Boston_Housing_Price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d00cc9c2",
   "metadata": {},
   "source": [
    "# 1. 연구목적 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6a9821",
   "metadata": {},
   "source": [
    "### - 어떤 데이터를 target으로 지정했을 때 정확도가 높은가?\n",
    "\n",
    "==> 타겟 지정을 달리했을때의 훈련 점수와, 테스트 점수를 비교하여 \n",
    "\n",
    "정확도가 높은 모델을 선택한다. \n",
    "\n",
    "=> 상관관계에 따라 보스턴시 주택 데이터에 어떤 영향을 미치는지 탐색한다.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90bb3a90",
   "metadata": {},
   "source": [
    "# 2. 연구 데이터"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24258c8b",
   "metadata": {},
   "source": [
    "### 보스턴 시의 주택 가격 데이터\n",
    "\n",
    "0 CRIM     town별 1인당 범죄율 \n",
    "\n",
    "1 ZN       25,000평방피트를 초과하는 거주지역의 비율\n",
    "\n",
    "2 INDUS    비소매상업지역이 점유하고 있는 토지의 비율\n",
    "\n",
    "3 CHAS     찰스강에 대한 더미변수(강의 경계에 위치한 경우는 1, 아니면 0)\n",
    "\n",
    "4 NOX      10ppm당 농축 일산화질소\n",
    "\n",
    "5 RM       주택 1가구당 평균 방의 개수\n",
    "\n",
    "6 AGE      1940년 이전에 건축된 소유주택의 비율\n",
    "\n",
    "7 DIS      5개의 보스턴 직업센터까지의 접근성 지수\n",
    "\n",
    "8 RAD      방사형 도로까지의 접근성 지수\n",
    "\n",
    "9 TAX      10,000달러 당 재산세율\n",
    "\n",
    "10 PTRATIO  town별 학생/교사 비율\n",
    "\n",
    "11 B        town별 흑인 비율\n",
    "\n",
    "12 LSTAT    모집단의 하위계층의 비율(%)\n",
    "\n",
    "13 MEDV     본인 소유의 주택 가격(중앙값, 단위:$1,000)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06f9d223",
   "metadata": {},
   "source": [
    "## 3. 분석 내용 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bda15f7",
   "metadata": {},
   "source": [
    "## 1) 데이터 탐색 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb04ccea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "16dc8c47",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>0.538</td>\n",
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       "      <td>9.14</td>\n",
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       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
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       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</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>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
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       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
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       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
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       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
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       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
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       "      <td>6.48</td>\n",
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       "      <td>80.8</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
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       "<p>506 rows × 14 columns</p>\n",
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      ],
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       "          0     1      2   3      4      5     6       7   8      9     10  \\\n",
       "0    0.00632  18.0   2.31   0  0.538  6.575  65.2  4.0900   1  296.0  15.3   \n",
       "1    0.02731   0.0   7.07   0  0.469  6.421  78.9  4.9671   2  242.0  17.8   \n",
       "2    0.02729   0.0   7.07   0  0.469  7.185  61.1  4.9671   2  242.0  17.8   \n",
       "3    0.03237   0.0   2.18   0  0.458  6.998  45.8  6.0622   3  222.0  18.7   \n",
       "4    0.06905   0.0   2.18   0  0.458  7.147  54.2  6.0622   3  222.0  18.7   \n",
       "..       ...   ...    ...  ..    ...    ...   ...     ...  ..    ...   ...   \n",
       "501  0.06263   0.0  11.93   0  0.573  6.593  69.1  2.4786   1  273.0  21.0   \n",
       "502  0.04527   0.0  11.93   0  0.573  6.120  76.7  2.2875   1  273.0  21.0   \n",
       "503  0.06076   0.0  11.93   0  0.573  6.976  91.0  2.1675   1  273.0  21.0   \n",
       "504  0.10959   0.0  11.93   0  0.573  6.794  89.3  2.3889   1  273.0  21.0   \n",
       "505  0.04741   0.0  11.93   0  0.573  6.030  80.8  2.5050   1  273.0  21.0   \n",
       "\n",
       "         11    12    13  \n",
       "0    396.90  4.98  24.0  \n",
       "1    396.90  9.14  21.6  \n",
       "2    392.83  4.03  34.7  \n",
       "3    394.63  2.94  33.4  \n",
       "4    396.90  5.33  36.2  \n",
       "..      ...   ...   ...  \n",
       "501  391.99  9.67  22.4  \n",
       "502  396.90  9.08  20.6  \n",
       "503  396.90  5.64  23.9  \n",
       "504  393.45  6.48  22.0  \n",
       "505  396.90  7.88  11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"./dataset/housing.csv\", delim_whitespace=True, header=None)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7af9bca4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       506 non-null    float64\n",
      " 1   1       506 non-null    float64\n",
      " 2   2       506 non-null    float64\n",
      " 3   3       506 non-null    int64  \n",
      " 4   4       506 non-null    float64\n",
      " 5   5       506 non-null    float64\n",
      " 6   6       506 non-null    float64\n",
      " 7   7       506 non-null    float64\n",
      " 8   8       506 non-null    int64  \n",
      " 9   9       506 non-null    float64\n",
      " 10  10      506 non-null    float64\n",
      " 11  11      506 non-null    float64\n",
      " 12  12      506 non-null    float64\n",
      " 13  13      506 non-null    float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f6b48b14",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "colormap=plt.cm.gist_heat\n",
    "plt.figure(figsize=(12,12))\n",
    "sns.heatmap(df.corr(),linewidths=0.1,vmax=0.5,cmap=colormap,linecolor='white',annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e12be892",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "colormap=plt.cm.gist_heat\n",
    "plt.figure(figsize=(12,12))\n",
    "d=df.corr()\n",
    "d[d<0.7]=0\n",
    "sns.heatmap(d,linewidths=0.1,cmap=colormap,linecolor='white',annot=True)\n",
    "plt.show()\n",
    "\n",
    "#10,000달러당 재산세율(9)과 상관관계가 높은 것: 방사형 도로까지의 접근성 지수(8, 0.91), 비소매상업지역이 점유하고 있는 토지의 비율(2, 0.72), 10ppm당 농축 일산화질소(4, 0.67) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b10257c1",
   "metadata": {},
   "source": [
