Data Preprocess4.ipynb

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   "source": [
    "# 변수 선택 기법\n",
    "## 필요 라이브러리 import"
   ]
  },
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   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "import os\n",
    "if os.name == 'nt':\n",
    "    font_family = 'Malgun Gothic'\n",
    "\n",
    "sns.set(font=font_family, rc={'axes.unicode_minus' : False})"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## 붓꽃 데이터 로드\n",
    "- sklearn.datasets.load_iris\n",
    "- 타겟 데이터 : setosa, versicolor, virginica의 세 가지 붓꽃 종\n",
    "- 특징 데이터 : 꽃받침 길이(Sepal length), 꽃받침 폭(Sepal Width), 꽃잎 길이(Petal length), 꽃잎 폭(Petal Width)"
   ]
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     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 150 (50 in each of three classes)\n",
      "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      "        - sepal length in cm\n",
      "        - sepal width in cm\n",
      "        - petal length in cm\n",
      "        - petal width in cm\n",
      "        - class:\n",
      "                - Iris-Setosa\n",
      "                - Iris-Versicolour\n",
      "                - Iris-Virginica\n",
      "                \n",
      "    :Summary Statistics:\n",
      "\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "                    Min  Max   Mean    SD   Class Correlation\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "    ============== ==== ==== ======= ===== ====================\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: 33.3% for each of 3 classes.\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "     Structure and Classification Rule for Recognition in Partially Exposed\n",
      "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "     on Information Theory, May 1972, 431-433.\n",
      "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "     conceptual clustering system finds 3 classes in the data.\n",
      "   - Many, many more ...\n"
     ]
    }
   ],
   "source": [
    "# 붓꽃 데이터 로드\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(iris.DESCR)  # 붓꽃 데이터 상세 정보"
   ]
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    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 붓꽃 데이터 특성 확인\n",
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff4a050d-002d-4405-9742-f440081db724",
   "metadata": {},
   "source": [
    "## 데이터프레임 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "631fcb4b-c9b0-4005-b1a3-2f740cf93273",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "       species  \n",
       "145  virginica  \n",
       "146  virginica  \n",
       "147  virginica  \n",
       "148  virginica  \n",
       "149  virginica  "
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "# 데이터 생성 및 준비\n",
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "sy = pd.Series(iris.target, dtype='category')\n",
    "sy = sy.cat.rename_categories(iris.target_names)\n",
    "df['species'] = sy\n",
    "\n",
    "# 필터명 정의\n",
    "cols = iris.feature_names\n",
    "\n",
    "df.tail()"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "\u001b[1;31mType:\u001b[0m        CategoricalAccessor\n",
       "\u001b[1;31mString form:\u001b[0m <pandas.core.arrays.categorical.CategoricalAccessor object at 0x0000021357187EE0>\n",
       "\u001b[1;31mFile:\u001b[0m        c:\\users\\user\\anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\categorical.py\n",
       "\u001b[1;31mDocstring:\u001b[0m  \n",
       "Accessor object for categorical properties of the Series values.\n",
       "\n",
       "Be aware that assigning to `categories` is a inplace operation, while all\n",
       "methods return new categorical data per default (but can be called with\n",
