linear_regression_boston.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 房價預測"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 載入相關套件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 載入 Boston 房價資料集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  MEDV  \n",
       "0     15.3  396.90   4.98  24.0  \n",
       "1     17.8  396.90   9.14  21.6  \n",
       "2     17.8  392.83   4.03  34.7  \n",
       "3     18.7  394.63   2.94  33.4  \n",
       "4     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('./data/housing.data', encoding='utf8') as f:\n",
    "    data = f.readlines()\n",
    "all_fields = []\n",
    "for line in data:\n",
    "    line2 = line[1:].replace('   ', ' ').replace('  ', ' ')\n",
    "    fields = []\n",
    "    for item in line2.split(' '):\n",
    "        fields.append(float(item.strip()))\n",
    "        if len(fields) == 14:\n",
    "            all_fields.append(fields)\n",
    "df = pd.DataFrame(all_fields)\n",
    "df.columns = 'CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV'.split(',')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 資料清理、資料探索與分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "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   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    float64\n",
      " 9   TAX      506 non-null    float64\n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "# 觀察資料集彙總資訊\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.716290</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.653510</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.081900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.250895</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.326717</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.966540</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     1.716290   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      2.653510   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.081900    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.250895    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      2.326717   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max      9.966540  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 描述統計量\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "MEDV       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 是否有含遺失值(Missing value)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 繪圖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "# 直方圖\n",
    "import seaborn as sns\n",
    "\n",
    "X, y = df.drop('MEDV', axis=1).values, df.MEDV.values\n",
    "sns.histplot(x=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 不須進行特徵工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 資料分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((404, 13), (102, 13), (404,), (102,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 資料分割\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)\n",
    "\n",
    "# 查看陣列維度\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特徵縮放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train_std = scaler.fit_transform(X_train)\n",
    "X_test_std = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 選擇演算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 模型訓練"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 模型評分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 = 73.11\n",
      "MSE = 23.992721973991944\n",
      "MAE = 3.34219941994402\n"
     ]
    }
   ],
   "source": [
    "# R2、MSE、MAE\n",
    "y_pred = model.predict(X_test_std)\n",
    "print(f'R2 = {r2_score(y_test, y_pred)*100:.2f}') \n",
    "print(f'MSE = {mean_squared_error(y_test, y_pred)}') \n",
    "print(f'MAE = {mean_absolute_error(y_test, y_pred)}') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.10520073,  1.21539942,  0.24922505,  0.72602535, -2.2115316 ,\n",
       "        2.43141226,  0.23189051, -3.31189482,  2.4682816 , -2.16240522,\n",
       "       -1.93803896,  1.0698765 , -4.20907748])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 權重\n",
    "model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 模型評估，暫不進行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 模型佈署"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型存檔\n",
    "import joblib\n",
    "\n",
    "joblib.dump(model, 'lr_model.joblib')\n",
    "joblib.dump(scaler, 'lr_scaler.joblib');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10.模型預測，請參見 linear_regression_prediction.py"
   ]
  },
  {
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