    "### 1. 상관관계가 높은 09(만달러당 재산세율)을 target으로 둔 경우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e41393fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=df.values\n",
    "\n",
    "X=dataset[:,[0,1,2,3,4,5,6,7,8,10,11,12,13]]\n",
    "Y=dataset[:,9]\n",
    "\n",
    "train_input, test_input, train_target, test_target=train_test_split(X,Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ad978310",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss=StandardScaler()\n",
    "ss.fit(train_input)\n",
    "train_scaled=ss.transform(train_input)\n",
    "test_scaled=ss.transform(test_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "727a4b31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8714183507360396\n",
      "0.857275340407696\n"
     ]
    }
   ],
   "source": [
    "# KNR \n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knr=KNeighborsRegressor(n_neighbors=20)\n",
    "knr.fit(train_scaled, train_target)\n",
    "print(knr.score(train_scaled, train_target))\n",
    "print(knr.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "92ea4aff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8899596728304764\n",
      "0.8875804174371742\n"
     ]
    }
   ],
   "source": [
    "# 선형회귀 (GOOD!!)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr=LinearRegression()\n",
    "lr.fit(train_scaled, train_target)\n",
    "print(lr.score(train_scaled, train_target))\n",
    "print(lr.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb69af9",
   "metadata": {},
   "source": [
    " - 훈련 score 테스트 score 차이가 크지 않다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f10bca71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -2.21972888  25.06703242  42.513667    -4.88511153   8.32533391\n",
      "  -1.56783496  -0.65372006  -9.40561025 123.0860905    2.86942958\n",
      "  -0.55029102  -7.84165588 -13.89859882] 404.03217821782164\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_, lr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d8b95997",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8972527373179969\n",
      "0.9008326759620569\n"
     ]
    }
   ],
   "source": [
    "#다항회귀\n",
    "train_poly=np.column_stack((train_input**3, train_input))\n",
    "test_poly=np.column_stack((test_input**3, test_input))\n",
    "lr=LinearRegression()\n",
    "lr.fit(train_poly, train_target)\n",
    "print(lr.score(train_poly, train_target))\n",
    "print(lr.score(test_poly, test_target))\n",
    "# 정확도가 높아짐"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5016bc2e",
   "metadata": {},
   "source": [
    "- 3차 다항회귀를 통해 훈련 점수와 테스트 점수가 상승되었다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "306f49a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 다중회귀\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly=PolynomialFeatures(degree=2, include_bias=False)\n",
    "poly.fit(train_scaled)\n",
    "train_poly=poly.transform(train_scaled)\n",
    "test_poly=poly.transform(test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "ed5ad622",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9623208876613644\n",
      "0.941845239708462\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr=LinearRegression()\n",
    "lr.fit(train_poly, train_target)\n",
    "print(lr.score(train_poly, train_target))\n",
    "print(lr.score(test_poly, test_target))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1395d368",
   "metadata": {},
   "source": [
    "- 특성이 늘어나면서 선형회귀의 학습 능력 UP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e163fdf1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8891275028083886\n",
      "0.8878771574426152\n"
     ]
    }
   ],
   "source": [
    "# 릿지 \n",
    "from sklearn.linear_model import Ridge\n",
    "ridge=Ridge(alpha=10)\n",
    "ridge.fit(train_scaled, train_target)\n",
    "print(ridge.score(train_scaled, train_target))\n",
    "print(ridge.score(test_scaled, test_target))\n",
    "# 오차가 거의 없음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a22427ab",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "train_score = []\n",
    "test_score = []\n",
    "\n",
    "alpha_list = [0.001, 0.01, 0.1, 1, 10, 100] # 0.001에서 100까지 10배씩 모델 훈련 \n",
    "for alpha in alpha_list:\n",
    "    ridge = Ridge(alpha=alpha) # 릿지 모델 생성 \n",
    "    ridge.fit(train_scaled, train_target) # 훈련 \n",
    "    train_score.append(ridge.score(train_scaled, train_target))# 점수 저장\n",
    "    test_score.append(ridge.score(test_scaled, test_target))\n",
    "    \n",
    "plt.plot(np.log10(alpha_list), train_score)\n",
    "plt.plot(np.log10(alpha_list), test_score)\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"R^2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc548b6a",
   "metadata": {},
   "source": [
    "- 과적합이 아니기 때문에 오차가 거의 없다/  좁혀지는 지점 = 과소적합 \n",
    "- alpha의 최적값은 10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a66fe866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8899596715319477\n",
      "0.8875845716433675\n"
     ]
    }
   ],
   "source": [
    "# 라쏘\n",
    "from sklearn.linear_model import Lasso\n",
    "lasso=Lasso(alpha=0.001)\n",
    "lasso.fit(train_scaled, train_target)\n",
    "print(lasso.score(train_scaled, train_target))\n",
    "print(lasso.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9dc5363c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "train_score = []\n",
    "test_score = []\n",
    "\n",
    "alpha_list = [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "for alpha in alpha_list:\n",
    "    lasso = Lasso(alpha=alpha, max_iter=10000)\n",
    "    lasso.fit(train_scaled, train_target)\n",
    "    train_score.append(lasso.score(train_scaled, train_target))\n",
    "    test_score.append(lasso.score(test_scaled, test_target))\n",
    "\n",
    "plt.plot(np.log10(alpha_list), train_score)\n",
    "plt.plot(np.log10(alpha_list), test_score)\n",
    "plt.xlabel(\"alpha\")\n",
    "plt.ylabel(\"R^2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99496a4a",
   "metadata": {},
   "source": [
    "- alpha의 최적값은 0.001 or 0.1 or 1\n",
    "- 위의 score가 과적합이 아니기 때문에 차이가 별로 없다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "4e4e3f22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7698019801980198\n",
      "0.5588235294117647\n"
     ]
    }
   ],
   "source": [
    "# SGDClassifier (손실함수)\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sc2= SGDClassifier(loss='log',max_iter=100,random_state=42)\n",
