preprocessor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e3aba8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#먼저 필요한 패키지를 가져옴.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faf92902",
   "metadata": {},
   "source": [
    "## 전복데이터(붓꽃, 출생율, 보스턴 집값에 이어서)\n",
    "전복=중성으로 있다가 암수가 결정됨 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa48b53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Sex',\n",
       " 'Length',\n",
       " 'Diameter',\n",
       " 'Height',\n",
       " 'Whole weight',\n",
       " 'Shucked weight',\n",
       " 'Viscera weight',\n",
       " 'Shell weight',\n",
       " 'Rings']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abalone_columns=list() #파이썬에서 리스트 타입의 저장공간 생성\n",
    "for l in open('AI school/4.Data 0411-/abalone_attributes.txt'):\n",
    "    abalone_columns.append(l.strip())\n",
    "\n",
    "abalone_columns # 볼러온 데이터 출력 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26bb4b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('AI school/4.Data 0411-/abalone.txt', header=None, names=abalone_columns)\n",
    "#pd.read_csv('읽어올 텍스트경로',header=없음,name=abalone_columns<- 위에 만든 아발론 컬럼을 여기에 불러옴으로 설정)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca50d46f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.1550</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4172</th>\n",
       "      <td>F</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.8870</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2390</td>\n",
       "      <td>0.2490</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>M</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.9660</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2605</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>M</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.1760</td>\n",
       "      <td>0.5255</td>\n",
       "      <td>0.2875</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>F</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1.0945</td>\n",
       "      <td>0.5310</td>\n",
       "      <td>0.2610</td>\n",
       "      <td>0.2960</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>M</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.195</td>\n",
       "      <td>1.9485</td>\n",
       "      <td>0.9455</td>\n",
       "      <td>0.3765</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4177 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
       "0      M   0.455     0.365   0.095        0.5140          0.2245   \n",
       "1      M   0.350     0.265   0.090        0.2255          0.0995   \n",
       "2      F   0.530     0.420   0.135        0.6770          0.2565   \n",
       "3      M   0.440     0.365   0.125        0.5160          0.2155   \n",
       "4      I   0.330     0.255   0.080        0.2050          0.0895   \n",
       "...   ..     ...       ...     ...           ...             ...   \n",
       "4172   F   0.565     0.450   0.165        0.8870          0.3700   \n",
       "4173   M   0.590     0.440   0.135        0.9660          0.4390   \n",
       "4174   M   0.600     0.475   0.205        1.1760          0.5255   \n",
       "4175   F   0.625     0.485   0.150        1.0945          0.5310   \n",
       "4176   M   0.710     0.555   0.195        1.9485          0.9455   \n",
       "\n",
       "      Viscera weight  Shell weight  Rings  \n",
       "0             0.1010        0.1500     15  \n",
       "1             0.0485        0.0700      7  \n",
       "2             0.1415        0.2100      9  \n",
       "3             0.1140        0.1550     10  \n",
       "4             0.0395        0.0550      7  \n",
       "...              ...           ...    ...  \n",
       "4172          0.2390        0.2490     11  \n",
       "4173          0.2145        0.2605     10  \n",
       "4174          0.2875        0.3080      9  \n",
       "4175          0.2610        0.2960     10  \n",
       "4176          0.3765        0.4950     12  \n",
       "\n",
       "[4177 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89203e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4177, 9)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #습관적으로 데이터 모양을 확인 해주는 게 좋음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "28936872",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = data['Sex'] #설명 추가 11:10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "841baed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
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       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
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       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings  \n",
       "0         0.150     15  \n",
       "1         0.070      7  \n",
       "2         0.210      9  \n",
       "3         0.155     10  \n",
       "4         0.055      7  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8601d78a",
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9784ce42",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings  \n",
       "0         0.150     15  \n",
       "1         0.070      7  \n",
       "2         0.210      9  \n",
       "3         0.155     10  \n",
       "4         0.055      7  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() #데이터에서 성별 컬럼 삭제해서 가져옴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d321974",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.523992</td>\n",
       "      <td>0.407881</td>\n",
