HousePrice_Linear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b430fefd-0983-4acb-bf3e-9cd40e3b8a29",
    "_uuid": "3ef31ce7b79aeeb6dba29eb40bc62f7b34a69740"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample_submission.csv\n",
      "test.csv\n",
      "train.csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "from subprocess import check_output\n",
    "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b5a0ed1c-704e-427a-81ab-0fca4e26165f",
    "_uuid": "a63c96751193c55e637e73bf05ccf9f8663bb6ba"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</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>...</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",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\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>...</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",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\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>...</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",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\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>...</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",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\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>...</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",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\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>...</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",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>14115</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>143000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10084</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>307000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10382</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "      <td>350</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>RM</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6120</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>129900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>190</td>\n",
       "      <td>RL</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7420</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "5   6          50       RL         85.0    14115   Pave   NaN      IR1   \n",
       "6   7          20       RL         75.0    10084   Pave   NaN      Reg   \n",
       "7   8          60       RL          NaN    10382   Pave   NaN      IR1   \n",
       "8   9          50       RM         51.0     6120   Pave   NaN      Reg   \n",
       "9  10         190       RL         50.0     7420   Pave   NaN      Reg   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "5         Lvl    AllPub    ...            0    NaN  MnPrv        Shed     700   \n",
       "6         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "7         Lvl    AllPub    ...            0    NaN    NaN        Shed     350   \n",
       "8         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "9         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "5     10   2009        WD         Normal     143000  \n",
       "6      8   2007        WD         Normal     307000  \n",
       "7     11   2009        WD         Normal     200000  \n",
       "8      4   2008        WD        Abnorml     129900  \n",
       "9      1   2008        WD         Normal     118000  \n",
       "\n",
       "[10 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train_data = pd.read_csv(\"../input/train.csv\")\n",
    "# 前十筆資料\n",
    "train_data.head(10)\n",
    "# 後十筆資料\n",
    "# train_data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "35ecfa0a-a0d3-46db-b086-e2d40fb134cf",
    "_uuid": "aa6fc2564cefd24e4bb18b2e66b29aeb534202be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# 資料資訊，可檢查缺項\n",
    "train_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "2594fb2c-0a11-46ab-ab19-48c575e8b983",
    "_uuid": "924e32cd95999494442da14c869a2fa79d0b38b5"
   },
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
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       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</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>...</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",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000   \n",
       "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
       "\n",
       "           ...         WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  \\\n",
       "count      ...        1460.000000  1460.000000    1460.000000  1460.000000   \n",
       "mean       ...          94.244521    46.660274      21.954110     3.409589   \n",
       "std        ...         125.338794    66.256028      61.119149    29.317331   \n",
       "min        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "25%        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "50%        ...           0.000000    25.000000       0.000000     0.000000   \n",
       "75%        ...         168.000000    68.000000       0.000000     0.000000   \n",
       "max        ...         857.000000   547.000000     552.000000   508.000000   \n",
       "\n",
       "       ScreenPorch     PoolArea       MiscVal       MoSold       YrSold  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1460.000000  1460.000000   \n",
       "mean     15.060959     2.758904     43.489041     6.321918  2007.815753   \n",
       "std      55.757415    40.177307    496.123024     2.703626     1.328095   \n",
       "min       0.000000     0.000000      0.000000     1.000000  2006.000000   \n",
       "25%       0.000000     0.000000      0.000000     5.000000  2007.000000   \n",
       "50%       0.000000     0.000000      0.000000     6.000000  2008.000000   \n",
       "75%       0.000000     0.000000      0.000000     8.000000  2009.000000   \n",
       "max     480.000000   738.000000  15500.000000    12.000000  2010.000000   \n",
       "\n",
       "           SalePrice  \n",
       "count    1460.000000  \n",
       "mean   180921.195890  \n",
       "std     79442.502883  \n",
       "min     34900.000000  \n",
       "25%    129975.000000  \n",
       "50%    163000.000000  \n",
       "75%    214000.000000  \n",
       "max    755000.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "eed9a4e8-6444-489c-a724-556393b354a9",
    "_uuid": "831eb8b53c980db5de970b342e028c06b5648741"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage      259\n",
       "Alley           1369\n",
       "MasVnrType         8\n",
       "MasVnrArea         8\n",
       "BsmtQual          37\n",
       "BsmtCond          37\n",
       "BsmtExposure      38\n",
       "BsmtFinType1      37\n",
       "BsmtFinType2      38\n",
       "Electrical         1\n",
       "FireplaceQu      690\n",
       "GarageType        81\n",
       "GarageYrBlt       81\n",
       "GarageFinish      81\n",
       "GarageQual        81\n",
       "GarageCond        81\n",
       "PoolQC          1453\n",
       "Fence           1179\n",
       "MiscFeature     1406\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NA 個數\n",
    "naCount = train_data.isnull().sum()\n",
    "naCount[naCount > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "c1163c7a-c152-4212-a279-62e2563e95c4",
    "_uuid": "9d3658dd8d612e2685530756394d50ca5af2541b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                0.000000\n",
