Drug_Classification.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import cross_val_score"
      ]
    },
    {
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      "source": [
        "<a id='1'></a>\n",
        "# Read Data and PreCheck"
      ]
    },
    {
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      "execution_count": 8,
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      "outputs": [],
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/drug200.csv\")"
      ]
    },
    {
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              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
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              "      <th>0</th>\n",
              "      <td>23</td>\n",
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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"
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              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>44.315000</td>\n",
              "      <td>16.084485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.544315</td>\n",
              "      <td>7.223956</td>\n",
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              "      <th>min</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>6.269000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>10.445500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>13.936500</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>19.380000</td>\n",
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              "      <th>max</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "              Age     Na_to_K\n",
              "count  200.000000  200.000000\n",
              "mean    44.315000   16.084485\n",
              "std     16.544315    7.223956\n",
              "min     15.000000    6.269000\n",
              "25%     31.000000   10.445500\n",
              "50%     45.000000   13.936500\n",
              "75%     58.000000   19.380000\n",
              "max     74.000000   38.247000"
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          "execution_count": 10
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          "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"
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        "df.isnull().sum()"
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          "iopub.execute_input": "2021-01-01T13:53:53.035977Z",
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          "iopub.status.idle": "2021-01-01T13:53:53.039320Z",
          "shell.execute_reply": "2021-01-01T13:53:53.039939Z"
        },
        "papermill": {
          "duration": 0.065222,
          "end_time": "2021-01-01T13:53:53.040075",
          "exception": false,
          "start_time": "2021-01-01T13:53:52.974853",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "clIbEzM9_imE",
        "outputId": "205a606e-ffd1-46d0-b500-8abc6c728090"
      },
      "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": 12
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.055996,
          "end_time": "2021-01-01T13:53:53.262061",
          "exception": false,
          "start_time": "2021-01-01T13:53:53.206065",
          "status": "completed"
        },
        "tags": [],
        "id": "pSOhTexA_imF"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Variable Description\n",
        "\n",
        "* Age: Age of patient\n",
        "* Sex: Gender of patient\n",
        "* BP: Blood pressure of patient\n",
        "* Cholesterol: Cholesterol of patient\n",
        "* Na_to_K: Sodium to Potassium Ratio in Blood \n",
        "* Drug: Drug Type"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:53.377984Z",
          "iopub.status.busy": "2021-01-01T13:53:53.377272Z",
          "iopub.status.idle": "2021-01-01T13:53:53.390245Z",
          "shell.execute_reply": "2021-01-01T13:53:53.391154Z"
        },
        "papermill": {
          "duration": 0.073554,
          "end_time": "2021-01-01T13:53:53.391365",
          "exception": false,
          "start_time": "2021-01-01T13:53:53.317811",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "USTiCZlJ_imG",
        "outputId": "e31e068e-ff97-4457-f2a4-510bd4f295c4"
      },
      "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": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.057526,
          "end_time": "2021-01-01T13:53:53.506554",
          "exception": false,
          "start_time": "2021-01-01T13:53:53.449028",
          "status": "completed"
        },
        "tags": [],
        "id": "KfXGt4Qt_imG"
      },
      "source": [
        "* float64(1): Na_to_K\n",
        "* int64(1): Age\n",
        "* object(4): Sex, BP, Cholesterol, Drug"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.056461,
          "end_time": "2021-01-01T13:53:53.619757",
          "exception": false,
          "start_time": "2021-01-01T13:53:53.563296",
          "status": "completed"
        },
        "tags": [],
        "id": "k6YT9EAG_imG"
      },
      "source": [
        "<a id='3'></a>\n",
        "## Univariate Variable Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.056572,
          "end_time": "2021-01-01T13:53:53.732363",
          "exception": false,
          "start_time": "2021-01-01T13:53:53.675791",
          "status": "completed"
        },
        "tags": [],
        "id": "kI3v-6y6_imH"
      },
      "source": [
        "<a id='4'></a>\n",
        "### Age Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:53.854870Z",
          "iopub.status.busy": "2021-01-01T13:53:53.854172Z",
          "iopub.status.idle": "2021-01-01T13:53:53.859660Z",
          "shell.execute_reply": "2021-01-01T13:53:53.860447Z"
        },
        "papermill": {
          "duration": 0.070193,
          "end_time": "2021-01-01T13:53:53.860651",
          "exception": false,
          "start_time": "2021-01-01T13:53:53.790458",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6qy0L07_imH",
        "outputId": "00804fa4-8c6d-4afb-c868-b122c99f5e81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max Age: 74\n",
            "Min Age: 15\n"
          ]
        }
      ],
      "source": [
        "print(\"Max Age:\", df.Age.max())\n",
        "print(\"Min Age:\", df.Age.min())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:53.982721Z",
          "iopub.status.busy": "2021-01-01T13:53:53.982008Z",
          "iopub.status.idle": "2021-01-01T13:53:54.200340Z",
          "shell.execute_reply": "2021-01-01T13:53:54.199629Z"
        },
        "papermill": {
          "duration": 0.281926,
          "end_time": "2021-01-01T13:53:54.200449",
          "exception": false,
          "start_time": "2021-01-01T13:53:53.918523",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "7cgHyZd__imH",
        "outputId": "2795b053-e9b3-4ac9-8fc8-8a66682f3f21"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Age distribution\n",
        "plt.figure(figsize = (9,5))\n",
        "sns.distplot(df.Age)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.057472,
          "end_time": "2021-01-01T13:53:54.315918",
          "exception": false,
          "start_time": "2021-01-01T13:53:54.258446",
          "status": "completed"
        },
        "tags": [],
        "id": "RGFxjQQe_imI"
      },
      "source": [
        "* Age range is between 15 and 74."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.057075,
          "end_time": "2021-01-01T13:53:54.430627",
          "exception": false,
          "start_time": "2021-01-01T13:53:54.373552",
          "status": "completed"
        },
        "tags": [],
        "id": "PEZ-WH_f_imI"
      },
      "source": [
        "<a id='5'></a>\n",
        "### Sex Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:54.553290Z",
          "iopub.status.busy": "2021-01-01T13:53:54.552550Z",
          "iopub.status.idle": "2021-01-01T13:53:54.557254Z",
          "shell.execute_reply": "2021-01-01T13:53:54.556581Z"
        },
        "papermill": {
          "duration": 0.069281,
          "end_time": "2021-01-01T13:53:54.557367",
          "exception": false,
          "start_time": "2021-01-01T13:53:54.488086",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "68VDM-oc_imI",
        "outputId": "512a5575-eb4b-4f5b-d9b4-21c96535970f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M    104\n",
              "F     96\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df.Sex.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:54.691040Z",
          "iopub.status.busy": "2021-01-01T13:53:54.688752Z",
          "iopub.status.idle": "2021-01-01T13:53:54.797954Z",
          "shell.execute_reply": "2021-01-01T13:53:54.798760Z"
        },
        "papermill": {
          "duration": 0.18353,
          "end_time": "2021-01-01T13:53:54.798987",
          "exception": false,
          "start_time": "2021-01-01T13:53:54.615457",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Nax2uh6g_imJ",
        "outputId": "28eba41c-71d1-4888-87c5-9a1537a49c34"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Sex Distribution\n",
        "plt.figure(figsize=(9,5))\n",
        "sns.countplot(x = df.Sex)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.068769,
          "end_time": "2021-01-01T13:53:54.934054",
          "exception": false,
          "start_time": "2021-01-01T13:53:54.865285",
          "status": "completed"
        },
        "tags": [],
        "id": "vmsl5qwX_imJ"
      },
      "source": [
        "* The ratio of gender seems balanced in the data\n",
        "* This is a categorical variable. It would be better if we apply label encoder to avoid any error during model implementation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.05847,
          "end_time": "2021-01-01T13:53:55.055626",
          "exception": false,
          "start_time": "2021-01-01T13:53:54.997156",
          "status": "completed"
        },
        "tags": [],
        "id": "tUGJS9TJ_imK"
      },
      "source": [
        "<a id='6'></a>\n",
        "### BP Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:55.180682Z",
          "iopub.status.busy": "2021-01-01T13:53:55.180014Z",
          "iopub.status.idle": "2021-01-01T13:53:55.184505Z",
          "shell.execute_reply": "2021-01-01T13:53:55.184008Z"
        },
        "papermill": {
          "duration": 0.070169,
          "end_time": "2021-01-01T13:53:55.184620",
          "exception": false,
          "start_time": "2021-01-01T13:53:55.114451",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dynpD0S3_imK",
        "outputId": "8ab815ff-5759-4ef3-b82d-fed4ceadc9fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HIGH      77\n",
              "LOW       64\n",
              "NORMAL    59\n",
              "Name: BP, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df.BP.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:55.319349Z",
          "iopub.status.busy": "2021-01-01T13:53:55.317253Z",
          "iopub.status.idle": "2021-01-01T13:53:55.437139Z",
          "shell.execute_reply": "2021-01-01T13:53:55.436434Z"
        },
        "papermill": {
          "duration": 0.193165,
          "end_time": "2021-01-01T13:53:55.437264",
          "exception": false,
          "start_time": "2021-01-01T13:53:55.244099",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "2Hufhk8o_imK",
        "outputId": "c4c29612-596e-4ffb-e675-c391a9b4f170"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.countplot(df.BP)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.059496,
          "end_time": "2021-01-01T13:53:55.566309",
          "exception": false,
          "start_time": "2021-01-01T13:53:55.506813",
          "status": "completed"
        },
        "tags": [],
        "id": "y67CfVzB_imL"
      },
      "source": [
        "<a id='7'></a>\n",
        "### Cholesterol Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:55.695934Z",
          "iopub.status.busy": "2021-01-01T13:53:55.694954Z",
          "iopub.status.idle": "2021-01-01T13:53:55.699974Z",
          "shell.execute_reply": "2021-01-01T13:53:55.699376Z"
        },
        "papermill": {
          "duration": 0.073184,
          "end_time": "2021-01-01T13:53:55.700097",
          "exception": false,
          "start_time": "2021-01-01T13:53:55.626913",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2OiaiXot_imO",
        "outputId": "0151fd0d-540d-4a8a-bebd-2e91ca658394"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HIGH      103\n",
              "NORMAL     97\n",
              "Name: Cholesterol, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df.Cholesterol.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:55.839892Z",
          "iopub.status.busy": "2021-01-01T13:53:55.838839Z",
          "iopub.status.idle": "2021-01-01T13:53:55.945903Z",
          "shell.execute_reply": "2021-01-01T13:53:55.946458Z"
        },
        "papermill": {
          "duration": 0.185317,
          "end_time": "2021-01-01T13:53:55.946609",
          "exception": false,
