Camytha_Octanuryati_Rochmad_Script_Final_Project.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/camythaocta/Stroke-Predictions_Imbalanced-Data/blob/main/Camytha_Octanuryati_Rochmad_Script_Final_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6aPWWy1-RvhW"
      },
      "source": [
        "The process to do with this project are:\n",
        "1. Import libraries and load the data\n",
        "2. Exploratory data analysis (EDA) which consists of preliminary look at the data, data cleansing, and data deep-dive understanding\n",
        "3. Modelling machine learning"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "output: which parameters have the highest contributions to the prediction of people having stroke?"
      ],
      "metadata": {
        "id": "2JNj84Uz9bs6"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3NsLVIL-1wD"
      },
      "source": [
        "#Import Libraries and Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sVcDATK41yQM"
      },
      "outputs": [],
      "source": [
        "import pandas as pd #for data processing, data manipulation, read csv file\n",
        "import matplotlib.pyplot as plt #for graphic\n",
        "import seaborn as sns #for data visualization\n",
        "import numpy as np #for linear algebra, data analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "QNOnmWx50Zt9",
        "outputId": "16d6f77c-5d28-42cd-ed7e-9b61d32d71f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KT2Pivr81l2e"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/project/dataset.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HiwA507tX-at"
      },
      "source": [
        "#Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qCormd1d_lB_"
      },
      "source": [
        "##Preliminary Look at the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "occTxhghANPT"
      },
      "source": [
        "###Sample of Data"
      ]
    },
    {
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        "outputId": "18a8b2a5-d89d-46f5-88d8-ff69d5e5ab7b"
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              "      id  gender   age  ...   bmi   smoking_status stroke\n",
              "0  30669    Male   3.0  ...  18.0              NaN      0\n",
              "1  30468    Male  58.0  ...  39.2     never smoked      0\n",
              "2  16523  Female   8.0  ...  17.6              NaN      0\n",
              "3  56543  Female  70.0  ...  35.9  formerly smoked      0\n",
              "4  46136    Male  14.0  ...  19.1              NaN      0\n",
              "\n",
              "[5 rows x 12 columns]"
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        "data.head(5)"
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              "                                                     [key], {});\n",
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              "          id  gender   age  ...   bmi   smoking_status stroke\n",
              "43395  56196  Female  10.0  ...  20.4     never smoked      0\n",
              "43396   5450  Female  56.0  ...  55.4  formerly smoked      0\n",
              "43397  28375  Female  82.0  ...  28.9  formerly smoked      0\n",
              "43398  27973    Male  40.0  ...  33.2     never smoked      0\n",
              "43399  36271  Female  82.0  ...  20.6     never smoked      0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "data.tail(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "K9rXp7jE_A-j",
        "outputId": "aff3d5e4-9511-47fc-9f8f-7601c988fca5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>27804</th>\n",
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              "      <td>61.0</td>\n",
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              "      <td>Rural</td>\n",
              "      <td>147.57</td>\n",
              "      <td>21.5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12409</th>\n",
              "      <td>18294</td>\n",
              "      <td>Female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No</td>\n",
              "      <td>children</td>\n",
              "      <td>Urban</td>\n",
              "      <td>87.21</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40516</th>\n",
              "      <td>37542</td>\n",
              "      <td>Female</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Govt_job</td>\n",
              "      <td>Urban</td>\n",
              "      <td>69.73</td>\n",
              "      <td>18.1</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "          const element = document.querySelector('#df-e2a08e0d-e8aa-4358-9180-efbc54896900');\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",
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              "  "
            ],
            "text/plain": [
              "          id  gender   age  ...   bmi   smoking_status stroke\n",
              "27804  26345  Female  64.0  ...  33.8     never smoked      0\n",
              "30791  24471  Female  34.0  ...  26.3     never smoked      0\n",
              "40799  57591  Female  61.0  ...  21.5              NaN      0\n",
              "12409  18294  Female   4.0  ...  18.0              NaN      0\n",
              "40516  37542  Female  80.0  ...  18.1  formerly smoked      0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "data.sample(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFDjQ8rnAUwo"
      },
      "source": [
        "1. `id` column is the key (identifier) of the dataset, meaningless to be explored further\n",
        "2. No obvious defect on the data (column name vs its entries), all looks good"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UjWAbkFFBGoH"
      },
      "source": [
        "###General Information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FOUORIMN_tlC",
        "outputId": "e8731b04-364b-47ac-f479-3776efc481bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 43400 entries, 0 to 43399\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 43400 non-null  int64  \n",
            " 1   gender             43400 non-null  object \n",
            " 2   age                43400 non-null  float64\n",
            " 3   hypertension       43400 non-null  int64  \n",
            " 4   heart_disease      43400 non-null  int64  \n",
            " 5   ever_married       43400 non-null  object \n",
            " 6   work_type          43400 non-null  object \n",
            " 7   Residence_type     43400 non-null  object \n",
            " 8   avg_glucose_level  43400 non-null  float64\n",
            " 9   bmi                41938 non-null  float64\n",
            " 10  smoking_status     30108 non-null  object \n",
            " 11  stroke             43400 non-null  int64  \n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 4.0+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QxcNMfqrBL0t"
      },
      "source": [
        "1. Dataset contains 12 columns and 43400 rows\n",
        "2. There are 7 numerical columns and 5 categorical columns\n",
        "3. All, except `bmi` and `smoking_status` columns, has some missing values\n",
        "4. All dtypes is appropriate, given the corresponding column name"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGYtpYQkCWWu"
      },
      "source": [
        "##Data Cleansing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "00VRV2d8Bw8m"
      },
      "source": [
        "###Missing Value Handling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhL6zkgyBjJr",
        "outputId": "2ae984c9-86dc-47c1-e67c-b3029d97710f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                       0\n",
              "gender                   0\n",
              "age                      0\n",
              "hypertension             0\n",
              "heart_disease            0\n",
              "ever_married             0\n",
              "work_type                0\n",
              "Residence_type           0\n",
              "avg_glucose_level        0\n",
              "bmi                   1462\n",
              "smoking_status       13292\n",
              "stroke                   0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "## number of missing values per column\n",
        "data_NaN_missing = data.isna().sum()\n",
        "data_NaN_missing"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_NaN_missing / len(data) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xvkAskNwcDaP",
        "outputId": "16948605-08a5-460c-b99b-eb59da32a883"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                    0.000000\n",
              "gender                0.000000\n",
              "age                   0.000000\n",
              "hypertension          0.000000\n",
              "heart_disease         0.000000\n",
              "ever_married          0.000000\n",
              "work_type             0.000000\n",
              "Residence_type        0.000000\n",
              "avg_glucose_level     0.000000\n",
              "bmi                   3.368664\n",
              "smoking_status       30.626728\n",
              "stroke                0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is no column that has more than 35% `NaN` missing values. Therefore, none of the columns need to be dropped. In the other hand, the imputation is conducted."
