "cell_type": "code",
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              "     Age Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0     23   F    HIGH        HIGH   25.355  DrugY\n",
              "1     47   M     LOW        HIGH   13.093  drugC\n",
              "2     47   M     LOW        HIGH   10.114  drugC\n",
              "3     28   F  NORMAL        HIGH    7.798  drugX\n",
              "4     61   F     LOW        HIGH   18.043  DrugY\n",
              "..   ...  ..     ...         ...      ...    ...\n",
              "195   56   F     LOW        HIGH   11.567  drugC\n",
              "196   16   M     LOW        HIGH   12.006  drugC\n",
              "197   52   M  NORMAL        HIGH    9.894  drugX\n",
              "198   23   M  NORMAL      NORMAL   14.020  drugX\n",
              "199   40   F     LOW      NORMAL   11.349  drugX\n",
              "\n",
              "[200 rows x 6 columns]"
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              "      <td>11.349</td>\n",
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      ],
      "source": [
        "#loading the dataset\n",
        "data= pd.read_csv(\"/content/drug200.csv\")\n",
        "data"
      ]
    },
    {
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imblearn in /usr/local/lib/python3.8/dist-packages (0.0)\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.8/dist-packages (from imblearn) (0.8.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.2.0)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.7.3)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.8/dist-packages (from imbalanced-learn->imblearn) (1.0.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (3.1.0)\n"
          ]
        }
      ],
      "source": [
        "pip install imblearn #oversampling and undersampling techniques to handle class imbalance in a dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "96300e0c",
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          "data": {
            "text/plain": [
              "'(Synthetic Minority Over-sampling Technique) is a technique for addressing class imbalance in a dataset\\n by generating synthetic samples'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression \n",
        "from sklearn.preprocessing import StandardScaler \n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.metrics import * \n",
        "\n",
        "from imblearn.over_sampling import SMOTE \n",
        "'''(Synthetic Minority Over-sampling Technique) is a technique for addressing class imbalance in a dataset\n",
        " by generating synthetic samples'''"
      ]
    },
    {
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        "tags": [],
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      },
      "source": [
        "##Data Description"
      ]
    },
    {
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              "     Age Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0     23   F    HIGH        HIGH   25.355  DrugY\n",
              "1     47   M     LOW        HIGH   13.093  drugC\n",
              "2     47   M     LOW        HIGH   10.114  drugC\n",
              "3     28   F  NORMAL        HIGH    7.798  drugX\n",
              "4     61   F     LOW        HIGH   18.043  DrugY\n",
              "..   ...  ..     ...         ...      ...    ...\n",
              "195   56   F     LOW        HIGH   11.567  drugC\n",
              "196   16   M     LOW        HIGH   12.006  drugC\n",
              "197   52   M  NORMAL        HIGH    9.894  drugX\n",
              "198   23   M  NORMAL      NORMAL   14.020  drugX\n",
              "199   40   F     LOW      NORMAL   11.349  drugX\n",
              "\n",
              "[200 rows x 6 columns]"
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              "      <td>NORMAL</td>\n",
              "      <td>11.349</td>\n",
              "      <td>drugX</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-66df3a83-f3b4-4f5a-ba82-dcf31def0a81')\"\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",
              "  <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-66df3a83-f3b4-4f5a-ba82-dcf31def0a81 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-66df3a83-f3b4-4f5a-ba82-dcf31def0a81');\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
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c11db370",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:30.064058Z",
          "iopub.status.busy": "2022-12-20T07:31:30.063646Z",
          "iopub.status.idle": "2022-12-20T07:31:30.088850Z",
          "shell.execute_reply": "2022-12-20T07:31:30.087617Z"
        },
        "papermill": {
          "duration": 0.040641,
          "end_time": "2022-12-20T07:31:30.091808",
          "exception": false,
          "start_time": "2022-12-20T07:31:30.051167",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c11db370",