    "sc2.fit(train_scaled,train_target)\n",
    "print(sc2.score(train_scaled, train_target))\n",
    "print(sc2.score(test_scaled, test_target))\n",
    "\n",
    "# 훈련세트와 테스트 세트의 정확도가 낮다 100번으로 부족 \n",
    "\n",
    "# 변화무쌍하다, 돌릴때마다, 에러가났다, 레포트거리!! 이유 \n",
    "# 경향을 띄기 때문에  50점에서 iter=80일때와 90일때 점수가 크게 차이가 나지 않는 이유는 지그재그로 변화무쌍하기 떄문에 큰 차이가 나지 않았다.\n",
    "# 그래서 에포크 50회가 가장 적당한 훈련수 임은 알수 있다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "4f30638d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8366336633663366\n",
      "0.6764705882352942\n"
     ]
    }
   ],
   "source": [
    "sc2.partial_fit(train_scaled,train_target)\n",
    "print(sc2.score(train_scaled,train_target))\n",
    "print(sc2.score(test_scaled,test_target))\n",
    "# 에포크 실행하니 정확도 조금 향상. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "1d70b9ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 에포크와 과대/과소 적합 \n",
    "\n",
    "sc3 = SGDClassifier(loss='log',random_state=42)\n",
    "train_score=[]\n",
    "test_score=[]\n",
    "classes = np.unique(train_target)\n",
    "\n",
    "# 300번 에포크 동안 훈련\n",
    "for _ in range(0,300):\n",
    "    sc3.partial_fit(train_scaled,train_target,classes=classes)\n",
    "    train_score.append(sc3.score(train_scaled,train_target))\n",
    "    test_score.append(sc3.score(test_scaled,test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "564d37a1",
   "metadata": {},
   "outputs": [
    {
     "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.plot(train_score)\n",
    "plt.plot(test_score)\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "733704f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8910891089108911\n",
      "0.6764705882352942\n"
     ]
    }
   ],
   "source": [
    "sc3 = SGDClassifier(loss='log',max_iter=100,tol=None,random_state=42)\n",
    "sc3.fit(train_scaled,train_target)\n",
    "print(sc3.score(train_scaled,train_target))\n",
    "print(sc3.score(test_scaled,test_target))\n",
    "\n",
    "# 더이상 높아지지 않는 지점이 50회 이상부터는 test가 높아지지 않는다 \n",
    "# 에포크 50회가 가장 적당한 훈련 횟수이다. 최적의 모델=50회 (조기종료)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "772a2bd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.9608540130358032\n"
     ]
    }
   ],
   "source": [
    "# 의사결정회귀\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt=DecisionTreeRegressor(random_state=42)\n",
    "dt.fit(train_scaled, train_target)\n",
    "print(dt.score(train_scaled, train_target))\n",
    "print(dt.score(test_scaled, test_target))\n",
    "#과대적합 (훈련good)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "a5e3487b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.9608540130358032\n"
     ]
    }
   ],
   "source": [
    "# 그리드서치\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "params={'min_impurity_decrease':[0.0001, 0.0001, 0.0003, 0.0004, 0.0005]}\n",
    "gs=GridSearchCV(DecisionTreeRegressor(random_state=42), params, n_jobs=-1)\n",
    "gs.fit(train_scaled, train_target)\n",
    "dt=gs.best_estimator_\n",
    "print(dt.score(train_scaled, train_target))\n",
    "print(dt.score(test_scaled, test_target))\n",
    "#과대적합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "8f4046ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor(min_impurity_decrease=0.0001, random_state=42)\n"
     ]
    }
   ],
   "source": [
    "print(gs.best_estimator_)\n",
    "\n",
    "# 최고의 estimator는 0.0001 일때 이고 랜덤이 42일때이다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "5436c36e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.94316297 0.94316297 0.94316297 0.94316297 0.94316297]\n"
     ]
    }
   ],
   "source": [
    "print(gs.cv_results_['mean_test_score'])\n",
    "\n",
    "# 위를 바탕으로 그 때의 점수의 평균이 이렇게 나왔다. 각각 테스트 스코아의 평균이 이렇게 나왔다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "85a5d7d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_impurity_decrease': 0.0001}\n"
     ]
    }
   ],
   "source": [
    "best_index=np.argmax(gs.cv_results_['mean_test_score'])\n",
    "print(gs.cv_results_['params'][best_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b63d559e",
   "metadata": {},
   "outputs": [],
   "source": [
    "params={'min_impurity_decrease':np.arange(0.0001, 0.001, 0.0001), 'max_depth':range(5,20,1), 'min_samples_split':range(2, 100, 10)}\n",
    "\n",
    "# 이 간격으로 찾았을때의 지점이 / 최적의 파라미터를 찾아서 대입한 것 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "d67656a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=DecisionTreeRegressor(random_state=42), n_jobs=-1,\n",
       "             param_grid={'max_depth': range(5, 20),\n",
       "                         'min_impurity_decrease': array([0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008,\n",
       "       0.0009]),\n",
       "                         'min_samples_split': range(2, 100, 10)})"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs=GridSearchCV(DecisionTreeRegressor(random_state=42), params, n_jobs=-1)\n",
    "gs.fit(train_scaled, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "cafbd55d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 7, 'min_impurity_decrease': 0.0001, 'min_samples_split': 12}\n"
     ]
    }
   ],
   "source": [
    "print(gs.best_params_)\n",
    "\n",
    "# 최고의 파라미터는 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "85d24e8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9538819759419443\n"
     ]
    }
   ],
   "source": [
    "print(np.max(gs.cv_results_['mean_test_score']))\n",
    "\n",
    "# 그떄의 점수 => 그래서 이게 그리드 서치에서의 제일 좋은 점수 \n",
    "# 하이퍼파라미터 탐색을 자동화, \n",
    "# 가장 좋은 검증점수 매개변수를 조합해서 최종 모델훈련 \n",
    "# 제일 좋은 매개변수 조합으로 최종 점수! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "3e604d67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9948273254398832 0.9654717156268917\n"
     ]
    }
   ],
   "source": [
    "# 랜덤 포레스트\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf=RandomForestRegressor(n_jobs=-1, random_state=42)\n",
    "scores=cross_validate(rf, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
    "print(np.mean(scores['train_score']), np.mean(scores['test_score']))\n",
    "\n",
    "# 과대적합 막아줌 성능 향상. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f599de0",
   "metadata": {},
   "source": [
    "=> 5개의 확률을 구하고, 경우마다 조금씩 다를 수 있으니, 5개의 평균값을 기대치로 본다. \n",
    "=> (테스트 만으로는 잘 작동하는지 확신이 어려우므로)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "bc9efe0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.27547890e-02 7.15649925e-03 6.79428868e-02 7.53147165e-05\n",
