ML_STUDY_Assignment_Week2_Problem.ipynb

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  "metadata": {
    "colab": {
      "name": "ML_STUDY_Assignment_Week2.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "qSYD2cTt5_c6",
        "10sbYhES6X1n"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSYD2cTt5_c6"
      },
      "source": [
        "# 1. 이론 주관식\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPWHS9Yr6wes"
      },
      "source": [
        "### 1.과적합(overfitting) 이란?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O1V5Rhmd7Vv_"
      },
      "source": [
        "### 2. 과적합을 개선하기 위한 방법 한가지 이상"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gbG6AgpNDmke"
      },
      "source": [
        "### 3. Imbalanced 분포도를 가진 레이블 데이터 집합에서 사용하는 검증 방법을 찾고 그 방법을 간단하게 설명하시오."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wgJUR6pzKfA5"
      },
      "source": [
        "### 4. Data Prepocessing (데이터 전처리)에서 인코딩(encoding)을 하는 이유"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yC4AclRWPb9z"
      },
      "source": [
        "### 5. One-hot encoding의 단점"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "el_-_7EKUmIk"
      },
      "source": [
        "### 6. 데이터의 스케일링 변환시 유의해야할 점"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5M4T_UrLYKkZ"
      },
      "source": [
        "# 2. Coding part\n",
        "\n",
        "Heart Failure Prediction의 자료를 다운 받고 이 자료를 이용하여 death event를 예측 해보기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzKErmR6Zn5m"
      },
      "source": [
        "1. 자료는 assignment를 위해서 조금 수정하였기에 제공된 파일 사용하기\n",
        "2. 참고 코드\n",
        "https://github.com/wikibook/pymldg-rev/blob/master/2%EC%9E%A5/2.6%20%EC%82%AC%EC%9D%B4%ED%82%B7%EB%9F%B0%EC%9C%BC%EB%A1%9C%20%EC%88%98%ED%96%89%ED%95%98%EB%8A%94%20%ED%83%80%EC%9D%B4%ED%83%80%EB%8B%89%20%EC%83%9D%EC%A1%B4%EC%9E%90%20%EC%98%88%EC%B8%A1%20.ipynb\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4MhdgPysX03D"
      },
      "source": [
        "자료 설명 (안 읽어도 됨)\n",
        "\n",
        "Heart Failure Prediction\n",
        "12 clinical features for predicting death events.\n",
        "\n",
        "Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide.\n",
        "Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.\n",
        "\n",
        "Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.\n",
        "\n",
        "People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help.\n",
        "\n",
        "출처 : https://www.kaggle.com/andrewmvd/heart-failure-clinical-data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10sbYhES6X1n"
      },
      "source": [
        "### 1. 데이터 불러오기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XOODNd6deX3L"
      },
      "source": [
        "저는 코랩을 사용해서 구글드라이브에서 아래처럼 파일을 불러왔는데, 각자 사용하는 프로그램에 따라서 파일을 불러오기만 하면 됩니다!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNBbF9EE0dv9",
        "outputId": "02c1f026-e4ba-4d45-f483-32e61167adbf"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "6NrqTjLEzPyT",
        "outputId": "5051db50-19f7-44d3-9ab7-ce407b5619bb"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "heart_df = pd.read_csv('./drive/MyDrive/Colab_Notebooks/heart_failure_clinical_records_dataset.csv')\n",
        "[                ]"
      ],
      "execution_count": 276,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>anaemia</th>\n",
              "      <th>creatinine_phosphokinase</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>ejection_fraction</th>\n",
              "      <th>high_blood_pressure</th>\n",
              "      <th>platelets</th>\n",
              "      <th>serum_creatinine</th>\n",
              "      <th>serum_sodium</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoking</th>\n",
              "      <th>time</th>\n",
              "      <th>DEATH_EVENT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>582.0</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1</td>\n",
              "      <td>265000.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>130.0</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7861.0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>263358.03</td>\n",
