11. 데이터 전처리.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 모듈 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 불러오기\n",
    "boston = pd.read_csv('input/boston_missing.csv')\n",
    "# column 파악 .. 평균 domain 정도\n",
    "# 범주형, 연속형에 따라 사용 가능한 model, function이 다름"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/데이터 설명.PNG\" style=\"width:600px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 20)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터의 shape확인\n",
    "boston.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>OBS.</th>\n",
       "      <th>TOWN</th>\n",
       "      <th>TOWN#</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>CMEDV</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Nahant</td>\n",
       "      <td>0</td>\n",
       "      <td>-70.955</td>\n",
       "      <td>42.2550</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>T</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Swampscott</td>\n",
       "      <td>1</td>\n",
       "      <td>-70.950</td>\n",
       "      <td>42.2875</td>\n",
       "      <td>21.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>T</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Swampscott</td>\n",
       "      <td>1</td>\n",
       "      <td>-70.936</td>\n",
       "      <td>42.2830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>T</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Marblehead</td>\n",
       "      <td>2</td>\n",
       "      <td>-70.928</td>\n",
       "      <td>42.2930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>T</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Marblehead</td>\n",
       "      <td>2</td>\n",
       "      <td>-70.922</td>\n",
       "      <td>42.2980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>T</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBS.        TOWN  TOWN#     LON      LAT  MEDV  CMEDV     CRIM    ZN  \\\n",
       "0     1      Nahant      0 -70.955  42.2550  24.0    NaN  0.00632  18.0   \n",
       "1     2  Swampscott      1 -70.950  42.2875  21.6    NaN  0.02731   0.0   \n",
       "2     3  Swampscott      1 -70.936  42.2830   NaN    NaN  0.02729   0.0   \n",
       "3     4  Marblehead      2 -70.928  42.2930   NaN    NaN  0.03237   0.0   \n",
       "4     5  Marblehead      2 -70.922  42.2980   NaN    NaN  0.06905   0.0   \n",
       "\n",
       "   INDUS CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO       B  LSTAT  \n",
       "0   2.31    T  0.538  6.575  65.2  4.0900    1  296     15.3  396.90   4.98  \n",
       "1   7.07    T  0.469  6.421  78.9  4.9671    2  242     17.8  396.90   9.14  \n",
       "2   7.07    T  0.469  7.185  61.1  4.9671    2  242     17.8  392.83   4.03  \n",
       "3   2.18    T  0.458  6.998  45.8  6.0622    3  222     18.7  394.63   2.94  \n",
       "4   2.18    T  0.458  7.147  54.2  6.0622    3  222     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data 값 확인\n",
    "boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 20 columns):\n",
      "OBS.       506 non-null int64\n",
      "TOWN       506 non-null object\n",
      "TOWN#      506 non-null int64\n",
      "LON        506 non-null float64\n",
      "LAT        506 non-null float64\n",
      "MEDV       501 non-null float64\n",
      "CMEDV      88 non-null float64\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       500 non-null object\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null int64\n",
      "TAX        506 non-null int64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "dtypes: float64(14), int64(4), object(2)\n",
      "memory usage: 79.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# 데이터 column 확인\n",
    "boston.info()\n",
    "# type 확인"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 전처리란 간단히 말해서 모델에 넣기전에 데이터를 가공하는 것!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (1) 요약통계량 확인\n",
    "### * describe() 함수를 사용\n",
    "### * 수치형 변수에 관해서만 결과를 보여준다, 명목형 제외\n",
    "### * 결측치 제거한 값만 계산해준다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>OBS.</th>\n",
       "      <th>TOWN#</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>CMEDV</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>253.500000</td>\n",
       "      <td>47.531621</td>\n",
       "      <td>-71.056389</td>\n",
       "      <td>42.216440</td>\n",
       "      <td>22.446507</td>\n",
       "      <td>16.370455</td>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>146.213884</td>\n",
       "      <td>27.571401</td>\n",
       "      <td>0.075405</td>\n",
       "      <td>0.061777</td>\n",
       "      <td>9.189284</td>\n",
       "      <td>4.782419</td>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-71.289500</td>\n",
       "      <td>42.030000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>26.250000</td>\n",
       "      <td>-71.093225</td>\n",
       "      <td>42.180775</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>13.225000</td>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>253.500000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>-71.052900</td>\n",
       "      <td>42.218100</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>379.750000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>-71.019625</td>\n",
       "      <td>42.252250</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>3.677082</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>-70.810000</td>\n",
       "      <td>42.381000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>29.800000</td>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             OBS.       TOWN#         LON         LAT        MEDV      CMEDV  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  501.000000  88.000000   \n",
       "mean   253.500000   47.531621  -71.056389   42.216440   22.446507  16.370455   \n",
       "std    146.213884   27.571401    0.075405    0.061777    9.189284   4.782419   \n",
       "min      1.000000    0.000000  -71.289500   42.030000    5.000000   7.000000   \n",
       "25%    127.250000   26.250000  -71.093225   42.180775   16.800000  13.225000   \n",
       "50%    253.500000   42.000000  -71.052900   42.218100   21.100000  16.100000   \n",
       "75%    379.750000   78.000000  -71.019625   42.252250   25.000000  20.100000   \n",
       "max    506.000000   91.000000  -70.810000   42.381000   50.000000  29.800000   \n",
       "\n",
       "             CRIM          ZN       INDUS         NOX          RM         AGE  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.554695    6.284634   68.574901   \n",
       "std      8.601545   23.322453    6.860353    0.115878    0.702617   28.148861   \n",
       "min      0.006320    0.000000    0.460000    0.385000    3.561000    2.900000   \n",
       "25%      0.082045    0.000000    5.190000    0.449000    5.885500   45.025000   \n",
       "50%      0.256510    0.000000    9.690000    0.538000    6.208500   77.500000   \n",