      ],
      "metadata": {
        "id": "3JTV5yGJe4U8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['bmi'] = data['bmi'].fillna(data['bmi'].median())"
      ],
      "metadata": {
        "id": "yjosYkOKpwrF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['smoking_status'] = data['smoking_status'].fillna(data['smoking_status'].mode()[0])"
      ],
      "metadata": {
        "id": "ZW_aiBWqlzCc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhgWx82nCaF8",
        "outputId": "53ccb67e-e874-4a52-9a4a-5829f1e1299c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                   0\n",
              "gender               0\n",
              "age                  0\n",
              "hypertension         0\n",
              "heart_disease        0\n",
              "ever_married         0\n",
              "work_type            0\n",
              "Residence_type       0\n",
              "avg_glucose_level    0\n",
              "bmi                  0\n",
              "smoking_status       0\n",
              "stroke               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "#sanity check\n",
        "data.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEfX_RylCqlw"
      },
      "source": [
        "Success removing NULL values. However, in `.isna()`, only `NaN` missing value is handled. Therefore, it is still needed to check the other types of missing value in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "orfSBUdBB4gd",
        "outputId": "91ef450d-e561-4039-f5d7-ae9001724a43"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============= id =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "2047     1\n",
              "42270    1\n",
              "66858    1\n",
              "7465     1\n",
              "25894    1\n",
              "        ..\n",
              "64106    1\n",
              "57961    1\n",
              "37479    1\n",
              "35428    1\n",
              "67585    1\n",
              "Name: id, Length: 43400, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= gender =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Female    25665\n",
              "Male      17724\n",
              "Other        11\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= age =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "51.00    738\n",
              "52.00    721\n",
              "53.00    701\n",
              "78.00    698\n",
              "50.00    694\n",
              "        ... \n",
              "0.48      37\n",
              "0.40      35\n",
              "1.00      34\n",
              "0.16      26\n",
              "0.08      17\n",
              "Name: age, Length: 104, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= hypertension =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0    39339\n",
              "1     4061\n",
              "Name: hypertension, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= heart_disease =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0    41338\n",
              "1     2062\n",
              "Name: heart_disease, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= ever_married =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Yes    27938\n",
              "No     15462\n",
              "Name: ever_married, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= work_type =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Private          24834\n",
              "Self-employed     6793\n",
              "children          6156\n",
              "Govt_job          5440\n",
              "Never_worked       177\n",
              "Name: work_type, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= Residence_type =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Urban    21756\n",
              "Rural    21644\n",
              "Name: Residence_type, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= avg_glucose_level =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "82.71     19\n",
              "87.07     18\n",
              "72.53     18\n",
              "84.40     17\n",
              "90.71     17\n",
              "          ..\n",
              "202.11     1\n",
              "121.82     1\n",
              "138.99     1\n",
              "152.24     1\n",
              "160.00     1\n",
              "Name: avg_glucose_level, Length: 12543, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= bmi =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "27.7    1733\n",
              "27.6     267\n",
              "26.8     266\n",
              "26.1     265\n",
              "27.1     260\n",
              "        ... \n",
              "10.1       1\n",
              "67.0       1\n",
              "11.2       1\n",
              "97.6       1\n",
              "65.2       1\n",
              "Name: bmi, Length: 555, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= smoking_status =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "never smoked       29345\n",
              "formerly smoked     7493\n",
              "smokes              6562\n",
              "Name: smoking_status, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "============= stroke =================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0    42617\n",
              "1      783\n",
              "Name: stroke, dtype: int64"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "for column in data.columns:\n",
        "    print(f\"============= {column} =================\")\n",
        "    display(data[column].value_counts())\n",
        "    print()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DP5wah-iDYHw"
      },
      "source": [
        "There is no strange value in each column. Therefore, now the dataset is clean from missing value."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "100 * len(data[data['gender']=='Other']) / len(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5tZ6HvLtdaoa",
        "outputId": "79ec38bc-eca6-4fa1-b60c-7acb764325dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02534562211981567"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "However, 'Other' in `gender` is only 0.025% of the data. Therefore, it can be omitted and focus on 'Female' and 'Male'"
      ],
      "metadata": {
        "id": "ZO7cQFo-dIEx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data[data.gender != 'Other']"
      ],
      "metadata": {
        "id": "As0zdU1ZfbkL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5b_G0frtDuf8"
      },
      "source": [
        "###Duplicate Rows Handling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScllwFOIDHvp",
        "outputId": "63b0ae80-4c7d-48f2-abe4-56e39f2d4cab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "## number of duplicated data\n",
        "data.duplicated().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DllbRrnDD-t1"
      },
      "source": [
        "There is no duplicate rows in the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hiWy_NtoEGMy"
      },
      "source": [
        "##Data deep-dive understanding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZA2e_e_KF_xF"
      },
      "source": [
        "###Statistical Summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-sIkdO4kFcEC"
      },
      "outputs": [],
      "source": [
        "numericals = ['id','age','hypertension','heart_disease','avg_glucose_level','bmi','stroke']\n",
        "categoricals = ['gender','ever_married','work_type','Residence_type','smoking_status']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "RzNWeZ87G1BN",
        "outputId": "b2d80b82-584a-40a1-9b95-b9f4b0da32cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>stroke</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>43389.000000</td>\n",
              "      <td>43389.000000</td>\n",
              "      <td>43389.000000</td>\n",
              "      <td>43389.000000</td>\n",
              "      <td>43389.000000</td>\n",
              "      <td>43389.000000</td>\n",
              "      <td>43389.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36326.360760</td>\n",
              "      <td>42.219747</td>\n",
              "      <td>0.093595</td>\n",
              "      <td>0.047524</td>\n",
              "      <td>104.486760</td>\n",