          "start_time": "2021-01-01T13:53:55.761292",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "fYdxpPz6_imP",
        "outputId": "7ef41b5b-7296-4c39-ee70-4408843fa51b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.countplot(df.Cholesterol)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.062409,
          "end_time": "2021-01-01T13:53:56.072553",
          "exception": false,
          "start_time": "2021-01-01T13:53:56.010144",
          "status": "completed"
        },
        "tags": [],
        "id": "DIP3L6Fg_imP"
      },
      "source": [
        "* Cholesterol is a balanced data. \n",
        "* It is categorical and label encoder will apply on it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.06265,
          "end_time": "2021-01-01T13:53:56.198368",
          "exception": false,
          "start_time": "2021-01-01T13:53:56.135718",
          "status": "completed"
        },
        "tags": [],
        "id": "k4SO2wXx_imP"
      },
      "source": [
        "<a id='8'></a>\n",
        "### Na_to_K Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:56.332323Z",
          "iopub.status.busy": "2021-01-01T13:53:56.331504Z",
          "iopub.status.idle": "2021-01-01T13:53:56.335706Z",
          "shell.execute_reply": "2021-01-01T13:53:56.336317Z"
        },
        "papermill": {
          "duration": 0.074694,
          "end_time": "2021-01-01T13:53:56.336456",
          "exception": false,
          "start_time": "2021-01-01T13:53:56.261762",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2YWBuag_imP",
        "outputId": "fc2552db-3a0a-4d74-f42a-76f031d34f85"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max Na_to_K: 38.247\n",
            "Min Na_to_K: 6.269\n",
            "Mean Na_to_K: 16.08448499999999\n"
          ]
        }
      ],
      "source": [
        "print(\"Max Na_to_K:\",df.Na_to_K.max())\n",
        "print(\"Min Na_to_K:\",df.Na_to_K.min())\n",
        "print(\"Mean Na_to_K:\",df.Na_to_K.mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:56.477281Z",
          "iopub.status.busy": "2021-01-01T13:53:56.472177Z",
          "iopub.status.idle": "2021-01-01T13:53:56.630160Z",
          "shell.execute_reply": "2021-01-01T13:53:56.629609Z"
        },
        "papermill": {
          "duration": 0.231793,
          "end_time": "2021-01-01T13:53:56.630288",
          "exception": false,
          "start_time": "2021-01-01T13:53:56.398495",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "l15dvbyO_imQ",
        "outputId": "c2ba7b15-0f14-4d2d-d7e9-4d67dac4c79c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.distplot(df.Na_to_K)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.062612,
          "end_time": "2021-01-01T13:53:56.756310",
          "exception": false,
          "start_time": "2021-01-01T13:53:56.693698",
          "status": "completed"
        },
        "tags": [],
        "id": "-HcOqMqe_imQ"
      },
      "source": [
        "<a id='9'></a>\n",
        "### Drug Variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:56.892865Z",
          "iopub.status.busy": "2021-01-01T13:53:56.891789Z",
          "iopub.status.idle": "2021-01-01T13:53:56.895758Z",
          "shell.execute_reply": "2021-01-01T13:53:56.895250Z"
        },
        "papermill": {
          "duration": 0.0751,
          "end_time": "2021-01-01T13:53:56.895878",
          "exception": false,
          "start_time": "2021-01-01T13:53:56.820778",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOteX6kv_imR",
        "outputId": "ffdb1f56-2bee-4f40-f5ff-450f8691baf6"
      },
      "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": 24
        }
      ],
      "source": [
        "df.Drug.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:57.120200Z",
          "iopub.status.busy": "2021-01-01T13:53:57.032688Z",
          "iopub.status.idle": "2021-01-01T13:53:57.234582Z",
          "shell.execute_reply": "2021-01-01T13:53:57.233935Z"
        },
        "papermill": {
          "duration": 0.275339,
          "end_time": "2021-01-01T13:53:57.234712",
          "exception": false,
          "start_time": "2021-01-01T13:53:56.959373",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "6fm6mSC3_imR",
        "outputId": "10b0d820-9dc3-4f2d-98e7-f62c0c68f318"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.countplot(df.Drug)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.064243,
          "end_time": "2021-01-01T13:53:57.364096",
          "exception": false,
          "start_time": "2021-01-01T13:53:57.299853",
          "status": "completed"
        },
        "tags": [],
        "id": "X6A8z_fD_imR"
      },
      "source": [
        "* Drug is target column and you can see that it is unbalanced dataset. Using K Fold cross-validation would be better for reliable results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.112296,
          "end_time": "2021-01-01T13:53:57.540508",
          "exception": false,
          "start_time": "2021-01-01T13:53:57.428212",
          "status": "completed"
        },
        "tags": [],
        "id": "QwoXSYD1_imR"
      },
      "source": [
        "<a id='10'></a>\n",
        "# Basic Data Analysis\n",
        "\n",
        "* Age -- Drug\n",
        "* Sex -- Drug\n",
        "* BP -- Drug\n",
        "* Cholesterol -- Drug"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.063505,
          "end_time": "2021-01-01T13:53:57.667974",
          "exception": false,
          "start_time": "2021-01-01T13:53:57.604469",
          "status": "completed"
        },
        "tags": [],
        "id": "MntnYN9u_imS"
      },
      "source": [
        "<a id='11'></a>\n",
        "## Age -- Drug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:57.799845Z",
          "iopub.status.busy": "2021-01-01T13:53:57.799124Z",
          "iopub.status.idle": "2021-01-01T13:53:58.035047Z",
          "shell.execute_reply": "2021-01-01T13:53:58.035589Z"
        },
        "papermill": {
          "duration": 0.304062,
          "end_time": "2021-01-01T13:53:58.035759",
          "exception": false,
          "start_time": "2021-01-01T13:53:57.731697",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "qxQCjoil_imS",
        "outputId": "8dd9f77c-8a9b-473c-9e73-957a20bb4671"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.swarmplot(x = \"Drug\", y = \"Age\",data = df)\n",
        "plt.legend(df.Drug.value_counts().index)\n",
        "plt.title(\"Age -- Drug\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:58.174991Z",
          "iopub.status.busy": "2021-01-01T13:53:58.174179Z",
          "iopub.status.idle": "2021-01-01T13:53:58.179410Z",
          "shell.execute_reply": "2021-01-01T13:53:58.178519Z"
        },
        "papermill": {
          "duration": 0.078084,
          "end_time": "2021-01-01T13:53:58.179569",
          "exception": false,
          "start_time": "2021-01-01T13:53:58.101485",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sQu2fwW_imS",
        "outputId": "85bbf6fd-636b-4c59-f4d3-dacd97b90a38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum Age of DrugB 51\n",
            "Maximum Age of DrugA 50\n"
          ]
        }
      ],
      "source": [
        "print(\"Minimum Age of DrugB\",df.Age[df.Drug == \"drugB\"].min())\n",
        "print(\"Maximum Age of DrugA\",df.Age[df.Drug == \"drugA\"].max())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.067688,
          "end_time": "2021-01-01T13:53:58.314170",
          "exception": false,
          "start_time": "2021-01-01T13:53:58.246482",
          "status": "completed"
        },
        "tags": [],
        "id": "NL1QbpSE_imT"
      },
      "source": [
        "* DrugB is taken only by older than 51 years old. \n",
        "* DrugA is taken only by younger than 50 years old. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.066256,
          "end_time": "2021-01-01T13:53:58.447432",
          "exception": false,
          "start_time": "2021-01-01T13:53:58.381176",
          "status": "completed"
        },
        "tags": [],
        "id": "ygwVOyhh_imT"
      },
      "source": [
        "<a id='12'></a>\n",
        "## Sex -- Drug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:58.597320Z",
          "iopub.status.busy": "2021-01-01T13:53:58.596337Z",
          "iopub.status.idle": "2021-01-01T13:53:58.601377Z",
          "shell.execute_reply": "2021-01-01T13:53:58.600838Z"
        },
        "papermill": {
          "duration": 0.087072,
          "end_time": "2021-01-01T13:53:58.601489",
          "exception": false,
          "start_time": "2021-01-01T13:53:58.514417",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "J6hz8YIr_imT",
        "outputId": "2dfc333f-2da6-41b1-9ef4-caca3946fb88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-fb5aa988-bc54-4ba0-956a-2068af35883c\">\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",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Drug</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>drugA</td>\n",
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              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>drugA</td>\n",
              "      <td>M</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>drugB</td>\n",
              "      <td>F</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>drugB</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>27</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>drugX</td>\n",
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              "      <td>27</td>\n",
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              "  </tbody>\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fb5aa988-bc54-4ba0-956a-2068af35883c 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-fb5aa988-bc54-4ba0-956a-2068af35883c');\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",
              "  "
            ],
            "text/plain": [
              "    Drug Sex  Count\n",
              "0  DrugY   F     47\n",
              "1  DrugY   M     44\n",
              "2  drugA   F      9\n",
              "3  drugA   M     14\n",
              "4  drugB   F      6\n",
              "5  drugB   M     10\n",
              "6  drugC   F      7\n",
              "7  drugC   M      9\n",
              "8  drugX   F     27\n",
              "9  drugX   M     27"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df_Sex_Drug = df.groupby([\"Drug\",\"Sex\"]).size().reset_index(name = \"Count\")\n",
        "df_Sex_Drug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:58.741083Z",
          "iopub.status.busy": "2021-01-01T13:53:58.740016Z",
          "iopub.status.idle": "2021-01-01T13:53:58.928833Z",
          "shell.execute_reply": "2021-01-01T13:53:58.928216Z"
        },
        "papermill": {
          "duration": 0.260438,
          "end_time": "2021-01-01T13:53:58.928951",
          "exception": false,
          "start_time": "2021-01-01T13:53:58.668513",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "8UhtvVnJ_imT",
        "outputId": "72411ef1-3606-4325-8ca3-2c2407c4b8a4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "\n",
        "plt.figure(figsize = (9,5))\n",
        "sns.barplot(x = \"Drug\",y=\"Count\", hue = \"Sex\",data = df_Sex_Drug)\n",
        "plt.title(\"Sex -- Drug\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.066955,
          "end_time": "2021-01-01T13:53:59.063786",
          "exception": false,
          "start_time": "2021-01-01T13:53:58.996831",
          "status": "completed"
        },
        "tags": [],
        "id": "ijAs4DQS_imU"
      },
      "source": [
        "* Male people get drugA, drugB and drugC more than male people.\n",
        "* Female people get DrugY more than female people.\n",
        "* drugX seems equal for male and female people.\n",
        "* According to this graph, Sex feature is not an important feature for classification."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.067509,
          "end_time": "2021-01-01T13:53:59.198942",
          "exception": false,
          "start_time": "2021-01-01T13:53:59.131433",
          "status": "completed"
        },
        "tags": [],
        "id": "_UW_yxpn_imU"
      },
      "source": [
        "<a id='13'></a>\n",
        "## BP -- Drug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:59.351032Z",
          "iopub.status.busy": "2021-01-01T13:53:59.349935Z",
          "iopub.status.idle": "2021-01-01T13:53:59.355340Z",
          "shell.execute_reply": "2021-01-01T13:53:59.354810Z"
        },
        "papermill": {
          "duration": 0.088839,
          "end_time": "2021-01-01T13:53:59.355459",
          "exception": false,
          "start_time": "2021-01-01T13:53:59.266620",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "BM54jUdD_imU",
        "outputId": "f252a69c-dbe2-4e9a-9755-86d36f3f6163"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "  <div id=\"df-3e0a6140-bbe3-48d8-ad4e-5bf005ab310e\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Drug</th>\n",