      " 3.31156114e-02 9.52943395e-04 3.40695399e-03 2.11183926e-02\n",
      " 8.18790386e-01 7.84506256e-03 1.67530705e-03 2.44313384e-03\n",
      " 2.72271953e-03]\n"
     ]
    }
   ],
   "source": [
    "rf.fit(train_input, train_target)\n",
    "print(rf.feature_importances_)\n",
    "#중요도는 8번(도로까지 접근성 지수)>2번(비소매상업지역 토지)>0,4번(범죄율, 일산화질소)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "04f2e69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9728049579109765\n"
     ]
    }
   ],
   "source": [
    "rf=RandomForestRegressor(oob_score=True, n_jobs=-1, random_state=42)\n",
    "rf.fit(train_input, train_target)\n",
    "print(rf.oob_score_)  # 누락 데이터 랜덤포레스트 0.97"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "0c65ddc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.9754840574538081\n"
     ]
    }
   ],
   "source": [
    "# 엑스트라 트리\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "\n",
    "et=ExtraTreesRegressor(n_jobs=-1, random_state=42)\n",
    "scores=cross_validate(et, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
    "\n",
    "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "02f925ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hk428\\anaconda3\\envs\\tf24\\lib\\site-packages\\sklearn\\model_selection\\_split.py:666: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.9790111250688168\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "splitter=StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
    "et=ExtraTreesRegressor(n_jobs=-1, random_state=42)\n",
    "scores=cross_validate(et, train_input, train_target, return_train_score=True, n_jobs=-1, cv=StratifiedKFold())\n",
    "\n",
    "print(np.mean(scores['train_score']), np.mean(scores['test_score']))\n",
    "\n",
    "# 디폴트 값은 5개, 교차검증 k-fold의 수를 늘려보았을때 점수가 더 높게나왔다.  0.9790111250688168 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "24c67367",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02046418 0.0091414  0.11688539 0.00109989 0.03187281 0.00147064\n",
      " 0.00825286 0.01161312 0.7475232  0.03698817 0.00243068 0.00469866\n",
      " 0.00755899]\n"
     ]
    }
   ],
   "source": [
    "et.fit(train_input, train_target)\n",
    "print(et.feature_importances_)\n",
    "# 중요도: 8번(방사도로수)> 2번(비소매상업지역 토지) -> 재산세율에는 도로 수와 비소매상업지역 토지가 미치는 영향이 큼\n",
    "# 엑스트라 트리에서 본 중요도가 차이를 한 눈에 볼 수 있게끔 해줌"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96911b25",
   "metadata": {},
   "source": [
    "## 그레이디언트 부스팅 => 손실 함수를 최소화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "aba8af0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9961570363254559 0.9619739071030855\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gb=GradientBoostingRegressor(random_state=42)\n",
    "scores=cross_validate(gb, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
    "print(np.mean(scores['train_score']), np.mean(scores['test_score']))\n",
    "# 비교적 높은 점수로 좋은 성능 발휘 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5a4b6a20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.81702539e-03 1.21110740e-02 7.12299026e-02 8.46651022e-06\n",
      " 4.07323549e-02 4.02089458e-04 6.46106822e-04 1.08051818e-02\n",
      " 8.44273063e-01 4.83180676e-03 1.67250554e-03 9.68889968e-04\n",
      " 7.50153372e-03]\n"
     ]
    }
   ],
   "source": [
    "gb.fit(train_input, train_target)\n",
    "print(gb.feature_importances_)\n",
    "#그레이디언트 부스트의 중요도: 8번>2번"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "a9b2b390",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9896025133269275 0.9465373264152269\n"
     ]
    }
   ],
   "source": [
    "# 히스토그램 기반 그레이디언트 부스팅\n",
    "from sklearn.experimental import enable_hist_gradient_boosting\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "\n",
    "hgb=HistGradientBoostingRegressor(random_state=42)\n",
    "scores=cross_validate(hgb, train_input, train_target, return_train_score=True)\n",
    "\n",
    "hgb.fit(train_input, train_target)\n",
    "print(np.mean(scores['train_score']), np.mean(scores['test_score']))\n",
    "#과대적합/ 테스트 스코어가 낮아짐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "fe37aa84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00959481 0.00419396 0.04416902 0.         0.06402234 0.00337159\n",
      " 0.00490221 0.03088765 1.30016989 0.0090849  0.00306419 0.00262478\n",
      " 0.01757062]\n"
     ]
    }
   ],
   "source": [
    "# 특성중요도\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "hgb.fit(train_input, train_target)\n",
    "result=permutation_importance(hgb, train_input, train_target, n_repeats=10, random_state=42, n_jobs=-1)\n",
    "print(result.importances_mean)\n",
    "# 특성중요도가 아주 잘 드러남(8번 특성이 가장 중요함.)\n",
    "# 또 높게나왔네 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "fb2cd80a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.67119768e-02  5.70755705e-04  3.79820086e-02  0.00000000e+00\n",
      "  7.18010167e-02  3.76218058e-03 -8.44407750e-04  1.29011665e-02\n",
      "  1.31423780e+00  1.27865469e-02  3.88715787e-04  1.68187573e-03\n",
      "  1.69072181e-02]\n"
     ]
    }
   ],
   "source": [
    "result=permutation_importance(hgb, test_input, test_target, n_repeats=10, random_state=42, n_jobs=-1)\n",
    "print(result.importances_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ad40cc8",
   "metadata": {},
   "source": [
    "### 2. 상관관계가 중등도인 13(본인 소유의 주택 가격)을  target으로 둔 경우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "2aa2029a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=dataset[:,:13]\n",
    "Y=dataset[:,13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "e16148e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_input, test_input, train_target, test_target=train_test_split(X,Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "dff976b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss=StandardScaler()\n",
    "ss.fit(train_input)\n",
    "train_scaled=ss.transform(train_input)\n",
    "test_scaled=ss.transform(test_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "6be808d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7465745203562384\n",
      "0.6338839503029159\n"
     ]
    }
   ],
   "source": [
    "# KNR \n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knr=KNeighborsRegressor(n_neighbors=20)\n",
    "knr.fit(train_scaled, train_target)\n",