        "outputId": "02e8dd0d-f0bc-4700-e179-b28bd3e24ce1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Age          200 non-null    int64  \n",
            " 1   Sex          200 non-null    object \n",
            " 2   BP           200 non-null    object \n",
            " 3   Cholesterol  200 non-null    object \n",
            " 4   Na_to_K      200 non-null    float64\n",
            " 5   Drug         200 non-null    object \n",
            "dtypes: float64(1), int64(1), object(4)\n",
            "memory usage: 9.5+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e5155736",
      "metadata": {
        "papermill": {
          "duration": 0.01039,
          "end_time": "2022-12-20T07:31:30.113560",
          "exception": false,
          "start_time": "2022-12-20T07:31:30.103170",
          "status": "completed"
        },
        "tags": [],
        "id": "e5155736"
      },
      "source": [
        "## Missing Values "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3172c910",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:30.137030Z",
          "iopub.status.busy": "2022-12-20T07:31:30.136255Z",
          "iopub.status.idle": "2022-12-20T07:31:30.146428Z",
          "shell.execute_reply": "2022-12-20T07:31:30.145249Z"
        },
        "papermill": {
          "duration": 0.024772,
          "end_time": "2022-12-20T07:31:30.148828",
          "exception": false,
          "start_time": "2022-12-20T07:31:30.124056",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3172c910",
        "outputId": "54c41307-5454-4824-9add-e02eb5b6ccbe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age            0\n",
              "Sex            0\n",
              "BP             0\n",
              "Cholesterol    0\n",
              "Na_to_K        0\n",
              "Drug           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "data.isna().sum() #checking missed values in the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "249f58f7",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:30.172106Z",
          "iopub.status.busy": "2022-12-20T07:31:30.171605Z",
          "iopub.status.idle": "2022-12-20T07:31:30.184998Z",
          "shell.execute_reply": "2022-12-20T07:31:30.183894Z"
        },
        "papermill": {
          "duration": 0.027831,
          "end_time": "2022-12-20T07:31:30.187206",
          "exception": false,
          "start_time": "2022-12-20T07:31:30.159375",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "249f58f7",
        "outputId": "f13823d8-ed7d-40ec-8d02-a0bca207ec48"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age             57\n",
              "Sex              2\n",
              "BP               3\n",
              "Cholesterol      2\n",
              "Na_to_K        198\n",
              "Drug             5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "data.nunique() # returning unique elements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "46e756f7",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:30.210815Z",
          "iopub.status.busy": "2022-12-20T07:31:30.210316Z",
          "iopub.status.idle": "2022-12-20T07:31:30.216078Z",
          "shell.execute_reply": "2022-12-20T07:31:30.214955Z"
        },
        "papermill": {
          "duration": 0.020566,
          "end_time": "2022-12-20T07:31:30.218445",
          "exception": false,
          "start_time": "2022-12-20T07:31:30.197879",
          "status": "completed"
        },
        "tags": [],
        "id": "46e756f7"
      },
      "outputs": [],
      "source": [
        "plt.style.use('ggplot')\n",
        "plt.rcParams[\"figure.figsize\"] = (10,5) #valuating data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be7b1c20",
      "metadata": {
        "papermill": {
          "duration": 0.010319,
          "end_time": "2022-12-20T07:31:30.239438",
          "exception": false,
          "start_time": "2022-12-20T07:31:30.229119",
          "status": "completed"
        },
        "tags": [],
        "id": "be7b1c20"
      },
      "source": [
        "## Feature's selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4f5873d4",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:30.263404Z",
          "iopub.status.busy": "2022-12-20T07:31:30.262103Z",
          "iopub.status.idle": "2022-12-20T07:31:30.607372Z",
          "shell.execute_reply": "2022-12-20T07:31:30.604262Z"
        },
        "papermill": {
          "duration": 0.360212,
          "end_time": "2022-12-20T07:31:30.610184",
          "exception": false,
          "start_time": "2022-12-20T07:31:30.249972",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "4f5873d4",
        "outputId": "a797b55a-0db4-45c9-f165-9a4de05da888"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Age')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(data=data , x='Age' , kde=True)\n",