       "75%      3.677082   12.500000   18.100000    0.624000    6.623500   94.075000   \n",
       "max     88.976200  100.000000   27.740000    0.871000    8.780000  100.000000   \n",
       "\n",
       "              DIS         RAD         TAX     PTRATIO           B       LSTAT  \n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000  \n",
       "mean     3.795043    9.549407  408.237154   18.455534  356.674032   12.653063  \n",
       "std      2.105710    8.707259  168.537116    2.164946   91.294864    7.141062  \n",
       "min      1.129600    1.000000  187.000000   12.600000    0.320000    1.730000  \n",
       "25%      2.100175    4.000000  279.000000   17.400000  375.377500    6.950000  \n",
       "50%      3.207450    5.000000  330.000000   19.050000  391.440000   11.360000  \n",
       "75%      5.188425   24.000000  666.000000   20.200000  396.225000   16.955000  \n",
       "max     12.126500   24.000000  711.000000   22.000000  396.900000   37.970000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 상관분석\n",
    "### * 수치형 변수중에서 각각 2개의 column씩 상관관계를 보여준다\n",
    "### * 값은 -1 ~ 1사이에서 존재한다\n",
    "### * 절대값이 0.7이상이면 둘 사이의 관계가 밀접하게 관련이 되어있다고 볼 수 있다\n",
    "### * 둘 사이의 관계가 높다고 인과관계로 잘못 해석하면 안 된다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>OBS.</th>\n",
       "      <th>TOWN#</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>CMEDV</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>OBS.</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992287</td>\n",
       "      <td>-0.048973</td>\n",
       "      <td>-0.651668</td>\n",
       "      <td>-0.214940</td>\n",
       "      <td>0.611685</td>\n",
       "      <td>0.407407</td>\n",
       "      <td>-0.103393</td>\n",
       "      <td>0.399439</td>\n",
       "      <td>0.398736</td>\n",
       "      <td>-0.079971</td>\n",
       "      <td>0.203784</td>\n",
       "      <td>-0.302211</td>\n",
       "      <td>0.686002</td>\n",
       "      <td>0.666626</td>\n",
       "      <td>0.291074</td>\n",
       "      <td>-0.295041</td>\n",
       "      <td>0.258465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TOWN#</td>\n",
       "      <td>0.992287</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000627</td>\n",
       "      <td>-0.631648</td>\n",
       "      <td>-0.257397</td>\n",
       "      <td>0.535115</td>\n",
       "      <td>0.447920</td>\n",
       "      <td>-0.123970</td>\n",
       "      <td>0.434426</td>\n",
       "      <td>0.429399</td>\n",
       "      <td>-0.117334</td>\n",
       "      <td>0.235141</td>\n",
       "      <td>-0.327280</td>\n",
       "      <td>0.724060</td>\n",
       "      <td>0.707982</td>\n",
       "      <td>0.341111</td>\n",
       "      <td>-0.306517</td>\n",
       "      <td>0.296600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LON</td>\n",
       "      <td>-0.048973</td>\n",
       "      <td>-0.000627</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.143054</td>\n",
       "      <td>-0.343840</td>\n",
       "      <td>-0.028841</td>\n",
       "      <td>0.065101</td>\n",
       "      <td>-0.218081</td>\n",
       "      <td>0.062702</td>\n",
       "      <td>0.160871</td>\n",
       "      <td>-0.257110</td>\n",
       "      <td>0.204739</td>\n",
       "      <td>-0.011243</td>\n",
       "      <td>0.034067</td>\n",
       "      <td>0.050663</td>\n",
       "      <td>0.312602</td>\n",
       "      <td>-0.018300</td>\n",
       "      <td>0.195630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LAT</td>\n",
       "      <td>-0.651668</td>\n",
       "      <td>-0.631648</td>\n",
       "      <td>0.143054</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002079</td>\n",
       "      <td>-0.009729</td>\n",
       "      <td>-0.084293</td>\n",
       "      <td>-0.129667</td>\n",
       "      <td>-0.041093</td>\n",
       "      <td>-0.068600</td>\n",
       "      <td>-0.069317</td>\n",
       "      <td>0.079035</td>\n",
       "      <td>-0.082981</td>\n",
       "      <td>-0.207013</td>\n",
       "      <td>-0.167718</td>\n",
       "      <td>-0.004527</td>\n",
       "      <td>0.105254</td>\n",
       "      <td>0.045660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MEDV</td>\n",
       "      <td>-0.214940</td>\n",
       "      <td>-0.257397</td>\n",
       "      <td>-0.343840</td>\n",
       "      <td>-0.002079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.983990</td>\n",
       "      <td>-0.386980</td>\n",
       "      <td>0.367456</td>\n",
       "      <td>-0.478908</td>\n",
       "      <td>-0.423380</td>\n",
       "      <td>0.692737</td>\n",
       "      <td>-0.374947</td>\n",
       "      <td>0.243707</td>\n",
       "      <td>-0.377489</td>\n",
       "      <td>-0.463498</td>\n",
       "      <td>-0.511775</td>\n",
       "      <td>0.331752</td>\n",
       "      <td>-0.735080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CMEDV</td>\n",
       "      <td>0.611685</td>\n",
       "      <td>0.535115</td>\n",
       "      <td>-0.028841</td>\n",
       "      <td>-0.009729</td>\n",
       "      <td>0.983990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.390414</td>\n",
       "      <td>-0.551992</td>\n",
       "      <td>0.212650</td>\n",
       "      <td>-0.563130</td>\n",
       "      <td>0.650996</td>\n",
       "      <td>-0.241038</td>\n",
       "      <td>-0.403820</td>\n",
       "      <td>-0.062891</td>\n",
       "      <td>0.631145</td>\n",
       "      <td>-0.776782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CRIM</td>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.447920</td>\n",
       "      <td>0.065101</td>\n",
       "      <td>-0.084293</td>\n",
       "      <td>-0.386980</td>\n",
       "      <td>-0.428257</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.455621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZN</td>\n",
       "      <td>-0.103393</td>\n",
       "      <td>-0.123970</td>\n",
       "      <td>-0.218081</td>\n",
       "      <td>-0.129667</td>\n",
       "      <td>0.367456</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.412995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>INDUS</td>\n",
       "      <td>0.399439</td>\n",
       "      <td>0.434426</td>\n",
       "      <td>0.062702</td>\n",
       "      <td>-0.041093</td>\n",
       "      <td>-0.478908</td>\n",
       "      <td>-0.390414</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.603800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NOX</td>\n",
       "      <td>0.398736</td>\n",
       "      <td>0.429399</td>\n",
       "      <td>0.160871</td>\n",
       "      <td>-0.068600</td>\n",
       "      <td>-0.423380</td>\n",
       "      <td>-0.551992</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.590879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RM</td>\n",
       "      <td>-0.079971</td>\n",