       "MSSubClass        1.407657\n",
       "LotFrontage       2.163569\n",
       "LotArea          12.207688\n",
       "OverallQual       0.216944\n",
       "OverallCond       0.693067\n",
       "YearBuilt        -0.613461\n",
       "YearRemodAdd     -0.503562\n",
       "MasVnrArea        2.669084\n",
       "BsmtFinSF1        1.685503\n",
       "BsmtFinSF2        4.255261\n",
       "BsmtUnfSF         0.920268\n",
       "TotalBsmtSF       1.524255\n",
       "1stFlrSF          1.376757\n",
       "2ndFlrSF          0.813030\n",
       "LowQualFinSF      9.011341\n",
       "GrLivArea         1.366560\n",
       "BsmtFullBath      0.596067\n",
       "BsmtHalfBath      4.103403\n",
       "FullBath          0.036562\n",
       "HalfBath          0.675897\n",
       "BedroomAbvGr      0.211790\n",
       "KitchenAbvGr      4.488397\n",
       "TotRmsAbvGrd      0.676341\n",
       "Fireplaces        0.649565\n",
       "GarageYrBlt      -0.649415\n",
       "GarageCars       -0.342549\n",
       "GarageArea        0.179981\n",
       "WoodDeckSF        1.541376\n",
       "OpenPorchSF       2.364342\n",
       "EnclosedPorch     3.089872\n",
       "3SsnPorch        10.304342\n",
       "ScreenPorch       4.122214\n",
       "PoolArea         14.828374\n",
       "MiscVal          24.476794\n",
       "MoSold            0.212053\n",
       "YrSold            0.096269\n",
       "SalePrice         1.882876\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# skewness\n",
    "train_data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "6f1e8286-27f1-441d-89b5-be4df75aa2ff",
    "_uuid": "77fef9cad5d4ba5e74bce073ebe67221d0dc16ec"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AaWZ2pKRLCMNAv4lt+TVwn6QHgf8AfzGz+fE4+2Vi+6+JWajKz3mZVMEhg0az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\n/w+FnoSxcoeFrwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd5301375f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 計算相關性\n",
    "corrmat = train_data.corr()\n",
    "# 畫圖\n",
    "sns.heatmap(corrmat, square=True)\n",
    "# 顯示圖片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "30f5a679-81fe-4590-bd90-c1abc4defd7f",
    "_uuid": "563e283dc6b59c4ac594d77251ca9124d1a99fee"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CkkOAVwNXLFUn9yE7x+tI4CHgzKXu0H7AMZudPY3Td4HhPytwKrCgN83s8wGQJMAXgeuq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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d4548be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jgGXAcmCUWzHKwptuvaJe9qK3gBtc/9nAgkA/iREX0tO8qN70BuJd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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d447b208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8nleuSm3ApoKCArp360YyZ0sqUbYA+x9wQBLvCsXFxVx//Q1UVlRx1NDvkxHI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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d447b2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 離散資料\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "label = 'MSZoning'\n",
    "# 畫出直方圖\n",
    "data = train_data[label]\n",
    "plt.hist(data)\n",
    "\n",
    "# seaborn 會自動移除 NaN 值，需注意\n",
    "# 畫出直方圖\n",
    "# sns.factorplot(label, hue='SalePrice', data=train_data, kind=\"count\")\n",
    "# 顯示中心值與信賴區間\n",
    "sns.factorplot(label, 'SalePrice', data=train_data, kind=\"point\")\n",
    "# 顯示中位數與四分位距\n",
    "sns.factorplot(label, 'SalePrice', data=train_data, kind=\"violin\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "757c870e-5605-4fe3-ba89-d48400930a4e",
    "_uuid": "032d59eb20cda785249c259640e21988ca16781d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d44e9b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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OPvtsfv3rX89ZGxa6/v5+LrnkksyM0/e///2cffbZnH/++XPQsrnT4vd6wtFxkV7zbAV2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\nvsIMn2EYhtFXmOEzDMMw+or/DznOPb3f5ABkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d4573240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/v70+6DUF+VnsraqnLsREwvRUDyeNi7z0JHh/B7MOG8SsgEbW604Z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d41e2e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UlISPPnJM6VRQUIC0tDSUlJTAZrNhzJgxyMnJ0ZpGY8aMQXJyMi5duoTDhw9r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\nMVncsYIFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d4162128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 連續資料\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "label = 'LotFrontage'\n",
    "# 畫出直方圖\n",
    "# 移除 NA 值\n",
    "data = train_data[label].dropna(how='any')\n",
    "plt.hist(data, bins=range(0,100,10), rwidth=0.8)\n",
    "\n",
    "# seaborn 會自動移除 NaN 值，需注意\n",
    "# 畫出相關性的圖\n",
    "sns.jointplot(label, 'SalePrice', data=train_data, kind='hex')\n",
    "# 顯示中心值與信賴區間\n",
    "sns.factorplot('SalePrice', label, data=train_data, kind=\"point\")\n",
    "# 顯示中位數與四分位距\n",
    "sns.factorplot('SalePrice', label, data=train_data, kind=\"violin\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e7bceeb4-9482-4bd3-ad45-f9d8bc3efdab",
    "_uuid": "a1200e55fcf4a95b0955b76db721096c8d0c6f2d"
   },
   "source": [
    "資料處理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "8814f552-ba74-400f-a8ce-4ae03a93312c",
    "_uuid": "1e5a826a0c944847a387d9b44aadaec181e2b1d3",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_origin = train_data.drop(['Id', 'SalePrice'], axis='columns')\n",
    "y = train_data['SalePrice']\n",
    "y = np.log1p(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "44670cc0-e3bb-4d1f-a7c8-b422beaffc89",
    "_uuid": "48b1a0c4e5da41b861ab7d7a5031bd07bb4538c5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</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>...</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>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</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>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities      ...       ScreenPorch PoolArea PoolQC  Fence  \\\n",
       "0         Lvl    AllPub      ...               120        0    NaN  MnPrv   \n",
       "1         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "2         Lvl    AllPub      ...                 0        0    NaN  MnPrv   \n",
       "3         Lvl    AllPub      ...                 0        0    NaN    NaN   \n",
       "4         HLS    AllPub      ...               144        0    NaN    NaN   \n",
       "\n",
       "  MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0         NaN       0      6    2010        WD         Normal  \n",
       "1        Gar2   12500      6    2010        WD         Normal  \n",
       "2         NaN       0      3    2010        WD         Normal  \n",
       "3         NaN       0      6    2010        WD         Normal  \n",
       "4         NaN       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv(\"../input/test.csv\")\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "f4fa5e67-5227-49d1-87bd-dcfd155edd92",
    "_uuid": "61cda3218ea612217304979ba8f32129898f54a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 80 columns):\n",
      "Id               1459 non-null int64\n",
      "MSSubClass       1459 non-null int64\n",
      "MSZoning         1455 non-null object\n",
      "LotFrontage      1232 non-null float64\n",
      "LotArea          1459 non-null int64\n",
      "Street           1459 non-null object\n",
      "Alley            107 non-null object\n",