       "      <td>0.139516</td>\n",
       "      <td>0.828742</td>\n",
       "      <td>0.359367</td>\n",
       "      <td>0.180594</td>\n",
       "      <td>0.238831</td>\n",
       "      <td>9.933684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.120093</td>\n",
       "      <td>0.099240</td>\n",
       "      <td>0.041827</td>\n",
       "      <td>0.490389</td>\n",
       "      <td>0.221963</td>\n",
       "      <td>0.109614</td>\n",
       "      <td>0.139203</td>\n",
       "      <td>3.224169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.441500</td>\n",
       "      <td>0.186000</td>\n",
       "      <td>0.093500</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.545000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.799500</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.234000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.615000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>1.153000</td>\n",
       "      <td>0.502000</td>\n",
       "      <td>0.253000</td>\n",
       "      <td>0.329000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.815000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>2.825500</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Length     Diameter       Height  Whole weight  Shucked weight  \\\n",
       "count  4177.000000  4177.000000  4177.000000   4177.000000     4177.000000   \n",
       "mean      0.523992     0.407881     0.139516      0.828742        0.359367   \n",
       "std       0.120093     0.099240     0.041827      0.490389        0.221963   \n",
       "min       0.075000     0.055000     0.000000      0.002000        0.001000   \n",
       "25%       0.450000     0.350000     0.115000      0.441500        0.186000   \n",
       "50%       0.545000     0.425000     0.140000      0.799500        0.336000   \n",
       "75%       0.615000     0.480000     0.165000      1.153000        0.502000   \n",
       "max       0.815000     0.650000     1.130000      2.825500        1.488000   \n",
       "\n",
       "       Viscera weight  Shell weight        Rings  \n",
       "count     4177.000000   4177.000000  4177.000000  \n",
       "mean         0.180594      0.238831     9.933684  \n",
       "std          0.109614      0.139203     3.224169  \n",
       "min          0.000500      0.001500     1.000000  \n",
       "25%          0.093500      0.130000     8.000000  \n",
       "50%          0.171000      0.234000     9.000000  \n",
       "75%          0.253000      0.329000    11.000000  \n",
       "max          0.760000      1.005000    29.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe() #각 변수별 기초통계량 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "48681d03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4177 entries, 0 to 4176\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Length          4177 non-null   float64\n",
      " 1   Diameter        4177 non-null   float64\n",
      " 2   Height          4177 non-null   float64\n",
      " 3   Whole weight    4177 non-null   float64\n",
      " 4   Shucked weight  4177 non-null   float64\n",
      " 5   Viscera weight  4177 non-null   float64\n",
      " 6   Shell weight    4177 non-null   float64\n",
      " 7   Rings           4177 non-null   int64  \n",
      "dtypes: float64(7), int64(1)\n",
      "memory usage: 261.2 KB\n"
     ]
    }
   ],
   "source": [
    "#전체데이터 형태 불러오기\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "abc72b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=(data-np.min(data))/(np.max(data)-np.min(data)) #min-max 수식을 코딩함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c87c7881",
   "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>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.521008</td>\n",
       "      <td>0.084071</td>\n",
       "      <td>0.181335</td>\n",
       "      <td>0.150303</td>\n",
       "      <td>0.132324</td>\n",
       "      <td>0.147982</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.371622</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.079646</td>\n",
       "      <td>0.079157</td>\n",
       "      <td>0.066241</td>\n",
       "      <td>0.063199</td>\n",
       "      <td>0.068261</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.614865</td>\n",
       "      <td>0.613445</td>\n",
       "      <td>0.119469</td>\n",
       "      <td>0.239065</td>\n",
       "      <td>0.171822</td>\n",
       "      <td>0.185648</td>\n",
       "      <td>0.207773</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493243</td>\n",
       "      <td>0.521008</td>\n",
       "      <td>0.110619</td>\n",
       "      <td>0.182044</td>\n",
       "      <td>0.144250</td>\n",
       "      <td>0.149440</td>\n",
       "      <td>0.152965</td>\n",
       "      <td>0.321429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.344595</td>\n",
       "      <td>0.336134</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>0.071897</td>\n",
       "      <td>0.059516</td>\n",
       "      <td>0.051350</td>\n",
       "      <td>0.053313</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Length  Diameter    Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0  0.513514  0.521008  0.084071      0.181335        0.150303        0.132324   \n",
       "1  0.371622  0.352941  0.079646      0.079157        0.066241        0.063199   \n",
       "2  0.614865  0.613445  0.119469      0.239065        0.171822        0.185648   \n",