        "plt.title('Age')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2e85aca7",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:30.636785Z",
          "iopub.status.busy": "2022-12-20T07:31:30.635514Z",
          "iopub.status.idle": "2022-12-20T07:31:30.906020Z",
          "shell.execute_reply": "2022-12-20T07:31:30.904890Z"
        },
        "papermill": {
          "duration": 0.2866,
          "end_time": "2022-12-20T07:31:30.908858",
          "exception": false,
          "start_time": "2022-12-20T07:31:30.622258",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "2e85aca7",
        "outputId": "0d6d9463-4002-4e79-874d-6692286feaca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Na_to_K')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(data=data , x='Na_to_K' , kde=True)\n",
        "plt.title('Na_to_K')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0db3f234",
      "metadata": {
        "papermill": {
          "duration": 0.015297,
          "end_time": "2022-12-20T07:31:33.734946",
          "exception": false,
          "start_time": "2022-12-20T07:31:33.719649",
          "status": "completed"
        },
        "tags": [],
        "id": "0db3f234"
      },
      "source": [
        "## Features with target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5df7e630",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:33.766188Z",
          "iopub.status.busy": "2022-12-20T07:31:33.765706Z",
          "iopub.status.idle": "2022-12-20T07:31:34.476983Z",
          "shell.execute_reply": "2022-12-20T07:31:34.475731Z"
        },
        "papermill": {
          "duration": 0.729801,
          "end_time": "2022-12-20T07:31:34.479512",
          "exception": false,
          "start_time": "2022-12-20T07:31:33.749711",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 863
        },
        "id": "5df7e630",
        "outputId": "a3539db1-7bb6-4088-b7ad-09276a1e8eef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for i in data.columns : \n",
        "    if (data[i].dtype == 'object') & ( i != 'Drug') : \n",
        "        plt.figure(figsize=(8,4))\n",
        "        sns.countplot(data=data , hue=i , x='Drug')\n",
        "        plt.title(i)\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a7e082a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.513644Z",
          "iopub.status.busy": "2022-12-20T07:31:34.513150Z",
          "iopub.status.idle": "2022-12-20T07:31:34.519010Z",
          "shell.execute_reply": "2022-12-20T07:31:34.517848Z"
        },
        "papermill": {
          "duration": 0.024802,
          "end_time": "2022-12-20T07:31:34.521314",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.496512",
          "status": "completed"
        },
        "tags": [],
        "id": "3a7e082a"
      },
      "outputs": [],
      "source": [
        "data_final = data.copy() #copy of the variable data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fbe2ff12",
      "metadata": {
        "papermill": {
          "duration": 0.014704,
          "end_time": "2022-12-20T07:31:34.550877",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.536173",
          "status": "completed"
        },
        "tags": [],
        "id": "fbe2ff12"
      },
      "source": [
        "## Features Engineering "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fbea95fd",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.583530Z",
          "iopub.status.busy": "2022-12-20T07:31:34.583020Z",
          "iopub.status.idle": "2022-12-20T07:31:34.601054Z",
          "shell.execute_reply": "2022-12-20T07:31:34.599882Z"
        },
        "papermill": {
          "duration": 0.037532,
          "end_time": "2022-12-20T07:31:34.603412",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.565880",
          "status": "completed"
        },
        "tags": [],
        "id": "fbea95fd"
      },
      "outputs": [],
      "source": [
        "for i in data_final.drop('Drug' , axis=1).columns : \n",
        "    if data_final[i].dtype == 'object' : \n",
        "        dummies = pd.get_dummies(data[i] , prefix=f'{i}_')\n",
        "        data_final = pd.concat([data_final , dummies] , axis=1)\n",
        "        data_final = data_final.drop(i , axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b1ba985c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.636951Z",
          "iopub.status.busy": "2022-12-20T07:31:34.635620Z",
          "iopub.status.idle": "2022-12-20T07:31:34.651998Z",
          "shell.execute_reply": "2022-12-20T07:31:34.650755Z"
        },
        "papermill": {
          "duration": 0.035675,
          "end_time": "2022-12-20T07:31:34.654402",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.618727",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "b1ba985c",
        "outputId": "cff3ee53-3883-48b5-a220-4795ed44bda9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Na_to_K   Drug  Sex__F  Sex__M  BP__HIGH  BP__LOW  BP__NORMAL  \\\n",