              "      <td>28.574740</td>\n",
              "      <td>0.018046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>21073.474716</td>\n",
              "      <td>22.520750</td>\n",
              "      <td>0.291268</td>\n",
              "      <td>0.212758</td>\n",
              "      <td>43.115157</td>\n",
              "      <td>7.640258</td>\n",
              "      <td>0.133119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>10.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>18034.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>77.540000</td>\n",
              "      <td>23.400000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36352.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>91.580000</td>\n",
              "      <td>27.700000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54516.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>112.070000</td>\n",
              "      <td>32.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72943.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>291.050000</td>\n",
              "      <td>97.600000</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-d961bacf-36dd-4ec6-acd6-c6f635da98b8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "        async function convertToInteractive(key) {\n",
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              "          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",
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              "  "
            ],
            "text/plain": [
              "                 id           age  ...           bmi        stroke\n",
              "count  43389.000000  43389.000000  ...  43389.000000  43389.000000\n",
              "mean   36326.360760     42.219747  ...     28.574740      0.018046\n",
              "std    21073.474716     22.520750  ...      7.640258      0.133119\n",
              "min        1.000000      0.080000  ...     10.100000      0.000000\n",
              "25%    18034.000000     24.000000  ...     23.400000      0.000000\n",
              "50%    36352.000000     44.000000  ...     27.700000      0.000000\n",
              "75%    54516.000000     60.000000  ...     32.600000      0.000000\n",
              "max    72943.000000     82.000000  ...     97.600000      1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "data[numericals].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kuZmWEqJHdS_"
      },
      "source": [
        "1. `id` is the unique key\n",
        "2. `hypertension`, `heart_disease`, and `stroke` are boolean columns\n",
        "3. Overall, the minimum and maximum values make sense for each column \n",
        "4. Mean ~ 50% (Median) in `age` and `bmi` column, indicating somewhat a symmetrical distribution\n",
        "5. Mean >> 50% (Median) in `avg_glucose_level`, indicating it is positively skewed distribution"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`id` column is the identifier, so it is meaningless to process further. Therefore, this column needs to be omitted. "
      ],
      "metadata": {
        "id": "LDByN0oGWVun"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(columns='id')"
      ],
      "metadata": {
        "id": "CYTO7X2rW7xJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
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              "      <th>unique</th>\n",
              "      <td>2</td>\n",
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              "      <td>3</td>\n",
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              "                                                     [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": [
              "        gender ever_married work_type Residence_type smoking_status\n",
              "count    43389        43389     43389          43389          43389\n",
              "unique       2            2         5              2              3\n",
              "top     Female          Yes   Private          Urban   never smoked\n",
              "freq     25665        27933     24827          21751          29341"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "data[categoricals].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmWIo-e0VmYm",
        "outputId": "325cc732-4ca8-4c21-8e9f-ee80c76a1292"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Value counts of gender column\n",
            "Female    25665\n",
            "Male      17724\n",
            "Name: gender, dtype: int64 \n",
            "\n",
            "Value counts of ever_married column\n",
            "Yes    27933\n",
            "No     15456\n",
            "Name: ever_married, dtype: int64 \n",
            "\n",
            "Value counts of work_type column\n",
            "Private          24827\n",
            "Self-employed     6793\n",
            "children          6154\n",
            "Govt_job          5438\n",
            "Never_worked       177\n",
            "Name: work_type, dtype: int64 \n",
            "\n",
            "Value counts of Residence_type column\n",
            "Urban    21751\n",
            "Rural    21638\n",
            "Name: Residence_type, dtype: int64 \n",
            "\n",
            "Value counts of smoking_status column\n",
            "never smoked       29341\n",
            "formerly smoked     7487\n",
            "smokes              6561\n",
            "Name: smoking_status, dtype: int64 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "# showing the precise value counts\n",
        "for col in categoricals:\n",
        "  print(f\"Value counts of {col} column\")\n",
        "  print(data[col].value_counts(), '\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWhyYu3SWIRG"
      },
      "source": [
        "Private work type and never smoked people are quite dominant"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M-CwICdeWhY7"
      },
      "source": [
        "###Univariate Analysis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jcCzVhjzGarS"
      },
      "source": [
        "Univariate analysis consists of boxplot for detecting the outliers, and Kernel Density Estimation (KDE) plot and bar graph for validating the distribution form."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylk7rjIdWtBT"
      },
      "source": [
        "####Boxplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "rCPkAskCWmHj",
        "outputId": "46d60917-32e9-439e-eba3-858689d328d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "features = numericals[1:]\n",
        "for i in range(0, len(features)):\n",
        "    plt.subplot(1, len(features), i+1)\n",
        "    sns.boxplot(y=data[features[i]], color='gold')\n",
        "    plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kTKGWvUeGyav"
      },
      "source": [
        "There are outliers in `avg_glucose_level` and `bmi` columns. Therefore, these columns need to be proceed further"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1_agl = np.percentile(data['avg_glucose_level'], 25, interpolation = 'midpoint') \n",
        "#Q2 = np.percentile(data, 50, interpolation = 'midpoint') \n",
        "Q3_agl = np.percentile(data['avg_glucose_level'], 75, interpolation = 'midpoint') \n",
        "IQR_agl = Q3_agl - Q1_agl\n",
        "low_lim_agl = Q1_agl - 1.5 * IQR_agl\n",
        "up_lim_agl = Q3_agl + 1.5 * IQR_agl"
      ],
      "metadata": {
        "id": "VmIAXdW-rEZq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outlier_avg = []\n",
        "for x in data['avg_glucose_level']:\n",
        "    if ((x> up_lim_agl) or (x<low_lim_agl)):\n",
        "         outlier_avg.append(x)\n",
        "print('There are', len(outlier_avg), 'outliers in avg_glucose_level column and it is', round(len(outlier_avg)/len(data)*100,2),'% of the total data' )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "txc-kriRrhzt",
        "outputId": "15a1ebc5-5e67-4efd-9d87-f5817dc26f2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 4978 outliers in avg_glucose_level column and it is 11.47 % of the total data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1_bmi = np.percentile(data['bmi'], 25, interpolation = 'midpoint') \n",
        "Q3_bmi = np.percentile(data['bmi'], 75, interpolation = 'midpoint') \n",
        "IQR_bmi = Q3_bmi - Q1_bmi\n",
        "low_lim_bmi = Q1_bmi - 1.5 * IQR_bmi\n",
