Day_010_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業 : (Kaggle)房價預測"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著模仿範例寫法, 在房價預測中, 觀察去除離群值的影響"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 觀察將極端值以上下限值取代, 對於分布與迴歸分數的影響 (In[5], Out[5])\n",
    "- 觀察將極端值資料直接刪除, 對於分布與迴歸分數的影響 (In[6], Out[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig  ... ScreenPorch PoolArea PoolQC Fence  \\\n",
       "0         Lvl    AllPub    Inside  ...           0        0    NaN   NaN   \n",
       "1         Lvl    AllPub       FR2  ...           0        0    NaN   NaN   \n",
       "2         Lvl    AllPub    Inside  ...           0        0    NaN   NaN   \n",
       "3         Lvl    AllPub    Corner  ...           0        0    NaN   NaN   \n",
       "4         Lvl    AllPub       FR2  ...           0        0    NaN   NaN   \n",
       "\n",
       "  MiscFeature MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0       2    2008        WD         Normal  \n",
       "1         NaN       0       5    2007        WD         Normal  \n",
       "2         NaN       0       9    2008        WD         Normal  \n",
       "3         NaN       0       2    2006        WD        Abnorml  \n",
       "4         NaN       0      12    2008        WD         Normal  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 做完特徵工程前的所有準備 (與前範例相同)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "data_path = 'data/'\n",
    "df_train = pd.read_csv(data_path + 'house_train.csv.gz')\n",
    "\n",
    "train_Y = np.log1p(df_train['SalePrice'])\n",
    "df = df_train.drop(['Id', 'SalePrice'] , axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36 Numeric Features : ['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#只取 int64, float64 兩種數值型欄位, 存於 num_features 中\n",
    "num_features = []\n",
    "for dtype, feature in zip(df.dtypes, df.columns):\n",
    "    if dtype == 'float64' or dtype == 'int64':\n",
    "        num_features.append(feature)\n",
    "print(\"{} Numeric Features : {}\\n\".format(len(num_features), num_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>460</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>642</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>836</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          60         65.0     8450            7            5       2003   \n",
       "1          20         80.0     9600            6            8       1976   \n",
       "2          60         68.0    11250            7            5       2001   \n",
       "3          70         60.0     9550            7            5       1915   \n",
       "4          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  GarageArea  \\\n",
       "0          2003       196.0         706           0  ...         548   \n",
       "1          1976         0.0         978           0  ...         460   \n",
       "2          2002       162.0         486           0  ...         608   \n",
       "3          1970         0.0         216           0  ...         642   \n",
       "4          2000       350.0         655           0  ...         836   \n",
       "\n",
       "   WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
       "0           0           61              0          0            0         0   \n",
       "1         298            0              0          0            0         0   \n",
       "2           0           42              0          0            0         0   \n",
       "3           0           35            272          0            0         0   \n",
       "4         192           84              0          0            0         0   \n",
       "\n",
       "   MiscVal  MoSold  YrSold  \n",
       "0        0       2    2008  \n",
       "1        0       5    2007  \n",
       "2        0       9    2008  \n",
       "3        0       2    2006  \n",
       "4        0      12    2008  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 削減文字型欄位, 只剩數值型欄位\n",
    "df = df[num_features]\n",
    "df = df.fillna(-1)\n",
    "MMEncoder = MinMaxScaler()\n",
    "train_num = train_Y.shape[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業1\n",
    "* 試著限制 '1樓地板面積(平方英尺)' (1stFlrSF) 欄位的上下限, 看看能否再進一步提高分數?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1857: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\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"
    },
    {
     "data": {
      "text/plain": [
       "0.8466092918177186"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 顯示 1stFlrSF 與目標值的散佈圖\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.regplot(x = df['1stFlrSF'][:train_num], y=train_Y)\n",
    "plt.show()\n",
    "\n",
    "# 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1857: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\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"
    },
    {
     "data": {
      "text/plain": [
       "0.8855534006970955"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將 1stFlrSF 限制在你覺得適合的範圍內, 調整離群值\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "keep_indexes = (df['1stFlrSF'] < 2000) & (df['1stFlrSF'] > 500)\n",
    "tmp_x = df[keep_indexes]\n",
    "\n",
    "sns.regplot(x = tmp_x['1stFlrSF'], y=train_Y[keep_indexes])\n",
    "plt.show()\n",
    "\n",
    "# 做線性迴歸, 觀察分數\n",
    "alter_train_y = train_Y[keep_indexes]\n",
    "alter_train_x = MMEncoder.fit_transform(tmp_x)\n",
    "\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, alter_train_x, alter_train_y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 作業2\n",
    "* 續前題, 去除離群值有兩類方式 :  捨棄離群值(刪除離群的資料) 以及調整離群值,  \n",
    "請試著用同樣的上下限, 改為 '捨棄離群值' 的方法, 看看結果會變好還是變差? 並試著解釋原因。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/stats/stats.py:1857: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\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"
    },
    {
     "data": {
      "text/plain": [
       "0.8762147522681326"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 將 1stFlrSF 限制在你覺得適合的範圍內, 捨棄離群值\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "df['1stFlrSF'] = df['1stFlrSF'].clip(500,2000)\n",
    "\n",
    "sns.regplot(x = df['1stFlrSF'], y=train_Y)\n",
    "plt.show()\n",
    "\n",
    "# 做線性迴歸, 觀察分數\n",
    "train_X = MMEncoder.fit_transform(df)\n",
    "estimator = LinearRegression()\n",
    "cross_val_score(estimator, train_X, train_Y, cv=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "結果是較差的  \n",
    "起因在於我擷取的區間是比較狹窄  \n",
    "在區間邊界外的資料數量是相對比多  \n",
    "調整後邊界數值的資料分布較分散  \n",
    "間接影響回歸的分析的符合程度  "
   ]
  }
 ],
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