      "LotShape         1459 non-null object\n",
      "LandContour      1459 non-null object\n",
      "Utilities        1457 non-null object\n",
      "LotConfig        1459 non-null object\n",
      "LandSlope        1459 non-null object\n",
      "Neighborhood     1459 non-null object\n",
      "Condition1       1459 non-null object\n",
      "Condition2       1459 non-null object\n",
      "BldgType         1459 non-null object\n",
      "HouseStyle       1459 non-null object\n",
      "OverallQual      1459 non-null int64\n",
      "OverallCond      1459 non-null int64\n",
      "YearBuilt        1459 non-null int64\n",
      "YearRemodAdd     1459 non-null int64\n",
      "RoofStyle        1459 non-null object\n",
      "RoofMatl         1459 non-null object\n",
      "Exterior1st      1458 non-null object\n",
      "Exterior2nd      1458 non-null object\n",
      "MasVnrType       1443 non-null object\n",
      "MasVnrArea       1444 non-null float64\n",
      "ExterQual        1459 non-null object\n",
      "ExterCond        1459 non-null object\n",
      "Foundation       1459 non-null object\n",
      "BsmtQual         1415 non-null object\n",
      "BsmtCond         1414 non-null object\n",
      "BsmtExposure     1415 non-null object\n",
      "BsmtFinType1     1417 non-null object\n",
      "BsmtFinSF1       1458 non-null float64\n",
      "BsmtFinType2     1417 non-null object\n",
      "BsmtFinSF2       1458 non-null float64\n",
      "BsmtUnfSF        1458 non-null float64\n",
      "TotalBsmtSF      1458 non-null float64\n",
      "Heating          1459 non-null object\n",
      "HeatingQC        1459 non-null object\n",
      "CentralAir       1459 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1459 non-null int64\n",
      "2ndFlrSF         1459 non-null int64\n",
      "LowQualFinSF     1459 non-null int64\n",
      "GrLivArea        1459 non-null int64\n",
      "BsmtFullBath     1457 non-null float64\n",
      "BsmtHalfBath     1457 non-null float64\n",
      "FullBath         1459 non-null int64\n",
      "HalfBath         1459 non-null int64\n",
      "BedroomAbvGr     1459 non-null int64\n",
      "KitchenAbvGr     1459 non-null int64\n",
      "KitchenQual      1458 non-null object\n",
      "TotRmsAbvGrd     1459 non-null int64\n",
      "Functional       1457 non-null object\n",
      "Fireplaces       1459 non-null int64\n",
      "FireplaceQu      729 non-null object\n",
      "GarageType       1383 non-null object\n",
      "GarageYrBlt      1381 non-null float64\n",
      "GarageFinish     1381 non-null object\n",
      "GarageCars       1458 non-null float64\n",
      "GarageArea       1458 non-null float64\n",
      "GarageQual       1381 non-null object\n",
      "GarageCond       1381 non-null object\n",
      "PavedDrive       1459 non-null object\n",
      "WoodDeckSF       1459 non-null int64\n",
      "OpenPorchSF      1459 non-null int64\n",
      "EnclosedPorch    1459 non-null int64\n",
      "3SsnPorch        1459 non-null int64\n",
      "ScreenPorch      1459 non-null int64\n",
      "PoolArea         1459 non-null int64\n",
      "PoolQC           3 non-null object\n",
      "Fence            290 non-null object\n",
      "MiscFeature      51 non-null object\n",
      "MiscVal          1459 non-null int64\n",
      "MoSold           1459 non-null int64\n",
      "YrSold           1459 non-null int64\n",
      "SaleType         1458 non-null object\n",
      "SaleCondition    1459 non-null object\n",
      "dtypes: float64(11), int64(26), object(43)\n",
      "memory usage: 912.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "1519a5ce-0df5-41d3-80a8-581535026205",
    "_uuid": "05183c7315b5db45203767690f6214d3abe97a38"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSZoning           4\n",
       "LotFrontage      227\n",
       "Alley           1352\n",
       "Utilities          2\n",
       "Exterior1st        1\n",
       "Exterior2nd        1\n",
       "MasVnrType        16\n",
       "MasVnrArea        15\n",
       "BsmtQual          44\n",
       "BsmtCond          45\n",
       "BsmtExposure      44\n",
       "BsmtFinType1      42\n",
       "BsmtFinSF1         1\n",
       "BsmtFinType2      42\n",
       "BsmtFinSF2         1\n",
       "BsmtUnfSF          1\n",
       "TotalBsmtSF        1\n",
       "BsmtFullBath       2\n",
       "BsmtHalfBath       2\n",
       "KitchenQual        1\n",
       "Functional         2\n",
       "FireplaceQu      730\n",
       "GarageType        76\n",
       "GarageYrBlt       78\n",
       "GarageFinish      78\n",
       "GarageCars         1\n",
       "GarageArea         1\n",
       "GarageQual        78\n",
       "GarageCond        78\n",
       "PoolQC          1456\n",
       "Fence           1169\n",
       "MiscFeature     1408\n",
       "SaleType           1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NA 個數\n",
    "naCount = test_data.isnull().sum()\n",
    "naCount[naCount > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "da826163-2e5a-41f7-a807-c85427e286db",
    "_uuid": "53a02530e1cc2733ac4c72d4ea8b27da99e9c544",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test_origin = test_data.drop(['Id'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "68a3940b-79dd-4733-bb6e-e7cbd14cd396",
    "_uuid": "c0667ef68caa012098473232e3b103b712e27639"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_all_origin = pd.concat([X_origin, X_test_origin], ignore_index=True)\n",
    "trainL = len(X_origin)\n",
    "testL = len(X_test_origin)\n",
    "\n",
    "X_all_origin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "77e67e41-4ea1-48be-85f4-9346232a36f3",
    "_uuid": "133ee39554bc286cfd823aeec19b67b7c580eadd",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "def transSymbolEncoder(x):\n",
    "    lb = preprocessing.LabelEncoder()\n",
    "    lb.fit(x)\n",
    "    return lb.transform(x)\n",
    "\n",
    "def transSymbolBinary(XX, colName):\n",
    "    X = XX\n",
    "    x = X[colName].fillna(\"NA\")\n",
    "    lb = preprocessing.LabelBinarizer()\n",
    "    lb.fit(x)\n",
    "    value = lb.transform(x)\n",
    "    for i in range(len(value[0])):\n",
    "        addName = \"{}_{}\".format(colName, lb.classes_[i])\n",
    "        X[addName] = value[:, i]\n",
    "\n",
    "    return X.drop([colName], axis='columns')\n",
    "\n",
    "# 缺項處理\n",
    "def fillNA(XX):\n",
    "    X = XX.copy(deep=True)\n",
    "    for colName in X.columns:\n",
    "        if sum(X[colName].isnull()) > 0:\n",
    "            if X.dtypes[colName] in [np.int64, np.float64]:\n",
    "                # print(\"{} is filled by {}\".format(colName, X[colName].mean()))\n",
    "                X[colName] = X[colName].fillna(X[colName].mean())\n",
    "            elif X.dtypes[colName] in [object]:\n",
    "                # print(\"{} is filled by NA\".format(colName))\n",
    "                fillVal = \"NA\"# X[colName].value_counts().index[0]\n",
    "                X[colName] = X[colName].fillna(fillVal)\n",
    "            else:\n",
    "                print(\"{} is not define\".format(colName))\n",
    "    return X\n",
    "\n",
    "def transAll(XX):\n",
    "    X = XX.copy(deep=True)\n",
    "    for colName in X.columns:\n",
    "        if X.dtypes[colName] in [np.int64, np.float64]:\n",
    "            # print(\"{} is not transformed\".format(colName))\n",