        "up_lim_bmi = Q3_bmi + 1.5 * IQR_bmi"
      ],
      "metadata": {
        "id": "fzOUYuRM28rP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outlier_bmi = []\n",
        "for x in data['bmi']:\n",
        "    if ((x> up_lim_bmi) or (x<low_lim_bmi)):\n",
        "         outlier_bmi.append(x)\n",
        "print('There are', len(outlier_bmi), 'outliers in bmi column and it is', round(len(outlier_bmi)/len(data)*100,2), '% of the total data' )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45jUPfcM3uf6",
        "outputId": "3f81a890-ce88-4c12-8a28-b559efe0133b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1084 outliers in bmi column and it is 2.5 % of the total data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Outliers in `avg_glucose_level` is 11.47% (greater than 5%), so the clip method in pandas is used. Meanwhile, outliers in `bmi` is 2.5% (less than 5%), so it can be dropped"
      ],
      "metadata": {
        "id": "G3aKCIa745wE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#outliers handling for avg_glucose_level column\n",
        "data['avg_glucose_level'] = data['avg_glucose_level'].clip(upper=275)"
      ],
      "metadata": {
        "id": "G_nVzO7woSuR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WpL9OK0pJjcM",
        "outputId": "2aab5601-14ba-4c31-9539-472e697eee4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number of rows before filtering outliers are 43389 rows\n",
            "number of rows after filtering outliers are 42305 rows\n"
          ]
        }
      ],
      "source": [
        "#outliers handling for bmi column\n",
        "\n",
        "print(f'number of rows before filtering outliers are {len(data)} rows')\n",
        "\n",
        "filtered_entries = np.array([True] * len(data))\n",
        "for col in ['bmi']:\n",
        "    filtered_entries = ((data[col] >= low_lim_bmi) & (data[col] <= up_lim_bmi)) & filtered_entries\n",
        "    \n",
        "data_filtered= data[filtered_entries]\n",
        "\n",
        "print(f'number of rows after filtering outliers are {len(data_filtered)} rows')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_avKgR1nkil"
      },
      "source": [
        "####KDE plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "QFrs3YLjk8Rh",
        "outputId": "7e9965ab-22ee-494f-8fee-262c8f9a3caf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x504 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "\n",
        "features = numericals[1:]\n",
        "plt.figure(figsize=(30,7))\n",
        "for i in range(0, len(features)):\n",
        "    plt.subplot(1, len(features), i+1)\n",
        "    sns.distplot(x=data_filtered[features[i]], color='olive')\n",
        "    plt.xlabel(features[i])\n",
        "    plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HViV7NgPk0wu"
      },
      "source": [
        "1. `avg_glucose_level` is positively skewed\n",
        "2. `bmi` is slightly symmetrical distribution"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9SRK9Brq8AI"
      },
      "source": [
        "####Barplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "doE0xGn9n2A5",
        "outputId": "a522ed17-fb4e-47e6-ead0-01387ff98ffc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x288 with 5 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "features = categoricals\n",
        "plt.figure(figsize=(18,4))\n",
        "for i in range(0, len(features)):\n",
        "    plt.subplot(1, len(features), i+1)\n",
        "    sns.countplot(data=data_filtered,\n",
        "            x=features[i],\n",
        "            hue='stroke')\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ss5Tm10fp5cj"
      },
      "source": [
        "1. Male ~ Female to have stroke\n",
        "2. People who has married is more likely to have stroke\n",
        "3. People who work in private and self-employed are more likely to have stroke\n",
        "4. Urban ~ rural people to have stroke\n",
        "5. People who never smoked has tendency to have stroke"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "buHM-ZXBTzf0"
      },
      "source": [
        "###Multivariate (Bivariate) Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "YWqOzugtq3hh",
        "outputId": "bea56a26-d295-4d36-e4cf-4bf1beee5cd3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6320192c10>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Heat Map\n",
        "correlation = data_filtered.corr()\n",
        "sns.heatmap(correlation, annot=True, fmt='.2f')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmavnJGNZofk"
      },
      "source": [
        "There is no redundant features in this dataset. Therefore, all columns is used."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920
        },
        "id": "B8w8hLaAbmew",
        "outputId": "a9a2c07b-ed25-4701-e020-b9652b6951ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f63201b2a50>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 942.375x900 with 30 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Pair Plot\n",
        "sns.pairplot(data_filtered, hue='stroke')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_78Ozlv1fSSp"
      },
      "source": [
        "1. People who are 50-75 years old tend to have stroke\n",
        "2. People who have hypertension and heart disease tend to have stroke\n",
        "3. People who have normal average glucose level (in range 75-125) tend to have stroke\n",
        "4. People who have excess weight (bmi value more than 25) tend to have stroke"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Kr31wsBYPCB"
      },
      "source": [
        "#Modelling machine learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0WZW_wskb3bw"
      },
      "source": [
        "##Split Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sjX2uIWKb5b2"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_data, test_data = train_test_split(data_filtered, test_size=0.20, random_state=6)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BXuXtBHQcv7l"
      },
      "source": [
        "##Categorical feature handling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yocvbq4ucx_P"
      },
      "outputs": [],
      "source": [
        "# convert columns with 2 distinct values to boolean\n",
        "bool_categoricals = ['gender','ever_married', 'Residence_type']\n",
        "\n",
        "for feature in bool_categoricals:\n",
        "  if feature == 'gender':\n",
        "    train_data[feature] = train_data[feature].apply(lambda x: 1 if x=='Female' else 0).astype(int)\n",
        "    test_data[feature] = test_data[feature].apply(lambda x: 1 if x=='Female' else 0).astype(int)\n",
        "  if feature == 'Residence_type':\n",
        "    train_data[feature] = train_data[feature].apply(lambda x: 1 if x=='Urban' else 0).astype(int)\n",
        "    test_data[feature] = test_data[feature].apply(lambda x: 1 if x=='Urban' else 0).astype(int)\n",
        "  else :\n",
        "    train_data[feature] = train_data[feature].apply(lambda x: 1 if x=='Yes' else 0).astype(int)\n",
        "    test_data[feature] = test_data[feature].apply(lambda x: 1 if x=='Yes' else 0).astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "5jdtLXHGmEp3",
        "outputId": "ce02a94f-23c2-4bde-beae-e664373a8b74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-de4b6905-a118-4d79-b151-f8048d0fa67f\">\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>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>stroke</th>\n",
              "      <th>work_type_Govt_job</th>\n",
              "      <th>work_type_Never_worked</th>\n",
              "      <th>work_type_Private</th>\n",
              "      <th>work_type_Self-employed</th>\n",
              "      <th>work_type_children</th>\n",
              "      <th>smoking_status_formerly smoked</th>\n",
              "      <th>smoking_status_never smoked</th>\n",
              "      <th>smoking_status_smokes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>32317</th>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>87.45</td>\n",
              "      <td>23.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24317</th>\n",
              "      <td>0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>123.46</td>\n",