       "`inplace=True`).\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "data : Series or CategoricalIndex\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> s = pd.Series(list(\"abbccc\")).astype(\"category\")\n",
       ">>> s\n",
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    c\n",
       "4    c\n",
       "5    c\n",
       "dtype: category\n",
       "Categories (3, object): ['a', 'b', 'c']\n",
       "\n",
       ">>> s.cat.categories\n",
       "Index(['a', 'b', 'c'], dtype='object')\n",
       "\n",
       ">>> s.cat.rename_categories(list(\"cba\"))\n",
       "0    c\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    a\n",
       "dtype: category\n",
       "Categories (3, object): ['c', 'b', 'a']\n",
       "\n",
       ">>> s.cat.reorder_categories(list(\"cba\"))\n",
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    c\n",
       "4    c\n",
       "5    c\n",
       "dtype: category\n",
       "Categories (3, object): ['c', 'b', 'a']\n",
       "\n",
       ">>> s.cat.add_categories([\"d\", \"e\"])\n",
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    c\n",
       "4    c\n",
       "5    c\n",
       "dtype: category\n",
       "Categories (5, object): ['a', 'b', 'c', 'd', 'e']\n",
       "\n",
       ">>> s.cat.remove_categories([\"a\", \"c\"])\n",
       "0    NaN\n",
       "1      b\n",
       "2      b\n",
       "3    NaN\n",
       "4    NaN\n",
       "5    NaN\n",
       "dtype: category\n",
       "Categories (1, object): ['b']\n",
       "\n",
       ">>> s1 = s.cat.add_categories([\"d\", \"e\"])\n",
       ">>> s1.cat.remove_unused_categories()\n",
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    c\n",
       "4    c\n",
       "5    c\n",
       "dtype: category\n",
       "Categories (3, object): ['a', 'b', 'c']\n",
       "\n",
       ">>> s.cat.set_categories(list(\"abcde\"))\n",
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    c\n",
       "4    c\n",
       "5    c\n",
       "dtype: category\n",
       "Categories (5, object): ['a', 'b', 'c', 'd', 'e']\n",
       "\n",
       ">>> s.cat.as_ordered()\n",
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    c\n",
       "4    c\n",
       "5    c\n",
       "dtype: category\n",
       "Categories (3, object): ['a' < 'b' < 'c']\n",
       "\n",
       ">>> s.cat.as_unordered()\n",
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    c\n",
       "4    c\n",
       "5    c\n",
       "dtype: category\n",
       "Categories (3, object): ['a', 'b', 'c']\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sy.cat?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2028e4f-f221-4a33-a389-702552849f61",
   "metadata": {},
   "source": [
    "## 필터 기법 이용한 변수 선택\n",
    "### 분산을 이용한 필터 기법\n",
    "- VarianceThreshold : 분산이 기준치보다 낮은 특징 데이터는 사용하지 않는 방법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f8a1fee-29a7-4bdd-9c00-91fcb6ae361a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scikit-learn의 VarianceThreshold 사용\n",
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9a2e929-e9b2-44f1-bc7d-6426f8d7da9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.68112222 0.18871289 3.09550267 0.57713289]\n"
     ]
    }
   ],
   "source": [
    "# 분산 스레스 홀더 생성\n",
    "selector = VarianceThreshold()\n",
    "\n",
    "# Threshold 설정 없이 분산 산출\n",
    "selector.fit(df[cols])\n",
    "\n",
    "print(selector.variances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2b527a95-2ca5-43dc-a380-019fff6e6ddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.68112222 0.18871289 3.09550267 0.57713289]\n",
      "[ True False  True False]\n"
     ]
    }
   ],
   "source": [
    "# threshold 설정\n",
    "selector.set_params(threshold=0.6)\n",
    "\n",
    "# refit and transform\n",