       "      <td>-0.117334</td>\n",
       "      <td>-0.257110</td>\n",
       "      <td>-0.069317</td>\n",
       "      <td>0.692737</td>\n",
       "      <td>0.212650</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.613808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AGE</td>\n",
       "      <td>0.203784</td>\n",
       "      <td>0.235141</td>\n",
       "      <td>0.204739</td>\n",
       "      <td>0.079035</td>\n",
       "      <td>-0.374947</td>\n",
       "      <td>-0.563130</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.602339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DIS</td>\n",
       "      <td>-0.302211</td>\n",
       "      <td>-0.327280</td>\n",
       "      <td>-0.011243</td>\n",
       "      <td>-0.082981</td>\n",
       "      <td>0.243707</td>\n",
       "      <td>0.650996</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.496996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RAD</td>\n",
       "      <td>0.686002</td>\n",
       "      <td>0.724060</td>\n",
       "      <td>0.034067</td>\n",
       "      <td>-0.207013</td>\n",
       "      <td>-0.377489</td>\n",
       "      <td>-0.241038</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>0.488676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TAX</td>\n",
       "      <td>0.666626</td>\n",
       "      <td>0.707982</td>\n",
       "      <td>0.050663</td>\n",
       "      <td>-0.167718</td>\n",
       "      <td>-0.463498</td>\n",
       "      <td>-0.403820</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>0.543993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>0.291074</td>\n",
       "      <td>0.341111</td>\n",
       "      <td>0.312602</td>\n",
       "      <td>-0.004527</td>\n",
       "      <td>-0.511775</td>\n",
       "      <td>-0.062891</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>0.374044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B</td>\n",
       "      <td>-0.295041</td>\n",
       "      <td>-0.306517</td>\n",
       "      <td>-0.018300</td>\n",
       "      <td>0.105254</td>\n",
       "      <td>0.331752</td>\n",
       "      <td>0.631145</td>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.366087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LSTAT</td>\n",
       "      <td>0.258465</td>\n",
       "      <td>0.296600</td>\n",
       "      <td>0.195630</td>\n",
       "      <td>0.045660</td>\n",
       "      <td>-0.735080</td>\n",
       "      <td>-0.776782</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             OBS.     TOWN#       LON       LAT      MEDV     CMEDV      CRIM  \\\n",
       "OBS.     1.000000  0.992287 -0.048973 -0.651668 -0.214940  0.611685  0.407407   \n",
       "TOWN#    0.992287  1.000000 -0.000627 -0.631648 -0.257397  0.535115  0.447920   \n",
       "LON     -0.048973 -0.000627  1.000000  0.143054 -0.343840 -0.028841  0.065101   \n",
       "LAT     -0.651668 -0.631648  0.143054  1.000000 -0.002079 -0.009729 -0.084293   \n",
       "MEDV    -0.214940 -0.257397 -0.343840 -0.002079  1.000000  0.983990 -0.386980   \n",
       "CMEDV    0.611685  0.535115 -0.028841 -0.009729  0.983990  1.000000 -0.428257   \n",
       "CRIM     0.407407  0.447920  0.065101 -0.084293 -0.386980 -0.428257  1.000000   \n",
       "ZN      -0.103393 -0.123970 -0.218081 -0.129667  0.367456       NaN -0.200469   \n",
       "INDUS    0.399439  0.434426  0.062702 -0.041093 -0.478908 -0.390414  0.406583   \n",
       "NOX      0.398736  0.429399  0.160871 -0.068600 -0.423380 -0.551992  0.420972   \n",
       "RM      -0.079971 -0.117334 -0.257110 -0.069317  0.692737  0.212650 -0.219247   \n",
       "AGE      0.203784  0.235141  0.204739  0.079035 -0.374947 -0.563130  0.352734   \n",
       "DIS     -0.302211 -0.327280 -0.011243 -0.082981  0.243707  0.650996 -0.379670   \n",
       "RAD      0.686002  0.724060  0.034067 -0.207013 -0.377489 -0.241038  0.625505   \n",
       "TAX      0.666626  0.707982  0.050663 -0.167718 -0.463498 -0.403820  0.582764   \n",
       "PTRATIO  0.291074  0.341111  0.312602 -0.004527 -0.511775 -0.062891  0.289946   \n",
       "B       -0.295041 -0.306517 -0.018300  0.105254  0.331752  0.631145 -0.385064   \n",
       "LSTAT    0.258465  0.296600  0.195630  0.045660 -0.735080 -0.776782  0.455621   \n",
       "\n",
       "               ZN     INDUS       NOX        RM       AGE       DIS       RAD  \\\n",
       "OBS.    -0.103393  0.399439  0.398736 -0.079971  0.203784 -0.302211  0.686002   \n",
       "TOWN#   -0.123970  0.434426  0.429399 -0.117334  0.235141 -0.327280  0.724060   \n",
       "LON     -0.218081  0.062702  0.160871 -0.257110  0.204739 -0.011243  0.034067   \n",
       "LAT     -0.129667 -0.041093 -0.068600 -0.069317  0.079035 -0.082981 -0.207013   \n",
       "MEDV     0.367456 -0.478908 -0.423380  0.692737 -0.374947  0.243707 -0.377489   \n",
       "CMEDV         NaN -0.390414 -0.551992  0.212650 -0.563130  0.650996 -0.241038   \n",
       "CRIM    -0.200469  0.406583  0.420972 -0.219247  0.352734 -0.379670  0.625505   \n",
       "ZN       1.000000 -0.533828 -0.516604  0.311991 -0.569537  0.664408 -0.311948   \n",
       "INDUS   -0.533828  1.000000  0.763651 -0.391676  0.644779 -0.708027  0.595129   \n",
       "NOX     -0.516604  0.763651  1.000000 -0.302188  0.731470 -0.769230  0.611441   \n",
       "RM       0.311991 -0.391676 -0.302188  1.000000 -0.240265  0.205246 -0.209847   \n",
       "AGE     -0.569537  0.644779  0.731470 -0.240265  1.000000 -0.747881  0.456022   \n",
       "DIS      0.664408 -0.708027 -0.769230  0.205246 -0.747881  1.000000 -0.494588   \n",
       "RAD     -0.311948  0.595129  0.611441 -0.209847  0.456022 -0.494588  1.000000   \n",
       "TAX     -0.314563  0.720760  0.668023 -0.292048  0.506456 -0.534432  0.910228   \n",
       "PTRATIO -0.391679  0.383248  0.188933 -0.355501  0.261515 -0.232471  0.464741   \n",
       "B        0.175520 -0.356977 -0.380051  0.128069 -0.273534  0.291512 -0.444413   \n",
       "LSTAT   -0.412995  0.603800  0.590879 -0.613808  0.602339 -0.496996  0.488676   \n",
       "\n",
       "              TAX   PTRATIO         B     LSTAT  \n",
       "OBS.     0.666626  0.291074 -0.295041  0.258465  \n",
       "TOWN#    0.707982  0.341111 -0.306517  0.296600  \n",
       "LON      0.050663  0.312602 -0.018300  0.195630  \n",
       "LAT     -0.167718 -0.004527  0.105254  0.045660  \n",
       "MEDV    -0.463498 -0.511775  0.331752 -0.735080  \n",
       "CMEDV   -0.403820 -0.062891  0.631145 -0.776782  \n",
       "CRIM     0.582764  0.289946 -0.385064  0.455621  \n",
       "ZN      -0.314563 -0.391679  0.175520 -0.412995  \n",
       "INDUS    0.720760  0.383248 -0.356977  0.603800  \n",
       "NOX      0.668023  0.188933 -0.380051  0.590879  \n",
       "RM      -0.292048 -0.355501  0.128069 -0.613808  \n",
       "AGE      0.506456  0.261515 -0.273534  0.602339  \n",
       "DIS     -0.534432 -0.232471  0.291512 -0.496996  \n",