              "      <td>31.2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7455</th>\n",
              "      <td>0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>128.77</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17804</th>\n",
              "      <td>0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>80.91</td>\n",
              "      <td>26.9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16435</th>\n",
              "      <td>0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>243.22</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32924</th>\n",
              "      <td>0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>72.14</td>\n",
              "      <td>26.8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42361</th>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>87.28</td>\n",
              "      <td>22.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4834</th>\n",
              "      <td>0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>63.28</td>\n",
              "      <td>40.2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42248</th>\n",
              "      <td>0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>106.77</td>\n",
              "      <td>22.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32426</th>\n",
              "      <td>0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>62.05</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>33844 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de4b6905-a118-4d79-b151-f8048d0fa67f')\"\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-de4b6905-a118-4d79-b151-f8048d0fa67f 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-de4b6905-a118-4d79-b151-f8048d0fa67f');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       gender   age  ...  smoking_status_never smoked  smoking_status_smokes\n",
              "32317       0  24.0  ...                            1                      0\n",
              "24317       0  66.0  ...                            1                      0\n",
              "7455        0  46.0  ...                            1                      0\n",
              "17804       0  82.0  ...                            1                      0\n",
              "16435       0  78.0  ...                            0                      0\n",
              "...       ...   ...  ...                          ...                    ...\n",
              "32924       0  16.0  ...                            1                      0\n",
              "42361       0  30.0  ...                            1                      0\n",
              "4834        0  17.0  ...                            1                      0\n",
              "42248       0  63.0  ...                            1                      0\n",
              "32426       0  76.0  ...                            0                      0\n",
              "\n",
              "[33844 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "train_data = pd.get_dummies(train_data)\n",
        "test_data = pd.get_dummies(test_data)\n",
        "\n",
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = train_data.drop(columns='stroke')\n",
        "x_test = test_data.drop(columns='stroke')\n",
        "\n",
        "y_train = train_data['stroke']\n",
        "#y_train = y_train.reshape(len(y_train),)\n",
        "\n",
        "y_test = test_data['stroke']\n",
        "#y_test = y_test.reshape(len(y_test),)"
      ],
      "metadata": {
        "id": "u8Vuq9_6BiNI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4a0N0W4neR1"
      },
      "source": [
        "##Imbalance Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0UCMWMVngJL",
        "outputId": "3150f4a7-b9ec-400a-d055-c301acf00d6e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    33219\n",
              "1      625\n",
              "Name: stroke, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "train_data['stroke'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data['stroke'].value_counts().plot.pie(autopct='%.2f')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "2w1_F9WgBZac",
        "outputId": "bcdb749c-f0cc-4d55-ddf8-cadb5fc6d59f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f631dc3f5d0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hAPDBMoLnm2e"
      },
      "source": [
        "It is known that people who has stroke is very rare compared to people who don't have stroke. Therefore, this data is called imbalanced data and it needs to be proceed further since the focus of this project is the people who tend to have stroke"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Oversampling Method"
      ],
      "metadata": {
        "id": "MczJdspMCsLA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Random Over Sampler "
      ],
      "metadata": {
        "id": "K2GF4RaWCEV-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "ros = RandomOverSampler(sampling_strategy='not majority', random_state=6)\n",
        "x_train_res, y_train_res = ros.fit_resample(x_train,y_train)\n",
        "\n",
        "ax = y_train_res.value_counts().plot.pie(autopct='%.2f')"
      ],
      "metadata": {
        "id": "c00-67XzC-Bs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "096ed7d8-57bf-4425-c326-77e9998b0b35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_res.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZAEjokOxu7g",
        "outputId": "1eb1574b-e4a6-4634-a03e-5a9b0837b0e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    33219\n",
              "0    33219\n",
              "Name: stroke, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####SMOTE Algorithm"
      ],
      "metadata": {
        "id": "zmc3JF-YCP77"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "# summarize class distribution\n",
        "print(\"Before oversampling: \",Counter(y_train))\n",
        "\n",
        "# define oversampling strategy\n",
        "SMOTE = SMOTE()\n",
        "\n",
        "# fit and apply the transform\n",
        "x_train_SMOTE, y_train_SMOTE = SMOTE.fit_resample(x_train, y_train)\n",
        "\n",
        "# summarize class distribution\n",
        "print(\"After oversampling: \",Counter(y_train_SMOTE))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lL0hyQD_x5WX",
        "outputId": "00b75766-97a6-4768-9033-1663a218f620"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before oversampling:  Counter({0: 33219, 1: 625})\n",
            "After oversampling:  Counter({0: 33219, 1: 33219})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix, classification_report, recall_score, f1_score, roc_curve, auc\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "7vcpKIOj6u7S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def eval_classification(model, pred, x_train_res, y_train_res, x_test, y_test):\n",
        "    print(\"Recall score (Test Set): %.2f\" % recall_score(y_test, pred))\n",
        "    print(\"F1-Score (Test Set): %.2f\" % f1_score(y_test, pred))\n",
        "    \n",
        "    fpr, tpr, thresholds = roc_curve(y_test, pred, pos_label=1)\n",
        "    print(\"AUC: %.2f\" % auc(fpr, tpr))"
      ],
      "metadata": {
        "id": "NzHwbh4z81E1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Metric Choice"
      ],
      "metadata": {
        "id": "sHqiyYNtHgfD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Logistic Regression"
      ],
      "metadata": {
        "id": "Z4zQ2HE6yKyr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(random_state=6)\n",
        "model.fit(x_train_res, y_train_res)\n",
        "y_pred = model.predict(x_test)\n",
        "eval_classification(model, y_pred, x_train_res, y_train_res, x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmnmLIP5yNb2",
        "outputId": "4ea810bf-8dcf-4d87-a4f8-68339c4df349"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall score (Test Set): 0.82\n",
            "F1-Score (Test Set): 0.10\n",
            "AUC: 0.78\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rLm47hR30A_"
      },
      "source": [
        "####Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-7136MBH3xqX",
        "outputId": "400f08b8-0668-4c2c-c407-361d0b1ac1b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall score (Test Set): 0.03\n",
            "F1-Score (Test Set): 0.04\n",