    "print(knr.score(train_scaled, train_target))\n",
    "print(knr.score(test_scaled, test_target))\n",
    "#전체적인 스코어가 9번을 target으로 정했을 때보다 많이 낮아짐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f7a7d533",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7508856358979673\n",
      "0.6687594935356322\n"
     ]
    }
   ],
   "source": [
    "# 선형회귀(KNR보다는 높음)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr=LinearRegression()\n",
    "lr.fit(train_scaled, train_target)\n",
    "print(lr.score(train_scaled, train_target))\n",
    "print(lr.score(test_scaled, test_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "aa587e35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8273631144456788\n",
      "0.8174569324763867\n"
     ]
    }
   ],
   "source": [
    "# 다항회귀(3차 / 정확도가 높아짐)\n",
    "train_poly=np.column_stack((train_input**3, train_input))\n",
    "test_poly=np.column_stack((test_input**3, test_input))\n",
    "lr=LinearRegression()\n",
    "lr.fit(train_poly, train_target)\n",
    "print(lr.score(train_poly, train_target))\n",
    "print(lr.score(test_poly, test_target))\n",
    "\n",
    "# 모델 중 가장 적게 점수 사이의 차이가 제일 적었다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "1d01d934",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 다중회귀\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly=PolynomialFeatures(degree=2, include_bias=False)\n",
    "poly.fit(train_scaled)\n",
    "train_poly=poly.transform(train_scaled)\n",
    "test_poly=poly.transform(test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "6d86796c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9409048466481141\n",
      "0.806037661046026\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr=LinearRegression()\n",
    "lr.fit(train_poly, train_target)\n",
    "print(lr.score(train_poly, train_target))\n",
    "print(lr.score(test_poly, test_target))\n",
    "\n",
    "# 차이가 너무 크고, 테스트 점수 값이 낮음. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "55e35231",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5304419445901294\n",
      "0.5296954117516082\n"
     ]
    }
   ],
   "source": [
    "# 릿지\n",
    "from sklearn.linear_model import Ridge\n",
    "ridge=Ridge(alpha=1000)\n",
    "ridge.fit(train_scaled, train_target)\n",
    "print(ridge.score(train_scaled, train_target))\n",
    "print(ridge.score(test_scaled, test_target))\n",
    "# 그래프를 참고로 하여 그렸으나 전체적인 score가 낮아짐"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b7c55d",
   "metadata": {},
   "source": [
    "=> alpha의 최적값은 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "33e87fe3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7508856358979673\n",
      "0.6687594935356324\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-146-0ce567cf2af3>:4: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n",
      "  lasso.fit(train_scaled, train_target)\n",
      "C:\\Users\\hk428\\anaconda3\\envs\\tf24\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "C:\\Users\\hk428\\anaconda3\\envs\\tf24\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:530: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4371.565376151721, tolerance: 3.509685514851485\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    }
   ],
   "source": [
    "# 라쏘\n",
    "from sklearn.linear_model import Lasso\n",
    "lasso=Lasso(alpha=0)\n",
    "lasso.fit(train_scaled, train_target)\n",
    "print(lasso.score(train_scaled, train_target))\n",
    "print(lasso.score(test_scaled, test_target))\n",
    "#훈련SET과 테스트SET의 값이 함께 낮아짐"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "528b1d87",
   "metadata": {},
   "source": [
    "=> alpha의 최적값은 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "d3f63011",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.8579634380978161\n"
     ]
    }
   ],
   "source": [
    "# 의사결정회귀\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt=DecisionTreeRegressor(random_state=42)\n",
    "dt.fit(train_scaled, train_target)\n",
    "print(dt.score(train_scaled, train_target))\n",
    "print(dt.score(test_scaled, test_target))\n",
    "#과대적합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "e1039e0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999857537093314\n",
      "0.8307959354029337\n"
     ]
    }
   ],
   "source": [
    "# 그리드서치\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "params={'min_impurity_decrease':[0.0001, 0.0001, 0.0003, 0.0004, 0.0005]}\n",
    "gs=GridSearchCV(DecisionTreeRegressor(random_state=42), params, n_jobs=-1)\n",
    "gs.fit(train_scaled, train_target)\n",
    "dt=gs.best_estimator_\n",
    "print(dt.score(train_scaled, train_target))\n",
    "print(dt.score(test_scaled, test_target))\n",
    "#과대적합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "7f9c4a0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor(min_impurity_decrease=0.0001, random_state=42)\n"
     ]
    }
   ],
   "source": [
    "print(gs.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "99eac406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.65932551 0.65932551 0.65856244 0.65324236 0.64466203]\n"
     ]
    }
   ],
   "source": [
    "print(gs.cv_results_['mean_test_score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "ecb4ab0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_impurity_decrease': 0.0001}\n"
     ]
    }
   ],
   "source": [
    "best_index=np.argmax(gs.cv_results_['mean_test_score'])\n",
    "print(gs.cv_results_['params'][best_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "4c8e0a86",
   "metadata": {},
   "outputs": [],
   "source": [
    "params={'min_impurity_decrease':np.arange(0.0001, 0.001, 0.0001), 'max_depth':range(5,20,1), 'min_samples_split':range(2, 100, 10)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "bc26e893",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=DecisionTreeRegressor(random_state=42), n_jobs=-1,\n",
       "             param_grid={'max_depth': range(5, 20),\n",
       "                         'min_impurity_decrease': array([0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008,\n",
       "       0.0009]),\n",
       "                         'min_samples_split': range(2, 100, 10)})"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs=GridSearchCV(DecisionTreeRegressor(random_state=42), params, n_jobs=-1)\n",