       "3  0.493243  0.521008  0.110619      0.182044        0.144250        0.149440   \n",
       "4  0.344595  0.336134  0.070796      0.071897        0.059516        0.051350   \n",
       "\n",
       "   Shell weight     Rings  \n",
       "0      0.147982  0.500000  \n",
       "1      0.068261  0.214286  \n",
       "2      0.207773  0.285714  \n",
       "3      0.152965  0.321429  \n",
       "4      0.053313  0.214286  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() #위 수식 계산확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bddbb9ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler #위 수식을 사이킷런 패키지에서 가져옴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98c0d7a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "mMscaler=MinMaxScaler() #이름 축약시킴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bc9e5b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mMscaler.fit(data) #fit=학습적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "683bb717",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mMscaled_data=mMscaler.transform(data) #transform학습한형태로 데이터 전환\n",
    "mMscaled_data=mMscaler.fit_transform(data) #위의 핏, 트랜스폼작업 한줄로 합친것"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5732826d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.51351351, 0.5210084 , 0.0840708 , ..., 0.1323239 , 0.14798206,\n",
       "        0.5       ],\n",
       "       [0.37162162, 0.35294118, 0.07964602, ..., 0.06319947, 0.06826109,\n",
       "        0.21428571],\n",
       "       [0.61486486, 0.61344538, 0.11946903, ..., 0.18564845, 0.2077728 ,\n",
       "        0.28571429],\n",
       "       ...,\n",
       "       [0.70945946, 0.70588235, 0.18141593, ..., 0.37788018, 0.30543099,\n",
       "        0.28571429],\n",
       "       [0.74324324, 0.72268908, 0.13274336, ..., 0.34298881, 0.29347285,\n",
       "        0.32142857],\n",
       "       [0.85810811, 0.84033613, 0.17256637, ..., 0.49506254, 0.49177877,\n",
       "        0.39285714]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mMscaled_data #fit하면 판다스->넘파이 배열로 변경됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b9cf7553",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a74a6657",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sdscaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dedd3d70",
   "metadata": {},
   "outputs": [],
   "source": [
    "sdscaled_data = sdscaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4f3a3a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.57455813, -0.43214879, -1.06442415, ..., -0.72621157,\n",
       "        -0.63821689,  1.57154357],\n",
       "       [-1.44898585, -1.439929  , -1.18397831, ..., -1.20522124,\n",
       "        -1.21298732, -0.91001299],\n",
       "       [ 0.05003309,  0.12213032, -0.10799087, ..., -0.35668983,\n",
       "        -0.20713907, -0.28962385],\n",
       "       ...,\n",
       "       [ 0.6329849 ,  0.67640943,  1.56576738, ...,  0.97541324,\n",
       "         0.49695471, -0.28962385],\n",
       "       [ 0.84118198,  0.77718745,  0.25067161, ...,  0.73362741,\n",
       "         0.41073914,  0.02057072],\n",
       "       [ 1.54905203,  1.48263359,  1.32665906, ...,  1.78744868,\n",
       "         1.84048058,  0.64095986]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sdscaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fe92be26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting imbalanced-learn\n",
      "  Using cached imbalanced_learn-0.9.0-py3-none-any.whl (199 kB)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from imbalanced-learn) (2.2.0)\n",
      "Requirement already satisfied: scikit-learn>=1.0.1 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.0.2)\n",
      "Requirement already satisfied: numpy>=1.14.6 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.20.3)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.1.0)\n",
      "Requirement already satisfied: scipy>=1.1.0 in c:\\users\\admin\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.7.1)\n",
      "Installing collected packages: imbalanced-learn\n",
      "Successfully installed imbalanced-learn-0.9.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "648c60af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "16f49043",
   "metadata": {},
   "outputs": [],
   "source": [
    "ros = RandomOverSampler()\n",
    "rus = RandomUnderSampler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bd4aa78a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4177, 8)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5c04faeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "oversampled_data, oversampled_label = ros.fit_resample(data, label)\n",
    "oversampled_data = pd.DataFrame(oversampled_data, columns=data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "eeca2ad9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4584, 8)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oversampled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0d8c592c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.521008</td>\n",
       "      <td>0.084071</td>\n",
       "      <td>0.181335</td>\n",
       "      <td>0.150303</td>\n",
       "      <td>0.132324</td>\n",
       "      <td>0.147982</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.371622</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.079646</td>\n",
       "      <td>0.079157</td>\n",
       "      <td>0.066241</td>\n",
       "      <td>0.063199</td>\n",