            "AUC: 0.51\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier(random_state = 6)\n",
        "dt.fit(x_train_res, y_train_res)\n",
        "y_pred = dt.predict(x_test)\n",
        "eval_classification(model, y_pred, x_train_res, y_train_res, x_test, y_test)\n",
        "#print('recall score using decision tree classifier is', recall_score(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Hyperparameter Tunning (RandomizedSearchCV)"
      ],
      "metadata": {
        "id": "i36yyzIOebnI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "#n_estimators = [200, 250, 300, 350]\n",
        "#max_features = ['auto', 'sqrt', 'log2']\n",
        "max_depth = [1,3,5,7]\n",
        "min_samples_split = [2, 5, 10]\n",
        "min_samples_leaf = [1, 2, 4]\n",
        "# bootstrap = [True, False]\n",
        "\n",
        "random_grid = {'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf}"
      ],
      "metadata": {
        "id": "Tq3SWrB7ebOv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier( random_state = 6)\n",
        "dt_random = RandomizedSearchCV(estimator = dt, \n",
        "                              param_distributions = random_grid,\n",
        "                              cv = 5,\n",
        "                              scoring = 'recall')\n",
        "\n",
        "dt_random.fit(x_train_res, y_train_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BkGHHQiZeqW7",
        "outputId": "739c13ed-36e7-4c37-a8fd-944a22c5b70b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=6),\n",
              "                   param_distributions={'max_depth': [1, 3, 5, 7],\n",
              "                                        'min_samples_leaf': [1, 2, 4],\n",
              "                                        'min_samples_split': [2, 5, 10]},\n",
              "                   scoring='recall')"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt_random.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dz2g_ZwUezCz",
        "outputId": "c53e7c4b-6a6c-44e1-980a-326ac6d0103f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 7, 'min_samples_leaf': 4, 'min_samples_split': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_dt = DecisionTreeClassifier(max_depth = 7, min_samples_split = 10, min_samples_leaf = 4, random_state = 6)\n",
        "final_dt.fit(x_train_res, y_train_res)\n",
        "y_pred = final_dt.predict(x_test)\n",
        "\n",
        "print('recall:', recall_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mINHB8G8e5pM",
        "outputId": "4b667a4b-f870-4787-a1c5-e382a6421854"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall: 0.7074829931972789\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Hyperparameter Tunning (GridSearchCV)"
      ],
      "metadata": {
        "id": "liwt-XksgsEd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "parameters = {\n",
        "    # 'n_estimators': (200, 250, 300, 350),\n",
        "    # 'max_features': ('auto', 'sqrt', 'log2'),\n",
        "    'max_depth': (1,3,5,7),\n",
        "    'min_samples_split':(2, 5, 10),\n",
        "    'min_samples_leaf': (1, 2, 4)\n",
        "    # 'bootstrap': (True, False)\n",
        "}\n",
        "\n",
        "# note: we use recall\n",
        "dt_gridcv = GridSearchCV(dt, parameters, cv=5, scoring='recall')\n",
        "dt_gridcv.fit(x_train_res, y_train_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtWcbbHbgnmu",
        "outputId": "7967b0ff-9f74-4481-ae73-a99e54531f09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 16.1 s, sys: 39.7 ms, total: 16.1 s\n",
            "Wall time: 16.1 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_result = pd.DataFrame(dt_gridcv.cv_results_)\n",
        "retain_cols = ['params','mean_test_score','rank_test_score']\n",
        "cv_result[retain_cols].sort_values('rank_test_score')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "o8w9JaQeg8ew",
        "outputId": "451c2174-fb6c-4bde-b971-d3e40fd32d7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7d2a623a-bdc8-475e-a77d-866571886ef5\">\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>params</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.913152</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.913152</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.913152</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.913152</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.913152</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.913152</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.913152</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.913152</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.913152</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.879737</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.879737</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.879737</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.879737</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.879737</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.879737</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.879737</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.879737</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.879737</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.835094</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.835094</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.835094</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.835094</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.835094</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.835094</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.835094</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.835094</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.835094</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.815589</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.815589</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.815589</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.815589</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.815589</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.815589</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.815589</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.815589</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.815589</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d2a623a-bdc8-475e-a77d-866571886ef5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      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",
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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-7d2a623a-bdc8-475e-a77d-866571886ef5 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-7d2a623a-bdc8-475e-a77d-866571886ef5');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                               params  ...  rank_test_score\n",
              "35  {'max_depth': 7, 'min_samples_leaf': 4, 'min_s...  ...                1\n",
              "33  {'max_depth': 7, 'min_samples_leaf': 4, 'min_s...  ...                1\n",
              "32  {'max_depth': 7, 'min_samples_leaf': 2, 'min_s...  ...                1\n",
              "31  {'max_depth': 7, 'min_samples_leaf': 2, 'min_s...  ...                1\n",
              "30  {'max_depth': 7, 'min_samples_leaf': 2, 'min_s...  ...                1\n",
              "29  {'max_depth': 7, 'min_samples_leaf': 1, 'min_s...  ...                1\n",
              "28  {'max_depth': 7, 'min_samples_leaf': 1, 'min_s...  ...                1\n",
              "27  {'max_depth': 7, 'min_samples_leaf': 1, 'min_s...  ...                1\n",
              "34  {'max_depth': 7, 'min_samples_leaf': 4, 'min_s...  ...                1\n",
              "0   {'max_depth': 1, 'min_samples_leaf': 1, 'min_s...  ...               10\n",
              "1   {'max_depth': 1, 'min_samples_leaf': 1, 'min_s...  ...               10\n",
              "2   {'max_depth': 1, 'min_samples_leaf': 1, 'min_s...  ...               10\n",
              "3   {'max_depth': 1, 'min_samples_leaf': 2, 'min_s...  ...               10\n",
              "4   {'max_depth': 1, 'min_samples_leaf': 2, 'min_s...  ...               10\n",
              "5   {'max_depth': 1, 'min_samples_leaf': 2, 'min_s...  ...               10\n",