    "gs.fit(train_scaled, train_target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "d3fefd82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 8, 'min_impurity_decrease': 0.0001, 'min_samples_split': 42}\n"
     ]
    }
   ],
   "source": [
    "print(gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "4f46f46e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7189878924213235\n"
     ]
    }
   ],
   "source": [
    "print(np.max(gs.cv_results_['mean_test_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "5756ea70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.976740040838022 0.8260974564965702\n"
     ]
    }
   ],
   "source": [
    "# 랜덤 포레스트\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf=RandomForestRegressor(n_jobs=-1, random_state=42)\n",
    "scores=cross_validate(rf, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
    "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "524c4eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.03806177 0.00175615 0.00795268 0.00100426 0.01554377 0.50384493\n",
      " 0.01383994 0.06054907 0.00381091 0.01566064 0.01631341 0.01215362\n",
      " 0.30950883]\n"
     ]
    }
   ],
   "source": [
    "rf.fit(train_input, train_target)\n",
    "print(rf.feature_importances_)\n",
    "#중요도는 5번(주택 1가구당 평균 방의 개수)>12번(모집단의 하위계층 비율)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "5faf1b3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8367691571150133\n"
     ]
    }
   ],
   "source": [
    "rf=RandomForestRegressor(oob_score=True, n_jobs=-1, random_state=42)\n",
    "rf.fit(train_input, train_target)\n",
    "print(rf.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "63d68a73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999996310637 0.857992987573161\n"
     ]
    }
   ],
   "source": [
    "# 엑스트라 트리\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "\n",
    "et=ExtraTreesRegressor(n_jobs=-1, random_state=42)\n",
    "scores=cross_validate(et, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
    "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "f23a4ccc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02670059 0.00503923 0.04075369 0.02311007 0.04192078 0.33919036\n",
      " 0.01984859 0.02844052 0.02189783 0.04946183 0.05005573 0.01971589\n",
      " 0.33386488]\n"
     ]
    }
   ],
   "source": [
    "et.fit(train_input, train_target)\n",
    "print(et.feature_importances_)\n",
    "# 중요도: 5번과 12번이 거의 비슷함. 랜덤포레스트의 중요도와 차이가 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "62baa91e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999938557077291 0.8468577834920998\n"
     ]
    }
   ],
   "source": [
    "# 그레이디언트 부스팅\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gb=GradientBoostingRegressor(n_estimators=500, learning_rate=0.2, random_state=42)\n",
    "scores=cross_validate(gb, train_input, train_target, return_train_score=True, n_jobs=-1)\n",
    "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "3b82b8ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.99489734e-02 4.48248210e-04 1.80081400e-03 3.20569462e-04\n",
      " 3.13694183e-02 4.15049468e-01 1.55272641e-02 7.46038989e-02\n",
      " 2.87198949e-03 1.29505653e-02 3.33106944e-02 1.28146547e-02\n",
      " 3.68983442e-01]\n"
     ]
    }
   ],
   "source": [
    "gb.fit(train_input, train_target)\n",
    "print(gb.feature_importances_)\n",
    "#그레이디언트 부스트의 중요도: 5번>12번"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "28e09ac6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.972487748382218 0.8361021012141739\n"
     ]
    }
   ],
   "source": [
    "# 히스토그램 기반 그레이디언트 부스팅\n",
    "from sklearn.experimental import enable_hist_gradient_boosting\n",
    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
    "\n",
    "hgb=HistGradientBoostingRegressor(random_state=42)\n",
    "scores=cross_validate(hgb, train_input, train_target, return_train_score=True)\n",
    "print(np.mean(scores['train_score']), np.mean(scores['test_score']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "711d6560",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.85320474e-02 5.06238341e-04 5.61829374e-03 2.14230580e-03\n",
      " 4.45497986e-02 4.13792706e-01 2.56672509e-02 6.15457909e-02\n",
      " 5.13503179e-04 1.86182321e-02 2.38105663e-02 1.32477468e-02\n",
      " 5.59075861e-01]\n"
     ]
    }
   ],
   "source": [
    "# 특성중요도\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "hgb.fit(train_input, train_target)\n",
    "result=permutation_importance(hgb, train_input, train_target, n_repeats=10, random_state=42, n_jobs=-1)\n",
    "print(result.importances_mean)\n",
    "# 특성중요도(12번>5번)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "3c765b58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.54831541e-02 2.29589529e-04 1.82213731e-03 8.93237160e-05\n",
      " 4.63238700e-02 3.11380229e-01 1.45581528e-02 6.57137233e-02\n",
      " 1.32507452e-03 4.25263833e-03 1.90387738e-02 3.96395960e-03\n",
      " 5.47357702e-01]\n"
     ]
    }
   ],
   "source": [
    "result=permutation_importance(hgb, test_input, test_target, n_repeats=10, random_state=42, n_jobs=-1)\n",
    "print(result.importances_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd3e8b8",
   "metadata": {},
   "source": [
    "#### 나이브 베이즈, SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "a8de4aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 분류에만 사용. 자료가 회귀면 불가능"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "073a85cb",
   "metadata": {},
   "source": [
    "## 3) 비지도 학습 머신러닝 기법 적용 분석 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8e7fb0",
   "metadata": {},
   "source": [
    "# K-평균 알고리즘"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "38f4bb6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 3.9690e+02, 4.9800e+00,\n",
       "        2.4000e+01],\n",
       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 3.9690e+02, 9.1400e+00,\n",
       "        2.1600e+01],\n",
       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 3.9283e+02, 4.0300e+00,\n",
       "        3.4700e+01],\n",
       "       ...,\n",
       "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 3.9690e+02, 5.6400e+00,\n",
       "        2.3900e+01],\n",
       "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 3.9345e+02, 6.4800e+00,\n",
       "        2.2000e+01],\n",
       "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 3.9690e+02, 7.8800e+00,\n",
       "        1.1900e+01]])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distortions=[]\n",
    "X=dataset[:,:]\n",
    "X\n",
    "\n",
    "# 모든 데이터 집어넣음.  \n",
    "# 회귀라서 타겟이 따로 없이 군집(클러스터)를 만듦"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1efb34e",
   "metadata": {},
   "source": [
    "## 엘보우 방법을 사용하여 최적의 클러스터 개수 찾기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "e57e9a1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hk428\\anaconda3\\envs\\tf24\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "for i in range(1,20): # 클러스터 개수 K를 바꿔가며 kmeans 20번 훈련 \n",
    "    km=KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=200, random_state=0)\n",
    "    km.fit(X) , \n",
    "    distortions.append(km.inertia_) # 훈련한 후 inertia 리스트에 추가 \n",
    "    # 최적의 점을 찾아서 넣어본다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "9c09535b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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.plot(range(1,20), distortions, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Distortion')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d45381",
   "metadata": {},
   "source": [
    "- n_clusters=3 에서 그래프가 감소하는 속도가 꺾이므로 적절한 클러스터는 3개라 예측"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ba3ce6",
   "metadata": {},
   "source": [
    "### 표준화 작업 추가 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "68d55a3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss=StandardScaler()\n",
    "ss.fit(X)\n",
    "train_scaled=ss.transform(X)\n",
    "#test_scaled=ss.transform(test_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "5aba06e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hk428\\anaconda3\\envs\\tf24\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=2.\n",
      "  warnings.warn(\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": [
    "distortions = []\n",
    "for i in range(1, 20):\n",
    "    km = KMeans(n_clusters=i, \n",
    "                init='k-means++', \n",
    "                n_init=10, \n",
    "                max_iter=300, \n",
    "                random_state=0)\n",
    "    km.fit(train_scaled)\n",
    "    distortions.append(km.inertia_)\n",
    "plt.plot(range(1, 20), distortions, marker='o')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('Distortion')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 마찬가지로 n_clusters=3 에서 그래프가 감소하는 속도가 꺾이므로 적절한 클러스터는 3개라 예측 \n",
    "# (완만)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "de60242d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hk428\\anaconda3\\envs\\tf24\\lib\\site-packages\\sklearn\\metrics\\cluster\\_supervised.py:58: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and binary values for target\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.4160826448759576"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "km = KMeans(n_clusters=2, \n",
    "            init='random', \n",
    "            n_init=10, \n",
    "            max_iter=300,\n",
    "            tol=1e-04,\n",
    "            random_state=0)\n",
    "\n",
    "y_km = km.fit_predict(train_scaled)\n",
    "\n",
    "from sklearn.metrics import mutual_info_score\n",
    "mutual_info_score(Y, y_km) # 두 군집 간의 상호 정보 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "1bc2ffbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hk428\\anaconda3\\envs\\tf24\\lib\\site-packages\\sklearn\\metrics\\cluster\\_supervised.py:58: UserWarning: Clustering metrics expects discrete values but received continuous values for label, and multiclass values for target\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6907068878681815"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters=3, \n",
    "            init='random', \n",
    "            n_init=10, \n",
    "            max_iter=300,\n",
    "            tol=1e-04,\n",
    "            random_state=0)\n",
    "\n",
    "y_km = km.fit_predict(train_scaled)\n",
    "\n",
    "mutual_info_score(Y, y_km)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a73ba8a",
   "metadata": {},
   "source": [
    "- n_clusters=3 더 나눌수록 군집들 간 유사도 ↑"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0552a284",
   "metadata": {},
   "source": [
    "- 특성이 잘 없어서 군집이 안나눠지기 때문에 더 잘게 나눠서 비슷한 특성을 보인다, 유사도가 높게 보인다. \n",
    "- 클래스가 많아질수록 유사도가 높아짐, 잘게 쪼갤수록 스코어가 높아짐 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea0a81e6",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "1f97eae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.46757068, 0.11782371, 0.09635042, 0.06332428, 0.06077853,\n",
       "       0.04714363, 0.03824363, 0.02879118, 0.01980454, 0.01801839,\n",
       "       0.0151993 , 0.01307054, 0.00957212, 0.00430905])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA()\n",
    "train_scaled_pca=pca.fit_transform(train_scaled)\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "cc13429d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+UdKTkjYUGZyZmRUvz4CyHcsIxMzM2iNPr6EP9NsfJumc4kIyM7My5akaOkzSfEm7Svpz4G7ApQQzsyEiT9XQ8ZKOAe4HngGOi4ifFh6ZmZmVIk/V0CTg48C1wK+A90ravuC4zMysJHmqhm4A/iUiTiFb0P6XwIJCozIzs9LkGVC2f0RsAIiIAL4saV6xYZmZWVkalggkfQogIjZIene/056QzsxsiGhWNXRszfan+52bVkAsZmbWBs0SgRps19s3M7Mu1SwRRIPtevtmZtalmjUW753mEhIwomZeIQHDC4/MzMxK0WyFsmFlBmJmZu2RZxyBmZkNYYUmAknTJK2UtErS2XXOv0fSfelxl6S9i4zHzMy2VFgikDQMuAg4ApgMHCdpcr/LHgIOiog3A+cBs4uKx8zM6iuyRLA/sCoiVkfE82QL2kyvvSAi7oqIx9Pu3cDYAuMxM7M6ikwEY4CHa/Z707FGPgDcVO+EpJmSFkpauH79+haGaGZmRSaCeoPO6o4/kHQIWSI4q975iJgdET0R0TN69OgWhmhmZnkmndtavcC4mv2xwNr+F0l6MzAHOCIifldgPGZmVkeRJYIFwCRJEyVtRzZ30WazlkoaD1wHvDciHiwwFjMza6CwEkFEbJR0OnAzMAy4NCKWSTo1nZ8F/CuwC/BNSQAbI6KnqJjMzGxLRVYNERHzgfn9js2q2f4g8MEiYzAzs+Y8stjMrOKcCMzMKs6JwMys4pwIzMwqzonAzKzinAjMzCrOicDMrOKcCMzMKs6JwMys4pwIzMwqzonAzKziCp1rqCouuKU1E6eeefgeLbmPmdlguERgZlZxTgRmZhXnRGBmVnFOBGZmFefG4g7XioZoN0KbWTMuEZiZVZwTgZlZxTkRmJlVnBOBmVnFORGYmVWcE4GZWcW5+2hFFdUt1d1dzbqPSwRmZhXnRGBmVnGuGrKu4Cons+K4RGBmVnEuEViluaRh5hKBmVnluURgVgCXNKybuERgZlZxLhGYdZFWlDRgy9JGUfe17lBoIpA0DfgaMAyYExFf6Hde6fyRwDPA+yJicZExmVl5nLi6Q2GJQNIw4CLgcKAXWCBpXkQsr7nsCGBSekwFLk7PZmalq2qCKbJEsD+wKiJWA0i6ApgO1CaC6cB3IiKAuyXtJGnXiFhXYFxmZqXq9ASj7G9wATeWZgDTIuKDaf+9wNSIOL3mmhuBL0TET9L+rcBZEbGw371mAjPT7p7ASmAU8FghwRfD8Rav22J2vMVyvJvbPSJG1ztRZIlAdY71zzp5riEiZgOzN3uhtDAierY+vHI53uJ1W8yOt1iON78iu4/2AuNq9scCa7fiGjMzK1CRiWABMEnSREnbAccC8/pdMw84UZkDgCfcPmBmVq7CqoYiYqOk04GbybqPXhoRyySdms7PAuaTdR1dRdZ99ORBvMXsgS/pKI63eN0Ws+MtluPNqbDGYjMz6w6eYsLMrOKcCMzMKq