              "0   23   25.355  DrugY       1       0         1        0           0   \n",
              "1   47   13.093  drugC       0       1         0        1           0   \n",
              "2   47   10.114  drugC       0       1         0        1           0   \n",
              "3   28    7.798  drugX       1       0         0        0           1   \n",
              "4   61   18.043  DrugY       1       0         0        1           0   \n",
              "\n",
              "   Cholesterol__HIGH  Cholesterol__NORMAL  \n",
              "0                  1                    0  \n",
              "1                  1                    0  \n",
              "2                  1                    0  \n",
              "3                  1                    0  \n",
              "4                  1                    0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b1e8beb-3f0e-4264-815d-3c55010ecdfa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "      <th>Sex__F</th>\n",
              "      <th>Sex__M</th>\n",
              "      <th>BP__HIGH</th>\n",
              "      <th>BP__LOW</th>\n",
              "      <th>BP__NORMAL</th>\n",
              "      <th>Cholesterol__HIGH</th>\n",
              "      <th>Cholesterol__NORMAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>25.355</td>\n",
              "      <td>DrugY</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>18.043</td>\n",
              "      <td>DrugY</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      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-3b1e8beb-3f0e-4264-815d-3c55010ecdfa 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-3b1e8beb-3f0e-4264-815d-3c55010ecdfa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "data_final.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c8054d3d",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.687995Z",
          "iopub.status.busy": "2022-12-20T07:31:34.686827Z",
          "iopub.status.idle": "2022-12-20T07:31:34.694144Z",
          "shell.execute_reply": "2022-12-20T07:31:34.692988Z"
        },
        "papermill": {
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          "status": "completed"
        },
        "tags": [],
        "id": "c8054d3d"
      },
      "outputs": [],
      "source": [
        "features = data_final.drop('Drug' , axis=1)\n",
        "target = data_final['Drug']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b41bb86c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.730792Z",
          "iopub.status.busy": "2022-12-20T07:31:34.730327Z",
          "iopub.status.idle": "2022-12-20T07:31:34.743600Z",
          "shell.execute_reply": "2022-12-20T07:31:34.742757Z"
        },
        "papermill": {
          "duration": 0.032819,
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          "start_time": "2022-12-20T07:31:34.712794",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "b41bb86c",
        "outputId": "4509cd21-6bc7-4e24-f48b-98d764d03d4b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Na_to_K  Sex__F  Sex__M  BP__HIGH  BP__LOW  BP__NORMAL  \\\n",
              "0   23   25.355       1       0         1        0           0   \n",
              "1   47   13.093       0       1         0        1           0   \n",
              "2   47   10.114       0       1         0        1           0   \n",
              "3   28    7.798       1       0         0        0           1   \n",
              "4   61   18.043       1       0         0        1           0   \n",
              "\n",
              "   Cholesterol__HIGH  Cholesterol__NORMAL  \n",
              "0                  1                    0  \n",
              "1                  1                    0  \n",
              "2                  1                    0  \n",
              "3                  1                    0  \n",
              "4                  1                    0  "
            ],
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-afa91163-8cf0-4b2e-97d3-1866a8615893 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-afa91163-8cf0-4b2e-97d3-1866a8615893');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "features.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c52c84a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.779215Z",
          "iopub.status.busy": "2022-12-20T07:31:34.778809Z",
          "iopub.status.idle": "2022-12-20T07:31:34.787029Z",
          "shell.execute_reply": "2022-12-20T07:31:34.786085Z"
        },
        "papermill": {
          "duration": 0.027658,
          "end_time": "2022-12-20T07:31:34.789204",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.761546",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c52c84a",
        "outputId": "60e343d7-821c-4d73-be9f-284d6644f650"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DrugY    91\n",
              "drugX    54\n",