              "      <td>1.1</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>162000.00</td>\n",
              "      <td>1.3</td>\n",
              "      <td>129.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Y</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  anaemia  creatinine_phosphokinase  ...  smoking  time  DEATH_EVENT\n",
              "0  75.0        0                     582.0  ...        N   4.0            1\n",
              "1  55.0        0                    7861.0  ...        N   6.0            1\n",
              "2  65.0        0                     146.0  ...        Y   7.0            1\n",
              "\n",
              "[3 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-o-F_h64QNm"
      },
      "source": [
        "print('\\n ### train 데이터 정보 ###  \\n')\n",
        "[                  ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCJ_EKA36U2Q"
      },
      "source": [
        "### 2. 결손 데이터 확인"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T0JNAIAV4tMr",
        "outputId": "2e0d9dfd-9bd2-45c7-f701-e127ad03047e"
      },
      "source": [
        "print('데이터 세트 Null 값 갯수 ', [            ])"
      ],
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "데이터 세트 Null 값 갯수  age                         18\n",
            "anaemia                      0\n",
            "creatinine_phosphokinase     1\n",
            "diabetes                     0\n",
            "ejection_fraction            7\n",
            "high_blood_pressure          0\n",
            "platelets                    5\n",
            "serum_creatinine             2\n",
            "serum_sodium                 3\n",
            "sex                          0\n",
            "smoking                      0\n",
            "time                         4\n",
            "DEATH_EVENT                  0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULIcwW8pQ0Kb",
        "outputId": "b4e8297e-abb9-4bfb-b91c-2d71e706f537"
      },
      "source": [
        "heart_df['age'].fillna(heart_df['age'].mean(),inplace=True)\n",
        "[                                                       \n",
        "                                                       \n",
        "                                                       \n",
        "                                                       \n",
        "      \n",
        "                                                        ]\n",
        "print('데이터 세트 Null 값 갯수 ',covid_df.isnull().sum())"
      ],
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "데이터 세트 Null 값 갯수  age                         0\n",
            "anaemia                     0\n",
            "creatinine_phosphokinase    0\n",
            "diabetes                    0\n",
            "ejection_fraction           0\n",
            "high_blood_pressure         0\n",
            "platelets                   0\n",
            "serum_creatinine            0\n",
            "serum_sodium                0\n",
            "sex                         0\n",
            "smoking                     0\n",
            "time                        0\n",
            "DEATH_EVENT                 0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6RBt3ixnZ3Dv"
      },
      "source": [
        "### 3. 나이, 성별, 흡연여부로 다양하게 분포 파악해보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4kklNjys6cgH",
        "outputId": "31928a11-d472-4f6d-847c-cffbe1e9d12f"
      },
      "source": [
        "print(' Sex 값 분포 :\\n', heart_df['sex'].value_counts())\n",
        "[\n",
        "  \n",
        "                                                        ]"
      ],
      "execution_count": 280,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Sex 값 분포 :\n",
            " 1    194\n",
            "0    105\n",
            "Name: sex, dtype: int64\n",
            "\n",
            " age 값 분포 :\n",
            " 60.000000    30\n",
            "50.000000    25\n",
            "70.000000    25\n",
            "65.000000    24\n",
            "60.827996    18\n",
            "55.000000    17\n",