              "6   {'max_depth': 1, 'min_samples_leaf': 4, 'min_s...  ...               10\n",
              "8   {'max_depth': 1, 'min_samples_leaf': 4, 'min_s...  ...               10\n",
              "7   {'max_depth': 1, 'min_samples_leaf': 4, 'min_s...  ...               10\n",
              "22  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  ...               19\n",
              "24  {'max_depth': 5, 'min_samples_leaf': 4, 'min_s...  ...               19\n",
              "23  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  ...               19\n",
              "18  {'max_depth': 5, 'min_samples_leaf': 1, 'min_s...  ...               19\n",
              "20  {'max_depth': 5, 'min_samples_leaf': 1, 'min_s...  ...               19\n",
              "19  {'max_depth': 5, 'min_samples_leaf': 1, 'min_s...  ...               19\n",
              "25  {'max_depth': 5, 'min_samples_leaf': 4, 'min_s...  ...               19\n",
              "21  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  ...               19\n",
              "26  {'max_depth': 5, 'min_samples_leaf': 4, 'min_s...  ...               19\n",
              "10  {'max_depth': 3, 'min_samples_leaf': 1, 'min_s...  ...               28\n",
              "16  {'max_depth': 3, 'min_samples_leaf': 4, 'min_s...  ...               28\n",
              "15  {'max_depth': 3, 'min_samples_leaf': 4, 'min_s...  ...               28\n",
              "14  {'max_depth': 3, 'min_samples_leaf': 2, 'min_s...  ...               28\n",
              "13  {'max_depth': 3, 'min_samples_leaf': 2, 'min_s...  ...               28\n",
              "12  {'max_depth': 3, 'min_samples_leaf': 2, 'min_s...  ...               28\n",
              "11  {'max_depth': 3, 'min_samples_leaf': 1, 'min_s...  ...               28\n",
              "9   {'max_depth': 3, 'min_samples_leaf': 1, 'min_s...  ...               28\n",
              "17  {'max_depth': 3, 'min_samples_leaf': 4, 'min_s...  ...               28\n",
              "\n",
              "[36 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The best model is when the hyperparameters is {'max_depth': 7, 'min_samples_leaf': 4, 'min_samples_split': 10}"
      ],
      "metadata": {
        "id": "fGqWgz2GhIph"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "y_pred = dt_gridcv.best_estimator_.predict(x_test)\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred, labels=dt_gridcv.best_estimator_.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
        "                             display_labels=dt_gridcv.best_estimator_.classes_)\n",
        "disp.plot(ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "nNxOP7rShbfz",
        "outputId": "bdb2af4e-764b-405f-d44f-19b81187fb62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# classification report\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7L1FpO0htve",
        "outputId": "424f718e-3aa3-4d21-e596-8618e8d872aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.73      0.84      8314\n",
            "           1       0.04      0.71      0.08       147\n",
            "\n",
            "    accuracy                           0.73      8461\n",
            "   macro avg       0.52      0.72      0.46      8461\n",
            "weighted avg       0.98      0.73      0.83      8461\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cqSn93Nv4aJE"
      },
      "source": [
        "####Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6QR3b064l1i",
        "outputId": "358fec99-e981-44d4-ccd0-1a0d42a6c9ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall score (Test Set): 0.02\n",
            "F1-Score (Test Set): 0.03\n",
            "AUC: 0.51\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(random_state = 6)\n",
        "rf.fit(x_train_res, y_train_res)\n",
        "y_pred = rf.predict(x_test)\n",
        "eval_classification(model, y_pred, x_train_res, y_train_res, x_test, y_test)\n",
        "#print('recall score using random forest classifier is', recall_score(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Hyperparameter Tunning (RandomizedSearchCV)"
      ],
      "metadata": {
        "id": "aIM589PzfSmZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "max_depth = [1,3,5,7]\n",
        "min_samples_split = [2, 5, 10]\n",
        "min_samples_leaf = [1, 2, 4]\n",
        "\n",
        "random_grid = {'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf\n",
        "               }"
      ],
      "metadata": {
        "id": "UJM0vWlrfRkJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf = RandomForestClassifier(n_jobs = -1, random_state = 6)\n",
        "rf_random = RandomizedSearchCV(estimator = rf, \n",
        "                              param_distributions = random_grid,\n",
        "                              n_iter = 9,\n",
        "                              cv = 5,\n",
        "                              scoring = 'recall')\n",
        "\n",
        "rf_random.fit(x_train_res, y_train_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9C_W456dfjD6",
        "outputId": "7c218753-b2d0-4056-ab48-21955433508d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5,\n",
              "                   estimator=RandomForestClassifier(n_jobs=-1, random_state=6),\n",
              "                   n_iter=9,\n",
              "                   param_distributions={'max_depth': [1, 3, 5, 7],\n",
              "                                        'min_samples_leaf': [1, 2, 4],\n",
              "                                        'min_samples_split': [2, 5, 10]},\n",
              "                   scoring='recall')"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_random.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xeFn_UCwf3g8",
        "outputId": "90fc900b-e7d3-4784-9242-c0924e558554"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': 7, 'min_samples_leaf': 1, 'min_samples_split': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_rf = RandomForestClassifier(max_depth = 7, min_samples_split = 10, min_samples_leaf = 1,\n",
        "                                 n_jobs = -1, random_state = 6)\n",
        "final_rf.fit(x_train_res, y_train_res)\n",
        "y_pred = final_rf.predict(x_test)\n",
        "\n",
        "print('recall:', recall_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8ambxuaf7W5",
        "outputId": "c7689f23-ca3b-4730-e3b5-5b366d128b28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "recall: 0.7891156462585034\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#####Hyperparameter Tunning (GridSearchCV)"
      ],
      "metadata": {
        "id": "BaRO6CIhiBAY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "parameters = {\n",
        "    'max_depth': (1,3,5,7),\n",
        "    'min_samples_split':(2, 5, 10),\n",
        "    'min_samples_leaf': (1, 2, 4)\n",
        "}\n",
        "\n",
        "# note: we use recall\n",
        "rf_gridcv = GridSearchCV(rf, parameters, cv=5, scoring='recall')\n",
        "rf_gridcv.fit(x_train_res, y_train_res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GX6whTO-h7GP",
        "outputId": "051af887-11fa-4ee3-a223-ba8e2fb932d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1min 11s, sys: 4.22 s, total: 1min 15s\n",
            "Wall time: 5min 32s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_result = pd.DataFrame(rf_gridcv.cv_results_)\n",
        "retain_cols = ['params','mean_test_score','rank_test_score']\n",
        "cv_result[retain_cols].sort_values('rank_test_score')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "I9Wg1aU6iSaP",