    "            # X[colName] = (X[colName] - X[colName].mean()) / X[colName].std()\n",
    "            pass\n",
    "        elif X.dtypes[colName] in [object]:\n",
    "            # print(\"{} is transformed\".format(colName))\n",
    "            X = transSymbolBinary(X, colName)\n",
    "        else:\n",
    "            print(\"{} is not define\".format(colName))\n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "e9ca19a0-9577-4c5e-8ad7-c64e46770a0f",
    "_uuid": "99105c8e2b5d2584a0dc7a04abd665b4c516f1e9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>SaleType_NA</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>7.602900</td>\n",
       "      <td>5.283204</td>\n",
       "      <td>6.561031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.589336</td>\n",
       "      <td>7.589336</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.886532</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.234107</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.601902</td>\n",
       "      <td>7.602401</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>6.188264</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.262680</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.557995</td>\n",
       "      <td>7.586296</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.379897</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.601402</td>\n",
       "      <td>7.601402</td>\n",
       "      <td>5.860786</td>\n",
       "      <td>6.486161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.931826</td>\n",
       "      <td>4.454347</td>\n",
       "      <td>9.555064</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.597898</td>\n",
       "      <td>7.598900</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.597146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>4.330733</td>\n",
       "      <td>9.218804</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.603399</td>\n",
       "      <td>7.603898</td>\n",
       "      <td>5.231109</td>\n",
       "      <td>7.222566</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.252854</td>\n",
       "      <td>9.247925</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.587817</td>\n",
       "      <td>7.587817</td>\n",
       "      <td>5.484797</td>\n",
       "      <td>6.756932</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.931826</td>\n",
       "      <td>3.951244</td>\n",
       "      <td>8.719481</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.566311</td>\n",
       "      <td>7.576097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.252273</td>\n",
       "      <td>3.931826</td>\n",
       "      <td>8.912069</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.570443</td>\n",
       "      <td>7.576097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.747587</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 309 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0    4.110874     4.189655  9.042040     2.079442     1.791759   7.602900   \n",
       "1    3.044522     4.394449  9.169623     1.945910     2.197225   7.589336   \n",
       "2    4.110874     4.234107  9.328212     2.079442     1.791759   7.601902   \n",
       "3    4.262680     4.110874  9.164401     2.079442     1.791759   7.557995   \n",
       "4    4.110874     4.442651  9.565284     2.197225     1.791759   7.601402   \n",
       "5    3.931826     4.454347  9.555064     1.791759     1.791759   7.597898   \n",
       "6    3.044522     4.330733  9.218804     2.197225     1.791759   7.603399   \n",
       "7    4.110874     4.252854  9.247925     2.079442     1.945910   7.587817   \n",
       "8    3.931826     3.951244  8.719481     2.079442     1.791759   7.566311   \n",
       "9    5.252273     3.931826  8.912069     1.791759     1.945910   7.570443   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2          ...            \\\n",
       "0      7.602900    5.283204    6.561031    0.000000          ...             \n",
       "1      7.589336    0.000000    6.886532    0.000000          ...             \n",
       "2      7.602401    5.093750    6.188264    0.000000          ...             \n",
       "3      7.586296    0.000000    5.379897    0.000000          ...             \n",
       "4      7.601402    5.860786    6.486161    0.000000          ...             \n",
       "5      7.598900    0.000000    6.597146    0.000000          ...             \n",
       "6      7.603898    5.231109    7.222566    0.000000          ...             \n",
       "7      7.587817    5.484797    6.756932    3.496508          ...             \n",
       "8      7.576097    0.000000    0.000000    0.000000          ...             \n",
       "9      7.576097    0.000000    6.747587    0.000000          ...             \n",
       "\n",
       "   SaleType_NA  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0            0             0             0            1   \n",
       "1            0             0             0            1   \n",
       "2            0             0             0            1   \n",
       "3            0             0             0            1   \n",
       "4            0             0             0            1   \n",
       "5            0             0             0            1   \n",
       "6            0             0             0            1   \n",
       "7            0             0             0            1   \n",
       "8            0             0             0            1   \n",
       "9            0             0             0            1   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0                      0                      0                     0   \n",
       "1                      0                      0                     0   \n",
       "2                      0                      0                     0   \n",
       "3                      1                      0                     0   \n",
       "4                      0                      0                     0   \n",
       "5                      0                      0                     0   \n",
       "6                      0                      0                     0   \n",
       "7                      0                      0                     0   \n",
       "8                      1                      0                     0   \n",
       "9                      0                      0                     0   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                     0                     1                      0  \n",
       "1                     0                     1                      0  \n",
       "2                     0                     1                      0  \n",
       "3                     0                     0                      0  \n",
       "4                     0                     1                      0  \n",
       "5                     0                     1                      0  \n",
       "6                     0                     1                      0  \n",
       "7                     0                     1                      0  \n",
       "8                     0                     0                      0  \n",
       "9                     0                     1                      0  \n",
       "\n",
       "[10 rows x 309 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skewed_feats = (X_all_origin.skew() > 0.75)\n",