            "45.000000    17\n",
            "75.000000    10\n",
            "58.000000    10\n",
            "53.000000     9\n",
            "72.000000     7\n",
            "40.000000     7\n",
            "42.000000     7\n",
            "80.000000     7\n",
            "63.000000     6\n",
            "85.000000     6\n",
            "62.000000     5\n",
            "52.000000     5\n",
            "49.000000     4\n",
            "61.000000     4\n",
            "59.000000     4\n",
            "68.000000     4\n",
            "51.000000     4\n",
            "73.000000     4\n",
            "64.000000     3\n",
            "69.000000     3\n",
            "82.000000     3\n",
            "48.000000     2\n",
            "95.000000     2\n",
            "90.000000     2\n",
            "57.000000     2\n",
            "66.000000     2\n",
            "46.000000     2\n",
            "67.000000     2\n",
            "54.000000     2\n",
            "44.000000     2\n",
            "77.000000     2\n",
            "47.000000     1\n",
            "78.000000     1\n",
            "81.000000     1\n",
            "94.000000     1\n",
            "43.000000     1\n",
            "56.000000     1\n",
            "86.000000     1\n",
            "87.000000     1\n",
            "79.000000     1\n",
            "41.000000     1\n",
            "60.667000     1\n",
            "Name: age, dtype: int64\n",
            "\n",
            " smoking 값 분포 :\n",
            " N    203\n",
            "Y     96\n",
            "Name: smoking, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qSP6V2pAJFp",
        "outputId": "481447dc-4254-4235-ff8b-4e33b30a189c"
      },
      "source": [
        "[                                                  ]"
      ],
      "execution_count": 281,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex  DEATH_EVENT\n",
              "0    0               71\n",
              "     1               34\n",
              "1    0              132\n",
              "     1               62\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 281
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "rzuI-ZCbAJg5",
        "outputId": "4e84ab81-7519-423f-f675-ee65eb63be72"
      },
      "source": [
        "sns.barplot(x='sex', y = 'DEATH_EVENT', data=heart_df)"
      ],
      "execution_count": 282,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9caeddded0>"
            ]
          },
          "metadata": {},
          "execution_count": 282
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eMb+ZboxjADuAVUlWJjkNWAfMHGUMAEnOSHJ6s30W8AJg15FHSZLm0zi3wP50kntHtAd47JEGVtWBJBuAGxjcAru1qnYm2QTMVtVMkucBfwScAfxSkn9XVecBzwA+nOQggzB7z9BdUZKkno1zC+ySR/MFVbUd2D7UtrG1vYPBZajhcV8Ezn803y1JenTGXipcknTyMSQkSZ0MCUlSJ0NCktTJkJAkdTIkJEmdDAlJUidDQpLUyZCQJHUyJCRJnQwJSVInQ0KS1MmQkCR1MiQkSZ0MCUlSJ0NCktSp95BIsibJ7iRzSa4ccfyFSf46yYEkrxw6dkWSbzY/V/RdqyTpcL2GRJIlwGbgpcC5wOVJzh3qdhvwq8DHh8aeCVwFXACsBq5Kckaf9UqSDtf3mcRqYK6q9lTVg8A2YG27Q1V9u6q+BhwcGnsJ8Nmquruq7gE+C6zpuV5JUkvfIbEUuL21v7dpm7exSdYnmU0yu3///mMuVJL0cMf9xHVVbamq6aqanpqamnQ5knRC6Tsk9gFnt/aXNW19j5UkzYO+Q2IHsCrJyiSnAeuAmTHH3gBcnOSMZsL64qZNkrRAeg2JqjoAbGDwj/s3gOuqameSTUkuBUjyvCR7gVcBH06ysxl7N/AuBkGzA9jUtEmSFsgpfX9BVW0Htg+1bWxt72BwKWnU2K3A1l4LlCR1Ou4nriVJ/TEkJEmdDAlJUidDQpLUyZCQJHUyJCRJnQwJSVInQ0KS1MmQkCR1MiQkSZ0MCUlSJ0NCktTJkJAkdTIkJEmdDAlJUidDQpLUqfeQSLImye4kc0muHHH89CSfbI5/KcmKpn1FkvuTfLX5+VDftUqSDtfrm+mSLAE2AxcBe4EdSWaqaler22uBe6rqJ5OsA94LXNYcu7WqntVnjZKkbn2fSawG5qpqT1U9CGwD1g71WQt8rNn+NPCSJOm5LknSGPoOiaXA7a39vU3byD5VdQD4PvCk5tjKJF9JcmOSn+u5VknSkF4vNz1K3wWWV9VdSZ4L/HGS86rq3nanJOuB9QDLly+fQJmSdOLq+0xiH3B2a39Z0zayT5JTgCcCd1XVA1V1F0BV3QzcCjx9+AuqaktVTVfV9NTUVA+/BUk6efUdEjuAVUlWJjkNWAfMDPWZAa5otl8JfK6qKslUM/FNknOAVcCenuuVJLX0ermpqg4k2QDcACwBtlbVziSbgNmqmgF+D/jDJHPA3QyCBOCFwKYkPwIOAq+vqrv7rFeSdLje5ySqajuwfahtY2v774FXjRh3PXB93/VJkrr5xLUkqZMhIUnqZEhIkjoZEpKkToaEJKmTISFJ6mRISJI6GRKSpE6GhCSpkyEhSepkSEiSOhkSkqROhoQkqZMhIUnqZEhIkjoZEpKkToaEJKlT7yGRZE2S3Unmklw54vjpST7ZHP9SkhWtY29v2ncnuaTvWiVJh+s1JJIsATYDLwXOBS5Pcu5Qt9cC91TVTwL/EXhvM/ZcBu+7Pg9YA3yw+TxJ0gLp+0xiNTBXVXuq6kFgG7B2qM9a4GPN9qeBlyRJ076tqh6oqm8Bc83nSZIWyCk9f/5S4PbW/l7ggq4+VXUgyfeBJzXtNw2NXTr8BUnWA+ub3R8m2T0/pQs4C7hz0kUsBrn2ikmXoMP5d/OQqzIfn/LUrgN9h0TvqmoLsGXSdZyIksxW1fSk65CG+Xdz4fR9uWkfcHZrf1nTNrJPklOAJwJ3jTlWktSjvkNiB7AqycokpzGYiJ4Z6jMDHDqXfyXwuaqqpn1dc/fTSmAV8OWe65UktfR6uamZY9gA3AAsAbZW1c4km4DZqpoBfg/4wyRzwN0MgoSm33XALuAA8MaqeqjPevUwXsbTYuXfzQWSwX/aJUl6OJ+4liR1MiQkSZ0MCY10tOVUpElIsjXJ95J8fdK1nCwMCT3MmMupSJPwUQbL9GiBGBIaZZzlVKQFV1V/zuAuSC0QQ0KjjFpO5WFLokg68RkSkqROhoRGcUkUSYAhodHGWU5F0knAkNDDVNUB4NByKt8ArquqnZOtSoIknwD+CvipJHuTvHbSNZ3oXJZDktTJMwlJUidDQpLUyZCQJHUyJCRJnQwJSVInQ0KS1MmQkCR1MiSkeZLkcUn+JMn/TvL1JJcleW6SG5PcnOSGJP8kyRObd3X8VDPuE0n+1aTrl0Y5ZdIFSCeQNcDfVtXLAJI8EfgMsLaq9ie5DHh3Vf1akg3AR5N8ADijqj4yubKlbj5xLc2TJE8H/ifwSeB/APcAXwT2NF2WAN+tqoub/luAXwZ+uqr2LnzF0tF5JiHNk6r6myTPAf458O+BzwE7q+pnhvsmeQzwDOA+4AwG7+yQFh3nJKR5kuQpwH1V9V+Aa4ALgKkkP9McPzXJeU33NzNYPPHVwO8nOXUSNUtH45mENH/OB65JchD4EfAG4ADwO838xCnA+5McAF4HrK6qHyT5c+CdwFUTqlvq5JyEJKmTl5skSZ0MCUlSJ0NCktTJkJAkdTIkJEmdDAlJUidDQpLU6f8BthhDu3q7FDsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "q4xHXV2BAMD7",
        "outputId": "60201ad8-0be7-4a1e-c446-76eba4599869"
      },
      "source": [
        "[                                                 ]"
      ],
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9caee82ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 264
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "xzWSr7MWANeJ",
        "outputId": "cbdad12a-e3b2-46b3-b085-c22abe739ffc"
      },
      "source": [
        "# 입력 age에 따라 구분값을 반환하는 함수 설정. DataFrame의 apply lambda식에 사용. \n",
        "def get_category(age):\n",
        "    cat = ''\n",
        "    if age <= 55: cat = 'below55'\n",
        "    elif age <= 65: cat = '56To65'\n",
        "    elif age <= 75: cat = '66To75'\n",
        "    elif age <= 85: cat = '76To85'\n",
        "    else : cat = 'Above86'\n",
        "    \n",
        "    return cat\n",
        "\n",
        "# 막대그래프의 크기 figure를 더 크게 설정 \n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "#X축의 값을 순차적으로 표시하기 위한 설정 \n",
        "group_names = ['below55', '56To65', '66To75', '76To85', 'Above86']\n",
        "\n",
        "# lambda 식에 위에서 생성한 get_category( ) 함수를 반환값으로 지정. \n",
        "# get_category(X)는 입력값으로 'Age' 컬럼값을 받아서 해당하는 cat 반환\n",
        "heart_df['Age_cat'] = heart_df['age'].apply(lambda x : get_category(x))\n",
        "sns.barplot(x='Age_cat', y='DEATH_EVENT', hue='sex', data=heart_df, order=group_names)\n",
        "heart_df.drop('Age_cat', axis=1, inplace=True)"
      ],
      "execution_count": 283,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "OsHxONnASiVQ",
        "outputId": "43ceeb63-02cf-4d56-d33e-4d4fa2979ddd"
      },
      "source": [
        "[\n",
        "  \n",
        "                                                        ]"
      ],
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rS0mcT5caOMh"
      },
      "source": [
        "### 4. 데이터 전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "id": "Zgiupup_AO_C",
        "outputId": "1d031d83-99bf-41e7-a0db-1be3f1a0bbe2"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "def encode_features(dataDF):\n",
        "    features = ['smoking']\n",
        "    for feature in features:\n",
        "        le = preprocessing.LabelEncoder()\n",
        "        le = le.fit(dataDF[feature])\n",
        "        dataDF[feature] = le.transform(dataDF[feature])\n",
        "        \n",
        "    return dataDF\n",
        "\n",
        "heart_df = encode_features(heart_df)\n",
        "heart_df.head()"
      ],
      "execution_count": 285,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>anaemia</th>\n",
              "      <th>creatinine_phosphokinase</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>ejection_fraction</th>\n",
              "      <th>high_blood_pressure</th>\n",
              "      <th>platelets</th>\n",
              "      <th>serum_creatinine</th>\n",
              "      <th>serum_sodium</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoking</th>\n",
              "      <th>time</th>\n",
              "      <th>DEATH_EVENT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>582.0</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1</td>\n",