              "drugA    23\n",
              "drugC    16\n",
              "drugB    16\n",
              "Name: Drug, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "target.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ee89508",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.823778Z",
          "iopub.status.busy": "2022-12-20T07:31:34.823314Z",
          "iopub.status.idle": "2022-12-20T07:31:34.829638Z",
          "shell.execute_reply": "2022-12-20T07:31:34.828874Z"
        },
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          "end_time": "2022-12-20T07:31:34.831683",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.805661",
          "status": "completed"
        },
        "tags": [],
        "id": "4ee89508"
      },
      "outputs": [],
      "source": [
        "x_train , x_test , y_train , y_test = train_test_split(features , target , test_size=0.2 , shuffle=True  )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "15c19e9f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.865913Z",
          "iopub.status.busy": "2022-12-20T07:31:34.865114Z",
          "iopub.status.idle": "2022-12-20T07:31:34.872008Z",
          "shell.execute_reply": "2022-12-20T07:31:34.870501Z"
        },
        "papermill": {
          "duration": 0.026536,
          "end_time": "2022-12-20T07:31:34.874438",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.847902",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "15c19e9f",
        "outputId": "4bf50b04-b82a-40b6-8099-a7729bb56c9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Data = 160\n",
            "Test Data = 40\n"
          ]
        }
      ],
      "source": [
        "print(f'Train Data = {len(x_train)}')\n",
        "print(f'Test Data = {len(x_test)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7259d195",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.910535Z",
          "iopub.status.busy": "2022-12-20T07:31:34.910132Z",
          "iopub.status.idle": "2022-12-20T07:31:34.915433Z",
          "shell.execute_reply": "2022-12-20T07:31:34.914088Z"
        },
        "papermill": {
          "duration": 0.025891,
          "end_time": "2022-12-20T07:31:34.917794",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.891903",
          "status": "completed"
        },
        "tags": [],
        "id": "7259d195"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler() #normalisation of data\n",
        "#he distribution of each feature is centered around 0 with a standard deviation of 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1139a23f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:34.952107Z",
          "iopub.status.busy": "2022-12-20T07:31:34.951739Z",
          "iopub.status.idle": "2022-12-20T07:31:34.963154Z",
          "shell.execute_reply": "2022-12-20T07:31:34.961862Z"
        },
        "papermill": {
          "duration": 0.031709,
          "end_time": "2022-12-20T07:31:34.965789",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.934080",
          "status": "completed"
        },
        "tags": [],
        "id": "1139a23f"
      },
      "outputs": [],
      "source": [
        "#standardisation of dataset\n",
        "\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_test = scaler.transform(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "36f4fb19",
      "metadata": {
        "papermill": {
          "duration": 0.015888,
          "end_time": "2022-12-20T07:31:34.998383",
          "exception": false,
          "start_time": "2022-12-20T07:31:34.982495",
          "status": "completed"
        },
        "tags": [],
        "id": "36f4fb19"
      },
      "source": [
        "## Fix Imbalance Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73570caf",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.033215Z",
          "iopub.status.busy": "2022-12-20T07:31:35.032837Z",
          "iopub.status.idle": "2022-12-20T07:31:35.037923Z",
          "shell.execute_reply": "2022-12-20T07:31:35.036625Z"
        },
        "papermill": {
          "duration": 0.025553,
          "end_time": "2022-12-20T07:31:35.040181",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.014628",
          "status": "completed"
        },
        "tags": [],
        "id": "73570caf"
      },
      "outputs": [],
      "source": [
        "smote = SMOTE()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "38bd6016",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.075596Z",
          "iopub.status.busy": "2022-12-20T07:31:35.075165Z",
          "iopub.status.idle": "2022-12-20T07:31:35.088747Z",
          "shell.execute_reply": "2022-12-20T07:31:35.087548Z"
        },
        "papermill": {
          "duration": 0.034179,
          "end_time": "2022-12-20T07:31:35.091419",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.057240",
          "status": "completed"
        },
        "tags": [],
        "id": "38bd6016"