    "v_sel = selector.fit_transform(df[cols])\n",
    "\n",
    "# 분산 확인\n",
    "print(selector.variances_)\n",
    "\n",
    "# 분산이 임계치보다 낮은 feature 확인 ( True : 높음, False : 낮음 )\n",
    "print(selector.get_support())"
   ]
  },
  {
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   "execution_count": 11,
   "id": "03566d2e-5e38-4f59-86b5-6fbc314aa80d",
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  petal length (cm)\n",
       "0                5.1                1.4\n",
       "1                4.9                1.4\n",
       "2                4.7                1.3\n",
       "3                4.6                1.5\n",
       "4                5.0                1.4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 특성 필터 하여 데이터프레임에 저장\n",
    "iris_sel_df = df.iloc[:, [0,2]]\n",
    "iris_sel_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9eab36a4-00b0-4f23-bcc6-fad6f5e81a93",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>5.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>5.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  petal length (cm)    species\n",
       "0                  5.1                1.4     setosa\n",
       "1                  4.9                1.4     setosa\n",
       "2                  4.7                1.3     setosa\n",
       "3                  4.6                1.5     setosa\n",
       "4                  5.0                1.4     setosa\n",
       "..                 ...                ...        ...\n",
       "145                6.7                5.2  virginica\n",
       "146                6.3                5.0  virginica\n",
       "147                6.5                5.2  virginica\n",
       "148                6.2                5.4  virginica\n",
       "149                5.9                5.1  virginica\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결과 데이터프레임 생성\n",
    "iris_sel_df = pd.concat([iris_sel_df, df[['species']]], axis=1)\n",
    "iris_sel_df\n",
    "                         "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47622881-63e2-4a90-8f0e-e27f6eca4486",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 큰 상관계수 (Correlation)을 이용한 필터 기법\n",
    "- 데이터셋에서 'sepal length (cm)' 속성과 상관계수가 0.6보다 큰 속성들을 필터링\n",
    "- 3.데이터프레임 변환의 df 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c714de12-69e4-4acb-8758-1f69928d2065",
   "metadata": {},
   "outputs": [
    {
     "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)  \n",
       "count        150.000000  \n",
       "mean           1.199333  \n",
       "std            0.762238  \n",
       "min            0.100000  \n",
       "25%            0.300000  \n",
       "50%            1.300000  \n",
       "75%            1.800000  \n",
       "max            2.500000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iris 데이터프레임 확인\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2924cfb6-ee01-4107-966e-14753a6d90b6",
   "metadata": {},
   "outputs": [
    {
     "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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length (cm)</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width (cm)</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "sepal length (cm)           1.000000         -0.117570           0.871754   \n",
       "sepal width (cm)           -0.117570          1.000000          -0.428440   \n",
       "petal length (cm)           0.871754         -0.428440           1.000000   \n",
       "petal width (cm)            0.817941         -0.366126           0.962865   \n",
       "\n",
       "                   petal width (cm)  \n",
       "sepal length (cm)          0.817941  \n",
       "sepal width (cm)          -0.366126  \n",