    "skewed_feats = skewed_feats.index\n",
    "\n",
    "X_all = fillNA(X_all_origin)\n",
    "X_all[skewed_feats] = np.log1p(X_all[skewed_feats])\n",
    "X_all = transAll(X_all)\n",
    "X_all.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "f0b77200-ea9c-4d4e-aca0-a64c562b6fe7",
    "_uuid": "a37ba302560f255ba757a4e871b62d7fc206d462",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X_all[:trainL]\n",
    "X_test = X_all[trainL:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "97f14193-7d59-42d4-bc9c-7af0424033bf",
    "_uuid": "1184ba53b405a353c87690f1eda5e2f0eaa2a81b"
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "  <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",
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       "      <th>SaleCondition_Abnorml</th>\n",
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       "      <td>3.044522</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.589336</td>\n",
       "      <td>7.589336</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.886532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.234107</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.601902</td>\n",
       "      <td>7.602401</td>\n",
       "      <td>5.093750</td>\n",
       "      <td>6.188264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.262680</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.557995</td>\n",
       "      <td>7.586296</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.379897</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.601402</td>\n",
       "      <td>7.601402</td>\n",
       "      <td>5.860786</td>\n",
       "      <td>6.486161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 309 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0    4.110874     4.189655  9.042040     2.079442     1.791759   7.602900   \n",
       "1    3.044522     4.394449  9.169623     1.945910     2.197225   7.589336   \n",
       "2    4.110874     4.234107  9.328212     2.079442     1.791759   7.601902   \n",
       "3    4.262680     4.110874  9.164401     2.079442     1.791759   7.557995   \n",
       "4    4.110874     4.442651  9.565284     2.197225     1.791759   7.601402   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2          ...            \\\n",
       "0      7.602900    5.283204    6.561031         0.0          ...             \n",
       "1      7.589336    0.000000    6.886532         0.0          ...             \n",
       "2      7.602401    5.093750    6.188264         0.0          ...             \n",
       "3      7.586296    0.000000    5.379897         0.0          ...             \n",
       "4      7.601402    5.860786    6.486161         0.0          ...             \n",
       "\n",
       "   SaleType_NA  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0            0             0             0            1   \n",
       "1            0             0             0            1   \n",
       "2            0             0             0            1   \n",
       "3            0             0             0            1   \n",
       "4            0             0             0            1   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0                      0                      0                     0   \n",
       "1                      0                      0                     0   \n",
       "2                      0                      0                     0   \n",
       "3                      1                      0                     0   \n",
       "4                      0                      0                     0   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                     0                     1                      0  \n",
       "1                     0                     1                      0  \n",
       "2                     0                     1                      0  \n",
       "3                     0                     0                      0  \n",
       "4                     0                     1                      0  \n",
       "\n",
       "[5 rows x 309 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "70db24e6-df84-434b-8d60-2afcc879e18a",
    "_uuid": "df49349df8ca08142911bf55eb999ac426513cd7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>SaleType_NA</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.818794</td>\n",
       "      <td>4.215224</td>\n",
       "      <td>9.110966</td>\n",
       "      <td>1.940309</td>\n",
       "      <td>1.869266</td>\n",
       "      <td>7.586821</td>\n",
       "      <td>7.593756</td>\n",
       "      <td>2.145670</td>\n",
       "      <td>4.229731</td>\n",
       "      <td>0.655398</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.083562</td>\n",
       "      <td>0.002055</td>\n",
       "      <td>0.867808</td>\n",
       "      <td>0.069178</td>\n",
       "      <td>0.002740</td>\n",
       "      <td>0.008219</td>\n",
       "      <td>0.013699</td>\n",
       "      <td>0.820548</td>\n",
       "      <td>0.085616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.688354</td>\n",
       "      <td>0.314483</td>\n",
       "      <td>0.517369</td>\n",
       "      <td>0.201983</td>\n",
       "      <td>0.168400</td>\n",
       "      <td>0.015389</td>\n",
       "      <td>0.010424</td>\n",
       "      <td>2.630552</td>\n",
       "      <td>2.992052</td>\n",
       "      <td>1.845045</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.276824</td>\n",
       "      <td>0.045299</td>\n",
       "      <td>0.338815</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.052289</td>\n",
       "      <td>0.090317</td>\n",
       "      <td>0.116277</td>\n",
       "      <td>0.383862</td>\n",
       "      <td>0.279893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>7.170888</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>7.535297</td>\n",
       "      <td>7.576097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>8.929898</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.578145</td>\n",
       "      <td>7.584773</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.931826</td>\n",
       "      <td>4.252854</td>\n",
       "      <td>9.156887</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.587817</td>\n",
       "      <td>7.598399</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.951943</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.262680</td>\n",
       "      <td>4.382027</td>\n",
       "      <td>9.358976</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.601402</td>\n",
       "      <td>7.603399</td>\n",
       "      <td>5.107456</td>\n",
       "      <td>6.569832</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.252273</td>\n",
       "      <td>5.749393</td>\n",
       "      <td>12.279537</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>7.378384</td>\n",
       "      <td>8.638525</td>\n",
       "      <td>7.296413</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 309 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MSSubClass  LotFrontage      LotArea  OverallQual  OverallCond  \\\n",
       "count  1460.000000  1460.000000  1460.000000  1460.000000  1460.000000   \n",
       "mean      3.818794     4.215224     9.110966     1.940309     1.869266   \n",
       "std       0.688354     0.314483     0.517369     0.201983     0.168400   \n",
       "min       3.044522     3.091042     7.170888     0.693147     0.693147   \n",