      },
      "outputs": [],
      "source": [
        "x_train , y_train = smote.fit_resample(x_train , y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c365abfc",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.125976Z",
          "iopub.status.busy": "2022-12-20T07:31:35.125158Z",
          "iopub.status.idle": "2022-12-20T07:31:35.135223Z",
          "shell.execute_reply": "2022-12-20T07:31:35.134178Z"
        },
        "papermill": {
          "duration": 0.029854,
          "end_time": "2022-12-20T07:31:35.137353",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.107499",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c365abfc",
        "outputId": "34fb20ad-4423-4822-dfe0-b5e9a651c6ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DrugY    77\n",
              "drugB    77\n",
              "drugX    77\n",
              "drugA    77\n",
              "drugC    77\n",
              "Name: Drug, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "y_train.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00e535dd",
      "metadata": {
        "papermill": {
          "duration": 0.016408,
          "end_time": "2022-12-20T07:31:35.169431",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.153023",
          "status": "completed"
        },
        "tags": [],
        "id": "00e535dd"
      },
      "source": [
        "## Modeling "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4088294f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.203685Z",
          "iopub.status.busy": "2022-12-20T07:31:35.202627Z",
          "iopub.status.idle": "2022-12-20T07:31:35.207611Z",
          "shell.execute_reply": "2022-12-20T07:31:35.206893Z"
        },
        "papermill": {
          "duration": 0.024287,
          "end_time": "2022-12-20T07:31:35.209804",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.185517",
          "status": "completed"
        },
        "tags": [],
        "id": "4088294f"
      },
      "outputs": [],
      "source": [
        "model = LogisticRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "19fa75c9",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.243206Z",
          "iopub.status.busy": "2022-12-20T07:31:35.242497Z",
          "iopub.status.idle": "2022-12-20T07:31:35.271028Z",
          "shell.execute_reply": "2022-12-20T07:31:35.269727Z"
        },
        "papermill": {
          "duration": 0.048096,
          "end_time": "2022-12-20T07:31:35.273469",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.225373",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19fa75c9",
        "outputId": "7c52e720-f2da-4f54-8180-231341e8cf1c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "model.fit(x_train , y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "718468dd",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.308127Z",
          "iopub.status.busy": "2022-12-20T07:31:35.306852Z",
          "iopub.status.idle": "2022-12-20T07:31:35.312923Z",
          "shell.execute_reply": "2022-12-20T07:31:35.311918Z"
        },
        "papermill": {
          "duration": 0.025565,
          "end_time": "2022-12-20T07:31:35.315083",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.289518",
          "status": "completed"
        },
        "tags": [],
        "id": "718468dd"
      },
      "outputs": [],
      "source": [
        "prediction = model.predict(x_test) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd53bdcb",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.348986Z",
          "iopub.status.busy": "2022-12-20T07:31:35.348043Z",
          "iopub.status.idle": "2022-12-20T07:31:35.355889Z",
          "shell.execute_reply": "2022-12-20T07:31:35.354574Z"
        },
        "papermill": {
          "duration": 0.027296,
          "end_time": "2022-12-20T07:31:35.358048",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.330752",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dd53bdcb",
        "outputId": "67f1f691-89f4-426c-e336-776ed881aa42"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.975"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "accuracy_score(y_test , prediction)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b3cd5d5",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.392866Z",
          "iopub.status.busy": "2022-12-20T07:31:35.391686Z",
          "iopub.status.idle": "2022-12-20T07:31:35.404744Z",
          "shell.execute_reply": "2022-12-20T07:31:35.403059Z"
        },
        "papermill": {
          "duration": 0.033481,
          "end_time": "2022-12-20T07:31:35.407756",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.374275",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7b3cd5d5",
        "outputId": "f7985776-4b2c-456b-98d8-351f7fc95c7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       DrugY       0.93      1.00      0.97        14\n",
            "       drugA       1.00      1.00      1.00         6\n",