       "RAD      0.910228  0.464741 -0.444413  0.488676  \n",
       "TAX      1.000000  0.460853 -0.441808  0.543993  \n",
       "PTRATIO  0.460853  1.000000 -0.177383  0.374044  \n",
       "B       -0.441808 -0.177383  1.000000 -0.366087  \n",
       "LSTAT    0.543993  0.374044 -0.366087  1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column이 많아질 수록 데이터 분석이 어려움 ... 차원의 저주\n",
    "# column이 많다고 좋은 게 아님, 무작정 없앨 수는 없음 - 차원축소\n",
    "boston.corr()\n",
    "# 자기 자신과의 상관관계는 1이고 대각선 기준으로 대칭임\n",
    "# 의미 없는 값은 버림\n",
    "# 명목형 column은 존재하지 않음\n",
    "# scatter로 그리면 잘 보임\n",
    "# 낮을 수록 아무 관계가 없음\n",
    "# 반응 속도가 빨라야 하기 때문에 차원을 줄이면서도 성능은 좋게"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e086ee5e88>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상관관계 시각화\n",
    "# seaborn모듈 안 heatmap함수를 통해서 구현 가능\n",
    "# data : corr()함수를 통해 나온 결과\n",
    "# annot : default는 False / True로 설정하면 해당 필드에 값을 보여줌, 표기법\n",
    "# fmt : 값 소숫점 표현\n",
    "# cmap : 그라데이션 색깔 설정\n",
    "# Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds', 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu','GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn'\n",
    "# 'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic'\n",
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(data=boston.corr(), annot=False, cmap= 'Blues')\n",
    "\n",
    "# 특징 파악 - targeting, 시각화가 필요\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e087caa1c8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상관관계 시각화\n",
    "# seaborn모듈 안 heatmap함수를 통해서 구현 가능\n",
    "# data : corr()함수를 통해 나온 결과\n",
    "# annot : default는 False / True로 설정하면 해당 필드에 값을 보여줌, 표기법\n",
    "# fmt : 값 소숫점 표현\n",
    "# cmap : 그라데이션 색깔 설정\n",
    "# Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds', 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu','GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn'\n",
    "# 'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic'\n",
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(data=boston.corr(), fmt='.1f', annot=True, cmap= 'Blues')\n",
    "\n",
    "# 특징 파악 - targeting, 시각화가 필요"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 결측치 처리\n",
    "### 결측치란 해당 필드에 값이 존재하지 않은것을 의미\n",
    "### 1) 결측치의 숫자가 매우 적다면 이들을 삭제하고 분석\n",
    "### 2) 결측치의 숫자가 어느정도 존재하면 값을 대체"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBS.</th>\n",
       "      <th>TOWN</th>\n",
       "      <th>TOWN#</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>CMEDV</th>\n",
       "      <th>CRIM</th>\n",
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       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>index</td>\n",
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       "      <td>3</td>\n",
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       "      <td>19</td>\n",
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       "      <td>int64</td>\n",
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       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
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       "    <tr>\n",
       "      <td>null_value</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>418</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>null_value(%)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.988142</td>\n",
       "      <td>82.6087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "                OBS.    TOWN  TOWN#      LON      LAT      MEDV    CMEDV  \\\n",
       "index              0       1      2        3        4         5        6   \n",
       "type           int64  object  int64  float64  float64   float64  float64   \n",
       "null_value         0       0      0        0        0         5      418   \n",
       "null_value(%)      0       0      0        0        0  0.988142  82.6087   \n",
       "\n",
       "                  CRIM       ZN    INDUS     CHAS      NOX       RM      AGE  \\\n",
       "index                7        8        9       10       11       12       13   \n",
       "type           float64  float64  float64   object  float64  float64  float64   \n",
       "null_value           0        0        0        6        0        0        0   \n",
       "null_value(%)        0        0        0  1.18577        0        0        0   \n",
       "\n",
       "                   DIS    RAD    TAX  PTRATIO        B    LSTAT  \n",
       "index               14     15     16       17       18       19  \n",
       "type           float64  int64  int64  float64  float64  float64  \n",
       "null_value           0      0      0        0        0        0  \n",
       "null_value(%)        0      0      0        0        0        0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NA 확인\n",
    "na_dic={'index': [x for x in range(boston.shape[1])],\n",
    "       'type': boston.dtypes,\n",
    "       'null_value': boston.isnull().sum(),\n",
    "       'null_value(%)' : boston.isnull().sum()*100/boston.shape[0]}\n",
    "na = pd.DataFrame(na_dic).T\n",
    "na\n",
    "# null이 있는 column 3개\n",
    "# null 적을 때는 column 지우기 아까우니까 값을 채워줌\n",
    "# 결측치가 있으면 안들아감 - 없애거나 채우거나 : 임의로 판단해서 채우는 것이므로 좋은 것이 아님...\n",
    "# 418개를 지우면 데이터가 적으므로 분석하기 어려움, 특정 방향으로 쏠릴 수도\n",
    "# like 스미싱 문자... 데이터가 너무 없는 경우 : 클래스 불균형 - 반반이 나뉘어야 공평하게 분류 가능\n",
    "# 어쩔 수 없이 해야 하는데 데이터 개수가 너무 없는 경우 column을 지워야 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Swampscott</td>\n",
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       "      <td>21.6</td>\n",
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       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
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       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>T</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
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       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Marblehead</td>\n",
       "      <td>2</td>\n",
       "      <td>-70.928</td>\n",
       "      <td>42.2930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>T</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Marblehead</td>\n",
       "      <td>2</td>\n",
       "      <td>-70.922</td>\n",
       "      <td>42.2980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06905</td>\n",
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       "      <td>2.18</td>\n",
       "      <td>T</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBS.        TOWN  TOWN#     LON      LAT  MEDV     CRIM    ZN  INDUS CHAS  \\\n",
       "0     1      Nahant      0 -70.955  42.2550  24.0  0.00632  18.0   2.31    T   \n",
       "1     2  Swampscott      1 -70.950  42.2875  21.6  0.02731   0.0   7.07    T   \n",
       "2     3  Swampscott      1 -70.936  42.2830   NaN  0.02729   0.0   7.07    T   \n",
       "3     4  Marblehead      2 -70.928  42.2930   NaN  0.03237   0.0   2.18    T   \n",