              "      <th>BP</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DrugY</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DrugY</td>\n",
              "      <td>LOW</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DrugY</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>drugA</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>drugB</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>drugC</td>\n",
              "      <td>LOW</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>drugX</td>\n",
              "      <td>LOW</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>drugX</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e0a6140-bbe3-48d8-ad4e-5bf005ab310e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \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-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-3e0a6140-bbe3-48d8-ad4e-5bf005ab310e 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-3e0a6140-bbe3-48d8-ad4e-5bf005ab310e');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Drug      BP  Count\n",
              "0  DrugY    HIGH     38\n",
              "1  DrugY     LOW     30\n",
              "2  DrugY  NORMAL     23\n",
              "3  drugA    HIGH     23\n",
              "4  drugB    HIGH     16\n",
              "5  drugC     LOW     16\n",
              "6  drugX     LOW     18\n",
              "7  drugX  NORMAL     36"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "df_BP_Drug = df.groupby([\"Drug\",\"BP\"]).size().reset_index(name = \"Count\")\n",
        "df_BP_Drug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:53:59.517450Z",
          "iopub.status.busy": "2021-01-01T13:53:59.515942Z",
          "iopub.status.idle": "2021-01-01T13:53:59.732173Z",
          "shell.execute_reply": "2021-01-01T13:53:59.731620Z"
        },
        "papermill": {
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          "start_time": "2021-01-01T13:53:59.423825",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "nStZ8rI2_imV",
        "outputId": "613a477c-e6a3-4068-f95a-89b8ca03e1db"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.barplot(x = \"Drug\",y=\"Count\", hue = \"BP\",data = df_BP_Drug)\n",
        "plt.title(\"BP -- Drug\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.069278,
          "end_time": "2021-01-01T13:53:59.870338",
          "exception": false,
          "start_time": "2021-01-01T13:53:59.801060",
          "status": "completed"
        },
        "tags": [],
        "id": "bPH2oiJD_imV"
      },
      "source": [
        "* drugA and drugB are got only by people who have HIGH blood pressure.\n",
        "* drugC is got by people who have LOW blood pressure.\n",
        "* drugX is got by people who have HIGH blood pressure.\n",
        "* BP is an important feature for classification."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.069005,
          "end_time": "2021-01-01T13:54:00.008178",
          "exception": false,
          "start_time": "2021-01-01T13:53:59.939173",
          "status": "completed"
        },
        "tags": [],
        "id": "_b61A7wb_imV"
      },
      "source": [
        "<a id='14'></a>\n",
        "## Na_to_K -- Drug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:00.155794Z",
          "iopub.status.busy": "2021-01-01T13:54:00.154737Z",
          "iopub.status.idle": "2021-01-01T13:54:00.351320Z",
          "shell.execute_reply": "2021-01-01T13:54:00.350649Z"
        },
        "papermill": {
          "duration": 0.27426,
          "end_time": "2021-01-01T13:54:00.351451",
          "exception": false,
          "start_time": "2021-01-01T13:54:00.077191",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "goOBreEP_imW",
        "outputId": "d540f848-b216-4357-be6e-3ca34c2ae7f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.swarmplot(x = \"Drug\", y = \"Na_to_K\",data = df)\n",
        "plt.title(\"Na_to_K -- Drug\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:00.504477Z",
          "iopub.status.busy": "2021-01-01T13:54:00.503588Z",
          "iopub.status.idle": "2021-01-01T13:54:00.507472Z",
          "shell.execute_reply": "2021-01-01T13:54:00.506848Z"
        },
        "papermill": {
          "duration": 0.082648,
          "end_time": "2021-01-01T13:54:00.507582",
          "exception": false,
          "start_time": "2021-01-01T13:54:00.424934",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5jg9yJ4_imW",
        "outputId": "3881b297-92bb-4abe-dda0-19984bad0e6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum Na_to_K for DrugY: 15.015\n"
          ]
        }
      ],
      "source": [
        "print(\"Minimum Na_to_K for DrugY:\",df.Na_to_K[df.Drug == \"DrugY\"].min())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.070586,
          "end_time": "2021-01-01T13:54:00.649547",
          "exception": false,
          "start_time": "2021-01-01T13:54:00.578961",
          "status": "completed"
        },
        "tags": [],
        "id": "795HqGXH_imW"
      },
      "source": [
        "* People who have Na_to_K ratio is bigger than 15, get DrugY.\n",
        "* We can create a new feature from here."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.071075,
          "end_time": "2021-01-01T13:54:00.792430",
          "exception": false,
          "start_time": "2021-01-01T13:54:00.721355",
          "status": "completed"
        },
        "tags": [],
        "id": "DFZvQGY9_imW"
      },
      "source": [
        "<a id='15'></a>\n",
        "## Cholesterol -- Drug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:00.952415Z",
          "iopub.status.busy": "2021-01-01T13:54:00.948524Z",
          "iopub.status.idle": "2021-01-01T13:54:00.957208Z",
          "shell.execute_reply": "2021-01-01T13:54:00.956599Z"
        },
        "papermill": {
          "duration": 0.092399,
          "end_time": "2021-01-01T13:54:00.957323",
          "exception": false,
          "start_time": "2021-01-01T13:54:00.864924",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "wnrXbBsI_imX",
        "outputId": "3511d68b-b3ed-4fec-9c8e-21beacbec82e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-8f3981e1-786d-47a3-98f4-f6cbbf80d315\">\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>Drug</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DrugY</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DrugY</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>drugA</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>drugA</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>drugB</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>drugB</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>drugC</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>drugX</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>drugX</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f3981e1-786d-47a3-98f4-f6cbbf80d315')\"\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-8f3981e1-786d-47a3-98f4-f6cbbf80d315 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-8f3981e1-786d-47a3-98f4-f6cbbf80d315');\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",
              "  "
            ],
            "text/plain": [
              "    Drug Cholesterol  Count\n",
              "0  DrugY        HIGH     47\n",
              "1  DrugY      NORMAL     44\n",
              "2  drugA        HIGH     12\n",
              "3  drugA      NORMAL     11\n",
              "4  drugB        HIGH      8\n",
              "5  drugB      NORMAL      8\n",
              "6  drugC        HIGH     16\n",
              "7  drugX        HIGH     20\n",
              "8  drugX      NORMAL     34"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "df_CH_Drug = df.groupby([\"Drug\",\"Cholesterol\"]).size().reset_index(name = \"Count\")\n",
        "df_CH_Drug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:01.126698Z",
          "iopub.status.busy": "2021-01-01T13:54:01.117716Z",
          "iopub.status.idle": "2021-01-01T13:54:01.305116Z",
          "shell.execute_reply": "2021-01-01T13:54:01.304424Z"
        },
        "papermill": {
          "duration": 0.268592,
          "end_time": "2021-01-01T13:54:01.305253",
          "exception": false,
          "start_time": "2021-01-01T13:54:01.036661",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "hWoA7OEh_imX",
        "outputId": "b3efb79f-9290-4835-84fd-eee8ded44b48"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.barplot(x = \"Drug\",y=\"Count\", hue = \"Cholesterol\",data = df_CH_Drug)\n",
        "plt.title(\"Cholesterol -- Drug\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.072596,
          "end_time": "2021-01-01T13:54:01.450925",
          "exception": false,
          "start_time": "2021-01-01T13:54:01.378329",
          "status": "completed"
        },
        "tags": [],
        "id": "l9e-Qzl6_imX"
      },
      "source": [
        "* drugC is got by people who have HIGH cholesterol.\n",
        "* Cholesterol is an important feature to classify drugC"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.072739,
          "end_time": "2021-01-01T13:54:01.596761",
          "exception": false,
          "start_time": "2021-01-01T13:54:01.524022",
          "status": "completed"
        },
        "tags": [],
        "id": "VYKGX4w3_imX"
      },
      "source": [
        "<a id='16'></a>\n",
        "## Na_to_K -- BP -- Drug"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:01.752426Z",
          "iopub.status.busy": "2021-01-01T13:54:01.748433Z",
          "iopub.status.idle": "2021-01-01T13:54:02.095571Z",
          "shell.execute_reply": "2021-01-01T13:54:02.095032Z"
        },
        "papermill": {
          "duration": 0.426301,
          "end_time": "2021-01-01T13:54:02.095728",
          "exception": false,
          "start_time": "2021-01-01T13:54:01.669427",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "4Yu4NL_5_imY",
        "outputId": "84d6352c-e619-46d1-cf74-e32e09b420e4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BVwA7lVItLXUKuAHYZs84Glrrpt7MntSX7S9dzWd3x9O6iVeNNv5ebg6ITAghREMwaROL9y1m3OJxDPlmCOMWj2PxvsWY9IW9kdjDw4P58+eTnZ1d49jixYv56KOPWL16NTt37uTDDz/ktttu48iRI9Y2s2fPZvPmzdx33308/vjjNv0LCgo4dOgQACkpKTXvyWRiwYIFtG7dmj///POC7uNc7D1y0hJYoZTaAqzHvOZkMTBbKbUV2Ao0A6af5RyN3vBOzYmPbGotRzbzYVxcawdGJIQQwl5M2sTDKx7mpcSX2JGzg+Olx9mRs4OXEl/i7yv+fkEJiqurK1OmTGHGjBk1jr366qu89tprNGvWDICePXsyYcIE3n///Rpt+/XrR0aG7Qj+2LFj+eabbwCYM2cOt956q83xP/74gy5dujB16lTmzJlz3vdQG/Z+WmeL1rqH1rqb1jpGa/2SpX641rqrpe6Oak/0XJRcXQzMndyXLyfG88n4OH5+eBBNfNwdHZYQQgg7WJK2hDWZayipKLGpL6koITEzkaVpS8/Qs3buv/9+Zs+eTV6e7cMX27dvp1evXjZ1cXFxbN++vcY5fv75Z2644QabuhtvvJH58+cD8OOPP3LdddfZHD+ZsIwePZqffvoJo9F4QfdxNrJDbAMxGBSD2jeORb1CCCHO35c7vqyRmJxUUlHCFzu+YETbEed9fn9/f8aPH88777yDl1fNZQNnc/vtt1NeXk5hYaHNmhOAoKAgmjRpwty5c+ncuTPe3lXvByovL2fJkiW8+eab+Pn50adPH5YtW8bIkSPP+z7ORvbQFUIIIerRkaIjZz1+tOjoBV/j4YcfZubMmRQVFVnroqOjSUpKsmmXlJREly5drOXZs2ezb98+JkyYwLRp02qcd9y4cdx///01pnSWLVvGiRMn6Nq1KxEREaxevdquUzuSnAghhBD1qIVPi7MeD/EJueBrNG3alLFjx9q8ffiJJ57gySefJCcnB4Dk5GRmzZrFfffdZ9NXKcXLL7/MmjVr2Llzp82x0aNH88QTT3DVVVfZ1M+ZM4dPP/2U/fv3s3//ftLS0vjll18oLi7GHiQ5sbfcA+aXP+UeMJe1hrUfw/+ugfn3wvE0x8YnhBCiXt0ZfSderqefbvFy9WJ89Ph6uc6jjz5q89TO9ddfzz333EP//v3p1KkTkydP5quvvqJly5rvcvPy8uLRRx/ltddes6n38/PjySefxN29al1kcXExP//8MyNGVE1F+fj4MHDgQH788UfA/DhxWFiY9ZOenn5B96ZOfY2ys4qLi9MbNmxwdBh1s+U7WHAv6EpQLjD6QyjNgyWPVbVpEgHTNoLBxWFhCiGEOLeUlBQ6d+58znYnn9Y5dVGsl6sX/Vr2Y8awGRjUpTU2cLrvTimVpLWOO117WRBrT7++YE5MwPzz1xchqK1tm9z9kLkZWvVs6OiEEELYgUEZeGvYWyxNW8oXO77gaNFRQnxCGB89nmsir7nkEpPzIcmJPZWesC2X5EKTSEhbWVVncIMA2fNECCEuJgZlYETbERf0VM6lTNI3e+o5wbbcawIMeQKadTSXXdzhihfBVx4xFkIIIU6SkRN7unI6BHeAQ+ugsgK2fg+bZkP8ZIj5DHxbgE+Qo6MUQghRS1przG9eEbV1PmtbJTmxJ4MBet0FreLgwwFV9atehxZdIaTLGbsKIYRwLp6enuTk5BAUFCQJSi1prcnJycHT07NO/SQ5aQjp62rWHVoLXW6oWS+EEMIpnXxENisry9GhNCqenp6EhYXVqY8kJw0hLL5mXes+DR+HEEKI8+bm5kZkZKSjw7gkyILYhtAiBkbOAN8Q8AiAwY/LqIkQQghxBjJy0lDi7jF/hBBCCHFWMnIihBBCCKciyYkQQgghnIpM69jBj5sP897veymvNHH3gAjG94twdEhCCCFEoyHJST3bc7SAh+ZuwmTZc+a5hduJbOZD70g/fjv4G2WVZVwWfhkBHgGODVQIIYRwUpKc1LOE1BxrYnLSyt3HeDtlKrtydwHw/qb3mTtyLsHesm29EEIIcSpZc1LPuoT616gzuR+0JiYAx0qOMX/P/IYMSwghhGg0JDmpZ3ERTXn48vZ4ubng5qK4s28buretrNHOaDI6IDohhBDC+cm0jh08fHkH/jYkCq3By92FYmNb/uvbiozCDAD83PwY1W6Ug6MUQgghnJMkJ3bi6eZi/d3bzZs5I+awcO9CSitLuS7qOlr5tnJgdEIIIYTzkuSkgTTxbMJdMXc5OgwhhBDC6cmaEyGEEEI4FUlOhBBCCOFUZFrHEcqLYMUrcOAvCOsNw54Fr0BHRyWEEEI4BUlOGkBRWQUpmfm0D/EjwMsNfnoUNs8xHzy8CfIPwy2zHRukEEII4SQkObGzxNQcpny5gYLSCrzcXHjrlliuSlls22jXEjCZwCCzbEIIIYRd/xoqpTyVUuuUUpuVUtuVUi9a6iOVUmuVUnuVUt8opdztGYcjvbx4BwWlFQCUGCt5cdF2CGpr26hJhCQmQgghhIW9/yKWAcO11t2BWOBqpVRf4FVghta6HZALTLRzHA5zOK/Epny0oIyKq/8DviHmCq+mMHKGAyITQgghnJNdkxNtVmgpulk+GhgOzLPUfw7cYM84HGlkt5Y25as7B+GasxuG/xMmr4BHUqDtUIfEJoQQQjgju685UUq5AElAO+B9IBU4obWusDRJBy7a7VKfG9mFED9P1qYdp1uIO/fvngipqeaDzTrClBWODVAIIYRwMnZPTrTWlUCsUioQWAB0qm1fpdQUYApAeHi4fQK0M3dXA9Mua880gIR3oSC16mD2LtixCGJvdVR4QgghhNNpsFWYWusTwAqgHxColDqZGIUBGWfo87HWOk5rHRccHNxAkdaPYwWlHMkrta2sLK/Z8HR1QgghxCXM3k/rBFtGTFBKeQFXACmYk5SbLM0mAAvtGUdD0lrz1Pdb6PvKb/T9929Mm7OJikqT+WD3W80LYE/yD4NoeTuxEEIIUZ29p3VaAp9b1p0YgG+11ouVUjuAuUqp6cAmYKad42gwK3YdY+76Q9byj5sPc3nn5owKyoDVb0FwJwhsDSExEHub7AwrhBBCnMKuyYnWegvQ4zT1+4B4e17bUfZlFdWoS00/AouvhwrLY8UHFdwzEXyaNXB0QgghhPOTnb/q2bBOzXE1KGvZoOByj5SqxAQADSmLGj44IYQQohGQ7evrWVSwL59OiOPjlfuoqNTcMzCSbt474a9TGjaJcER4QgghhNOT5MQOhnZsztCOzasqdAj0uBM2fQVoiBoOPe5wWHxCCCGEM5PkpCEoBaPeY2nQeBYlpZFTHM4DaYUM7uDl6MiEEEIIpyPJSQNJSM1m6uIswBc4zsSD6/n90aG0burt6NCEEEIIpyILYu0ov9TIxoO5lJRX8nvKMZtjxkrNyj1ZDopMCCGEcF4ycmIny7Yf4e/fJFNcXkmAlxu3xreu0aZ9cz8HRCaEEEI4Nxk5sQOtNc8v3E5xeSUAeSVGVu/NZkS3ligFrgbF5EGRxEc2PceZhBBCiEuPjJzYQXmliWMFtu/VOXyilMXTBpFVUIabiyLQ291B0QkhhBDOTUZO7MDD1YUrokNs6oZ2DOY/P+/kv3/s5Uh+6Rl6CiGEEEJGTuzkjbGxRPy+hy2H8ogND+S7DYfILjS/gXjOuoMsnjaQdrLmRAghhKhBRk7sxNfDlaev6cycKX2JCva1JiYApUYT8zdmODA6IYQQwnlJctIA/DxrDlD5ebo5IBIhhBDC+Uly0gCGd2pO74gm1nJEkDfjetd8tFgIIYQQsuakQbi5GJg7pR+r92ZTaqxkSIdgPN1cajYsyoG1H0J+BsTcCO0ua/hghRBCCAeT5MRO0vLSmLd7HgZl4OYONxPuH86QDsHW47uO72LW9lmUVZYxtuNY+obEw+fXwbHt5gbJs+GWOdDpWgfdgRBCCOEYkpzYweHCw9z2020UGgsBmL9nPj+M+oFgb3Nykl2SzYSfJ1BkLALg94O/M7/nM7Q9mZictOkrSU6EEEJccmTNiR0sTVtqTUwA8svzWX5gubW8Mn2lNTEBqNSV/JG9ueaJvJvUrBNCCCEucpKc2IGfe839S/xwhQ2fwZoPCFE1d4f1CImGuHuqKnyCYcDD9gxTCCGEcEoyrWMHI9qOYO6uuezJ3QNAdNNOXPnba5BtLvfzDuKqntewLGMlAN2DuzO63WjofDv0nGBeEBs5BDx8HXYPQgghhKMorbWjY6iVuLg4vWHDBkeHUWtGk5GEjAQMykC/45m4zp9s22Do06TG3kxpZSldgro4JkghhBDCQZRSSVrruNMdk5ETO3EzuDGk9RBzIXfeadtEBUY1YERCCCFE4yBrThpCx2shqH1V2TsIetzhuHiEEEIIJyYjJw0gu9yF1Mu/o3veb3jqcvMGa34h5+54DhsP5lJmNNEnsikGg6qHSIUQQgjHk+SkDjYe3ch7ye+RW5rLDe1uYEKXCefsM39jOk99v5XyShN+nq3531296e3XtE7XXZe5jvl75xPgHsD4LuNp4R3K3bPWs3J3FgBdQv2ZO6WvvK9HCCHERUGSk1rKK8tj6q9TKa4oBuD1Da/T1LMpMc1imJE0g4P5BxkePpypsVNxM5iThIpKEy8v3kF5pQmAgtIK/r0khfn3DTjzhbJ2QcqPEBAGXcaQlLOVyb9MxqTN5/jlwC880mmWNTEB2H44n+82pHPPwEg73b0QQgjRcCQ5qaWNRzdaE5OTVqWv4oPNH3Co4BAAqVtTcTO4MTV2KgClFSZOlBht+hzJKz3zRQ4kwBejoLLcXN7yLT9GdbMmJgBZJVmsP5xSo2tOUdn53JYQQgjhdGRBbC1FBUahsF3X0dSrqTUxOWll+kpOFJfz9dqD/LLjCMM6Nrc5PqpHqzNfZM0HVYkJQOpvBJlMNZoN69QMf8+qvNLdxcD13c9yXiGEEKIRkZGTWgr3D+fRuEd5P/l9SipKGBw2mMldJ7NgzwKbEZVQnwiumLGSrALzSEbHED+mDo1iW0Ye3u4u+Hi4cPhECaGBXuzPLuK1ZbvYl13EldEhPIShRrZ4a5trWJa1kQP5BwC4JuIahkb0Yv59BXz2137KKkzc3iecji1q7korhBBCNEayCVsdFRuLKakoIcgrCDC/R+flNS9TUF5AdFA03d3+zscrcmz6vDWuOzN+3cOBHHMSE+DlxqIHBnD3Z+vZl131jp3/9Clh7Lb7oNIyRdPharjtG4wmI0lHk/B39yc6KLphblQIIYSwI4dtwqaUag18AYQAGvhYa/22UuoFYDJwclXnM1rrJfaMpb54u3nj7eZtLV8TeQ3Dw4eTW5pLC58WvLF8F2CbnGw8eMKamADklRj5ZFWaTWIC8FVGC8belwg7fzIviO18HWDe0K1vy772uykhhBDCidh7zUkF8KjWOhroC9yvlDr5X/8ZWutYy6dRJCZn4uHiQQufFgDc3Ks1ftXWg7QJ8qZ764AafQK93PBxd7GpaxfsC0FRMOBBiBkDLvJosBBCiEuPXUdOtNaZQKbl9wKlVApwUa/cDA/yZulDg/hhUwZe7q7c2LMVnm4ufPxnGruOFgAQ7OfBHX3bENXch+d+2E5BWQWdWvjx6FUdHRy9EEII4XgNtuZEKRUBrARigEeAu4B8YAPm0ZXcs/V3ljUn56u4vIIlW49QaqxkRNeWNPFxp7SilMP52bjqpoQHeZ/7JEIIIcRF4mxrThokOVFK+QJ/Av/SWs9XSoUA2ZjXobwMtNRa33OaflOAKQDh4eG9Dhw4YPdYG8qSfUuYvmY6BcYCOjftzDvD37FODQkhhBAXu7MlJ3bf50Qp5QZ8D8zWWs8H0Fof1VpXaq1NwCdA/On6aq0/1lrHaa3jgoOD7R1qgyk2FvNi4osUGM3TPCnHU3h307sOjkoIIYRwDhe05kQp5a61Lj/LcQXMBFK01m9Wq29pWY8CMBrYdiFxNBZH80sprzBRbsissdvsvhP7HBSVEEII4VzOmZwopZ7TWr90mvoAYCEw9CzdBwB3AluVUsmWumeAW5VSsZindfYD99Yt7Mbn2QVb+XrdQbSGoR2DCPNvTXph1e6yg9AuMkwAACAASURBVFsPdmB0QgghhPOozcjJQKXUv7TWz56ssKwZWQbMP1tHrfVqOGXPd7NG/ehwXSWkZjN77UFr+Y9dOTxy7T9IbfINBwsOMqz1MCZ1neTACIUQQgjnUZvk5HpgnlLqTa31I0qp9sBS4HWt9Yf2De/iUH0DtpMKCgJ5e8TbDohGCCGEcG7nXBCrtS7FvC4kQik1B/gVeFwSk9ob0iEYD9eqr1opuCJanswRQgghTqc2a04esfy6FngCWAVEnqyvvtBVnF5ooBdfTuzDB3/spdRoYny/NsRHNnV0WEIIIYRTqs20TvXX3b5zmjpRC/GRTYmPPO0T00IIIYSo5pzJidb6xdqcSCn1tNb63xcekhBCCCEuZfW5CdvN9XguIYQQQlyi6jM5Od0jwxelikqTo0MQQgghLlr1+VbihnmDoAP9vvMo//xhO5l5JVzVpQWv3dwdXw/LV1hRBq4e9rlweRGkrgCfYAjvY59rCCGEEE6iPpOTi3rkpLCsggfnJFNYVgHA0m1HaN3Um2e6F8OCqZC9C8L7wZhPILD1eV9nZfpKVqWvon2T9oxuNxq3gkyYeSUUWHb77zIGbv6sPm5JCCGEcEr1mZx8V4/ncjqpxwqticlJyYdOwN774XiqueJgIix9Am6dc87zHcwp5rlF29iWkc+AdkG8eH0XfklfyEuJVW8KWH9kPa8Vu1QlJgDb58OAByG0R73clxBCCOFsap2cKKXCgHeBgZincFYBD2mt0wG01q/YJUIn0bGFH4HebpwoNlrr+rb2hnWptg0zNsLOJbDsGSg8Bt3HwTX/ARc3m2bT5mxkc3oeAAuTD5u7en9j02bZ/mVM9+5Fjcmiktx6uSchhBDCGdVlQexnwCKgJRAK/GipuyR4urnw0R296BLqj7+nK7fGh3PfFTEQ0tW2YeveMO9uyE0DYxFs+B+s+QCAwvJCTpSeoLCswpqYnPTX3my83bxt6twMbph63Aaq2j9T07YQMcgu9yiEEEI4A6V17daxKqWStdax56qzl7i4OL1hw4aGuFTdZO+Fn/4OmVug7VDocgN8d5dtm04jeat9b77Y8QUVpgqujriGdeuuZn+1d+4M6RDMvVdV8MDvD2A0mUdn7u12Lw/0eAAOJMKWueYFsfFTwLd5g92eEEIIYQ9KqSStddzpjtVlzUmOUuoO4OSCiluBnAsNrtFr1g4m/FhVLsoGFw+oLLNWZTRpxcxtM63lpfuXMLF/P35ICGB/TjHdwgJ4eVQM4UHeLBmzhMTDibRv0p6YZjHmDm36mT9CCCHEJaAuyck9mNeczMC85iQBuMsOMTVuPs3gppnmNScFR6H7LSS2joUjS22albvvZsVjj1NYVoGfZ9V6lBY+LRjdfnRDRy2EEEI4jbokJ2Fa6+urVyilBgCH6jeki0Dn68wfiz4Fh3BNeo0KU9XTPoNaDUIpZZOYCCGEEKJuC2LfrWWdOEVrv9a8PextYoNj6dy0My/1f4l+oTJNI4QQQpzOOUdOlFL9gP5AsFLqkWqH/AEXewV2sRkcNpjBYYMdHYYQQgjh9GozreMO+Fra+lWrzwduskdQQgghhLh0nTM50Vr/CfyplJqltT5wpnZKqXe11tPqNTohhBBCXHJqvebkbImJxYALjEUIIYQQok4LYoUQQggh7E6Sk0agotLEgZwiKipNjg5FCCGEsLv6fCuxqsdzNR4mE6T+Dvnp0P4q8G9Z+76Fx6CyHALCrFUpmfmkZRfRPyqIQG93kg7kct/sJI7ml9HC35P3b+9JrzZN7HAjQgghhHOoz+Tk7Xo8V+Px/UTYPt/8u7sv3LUYQnucu99Pj8GGmaBN0Gkk3PQ/XvstjfdXmN9y7OvhyuxJfXh2wVaO5pu3wj+SX8qzC7by88PySLIQQoiLV62TE6VUMPAkEA14nqzXWg+3/JxV38E5vazdVYkJQHkhJLwHI2fAtnlQXgQxN9UcTUlbCes/qSrvXEzOurl89GewtaqwrIJ3fttDalahTdd9WUX2uBMhhBDCadRlzclsIAWIBF4E9gPr7RBT41FRWrOuvAg+vRwW/x2W/wM+6Ae5pzzolL2nRreirINUmGzfEJ1XYuSyTiE2dZd1ljcSCyGEuLjVZVonSGs9Uyn1ULW9Ty7t5KRlNwjvDwcTzGXlAi1iYHe1l/yV5MLGL/i5bW8+3PwhpZWlTGp9JTfZvLlYER57Gf2OGkjcV/Wi53G9W3NldAua+Liz8UAuvSKa8OTVnRru/oQQQggHqEtyYrT8zFRKjQAOA03rP6RG5o55kPw15B2C6BvgxMEaTXLLT/DkqicxafPTNi+mfEb7K/9J992/g7EU+kyBNv35ZEIFXyTuJy2riCu7tOCKaPOoyb/HdG3IOxJCCCEcqi7JyXSlVADwKOYX/vkDf7dLVI2Juw/ET64qN4+G4M6QlWIuezXlr5B2mI4tt+n2syqm+50LbOp8PVy5b2g7e0cshBBCOLVaJyda68WWX/OAYbXpo5RqDXwBhAAa+Fhr/bZSqinwDRCBee3KWK11bu3DdmJunjDpV/NC2fIi6DKGiLIs2Ppfm2bRQdGn739kKxjcoLlM3wghhLg01eatxM+d5bDWWr98luMVwKNa641KKT8gSSn1C3AX8JvW+v+UUk8BT2F+Euji4OELPcdbizF+ITzU8yE+3fop5ZXljGk/hhFtR9j2MZbC1zebn+QB8+PFY78Ag7z4WQghxKVFaa3P3kCpR09T7QNMxLxI1rfWF1NqIfCe5TNUa52plGoJ/KG17ni2vnFxcXrDhg21vZRTMpqMmLQJDxcPyMuATV+C1tDjdkhdAT8+aNvhlq+h04jTn0wIIYRoxJRSSVrruNMdq81bid+odiI/4CHgbmAu8MaZ+p0miAigB7AWCNFaZ1oOHcE87XO6PlOAKQDh4eG1vZTDVZgqmLd7HtuytxHXIo5RUaNQSuFmcDM3KMyCj4dAUZa5vO5j6H5LzRPlpTdc0EIIIYSTqNWaE8sakUeA24HPgZ51WSOilPIFvgce1lrnK1W1073WWiulTjt8o7X+GPgYzCMntb2eo01fM53v93wPwMLUhRwqOMS0HtOqGmxfUJWYAJQcB2UAgyuYKsx1bt7Q8doGjFoIIYRwDufchE0p9RrmzdYKgK5a6xfqmJi4YU5MZmutT26netQynYPl57E6R+6kjCYjC1MX2tTN32O57WMpkJEErh41OzaJgDt/gM7XQZcxMGExBLY+rxi01ny34RCPfbeZL9cckBcGCiGEaFRqM3LyKFAG/AN4ttqoh8I88OF/po7K3HgmkKK1frPaoUXABOD/LD8Xnqa7Q2SXZOPt6o23m/d59XdRLvi5+ZFbVpW/+bv7w7fjYYflNkN7QrMOkL3bXA5qB93GgmcARA660FvgjeW7eW/FXgDmJaWTkpnPK6NlrxQhhBCNQ23WnNRli/tTDQDuBLYqpZItdc9gTkq+VUpNBA4AYy/gGvWisLyQR/54hMTMRLxcvXio50Pc3vl2MJbALsuOrx2vATevGn23H87jqzUHcTUoJvRvw4M9H+SlxJfQaFwNrjzYcggsfbGqw+GNcMV0CAg1L4jteC24n18ylFtUzjMLtrJ6TzadW/rzypgY5q633Qhu3oZ0Xh4Vg4vh0nxxtBBCiMalPt9KXIPWejXmEZbTucye166rWdtnkZiZCEBJRQn/Wf8fhjXvTeic26tGOJp1hMm/gYeftV9adhE3fpBAqdE8dfLDpgx+fXQkP43uw/ac7cQ2j6XFruU1rkfRMQrjprJ6TxYhR8roEW5OTvJLjfy0JZNKk2Zkt5YEervbdNt4dCNrj6ylS1AXBocN5oUft7N02xEA1u0/zn2zN+Lv6UZ2Ybm1j5+nK5KXCCGEaCzsmpw0JqknUm3KJm0ibetXhJ5MTACyd8G276HXXdaqxZsPWxMTgIKyCpZuzeSuAZG09resGelwNXj4Q1m+uaxcSG91NTe8tsKaRNzcK4znrovmundXcyCnGID3V+xl8bSBBPma16h8t/s7Xkp8yXqtu2PuZl1ad5u4dx8t5LWbuvH0/K1UmDRKwRNXd6T6ImQhhBDCmUlyYjEobBC/HvzVWvZz9yPWrUmNdkWF+Tz2VRIJqTl0bRVAfGTN1ws1da+AzXPBOwiihoNvc7h7CcZV76CNJbj3ncx7m/zILjxh7fNdUjqtm3pZExOAzLxSFmzKYNKgtgDM2jbL5jpzUubQI2wQmXlVb0eOCPLmpl5hDGjXjKQDuXQLC6BNkM95fy9CCCFEQ5PkxGJ0u9HkleWxKHURzbyaMa3HNHw8m8Pqd8yP+gJ4BzF9fzRLU8zTKKv3ZpNbXE7XVgFszcgDID7Mm6t/HwElR8192l0Od3zPq8luzEweg9Fk4nqDPyXG8hoxFJVX1qirvkeeQdVc/vPPEZ0oKKkkcV8OHUJ8ef3m7iilCA30IjSw5voYIYQQwtmdc4dYZ+GwHWJPHIRNX5l/73Envd7dSU6RbWKx5fkr2ZaRh4tBEb/zNdS6D2yO77zmO65eYLSpu2dABJ8l7LcmHx1D/PhmSl9GvLuajBMlAAT7ebDkwUEE+3mQkplP4rGlzEiebj3H5K6TebCneVfZikoTri4XsnZZCCGEaDgXtEPsJS8wHIY9Yy12Dcvkj11VG6i1a+7LziMFZBWWMbh9MGpbUY1TZGTlYH6JcxWlFLMn9WFR8mFC/D2Z0D+CQB93Fk8byIJNGZi0ZlRsK9xdDYx6/y82HzqBUr5c13s63dpnEd00mv6t+lvPJ4mJEEKIi4X8Raujl0fF0KuNeS1KhxBfIoK8GftRIg/NTWbwaytIa3OT+a3CJzXrSPv4a3FzsV2Q2i0sgA/+SOWH5AzWpR2nsLQCKsppsuIp7lk1mEmbbyH46Co+T9jP5kPmtSlaw6J1rvRpcrNNYiKEEEJcTGRa5zyVGivJzCtl2Ot/2NSP7tGKGUNcLAtim0GvCeDdlBW7jvHe73spNVYyoV8EP245zKo92dZ+cW2aMC8mEX6rehoHNx+eiFrIt8m2G+h+cHtPruna0p63J4QQQtiVTOvYgaebC0VlFTXqC0qNsHE2JH0OHr7gFQhxdzOsY3OGee8HoxEiQnlu0TabfhsO5KL9Em03hTEWcW3LAr5NrqoK8HJjQPtmdrknIYQQwhlIcnIBuoT60711oHXaRSm4LWiv+S3DAMVlsPjvEN4Plj8Ley2PKjfvQo9W/yFxf571XN1bB6LCesPeX6ou4OrF0N6x/LdpGXPXHyLAy437h0Xh71lt2kgIIYS4yEhycgGUUnw5MZ6v1x7k8IkSRnYLJX7rC6e00rDpy6rEBODYdl4dsI1HdDc2HMile+tA3ri5OzTpCcf3mTd682sJV/8bvJtybVe4VqZxhBBCXCIkOblA/p5u/G1IVFVF/gDY+HlVWbmAT3CNfuFkMm/q39Ba2+7eOuYjGP2heRhGCCGEuATJ0zr1rdtYGPy4eTFsk0i48RPocSd4Vdtt1sUdut4McPpt5SUxEUIIcQmTp3UaSvZeWPsBGEsh7m4IO+0CZSGEEOKSIE/rXKBD+YeYtX0WeeV5jG43mgGtBtgcN2kTi/ctZkvWFnqF9OLqiKtrjog0awcj3mDjwVyWbT1Cm4yDjOnZCk83lwa8EyGEEML5SXJyDsXGYsb/PJ7sEvOeJMv3L+eTKz+hT8s+1javb3idL3d8CcA3u75hX94+7o+9v8a5ftlxlClfbrBuWf/LjiN8dne8TZvth/NYsfMYUcG+XNmlBS4GmeIRQghxaZE1J+ewJnONNTEB0GiWpC2pKmvNd7u+s+nzzc5vTnuuz6u9Swdgxa4s9mdXbXe/fPsRrnt3Na8v383U2Rt5Yt6WeroLIYQQovGQ5OQcgr1qPmnTzKsZpRWlmLQJpRRerrZv//V28z7tuTxcbb9upcC9Wt2nq9IwVUte5m9K51hBqU2f1RmrmbR8EpOWT2Jl+sq63o4QQgjh9CQ5OYeuwV0ZFTXKWo7wj2Bb9jbiZ8dz+XeX88uBX3igxwPW4wZl4P6gePj0Cpg1ElJXWI/9bWiUTYJyeecQnl2wlWvfXsXHK1PR2C5OVoCqtmfs3ty9TPttGmsz17I2cy0P/v4gu47vssNdCyGEEI4ja05qYfrA6UzoMoG8sjz+OPQHn+8w72OSVZLFs6uf5debf6VXSC+2ZG2hp1HT5tu7qzofWgsPrGdN6RE+3f0pXXsaaOc+isvbxvH4vM3kFhsB2JGZz/h+bdh48ASVluGTG3uGEeznYT3Vn+l/UqGrtsyv1JX8mf4nHZt2bIBvQQghhGgYkpzUUvsm7QH4cPOHNvUlFSXsO7GP2OaxRAVGwfJ/2HasLOfE9vncl/YVRpM5EdlFIh1CPrQmJicdPlHKTw8O5HfLgtgrOofYHG/j36ZGXOH+4Rd6a0IIIYRTkWmdOopvaft0TYBHAJ0ytsIP98Haj6BpVI0+ybrImpictL94XY0ncdo198XHO4824bvoEl6BwaAoq6jk+YXbiP/Xr3y41JMBLa60TvVcE3kNV4RfUc93KIQQQjiWjJzU0d0xd5NbmsvP+38m1CeUx1xD8VxoWXOSPBu6joPoUbBjoXnr+vgp+La/BvZ9a3Oebi1bEzmiM//5eRclxkriI5rSuvVORv7wPCZtwqAMvDLwFVL2tOPzxAMAHCsoo3nONSyZ9hiuLooWPi0a+vaFEEIIu5MdYi/UjBjIO1RVNrjC0+lQcsK8Tb1PEACvrnuVOTvnUKkruTz8cl4d/CruLu4UllWQX2IkNNCL4d8OJ6sky3qqVr6t8Dr6TzYdPGFzySUPDiI61L9Bbk8IIYSwB9kh1p48A2yTE3cfvtr1DR9v+x8VuoIJ0RO4t/u9PBn/JFO6TaG8spwQn6q1JCm5mzhceJiBHgMpNBbanDq/PJ/eoQE2yYmfhysRzU7/qLIQQghxMZDk5EIN/yd8eydUlgOK9N5382rSG9bD7yW/R+egznRo0oFvdn1DaUUpY9qPoX2T9vxj9T9YmLoQAB83H4a2HsrStKXWvjd1uIl7Ondgf04Rq/ZkE+LvwfQbuuLtLv9sQgghLl7yV+5CdbwaHtoMBxOhRXd+O/oXpNs2WXdkHc8nPG/daXbe7nm8Newta2ICUGQswmQy8WL/F9mctZkezXtwfdT1GJSBLyf2oaisAi83Fwyynb0QQoiLnCQnZ1Bp0qzffxwfd1e6hgWcvbF/KMTcCEB3U2GNw1prmy3wSytLWbZ/WY12JZUljGk/hjHtx9Q45lOSCQVl5hcIWuJbui2TAznFXNa5OZ1ayBoUIYQQFwdJTk4jr9jIuI8T2XmkAIAro0P46M5e/LU3hzd/2UV+aQW39G7NpEFtzR0OJELWTogaRmzzWJ6Kf4pPtnxCpa5kfPR42ga25YsdX9hco41/G3o078GmY5sA806wYzuMrRmM1rDoAdg0G9AQNRxu+Zq/z9vJos2HAXjzl918Oj6OYZ2a2+07EUIIIRqKJCenMWf9QWtiArB8x1EWbznMY99toazCBMD0n1JoGeDFiKMfwF9vmxsa3ODWudze+XZu73y7tb/RZKRn855sPLYRgHC/cMa0H8OtnW7l+z3fc7jwMFe0uYKeIT1rBrNvBWz6qqqc+juHE+ayaHNVIlJp0ny6ep8kJ0IIIS4Kdk1OlFL/A0YCx7TWMZa6F4DJwMlnZp/RWi85/RkcI6ugrEbd2rRca2Jy0sqUdEbs+m9VhckIq16H9pfbtHMzuPG/q/7Hmsw1lFaWMrDVQDxcPDh0vJidu2LJKuhMd+9QCIHjpcd5K+kttuVsI75FPI/oQNxPDSYvHbBNRKq/g0cIIYRozOw9cjILeA/44pT6GVrr1+187fM2KjaUzxP2U2F5x00TbzfG9AjlqzUHbNp1DPaClArbzsYSSP7aPA3jEwSDH4cWXXHJz2DAjmVgLAX3EMqadeaWj9eQcaIEMI/OfHxnL+ZlPEdiZiIAe3L34NJqOI+7ekFFieUCitAeV3FDsRs/JJundVwNikmDIu33hQghhBANyK7JidZ6pVIqwp7XsIduYYF8Pbkvc9YdxNvdhYkDI2kb7MtT13Ti7V/3UFpRybUxLbl9cGc4Pga2fV/VOaw3/DC1qpy2Eu5dBZ8MhyLLYNHGz9lw9VJrYnLSguT9JFYk2tQtyt7I4+MXmqeOKkqg92RoHc8bYzVXdWlBWk4Rl3cOoUOIn72+DiGEEKJBOWrNyQNKqfHABuBRrXWug+I4o/jIpsRHNrWp+9uQKCb0i6C8wkSAt5u5cvRH0HYoHNsJHa60LFytpiQXEt+vSkwAKkoJyfgFiLFp2sLPj1bGVmQUZljrIgMiIbwPhH9t09bFoLima8sLvEshhBDC+TjixX8fAFFALJAJvHGmhkqpKUqpDUqpDVlZWWdq1qC83F2qEhMAFzfoOR6ufsWcpDQ9zfTKaeraBXlw94AIazmymQ9/GxrFi/1fpKmnOSkK9Qnl6fin6/cGhHB2xcfh52fgi1GQ8B6YKh0dkRCigdn93TqWaZ3FJxfE1vbYqZz23TqnKsmF2TdD+nrzi/8GPgxDn4EvrocDf5nbBLWDib+Ad1PSsovILiyjR+tAXF3MuaKx0khmUSatfFvhYnBx4M0I4QCzRsL+VVXlIU/BMEnShbjYONW7dZRSLbXWmZbiaGBbQ8dgV15NYNKvkLULPAPBz/IenQmLIe1PqCg171Xi6gGYR0wim/nYnMLNxY1w//CGjlwIxys8ZpuYgHlNlyQnQlxS7P0o8RxgKNBMKZUOPA8MVUrFAhrYD9xrzxgcJrijbdlggKhhjolFnJmpEmR0yuHWH1lPSk4KfYNj6eARAGV5VQcDwhwXmBCNiNaaBZsySEzNoVtYALfEh+Pm4ojVGxfO3k/r3Hqa6pn2vKYQtXJ8H8y/F9LXQWhP88Lm4A6OjuqS9N/k//LB5g8A8349X/QcS+y6L6CyDHyC4fIXznmOpKNJfLLlE0oqShjXcRzXtr3WvkEL4YTe/m0Pb/26B4DvktLZmpHHf27q7uCozo/sECsuTQunmRMTgMMbzY9/T/7NsTE1clprPtj8Ad/v/h4/dz+m9ZzGZeGXnbVPeWU5s7bPqjoHmudL9rDwkRTI2Qstu4ObZ82O6UlgLIY2/Tlaks29v9xLWaV588SNxzbSzKsZ8S3j6/P2LjkVlSZ+SD5MSmY+gzsEM6RDsKNDuiRorVm2/QjbMvLp3y6I/lHNACg1VrLxQC5tmvnQKtALAJNJs+lQLh6uLsS0CmDuukM251qwKYOXb4jBw7XxjQ5LciIuTRmnLK7OSHJMHBeRxfsWW0dAjpUc47E/H2PpmKWsP7LemoDcHXM3I9uOtPbRaCpPeRqnwlRh3sDQJ6jmRUwmmHsr7P7ZXA6JIWHwfdbE5KQVh1ZIcnKBnp6/le+SzK9Yn7k6jek3xHBH3zYOjuriN/2nFGauTgPgvRV7eXlUF+IimnLnzLVkF5ZjUPD0NZ25Jb41t32ylq0Z5inQK6JD8Pd05Uh+1bl8PFxxNTTOaZ3GGbUQF6pN/7OXRZ2tO7LOplxhqmDh3oU8u/pZdufuZnfubp5Z9Qzbs7db23i4eDC2o+0LL++MvrOqkPAuvBoJ/9cGVr4Oqb9VJSYAR7fRJnM7p2rjL39EL0RBqZH5mzJs6j5P2O+YYC4hpcZKvjxlJ/JPV6fx5i+7yS4sB8Ck4fXlu/gi8YA1MQH4ZcdRro5pgZuL+VUmSsFjV3bExdA4X20iIyfi0nT9e7BoGhxcA2FxcP27jo6o0esS1IUf9v5gLSsUeWV5aKq2K9BoEg4n0OXobtixCJpG8kTf++kZ0pOV6SvJLslm74m97Duxj7Z5R2D5P6ou8PvL0G9ajev2NBm4vfPtzN05l0pdyeCwwYxuP9qu93qxczUYcDUoKk1V/3aebo1vaqCxMSiFi7JNJlwNimP5pTZ1ZRUm0o8X1+gf1sSblU8MY/3+XLq2CqjxJGhjIsmJuDQFtII75zs6iovKTR1uYufxnSxMXYiPmw8P9niQ5t7N+TLlS5t2HXMPw4rHrGWVuoKocTN5etXT1umZJfuWsLzltfieehGDwfy4follU2mDG3S9madadmdS10mUV5YT6htqx7u8NHi5u3DvkCje+c28uNLVoJg2vJ2Do7r4ubsamDK4LW9bvnel4P5h7ThRbGRzetUoSY/wQG7v24bvktKt74Dz83Tlss7NCfL14PruXg6Jvz5JciKEqBeuBlde6P8Cz/Z5FheDCwZlQGvNHZ3vYO6uuSgU4zqOY9D2P207Ht7I2s2f26wbKTAWsNYVaiynbTsMeoyHNf81L4jtdbd50SzQzKuZfW/wErE1PY+/UrPp1aYJ30/tz84j+QyIakZEI/5feGPy9ys60C8qiG0ZefSLCqJLaAAAvh6uLNmWiQJu7BlGdEt/Zk/qw+y1B/Fyc2HioEiCfD0cG3w9kuREXBr0/7d33+FRldkDx79vyqR3QgoQAiGUBJASQJqAgIBiX7GgYmFZxcXdRdey69p2XXVV3MJPxVXAAigWREFAioA0kd5C6CGEFNJ7f39/3MlMhsBayGSSmfN5Hh7m3rl3cm5uJnPylvNqqK0GD5OjI3F6nu7W5R2UUjw+8HFm9DW6Y3w9feHUIdsT3DzwDmzc2lHbYSCMexE2zQJdB0NmWGsFTZxlt/hd2ZLdZ5i5eC/1hcN/M6IzT07o4digXNDlncO4vHNYo33/WHWYnJIqvk05x4idZ3jvvoEM6nyBgeNOQAbECueXsgJe7wkvRMBHk6GiCKrKYM2z8O44Y1xDZbGjo3Rqvp6+RmICMOIx8G3wC3XYTK5KvJMeodYPwYGRAxkVMwoGT4c/HoPHTsCwPzRz1M5n+b4M7p67nd99tJuUzMY/82+tP0HDFU3mbT5FRbWsbdQSzN9yyjIoMh0S0gAAIABJREFUFmDDkXPsOJXnwIjsS1pOhHOrLIbPpkJVibF9eBlsiDUWl9trXuk5bRsUpsMt8xwVpWuJSCTvgY34pu/GO7w7tInHD1h0zSJ2Zu3E092TPuF9UOoiswzqao2Es+A0dBsPoZ2bNfzW6tvD2Ty0cJdle+ORc2x8bBQB3taWrrrz11rT8FOWX9NacyC9iGBfTzqE+jZVyKKBsqqaRvtKq5w3cZTkRDi3nCPWxKTe2d1wdo/tvuQvmy8mF1ZZW8njGx9n7em1eLl7Ma33NKa1iQfA3c29UW2ST498ypx9c6iqreKuhLuY2muqkWweNA9mXvMs3P2FTAX/CZbvz7DZzi+rZuORc2w7kceyfWdpH+LL6B4RHM22vl9u7t+OA2cL6dUuCG9PdwrLq3lm6QG+O5pDj6hAnr8+kWBfE3e+8z2HMopQCqYMjuXZ6xKb+/Kc3m0DY/hs1xmqa41sMS7cjyFxztmlA5KcCGfXNgF8QqG8QfNn7DAoL4DsBvUxgmWhxeawOGUxa08blXgrayv5z+7/MLLDSLqGdGVL+hYWHF6Ap5sn9yTeg5e7F89tfc5y7r92/Yve7gEMPNhgllVtJWyZLcnJT9A+pPEMji3Hc1nw/WkA8ssKOZ1XxsKpg9h+Ko/Csmo+3JbKou1phPh6Mu/egXywNZUv9pwFYNOxHKYv2MXYhAgOZRiVv7Q2uh8mJXUgITqw+S7OBfTpEMwXDw3li93phPiZuKMVr5vzU0hyIpybpw/cthBWPQn5qZB4AwybCZ2ugI/vNKakegfBNa85OlKXcKzgWKN9xwuOU1tXy/S106nVRjP1pvRN3NfzvkbHHjq3j0Z1X+saN3eLxu4d0om1ydnsTy9EKbhnSCyHM2zHnRSWV+Ntcmf6yC4MeGEN1eZpqvll1by0IpnTuba1NQ5nFtOlbaMJ36QXlEtyYgeJ0UEE+5rwN3kQ5Ov54ye0YpKcCOfXcTBMW2+7L3YYzEyGc4ehTTcwST95cxjebjifH7W2fHi7ezMgcgCLDi+yJCZgtKqUVpc2Or9D7JUQfxSOrjJ2uHnA5Q/YPW5nEOTryVczhnHobBFBvp60C/bhxa+T2Xoi13KMr8mdLm39KauqobC82ub8jMIKerYL4myhtSBY+xAfbuzbjmX7rF1GYX4mp+5ucJSSyhp+88EONh/LxeTuxm+v7MLDo+MdHZbdSHIiXJenD0T3dXQUTuuHzB9YdWoVUX5RTOo2iQBTAGM6juFPg/7Ep0c+xdPNkyvaX4FC0c6/XaPzB0YOJMI3gjn75lBTV8PkHpMZ3XE0tBsOBz6DglTofg1E9nLA1bVeDVs0ZoyOJzW3jG8OZdI20JuhcWHM/HgvfWOCGRoXxubj1sTl2t7R3D4ohtzSKnam5tOpjR+v3tKb/h1DeXNyPxbvSCPEz8T0kXH4eclHS1Obt+kkm48Z96Oqto5Zq49wda+oC7ZcOQOlf8pQ7BYgKSlJ79ix48cPFEI43Ia0DcxYN8NSur53m94suGaB5fkPDn3AKz+8gkbj5e7F66NeZ8GhBWw+uxmACbETeOmKl3BTRp+61vris3fEJauqqeP5rw7yoXn8CcANfaKJCPTmUEYRI7qGc+/QTpZ1WsqqavDxdJd70oz+8PEelpy33tGcu/ozLjHSQRFdOqXUTq110oWek/RWiHoZeyH7MHQaDhcoCiZ+uk+OfGKzps6+nH0k5ybTI6wHlbWVzN492/J8ZW0lc/bO4cOrP+R4wXE83TyJCbQdoHzBD8G8k5D2vdH6Fd7Nrtfj7EwebpaBrvW+3p/JkRcmXPB4X5N8dDS30T3a2iQnAV4ejQq1ORP5CRMCYP3LsP7vxmN3L7jjY2s1UvGz+Xk2LnXu72k0P1fWVlJeU27zXGFlIez9mLhtb4C7pzFoufvVbDyzkVd3vEpOeQ7Xdr6WPw74Ix5uHnDgc2NKcf04lWtegwFT7X5dzqxtgBclldbBxeEBXpzMKSUls4gBsaFOVRq9NZrYO5qCsmo+MXef/W50PEE+zjso1nnnIQnR0LE1sPZ5o3jX+SqLjRLp9WorYcPLzRebE7qv530EmAIs2zfF30SHwA4ABJoCGdNxjM3xN7bpB0umQcYeOPMDfHwnBek7eWT9I5wsPElxVTELDy9kQbK5a+jbF6yJCcC6F35atTBxUU9N7IG3p/GRYPJwY0hcGFe+tp4HPtzF0JfXseVYjoMjFHde3pGlvx3G/HsH0jcmxNHh2JW0nAjnt+l1o1hXvSsegyv/bN2uqYKaSttzpJz9JekW2o0VN61g84mVRO39hL5bP4a04zDhFQiM4sXhL3JZ+GWk5KUwpN0QJp7YafsCupaDhz+lotZ2qfhdWbuYkjil8f2pKjUqx7rLr7Rf6sruEWx7cjT70wuJC/dn7KwNlnyvorqOV79J4fMusriiaB7yThbOb+sbttvb3oC+d8KXv4XT26D9QOgy2mhdqdf/nmYN0RkFeQVx9Z6lkLLa2JH8lZFU3L0UL3cvI8moV1Le6Pzu7YbgeXYV1XXWKa29wntBSTb0uRM2NahN0+8uSUyaQLCvieHx4RSWV1N23po6BWXVFzlLiKYn72bh/NzO+zF3c4cvZ8DJjcZ26iYjQZnwCmQfgvixxhRVcelOfHve9gbISobVf4Hco9B9Iox+GnreZBy7dxEodxgwlTCvIF7s+RteOfYJuRW5XBNzFXfv+go+ewQ8/eCyO4xVptv1hz6THXN9rVRBWRVKqYuOWQjy8WR8YiQrDmRa9k0a0KG5whM/w9mCcvJKq0iMDrQMHM8vreJIVjE92wW12mndMpVYOL8f3oHlj1i3x/7VGLNQ07DLQMGzBc0emtN79ypjRk29iJ7GWkf5p6z7hj9iJChgLMhYXgiLboWcFGNfwg3U3TIPt3V/g+8atJYod/j9fghqXCNFXFhtneaJz/bx2a4zuCnF3YNjefraBLTWfH8yj4KyakZ0DcfH5E5FdS1zN51k75kCxiVGclO/9oCxsvGGI9l0iwxk8qAYvD3dHXxVruvFr5N5+ztjJekeUYF8eP9AthzP5ZFP9lJVU0egtwfvTBnAwE6hjg71gmQqsXBtA6Ya001Tt0L7ARAzCI5+A6e+sx7TfoDj4nNm1/4LPrkXziVDWBcY8QQsvtP2mGNrrMmJbyhs/681MQE49AVuqdOMad4N6VpjYUdJTn6y5fsz+GTnGcBYgXju5pOM6h7Oe1tSWZOcBUBUkDefTx/C4cxi3t10ktzSKo5ll9CnQzDrU87x/LJDltfbcSqPN+/s75BrcXVHs4qZs/GEZTs5o4j/fneCT3emU1VTB0BRRQ0vfJ3M0oeGOirMX0ySE+Ea2vU3/tW7fjZ8MR1OmxOWDpfDf/qDTwhc+RR0HumoSJ1L2x7w0DajRcQnBKrLwSsIKgutx7TpCmueM7rUuoyBkqzGr1OaDfFjIGW5dZ93sCSVP9ORzMYDvdccyrIkJmCUqZ+36SRf7DlLbmkVAMfPlfK35cmcybddW2flwUzyS6sI8TPZN3AXo7Xm4x/S+O5YDglRgdw3tBM+JnfySqtYfSiTEF8T7heo/XM6r4zcUtvB/ZmFjcdztQaSnAjXFBIL935tPN63GD7/tfW5hbcZ3QX+4Q4JzSn5hBgtVeX5MHEWrHgcynKM5KLoLOz/xDjuyEroN8XosqmfKuwXDl3GcKaygOqku4k5uRX3wGiqRj3J/x34L1vPbqVbaDd+1+93tPGR2ST/y8hu4cz+1rr4orubumD585ySKrKLbT/kjmQVExnobbPPy8MNL0+pSNHU/rPuGLNWHwGMbrQD6YU8Nr47N72xmXzzwOTLO4fSxt9ETkmV5bzr+7SjplbzzSFrsnndZa2zoKQkJ0IcX2e7XVMOqZuNFYxF01h0m5F4AAREw9Q1YPIH5QavdLY9NnUL3L0Udn8AXoEweDofHP+CV3e8Sp2uwz/MnzfHPM+KkytYeHghAMl5yaQVpzF//Pzmva5WJik2lNdvvYy5m07h7qZ4cGQcI7qGM2fjCc7kG39he7gp7hzckePnSth7xtrCdWX3toxLjOS++T9Qae42eHh0vFSLtYNPzV1v9VYeNFpL8hvMmNp2Io/XJ/Xhu6PnyCmt4lf92zMuMZKhXdrwxrfHOHC2iCFxYUwd1qm5w28S8lMlXFttDUQknrdTGQM3RdNI3WpNTACKz8KOuXDVX6G6wkhAKossT+uASL5RZRyM6U5SZBJJAZH8Z8V/qNPGB2JJdQmzd88mtTjV5svszNpJYWUhQV5BzXJZrdWNfdtzY9/2Nvs+f3AI729NpaC8ipv7tadvTAhv3tmfv3+dTHJGEVd0Deexcd3xMbmz6fEr2XYil26RAXSNCLjIVxGXIszfxOk8axeav5cHNXWNJ6+E+ZuYdWsfm33+Xh48Nr673WO0N0lOhHOqrTZaP7yDIbpP4+dzj8OS3xjVSKMug7gr4fi3YPKDUX+CNl2aP2Zn1SDxsKgoJLUolTPFZxgw+i+YVj4JdTXgHcyC6Hhe3vAoAPMOzuOB3g80KnefW5FLbGAsmaXWqa7hPuGWEvni52kb6M2j42zXJ4oO9mH2Hf0aHVtbp/HycHPq0umO9sdx3Zj63g7KqmpxU/D4+O5c1j6YL/ZYB7t2iwhgSJysrSNE61GSDfMmQK65bz3xJrhlnu0xXz5sJCZgLPjXrj88fgo8vMHTtl+dorPgGwYesrbIL9J5pDHGp376sJsHn/n78dySa9FoQr1DmXvvl8RV11ATfRn//NS2tP2SY0sY2m4om9M3W/ZdF3cdQ9sN5eF1D5Nekk6gKZCnBz+Nu5tMa/0l1iZn8ddlh8gsquCGPu147vpEvDzcqaqp41xJJdFB3iil+GrvWf7w8R5q6jSe7op/3daXq3tFOTp8pzMkrg1bnriSnan5dIsMoH2ILwDLZgxj6Z50QnxNTBrQAQ935x3vI8mJcD7b37YmJgAHP4dBv4GYy6370s+rmZO+C3yCbfcVnYWP7oCzu40BnRNfh8Qb7Re3s/LwgvtXG/VmyvIo6HE1f9v0iGVV4ryKPN44/jmvjXwNVVeLp5snlbXWwZgmdxOvjXiN9w++z7GCYwxvP5wbuhjjgZbfuJzU4lSi/aLx9vC+4JcX/1tBWRUPLdxFRbXxF/lHP6TRIdSXxOhAHlm8l9zSKuLC/fjv3Um8tOKwpXuhulbz4opkSU7sJNjXxOgeEZbtE+dKOJ5dwn1DO7nEIox2TU6UUnOBiUC21rqneV8o8DEQC5wCJmmt8+0Zh3Axpeca7yvJgiPfGF0M8VdBxyG2A2FjBsPmfxv1T9omwIjHYO1fjcQEjFkmXz5snGtqvOKuMFTVVrEweSEHcg/QP6I/k7pOMloz/Nsa3WVAXsEJanSNzXk55TmcKT7Druxd3NTlJt5Pfh8AheKByx7Az9OPB/s8CMD+c/t55YdXaOvblpvjb6Zz0HkDasXPcuhskSUxqbfjVB7vbTnVaCrx+dNU8xrMFBH28853J3jh62S0Bm9PN+beM4Ahcc49M83eLSfzgdnA+w32PQGs1Vq/pJR6wrz9uJ3jEK6k922w630wD6DEPxK2vQWntxjbAVFw6wJjump9nZPwbkZJdTCmvGbuM2pyNFRZBIXpEN61+a6llXl+6/MsPb4UgFWnVpFRksHMxHvhi4eMQbFhXeh87b/oGdaTA7kHLOfFB8czcclEas3Th+9OuJt2/u1IikwiJiCG2rpa3N3c2ZaxjQdWP2A5buXJlSy8ZqGlbLf4+RKiA/H2dLNJUBKiAvk2xTbJP5JVzC39O/DBNutA5FuSpKS9vVVU1zJr9RGbRRhnfXOEIQ9ePDlJLyhnzaEsooK8Gd0jAne31vf+sGtyorXeqJSKPW/39cBI8+P3gPVIciKaUsfB5qmoHxrdMVF94IsHrM8XZxjFvO781Lpv9nnFvE5vhcunQ8Ye676QTkaVU3FBNXU1LD+x3Gbf0uNLmZmRai2elpMCn0zhzembee/wQk4XnWZsx7G8tfctS8IBxjiTlTet5Jktz7AubR3BXsE8PuBx1pxeY3PcgdwD7M/ZT+/w3s1yjc4o2NfE7Nv78dflh8gsNMacPDwmnk3HchpNJX56YgJx4X7sPF3AgNgQJg/q6MDIXUNFdS3l5y3CmF9mtFjV1NYxf8spth7PpVf7IH5zRRwpWcXc9vZWS7I5PjGSt+5qfVV8HTHmJEJrnWF+nAlE/K+DhfhFOl1h/ANjNdzzVRbDD+8aqxJ3GAhBHYxS6PW8gmDkn8DdBClfG0nJ2OfBzXkHoF0qd+VOkFcQuRW5ln2h3qGQ9oPtgSVZeBSdxdfDlwBTAAGmAEqqS2wOKa8pZ96Beaw5bawUnVeRx1Obn2J0zOhGX9fHw6fpL8bFjEmIYEyC7a/iC00l9nB3Y2xiJPERAfSLCWmVf5G3FtlFFWw6lkNcuD/jEiJZedA6M+1W8yKM/1iVwtvmEvZrD2dzNKsEpbBpBVt5MJOjWcXEt7Jp3w4dEKu11kqpi648qJSaBkwDiImJaba4hJPpMgaCO0KBuTna3QsqimD5TGN7/2JjoKt/JJRkGjN2xr8I3gEw9jnjn/hRSikeSXqEpzc/TY2uweRm4vf9fg8eX0L2QeuBAdE8tPMf7MrZB8BnRz9jfOx4Vp6y1kK5ocsNpOSn2Lx+dV01g6MGszl9M8XVRhn2cbHjiA+Jv2hMdbqO7LJs2vq2xU1JYvlzXGgq8TvfneDvXydTpyHUz8QH9w8kMVrqyjS170/kMmXedkuSMXVYJ56c0J0dqfm4K4WPpzsllTUs2Z1uc96KAxk2g2jrVde2jgV+G3JEcpKllIrSWmcopaKA7IsdqLV+G3gbjFWJmytA4WQ8fWDqWtg5HyoKoM8dMHeC7TEpK+CJNMjcD6GdjAXoxM92bdy1DIgcwOG8w/Rq04swnzAI72OsrZOyAtrEkzZiJru2P21zXmFlIa+MeIX1aespqSohzDuMUK9Qvku3Ls7o7+nPuE7jGBkzko1nNhLhG8HlUdYZWLV1tRRVFRHiHQLAodxDzFw/k/SSdKL9onl1xKv0Cu/VPN+IVmb36Xz+vfYoBeXV3JrUgdsGxlBdW8dnO89YWk4GdArllVUp1NcCyyut4vXVR3hniqxv1NT+s+6YTevHe1tP8cH9A3l9zREqqutYeTCTRdvTaBvgxbkGywyE+nlxz+BYvj2cbZlVNSQujITowOa+hEvmiOTkS2AK8JL5/6UOiEG4Gv9wGPFH67ZviO3icz4h4GGC9q2vb7YlSclLIassi0FRg6zdLd5BNnVmPEszUduVZSoxQIApgKSIJP6+7e/kV+az4cwGgkxBTOo6iW/TviXCN4KZSTPx8/TDz9PPMpW43pazW/jLpr+QXZ5NYlgis0bO4vmtz5NeYvxlebb0LM9sfYbPr/vc/t+EVqagrIq73t1OSaUxg2r36QKCfU18cyiTz3cZ37/3tqby+zHxlrL19bKKKhu9nrh09feiXnWt5qPtaTYJy6GMIv40oTupuWWUVNZgcnfjLxN7MDS+DV/NGMaK/RlEB/twQ9/WuWq3vacSL8IY/NpGKXUGeAYjKVmslLofSAUm2TMGIS5ozHPw2VSoqzZm7Yx93tERtXovfv+iZa2bMO8w5o+fT2xQLJzYYMx+6jQConoT6RfJHT3uYEHyAgACPAP4de9fs+rUKvIrrVUFCqsKiQmMYd2kdRf6chY1dTU8tekpzpUbs0sO5h7k1R2vcrzguM1x528Lw9bjuY0+DL/en8GyfWdt9i3bl0FSxxB2pFrvUWv94GvpJg+KYU9agWV7dPe2F6zI27tDMFufvJJ9ZwrpFhlAG3P9kx5RgfSIan2tJQ3Ze7bO7Rd5qvGoNiGaU+INxkDYMzsgui8Ey5TIS5FWlGZJTMAoLz/3wFyeL3eHTbPMexXc9Db0nsQTA59gQqcJfHDoA9KK0liQvOCC9Up+ymDXvIo8S2JSLyUvhWHthlkG1AIMazfsl12ck+sc3rjkf1y4HyYP2+nFfiZ33pmSxFsbTnD8XAlje0QwaYC8b+zhlqQOtAnwYm1yFnHh/tw+MIb0gnKW7E6nqMJIJAfGhjKoUyhKKYZ2cb6aJ1IhVriuwGhIuM7RUTiFhi0eln3lObDtswZ7NHw3C3objaUb0jaw6tQqAA7lHaJ7aHc6B3XmRKEx+yA2MJarO13N+rT1vLX3LSpqKri9++3c2v1WyE6GlU9AzjHadptAl6A4jhVaW0YGRw9mRt8Z+Jv82Z29m95tevPogEft9w1oxbpFBvDw6HjeXH+M6lrN0C5h3D+8M0opZq02ZrB5uCkeHh1PsK+JJya0/kXlWoNR3doyqltby3ZcuD9rHhnBqgOZhPiZuCoh0qnr+yitW8c406SkJL1jx44fP1AI0ezqdB03f3kzxwqsywb8c9jLjF44BWobVBENi4cZxvv4hi9u4HihbVfLshuXsfXsVspqyri9++3klOdw3ZLrbCrKvnHlbIZ//rB1rR4gdeC9/MOrmmP5xxjWbhiPJD2Cr6evfS7WSeWXVlFSWUOHUOv3bd+ZAg5nFDM4LsxmvxBNQSm1U2uddKHnpOVECHHJ3JQb71z1Dh8mf0hmaSYTOk3givZXQNJ98P1bluMqBk6luqqYAFMAHQI72CQnAaYAPjj4AYuPLEajWXt6LRNiJzQqdZ987GuGN0hMADqm7uD/Htxk12t0diF+JkL8TJbtrKIK5m0+RXJGEcfOlTBzbFe8PWVhRUdLzigiyMeT6GDnru8jyYkQokmE+YTxu36/s905/iXoOBSduY8PKs7wesocSJnDr7r+ij/0+wNH84+SXpKOr4cvUxKmMHvPbMup+87to3tI4y6EiKh+4BMK5XnWnZEyRbipPfDhTnafNgZlHs4spqqmjmevS3RwVK6roKyKu+duZ9+ZQtwU3De0E09NTHB0WHYjVYmEEPajFCRcx4b4YbxybjM1uoYaXcNHKR9xuvg0y6//is+TnmHdiNnGzJ7z1Opapl82HW93b9yVO9fFXcc1XW+GG+cYayQBxAyBMc8251U5vbzSKktiUm9NcpaDohEAczedZJ95OYE6De9sOklyRpGDo7IfaTkRQtjd0fyjjfdl72Pksj8Tn2UsAHh5wnUEmAIoriq2HDO241iGthvKPT3voaauhgCTuQR316vgDweNxRh9QprlGlxJoLcHbfy9yCmx1jHp0rbxrB7RfM7kl19wX2ufMnwx0nIiXJPWsP9TWP4o7FsMrWRgeGs1pN0QFNaZBQrFkPwMyLKuTBx06Evm9XqY8bHjGRI9hNdGvMagqEHsyd5DQUWBNTGp5+YuiYmdeLi78fLNvQj2NWprxIb58tQ1PRwclWu7uleUzXaIryeD48IcFI39yWwd4ZrW/Q02vmLdHjIDrvqb4+JxAStPrmTugbkA3NvzXiYkr4Ptb9sedPO70OtXAGSUZHD/N/eTVpyGm3JjWu9pPNTnoeYO26VU19bx9f4MTueWMTYxgtgwPzILK4gJ9cVNFvlzuC/3nuWTHWmE+Jp4aFQXukW2rsX8zve/ZutIciJc08uxUN6gNoenH/z57EUPF3aQth3mjgNtLvTlE8rJKUtw9w0lJjCG57Y+x6dHPrUc7qbcWHnTSqL8oy7yguJS/fr9Haw+ZIwt8XBTzL93IMPina/Al2gZZCqxEOcz+dsmJyap4dDUaupq+Or4VxzJP8KQ6CEMbz/c9oAOA+GuJbDzPWpNfjxHLktW3QXA+NjxlFSX2Bxep+vIKsuyJCeZpZm8tuM1DucdZlDUIGb2nym1TS7BqZxSS2ICUFOneXfTCUlOhEPImBPhmkb9CRqMgWDUnx0WirN6dsuzPL3laT5M/pDpa6fz0eGPGh/UeSTcMo+lCVeypMA6/mTlqZV0DrQtZ98hoAO92linDD+y4RFWnlrJqaJTfJzyMf/44R92uhLXcKFio25OXIFUtGzSciJcU587oF0SpH0P7ZOgrQz2a0olVSUsO7HMZt+iw4u4rfttFzy+fvXghtoFtOPvw/7OipMriPSLZHKPyaxOXU2gVyAJoQnsO7fP5vjv0r9rugtwQR3D/LimVxTL92cAYHJ3Iyk2hDfWH2N4l3B6tQ9ycITClUhyIlxXeFfjn2hy7m7ueLp5Ultba9nn7eENeSeMdXFiBoNvqOW5MTFjeHf/u9Rq43gvdy9GdhhJtH8018ZdS0ZJBnd+fSfZ5dkADIkaQqRvJJllmZbX6BLcpZmuznn9+/a+TOwdRVp+GUeySnh5ZQoAr6gU/nlrH67vI6sQi+YhyYlwHbnHYelDxkDMmMvh+v+D0E6Ojsop+Xj4cF/P+3hj7xsAeCgPHvDuCP/uB2hjAPLkxdR1HMKiw4vYenYrV3W8iqKqInw8fAjxDmHqN1MJ9gpmRt8ZbM3YaklMALZkbOHRpEeZf3A+OeU5dArqxOMDH3fQ1ToPdzfFhF5RlFbW0Pf51Zb9WsNbG05IciKajSQnwnUsfQhObzUep26Gpb+Fe5c7NiYn9mCfBxnWbhgp+SkManMZHd4cAZhnB1aXwroXeKPvNczZN8dyzoj2IxgVM4o/bzLGAKUVpzFj3QzGxY5r9PodAzuy+lerySnPIcI3wqlXaHUEje1MztYys1M4B0lOhOtI+952+8x2x8ThQnqF96JXeC8oyzMSkobKchqNS9l4ZqPR/dNAZW0lcUFxeLp5Ul1XDUB7//YMiR6Ch5sHkX6Rdr0GV+Tn5cHkQR2Zv+WUZd+0Kzpf/AQhmpgkJ8J1dLgcTm9psD3IcbG4Gt9Q6HY1pHxt3ddnMhHFu20GwwaYAkgITWDVqVWWfQrFmI5jGBw9mC+Pf0mgVyCTuk7C5G5C2M8z1yYwJC6Mw5nFDI9vQ98YqcYrmo8UYROuI++k0ZWT9j206w9eAZC5D9oPgKtfhUDIdNhAAAAI00lEQVQp7mVXVWXw/VuQdRDix8Jlt7Enew/T10ynuLoYDzcPpvWaxsgOI3n3wLusTl2Nr4cvv+37Wyb3mOzo6IUQTUwqxApxvoW3wpGV1u3OI+HupY6KxqWVVpeyK2sXb+97mz3n9gDG7J1nBz+Lj6ePtJAI4aT+V3IiRdiEazqx/rztDQ4JQ4Cfpx9ZZVmWxARgzek17MreJYmJEC5KkhPhmqIus92O7HXh40SzyCzNbLQvozTDAZGI81VU1/74QUI0MUlOhGu69l/QNsF43KarUfNEOMzYjmPxUNbx+T4ePozqMMqBEYmdqXmMenU93f+ykklvbSWjsNzRIQkXImNOhGsrLwCfYEdHIYDtGdtZdHgRHm4eTEmcQs82PR0dksuqq9Nc8cq3nMm3JiQTekby5p39HRiVcDayKrEQFyOJSYsxMGogA6MGOjoMAeSVVdkkJgD7zhQ6KBrhiqRbRwghhI02/l50jfC32Tc4LsxB0QhXJMmJEEKIRt6Y3I/BncMI9vXkusui+cs1CY4OSbgQ6dYRQgjRSJe2ASyadrmjwxAuSlpOhBBCCNGiSHIihBBCiBbFYd06SqlTQDFQC9RcbDqREEIIIVyLo8ecjNJa5zg4BiGEEEK0INKtI4QQQogWxZHJiQa+UUrtVEpNc2AcQgghhGhBHNmtM0xrna6UagusVkod1lpvbHiAOWmZBhATE+OIGIUQQgjRzBzWcqK1Tjf/nw0sARrVrdZav621TtJaJ4WHhzd3iEIIIYRwAIckJ0opP6VUQP1j4CrggCNiEUIIIUTL4pBViZVSnTFaS8DoWlqotX7hR845B6TaO7Zm0AaQGUoti9yTlkXuR8si96PlcZZ70lFrfcFuEYckJ65MKbVDarq0LHJPWha5Hy2L3I+WxxXuiUwlFkIIIUSLIsmJEEIIIVoUSU6a39uODkA0IvekZZH70bLI/Wh5nP6eyJgTIYQQQrQo0nIihBBCiBZFkpNfSClVq5Tao5Q6qJTaq5R6RCnVJN9PpVSiUuqIUsqnwb7lSqnbm+L1XYFS6lml1KNN9FqeSqmXlFJHlVK7lFJblVITmuK1XUVT3Q+llLt5yYsrGuz7Ril1y6W+tqtpyveI+fXaKKWqlVIPNNVrupIm/p21XimVYv6MSm6NS8RIcvLLlWut+2itE4GxwATgmfMPUkr97CUCtNYHgc+BP5tf4wbAU2u96NJCdm2/5F6Y/RWIAnpqrfsBNwABTRaYi/qF741aYDow25w03g7Uaa0/afIAXdAlvEcAbgG2AfJHVBO5xPsxWWvdBxgKvKyUMjVRWM1Cxpz8QkqpEq21f4PtzsAPGMVxpgA3Af6AO0bS8qjWeqL52NnADq31fKXU1cAsoBTYDHTWWk9USvkCu4FbgY+Aa7XWR5vtAlshpdSfMb732UAasBOYCOwBhgGLgF7AMq31p+ZzSrTW/uZWr9nAleZzq4G5wNfm7U5a66LmvaLWzR73o8FxczCKUN0BjNVaH2vOa2ut7HxPNgKPAguBkVrrM815ba2Rve6HUmo9xmfODqVUDLAFo+BZbbNe4CVw5MJ/TkVrfUIp5Q60Ne/qB/TWWucppUZe6ByllDcwB7hCa31SKWVpGdFal5mb+DYCsyQx+d+UUv2B24A+GD/XuzDe6ACm+oJFSqn5F3mJm4BYIAHjHiZjJCddgNOSmPw8drwf9Z7E+IX8T0lMfhp73hOlVAcgSmu9XSm1GOOPqtfsciFOohneIwuUUpVAPPD71pSYgHTr2NNqrXXejxzTHTihtT5p3rbpttFafwUUAG/YIT5nMxxYorUuMycSXzZ47uOfcP4w4BOtdZ3WOhP41h5BuhB7348rgEKgZ5NE6xrseU9uBRabH3+EdO38FPZ+j0zWWvcGYoBHlVIdmyTqZiLJSRMxd+vUYjTPgdFNU68G2++198946TrzP/HLXfBemJtFf6wf9hgQo5QKtFNsruhS7kf9YqH/wGjObmvuGhWX5pLuCUYyco9S6hTGh2xvpVR8UwfpQi71flhorc9htMoMarLomoEkJ01AKRUOvAXM1hcexJMKJCilvJRSwcBo8/4UoLNSKta8fau9Y3ViG4EblFI+5hWvr73IcaeA/ubH1wGe5sebgZuVUm5KqQhgJBjda8C7wL/qB5QppcJldsiPssv9MHsaWKy1PowxOPZ1cxep+N/sck+UUl0Bf611O611rNY6FngRaT35MfZ8j1iYxy/2BY43UdzNQsac/HI+Sqk9GD8oNcAHGANbG9Fap5n7YQ8AJzEGuqK1LldKTQdWKqVKMQbUil9Aa71LKfUxsBej9epi38v/AkuVUnuBlVj/QvkMI2k8hDGWYRdGtwHAU8DfgENKqQrzOU/b4zqchb3uh1IqEbgRuMz8dXYrpVYBjwPP2elynIId3yO3Y11lngbHfgw835TX4Ezs/DsLjDEn5YAXMF9rvZNWRGbrOJhSyl9rXaKUUsD/AUe11q87Oi5X1OBehAHbgaHmvlzhAHI/Wh65Jy2LM98PaTlxvF8rpaZg9CPuxpi9IxxjmbnbzQT81Vne5K2Y3I+WR+5Jy+K090NaToQQQgjRosiAWCGEEEK0KJKcCCGEEKJFkeRECCGEEC2KDIgVQtidUqoW2I916v37wOtaaykwKIRoRJITIURzKDevkIpSqi3G4nCBnLeSt1LKQ2td44D4hBAtiHTrCCGaldY6G5gG/FYZ7lFKfamUWgesVUqNVEotqz9eKTVbKXWP+fHVSqnDSqmdSql/NzxOCOE8JDkRQjQ7rfUJ4PxVvH+ltR5xsXMarOI9QWvdHwi3e6BCCIeQ5EQI0RJc8ireQgjnIcmJEKLZ2XEVbyGEE5DkRAjRrGQVbyHEj5HZOkKI5iCreAshfjJZW0cI0WrIKt5CuAbp1hFCtCa/NrfAHASCkFW8hXBK0nIihBBCiBZFWk6EEEII0aJIciKEEEKIFkWSEyGEEEK0KJKcCCGEEKJFkeRECCGEEC2KJCdCCCGEaFH+H6JKiKaQrYl9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.swarmplot(x = \"Drug\", y = \"Na_to_K\",hue=\"BP\",data = df)\n",