            "       drugB       1.00      0.83      0.91         6\n",
            "       drugC       1.00      1.00      1.00         3\n",
            "       drugX       1.00      1.00      1.00        11\n",
            "\n",
            "    accuracy                           0.97        40\n",
            "   macro avg       0.99      0.97      0.97        40\n",
            "weighted avg       0.98      0.97      0.97        40\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test , prediction))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6c2f1690",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.443210Z",
          "iopub.status.busy": "2022-12-20T07:31:35.442737Z",
          "iopub.status.idle": "2022-12-20T07:31:35.454208Z",
          "shell.execute_reply": "2022-12-20T07:31:35.452752Z"
        },
        "papermill": {
          "duration": 0.032684,
          "end_time": "2022-12-20T07:31:35.456622",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.423938",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c2f1690",
        "outputId": "96e34943-2027-476b-dfc9-fa3c5b14571a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[14,  0,  0,  0,  0],\n",
              "       [ 0,  6,  0,  0,  0],\n",
              "       [ 1,  0,  5,  0,  0],\n",
              "       [ 0,  0,  0,  3,  0],\n",
              "       [ 0,  0,  0,  0, 11]])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "confusion_matrix(y_test , prediction) #defining the sa"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3f139180",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-12-20T07:31:35.490797Z",
          "iopub.status.busy": "2022-12-20T07:31:35.490383Z",
          "iopub.status.idle": "2022-12-20T07:31:35.739053Z",
          "shell.execute_reply": "2022-12-20T07:31:35.737761Z"
        },
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          "duration": 0.26882,
          "end_time": "2022-12-20T07:31:35.741627",
          "exception": false,
          "start_time": "2022-12-20T07:31:35.472807",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "3f139180",
        "outputId": "11c660e9-c11b-4a0f-f17b-bba31b325915"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f974b1a6e80>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAFlCAYAAADLZQJMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAVQElEQVR4nO3bcWzUd/3H8dfdtbTSsc42si3Xdh2jykoky2SdSDRDDquzUrK4BRpnyBbMMJUlc1ims1tIHMWlOpCOGrPq/tFGEqPVLGtyZkqimTvXVZRyo4dt6B0jZe1olcEo169/+LPfXwMcHWv53Pv6fCQkuzt633fe++6Zb+++C3ie5wkAYEbQ9QAAgPeHcAOAMYQbAIwh3ABgDOEGAGMINwAYk+d6gPkkEAi4HgGAEZnu1OaKGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxhBuADCGcAOAMYQbAIwh3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjci7cTzzxhFatWqW6urqLXuvo6NDHPvYxjY6OOpjMvdraWsXjcfX396upqcn1OE6xCx+78JnZhZdjXnvtNe8f//iH98UvfnHa8ydOnPAeeugh75577vFGRkaczCbJ2Z9gMOglEgnv1ltv9fLz873e3l7v9ttvdzoTu3D/h11k7y4yybkr7rvuukvFxcUXPb9r1y5t375dgUDAwVTu1dTUKJFIaGBgQBMTE+rs7FR9fb3rsZxgFz524bO0i7wr/YVUKqVYLDb18UJJSYlWrlypsrKyOR9utkSjUS1evFjLli1zPYoz4XBYQ0NDU4+TyaTuvvtuhxO5wy587MJnaRcZw/3rX/9af/rTn7R69WotXbpUkjQ6Oqo9e/Zo9erV2rBhwyV/LhqNKhqNSpJaWlpmeeT35+zZs/rxj3+sjo4Op3MAwGzJGO5XXnlFra2tysub/tfq6ur02GOPXTbckUhEkUhk9qb8AI4fP65kMjn1K8/Jkyd133336cCBA/rIRz7ieLprJ5VKqby8fOpxWVmZUqmUw4ncYRc+duEztYtMH4A/+uij3vDw8EXPDw8Pe9u2bftg39TNoaGhoYu+nPyfNWvWzMsvJ0OhkHfs2DGvsrJy6ouX6upq518IsQt2kS1/sm0XmWS84t68ebN27typm2++WaWlpZKkt99+WydPntTDDz+c6Uedeeyxx/Taa6/pnXfe0Wc+8xl94xvf0P333+96LOfS6bQaGxvV3d2tUCikjo4O9fX1uR7LCXbhYxc+S7sI/N+V4GVNTk4qkUhM+3Jy6dKlCgZz7oaUOTdf72gB8P5lSvMVw43ZQ7gBzFSmNHPZDADGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxhBuADCGcAOAMYQbAIwh3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMCZvrg8QCATm+hBm7Ny50/UIWaO5udn1CIBZXHEDgDGEGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxhBuADCGcAOAMYQbAIwh3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjcj7ctbW1isfj6u/vV1NTk+txnCooKNCXv/xlbd26VVu3blU4HHY9kjOcFz524bOyi4Dned6cHiAQmMu3zygYDOro0aNat26dksmkYrGYNm3apCNHjjiZZ+fOnU6O+z/r16/X8ePH1dvbq2AwqPz8fL333ntOZmlubnZyXCn7zguX2IUv23aRKc05fcVdU1OjRCKhgYEBTUxMqLOzU/X19a7HcqKgoEAVFRXq7e2VJE1OTjqLtmucFz524bO0i5wOdzgc1tDQ0NTjZDI5bz8euOGGG/Tuu+9q/fr12rJli+rq6pSfn+96LCc4L3zswmdpF3lX+4OvvPKK1qxZc8nXotGootHoVQ+F2RcMBnXzzTfr5Zdf1okTJ/S5z31Oq1ev1h/+8AfXowF4n676ivuXv/zlZV+LRCJqaWlRS0vL1b79rEilUiovL596XFZWplQq5XAid8bHxzU+Pq4TJ05Iko4cOaKbbrrJ8VRucF742IXP0i4yXnE//vjjl3ze8zyNjY3NyUCzKRaLqaqqSpWVlUqlUtq4caMaGhpcj+XEmTNnND4+rtLSUo2MjOjWW2/VqVOnXI/lBOeFj134LO0iY7jHxsb0ne98R0VFRdOe9zxP3/3ud+d0sNmQTqfV2Nio7u5uhUIhdXR0qK+vz/VYzrz88svasGGDQqGQTp8+ra6uLtcjOcF54WMXPku7yHg74P79+7VmzRotW7bsotf27NmjRx999MoHcHg7YLZxfTtgNnF5OyBgQabbATNecW/duvWyr80k2gCA2ZfTtwMCQC4i3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxhBuADCGcAOAMYQbAIwh3ABgDOEGAGMCnud5c3mAxYsXz+Xbm3Lq1CnXI2SNbdu2uR4ha+zdu9f1CMhCmdLMFTcAGEO4AcAYwg0AxhBuADCGcAOAMYQbAIwh3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxuR8uJ977jkdPnxYf/zjH12P4lxtba3i8bj6+/vV1NTkehynmpub1dTUpO3bt+ub3/ym63Gc4rzwWdlFnusB5lpnZ6deeOEF7du3z/UoTgWDQbW1tWndunVKJpOKxWLq6urSkSNHXI/mzL59+3TmzBnXYzjFeeGztIucv+J+9dVXdfr0addjOFdTU6NEIqGBgQFNTEyos7NT9fX1rseCY5wXPku7uGK4U6mU/v73v+vcuXPTnu/t7Z2zoTD7wuGwhoaGph4nk0mFw2GHE7m3detWPf7441q1apXrUZzhvPBZ2kXGj0peeukldXd3KxwOq729XZs3b9Zdd90lSfrFL36hO+6445oMCcy2PXv2aGxsTNddd52+/vWva3h4WMeOHXM9FjAjGcP9+9//Xrt371ZhYaGGh4f1gx/8QKdOndK9994rz/Mu+3PRaFTRaHTWh8XVS6VSKi8vn3pcVlamVCrlcCK3xsbGJEn//ve/dejQIVVUVMzLcHNe+CztIuNHJZ7nqbCwUJK0ePFiPf3003rjjTf04osvZgx3JBJRS0uLWlpaZndaXLVYLKaqqipVVlYqPz9fGzduVFdXl+uxnFiwYIEKCgqm/nnZsmV66623HE/lBueFz9IuMl5xFxcXa3BwUJWVlZKkwsJC7dixQ/v379fx48evxXwfWHt7u1avXq2SkhL19vbq+9//vn7+85+7HuuaS6fTamxsVHd3t0KhkDo6OtTX1+d6LCcWLVqkhx9+WNJ/7yR4/fXXFY/HHU/lBueFz9IuAl6GS+eRkRGFQiHdcMMNF70Wj8e1bNmyKx5g8eLFH2zCHHLq1CnXI2SNbdu2uR4ha+zdu9f1CMhCmT7VyHjFXVpaetnXZhJtAMDsy/n7uAEg1xBuADCGcAOAMYQbAIwh3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxhBuADCGcAOAMQHP87w5PUAgMJdvD5gXiURcj5A1otGo6xGyRqY0c8UNAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxhBuADCGcAOAMYQbAIwh3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMyflw19bWKh6Pq7+/X01NTa7HcYpd+NjFf+Xn5+tHP/qR2tvb9ZOf/ERf/epXXY/klJXzIuB5njenBwgE5vLtMwoGgzp69KjWrVunZDKpWCymTZs26ciRI85mcoVd+LJtF5FIxMlx/6ewsFDnzp1TKBTSD3/4Q+3fv9/ZLqLRqJPjStl3XmRKc05fcdfU1CiRSGhgYEATExPq7OxUfX2967GcYBc+djHduXPnJEl5eXnKy8vLGIxcZum8uGK4E4mEEomEJCmZTOp3v/udenp65nyw2RAOhzU0NDT1OJlMKhwOO5zIHXbhYxfTBYNBtbe368CBA+rp6VE8Hnc9khOWzou8TC8eOHBAvb29SqfTWrFihfr7+7V8+XL95je/0eDgoO67775L/lw0GnX6Kw+AmZucnNQjjzyioqIiPf3006qsrNTg4KDrsZBBxnC/+uqrevbZZzUxMaGvfe1r2r9/vxYuXKj169fr29/+9mXDHYlEpj6327179+xPPUOpVErl5eVTj8vKypRKpZzN4xK78LGLSztz5oz+9re/aeXKlfMy3JbOi4wflYRCIQWDQRUUFOjGG2/UwoULJUkLFixw+qXjTMViMVVVVamyslL5+fnauHGjurq6XI/lBLvwsQtfcXGxioqKJP33v+s777xz2scF84ml8yLjFXdeXp7ee+89FRQUqKWlZer5d999V8Fg9n+vmU6n1djYqO7uboVCIXV0dKivr8/1WE6wCx+78JWUlOhb3/qWgsGgAoGADh48qL/85S+ux3LC0nmR8XbAiYkJ5efnX/T8+Pi4Tp8+rYqKiisfwMCVOeCS69sBswnfjfky3d2T8Yr7UtGWpOuvv17XX3/9B5sKAHBVsv/zDgDANIQbAIwh3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxhBuADCGcAOAMYQbAIwJeJ7nzekBAoG5fHsAOWRwcND1CFnjlltuuexrXHEDgDGEGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxhBuADCGcAOAMYQbAIwh3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjcj7ctbW1isfj6u/vV1NTk+txnGIXPnbhm8+7aG1t1f33368tW7ZMPXfw4EFt2bJFtbW1Onr0qMPpLi+nwx0MBtXW1qYvfOELqq6u1qZNm3T77be7HssJduFjF775vot169bpmWeemfZcZWWlmpub9fGPf9zRVFeW0+GuqalRIpHQwMCAJiYm1NnZqfr6etdjOcEufOzCN993sWLFCi1atGjacxUVFSovL3c00cy873Dv27dvLuaYE+FwWENDQ1OPk8mkwuGww4ncYRc+duFjFzblZXpx9+7d0x57nqfDhw/rzJkzknTZz8Oi0aii0egsjQgA+P8yhnt0dFThcFhr165VIBCQ53n65z//qS996UsZ3zQSiSgSiUi6OP7XUiqVmvYrT1lZmVKplLN5XGIXPnbhYxc2ZfyoZNeuXVqyZIl+9atfaeHChVq+fLkWLFig6upqVVdXX6sZr1osFlNVVZUqKyuVn5+vjRs3qqury/VYTrALH7vwsQubMl5xB4NB1dXVadWqVXrxxRdVXFysdDp9rWb7wNLptBobG9Xd3a1QKKSOjg719fW5HssJduFjF775votnnnlGhw4d0tjYmBoaGvTggw9q0aJFev755zU2NqYnn3xSt912m3bt2uV61GkCnud5M/3LPT09isfjamhomPkBAoGrGgzA/DM4OOh6hKxxyy23XPa19xXuq0G4AcwU4fZlCndO38cNALmIcAOAMYQbAIwh3ABgDOEGAGMINwAYQ7gBwBjCDQDGEG4AMIZwA4AxhBsAjCHcAGAM4QYAYwg3ABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBjCDcAGEO4AcAYwg0AxhBuADCGcAOAMYQbAIzJm+sDeJ4314eYkWg0qkgk4nqMrMAufOzCxy582b6LeXPFHY1GXY+QNdiFj1342IUv23cxb8INALmCcAOAMfMm3Nn8edW1xi587MLHLnzZvouAly3fHgIAZmTeXHEDQK6Y89sBXevt7dVPf/pTTU5Oau3atdqwYYPrkZx5/vnn1dPTo+LiYrW2troex5m3335bbW1tOn36tAKBgCKRiO69917XYzlx/vx5PfXUU7pw4YLS6bQ++clP6oEHHnA9llOTk5PasWOHSkpKtGPHDtfjXFJOh3tyclIvvPCCnnzySZWWluqJJ57QypUrVVZW5no0J+655x59/vOfV1tbm+tRnAqFQnrwwQe1ZMkSnT17Vjt27NCKFSvm5XmRn5+vp556SoWFhbpw4YKam5t1xx136KMf/ajr0Zx56aWXFA6HdfbsWdejXFZOf1SSSCR000036cYbb1ReXp4+9alPKRaLuR7Lmerqal133XWux3Duwx/+sJYsWSJJ+tCHPqRwOKzR0VHHU7kRCARUWFgoSUqn00qn0woEAo6ncmdkZEQ9PT1au3at61Eyyukr7tHRUZWWlk49Li0tVX9/v8OJkG2Gh4c1MDCgpUuXuh7FmcnJSTU1NenkyZOqra1VVVWV65Gc+dnPfqavfOUrWX21LeX4FTeQyblz59Ta2qrNmzdr4cKFrsdxJhgM6tlnn1V7e7uOHTum48ePux7Jiddff13FxcVTv41ls5y+4i4pKdHIyMjU45GREZWUlDicCNniwoULam1t1ac//WndfffdrsfJCkVFRVq+fLl6e3tVUVHhepxr7s0339Rf//pXvfHGGzp//rzOnj2rvXv3atu2ba5Hu0hOh/u2227TW2+9peHhYZWUlOjPf/5zVv5LwLXleZ7a29sVDodVV1fnehynxsfHFQqFVFRUpPPnz+vQoUOqr693PZYTDQ0NamhokCQdPnxYv/3tb7O2Fzkd7lAopIceekjf+973NDk5qTVr1qi8vNz1WM4899xz6uvr07/+9S898sgjeuCBB/TZz37W9VjX3JtvvqmDBw+qoqJC27dvlyRt2rRJd955p+PJrr133nlHbW1tmpyclOd5WrVqlT7xiU+4HgtXwP85CQDG8OUkABhDuAHAGMINAMYQbgAwhnADgDGEGwCMIdwAYAzhBgBj/gOziBsJRxkY6AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(6,6))\n",
        "sns.heatmap(confusion_matrix(y_test , prediction ) , cbar=False , annot=True , cmap='Greys_r')"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ac72OR-u8Khu"
      },
      "id": "Ac72OR-u8Khu",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.13"
    },
    "papermill": {
      "default_parameters": {},
      "duration": 17.431724,
      "end_time": "2022-12-20T07:31:36.678287",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2022-12-20T07:31:19.246563",
      "version": "2.3.4"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}