       "4     5  Marblehead      2 -70.922  42.2980   NaN  0.06905   0.0   2.18    T   \n",
       "\n",
       "     NOX     RM   AGE     DIS  RAD  TAX  PTRATIO       B  LSTAT  \n",
       "0  0.538  6.575  65.2  4.0900    1  296     15.3  396.90   4.98  \n",
       "1  0.469  6.421  78.9  4.9671    2  242     17.8  396.90   9.14  \n",
       "2  0.469  7.185  61.1  4.9671    2  242     17.8  392.83   4.03  \n",
       "3  0.458  6.998  45.8  6.0622    3  222     18.7  394.63   2.94  \n",
       "4  0.458  7.147  54.2  6.0622    3  222     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CMEDV column은 제거\n",
    "boston.drop(['CMEDV'], axis = 1, inplace = True)\n",
    "boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MEDV는 중위 값으로 대체\n",
    "# 대체도 항상 근거가 있어야 함\n",
    "# 0 0 1 2 3 100 NA : 평균에서 너무 벗어남\n",
    "# 0 0 1 1 2 2 NA : 중위값 해도 ㄱㅊ\n",
    "temp = boston.MEDV.describe()['50%']\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#boston.fillna : boston에 있는 모든 값을 채워줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Marblehead</td>\n",
       "      <td>2</td>\n",
       "      <td>-70.928</td>\n",
       "      <td>42.2930</td>\n",
       "      <td>21.1</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2</td>\n",
       "      <td>-70.922</td>\n",
       "      <td>42.2980</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>T</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBS.        TOWN  TOWN#     LON      LAT  MEDV     CRIM    ZN  INDUS CHAS  \\\n",
       "0     1      Nahant      0 -70.955  42.2550  24.0  0.00632  18.0   2.31    T   \n",
       "1     2  Swampscott      1 -70.950  42.2875  21.6  0.02731   0.0   7.07    T   \n",
       "2     3  Swampscott      1 -70.936  42.2830  21.1  0.02729   0.0   7.07    T   \n",
       "3     4  Marblehead      2 -70.928  42.2930  21.1  0.03237   0.0   2.18    T   \n",
       "4     5  Marblehead      2 -70.922  42.2980  21.1  0.06905   0.0   2.18    T   \n",
       "\n",
       "     NOX     RM   AGE     DIS  RAD  TAX  PTRATIO       B  LSTAT  \n",
       "0  0.538  6.575  65.2  4.0900    1  296     15.3  396.90   4.98  \n",
       "1  0.469  6.421  78.9  4.9671    2  242     17.8  396.90   9.14  \n",
       "2  0.469  7.185  61.1  4.9671    2  242     17.8  392.83   4.03  \n",
       "3  0.458  6.998  45.8  6.0622    3  222     18.7  394.63   2.94  \n",
       "4  0.458  7.147  54.2  6.0622    3  222     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston['MEDV'].fillna(temp, inplace = True)\n",
    "boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CHAS 6개 값을 제거\n",
    "# notnull / dropna\n",
    "boston = boston[boston.CHAS.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>OBS.</th>\n",
       "      <th>TOWN</th>\n",
       "      <th>TOWN#</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>index</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>type</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>object</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>null_value</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>null_value(%)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                OBS.    TOWN  TOWN#      LON      LAT     MEDV     CRIM  \\\n",
       "index              0       1      2        3        4        5        6   \n",
       "type           int64  object  int64  float64  float64  float64  float64   \n",
       "null_value         0       0      0        0        0        0        0   \n",
       "null_value(%)      0       0      0        0        0        0        0   \n",
       "\n",
       "                    ZN    INDUS    CHAS      NOX       RM      AGE      DIS  \\\n",
       "index                7        8       9       10       11       12       13   \n",
       "type           float64  float64  object  float64  float64  float64  float64   \n",
       "null_value           0        0       0        0        0        0        0   \n",
       "null_value(%)        0        0       0        0        0        0        0   \n",
       "\n",
       "                 RAD    TAX  PTRATIO        B    LSTAT  \n",
       "index             14     15       16       17       18  \n",
       "type           int64  int64  float64  float64  float64  \n",
       "null_value         0      0        0        0        0  \n",
       "null_value(%)      0      0        0        0        0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NA 재확인\n",
    "na_dic={'index': [x for x in range(boston.shape[1])],\n",
    "       'type': boston.dtypes,\n",
    "       'null_value': boston.isnull().sum(),\n",
    "       'null_value(%)' : boston.isnull().sum()*100/boston.shape[0]}\n",
    "na = pd.DataFrame(na_dic).T\n",
    "na\n",
    "\n",
    "# na 값이 모두 사라짐, 예측을 해서 채우는 것과는 결과가 다름"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 이상치 처리\n",
    "### * 이상치란 기존 데이터들로부터 동떨어진 데이터\n",
    "### * 측정오류나 잘못된 입력으로 인해 이상치가 포함될 수 있음\n",
    "### * 이상치가 포함된 데이터로 만든 모델은 정확한 결과를 낼 수 없으며 신뢰도가 떨어짐\n",
    "### * 주로 boxplot을 통해서 판단"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/boxplot.PNG\" style=\"width:600px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e084748bc8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# boxplot 시각화\n",
    "# Q 4분위수 (25~75%) - 선이 medium\n",
    "# IQR = Q3-Q1\n",
    "# 이상치 범위 Q3+-1.5*IQR\n",
    "# outlier가 붙으면 신뢰도가 떨어짐\n",
    "# 가중치를 적게 주든지 해서...\n",
    "sns.catplot(x= 'CHAS', y='MEDV', kind = 'box', data= boston)\n",
    "# 강에 인접한가 : T\n",
    "# outlier라고 보기 어려움 - 지우기보다는 해석 쪽으로"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>OBS.</th>\n",
       "      <th>TOWN</th>\n",
       "      <th>TOWN#</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Nahant</td>\n",
       "      <td>0</td>\n",
       "      <td>-70.955</td>\n",
       "      <td>42.2550</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>T</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Swampscott</td>\n",
       "      <td>1</td>\n",
       "      <td>-70.950</td>\n",
       "      <td>42.2875</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>T</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Swampscott</td>\n",
       "      <td>1</td>\n",
       "      <td>-70.936</td>\n",
       "      <td>42.2830</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>T</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Marblehead</td>\n",
       "      <td>2</td>\n",
       "      <td>-70.928</td>\n",
       "      <td>42.2930</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>T</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Marblehead</td>\n",
       "      <td>2</td>\n",
       "      <td>-70.922</td>\n",
       "      <td>42.2980</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>T</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBS.        TOWN  TOWN#     LON      LAT  MEDV     CRIM    ZN  INDUS CHAS  \\\n",
       "0     1      Nahant      0 -70.955  42.2550  24.0  0.00632  18.0   2.31    T   \n",