        "plt.legend()\n",
        "plt.title(\"Na_to_K -- BP -- Drug\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.073476,
          "end_time": "2021-01-01T13:54:02.243337",
          "exception": false,
          "start_time": "2021-01-01T13:54:02.169861",
          "status": "completed"
        },
        "tags": [],
        "id": "40nQwIag_imY"
      },
      "source": [
        "* If people have HIGH blood pressure and Na_to_K ratio is lower than 15 , they get drugA and drugB only.\n",
        "* If people have LOW blood pressure and Na_to_K ratio is lower than 15 , they get drugC only."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.07403,
          "end_time": "2021-01-01T13:54:02.392097",
          "exception": false,
          "start_time": "2021-01-01T13:54:02.318067",
          "status": "completed"
        },
        "tags": [],
        "id": "d_fJfgOy_imY"
      },
      "source": [
        "<a id='17'></a>\n",
        "# Preparing Data and Feature Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.073422,
          "end_time": "2021-01-01T13:54:02.540340",
          "exception": false,
          "start_time": "2021-01-01T13:54:02.466918",
          "status": "completed"
        },
        "tags": [],
        "id": "R360m5BJ_imZ"
      },
      "source": [
        "<a id='18'></a>\n",
        "## Create New Features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.073368,
          "end_time": "2021-01-01T13:54:02.687534",
          "exception": false,
          "start_time": "2021-01-01T13:54:02.614166",
          "status": "completed"
        },
        "tags": [],
        "id": "w1gxBsAS_imZ"
      },
      "source": [
        "<a id='19'></a>\n",
        "### Na_to_K_Bigger_Than_15 \n",
        "\n",
        "If Na_to_K is bigger than 15, it is always drugY."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:02.856191Z",
          "iopub.status.busy": "2021-01-01T13:54:02.855252Z",
          "iopub.status.idle": "2021-01-01T13:54:02.859677Z",
          "shell.execute_reply": "2021-01-01T13:54:02.860219Z"
        },
        "papermill": {
          "duration": 0.098345,
          "end_time": "2021-01-01T13:54:02.860363",
          "exception": false,
          "start_time": "2021-01-01T13:54:02.762018",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ACwyplnX_imZ",
        "outputId": "7ca462bb-cf14-4dd8-8d35-d12af8b32caf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-54b05f15-ef7d-44ca-9ca3-890c570ed625\">\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>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "      <th>Na_to_K_Bigger_Than_15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>F</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>25.355</td>\n",
              "      <td>DrugY</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>DrugY</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-54b05f15-ef7d-44ca-9ca3-890c570ed625');\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",
              "  "
            ],
            "text/plain": [
              "   Age Sex      BP Cholesterol  Na_to_K   Drug  Na_to_K_Bigger_Than_15\n",
              "0   23   F    HIGH        HIGH   25.355  DrugY                       1\n",
              "1   47   M     LOW        HIGH   13.093  drugC                       0\n",
              "2   47   M     LOW        HIGH   10.114  drugC                       0\n",
              "3   28   F  NORMAL        HIGH    7.798  drugX                       0\n",
              "4   61   F     LOW        HIGH   18.043  DrugY                       1"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df['Na_to_K_Bigger_Than_15'] = [1 if i >=15.015 else 0 for i in df.Na_to_K]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:03.024803Z",
          "iopub.status.busy": "2021-01-01T13:54:03.024104Z",
          "iopub.status.idle": "2021-01-01T13:54:03.028733Z",
          "shell.execute_reply": "2021-01-01T13:54:03.028183Z"
        },
        "papermill": {
          "duration": 0.093086,
          "end_time": "2021-01-01T13:54:03.028866",
          "exception": false,
          "start_time": "2021-01-01T13:54:02.935780",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bZ3QQaL-_imZ",
        "outputId": "17d1686c-c1e3-4381-a6bd-36a50ab14b87"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\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",
              "\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>Drug</th>\n",
              "      <th>Na_to_K_Bigger_Than_15</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>DrugY</td>\n",
              "      <td>1</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>drugA</td>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>drugB</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>drugC</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>drugX</td>\n",
              "      <td>0</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\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-286ee1f5-c58f-46d1-b0a9-522aaebcfaf2 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-286ee1f5-c58f-46d1-b0a9-522aaebcfaf2');\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",
              "  "
            ],
            "text/plain": [
              "    Drug  Na_to_K_Bigger_Than_15  Count\n",
              "0  DrugY                       1     91\n",
              "1  drugA                       0     23\n",
              "2  drugB                       0     16\n",
              "3  drugC                       0     16\n",
              "4  drugX                       0     54"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "df_NaK15 = df.groupby([\"Drug\",\"Na_to_K_Bigger_Than_15\"]).size().reset_index(name = \"Count\")\n",
        "df_NaK15"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:03.188823Z",
          "iopub.status.busy": "2021-01-01T13:54:03.187514Z",
          "iopub.status.idle": "2021-01-01T13:54:03.399869Z",
          "shell.execute_reply": "2021-01-01T13:54:03.399342Z"
        },
        "papermill": {
          "duration": 0.296021,
          "end_time": "2021-01-01T13:54:03.399986",
          "exception": false,
          "start_time": "2021-01-01T13:54:03.103965",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "7jZUsZmb_imZ",
        "outputId": "6ea213f3-9ac8-4d8e-e668-d70a8f903e74"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize = (9,5))\n",
        "sns.barplot(x = \"Drug\",y=\"Count\", hue = \"Na_to_K_Bigger_Than_15\",data = df_NaK15)\n",
        "plt.title(\"Na_to_K_Bigger_Than_15 -- Drug\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.075625,
          "end_time": "2021-01-01T13:54:03.552798",
          "exception": false,
          "start_time": "2021-01-01T13:54:03.477173",
          "status": "completed"
        },
        "tags": [],
        "id": "S_NrjN0a_ima"
      },
      "source": [
        "* Na_to_K_Bigger_Than_15 feature will be important feature to drugY classification."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.075446,
          "end_time": "2021-01-01T13:54:03.704747",
          "exception": false,
          "start_time": "2021-01-01T13:54:03.629301",
          "status": "completed"
        },
        "tags": [],
        "id": "-iQ_Mhto_ima"
      },
      "source": [
        "<a id='20'></a>\n",
        "## Label Encoding\n",
        "\n",
        "We will convert from object to int64\n",
        "\n",
        "* Sex\n",
        "* BP\n",
        "* Cholesterol\n",
        "* Na_to_K\n",
        "* Na_to_K_Bigger_Than_15"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:03.864660Z",
          "iopub.status.busy": "2021-01-01T13:54:03.863851Z",
          "iopub.status.idle": "2021-01-01T13:54:03.866938Z",
          "shell.execute_reply": "2021-01-01T13:54:03.866366Z"
        },
        "papermill": {
          "duration": 0.086108,
          "end_time": "2021-01-01T13:54:03.867050",
          "exception": false,
          "start_time": "2021-01-01T13:54:03.780942",
          "status": "completed"
        },
        "tags": [],
        "id": "0TdCnvKJ_ima"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "def label_encoder(y):\n",
        "    le = LabelEncoder()\n",
        "    df[y] = le.fit_transform(df[y])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:04.032186Z",
          "iopub.status.busy": "2021-01-01T13:54:04.031273Z",
          "iopub.status.idle": "2021-01-01T13:54:04.034436Z",
          "shell.execute_reply": "2021-01-01T13:54:04.033889Z"
        },
        "papermill": {
          "duration": 0.088854,
          "end_time": "2021-01-01T13:54:04.034548",
          "exception": false,
          "start_time": "2021-01-01T13:54:03.945694",
          "status": "completed"
        },
        "tags": [],
        "id": "IBtRTrMZ_ima"
      },
      "outputs": [],
      "source": [
        "label_list = [\"Sex\",\"BP\",\"Cholesterol\",\"Na_to_K\",\"Na_to_K_Bigger_Than_15\",\"Drug\"]\n",
        "\n",
        "for l in label_list:\n",
        "    label_encoder(l)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:04.200165Z",
          "iopub.status.busy": "2021-01-01T13:54:04.199491Z",
          "iopub.status.idle": "2021-01-01T13:54:04.203937Z",
          "shell.execute_reply": "2021-01-01T13:54:04.203352Z"
        },
        "papermill": {
          "duration": 0.092345,
          "end_time": "2021-01-01T13:54:04.204080",
          "exception": false,
          "start_time": "2021-01-01T13:54:04.111735",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Z-qTsXLg_imb",
        "outputId": "15b41dd8-4d36-48eb-de9c-03b84864bdd4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5f811ef6-34ef-4a01-9856-3d6567103da3\">\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>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "      <th>Na_to_K_Bigger_Than_15</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>167</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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>89</td>\n",
              "      <td>3</td>\n",
              "      <td>0</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>43</td>\n",
              "      <td>3</td>\n",
              "      <td>0</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>10</td>\n",
              "      <td>4</td>\n",
              "      <td>0</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>133</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f811ef6-34ef-4a01-9856-3d6567103da3')\"\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-5f811ef6-34ef-4a01-9856-3d6567103da3 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-5f811ef6-34ef-4a01-9856-3d6567103da3');\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",
              "  "
            ],
            "text/plain": [
              "   Age  Sex  BP  Cholesterol  Na_to_K  Drug  Na_to_K_Bigger_Than_15\n",
              "0   23    0   0            0      167     0                       1\n",
              "1   47    1   1            0       89     3                       0\n",
              "2   47    1   1            0       43     3                       0\n",
              "3   28    0   2            0       10     4                       0\n",
              "4   61    0   1            0      133     0                       1"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.078603,
          "end_time": "2021-01-01T13:54:04.360825",
          "exception": false,
          "start_time": "2021-01-01T13:54:04.282222",
          "status": "completed"
        },
        "tags": [],
        "id": "ikn7YW-6_imb"
      },
      "source": [
        "<a id='21'></a>\n",
        "## Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:04.518425Z",
          "iopub.status.busy": "2021-01-01T13:54:04.517802Z",
          "iopub.status.idle": "2021-01-01T13:54:04.531270Z",
          "shell.execute_reply": "2021-01-01T13:54:04.530595Z"
        },
        "papermill": {
          "duration": 0.093451,
          "end_time": "2021-01-01T13:54:04.531383",
          "exception": false,
          "start_time": "2021-01-01T13:54:04.437932",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDaWF0jF_imb",
        "outputId": "11338edc-1954-47ca-8a60-ced0ceb17148"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train shape: (160, 6)\n",
            "x_test shape: (40, 6)\n",
            "y_train shape: (160, 1)\n",
            "y_test shape: (40, 1)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x = df.drop([\"Drug\"],axis=1)\n",
        "y = df.Drug\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2, random_state = 42, shuffle = True)\n",
        "\n",
        "y_train = y_train.values.reshape(-1,1)\n",
        "y_test = y_test.values.reshape(-1,1)\n",
        "\n",
        "print(\"x_train shape:\",x_train.shape)\n",
        "print(\"x_test shape:\",x_test.shape)\n",
        "print(\"y_train shape:\",y_train.shape)\n",
        "print(\"y_test shape:\",y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.077672,
          "end_time": "2021-01-01T13:54:04.686798",
          "exception": false,
          "start_time": "2021-01-01T13:54:04.609126",
          "status": "completed"
        },
        "tags": [],
        "id": "w7GbmlVg_imb"
      },
      "source": [
        "Data was splitted as 80% train data and 20% test data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.080772,
          "end_time": "2021-01-01T13:54:04.845353",
          "exception": false,
          "start_time": "2021-01-01T13:54:04.764581",
          "status": "completed"
        },
        "tags": [],
        "id": "nIdhda-k_imc"
      },
      "source": [
        "<a id='22'></a>\n",
        "# Model Implementation\n",
        "\n",
        "I will try three models and compare their results. For all models, I apply GridSearchCV method to find best score. Also, to be sure our models performance are random, I will use 5 Fold Cross Validation method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:05.011942Z",
          "iopub.status.busy": "2021-01-01T13:54:05.011171Z",
          "iopub.status.idle": "2021-01-01T13:54:05.013624Z",
          "shell.execute_reply": "2021-01-01T13:54:05.014150Z"
        },
        "papermill": {
          "duration": 0.087695,
          "end_time": "2021-01-01T13:54:05.014300",
          "exception": false,
          "start_time": "2021-01-01T13:54:04.926605",
          "status": "completed"
        },
        "tags": [],
        "id": "39D-2_of_imc"
      },
      "outputs": [],
      "source": [
        "# To store results of models\n",
        "result_dict_train = {}\n",
        "result_dict_test = {}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.076949,
          "end_time": "2021-01-01T13:54:05.168781",
          "exception": false,
          "start_time": "2021-01-01T13:54:05.091832",
          "status": "completed"
        },
        "tags": [],
        "id": "eA-BsYPR_imc"
      },
      "source": [
        "<a id='23'></a>\n",
        "## KNN Classifier\n",
        "\n",
        "To find best score of KNN model, I will try different value of n_neighbors, p, and weights parameters. If you are not sure about what these parameters are you can check my another kernel [Understanding Parameters of KNN](http://www.kaggle.com/gorkemgunay/understanding-parameters-of-knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.077743,
          "end_time": "2021-01-01T13:54:05.323948",
          "exception": false,
          "start_time": "2021-01-01T13:54:05.246205",
          "status": "completed"
        },
        "tags": [],
        "id": "gfU22pxe_imc"
      },
      "source": [
        "<a id='24'></a>\n",
        "### Default Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:05.482649Z",
          "iopub.status.busy": "2021-01-01T13:54:05.481983Z",
          "iopub.status.idle": "2021-01-01T13:54:05.695673Z",
          "shell.execute_reply": "2021-01-01T13:54:05.696727Z"
        },
        "papermill": {
          "duration": 0.295737,
          "end_time": "2021-01-01T13:54:05.696946",
          "exception": false,
          "start_time": "2021-01-01T13:54:05.401209",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CZ8ehakI_imc",
        "outputId": "78226e54-934f-418d-c1d3-580ffa253567"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Score: 0.59375\n",
            "Test Score: 0.65\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier()\n",
        "accuracies = cross_val_score(knn, x_train, y_train, cv=5)\n",
        "knn.fit(x_train,y_train)\n",
        "\n",
        "print(\"Train Score:\",np.mean(accuracies))\n",
        "print(\"Test Score:\",knn.score(x_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:05.877054Z",
          "iopub.status.busy": "2021-01-01T13:54:05.876327Z",
          "iopub.status.idle": "2021-01-01T13:54:05.884014Z",
          "shell.execute_reply": "2021-01-01T13:54:05.883224Z"
        },
        "papermill": {
          "duration": 0.10852,
          "end_time": "2021-01-01T13:54:05.884173",
          "exception": false,
          "start_time": "2021-01-01T13:54:05.775653",
          "status": "completed"
        },
        "tags": [],
        "id": "rbTRAIXO_imd"
      },
      "outputs": [],
      "source": [
        "result_dict_train[\"KNN Default Train Score\"] = np.mean(accuracies)\n",
        "result_dict_test[\"KNN Default Test Score\"] = knn.score(x_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.07839,
          "end_time": "2021-01-01T13:54:06.053568",
          "exception": false,
          "start_time": "2021-01-01T13:54:05.975178",
          "status": "completed"
        },
        "tags": [],
        "id": "X3uuvVAs_imd"
      },
      "source": [
        "<a id='25'></a>\n",
        "### GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:06.214303Z",
          "iopub.status.busy": "2021-01-01T13:54:06.213666Z",
          "iopub.status.idle": "2021-01-01T13:54:21.124526Z",
          "shell.execute_reply": "2021-01-01T13:54:21.123963Z"
        },
        "papermill": {
          "duration": 14.992265,
          "end_time": "2021-01-01T13:54:21.124674",
          "exception": false,
          "start_time": "2021-01-01T13:54:06.132409",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXM3Lnkk_imd",
        "outputId": "0a7bc3e5-6ef9-4814-ee01-1997df481ce5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hyperparameters: {'n_neighbors': 10, 'p': 1, 'weights': 'distance'}\n",
            "Train Score: 0.75625\n",
            "Test Score: 0.7\n"
          ]
        }
      ],
      "source": [
        "grid = {'n_neighbors':np.arange(1,120),\n",
        "        'p':np.arange(1,3),\n",
        "        'weights':['uniform','distance']\n",
        "       }\n",
        "\n",
        "knn = KNeighborsClassifier(algorithm = \"auto\")\n",
        "knn_cv = GridSearchCV(knn,grid,cv=5)\n",
        "knn_cv.fit(x_train,y_train)\n",
        "\n",
        "print(\"Hyperparameters:\",knn_cv.best_params_)\n",
        "print(\"Train Score:\",knn_cv.best_score_)\n",
        "print(\"Test Score:\",knn_cv.score(x_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:21.292187Z",
          "iopub.status.busy": "2021-01-01T13:54:21.291159Z",
          "iopub.status.idle": "2021-01-01T13:54:21.295782Z",
          "shell.execute_reply": "2021-01-01T13:54:21.296285Z"
        },
        "papermill": {
          "duration": 0.091357,
          "end_time": "2021-01-01T13:54:21.296439",
          "exception": false,
          "start_time": "2021-01-01T13:54:21.205082",
          "status": "completed"
        },
        "tags": [],
        "id": "RBU3HyLt_imd"
      },
      "outputs": [],
      "source": [
        "result_dict_train[\"KNN GridSearch Train Score\"] = knn_cv.best_score_\n",
        "result_dict_test[\"KNN GridSearch Test Score\"] = knn_cv.score(x_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.078545,
          "end_time": "2021-01-01T13:54:21.454622",
          "exception": false,
          "start_time": "2021-01-01T13:54:21.376077",
          "status": "completed"
        },
        "tags": [],
        "id": "4S8ug1PR_ime"
      },
      "source": [
        "<a id='26'></a>\n",
        "# Random Forest\n",
        "\n",
        "\n",
        "To find best score of Random Forest model, I will try different value of n_estimators and criterion parameters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.089649,
          "end_time": "2021-01-01T13:54:21.632988",
          "exception": false,
          "start_time": "2021-01-01T13:54:21.543339",
          "status": "completed"
        },
        "tags": [],
        "id": "QoiNQrAc_ime"
      },
      "source": [
        "<a id='27'></a>\n",
        "### Default Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:21.811697Z",
          "iopub.status.busy": "2021-01-01T13:54:21.810910Z",
          "iopub.status.idle": "2021-01-01T13:54:23.129528Z",
          "shell.execute_reply": "2021-01-01T13:54:23.130113Z"
        },
        "papermill": {
          "duration": 1.409596,
          "end_time": "2021-01-01T13:54:23.130266",
          "exception": false,
          "start_time": "2021-01-01T13:54:21.720670",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Cr6nMCv_ime",
        "outputId": "f40e9e6c-cde9-4778-e200-ff5125647513"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Score: 0.98125\n",
            "Test Score: 0.975\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rfc = RandomForestClassifier(random_state = 42)\n",
        "accuracies = cross_val_score(rfc, x_train, y_train, cv=5)\n",
        "rfc.fit(x_train,y_train)\n",
        "\n",
        "print(\"Train Score:\",np.mean(accuracies))\n",
        "print(\"Test Score:\",rfc.score(x_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:23.306292Z",
          "iopub.status.busy": "2021-01-01T13:54:23.305020Z",
          "iopub.status.idle": "2021-01-01T13:54:23.315491Z",
          "shell.execute_reply": "2021-01-01T13:54:23.314835Z"
        },
        "papermill": {
          "duration": 0.105582,
          "end_time": "2021-01-01T13:54:23.315602",
          "exception": false,
          "start_time": "2021-01-01T13:54:23.210020",
          "status": "completed"
        },
        "tags": [],
        "id": "AUgW3BDs_ime"
      },
      "outputs": [],
      "source": [
        "result_dict_train[\"Random Forest Default Train Score\"] = np.mean(accuracies)\n",
        "result_dict_test[\"Random Forest Default Test Score\"] = rfc.score(x_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.080248,
          "end_time": "2021-01-01T13:54:23.475507",
          "exception": false,
          "start_time": "2021-01-01T13:54:23.395259",
          "status": "completed"
        },
        "tags": [],
        "id": "ZKh4Gsln_imf"
      },
      "source": [
        "<a id='28'></a>\n",
        "### GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:54:23.654477Z",
          "iopub.status.busy": "2021-01-01T13:54:23.649007Z",
          "iopub.status.idle": "2021-01-01T13:55:54.258855Z",
          "shell.execute_reply": "2021-01-01T13:55:54.259400Z"
        },
        "papermill": {
          "duration": 90.704271,
          "end_time": "2021-01-01T13:55:54.259552",
          "exception": false,
          "start_time": "2021-01-01T13:54:23.555281",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8R2HtJZa_imf",
        "outputId": "3c5166d4-5253-4af2-8f68-7d4ba43250a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hyperparameters: {'criterion': 'entropy', 'n_estimators': 100}\n",
            "Train Score: 0.9875\n",
            "Test Score: 0.975\n"
          ]
        }
      ],
      "source": [
        "grid = {'n_estimators':np.arange(100,1000,100),\n",
        "        'criterion':['gini','entropy']\n",
        "       }\n",
        "\n",
        "rf = RandomForestClassifier(random_state = 42)\n",
        "rf_cv = GridSearchCV(rf,grid,cv=5)\n",
        "rf_cv.fit(x_train,y_train)\n",
        "\n",
        "print(\"Hyperparameters:\",rf_cv.best_params_)\n",
        "print(\"Train Score:\",rf_cv.best_score_)\n",
        "print(\"Test Score:\",rf_cv.score(x_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:55:54.425573Z",
          "iopub.status.busy": "2021-01-01T13:55:54.424949Z",