        "outputId": "dac7054f-1955-4c24-b005-e180cb476710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>params</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.888468</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.888468</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.888468</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.886390</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.886390</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.886390</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.885276</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.885276</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>{'max_depth': 7, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.885276</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.871850</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.871850</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.871850</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.870767</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.870767</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.870767</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.870496</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.870496</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>{'max_depth': 5, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.870496</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.867726</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.867726</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.867726</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.867726</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.867726</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.867726</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.867726</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.867726</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>{'max_depth': 3, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.867726</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.841085</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.841085</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.841085</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.841085</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.841085</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.841085</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 4, 'min_s...</td>\n",
              "      <td>0.841085</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 2, 'min_s...</td>\n",
              "      <td>0.841085</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>{'max_depth': 1, 'min_samples_leaf': 1, 'min_s...</td>\n",
              "      <td>0.841085</td>\n",
              "      <td>28</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-cc37dac8-4f33-4f0d-892b-03ee3df70040');\n",
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              "            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",
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            ],
            "text/plain": [
              "                                               params  ...  rank_test_score\n",
              "27  {'max_depth': 7, 'min_samples_leaf': 1, 'min_s...  ...                1\n",
              "28  {'max_depth': 7, 'min_samples_leaf': 1, 'min_s...  ...                1\n",
              "29  {'max_depth': 7, 'min_samples_leaf': 1, 'min_s...  ...                1\n",
              "35  {'max_depth': 7, 'min_samples_leaf': 4, 'min_s...  ...                4\n",
              "34  {'max_depth': 7, 'min_samples_leaf': 4, 'min_s...  ...                4\n",
              "33  {'max_depth': 7, 'min_samples_leaf': 4, 'min_s...  ...                4\n",
              "31  {'max_depth': 7, 'min_samples_leaf': 2, 'min_s...  ...                7\n",
              "32  {'max_depth': 7, 'min_samples_leaf': 2, 'min_s...  ...                7\n",
              "30  {'max_depth': 7, 'min_samples_leaf': 2, 'min_s...  ...                7\n",
              "25  {'max_depth': 5, 'min_samples_leaf': 4, 'min_s...  ...               10\n",
              "24  {'max_depth': 5, 'min_samples_leaf': 4, 'min_s...  ...               10\n",
              "26  {'max_depth': 5, 'min_samples_leaf': 4, 'min_s...  ...               10\n",
              "18  {'max_depth': 5, 'min_samples_leaf': 1, 'min_s...  ...               13\n",
              "20  {'max_depth': 5, 'min_samples_leaf': 1, 'min_s...  ...               13\n",
              "19  {'max_depth': 5, 'min_samples_leaf': 1, 'min_s...  ...               13\n",
              "23  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  ...               16\n",
              "22  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  ...               16\n",
              "21  {'max_depth': 5, 'min_samples_leaf': 2, 'min_s...  ...               16\n",
              "17  {'max_depth': 3, 'min_samples_leaf': 4, 'min_s...  ...               19\n",
              "15  {'max_depth': 3, 'min_samples_leaf': 4, 'min_s...  ...               19\n",
              "14  {'max_depth': 3, 'min_samples_leaf': 2, 'min_s...  ...               19\n",
              "13  {'max_depth': 3, 'min_samples_leaf': 2, 'min_s...  ...               19\n",
              "12  {'max_depth': 3, 'min_samples_leaf': 2, 'min_s...  ...               19\n",
              "11  {'max_depth': 3, 'min_samples_leaf': 1, 'min_s...  ...               19\n",
              "10  {'max_depth': 3, 'min_samples_leaf': 1, 'min_s...  ...               19\n",
              "9   {'max_depth': 3, 'min_samples_leaf': 1, 'min_s...  ...               19\n",
              "16  {'max_depth': 3, 'min_samples_leaf': 4, 'min_s...  ...               19\n",
              "1   {'max_depth': 1, 'min_samples_leaf': 1, 'min_s...  ...               28\n",
              "2   {'max_depth': 1, 'min_samples_leaf': 1, 'min_s...  ...               28\n",
              "3   {'max_depth': 1, 'min_samples_leaf': 2, 'min_s...  ...               28\n",
              "7   {'max_depth': 1, 'min_samples_leaf': 4, 'min_s...  ...               28\n",
              "5   {'max_depth': 1, 'min_samples_leaf': 2, 'min_s...  ...               28\n",
              "6   {'max_depth': 1, 'min_samples_leaf': 4, 'min_s...  ...               28\n",
              "8   {'max_depth': 1, 'min_samples_leaf': 4, 'min_s...  ...               28\n",
              "4   {'max_depth': 1, 'min_samples_leaf': 2, 'min_s...  ...               28\n",
              "0   {'max_depth': 1, 'min_samples_leaf': 1, 'min_s...  ...               28\n",
              "\n",
              "[36 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The best model is when the hyperparameters is {'max_depth': 7, 'min_samples_leaf': 1, 'min_samples_split': 2}"
      ],
      "metadata": {
        "id": "x0QR2reCijBz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,7))\n",
        "y_pred = rf_gridcv.best_estimator_.predict(x_test)\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred, labels=rf_gridcv.best_estimator_.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
        "                             display_labels=rf_gridcv.best_estimator_.classes_)\n",
        "disp.plot(ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "YHnw027GisCJ",
        "outputId": "8bf7c49b-092c-40a8-c5d7-2b9c4a08d495"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# classification report\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PtYVOrNKkjDG",
        "outputId": "f76561b9-5731-4b40-a89b-57c7bf80c7f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.75      0.86      8314\n",
            "           1       0.05      0.79      0.10       147\n",
            "\n",
            "    accuracy                           0.76      8461\n",
            "   macro avg       0.52      0.77      0.48      8461\n",
            "weighted avg       0.98      0.76      0.85      8461\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####XGBoost"
      ],
      "metadata": {
        "id": "KjnQ1-M0_-JG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "xg = XGBClassifier(random_state=6)\n",
        "xg.fit(x_train_res, y_train_res)\n",
        "\n",
        "y_pred = xg.predict(x_test)\n",
        "eval_classification(model, y_pred, x_train_res, y_train_res, x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrot4Xbt_XRA",
        "outputId": "975917a8-a1a3-4aa6-b565-6afa1f4356b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Recall score (Test Set): 0.82\n",
            "F1-Score (Test Set): 0.11\n",
            "AUC: 0.79\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Camytha Octanuryati Rochmad - Script Final Project",
      "provenance": [],
      "collapsed_sections": [
        "cqSn93Nv4aJE",
        "a0bWR_hr7HOr",
        "KoVcoD_iybn_",
        "gYlyXjxccpid",
        "txpzm8Ci2qOf",
        "xm1V9aBB_-fI",
        "KGJimZodcz2D",
        "RWxvZhbVoEgz"
      ],
      "authorship_tag": "ABX9TyO16sfVtsw9ICq4Bvkg6FoK",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}