       "25%       3.044522     4.110874     8.929898     1.791759     1.791759   \n",
       "50%       3.931826     4.252854     9.156887     1.945910     1.791759   \n",
       "75%       4.262680     4.382027     9.358976     2.079442     1.945910   \n",
       "max       5.252273     5.749393    12.279537     2.397895     2.302585   \n",
       "\n",
       "         YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1   BsmtFinSF2  \\\n",
       "count  1460.000000   1460.000000  1460.000000  1460.000000  1460.000000   \n",
       "mean      7.586821      7.593756     2.145670     4.229731     0.655398   \n",
       "std       0.015389      0.010424     2.630552     2.992052     1.845045   \n",
       "min       7.535297      7.576097     0.000000     0.000000     0.000000   \n",
       "25%       7.578145      7.584773     0.000000     0.000000     0.000000   \n",
       "50%       7.587817      7.598399     0.000000     5.951943     0.000000   \n",
       "75%       7.601402      7.603399     5.107456     6.569832     0.000000   \n",
       "max       7.606387      7.606387     7.378384     8.638525     7.296413   \n",
       "\n",
       "               ...            SaleType_NA  SaleType_New  SaleType_Oth  \\\n",
       "count          ...                 1460.0   1460.000000   1460.000000   \n",
       "mean           ...                    0.0      0.083562      0.002055   \n",
       "std            ...                    0.0      0.276824      0.045299   \n",
       "min            ...                    0.0      0.000000      0.000000   \n",
       "25%            ...                    0.0      0.000000      0.000000   \n",
       "50%            ...                    0.0      0.000000      0.000000   \n",
       "75%            ...                    0.0      0.000000      0.000000   \n",
       "max            ...                    0.0      1.000000      1.000000   \n",
       "\n",
       "       SaleType_WD  SaleCondition_Abnorml  SaleCondition_AdjLand  \\\n",
       "count  1460.000000            1460.000000            1460.000000   \n",
       "mean      0.867808               0.069178               0.002740   \n",
       "std       0.338815               0.253844               0.052289   \n",
       "min       0.000000               0.000000               0.000000   \n",
       "25%       1.000000               0.000000               0.000000   \n",
       "50%       1.000000               0.000000               0.000000   \n",
       "75%       1.000000               0.000000               0.000000   \n",
       "max       1.000000               1.000000               1.000000   \n",
       "\n",
       "       SaleCondition_Alloca  SaleCondition_Family  SaleCondition_Normal  \\\n",
       "count           1460.000000           1460.000000           1460.000000   \n",
       "mean               0.008219              0.013699              0.820548   \n",
       "std                0.090317              0.116277              0.383862   \n",
       "min                0.000000              0.000000              0.000000   \n",
       "25%                0.000000              0.000000              1.000000   \n",
       "50%                0.000000              0.000000              1.000000   \n",
       "75%                0.000000              0.000000              1.000000   \n",
       "max                1.000000              1.000000              1.000000   \n",
       "\n",
       "       SaleCondition_Partial  \n",
       "count            1460.000000  \n",
       "mean                0.085616  \n",
       "std                 0.279893  \n",
       "min                 0.000000  \n",
       "25%                 0.000000  \n",
       "50%                 0.000000  \n",
       "75%                 0.000000  \n",
       "max                 1.000000  \n",
       "\n",
       "[8 rows x 309 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "73f19384-80b0-41e5-95b7-abb3e139bfc4",
    "_uuid": "f09c5fb5dc95711d2326197903bbb50a871a37ac"
   },
   "source": [
    "演算法實驗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "34821802-8994-43f0-a34b-c701bfaecac1",
    "_uuid": "fc817de3b6d5e690f3aa68dbd43a6252a10700ac",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score  \n",
    "import seaborn as sns\n",
    "\n",
    "def evaluate_model(clf, X, y):\n",
    "    # 5-Fold Cross Validation\n",
    "    scores = cross_val_score(clf, X, y, cv=10, scoring=\"neg_mean_squared_error\")\n",
    "    # 平均埴\n",
    "    m = scores.mean()\n",
    "    # 標準差\n",
    "    sd = scores.std()\n",
    "    \n",
    "    # 訓練資料準確度\n",
    "    clf.fit(X, y)\n",
    "    score = clf.score(X, y)\n",
    "    \n",
    "    # 中位數\n",
    "    ax = sns.boxplot(scores)\n",
    "    ax.set_title(\"val_mean:{:.5f}, val_std:{:.5f}\\ntrain_score:{:.2f}\\n\\nclf:{}\".format(m, sd, score, clf))\n",
    "    \n",
    "    return clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "31880869-30d7-4667-b91e-ce1938544f30",
    "_uuid": "098330dd73bcd3dfe45705a4f6081a097cbf5d14",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D0gOS3tbGdLtImrosYlrRSHpU0jvz61Ml/U+3Y2pF0u6S7u1nGWMl3SVpaE/TdrJl4AvA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\nZoVzMmBmZlY4JwNmZmaF+/8QXtKutN1GkQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d2072080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "clf = LinearRegression()\n",
    "linear_clf = evaluate_model(clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "358b8d84-72f7-4aae-a677-623f7ec6837f",
    "_uuid": "ebb14756b349d179e334780d4b4c18dcdb2b8ae8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VmZn1Ki1VHCPipGaPASAvTVV+3XBBRDQtCcisO1STrQrOV7Xep6WKY08REbeS\nnqU0M7NeyEtWmZmZlemwOEp6Lwdkz5P0e0lr1vthks7N0WjnSjpTUkjavLD9hNzW7sXSvN+Awvsn\n8/OSbe0fkq4qvF9J0kJJN9b7Xcr6v6ujMZuZWe9RzczxrYgYHhFDSY9IHNuJzzsaGBYRJ+f3c4GD\nC9sPIgUFdOQElj4fWY03gaE5bg7gU8BzNRzfafkOWjMz6wVqPa36ALABpBU28gxwnqS5kkZ30H4D\nKQFmeqmNdDfpfnn7ZqTnEl8qfZikiyVNy7PNs3Lb8cD6wJ2S7qxh7DcDpbsCxpDyUEufs6qkcTmw\nfKak0pgOVwo5vy3PTr8h6Vt5nwcl/XOh/0MLM+wd8vFnSvqNpPuA37QVWK4Urn6XpN8pLZ11tQpZ\nd4VxHp1/H9MWLlxYw1c3M7NaVF0c88PpewI35KYvkhYA3oYU53aupPXaao+IfVk6C7029/F34Bml\nFSgOBq5lWafn51GGkdJvhkXE+cDzpIf196jhu44HDpbUL/f3UPFzgDsiYgdgjzzmVfO2ofk7bQ/8\nEFgUEduS/lD4aqGPARExHPhXYFyhfTCwV0SMYWlg+QhgNHB+Yb9tSTPiwcCmwM6UiYixETEyIkYO\nHDiwhq9uZma1qKY49s+JLi8A6wC35fZdgGsi4r2IeBG4m1RA2mpvy3hSYRxFenC/6EuSZgAzSSto\nDK7uay0vIuaQEnDGkGaRRZ8GTsvf8y7Sc5KlZS/ujIjXI2IhaWb7+9w+N/dXck3+nHtIweala7M3\nRMRb+fXKwCWS5gITyr7PlIh4NkfSzSrr28zMGqjqa47AJoDo3DXHSm4k5ZM+HRF/LzUqLTx8ErBn\nRAwjxbd1FDDekRtIkXDXlLWLtHTU8PyzcUQ8kre9U9jv/cL791n2UZjyqKHS+2K0XDGwfCTwgcK2\n4ue8hx+zMTNrmqpPq0bEIlJG6bfzzSWTgdGSVpQ0ENiNlGvaVnt7/Z5KOmVZ9EFSYXlN0jrAPoVt\nrwOrVzv2gnHAWRExt6z9VuC40nU+SdvW0Xfp2uouwGsR8VqFfbojsNzMzLpYTbOTiJiZw7vHAFcB\nHyMt9xTAKRHxgqSJldo76Hd8hbbZkmaSAsSfAe4rbB4L3CLp+VquO0bEsyx7na/kB8DPgDmSVgAW\nAJXWXGzP23m8KwNHtrFPZwLLzXocJ99Yq2qp4PG+xMHjZma1qzZ43Ak5ZmZmZVrmpg9JawF/qrBp\nz4h4udHjMesLqg0eB5+Ctd6lZWaOEfFy4W7TJT/ASpJ+K+kvkqZLekDScqtj5Af051Vo/77SMljt\nkjQ8x9Tt3UVfyczMmqRlimO0CpqpAAAPpElEQVQl+e7TScA9EbFpRGxHeqZyw7L92pxBR8QZEXF7\nFR83Brg3/1txLPlmHzMz6+Fa/f+sPwksjohflhoi4qmI+EWOhrtB0h1UPh0LgKTLJR0oaW9JEwrt\nu5eCy3MRPgg4HPhUTuEpzUYfk3QlMA/YSNKn8+x1hqQJklbL+54haWqOnxtbKT7OzMwao9WL4xBg\nRjvbRwAHRsQnqujrdmDHQqzcaFK6D8DHSYsZP0FK2CleXNkCuCgihpAe3fgeKU5uBDAN+Fbe74KI\n2D4HvPen9kdJzMysi7R6cVyGpAslzZY0NTfdFhGvVHNsRLxLejbxC/k07OeA/8mbx7C0UI5n2VOr\nT0XEg/n1TqTIuPtyVN1hpOQhgD0kPZSj5T5JKuzl43fwuJlZA7TM3aptmA8cUHoTEccqrftYekCw\n1ofwxwPfIC3dNS0iXs+B7AcA+0k6nRRFt5akUoJP8TNEKsjLXJfMp2EvAkZGxDOSzqRCVF5EjCUF\nIDBy5Eg/oGpm1k1afeZ4B9BP0tcLbbWsA1nubtKp2K+xdKa4JzAnIjaKiEERsQlwHbDcHbHAg8DO\nygs8Ky2VtSVLC+FL+RrkgZ0Yo5mZdVJLF8dI8T+jSMtdLZA0BbiClOVayUckPVv4Oaisv/dIQen7\n5H8hnUItX03kOirctZpX9jgcuCbH8D0AbBURrwKXkG7auRWYWn6smZk1juPjeinHx1lP4BAA622q\njY9r9WuOZtaNXPCsVbX0aVUzM7N6eOZoZnWr5bRqd/IM1rqaZ45mZmZl+lxxlPSepFk5pm2CpLoe\n7ZD0Rtn7EyS9LWmNrhmpmZk1S58rjsBbecWOocBi4Jgu6ncM6RGML1ba2F64uZmZ9Sx9sTgWTQZK\nD+R/K88m50k6obRDW+1FkjYDViPlpo4ptC8Xbi7p5BwwPkfSWYV9J+UlteZLOrpbvq2ZmVWlz85m\n8kxuH+AWSdsBRwA7kiLeHpJ0N+mPh+XaI2JmWXcHkxJzJpOCBNaJiBfzthHAsIh4RdKnSUHkO+T+\nbpC0W0TcAxyZ9+kPTJV0XfkizbloHg2w8cYbd+0vxMzMluiLM8f+OfR7GvA0cBmwCzAxIt6MiDeA\n64Fd22kvNwYYHxHvk9Jxisk6xXDzT+efmaTVQrYiFUuA4yXNJkXMbVRoXyIixkbEyIgYOXDgwPp/\nA2Zm1q6+OHN8KyKGFxs6s3SipK1Jhey23M8HgAXABXmX8uDx/4iIX5X1sTuwF/CxiFgk6S4qBI+b\nmVlj9MWZYyWTgVGSBuT1GvfPbW21F40Bzsyh44MiYn1gfUmbsLxbgSMLCxxvIOlDwBrA33Jh3Iq0\ntJWZmTVJX5w5LiciZki6HJiSmy4tXVdsq73gYOCzZW0Tc/uLxcaI+KOkjwIP5FnmG8BXSOtEHiPp\nEeAx0qlVMzNrEgeP91IOHjczq121weM+rWpmZlbGxdHMzKyMrzmaWd16SvB4tRxQbtVq2MxRyb2S\n9im0HSTpli7o+ypJC3Jm6mxJe3S2zxo//+yyVJ0PSHpF0tntHLOXpEltbHtW0prdMVYzM+tYw4pj\npDt/jgF+KqlffpzhR8Cxnem3kFl6Yn5+8STgok4NtvM+AzwMjG7yOMzMrA4NveYYEfOA3wOnAmcA\nV0bEE5IOkzQlz/wukrQCgKSxkqblvNEzSv3kmdU5kmaSnj0segDYoLDv9pLuzrmlf5C0Tm6/V9JP\nc/8PSxopaaKkP0s6s3D8KYVs1eMK7WdIelzSvSyfZjMG+CnwgqQdCsd8TtJjkmYA+xXaB0q6LX/P\nX5HCAszMrEmacUPOWcCXSbmmP5Y0lFTgPp5nfiuRnhEEOC3fcrsN8ClJgwv9/DUito2ICWX97w1M\nApC0CvBz4ICI2A64CvhBYd+3cv+X5WOOAbYGjpa0pqQdgUOA7YGPAf8qaetc8A7I4/ocKSuV/JkD\ngN2Bm4FryEHkuf1XpGcitwPWL/ud3BkRQ/JxxW1mZtZgDb8hJyLelHQt8EZEvCNpL1LxmZYfjO8P\nPJN3HyPpqDzO9YHBpNOVANeWdX2epB+TZo075raPAkOA23PfKwLPFo65If87F5hbCguX9CSwISlb\n9bqIeCu3TyJlqw4otL8l6feFPvcl5am+LWkCMF3St/PYH4+IJ3JfVwNfzcfsRg4SiIj/kfR6pd+d\ng8fNzBqjWXervp9/IJ1CHBcR/1bcQdIWwDeBHSLiVUlXsWzeaDGzFNI1x0mSTiTNBEsracyJiEph\n4QDvFMbzTqH9fer/3YwBdsoFFmAg8AmgYsGrRUSMBcZCCgHobH9mZlZZT3jO8XbgS5LWBpC0lqSN\ngQ+SCsrfJa1HusmlGj8DBkjakzTL3KB03S/fRTqkhrFNBvaX1D/fQLRfbrsnt/eT9EHg87n/NUm5\nqBuWslaB40kF82FgC0kfVprGjil8zj2kU81I+gKweg1jNDOzLtb04hgRc0nX3G6XNAf4I7AOaUmn\nh4FHgSuB+6rsL4CzgVMi4h3gQNIdsnNIS0Xt2N7xZX1NIV03nErKO704Iubm9onAHOAmlmavHkA6\npfqPQjeTgFHAP0jXNP9AWi7r/wr7/Duwl6R5pEL7fLVjNDOzruds1V7K2arWEzgEwHqbarNVnZBj\nZnVzsbFW1fTTqmZmZj2NZ45mVrfedlq1p/CMu+fzzNHMzKxMnymODQw+f1TS96o4Zn9JJ+fXS4LL\nJR0pad3OjsnMzOrXZ06rRkRIOgaYIOlO0nf/ESlurm5lweeTJPUHHpV0RUQ809ZxETGxjU1Hkh5j\neaEz4zIzs/r1mZkjNCz4vD8QwKLCvmvm1ztJuj2//n+SflY8UNJoYDhwbR7LB7rj92BmZu3rU8Ux\n667g8/MkzSLlwl4ZES/XOrCIuBaYBYyOiOERsbi4XdLRuVhPW7hwYa3dm5lZlfpccYyIN0mh5b/J\nCTrF4PNZpBzUzfLuY/LyUjNIIebF4lgefF5aT3Jd4LPFpaq6cOxjI2JkRIwcOHBgV3dvZmZZn7nm\nWKY7gs8BiIjXJd1NWtFjCvAuS/8I6VfpGDMz61n63Myxgi4NPpe0Mml9xydy05Ok9RshZa925HUc\nPG5m1lR9vjh2YfB56ZrjHGA6S9eKPBO4SNJUYHEbxxb9GrjUN+SYmTWPg8d7KQePm5nVrtrg8T4/\nczQzMyvn4mhmZlamr96tamZdwMHj1gyNCG73zNHMzKxMyxRHSe/lOzznSZogaUCd/bxReD1E0h2S\nHpP0hKSzStFyefs+ObHmYUkzJf1XO/2eKWmRpA9V+qz8fpSkkLRVPWM3M7Ou0TLFEXgrR64NJT0y\ncUxnOssB4jcA50TER4CtSc8vfjNvHwpcAHwlIgYDI4H/7aDbl4Bvt7N9DHBv/tfMzJqklYpj0WRg\ncwBJ38qzyXmlZaHaay/4MnBfRPwRICIWAd8ATs7bTwF+GBGP5u3vRcTFHYxrHDBa0j+Xb5C0GilV\n5yiWZruamVkTtFxxzEtI7QPMlbQdcASwI7AT8DVJ27bVXtbVENLD/EtExBNA/7zKxtDy7VV4g1Qg\nv1lh237ALRHxOPByHmP5d3PwuJlZA7RSceyfE2qmAU8Dl5FmYhMj4s2IeAO4Hti1nfZGOB84TFJ5\nRNwYYHx+PZ4Kp1YdPG5m1hit9CjHW3lVjCUkdaa/h4HdyvrbFHg5B5HPJ2Wmzq6l03zsb4FjC/3+\nM/BJYGtJAawIhKSTwxFGZmYN10ozx0omA6MkDZC0KmndxsnttBddDewiaS9YcoPO+cC/5+3nAt+V\ntGXevoKkam8C+inwLyz94+RA0hJam0TEoIjYCFhA42azZmZW0NLFMSJmAJeTlo56CLg0Ima21V52\n7FvAvsDpkh4n3Wl6X0RcnbfPAU4ArpH0CDAP2LTKcb0ETARWyU1j8vui6/Bdq2ZmTeHg8SpJGkWa\n8e0REU81ezwOHjczq52Dx7tYREyKiE17QmE0M7Pu1Uo35PQIkk4HDiprnhARP2zGeMzMrHYujl0s\nF0EXQjOzXsynVc3MzMq4OJqZmZVxcTQzMyvj4mhmZlbGxdHMzKyMi6OZmVkZJ+T0UpIWAt0RSLA2\nKSqvN/LYG6+3jhs89mZp9tg3iYgOlzVycbRlSJpWTbRST+SxN15vHTd47M3SW8bu06pmZmZlXBzN\nzMzKuDhaubHNHkAneOyN11vHDR57s/SKsfuao5mZWRnPHM3MzMq4OFpFko6T9Kik+ZJ+3Ozx1ErS\ntyWFpLWbPZZqSDo3/77nSJooac1mj6kjkvaW9Jik/5V0WrPHUy1JG0m6U9LD+b/vbzZ7TLWQtKKk\nmZJubPZYaiFpTUm/y/+dPyLpY80eU3tcHG05kvYA9gO2iYghwE+aPKSaSNoI+DTwdLPHUoPbgKER\nMQx4HPhOk8fTLkkrAhcC+wCDgTGSBjd3VFV7F/h2RAwGdgKO7UVjB/gm8EizB1GHnwO3RMRWwDb0\n8O/g4miVfB04JyLeAYiIvzZ5PLU6DzgF6DUX1CPijxHxbn77ILBhM8dThR2A/42Iv0TEYmA86Q+q\nHi8i/i8iZuTXr5P+T3qD5o6qOpI2BD4HXNrssdRC0hrAbsBlABGxOCJebe6o2ufiaJVsCewq6SFJ\nd0vavtkDqpak/YDnImJ2s8fSCUcCf2j2IDqwAfBM4f2z9JICUyRpELAt8FBzR1K1n5H+8Hu/2QOp\n0YeBhcCv8ynhSyWt2uxBtWelZg/AmkPS7cC6FTadTvrv4p9Jp5y2B/5b0qbRQ25t7mDs3yWdUu1x\n2ht3RPxP3ud00mm/qxs5tr5I0mrAdcAJEfH3Zo+nI5I+D/w1IqZL2r3Z46nRSsAI4LiIeEjSz4HT\ngH9r7rDa5uLYR0XEXm1tk/R14PpcDKdIep+Uh7iwUeNrT1tjl7Q16S/U2ZIgnZqcIWmHiHihgUOs\nqL3fOYCkw4HPA3v2lD9E2vEcsFHh/Ya5rVeQtDKpMF4dEdc3ezxV2hnYV9JngX7AByVdFRFfafK4\nqvEs8GxElGbovyMVxx7Lp1WtkknAHgCStgQ+QC8IOY6IuRHxoYgYFBGDSP+DHNETCmNHJO1NOl22\nb0QsavZ4qjAV2ELShyV9ADgYuKHJY6qK0l9OlwGPRMRPmz2eakXEdyJiw/zf9sHAHb2kMJL/N/iM\npI/kpj2Bh5s4pA555miVjAPGSZoHLAYO6wUzmd7uAmAV4LY8630wIo5p7pDaFhHvSvoGcCuwIjAu\nIuY3eVjV2hk4FJgraVZu+25E3NzEMfUFxwFX5z+m/gIc0eTxtMsJOWZmZmV8WtXMzKyMi6OZmVkZ\nF0czM7MyLo5mZmZlXBzNzMzKuDiamZmVcXE0MzMr4+JoZmZW5v8Dm7uk1Ngi+2oAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d16db6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature PoolQC_NA (6.736199)\n",
      "2. feature YearBuilt (3.304111)\n",
      "3. feature YearRemodAdd (1.871175)\n",
      "4. feature PoolArea (1.431733)\n",
      "5. feature GrLivArea (0.437569)\n",
      "6. feature RoofMatl_Membran (0.392356)\n",
      "7. feature GarageQual_Ex (0.377817)\n",
      "8. feature Condition2_PosA (0.374866)\n",
      "9. feature GarageCond_Ex (-0.311005)\n",
      "10. feature MSZoning_C (all) (-0.324474)\n",
      "11. feature Condition2_PosN (-0.549724)\n",
      "12. feature RoofMatl_ClyTile (-1.306213)\n",
      "13. feature PoolQC_Ex (-2.003042)\n",
      "14. feature PoolQC_Gd (-2.249830)\n",
      "15. feature PoolQC_Fa (-2.483327)\n",
      "16. feature YrSold (-6.277556)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "threhold = 3e-1\n",
    "# Make a fake dataset:\n",
    "features = linear_clf.coef_[abs(linear_clf.coef_) > threhold]\n",
    "names = X.columns[abs(linear_clf.coef_) > threhold]\n",
    "\n",
    "# feature 重要性\n",
    "importances = features\n",
    "# 由小到大排序並回傳對應的 index，再將之反向，也就是改為由大到小的排序\n",
    "indices = np.argsort(importances)[::-1]\n",
    "# 個數\n",
    "n = len(features)\n",
    "# 畫直方圖\n",
    "plt.barh(range(n), importances[indices[:n]], align=\"center\")\n",
    "plt.yticks(range(n), names[indices[:n]])\n",
    "plt.show()\n",
    "\n",
    "# 印出排序後的 feature 重要性\n",
    "print(\"Feature ranking:\")\n",
    "for f in range(n):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1, names[indices[f]], importances[indices[f]]))\n",
    "    \n",
    "linear_important_index = names[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "82cdc1c0-6888-4ae9-a07d-632a45ff5567",
    "_uuid": "ef2e892892aafd4764c7b05bed17ebbc246bae71"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UISNpL0m3SdqwMmxvSfMkrd/nMrao9VYeJU9av18wfFvQ0dHACeWYOT8iXhIR3ynpPUTS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d16c7d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0.12733\n",
    "from sklearn import linear_model\n",
    "\n",
    "clf = linear_model.Ridge()\n",
    "linearRidge_clf = evaluate_model(clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "fd9d146e-6790-4e36-aa45-dc1780999949",