          "iopub.status.idle": "2021-01-01T13:55:54.442359Z",
          "shell.execute_reply": "2021-01-01T13:55:54.441729Z"
        },
        "papermill": {
          "duration": 0.100947,
          "end_time": "2021-01-01T13:55:54.442482",
          "exception": false,
          "start_time": "2021-01-01T13:55:54.341535",
          "status": "completed"
        },
        "tags": [],
        "id": "oLzncIoD_imf"
      },
      "outputs": [],
      "source": [
        "result_dict_train[\"Random Forest GridSearch Train Score\"] = rf_cv.best_score_\n",
        "result_dict_test[\"Random Forest GridSearch Test Score\"] = rf_cv.score(x_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.080604,
          "end_time": "2021-01-01T13:55:54.604551",
          "exception": false,
          "start_time": "2021-01-01T13:55:54.523947",
          "status": "completed"
        },
        "tags": [],
        "id": "NRH1n7Fj_imf"
      },
      "source": [
        "<a id='29'></a>\n",
        "# SVM Classifier\n",
        "\n",
        "To find best score of SVM model, I will try different value of C, kernel, degree and gamma parameters. The easy way to do this is GridSearchCV method. If you are not sure about what these parameters are you can check my another kernel [Understanding Parameters of SVM](https://www.kaggle.com/gorkemgunay/understanding-parameters-of-svm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.081012,
          "end_time": "2021-01-01T13:55:54.766065",
          "exception": false,
          "start_time": "2021-01-01T13:55:54.685053",
          "status": "completed"
        },
        "tags": [],
        "id": "e3bX39pI_img"
      },
      "source": [
        "<a id='30'></a>\n",
        "### Default Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:55:54.934240Z",
          "iopub.status.busy": "2021-01-01T13:55:54.933568Z",
          "iopub.status.idle": "2021-01-01T13:55:54.979354Z",
          "shell.execute_reply": "2021-01-01T13:55:54.978755Z"
        },
        "papermill": {
          "duration": 0.13063,
          "end_time": "2021-01-01T13:55:54.979466",
          "exception": false,
          "start_time": "2021-01-01T13:55:54.848836",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_flBznn_img",
        "outputId": "a9369459-1938-4b87-af2c-23d769f87522"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Score: 0.7125\n",
            "Test Score: 0.65\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "svc = SVC(random_state = 42)\n",
        "accuracies = cross_val_score(svc, x_train, y_train, cv=5)\n",
        "svc.fit(x_train,y_train)\n",
        "\n",
        "print(\"Train Score:\",np.mean(accuracies))\n",
        "print(\"Test Score:\",svc.score(x_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:55:55.148558Z",
          "iopub.status.busy": "2021-01-01T13:55:55.147554Z",
          "iopub.status.idle": "2021-01-01T13:55:55.151660Z",
          "shell.execute_reply": "2021-01-01T13:55:55.151118Z"
        },
        "papermill": {
          "duration": 0.091263,
          "end_time": "2021-01-01T13:55:55.151801",
          "exception": false,
          "start_time": "2021-01-01T13:55:55.060538",
          "status": "completed"
        },
        "tags": [],
        "id": "s0JESV7z_img"
      },
      "outputs": [],
      "source": [
        "result_dict_train[\"SVM Default Train Score\"] = np.mean(accuracies)\n",
        "result_dict_test[\"SVM Default Test Score\"] = svc.score(x_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.080856,
          "end_time": "2021-01-01T13:55:55.315425",
          "exception": false,
          "start_time": "2021-01-01T13:55:55.234569",
          "status": "completed"
        },
        "tags": [],
        "id": "9ccUuKeY_img"
      },
      "source": [
        "<a id='31'></a>\n",
        "### GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:55:55.488871Z",
          "iopub.status.busy": "2021-01-01T13:55:55.488171Z",
          "iopub.status.idle": "2021-01-01T13:56:04.721278Z",
          "shell.execute_reply": "2021-01-01T13:56:04.720424Z"
        },
        "papermill": {
          "duration": 9.32334,
          "end_time": "2021-01-01T13:56:04.721410",
          "exception": false,
          "start_time": "2021-01-01T13:55:55.398070",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zSr0U8-L_imh",
        "outputId": "60ed2f84-063a-4edc-c329-a4575509655c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'C': 1, 'degree': 1, 'gamma': 0.01, 'kernel': 'linear'}\n",
            "Train Score: 0.9875\n",
            "Test Score: 0.975\n"
          ]
        }
      ],
      "source": [
        "grid = {\n",
        "    'C':[0.01,0.1,1,10],\n",
        "    'kernel' : [\"linear\",\"poly\",\"rbf\",\"sigmoid\"],\n",
        "    'degree' : [1,3,5,7],\n",
        "    'gamma' : [0.01,1]\n",
        "}\n",
        "\n",
        "svm  = SVC ();\n",
        "svm_cv = GridSearchCV(svm, grid, cv = 5)\n",
        "svm_cv.fit(x_train,y_train)\n",
        "print(\"Best Parameters:\",svm_cv.best_params_)\n",
        "print(\"Train Score:\",svm_cv.best_score_)\n",
        "print(\"Test Score:\",svm_cv.score(x_test,y_test))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:56:04.895051Z",
          "iopub.status.busy": "2021-01-01T13:56:04.894391Z",
          "iopub.status.idle": "2021-01-01T13:56:04.897209Z",
          "shell.execute_reply": "2021-01-01T13:56:04.897692Z"
        },
        "papermill": {
          "duration": 0.093853,
          "end_time": "2021-01-01T13:56:04.897840",
          "exception": false,
          "start_time": "2021-01-01T13:56:04.803987",
          "status": "completed"
        },
        "tags": [],
        "id": "fqiiw99N_imh"
      },
      "outputs": [],
      "source": [
        "result_dict_train[\"SVM GridSearch Train Score\"] = svm_cv.best_score_\n",
        "result_dict_test[\"SVM GridSearch Test Score\"] = svm_cv.score(x_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.082173,
          "end_time": "2021-01-01T13:56:05.062399",
          "exception": false,
          "start_time": "2021-01-01T13:56:04.980226",
          "status": "completed"
        },
        "tags": [],
        "id": "Tj3HVSs5_imh"
      },
      "source": [
        "<a id='32'></a>\n",
        "# Conclusion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:56:05.231617Z",
          "iopub.status.busy": "2021-01-01T13:56:05.230981Z",
          "iopub.status.idle": "2021-01-01T13:56:05.241740Z",
          "shell.execute_reply": "2021-01-01T13:56:05.242289Z"
        },
        "papermill": {
          "duration": 0.097927,
          "end_time": "2021-01-01T13:56:05.242444",
          "exception": false,
          "start_time": "2021-01-01T13:56:05.144517",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "mIP6llke_imh",
        "outputId": "b4573526-b743-417f-a370-6b146e3f12ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0bc1fcbb-4339-4a5f-a164-49cb5118533c\">\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>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KNN Default Train Score</th>\n",
              "      <td>0.59375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNN GridSearch Train Score</th>\n",
              "      <td>0.75625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest Default Train Score</th>\n",
              "      <td>0.98125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest GridSearch Train Score</th>\n",
              "      <td>0.98750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVM Default Train Score</th>\n",
              "      <td>0.71250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVM GridSearch Train Score</th>\n",
              "      <td>0.98750</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bc1fcbb-4339-4a5f-a164-49cb5118533c')\"\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",
              "      </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-0bc1fcbb-4339-4a5f-a164-49cb5118533c 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-0bc1fcbb-4339-4a5f-a164-49cb5118533c');\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",
              "  "
            ],
            "text/plain": [
              "                                        Score\n",
              "KNN Default Train Score               0.59375\n",
              "KNN GridSearch Train Score            0.75625\n",
              "Random Forest Default Train Score     0.98125\n",
              "Random Forest GridSearch Train Score  0.98750\n",
              "SVM Default Train Score               0.71250\n",
              "SVM GridSearch Train Score            0.98750"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "df_result_train = pd.DataFrame.from_dict(result_dict_train,orient = \"index\",columns=[\"Score\"])\n",
        "df_result_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:56:05.420973Z",
          "iopub.status.busy": "2021-01-01T13:56:05.420010Z",
          "iopub.status.idle": "2021-01-01T13:56:05.424515Z",
          "shell.execute_reply": "2021-01-01T13:56:05.423888Z"
        },
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          "exception": false,
          "start_time": "2021-01-01T13:56:05.325818",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "XCbc7yrd_imi",
        "outputId": "dc1c051a-7cc7-4eee-f200-8cde182de656"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-5bc55c88-320e-415f-a79e-c7a66814e512\">\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>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KNN Default Test Score</th>\n",
              "      <td>0.650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNN GridSearch Test Score</th>\n",
              "      <td>0.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest Default Test Score</th>\n",
              "      <td>0.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Random Forest GridSearch Test Score</th>\n",
              "      <td>0.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVM Default Test Score</th>\n",
              "      <td>0.650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVM GridSearch Test Score</th>\n",
              "      <td>0.975</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5bc55c88-320e-415f-a79e-c7a66814e512')\"\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-5bc55c88-320e-415f-a79e-c7a66814e512 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-5bc55c88-320e-415f-a79e-c7a66814e512');\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",
              "  "
            ],
            "text/plain": [
              "                                     Score\n",
              "KNN Default Test Score               0.650\n",
              "KNN GridSearch Test Score            0.700\n",
              "Random Forest Default Test Score     0.975\n",
              "Random Forest GridSearch Test Score  0.975\n",
              "SVM Default Test Score               0.650\n",
              "SVM GridSearch Test Score            0.975"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "df_result_test = pd.DataFrame.from_dict(result_dict_test,orient = \"index\",columns=[\"Score\"])\n",
        "df_result_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-01-01T13:56:05.607497Z",
          "iopub.status.busy": "2021-01-01T13:56:05.605479Z",
          "iopub.status.idle": "2021-01-01T13:56:05.913352Z",
          "shell.execute_reply": "2021-01-01T13:56:05.912729Z"
        },
        "papermill": {
          "duration": 0.405083,
          "end_time": "2021-01-01T13:56:05.913460",
          "exception": false,
          "start_time": "2021-01-01T13:56:05.508377",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "5hPkSZ83_imi",
        "outputId": "67d969d3-8a83-46ea-f29e-13211d5b2207"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig,ax = plt.subplots(1,2,figsize=(20,5))\n",
        "sns.barplot(x = df_result_train.index,y = df_result_train.Score,ax = ax[0])\n",
        "sns.barplot(x = df_result_test.index,y = df_result_test.Score,ax = ax[1])\n",
        "ax[0].set_xticklabels(df_result_train.index,rotation = 75)\n",
        "ax[1].set_xticklabels(df_result_test.index,rotation = 75)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.08512,
          "end_time": "2021-01-01T13:56:06.083623",
          "exception": false,
          "start_time": "2021-01-01T13:56:05.998503",
          "status": "completed"
        },
        "tags": [],
        "id": "aH2DPLJ6_imi"
      },
      "source": [
        "* Random Forest classifier  and SVM classifier (after hyperparameter tuning) have a good scores.\n",
        "* KNN classifier has worst score in three clasifiers."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.6"
    },
    "papermill": {
      "duration": 139.967883,
      "end_time": "2021-01-01T13:56:06.277794",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-01-01T13:53:46.309911",
      "version": "2.1.0"
    },
    "colab": {
      "name": "Drug Classification",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}