       "1     2  Swampscott      1 -70.950  42.2875  21.6  0.02731   0.0   7.07    T   \n",
       "2     3  Swampscott      1 -70.936  42.2830  21.1  0.02729   0.0   7.07    T   \n",
       "3     4  Marblehead      2 -70.928  42.2930  21.1  0.03237   0.0   2.18    T   \n",
       "4     5  Marblehead      2 -70.922  42.2980  21.1  0.06905   0.0   2.18    T   \n",
       "\n",
       "     NOX     RM   AGE     DIS  RAD  TAX  PTRATIO       B  LSTAT  \n",
       "0  0.538  6.575  65.2  4.0900    1  296     15.3  396.90   4.98  \n",
       "1  0.469  6.421  78.9  4.9671    2  242     17.8  396.90   9.14  \n",
       "2  0.469  7.185  61.1  4.9671    2  242     17.8  392.83   4.03  \n",
       "3  0.458  6.998  45.8  6.0622    3  222     18.7  394.63   2.94  \n",
       "4  0.458  7.147  54.2  6.0622    3  222     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBS.</th>\n",
       "      <th>TOWN</th>\n",
       "      <th>TOWN#</th>\n",
       "      <th>LON</th>\n",
       "      <th>LAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Nahant</td>\n",
       "      <td>0</td>\n",
       "      <td>-70.9550</td>\n",
       "      <td>42.2550</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>6.575</td>\n",
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       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Swampscott</td>\n",
       "      <td>1</td>\n",
       "      <td>-70.9500</td>\n",
       "      <td>42.2875</td>\n",
       "      <td>21.6</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>T</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Swampscott</td>\n",
       "      <td>1</td>\n",
       "      <td>-70.9360</td>\n",
       "      <td>42.2830</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>T</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Marblehead</td>\n",
       "      <td>2</td>\n",
       "      <td>-70.9280</td>\n",
       "      <td>42.2930</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>T</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Marblehead</td>\n",
       "      <td>2</td>\n",
       "      <td>-70.9220</td>\n",
       "      <td>42.2980</td>\n",
       "      <td>21.1</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>T</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>501</td>\n",
       "      <td>502</td>\n",
       "      <td>Winthrop</td>\n",
       "      <td>91</td>\n",
       "      <td>-70.9860</td>\n",
       "      <td>42.2312</td>\n",
       "      <td>22.4</td>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>T</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>502</td>\n",
       "      <td>503</td>\n",
       "      <td>Winthrop</td>\n",
       "      <td>91</td>\n",
       "      <td>-70.9910</td>\n",
       "      <td>42.2275</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>T</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>503</td>\n",
       "      <td>504</td>\n",
       "      <td>Winthrop</td>\n",
       "      <td>91</td>\n",
       "      <td>-70.9948</td>\n",
       "      <td>42.2260</td>\n",
       "      <td>23.9</td>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>T</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>504</td>\n",
       "      <td>505</td>\n",
       "      <td>Winthrop</td>\n",
       "      <td>91</td>\n",
       "      <td>-70.9875</td>\n",
       "      <td>42.2240</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>T</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>505</td>\n",
       "      <td>506</td>\n",
       "      <td>Winthrop</td>\n",
       "      <td>91</td>\n",
       "      <td>-70.9825</td>\n",
       "      <td>42.2210</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>T</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>384 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     OBS.        TOWN  TOWN#      LON      LAT  MEDV     CRIM    ZN  INDUS  \\\n",
       "0       1      Nahant      0 -70.9550  42.2550  24.0  0.00632  18.0   2.31   \n",
       "1       2  Swampscott      1 -70.9500  42.2875  21.6  0.02731   0.0   7.07   \n",
       "2       3  Swampscott      1 -70.9360  42.2830  21.1  0.02729   0.0   7.07   \n",
       "3       4  Marblehead      2 -70.9280  42.2930  21.1  0.03237   0.0   2.18   \n",
       "4       5  Marblehead      2 -70.9220  42.2980  21.1  0.06905   0.0   2.18   \n",
       "..    ...         ...    ...      ...      ...   ...      ...   ...    ...   \n",
       "501   502    Winthrop     91 -70.9860  42.2312  22.4  0.06263   0.0  11.93   \n",
       "502   503    Winthrop     91 -70.9910  42.2275  20.6  0.04527   0.0  11.93   \n",
       "503   504    Winthrop     91 -70.9948  42.2260  23.9  0.06076   0.0  11.93   \n",
       "504   505    Winthrop     91 -70.9875  42.2240  22.0  0.10959   0.0  11.93   \n",
       "505   506    Winthrop     91 -70.9825  42.2210  11.9  0.04741   0.0  11.93   \n",
       "\n",
       "    CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO       B  LSTAT  \n",
       "0      T  0.538  6.575  65.2  4.0900    1  296     15.3  396.90   4.98  \n",
       "1      T  0.469  6.421  78.9  4.9671    2  242     17.8  396.90   9.14  \n",
       "2      T  0.469  7.185  61.1  4.9671    2  242     17.8  392.83   4.03  \n",
       "3      T  0.458  6.998  45.8  6.0622    3  222     18.7  394.63   2.94  \n",
       "4      T  0.458  7.147  54.2  6.0622    3  222     18.7  396.90   5.33  \n",
       "..   ...    ...    ...   ...     ...  ...  ...      ...     ...    ...  \n",
       "501    T  0.573  6.593  69.1  2.4786    1  273     21.0  391.99   9.67  \n",
       "502    T  0.573  6.120  76.7  2.2875    1  273     21.0  396.90   9.08  \n",
       "503    T  0.573  6.976  91.0  2.1675    1  273     21.0  396.90   5.64  \n",
       "504    T  0.573  6.794  89.3  2.3889    1  273     21.0  393.45   6.48  \n",
       "505    T  0.573  6.030  80.8  2.5050    1  273     21.0  396.90   7.88  \n",
       "\n",
       "[384 rows x 19 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결측치 제거\n",
    "temp = boston[boston.CHAS=='T'].describe()['MEDV']['75%']\n",
    "temp2 = boston[boston.CHAS=='T'] \n",
    "temp3 = temp2[ temp2.MEDV > temp ]\n",
    "boston[~boston.index.isin(temp3.index)]\n",
    "# false면 지워야 함"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 표준화/정규화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"img/표준화.정규화.PNG\" style=\"width:600px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NOX</th>\n",
       "      <th>AGE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>65.2</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>501</td>\n",
       "      <td>0.573</td>\n",
       "      <td>69.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>502</td>\n",
       "      <td>0.573</td>\n",
       "      <td>76.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>503</td>\n",
