homework/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": 21,
   "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  \\\n",
       "0         Lvl    AllPub    Inside      ...                 0        0    NaN   \n",
       "1         Lvl    AllPub       FR2      ...                 0        0    NaN   \n",
       "2         Lvl    AllPub    Inside      ...                 0        0    NaN   \n",
       "3         Lvl    AllPub    Corner      ...                 0        0    NaN   \n",
       "4         Lvl    AllPub       FR2      ...                 0        0    NaN   \n",
       "\n",
       "  Fence MiscFeature MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
       "0   NaN         NaN       0       2    2008        WD         Normal  \n",
       "1   NaN         NaN       0       5    2007        WD         Normal  \n",
       "2   NaN         NaN       0       9    2008        WD         Normal  \n",
       "3   NaN         NaN       0       2    2006        WD        Abnorml  \n",
       "4   NaN         NaN       0      12    2008        WD         Normal  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "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(f'{len(num_features)} Numeric Features : {num_features}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": 23,
     "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": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: 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": [
       "0.8466356981855533"
      ]
     },
     "execution_count": 24,
     "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'], 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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: 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": [
       "0.8686118949890453"
      ]
     },
     "execution_count": 25,
     "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",
    "df['1stFlrSF'] = df['1stFlrSF'].clip(400, 1800)\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": "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": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: 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": {
      "text/plain": [
       "0.8759707946176956"
      ]
     },
     "execution_count": 26,
     "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": [
    "# 將 1stFlrSF 限制在你覺得適合的範圍內, 捨棄離群值\n",
    "keep_indexs = (df['1stFlrSF']> 400) & (df['1stFlrSF']< 1800)\n",
    "df = df[keep_indexs]\n",
    "train_Y = train_Y[keep_indexs]\n",
    "sns.regplot(x = df['1stFlrSF'], y=train_Y)\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": "markdown",
   "metadata": {},
   "source": [
    "分數有變高了\n",
    "捨棄離群值可以讓數據不會被那些極端值影響整體結果"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}