       "      <td>0.068261</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.614865</td>\n",
       "      <td>0.613445</td>\n",
       "      <td>0.119469</td>\n",
       "      <td>0.239065</td>\n",
       "      <td>0.171822</td>\n",
       "      <td>0.185648</td>\n",
       "      <td>0.207773</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.493243</td>\n",
       "      <td>0.521008</td>\n",
       "      <td>0.110619</td>\n",
       "      <td>0.182044</td>\n",
       "      <td>0.144250</td>\n",
       "      <td>0.149440</td>\n",
       "      <td>0.152965</td>\n",
       "      <td>0.321429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.344595</td>\n",
       "      <td>0.336134</td>\n",
       "      <td>0.070796</td>\n",
       "      <td>0.071897</td>\n",
       "      <td>0.059516</td>\n",
       "      <td>0.051350</td>\n",
       "      <td>0.053313</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4579</th>\n",
       "      <td>0.493243</td>\n",
       "      <td>0.487395</td>\n",
       "      <td>0.115044</td>\n",
       "      <td>0.158491</td>\n",
       "      <td>0.139879</td>\n",
       "      <td>0.109282</td>\n",
       "      <td>0.132038</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4580</th>\n",
       "      <td>0.527027</td>\n",
       "      <td>0.537815</td>\n",
       "      <td>0.106195</td>\n",
       "      <td>0.166106</td>\n",
       "      <td>0.148621</td>\n",
       "      <td>0.156024</td>\n",
       "      <td>0.138017</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4581</th>\n",
       "      <td>0.628378</td>\n",
       "      <td>0.563025</td>\n",
       "      <td>0.110619</td>\n",
       "      <td>0.220825</td>\n",
       "      <td>0.169132</td>\n",
       "      <td>0.207373</td>\n",
       "      <td>0.187843</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4582</th>\n",
       "      <td>0.371622</td>\n",
       "      <td>0.327731</td>\n",
       "      <td>0.061947</td>\n",
       "      <td>0.056136</td>\n",
       "      <td>0.047411</td>\n",
       "      <td>0.043450</td>\n",
       "      <td>0.044345</td>\n",
       "      <td>0.178571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4583</th>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.638655</td>\n",
       "      <td>0.097345</td>\n",
       "      <td>0.150345</td>\n",
       "      <td>0.130464</td>\n",
       "      <td>0.117841</td>\n",
       "      <td>0.118585</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4584 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Length  Diameter    Height  Whole weight  Shucked weight  \\\n",
       "0     0.513514  0.521008  0.084071      0.181335        0.150303   \n",
       "1     0.371622  0.352941  0.079646      0.079157        0.066241   \n",
       "2     0.614865  0.613445  0.119469      0.239065        0.171822   \n",
       "3     0.493243  0.521008  0.110619      0.182044        0.144250   \n",
       "4     0.344595  0.336134  0.070796      0.071897        0.059516   \n",
       "...        ...       ...       ...           ...             ...   \n",
       "4579  0.493243  0.487395  0.115044      0.158491        0.139879   \n",
       "4580  0.527027  0.537815  0.106195      0.166106        0.148621   \n",
       "4581  0.628378  0.563025  0.110619      0.220825        0.169132   \n",
       "4582  0.371622  0.327731  0.061947      0.056136        0.047411   \n",
       "4583  0.513514  0.638655  0.097345      0.150345        0.130464   \n",
       "\n",
       "      Viscera weight  Shell weight     Rings  \n",
       "0           0.132324      0.147982  0.500000  \n",
       "1           0.063199      0.068261  0.214286  \n",
       "2           0.185648      0.207773  0.285714  \n",
       "3           0.149440      0.152965  0.321429  \n",
       "4           0.051350      0.053313  0.214286  \n",
       "...              ...           ...       ...  \n",
       "4579        0.109282      0.132038  0.178571  \n",
       "4580        0.156024      0.138017  0.285714  \n",
       "4581        0.207373      0.187843  0.250000  \n",
       "4582        0.043450      0.044345  0.178571  \n",
       "4583        0.117841      0.118585  0.250000  \n",
       "\n",
       "[4584 rows x 8 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oversampled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0aac8893",
   "metadata": {},
   "outputs": [],
   "source": [
    "undersampled_data,undersampled_label=rus.fit_resample(data,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7653834d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3921, 8)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undersampled_data.shape"
   ]
  },
  {
   "cell_type": "raw",
   "id": "192881fd",
   "metadata": {},
   "source": [
    "## SMOTE \n",
    "\n",
    "랜덤으로 데이터수치가 나타나는 문제를 해결 할 수 있는 방법은 없을까? 에서 개발됨\n",
    "수가 적은 클래"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4cc46070",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "data, label = make_classification(\n",
    "    n_samples=1000,\n",
    "    n_classes=3,\n",
    "    n_features=2,\n",
    "    n_repeated=0,\n",
    "    n_informative=2,\n",
    "    n_redundant=0,\n",
    "    n_clusters_per_class=1,\n",
    "    weights=[0.05,0.15,0.8],\n",
    "    class_sep=0.8,\n",
    "    random_state=2022\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "90673010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.91117507,  0.90864868],\n",
       "       [ 0.23908554, -1.9966176 ],\n",