       "      <td>0.573</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>504</td>\n",
       "      <td>0.573</td>\n",
       "      <td>89.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>505</td>\n",
       "      <td>0.573</td>\n",
       "      <td>80.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NOX   AGE\n",
       "0    0.538  65.2\n",
       "1    0.469  78.9\n",
       "2    0.469  61.1\n",
       "3    0.458  45.8\n",
       "4    0.458  54.2\n",
       "..     ...   ...\n",
       "501  0.573  69.1\n",
       "502  0.573  76.7\n",
       "503  0.573  91.0\n",
       "504  0.573  89.3\n",
       "505  0.573  80.8\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NOX, AGE column 불러오기 \n",
    "# 표준화는 평균1, 분산1로 변환, \n",
    "# 정규화는 최소0, 최대1로 변환\n",
    "# 성능의 차이가 커서 거의 해줘야 함\n",
    "boston3=boston[['NOX', \"AGE\"]]\n",
    "boston3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>-0.126987</td>\n",
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       "    <tr>\n",
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       "      <td>-0.819230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-0.838070</td>\n",
       "      <td>-0.519496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>495</td>\n",
       "      <td>0.151911</td>\n",
       "      <td>0.012175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>496</td>\n",
       "      <td>0.151911</td>\n",
       "      <td>0.283363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>497</td>\n",
       "      <td>0.151911</td>\n",
       "      <td>0.793624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>498</td>\n",
       "      <td>0.151911</td>\n",
       "      <td>0.732964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>499</td>\n",
       "      <td>0.151911</td>\n",
       "      <td>0.429662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          NOX       AGE\n",
       "0   -0.149387 -0.126987\n",
       "1   -0.743376  0.361865\n",
       "2   -0.743376 -0.273286\n",
       "3   -0.838070 -0.819230\n",
       "4   -0.838070 -0.519496\n",
       "..        ...       ...\n",
       "495  0.151911  0.012175\n",
       "496  0.151911  0.283363\n",
       "497  0.151911  0.793624\n",
       "498  0.151911  0.732964\n",
       "499  0.151911  0.429662\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard Scaler 불러오기\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# scaler 생성\n",
    "standard_scaler = StandardScaler()\n",
    "# scaler 학습\n",
    "standard_scaler.fit(boston3)\n",
    "\n",
    "# scaler 적용\n",
    "boston_scaled = standard_scaler.transform(boston3)\n",
    "boston_scaled # numpy array 형태로 나옴\n",
    "# 최종 데이터프레임 만들기\n",
    "pd.DataFrame(boston_scaled, columns=boston3.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>NOX</th>\n",
       "      <th>AGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.314815</td>\n",
       "      <td>0.641607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.782698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.172840</td>\n",
       "      <td>0.599382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.441813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.150206</td>\n",
       "      <td>0.528321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>495</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.681771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>496</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.760041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>497</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.907312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>498</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.889804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>499</td>\n",
       "      <td>0.386831</td>\n",
       "      <td>0.802266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          NOX       AGE\n",
       "0    0.314815  0.641607\n",
       "1    0.172840  0.782698\n",
       "2    0.172840  0.599382\n",
       "3    0.150206  0.441813\n",
       "4    0.150206  0.528321\n",
       "..        ...       ...\n",
       "495  0.386831  0.681771\n",
       "496  0.386831  0.760041\n",
       "497  0.386831  0.907312\n",
       "498  0.386831  0.889804\n",
       "499  0.386831  0.802266\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Min Max Scaler 불러오기\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# scaler 생성\n",
    "MinMax_scaler = MinMaxScaler()\n",
    "# scaler 학습\n",
    "MinMax_scaler.fit(boston3)\n",
    "# sclaer 적용\n",
    "boston_scaled2 = MinMax_scaler.transform(boston3)\n",
    "# 최종 데이터프레임 만들기\n",
    "pd.DataFrame(boston_scaled2, columns=boston3.columns)\n",
    "# - 라는 것은 평균보다 작다는 것"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 새로운 feature 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 통신 txt 불러오기\n",
    "data = pd.read_csv(\"./input/통신.txt\", sep=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>index</th>\n",
       "      <th>ID</th>\n",
       "      <th>주민번호</th>\n",
       "      <th>직종</th>\n",
       "      <th>가입통신사</th>\n",
       "      <th>통화료</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2009SP001</td>\n",
       "      <td>720521-1******</td>\n",
       "      <td>컨설팅/솔루션</td>\n",
       "      <td>KTF</td>\n",
       "      <td>30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2009SP002</td>\n",
       "      <td>800310-2******</td>\n",
       "      <td>금융</td>\n",
       "      <td>SK 텔레콤</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2009SP003</td>\n",
       "      <td>810626-2******</td>\n",
       "      <td>금융</td>\n",
       "      <td>SK 텔레콤</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2009SP004</td>\n",
       "      <td>781007-1******</td>\n",
       "      <td>기타</td>\n",
       "      <td>SK 텔레콤</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2009SP005</td>\n",
       "      <td>771117-2******</td>\n",
       "      <td>기타</td>\n",
       "      <td>LG 텔레콤</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index         ID            주민번호                        직종  \\\n",
       "0      1  2009SP001  720521-1******      컨설팅/솔루션                \n",
       "1      2  2009SP002  800310-2******  금융                         \n",
       "2      3  2009SP003  810626-2******  금융                         \n",
       "3      4  2009SP004  781007-1******  기타                         \n",
       "4      5  2009SP005  771117-2******  기타                         \n",
       "\n",
       "             가입통신사    통화료  \n",
       "0  KTF              30000  \n",
       "1     SK 텔레콤        50000  \n",
       "2     SK 텔레콤        23000  \n",
       "3     SK 텔레콤        50000  \n",
       "4     LG 텔레콤        80000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 실습"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. '성별'이라는 column 추가하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gender']=['M' if x[7]=='1' else 'F' for x in data['주민번호'] ]\n",