              "      <td>265000.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>130.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>7861.0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>263358.03</td>\n",
              "      <td>1.1</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>146.0</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>162000.00</td>\n",
              "      <td>1.3</td>\n",
              "      <td>129.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>210000.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>65.0</td>\n",
              "      <td>1</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>327000.00</td>\n",
              "      <td>2.7</td>\n",
              "      <td>116.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  anaemia  creatinine_phosphokinase  ...  smoking  time  DEATH_EVENT\n",
              "0  75.0        0                     582.0  ...        0   4.0            1\n",
              "1  55.0        0                    7861.0  ...        0   6.0            1\n",
              "2  65.0        0                     146.0  ...        1   7.0            1\n",
              "3  50.0        1                     111.0  ...        0   7.0            1\n",
              "4  65.0        1                     160.0  ...        0   8.0            1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 285
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FQwI2bazbYsZ"
      },
      "source": [
        "### 5. Null 처리 & 불필요 feature 제거 & 레이블 인코딩 & 새로 호출"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdGQf3SGAQhS"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Null 처리 함수\n",
        "def fillna(df):\n",
        "     df['age'].fillna(df['age'].mean(),inplace=True)\n",
        "     [\n",
        "  \n",
        "                                                        ]\n",
        "     return df\n",
        "\n",
        "# 머신러닝 알고리즘에 불필요한 속성 제거\n",
        "def drop_features(df):\n",
        "    df.drop(['time'],axis=1,inplace=True)\n",
        "    return df\n",
        "\n",
        "# 레이블 인코딩 수행. \n",
        "def format_features(df):\n",
        "    features = ['smoking']\n",
        "    for feature in features:\n",
        "        le = LabelEncoder()\n",
        "        le = le.fit(df[feature])\n",
        "        df[feature] = le.transform(df[feature])\n",
        "    return df\n",
        "\n",
        "# 앞에서 설정한 Data Preprocessing 함수 호출\n",
        "def transform_features(df): \n",
        "    df = fillna(df)\n",
        "    df = drop_features(df)\n",
        "    df = format_features(df)\n",
        "    return df\n"
      ],
      "execution_count": 286,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKE_mrrrbiYk"
      },
      "source": [
        "### 6. 원본 데이터를 재로딩 하고, feature데이터 셋과 Label 데이터 셋 추출. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0K8k3gbbiPl"
      },
      "source": [
        "# 원본 데이터를 재로딩 하고, feature데이터 셋과 Label 데이터 셋 추출. \n",
        "heart_df = pd.read_csv('./drive/MyDrive/Colab_Notebooks/heart_failure_clinical_records_dataset.csv')\n",
        "y_heart_df = heart_df['DEATH_EVENT']\n",
        "X_heart_df= heart_df.drop('DEATH_EVENT',axis=1)\n",
        "X_heart_df = transform_features(X_heart_df)"
      ],
      "execution_count": 287,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2gf-kn2mAUZD"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test=train_test_split(X_heart_df, y_heart_df, \n",
        "                                                  test_size=0.3, random_state=2021)"
      ],
      "execution_count": 288,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLmUPuAPbve5"
      },
      "source": [
        "### 7. 결정트리, Random Forest, 로지스틱 회귀로 데이터를 학습시키고 각 classifier에서 정확도 도출"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbvfSXKrAVj0",
        "outputId": "91e9986f-4009-4dce-cfa0-ca8bf30b846b"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# 결정트리, Random Forest, 로지스틱 회귀를 위한 사이킷런 Classifier 클래스 생성\n",