       "       [ 0.41063734, -1.09651695],\n",
       "       ...,\n",
       "       [-0.74729639, -1.06505046],\n",
       "       [ 0.78391513, -1.6661851 ],\n",
       "       [-0.04871935,  2.48764856]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e274abc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2281cac9d00>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:,0], data[:,1],c=label,linewidth=1,edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "88ab3e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE(k_neighbors=5)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f1178cc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "smoted_data, smoted_label = smote.fit_resample(data, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8b6674a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 2)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "79fd0c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2382, 2)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smoted_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fde9a4b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "원본 데이터의 클래스 비율 \n",
      "0     54\n",
      "1    152\n",
      "2    794\n",
      "dtype: int64\n",
      "\n",
      "SMOTE 결과 \n",
      "0    794\n",
      "1    794\n",
      "2    794\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('원본 데이터의 클래스 비율 \\n{}'.format(pd.get_dummies(label).sum()))\n",
    "print('\\nSMOTE 결과 \\n{}'.format(pd.get_dummies(smoted_label).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9e97feab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2281daf4a90>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(smoted_data[:,0], smoted_data[:,1],\n",
    "            c=smoted_label, linewidth=1, edgecolor='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d8b9f55",
   "metadata": {},
   "source": [
    "## Dimension Reduction \n",
    "차원의 축소"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e7c7e50c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "11455e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "digits=load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "825d4d47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _digits_dataset:\n",
      "\n",
      "Optical recognition of handwritten digits dataset\n",
      "--------------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 1797\n",
      "    :Number of Attributes: 64\n",
      "    :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n",
      "    :Missing Attribute Values: None\n",
      "    :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n",
      "    :Date: July; 1998\n",
      "\n",
      "This is a copy of the test set of the UCI ML hand-written digits datasets\n",
      "https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n",
      "\n",
      "The data set contains images of hand-written digits: 10 classes where\n",
      "each class refers to a digit.\n",
      "\n",
      "Preprocessing programs made available by NIST were used to extract\n",
      "normalized bitmaps of handwritten digits from a preprinted form. From a\n",
      "total of 43 people, 30 contributed to the training set and different 13\n",
      "to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n",
      "4x4 and the number of on pixels are counted in each block. This generates\n",
      "an input matrix of 8x8 where each element is an integer in the range\n",
      "0..16. This reduces dimensionality and gives invariance to small\n",
      "distortions.\n",
      "\n",
      "For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\n",
      "T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\n",
      "L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n",
      "1994.\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n",
      "    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n",
      "    Graduate Studies in Science and Engineering, Bogazici University.\n",
      "  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n",
      "  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n",
      "    Linear dimensionalityreduction using relevance weighted LDA. School of\n",
      "    Electrical and Electronic Engineering Nanyang Technological University.\n",
      "    2005.\n",
      "  - Claudio Gentile. A New Approximate Maximal Margin Classification\n",
      "    Algorithm. NIPS. 2000.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(digits.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6681c127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3a50cf1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=digits.data\n",
    "label=digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3afd747d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label:0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data[0].reshape(8,8) #64로 1차원이 되었던 데이터를 8*8로 재조립\n",
    "plt.imshow(data[0].reshape(8,8))\n",
    "print('Label:{}'.format(label[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ef62c85c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3ec4716a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
       "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
       "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
       "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
       "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c8f18c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x,y 2개로 차원 대폭축소. 이름은 뉴데이터로 지정\n",