       "petal length (cm)          0.962865  \n",
       "petal width (cm)           1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 상관계수 생성\n",
    "cor = df.corr()\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "473c2b85-0075-4940-8e42-59f4a9aa3121",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Spk2bMvV/+umn3HvvvbbjpUuXkpSUhNVqpXPnznTu3JlRo0bRuHFjduzYwZ133klYWBjJycmcOHGCSZMmERYWxt/+9jeefPJJHnjgAUeaXESkSjhbD6u8HOqJbdiwAV9fXxYvXsySJUuoUaMGqampZGZmkpCQwKJFi1i2bBnHjx8HICUlhYkTJ/LBBx8QEhJCQkICAA899BCLFi1iwYIFzJw585J1RkREkJKSAsD69eupW7cue/fupaCggNOnT1OrVi3bvb/88guLFy8mMTGROXPm2M43adKEMWPG0LFjRxITE6lWrRoAhYWFzJkzhwkTJlx0Pm7r1q2Eh4cDsH37dpYtW8a8efP44IMP6Nu3L3DmPTgjR45k2bJlpKWl8f3335OQkMCTTz5pi8Pf35+ioiJKSkocaXIRkSrhqj0xh5JYaGgoKSkpJCYmUlhYiIeHB+vXryclJYXY2Fji4uI4efIkmZmZAERGRnLNNdcAcP/997N9+3YATp8+zZQpUxg7dixHjhy5bJ2//PILpaWlfPPNNwwePJgtW7bYJZezNm/ezAMPPGBLUue/fO1CunTpAkDz5s0vGofFYqF+/foAfP7558TExODj4wNA9erVAWjatCl169bFbDbTtGlT7rrrLuBM8jx/8UhgYCDZ2dmXjElExAhuMSd27bXXkpSURFFRET179sRisVBSUsKwYcNITEwkMTGRlStX0qxZMwC8vLxsz+bn51OtWjXS09N55ZVXuPvuu5k4cSK1a9e+bL1//etf2bZtG56enkRERPDNN9+QkpJChw4d7O47m1jPKi4uvmS5Z5ORh4fHRRddWK1WzGazrXxPz7IjsOf/nGazGW9vbwA8PT3tel5ms1mLO0TEKXmYTeX+OBOHklhWVha+vr4MHDiQNm3asGvXLu644w4++ugj2x/r3bt32+5fv349hYWFACQlJdG+fXvS09Np1qwZzZs358CBA+Va5t6xY0emTp1KmzZtuOaaaygqKmL37t20bNnS7r4WLVqwdu1aiouLsVqtrFy50nbNx8eHvLw8R35cAIKCgmy9tDZt2pCUlMTp06cByM3NdaisrKwsatSo4XAMIiKVzS2GE3fu3El0dLRt2LB9+/ZERkZy00030aNHDx555BG7Zew333wzI0aMoE+fPnh5edGtWzfat2/P3r176devHx9//DENGza8bL1t27Zl69atREREANCyZUv8/Pzsel0ArVq1onXr1vTo0YP4+Hiuv/5627WwsDAsFgv9+vWjoKCg3D9zq1atSEtLA6BTp060aNGC3r17Exsby+rVq8tdTn5+Pp6enhfsyYmIGM1VhxNN1koa30pOTsZisTBkyJDKKL7K5OXlMWLECGbPnn1F5SxcuBB/f38efvjhcj+z7ZecK6rzatKu2xijQ3AKJ76ZanQITiO/SIukzqrp63H5my7jvhmp5b537d/DL39TFXGb74lVlJ+fH126dKnwF6UBjh8/TkpKCl27dv0TIxMR+fO4ak9MY1vl0LNnzyt6vnbt2rz77rt/UjQiIn8+s5Mlp/KqtCR2qS8wi4iIc3GyRYflpuFEERGptOHEdevW2XZymjVr1gXv2bFjB7fcckuF4tZwooiIOLR0Picnh5ycsgvPAgICCAgIsB3n5eUxdepUFi5cSPXq1YmNjSUiIoLQ0FDbPVarlZkzZxIYGFihuJXERETEoTmx+fPnM3Vq2ZWyw4YNY/jw4bbjTZs2ERERgb+/PwBdu3Zl48aNdkls3rx5dO7cmR9//LFCcSuJiYiIQz2xuLg4unXrVub8+b0wgMzMTLvXZDVo0ID9+/fbjtPS0ti5cydvvPEG06ZNczxolMRERAQcetnlH4cNL6aoqAg/P7/z6jDbtvE7fPgwb7/99kXnycpLSUxERCpliX1QUBAZGRm2Y4vFQoMGDYAzr7XKzs4mPj7edq1Xr14sWLDAtv9seWh1ooiIYHLgU14dOnTgs88+o6CggJKSElauXGl7u8jQoUNZvXo1SUlJJCUlUb9+fZKSkhxKYKAkJiIiVM4S+9q1axMfH09MTAy9evXi/vvvJy8v74q38TufhhNFRKTSvuwcHR1NdHS03bmQkJAy961bt65C5SuJiYiI0+2JWF5KYiIi4tDqRGeiJCYiIi67d6KSmIiIaDhRRERcl2umMCUxERFB7xMTEREXpoUdIiLisly0I6YkJiIiGk4UEREX5qI5TElMRES0xF4qQbvB7xsdgtM48U3Zt8i6o5qthxkdgvPwrm50BE4jP/X1Ky7DVXeDVxITERE8tDpRRERclYvmMCUxERHRnJiIiLgw9cRERMRluWhHTElMRETA00WzmJKYiIioJyYiIq5L206JiIjLctEcpiQmIiKVtzpx3bp1zJgxA09PTyIjIxk0aJDt2qpVq1iwYAGlpaXceOON/POf/8TDw8Oh8l11pxEREfkTmU2mcn/KKy8vj6lTp5KQkMDixYtZv349e/bssV339fVl8eLFJCUlYTab+eSTTxyOWz0xERHBw4EuTU5ODjk5OWXOBwQEEBAQYDvetGkTERER+Pv7A9C1a1c2btxIaGgoAJ06dbLd27RpU44dO+Zw3EpiIiKCifL3sObPn8/UqWU35R42bBjDhw+3HWdmZhIcHGw7btCgAfv37y/zXGFhIatWrWLixIkORq0kJiIiODYnFhcXR7du3cqcP78XBlBUVISfn9+5OsxmzGb7Ll9mZiajRo3i0UcfpXHjxo4FjZKYiIjgWBL747DhxQQFBZGRkWE7tlgsNGjQwHa8e/duXnnlFV5++WVCQkIcivcsLewQERFMJlO5P+XVoUMHPvvsMwoKCigpKWHlypVERUXZro8bN44333yzwgkM1BMTEREqZ4l97dq1iY+PJyYmBqvVSo8ePcjLy2P27Nk88sgjpKen89xzz9nuv+2223jqqaccqkNJTEREKu2lmNHR0URHR9udO9vz+u677664fCUxERHRq1hERMR1adspERFxWWYHvifmTAxbnZiamsrYsWMveC02NhaLxVKp9T///PN8++23lVqHiIirMJnK/3EmlZLE9uzZw+eff14ZRVdYbm4u8+bNq9CzW7Zs4e23365w3fn5+TzxxBMUFBRUuAwRkcrkaTaV++NMKiWJ7dq1i3379lVG0RX2+++/s379eoefs1qtzJgxgyFDhlS47urVq9OzZ08SEhIqXIaISGVy1Z7YZefEMjIyGD16NDfffDN79+6ltLSUCRMm0LhxY7Kzs3nllVc4fvw4VquV8ePHc/jwYWbNmkVRURHff/89M2bMYP78+axdu5bi4mKioqIYPHhwuQNcs2YNixcvpqSkhLZt2zJs2DBSU1NZvHgxnp6e/Pzzz/zlL39h8uTJmEwmNmzYwNSpU7nmmmv461//yrZt25gyZQpPP/00P//8M7GxsUyYMAGAb775hpkzZ5KRkcHQoUPp2rVrmfq3bt1KixYt8Pb2BmDDhg3MmjULq9VK8+bNef7554mKiiIqKoq0tDSCg4Pp0aMHs2fP5ujRo4wePZoOHTrQqVMn5s6da/caAhERZ3FVvxRzx44djB07ltDQUFJTU/nHP/7BnDlzmDRpEgMHDiQsLIwff/yR119/nenTpzNo0CAsFout99KmTRvi4uIoKSnhvvvuIy4urlzB/fLLL3zxxRfMnz8fs9nMiBEj2L17N3AmuaxYsYKaNWsycOBAtm7dys0338zrr7/OggULCAwMZO7cuQDUqlWLN998k3HjxtkNKWZmZvL++++TmZlJXFzcBZPYt99+S3h4OHAmoU+ZMoW5c+dSo0YN8vPzbecffvhhnnvuOQYPHsyiRYuYO3cue/fu5eWXX6ZDhw6YzWbq1avH0aNHCQoKKtfPLyJSVVw0h5UviTVt2tS2dX54eDgvvPACcGab/YMHD172eW9vb2bMmEF6ejrZ2dlkZWWVK7hNmzaxa9cuW9I7efIkhw4d4pprriE8PJyaNWsC0KJFCzIyMsjLy6Nt27YEBgYCEBUVxZdffnnR8rt06QKc2Vm5pKTkgvdYLBYiIyMB+Oqrr3jooYeoUaMGcGaYEKBevXo0adIEgGbNmlGnTh1MJhMhISEcOXLEVla9evWwWCxKYiLidFx1D8JyJTEvLy+747N7Z3l5eZGYmHjJZ0+cOMHIkSMZN24cMTExDBkyBKvVWq7gSkpK6NOnT5meW2pqKj4+Pud+CE9PSktLKSgosIu1uLj4kuWfHSIEyuysfJbVarX9vIWFhXh6lm2y8+s0m822cv9Y5sXqEBExmiN7IjqTcv1V3blzp20n4tWrV9OiRQsAmjRpwpo1a4AzCSM9PR0AHx8f8vLygDNDbQ0bNqR169bk5eWxa9eucgfXunVrVqxYYRu2OzuUeDHNmjVj/fr15ObmArB06VLbtfNjckRQUBBHjx4F4M4772T58uWcOnUKwFZPeakXJiLOysNkKvfHmZSrJ9akSRPeffddDh8+jJ+fH6+++ipwZgfisWPH2npjQ4YM4aabbuLOO+9k5syZ7N+/n3fffRcPDw/69OnDddddZxt2K4+wsDAeeugh+vbti5+fH3Xq1GHKlCkXvT84OJi4uDj69etHrVq16Nixo633U7duXerUqUOvXr144403yh1Dq1atSE1NpU2bNoSGhtK7d29iY2Px9fUlPDycYcOGlascq9XK4cOHqV+/frnrFhGpKs6VmsrPZL3M2F5GRkaZBRGuYvv27SxYsIDJkydXuIzS0lIGDBjA7NmzywyrOmLDhg388MMPDi3Vr97lzQrXd7U5seppo0NwCjVbl+9/mtyCd3WjI3Aa+amvX3EZi7ZlXP6m/+l3W6Mrru/PctVN0vz000/AmeSTmJhIRETEFZVnNpt54okneO+99ypcRmFhIYsXL2bAgAFXFIuISGWpjPeJVYWrbu/El156idOnTwNn5rAefPDBKy6zXbt2tGvXrsLP+/j4MHPmzCuOQ0Sksrhqj+aySaxRo0YuNZS4YMECo0MQEXE5ztbDKq+rricmIiKOu6p37BARkavbVTucKCIiVz8NJ4qIiMtyzRSmJCYiIlzlGwCLiMjVrbK2k1q3bh0zZszA09OTyMhIu9dRHT16lOeff568vDxq1qzJpEmTbBusl5erzuWJiMifyOTAf+WVl5fH1KlTSUhIYPHixaxfv549e/bYrr/11lv079+fDz/8kLvvvpvp06c7HLeSmIiIOPRm55ycHDIyMsp8cnJy7MrctGkTERER+Pv74+HhQdeuXdm4caPt+rZt27jrrrsA6Nq1K5s3b3Y4bg0niogIZgd6WPPnz2fq1Kllzg8bNozhw4fbjjMzMwkODrYdN2jQgP379wOQnZ1te/cjgK+vL0VFRQ7HrSQmIiIOLeyIi4ujW7duZc4HBATYHRcVFeHn52c7NpvNtjeLFBcX4+HhYXf/hd7XeDlKYiIi4lASCwgIKJOwLiQoKMj2Lko4807FBg0aAFCrVi2OHz9uu1ZQUEC1atXKH8T/aE5MREQq5aWYHTp04LPPPqOgoICSkhJWrlxJVFQUcKZXdsMNN5CamgrAihUriIyMdDhuJTEREamU1Ym1a9cmPj6emJgYevXqxf33309eXh6zZ88G4IUXXmDatGn07t2bTZs2Veh1VRpOFBGRSvuyc3R0NNHR0XbnQkJCAGjYsCEJCQlXVL6SmIiIONTDciZKYiIigtk1c5iSmIiIqCcmIiIuTD0xERFxWXqzs/z5fB3bzflqll9UYnQIzsG7utEROI+ifKMjuKq4ZgpTEhMREXDZLKYkJiIiWtghIiKuSws7RETEdSmJiYiIq9JwooiIuCwXXWGvJCYiIi47mqgkJiIiuGwWUxITERHt2CEiIq7LNVOYkpiIiIDLZjElMRER0RJ7ERFxXS46JaYkJiIiLjuaqCQmIiJgctGumJKYiIhoOFFERFxXVeSwvLw8Ro8eTWZmJtWrV2fixIkEBwfbrp84cYLx48dz/PhxCgoKGDVqFK1bt75kmUpiIiJSJVls7ty5hIeH88gjj7Bp0yYmTpzI1KlTbdd///13RowYwY033sjBgwf5+9//zqpVqy5ZppKYiIg4tMQ+JyeHnJycMucDAgIICAi46HOff/45ixYtAqB9+/a89NJLdtcbN25s+/e1115LYWHhZWNREhMREYdeijl//ny7HtRZw4YNY/jw4Rd9rqCgAF9fX9txYGAgOTk5F0x8a9eu5fbbb79sLEpiIiLi0HBiXFwc3bp1K3P+j8lo//79tt5W48aNKS0ttbvu4eGB2WwuU87ChQtZv349b7311mVjKfu0QVJTUxk7dmyFnk1KSmLnzp1lzlssFmJjYy/4zPPPP8+3335bofpERK42Jgf+CwgIoFGjRmU+f0xiN9xwA4mJiSQmJvLKK6/g6elJUVGR7Xpubi7+/v52z7zwwgv89ttvzJw5s8y1C6mSJLZnzx4+//zzSiu/V69eNGvW7JL35ObmMm/evAqVv2XLFt5+++0KPQuQn5/PE088QUFBQYXLEBGpTCZT+T8V1bZtW9tCjc2bN3PrrbfaXV+3bh0eHh48+eSTeHh4lKvMKkliu3btYt++fVVR1UX9/vvvrF+/3uHnrFYrM2bMYMiQIRWuu3r16vTs2ZOEhIQKlyEiUplMDnwqavjw4Xz22Wf06dOHOXPm8OSTTwLw9ttvk5mZyXfffUdqaiqxsbG2z4EDBy5ZpsNzYhkZGYwePZqbb76ZvXv3UlpayoQJE2jcuDHZ2dm88sorHD9+HKvVyvjx4zl8+DCzZs2iqKiI77//nhkzZjB//nzWrl1LcXExUVFRDB48+KL1rV69ml27dvF///d/5Ofn07ZtWzZs2EBAQACjRo0iNjaWBQsW0KNHD26//XY2bdrEO++8g7+/P6GhoQBkZWXx9NNP8/PPPxMbG8uECRMA+Oabb5g5cyYZGRkMHTqUrl27lql/69attGjRAm9vbwA2bNjArFmzsFqtNG/enOeff56oqCiioqJIS0sjODiYHj16MHv2bI4ePcro0aPp0KEDnTp1Yu7cuQwaNMjRJhcRqXxVsMS+Ro0azJw5s8z5s8ns2Wef5dlnn3WozAot7NixYwdjx44lNDSU1NRU/vGPfzBnzhwmTZrEwIEDCQsL48cff+T1119n+vTpDBo0CIvFYuvNtGnThri4OEpKSrjvvvuIi4u7aF3t27e3DQNu2bKFRo0akZaWRmRkJD/++CO33HKL7d7s7GwmTZpEYmIigYGBzJ07F4BatWrx5ptvMm7cOLshxczMTN5//30yMzOJi4u7YBL79ttvCQ8PB84k8ClTpjB37lxq1KhBfn6+7fzDDz/Mc889x+DBg1m0aBFz585l7969vPzyy3To0AGz2Uy9evU4evQoQUFBFWl2EZFK41YvxWzatKmtlxMeHs4LL7wAwKZNmzh48OBln/f29mbGjBmkp6eTnZ1NVlbWRe+tUaMGXl5eZGdns2HDBp5++mk2b97MTTfdxI033mi339f27dtp27YtgYGBAERFRfHll19etOwuXboA0KBBA0pKSi54j8ViITIyEoCvvvqKhx56iBo1agBnhgkB6tWrR5MmTQBo1qwZderUwWQyERISwpEjR2xl1atXD4vFoiQmIk7HNVNYBZOYl5eX3fHZROLl5UViYuIlnz1x4gQjR45k3LhxxMTEMGTIEKxW6yWfad++PampqRw9epS77rqLuXPnkpKSQocOHezuKygosJsMLC4uvmS5Z4cIgQsu84Qzc2Jnf77CwkI8Pcs22fntYTabbeX+scyL1SEiYjgXzWIV+qu6c+dOMjIygDNzVi1atACgSZMmrFmzBjiTQNLT0wHw8fEhLy8PODP01rBhQ1q3bk1eXh67du26bH0dO3Zk0aJFhISEYDKZCAoKYt26dbRv397uvmbNmrFhwwZbXcuXL7ddOz8GRwQFBXH06FEA7rzzTpYvX86pU6eAMyseHaFemIg4K0eW2DuTCiWxJk2a8O677xIbG8vKlSsZNWoUAOPGjWPJkiX07duX/v37k5mZCZz5479x40b+/ve/07RpUzw8POjTpw9vvvmmbRjuUsLCwkhPT+euu+4CoF27dmRnZ1OrVi27+4KDg4mJiaFPnz4MGDDA7jsLdevWpU6dOvTq1atcQ55ntWrVirS0NABCQ0Pp3bu3bdWMI0v2rVYrhw8fpn79+uV+RkSkqlTFEvvKYLJebizvDzIyMsoskLialZaWMmDAAGbPnl1mGNURGzZs4IcffnBoqX717rMrXN/V5vCCAUaH4BQa3v280SE4j6J8oyNwGvn/LbsFlKMyTlx+n8KzGtX0ueL6/iyapLkMs9nME088wXvvvVfhMgoLC1m8eDEDBgz48wITEflTVcU3xf582juxHNq1a0e7du0q/LyPj88FvxshIuIsnG2YsLwcTmKNGjVym6FEERF34aI5TD0xERFxo56YiIhcfZxt6Xx5KYmJiIh6YiIi4rqUxERExGVpOFFERFyXa+YwJTEREXHZHKYkJiIimhMTEREX5qovxdTeiSIi4rLUExMREQ0nioiI63LVJfYaThQRkSp5KWZeXh4jRoygZ8+e9O/fn0OHDl303kGDBjF9+vTLlqkkJiIiVZLE5s6dS3h4OEuWLGHQoEFMnDjxgvd99dVXHDx4sFxlajhRREQcGk7MyckhJyenzPmAgAACAgIu+tznn3/OokWLAGjfvj0vvfRSmXuys7OZP38+AwYM4Pjx45eNRUlMREQc6mHNnz+fqVOnljk/bNgwhg8fftHnCgoK8PX1tR0HBgaSk5NjS3ylpaWMHj2ap556ir1795YrFiUxERFxaFlHXFwc3bp1K3P+j72w/fv323pbjRs3prS01O66h4cHZvO5Wa033niDiIgIbrnlFiUxERFxgANZ7HLDhmfdcMMNJCYm2o7vv/9+ioqK8Pb2BiA3Nxd/f3/gzKKP1atXExQURHJyMllZWRQXF1OjRg0eeeSRi9ahJCYiIlWyxL5t27asWrWK7t27s3nzZm699VbbNT8/P9avX287Tk5OxmKxXDKBAZisVqu1sgIWERE5Kzs7m1GjRvH777/j5+fHa6+9Rt26dXn77bfp3bs3DRo0sN17NokNGTLkkmUqiYmIiMvS98RERMRlKYmJiIjLUhITERGXpSQmIiIuS0lMRERclpKYiIi4LCUxERFxWUpiIiLispTERETEZSmJiYiIy9IGwFKG1Wrl999/x2q1EhgYaPeqBHejtjjn2LFjWCwWrFYrQUFB1KtXz+iQDKO2cB5KYmKzZs0ali5dSnFxMYGBgZhMJrKysvD09CQ6OpquXbsaHWKVUVucUVRUxLx589i4cSP16tWjXr16mM1mLBYLmZmZdOjQgQEDBlCtWjWjQ610agvnpA2ABYARI0bQunVrunfvjp+fn921goICkpOTSUlJ4Z133jEowqqjtjhn8ODB9OrVi7vvvhvTBV79u2HDBj788EOmT59uQHRVS23hnJTEBDgzPFKnTp1L3pOVlUWtWrWqKCLjqC3OKSgouGzP4vyXHF7N1BbOSUlM7OzZs4fZs2djsVgoLS3FarViMplYuHCh0aFVObXFOZs3b2batGkcPXoUq9Vqa4svvvjC6NCqnNrCuSiJiZ2HH36YkSNH0qRJEzw9z02ZuuPEtdrinAcffJA33niDkJCQCw6luRO1hXPRwg6xExgYSKdOnYwOwymoLc6pW7cuTZo0MToMp6C2cC7qiYmd5cuX8/3333P33Xfbje23bt3awKiMobY4Z9OmTSxevJiOHTvatUV0dLRxQRlEbeFc1BMTO7t27WLr1q3k5eXh4eEBgMlkcss/3GqLcz766CNKS0s5evSoXVu4I7WFc1ESEzspKSksX77c6DCcgtrinEOHDrFkyRKjw3AKagvn4r7bD8gFNW/enKysLKPDcApqi3PCw8NJT083OgynoLZwLpoTEzsxMTHs2bOHpk2b4uXlZVs+PGfOHKNDq3Jqi3M6d+5MRkYG9evXt2uLTz/91OjQqpzawrkoiYmdQ4cOXfB8cHBwFUdiPLWFiPPTcKLYWbduHXXq1CE4OJjg4GBq1KjB119/bXRYhlBbnDNp0iQKCwttx7m5uW67vZLawrkoiYmdzz77DB8fH9uxv78/K1euNDAi46gtzvn+++/LtEVKSoqBERlHbeFclMSkjOLiYtu/S0pKOHXqlIHRGEttcU5ubq7t3/n5+WqL/3H3tjCaltiLnZ49ezJo0CAeeeQRvLy8SEpKIioqyuiwDKG2OOfxxx+nf//+PPzww3h6erJixQr69etndFiGUFs4Fy3skDK+++47Pv/8cwoLC2nTpg1333230SEZRm1xzqFDh/jqq68oLCwkPDyc0NBQo0MyjNrCiVhFrFZrUlLSZe9ZunRp5QfiBNQW50yZMsVaVFR00eunT5+2Tps2rQojMo7awjlpOFEA8PDwYODAgdx7772Eh4dTv359TCYTR44cIS0tjU8++YQuXboYHWaVUFucc8cdd/DYY4/RsmVLwsPDbW8zPtsW27Zt44knnjA6zCqhtnBOGk4Um4KCAj799FO2bt2KxWIBICgoiFatWnHvvffi6+trcIRVR21h79tvv7W1hdVqtbXFHXfc4Xb7BqotnIuSmIiIuCwtsRcREZelJCYiIi5LSUxERFyWkpiIiLgsLbEXO6mpqUybNo0jR44AuPVrJtQW56xevZp33nmH0tJSzGaz2kJt4TS0OlHsPPDAA7z66qs0b94cs9m9O+pqi3Puv/9+5syZQ/369Y0OxXBqC+einpjYqV+/Pi1btjQ6DKegtjgnODhYf7T/R23hXNQTE9twGcDatWspKirivvvuw9vb23a+Xr16RoRW5dQW52zbts3277S0NDIzM+nSpYvda0huu+02I0KrcmoL56UkJsTGxmIymbjYr4LJZCIhIaGKozKG2uKc0aNHX/aeiRMnVkEkxlNbOC8lMbFTWFho93+XFzvnDtQW5xw6dIjg4