    "# for 문 자제 .. 데이터가 많을 수록 시간이 너무 오래걸림\n",
    "# 딕셔너리로 담아도 좋음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = {'1' : 'M', '2' : 'F', '3' : 'M', '4' : 'F'}\n",
    "[temp[x[7]] for x in data['주민번호']]\n",
    "# 3, 4도 처리 가능, 두 줄, if else는 한 줄이면 됨"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 가입통신사 띄어쓰기 지우기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SK 텔레콤             51\n",
       "KTF                25\n",
       "LG 텔레콤             10\n",
       "K T F               3\n",
       "LG  텔레콤             2\n",
       "SK  텔레콤             2\n",
       "S K 텔 레 콤           2\n",
       "L G 텔 레 콤           1\n",
       "SK 텔레 콤             1\n",
       "L G 텔레콤             1\n",
       "SK 텔 레콤             1\n",
       " SK 텔레콤             1\n",
       "Name: 가입통신사, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['가입통신사'].value_counts() # 같은 건데 다른 것으로 판단... 띄어쓰기를 없애야 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['가입통신사'] = [x.replace(\" \", \"\") for x in data['가입통신사']]\n",
    "# 코딩실력이 좋아야 데이터 전처리도 가능 ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>index</th>\n",
       "      <th>ID</th>\n",
       "      <th>주민번호</th>\n",
       "      <th>직종</th>\n",
       "      <th>가입통신사</th>\n",
       "      <th>통화료</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2009SP001</td>\n",
       "      <td>720521-1******</td>\n",
       "      <td>컨설팅/솔루션</td>\n",
       "      <td>KTF</td>\n",
       "      <td>30000</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2009SP002</td>\n",
       "      <td>800310-2******</td>\n",
       "      <td>금융</td>\n",
       "      <td>SK텔레콤</td>\n",
       "      <td>50000</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2009SP003</td>\n",
       "      <td>810626-2******</td>\n",
       "      <td>금융</td>\n",
       "      <td>SK텔레콤</td>\n",
       "      <td>23000</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2009SP004</td>\n",
       "      <td>781007-1******</td>\n",
       "      <td>기타</td>\n",
       "      <td>SK텔레콤</td>\n",
       "      <td>50000</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2009SP005</td>\n",
       "      <td>771117-2******</td>\n",
       "      <td>기타</td>\n",
       "      <td>LG텔레콤</td>\n",
       "      <td>80000</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index         ID            주민번호                        직종  가입통신사    통화료  \\\n",
       "0      1  2009SP001  720521-1******      컨설팅/솔루션                 KTF  30000   \n",
       "1      2  2009SP002  800310-2******  금융                        SK텔레콤  50000   \n",
       "2      3  2009SP003  810626-2******  금융                        SK텔레콤  23000   \n",
       "3      4  2009SP004  781007-1******  기타                        SK텔레콤  50000   \n",
       "4      5  2009SP005  771117-2******  기타                        LG텔레콤  80000   \n",
       "\n",
       "  gender  \n",
       "0      M  \n",
       "1      F  \n",
       "2      F  \n",
       "3      M  \n",
       "4      F  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 통신사별 평균요금 column 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "가입통신사\n",
       "KTF      35821.428571\n",
       "LG텔레콤    40071.428571\n",
       "SK텔레콤    43827.586207\n",
       "Name: 통화료, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = data.groupby('가입통신사')['통화료'].mean()\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data['cost by mean'] = pd.merge(data, temp, how='inner', on = '가입통신사', sort=False)\n",
    "# 딕셔너리로 넣어도 됨\n",
    "# left : 왼쪽 인자는 다 보임, right : 오른쪽 인자는 다 보임, outer : 둘 다 보여주고 없는 것은 null로 표현, inner : 둘이 똑같을 때\n",
    "# left_on, right_on\n",
    "# 겹치는 컬럼 명은 바꿔줌"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp2 = pd.merge(data, temp, how='inner', on = '가입통신사', sort=False)\n",
    "temp2.rename({'통화료_x' : '통화료', '통화료_y' : 'temp', inplace = True})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>통화료</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>가입통신사</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>KTF</td>\n",
       "      <td>35821.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>LG텔레콤</td>\n",
       "      <td>40071.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SK텔레콤</td>\n",
       "      <td>43827.586207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                통화료\n",
       "가입통신사              \n",
       "KTF    35821.428571\n",
       "LG텔레콤  40071.428571\n",
       "SK텔레콤  43827.586207"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = pd.DataFrame(data.groupby('가입통신사')['통화료'].mean())\n",
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>index</th>\n",
       "      <th>ID</th>\n",
       "      <th>주민번호</th>\n",
       "      <th>직종</th>\n",
       "      <th>가입통신사</th>\n",
       "      <th>통화료</th>\n",
       "      <th>gender</th>\n",
       "      <th>cost by mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2009SP001</td>\n",
       "      <td>720521-1******</td>\n",
       "      <td>컨설팅/솔루션</td>\n",
       "      <td>KTF</td>\n",
       "      <td>30000</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2009SP002</td>\n",
       "      <td>800310-2******</td>\n",
       "      <td>금융</td>\n",
       "      <td>SK텔레콤</td>\n",
       "      <td>50000</td>\n",
       "      <td>F</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2009SP003</td>\n",
       "      <td>810626-2******</td>\n",
       "      <td>금융</td>\n",
       "      <td>SK텔레콤</td>\n",
       "      <td>23000</td>\n",
       "      <td>F</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2009SP004</td>\n",
       "      <td>781007-1******</td>\n",
       "      <td>기타</td>\n",
       "      <td>SK텔레콤</td>\n",
       "      <td>50000</td>\n",
       "      <td>M</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2009SP005</td>\n",
       "      <td>771117-2******</td>\n",
       "      <td>기타</td>\n",
       "      <td>LG텔레콤</td>\n",
       "      <td>80000</td>\n",
       "      <td>F</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index         ID            주민번호                        직종  가입통신사    통화료  \\\n",
       "0      1  2009SP001  720521-1******      컨설팅/솔루션                 KTF  30000   \n",
       "1      2  2009SP002  800310-2******  금융                        SK텔레콤  50000   \n",
       "2      3  2009SP003  810626-2******  금융                        SK텔레콤  23000   \n",
       "3      4  2009SP004  781007-1******  기타                        SK텔레콤  50000   \n",
       "4      5  2009SP005  771117-2******  기타                        LG텔레콤  80000   \n",
       "\n",
       "  gender cost by mean  \n",
       "0      M            1  \n",
       "1      F           19  \n",
       "2      F           20  \n",
       "3      M           22  \n",
       "4      F           23  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  }
 ],
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