        "dt_clf = DecisionTreeClassifier(random_state=2021)\n",
        "rf_clf = RandomForestClassifier(random_state=2021)\n",
        "lr_clf = LogisticRegression()\n",
        "\n",
        "# DecisionTreeClassifier 학습/예측/평가\n",
        "[\n",
        "  \n",
        "                                                        ]\n",
        "\n",
        "# RandomForestClassifier 학습/예측/평가\n",
        "[\n",
        "  \n",
        "                                                        ]\n",
        "\n",
        "# LogisticRegression 학습/예측/평가\n",
        "[\n",
        "  \n",
        "                                                        ]"
      ],
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTreeClassifier 정확도: 0.7333\n",
            "RandomForestClassifier 정확도:0.7667\n",
            "LogisticRegression 정확도: 0.7667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YeyTi3U0btYW"
      },
      "source": [
        "### 8. KFold 이용 (fold = 5)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApqJ3HMlAXn4",
        "outputId": "2e7e1f61-4f50-4e2d-a7f8-5a57791ca4d7"
      },
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "def exec_kfold(clf, folds=5):\n",
        "    # 폴드 세트를 5개인 KFold객체를 생성, 폴드 수만큼 예측결과 저장을 위한  리스트 객체 생성.\n",
        "    kfold = KFold(n_splits=folds)\n",
        "    scores = []\n",
        "    \n",
        "    # KFold 교차 검증 수행. \n",
        "    for iter_count , (train_index, test_index) in enumerate(kfold.split(X_heart_df)):\n",
        "        # X_titanic_df 데이터에서 교차 검증별로 학습과 검증 데이터를 가리키는 index 생성\n",
        "        X_train, X_test = X_heart_df.values[train_index], X_heart_df.values[test_index]\n",
        "        y_train, y_test = y_heart_df.values[train_index], y_heart_df.values[test_index]\n",
        "        \n",
        "        # Classifier 학습, 예측, 정확도 계산 \n",
        "        [\n",
        "  \n",
        "                                                        ] \n",
        "    \n",
        "    # 5개 fold에서의 평균 정확도 계산. \n",
        "    mean_score = np.mean(scores)\n",
        "    print(\"평균 정확도: {0:.4f}\".format(mean_score)) \n",
        "# exec_kfold 호출\n",
        "exec_kfold(dt_clf , folds=5)"
      ],
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "교차 검증 0 정확도: 0.2833\n",
            "교차 검증 1 정확도: 0.6333\n",
            "교차 검증 2 정확도: 0.7333\n",
            "교차 검증 3 정확도: 0.6833\n",
            "교차 검증 4 정확도: 0.7288\n",
            "평균 정확도: 0.6124\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "97q-l1QkcBYH"
      },
      "source": [
        "### 9. cross_val_score 사용"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59UP2i7MAZDL",
        "outputId": "2a294dee-f9bc-4995-ff14-972368b57b66"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(dt_clf, X_heart_df , y_heart_df , cv=5)\n",
        "[\n",
        "  \n",
        "                                                        ]\n",
        "\n",
        "print(\"평균 정확도: {0:.4f}\".format(np.mean(scores)))"
      ],
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "교차 검증 0 정확도: 0.6833\n",
            "교차 검증 1 정확도: 0.7333\n",
            "교차 검증 2 정확도: 0.7000\n",
            "교차 검증 3 정확도: 0.6833\n",
            "교차 검증 4 정확도: 0.6441\n",
            "평균 정확도: 0.6888\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "62iMFxhOcF49"
      },
      "source": [
        "### 10. GridSearchCV를 이용해서 최적의 hyper-parameter를 구하고 최고의 정확도 구해보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QlSJ1Rf8Aab6",
        "outputId": "22c66cca-45f0-477e-a2c0-b22617cc47b2"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "parameters = {'max_depth':[2,3,5,10],\n",
        "             'min_samples_split':[2,3,5], 'min_samples_leaf':[1,5,8]}\n",
        "\n",
        "[\n",
        "  \n",
        "                                                        ]"
      ],
      "execution_count": 292,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GridSearchCV 최적 하이퍼 파라미터 : {'max_depth': 2, 'min_samples_leaf': 8, 'min_samples_split': 2}\n",
            "GridSearchCV 최고 정확도: 0.7179\n",
            "테스트 세트에서의 DecisionTreeClassifier 정확도 : 0.7556\n"
          ]
        }
      ]
    }
  ]
}