ODLnnMHagvn4t6z1VLG448/XubcwIEDDYjEeGqLcy7UE3nmmWcMiMR4agvnooUdAsDSpUvZunUrP//8My+88ILtfG5uLqdPnzYwsqqntjhn/vz5rF+/nr179xIfH287n5ubS506dQyMrOqpLZyThhMFgMzMTH799VcmTpzImDFjbOd9fHxo2rSp3cKGq53a4pzc3Fx+//13nnnmGd58803beR8fH7f7w622cE5KYmJn3759hISEGB2GU1BbnHPixAlq1qxpdBhOQW3hXDScKHbmzp1b5pyHhwd169YlIiKCW2+91YCoqo5WoV3YiBEjyrwr6/zfiwcffNCgyKrG2VWrl+Iuq1adjZKY2KlZsyZFRUV07tyZ4uJili1bxl/+8heuvfZa3nzzTe677z769etndJiVpkePHrZ/f/LJJ1itVltbrFy5kr/85S/GBWegVq1aERgYSFRUFMXFxSxdupSaNWty6623kpiYyP79+xkxYoTRYVaayZMn2/6dkJBA3bp1bW2xfPlyatSoYWB0bs4qcp6BAwfaHZeWllrj4+OtVqvVWlBQYO3du7cRYRni7M991vlt4W4u9HM/+uijVqvVai0pKbH26dOnqkMyzKXaQqqeltiLnezsbIqKimzHJSUlHD9+HDgzge1OewiePHnyom3hbk6ePMnJkydtx6dOnbK1hTv9TsCZBR7nt0V+fj7Hjh0zMCL3puFEsdOnTx/69+/PAw88gJeXF6tXryYyMhIAi8VCSUmJwRFWnX79+tG/f38efPBBPDw8WLNmDZ06dTI6LEMMHTqUfv360a5dOzw9PdmwYQP9+/cH4KeffsLT033+lAwfPpxHHnmEdu3aYTab2bhxIzExMUaH5ba0OlHK+OWXX9iyZQsFBQW0bNnStpjj1KlTFBUVERgYaGyAVejAgQNs3ryZwsJCWrRo4db74+Xk5LB9+3YKCgpo1qwZDRs2NDokw+Tk5PDf//6XwsJCt28LoymJSRkWi4VDhw5RWlpqO9e6dWsDI6o6mZmZNGjQALDfDPh87rIB8B9t3bq1zO9FdHS0cQFVoV27dhEWFgbYbwZ8Pnf+Hxwjuc8YgJTL66+/zsaNG2nWrBkeHh7AmU1v3SWJrVy5kkGDBgHw7LPP2s6f3RTYnTYAPt+zzz7LiRMnaNmypW3o8HJLzq8mP/zwgy2JLVmy5IL3KIkZQz0xsRMdHc3HH39sS2DuLCsri1q1ahkdhlPo0aMHH330kdFhOIXze2ViPPXExM51111HSUmJkhjw5JNPUlJSQnh4OB06dKBly5Zu1fs4X+PGjcnJySEgIMDoUAyXkJBAeno6N954Ix06dKBdu3bawcNA6omJnfHjx7Njxw46duxo98qRwYMHGxiVcQoLC9m6dSupqans3r0bPz8/3nrrLaPDqnJPPfUU27ZtIzw83O734h//+IeBURnrwIEDpKSksG7dOnJyckhKSjI6JLeknpjYadGiBS1atDA6DKfxyy+/sG/fPtLT07Fardx4441Gh2SIvn370rdvX6PDcAq5ubmkpaXx9ddf8+OPPxIUFHTVb7vlzNQTkzKysrKwWCxuP+7fpk0bgoKCeOKJJ4iIiMDf39/okAy1Z88eDh8+7LbflTvr1ltv5brrruPpp5+mY8eORofj9pTExM6SJUtYtmwZJ0+eZMWKFezZs4elS5eWa2Pcq83BgwdZv34933zzDV5eXrRp04aOHTtSt25do0OrctOmTWPfvn0cPHiQ5ORk9uzZw3vvveeWQ6sFBQV8/fXXbNy4kd9++42QkBDuvvtumjdvbnRobsm99ouRy0pOTmbBggW2DU1DQ0PZtWuXwVEZ49prryU2NpZ33nmHvn372u1e4m62bNnClClT8PPzA878XrjrVkvVqlWjU6dOPP3000RHR7N9+3aGDRtmdFhuS3NiYsfb2xuTyWRbhVdaWkpeXp7BURlj8eLFbNmyhV9//ZW//vWv9OvXj3fffdfosAxxdn/Es78XBQUF5ObmGhmSYSZPnkxaWhre3t60b9+e//u//3P7oXcjKYmJnXvuuYdJkyaRm5vLl19+yZIlS2jXrp3RYRni5MmTDB06lCZNmhgdiuH69u3LM888w/Hjx0lMTGT58uV0797d6LAMERYWxqBBg/R1AyehOTEpIy0tjf/85z+UlpbSunVrtx1CE3sHDhxg06ZNlJSU0Lp1a5o2bWp0SCJKYiJyYRfbO/J87rqPpDgPJTGhc+fOF92J4ux+gZ9++mkVRyVGi42Nte0ZeT5330dSnIuSmIiIuCwt7BA5z4wZMy67P6K7bsHlzsaNG3fZ3wt33oLLSEpiIuepX7++0SGIE3rooYeMDkEuQsOJIiIOKiwsJCsry26+UG93NoZ6YiIXsWzZMmbMmMHRo0epVasWR44cITg4WItc3NysWbP4+OOPyc7O5uabb2b37t2EhoayYMECo0NzS0pionmgi0hISCA5OZmxY8cyceJEfvnlF5KTk40Oq8poHujC1q1bx9q1a3nuuecYP348ubm5brmHpLNQEhPNA11E9erV8fPz44YbbmD//v3ccsst7N692+iwqozmgS7M09MTs9nMTTfdxN69e2nVqhW//vqr0WG5LSUxoVu3bkaH4JRuu+020tPTiYqK4uWXX6Z58+Zu9WbnO+64w+74QvNA7ui+++4jPT2d++67j5EjRxIcHEydOnWMDsttaWGH2NE80IX99NNPpKen065dO7d8r5jmgS7s999/JyMjg7CwMNsmyVK11Opi5+w8UMeOHVm1ahVLliwhIiLC6LAMUVBQwKxZs/j73//OtGnTyMvLs72KxN2cnQdq3749M2bMYNWqVQQHBxsdliGGDh1q+3dgYCDNmjVj+PDhBkbk3jScKHbcfR7ofKNGjSIkJITnnnuO/Px8kpKS2LNnD2PGjDE6tCqneSD44osv2LdvH3v37mXmzJm287m5uW7XFs5ESUzsuPs80PmOHj3KlClTbMfjx4+nX79+BkZkHM0DQXBwMDk5OXh5edltfHzdddcRHx9vYGTuTXNiclHuPg80fPhwXnvtNdsQYlFRESNGjLD7v3B35O7zQF9//TVt27Y1Ogz5H/XExE5BQQEJCQn897//pXr16rRv395t54GCgoLo3bs399xzD15eXmzYsIGbbrrJLom5y/fnhg4dyrRp04Az80CBgYF259xJw4YNGTp0KHl5ecybN4+ffvoJi8Xiti+PNZqSmNjRPNA5zZo1o1mzZrZjdxxK1DxQWS+//DJjx47l5ZdfBuD666/nxRdfVBIziJKY2NE80DndunUjKysLi8VCWFiY0eEYQvNAZZlMJm666Sbbsaen/owaSa0vdurUqWO3lLyoqIiAgACDozLGkiVLWLZsGSdPnmTFihXs2bOHpUuXMnr0aKNDqzKhoaGEhoZSr149zQP9T82aNdmzZ49twdPatWvx8fExOCr3pSQmdjQPdE5ycjKLFi2if//+wJk/6Lt27TI4KmNoHuicl156iUmTJnHgwAEiIyMJDQ3l1VdfNTost6UkJnY0D3SOt7c3JpPJ9n/cpaWl5OXlGRyVMTQPdE5AQICSlhNREhM7mgc655577mHSpEnk5uby5ZdfsmTJErf8ow2aBzrfgQMHeOWVV8jIyMBkMtGyZUuee+45atWqZXRobsl9fxPlgjQPdE7//v1JS0sDIDU1lb/97W9ERkYaHJUxNA90zpgxYxg5cqRtg+TVq1czbtw4pk+fbnBk7sn9vqkol5ScnMyCBQuoUaMG4N7zQCdOnOD2229n1KhRxMfH4+npSXFxsdFhGeKll14iMTHRNg+0evVqtx1SM5lMdjv8P/DAA5w8edLAiNybemJiR/NA5zz22GN89NFH5Ofn89hjjxEeHs6nn37KxIkTjQ6tymke6JwmTZqwb98+QkJCADh8+DCNGjUyOCr3pSQmdjQPdI6Pjw8mk4nk5GS6detGfHw8jz76qNFhGULzQOf8+OOP9O7dm9DQUDw9Pfnhhx8ICQkhPj4eq9WKyWRizpw5RofpNrR3opSRlpbGf/7zH0pLS2ndurXbzgONHj0aPz8/UlJS+PDDD/H19SU6Oprly5cbHVqVe+SRR8rMA61evdot54EOHTp02Xvc9TU1RlASEzsnTpygRo0amM1mjhw5wu7du2nXrh1eXl5Gh1blioqK2LhxI6GhoTRq1IgTJ06wc+dOOnToYHRoVS4mJqbMCzBjY2NJTEw0KCKRM7SwQ+w89thjmEwm2zzQpk2bePHFF40OyxDe3t5ERkba5jtq1qzplgkMzs0DnaV5IHEWmhMTO5oHkgvRPJA4KyUxsXP99dfzz3/+0zYPZLVaycrKMjosMZg7rsgU16A5MbGjeSARcSVKYiIi4rK0sENERFyWkpiIiLgsJTEREXFZSmIiIuKy/h/RAvlDRxEMjAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 히트맵으로 상관관계 가시화\n",
    "sns.heatmap(cor, annot=False, cmap= plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee6d363b-0c43-4b03-bf12-df0f607e2907",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sepal length (cm)    1.000000\n",
      "sepal width (cm)     0.117570\n",
      "petal length (cm)    0.871754\n",
      "petal width (cm)     0.817941\n",
      "Name: sepal length (cm), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 'sepal length (cm)' 속성의 상관관계 정의\n",
    "cor_target = abs(cor['sepal length (cm)'])\n",
    "print(cor_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73090293-51f7-44c7-93f4-5eb20b2417e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal length (cm)    1.000000\n",
       "petal length (cm)    0.871754\n",
       "petal width (cm)     0.817941\n",
       "Name: sepal length (cm), dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 상관관계가 0.6보다 큰 속성 확인\n",
    "cor_target[cor_target > 0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8ebb6577-ddb0-44e8-a1c9-32f6fdb8afc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "상관관계 계수 > 0.6인 속성\n",
      "sepal length (cm)    1.000000\n",
      "petal length (cm)    0.871754\n",
      "petal width (cm)     0.817941\n",
      "Name: sepal length (cm), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 상관관계가 0.6보다 큰 속성 확인\n",
    "selected_cols = cor_target[cor_target > 0.6]\n",
    "print('상관관계 계수 > 0.6인 속성')\n",
    "print(selected_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aed1ec40-bdf8-4590-bb2f-94993b888428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sepal length (cm)', 'petal length (cm)', 'petal width (cm)'], dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_cols.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14f40b84-335b-408f-9dfb-8f12c6e17d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1                1.4               0.2\n",
       "1                4.9                1.4               0.2\n",
       "2                4.7                1.3               0.2\n",
       "3                4.6                1.5               0.2\n",
       "4                5.0                1.4               0.2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[selected_cols.index].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91aa0c2e-4068-45d2-834c-4715006ca70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1                1.4               0.2\n",
       "1                4.9                1.4               0.2\n",
       "2                4.7                1.3               0.2\n",
       "3                4.6                1.5               0.2\n",
       "4                5.0                1.4               0.2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 필터링된 속성을 df_cor_sel에 저장\n",
    "df_cor_sel = df[selected_cols.index]\n",
    "df_cor_sel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "198422d9-1bbb-41cc-9e2d-63ef48737659",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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