    "new_data=pca.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "76aae5ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #기존데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "2d04ac9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.shape #축소데이터 (64->2로 줄어듬)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c49634fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.2594666 , 21.27488263])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "529876a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2281bc6b220>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(new_data[:,0],new_data[:,1],c=label) #c=원래 지정한 라벨색상을 출력해라"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "7acec085",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('AI school/4.Data 0411-/abalone.txt', header=None, names=abalone_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "488878ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = data['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ef8428a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    M\n",
       "1    M\n",
       "2    F\n",
       "3    M\n",
       "4    I\n",
       "Name: Sex, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ceda0d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1e828bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2],\n",
       "       [2],\n",
       "       [0],\n",
       "       ...,\n",
       "       [2],\n",
       "       [0],\n",
       "       [2]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoded_label = le.fit_transform(label)\n",
    "label_encoded_label.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ed11e241",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.DataFrame(data = \n",
    "                      np.concatenate([label.values.reshape((-1,1)), \n",
    "                      label_encoded_label.reshape((-1, 1))], axis=1), \n",
    "                      columns=['label', 'label_encoded']) #컬러헤더지정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "e9d710cb",
   "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>label</th>\n",
       "      <th>label_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4172</th>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4177 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     label label_encoded\n",
       "0        M             2\n",
       "1        M             2\n",
       "2        F             0\n",
       "3        M             2\n",
       "4        I             1\n",
       "...    ...           ...\n",
       "4172     F             0\n",
       "4173     M             2\n",
       "4174     M             2\n",
       "4175     F             0\n",
       "4176     M             2\n",
       "\n",
       "[4177 rows x 2 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e3a906b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from  sklearn.preprocessing import OneHotEncoder\n",
    "ohe=OneHotEncoder(sparse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "d573163a",
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoded = ohe.fit_transform(label.values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e080d3b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " ...\n",
      " [0. 0. 1.]\n",
      " [1. 0. 0.]\n",
      " [0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "print(one_hot_encoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcdd8fc6",
   "metadata": {},
   "source": [
    "## clustering\n",
    "in unsupervised learning (비지도학습=자율학습)\n",
    "1) k-means clustering : 가장가까운 중심값을 찾아 중심점에 해당하는 클러스트를 할당. 장점:학습시키지 않아도 데이터파악 단점:제멋대로 파악\n",
    " 맨하탄 디스턴스:수직으로만 이동/euclidean distance=최단거리로만 이동\n",
    "2) hierarchical clustering: 거리 혹은 유사도 기반으로 클러스트 형성. 토너먼트처럼 점층적으로 진행되어서 hierarchical.\n",
    "어떻게 가장가까운 클러스터를 찾나?\n",
    "single linkage: 가장가까운점사이 거리\n",
    "complete link: \n",
    "average link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "71593ed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# k-means clustering \n",
    "from sklearn.datasets import load_wine\n",
    "wine=load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "3bb16aa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _wine_dataset:\n",
      "\n",
      "Wine recognition dataset\n",
      "------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 178 (50 in each of three classes)\n",
      "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
      "    :Attribute Information:\n",
      " \t\t- Alcohol\n",
      " \t\t- Malic acid\n",
      " \t\t- Ash\n",
      "\t\t- Alcalinity of ash  \n",
      " \t\t- Magnesium\n",
      "\t\t- Total phenols\n",
      " \t\t- Flavanoids\n",
      " \t\t- Nonflavanoid phenols\n",
      " \t\t- Proanthocyanins\n",
      "\t\t- Color intensity\n",
      " \t\t- Hue\n",
      " \t\t- OD280/OD315 of diluted wines\n",
      " \t\t- Proline\n",
      "\n",
      "    - class:\n",
      "            - class_0\n",
      "            - class_1\n",
      "            - class_2\n",
      "\t\t\n",
      "    :Summary Statistics:\n",
      "    \n",
      "    ============================= ==== ===== ======= =====\n",
      "                                   Min   Max   Mean     SD\n",
      "    ============================= ==== ===== ======= =====\n",
      "    Alcohol:                      11.0  14.8    13.0   0.8\n",
      "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
      "    Ash:                          1.36  3.23    2.36  0.27\n",