4rE4GkaZJWSlol6ex2x9OMpHGSbpe0QtIySR9vd0x5SBom6edprEdHSwMRr5H0i/Q5H9jumJqRdGb6XXhA0vclDW93TLUkXSrpUUkP1BzbWdItkn6Znl/bzhj7axDz+el34j5JP5C0UxtD3Ey9eGvOfUJSSBpVVjxdlwhqpq44ApgMHCdpcnujamoj8E8R8WfAAcBpHR5vn48DK9odRE5fA34YEW8E9qaD45Y0BvgY0BMRbyLrSHFse6PawlxgWr9jZwO3RsQk4Na030nmsmXMtwBviog3Aw8Cny47qCbmsmW8SBpHNi3PmjKD6bpEQM3UFRHxPNA3dUVHioh1fRPpRcSTZH+kxrQ3quYkjQXeAcxpdywDkTQS+EvgEoCIeD4i/tDWoAa2LTBC0rbA9nTY2JmI+DHw+36HpwPfTtvfBv62zJgGUi/miPhRRGxMu3eTjVPqCA0+Y4ALgE9RZ2BtkboxEYwBHq7Z76XD/7D2kTQBeAtwT5tDGchXyX4ZX2xzHHm8AVgPfCtVZc2R9Op2B9VIRDwCfInsG986srEzP2pvVLm8vm+MT3p+XZvjGaz3Aze1O4hmJB0FPBIRS8t+725MBLmmpeg0knYArgXOiIgN7Y6nEUnvBB6NiEXtjiWnbYF9gIsj4i3A03RetcVLUt36dGAisBvwakkntDeqoU3SZ8iqaC9rdyyNSNoe+Azwr+14/25MBF03LYWkV5Elgcsi4rp2xzOAtwFHSfoVWbXboZK+196QmuoFeiOir5R1DVli6FR/BTwUEesj4gXgOuCtbY4pj99K2hUgPT/a5nhykXQS8E7gPdHZg6b+hOzLwdL0f28ssFjSH5fx5t2YCPJMXdEx0uI7lwArIuIr7Y5nIBHx6YgYGxETyD7b2yKiY7+xRsRvgIcl7ZkOHcbmU513mjXAAZK2T78bh9HBjds15gEnpe2TgOvbGEsuaWGss4CjIuKZdsfTTETcHxGvi4gJ6f9eL7BP+v0uXNclgtT40zd1xQrgqohY1t6omnob8F6yb9ZL0uPIdgc1xHwUuEzSfcAU4PPtDaexVHK5BlgM3E/2f7CjpkKQ9H3gZ8CeknolfQD4AnC4pF+S9Wr5QrN7lK1BzBcCOwK3pP93s9oaZI0G8bYvns4uLZmZWdG6rkRgZmat5URgZlZxTgRmZhXnRGBmVnFOBGZmFedEYG0jaVPq1veApKvT6Mp61921lffvkfT1VxDfU1v72m4i6YxGn71Vg7uPWttIeioidkjblwGLagfdSRoWEZs6Ib6hLI1k7YmIx9odi7WHSwTWKf4X+FNJB6f1Gy4nG3D10jfzdO6OmrUHLkujc5G0n6S7JC2VdK+kHdP1N6bz50r6rqTb0pz6H0rHd5B0q6TFku6XNOBMtpJOTHPcL5X03XRs93Sf+9Lz+HR8rqSL08+0WtJBaS76FZLm1tzzKUlfTnHcKml0Oj5F0t16eU7916bjd0j6YvpZH5T0F+n4MGXz8C9Irzml2Wcn6WNkcx7dnmIclmJ+IH0eZ7bg39Y6XUT44UdbHsBT6XlbsikLPgwcTDZx3MQ61x0MPEE2D8s2ZCMz3w5sB6wG9kvXjUz3PBi4MR07F1gKjABGkc1gu1u6bmS6ZhSwipdLyk/ViXkvYCUwKu3vnJ5vAE5K2+8H/ittzyWbs0lkk81tAP48xb8ImJKuC7L5cCCbeOzCtH0fcFDa/hzw1bR9B/DltH0k8D9peybwz2n7j4CFZHPY1P3s0nW/qvl59gVuqfl5d2r374kfxT9cIrB2GiFpCdkfqzWkNQWAeyPioQavuTcieiPiRWAJMAHYE1gXEQsAImJDvDwPfa3rI+LZyKpAbidb20LA59P0FP9DNqX565vEfChwTboHEdE3p/yBwOVp+7tkCarPDRERZCWc30Y2r8yLwLIUP2RTfl+Ztr8HvF3Sa8j+EN+Zjn+bbO2FPn0TGC6quc9fAyemz/UeYBdgUjpX77PrbzXwBknfSHP1dOxMudY627Y7AKu0ZyNiSu2BVNPzdJPXPFezvYnsd1jkm4q8/zUBvAcYDewbES+k+vJmS0duzXv1xfwim8f/Io3/D+Z5j7579X0OffF9NCJurr1Q0sHU/+w2f9OIxyXtDfwNcBpwNFkJx4YwlwhsKPgFsJuk/QBS+0C9P7DTJQ2XtAtZVckC4DVk6y+8IOkQYPcB3utW4Oh0DyTtnI7fxctLTr4H+Mkgf4ZtgBlp+3jgJxHxBPB4X/0/2eSFd9Z7cY2bgQ8rm/ocSXto4IV6niSbnA1l6+RuExHXAv9CZ0/pbS3iEoF1vYh4XtIxwDckjQCeJZv3v797gf8GxgPnRcTa1FvpBkkLyapLfjHAey2T9O/AnZI2AT8H3ke2DvGlkj5JtmLayYP8MZ4G9pK0iKwu/5h0/CRgVureuTrHfeeQVfksTg3p6xl4WcnZwE2S1gFnkK321vclsZPW+bWCuPuoVYKkc8kaf7/U7ljqqUpXVetMrhoyM6s4lwjMzCrOJQIzs4pzIjAzqzgnAjOzinMiMDOrOCcCM7OK+38vE3kbYv867wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(1, 15), pca.explained_variance_ratio_, alpha=0.5, align='center')\n",
    "plt.step(range(1, 15), np.cumsum(pca.explained_variance_ratio_), where='mid')\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.xlabel('Principal components')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# 14개의 특성을 그래프로 나타냄. 특정 특성이 영향을 미치는 정도를 그래프로 나타냄. 1~2개만 봤을때 이미 50퍼 이상 넘어감. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "60b31080",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)\n",
    "train_scaled_pca = pca.fit_transform(train_scaled)\n",
    "#test_scaled_pca = pca.transform(test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "7dfd7da2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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(train_scaled_pca[:, 0], train_scaled_pca[:, 1])\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.show()\n",
    "# 군집 없이 나눠짐 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "bfe36e65",
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2) #평면\n",
    "result_pca = pca.fit_transform(train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "c00dcf87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "e36d89ab",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "colors = ['r', 'b', 'g']\n",
    "markers = ['s', 'o', 'x'] # 3개로 색칠 해봤더니, \n",
    "\n",
    "for l, c, m in zip(np.unique(y_km), colors, markers):\n",
    "    plt.scatter(train_scaled_pca[y_km == l, 0], \n",
    "                train_scaled_pca[y_km == l, 1], \n",
    "                c=c, label=l, marker=m)\n",
    "\n",
    "plt.xlabel('PC 1')\n",
    "plt.ylabel('PC 2')\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "# 색칠해서 나눠봤더니 색으로 정확하게 구분할수있었다. 군집이 제대로 안됐다는 것을, 뭉쳐져있구나 이놈드! "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df6c2e0f",
   "metadata": {},
   "source": [
    "# 4. 특이점 \n",
    "## 확률적 경사하강법 (09)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e1516d9",
   "metadata": {},
   "source": [
    "=> 50회가 가장 적합한 에포크 횟수라고 생각했으나, iter=80 일때와 90일때 점수가 크게 차이가 나지 않는다??\n",
    "오히려 100회를 시행했을때,훈련 score가 더 낮아짐.\n",
    "\n",
    "\n",
    "이유?\n",
    "=> SGD의 랜덤한 특성으로 그래프의 50회~100회에서 정확도가 변화무쌍한 지점을 확인.\n",
    "\n",
    "\n",
    "결과\n",
    "=> 50회 실행값과 70회 실행값이 변화무쌍하기 때문에 점수가 낮아지거나 높아져도 큰 차이가 없음을 발견."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56ed4c64",
   "metadata": {},
   "source": [
    "# 5. 결론"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "996b76ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "09 (만달러당 재산세율) \n",
    "\n",
    "=> 재산세율을 생각할때는 엑스트라 트리의 정확도가 훈련 셋 1.0, 정확도의 평균 점수가 약 0.98로 높게 나왔다.\n",
    "\n",
    "=> 그래서 재산세율을 타겟으로 할때는 '엑스트라 트리' 모델을 사용하는것이 가장 적합하다고 생각한다.\n",
    "\n",
    "\n",
    "=> 재산세율에 가장 영향을 끼치는 특성은 8번 (도로가의 접근성 지수) 라고 볼 수 있다. (거의 모든 특성중요도에서 8번이 높게 나옴)\n",
    "\n",
    "\n",
    "=> 따라서, '방사형 도로의 접근성 지수' 는 재산세율에 많은 영향을 끼치며, 둘의 상관관계는 높은 것으로 직접 확인 할 수 있었다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e793caf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "13  (본인 소유의 주택 가격) \n",
    "\n",
    "=> 주택 가격을 생각할때는 다항회귀가 훈련 셋 0.8274 테스트 셋 0.8173 가장 점수 차이가 제일 적었다.\n",
    "=> test score의 점수는 엑스트라 트리의 점수가 0.857 정도로 가장 높게 나왔다. \n",
    "\n",
    "=> 그래서 주택 가격의 타겟으로 할때는 '다항회귀' , '엑스트라 트리' 모델을 사용하는 것이 가장 적합하다고 생각한다. \n",
    "\n",
    "\n",
    "=> 집값에 가장 영향을 끼치는 특성은 5번(방의개수), 12번(하위계층의 비율) 이라고 볼 수 있다. (특성중요도)\n",
    "\n",
    "\n",
    "=> 따라서, '방의 개수' 와, '하위계층의 비율'은 집값에 많은 영향을 끼치며, 상관성이 높음을 직접 모델을 실행하면서 확인하였다.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2580851d",
   "metadata": {},
   "outputs": [],
   "source": [
    "=> 처음에는 주택 가격을 예측하는 데이터로 주어졌지만, \n",
    "의도와 다르게 재산세를 타겟으로 두었을때 정확도가 더 높게 나왔으므로 \n",
    "보스턴시 주택 데이터는 '만 달러 당 재산세' 를 더 잘 설명해주는 데이터라 볼 수 있다."
   ]
  }
 ],
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