      "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
      "    Magnesium:                    70.0 162.0    99.7  14.3\n",
      "    Total Phenols:                0.98  3.88    2.29  0.63\n",
      "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
      "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
      "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
      "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
      "    Hue:                          0.48  1.71    0.96  0.23\n",
      "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
      "    Proline:                       278  1680     746   315\n",
      "    ============================= ==== ===== ======= =====\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
      "    :Creator: R.A. Fisher\n",
      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      "    :Date: July, 1988\n",
      "\n",
      "This is a copy of UCI ML Wine recognition datasets.\n",
      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
      "\n",
      "The data is the results of a chemical analysis of wines grown in the same\n",
      "region in Italy by three different cultivators. There are thirteen different\n",
      "measurements taken for different constituents found in the three types of\n",
      "wine.\n",
      "\n",
      "Original Owners: \n",
      "\n",
      "Forina, M. et al, PARVUS - \n",
      "An Extendible Package for Data Exploration, Classification and Correlation. \n",
      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
      "\n",
      "Citation:\n",
      "\n",
      "Lichman, M. (2013). UCI Machine Learning Repository\n",
      "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
      "School of Information and Computer Science. \n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  Comparison of Classifiers in High Dimensional Settings, \n",
      "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Technometrics). \n",
      "\n",
      "  The data was used with many others for comparing various \n",
      "  classifiers. The classes are separable, though only RDA \n",
      "  has achieved 100% correct classification. \n",
      "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
      "  (All results using the leave-one-out technique) \n",
      "\n",
      "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
      "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
      "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
      "  (Also submitted to Journal of Chemometrics).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(wine.DESCR) #사이킷런데이터에 속성에는 descript (설명)이 붙어있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a9c588e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=wine.data\n",
    "label=wine.target\n",
    "columns=wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "24da017f",
   "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>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  \n",
       "0                          3.92   1065.0  \n",
       "1                          3.40   1050.0  \n",
       "2                          3.17   1185.0  \n",
       "3                          3.45   1480.0  \n",
       "4                          2.93    735.0  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.DataFrame(data,columns=columns)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "5bbab37c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "faf37b0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#와인데이터의 편차가 심하므로 min max scaling해줌\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler()\n",
    "data=scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "2cc640db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.84210526, 0.1916996 , 0.57219251, ..., 0.45528455, 0.97069597,\n",
       "        0.56134094],\n",
       "       [0.57105263, 0.2055336 , 0.4171123 , ..., 0.46341463, 0.78021978,\n",
       "        0.55064194],\n",
       "       [0.56052632, 0.3201581 , 0.70053476, ..., 0.44715447, 0.6959707 ,\n",
       "        0.64693295],\n",
       "       ...,\n",
       "       [0.58947368, 0.69960474, 0.48128342, ..., 0.08943089, 0.10622711,\n",
       "        0.39728959],\n",
       "       [0.56315789, 0.36561265, 0.54010695, ..., 0.09756098, 0.12820513,\n",
       "        0.40085592],\n",
       "       [0.81578947, 0.66403162, 0.73796791, ..., 0.10569106, 0.12087912,\n",
       "        0.20114123]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "c2e9dede",
   "metadata": {},
   "outputs": [],
   "source": [
    "#속성 13개나 되기때문에 차원을 줄이고자 PCA알고리즘 사용\n",
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(n_components=2)\n",
    "data=pca.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1d32b3d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 2)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "34c7bc6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans=KMeans(n_clusters=3) #군집의 개수를 지정해서 쪼개보라고 명령내려야함."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "488be671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "fa300d6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster=kmeans.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "6fa87688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2281efcfeb0>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:,0],data[:,1],c=cluster,linewidth=1, edgecolor='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a46be7a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}