    "_uuid": "5a41a4d089d4a8ec7953d9cf7d510fc47d515fc7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hIA3LQRy3SZoj6RLSvGQknVhqm6SzJI3Lr3eUdFV+fWEO5Jgj6dRCnU9I+rmk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\nn/NGYBvSKLNS+XK5fH4uH1v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JEZGvSlob2L2wrTwlq5Z7SZeRX8oj35eA95M65FJnPL7Q\n7nVIC1cgqa+k7Qt1DQGebODcRQ8D6yutDgVtyWUhhBB6gKVi1GR7mlLS1kHAlaTObAYpj/ok2/+S\nNKZSeY16r6lQNkPSNGAu8DRtl5MhPd18q6Rn67lvTHpqey0WX/xiFtC3sO7zGNIqSg8CT9EW6ynS\nEpG/ARaQfiAcVsc5l2B7gaT/zm1/g9opYSGEOsW82tAMPSKBK3Q9SX1tz8tPV58PPJovf9clErhC\nCKFxS1sCV48k6SOkjmsg6ZL+zcCJ+SnorjrnPNt98yXlm/M8ayR9Bvgl6WGt+aQ1k4/Jl9vr8XVJ\nh5LmOk8jPV2N0rrOi9ZRDiE0JhK4GhNXEiqLzriKPIK8EbjQ9l55itVI0j3mEwv7rQncXqGKnWwv\nsTKSpBVsv9tgW9Ymrfd8oO2JuWx/0v3rujrjPAqueyQcQgih+/T4B7haaEfgTduXAdheCBxHWqZw\nkqRBufxFUp72/wO2Iq3n/DbwV6UVmEqRk2MljQNuzw9n3S5pag4oqbUoxlHA5aWOOJ/3etv/lvQf\nkm6SNFPSfZIG53OeIunSHALy9xxaQt42QtIjku4mzXEOIYTQQjEyrm4Q6VLwIrZfk/QUabrTAcAP\n8xPQ69ieLOmnwDjbX1PK0p6UU7UgRWIOtv1Sfip8n1zfWsB9ksa6+g38jYDLq2w7FZhme29JOwJX\nkJ68hjS/eAfSCPphSReSgkwOzPusQPrxMGWJWkMIIXSbGBl3zJ1AaaGGA4Dr8+vPAyfnsJI7SfOT\n18vb/pKnNkF6Uvqn+Qnxv5KiPovBHY3YGvgdgO1xwJqSVs/bbskxmi+Q1jVeG9gGGGN7fp5fPbZS\npRAJXCGE0F2iM67uQdLSjovkTm490tSgF/Ml4eG0xWkK2C9Hbw6xvZ7th/K2NwpVHUyKpByag03+\nTfvBInPK21KnTgWARAJXCCF0j+iMq7sdWEXSV2FRRvaZwKj8BPO1wEnAGrZn5mP+DBxdWJxh0yp1\nrwE8Z/sdSTuQUsbacx5wqKTPlgok7Zsf7JpA6tzJISEvFBPFKhgP7C2pj6TVgD1qnDuEEEIXi864\ninz/dh/gy5IeBR4B3gS+l3e5nnTv9feFw34MrAjMlDQnv6/kKmCYpFmkxSHm1mjLv/O5fplXd3oI\n2JWUCHYKMDRf8j4dOLRGXVNJPyRmAP9HBICEEELLRehHqEuEfoQQQuPqDf2IkXEIIYTQYjG1qQ49\nLIlra+BXpAUtBJxj+4LOnqcJTQ5hmRQJXJ0TiVxJjIxrKCRx3WR7Q+CTQF+WXO2p0Xob/iEk6UOk\nRSe+YXsAKWTkCEn7dKYtIYQQWis649rqSuICyGlXwyStmtOvJkma1uQkrlH5ISzy/OGTyPGckkbl\nmMxSe+blfxs9TwghhG4Ul6lr60lJXINYMolrMunyeXvebPA8IYQQulGMjDvnTnpOEld7OnSeSOAK\nIYTuEZ1xbT0piWuJtuT3pTlH75L/m0pajrRcYkfOA0QCVwghdJfojGvrSUlc5wOHSRqS612T9CBZ\nKVzkCdo66z1JASQdOU8IIYRuFJ1xDT0sieufwFeAkZIeBp4FzrV9V97lYmA7STOALWgbhTd0nhBC\nCN0rEriWYpL+G/gmsK3tl7vyXJHAFUIIjYsErmWA7Qtsb9zVHXEIIYSuFVObmiivonQW8DngZeBt\n4Be2x5Tt159Cqlah/EfAeNt/pR35nvE0YHfbtzbtA4QQGhYJXL1bdyWExci4SfLDWjeROtP1bQ8l\n3Uv+SNl+VX8A2f5BrY44Owi4O/9bsS35aeoQQghLgfg/7ObZEXjb9kWlAttP2v51efJWtQpKCVqS\ndpN0XaF8e0k359cCvgwcBuwiaeVc3j8vr3gFMBv4qKTPS5qYk7euk9Q37/sDSQ9Imi1pZOmp7xBC\nCK0RnXHzDAKmtrN9M2B/29vVUddfgc9KWjW/Hw5ck19vCTxu+zFSoEjxGsqGwAW2B5GepP4+sLPt\nzUhzkY/P+51ne/N8mbwP8KU62hRCCKGLRGfcRSSdL2mGpAdyUTF5q1223wVuBfbIl7W/CPwhbz6I\nto75Gha/VP2k7fvy68+RYjLvyUlgh9I2v3gHSffnqU47kn5IVPoMkcAVQgjdIB7gap45wH6lN7aP\nyjnQpflAb1Q8qrprgG8BLwGTbb+eA0f2A/aSNIKU9LWmpNUqnEOkHwCL3VfOl7UvAIbZflrSKVRJ\n47I9EhgJaWpTg+0PIYRQpxgZN884YGVJ3yyUrdKJ+u4iXdr+Om0j4Z2AmbY/aru/7Y8BN5BCScrd\nB2wl6RMAeSWpT9LW8b6Q7yHvX+HYEEII3Sg64ybJSV17kxKwHpc0ibTC0nerHPIpSc8U/r5cVt9C\n4GZg9/wvpEvSY8rquYEKT1Xbfp70kNfovEDERGCA7VdISV2zSbGdD5QfG0IIoXtFAleoSyRwhRBC\n4yKBK4QQQlhKRGccQgghtFh0xiGEEEKLRWccQgghtFh0xiGEEEKLRWccQgghtFh0xiGEEEKLRWcc\nQgghtFh0xiGEEEKLRf0bFhUAAASISURBVAJXqIuk50kLUbzQ6rb0AGsR3wPE91AS30N8ByWVvoeP\n2e5X68DojEPdJE2uJ9att4vvIYnvIYnvIb6Dks58D3GZOoQQQmix6IxDCCGEFovOODRiZKsb0EPE\n95DE95DE9xDfQUmHv4e4ZxxCCCG0WIyMQwghhBaLzjhUJek/JP1F0qP53w9U2W89SbdJekjSg5L6\nd29Lu1a930Ped3VJz0g6rzvb2B3q+R4kDZE0UdIcSTMlDW9FW5tN0m6SHpb0N0knV9i+kqRr8/b7\ne9v/Bkrq+B6Oz/8fMFPS7ZI+1op2drVa30Nhv/0k/f927idEizqO4/j7Y8ueNAUDFTUMMsgispal\nDibkBnbZOkQURSt4kyCIDoG3umRLfy4dojxol8rFTLDoz1Z02jBQiArSPNSWFUQJIinVx8P8Nobl\n2WcmmPnNPuv3BQszz/Pb4ft8dna+v2dmGEuqvMM6mnHo52lg2vZmYDqt93IQmLR9IzAK/Japvlzq\n5gDwLPB5lqryq5PDBeAx2zcBO4GXJa3KWGPjJF0FvALcC2wBHpa0Zd6w3cAftq8HXgL25a2yfTVz\nOAGM2L4FmAKez1tl+2rmgKQVwBPAF3W2G8049HMfcCAtHwDunz8g7YRDtj8CsH3e9oV8JWZRmQOA\npNuBNcCHmerKrTIH29/ZPpWWf6aYmFU+8GCRGwVO2z5j+xLwJkUWZeVspoAdkpSxxhwqc7D9aen/\nfwbYkLnGHOrsD1BMzPcBf9XZaDTj0M8a22fT8i8UjWa+G4A/JR2WdELSZJo5LiWVOUhaBrwAPJWz\nsMzq7A//kTQKDAPft11Yy9YDP5bWZ9NrPcfY/hs4B6zOUl0+dXIo2w2832pF3ajMQdJtwEbbx+pu\ndKiZ2sKgkvQxsLbHW3vLK7Ytqdet90PANmAr8APwFrAL2N9spe1qIIc9wHu2Zwf5C1EDOcxtZx3w\nBjBh+99mqwyLnaRHgRFge9e15JYm5i9SHAdri2Z8hbM9ttB7kn6VtM722XRw7XUteBY4aftM+p0j\nwB0MWDNuIIc7gW2S9gDLgWFJ5233u7686DSQA5KuBo4Be23PtFRqTj8BG0vrG9JrvcbMShoCVgK/\n5ykvmzo5IGmMYvK23fbFTLXlVJXDCuBm4LM0MV8LHJU0bvvLhTYap6lDP0eBibQ8AbzbY8xxYJWk\nueuCdwPfZKgtp8ocbD9i+1rbmyhOVR8ctEZcQ2UOkoaBdyg+/1TG2tp0HNgs6br0+R6iyKKsnM0D\nwCdeeg9xqMxB0lbgVWDc9lK7kXNO3xxsn7N9je1N6XgwQ5HHgo0YohmH/p4D7pF0ChhL60gakfQ6\ngO1/KJrPtKSvAAGvdVRvWypzuELUyeFB4C5gl6ST6efWbsptRroG/DjwAfAt8LbtryU9I2k8DdsP\nrJZ0GniS/nfcD6SaOUxSnBk6lP728yctA69mDv9bPIErhBBC6Fh8Mw4hhBA6Fs04hBBC6Fg04xBC\nCKFj0YxDCCGEjkUzDiGEEDoWzTiEEELoWDTjEEIIoWPRjEMIIYSOXQaSpNqN6lDucgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d16db438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature GrLivArea (0.367278)\n",
      "2. feature OverallQual (0.243561)\n",
      "3. feature OverallCond (0.232666)\n",
      "4. feature RoofMatl_WdShngl (0.209081)\n",
      "5. feature Neighborhood_StoneBr (0.148044)\n",
      "6. feature GarageQual_Ex (0.128140)\n",
      "7. feature PoolQC_Ex (0.121049)\n",
      "8. feature Neighborhood_NoRidge (0.107065)\n",
      "9. feature GarageCars (0.105387)\n",
      "10. feature Condition2_PosA (0.103901)\n",
      "11. feature Neighborhood_NridgHt (0.103485)\n",
      "12. feature Neighborhood_Edwards (-0.104077)\n",
      "13. feature KitchenAbvGr (-0.105917)\n",
      "14. feature Neighborhood_MeadowV (-0.108041)\n",
      "15. feature Heating_Grav (-0.109124)\n",
      "16. feature PoolQC_Gd (-0.121306)\n",
      "17. feature Functional_Maj2 (-0.140938)\n",
      "18. feature MSZoning_C (all) (-0.295872)\n",
      "19. feature Condition2_PosN (-0.323821)\n",
      "20. feature RoofMatl_ClyTile (-0.557862)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "threhold = 1e-1\n",
    "# Make a fake dataset:\n",
    "features = linearRidge_clf.coef_[abs(linearRidge_clf.coef_) > threhold]\n",
    "names = X.columns[abs(linearRidge_clf.coef_) > threhold]\n",
    "\n",
    "# feature 重要性\n",
    "importances = features\n",
    "# 由小到大排序並回傳對應的 index，再將之反向，也就是改為由大到小的排序\n",
    "indices = np.argsort(importances)[::-1]\n",
    "# 個數\n",
    "n = len(features)\n",
    "# 畫直方圖\n",
    "plt.barh(range(n), importances[indices[:n]], align=\"center\")\n",
    "plt.yticks(range(n), names[indices[:n]])\n",
    "plt.show()\n",
    "\n",
    "# 印出排序後的 feature 重要性\n",
    "print(\"Feature ranking:\")\n",
    "for f in range(n):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1, names[indices[f]], importances[indices[f]]))\n",
    "    \n",
    "linearRidge_important_index = names[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "898f79d2-32ac-446d-9729-288e24b7461d",
    "_uuid": "5722ede48328499300c07dec55a7973e17d069ff"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pLlVaGJK0vKSfKtsqvU3SoSrNPEraS9KfJB0taTpwWK3bQ8p2ml9Zuk8t89iz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\n1gAHYDMzswY4AJuZmTXAAdjMzKwB/w/zTborh1oV3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d1713be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "clf = linear_model.Lasso()\n",
    "linearLasso_clf = evaluate_model(clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "a5dcaab3-296d-4bd7-8e3e-6239da8f1af0",
    "_uuid": "13b56c458eae118a2d175ac78f3180cff16df977"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWQAAAD8CAYAAABAWd66AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d15d40b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "threhold = 3e-1\n",
    "# Make a fake dataset:\n",
    "features = linearLasso_clf.coef_[abs(linearLasso_clf.coef_) > threhold]\n",
    "names = X.columns[abs(linearLasso_clf.coef_) > threhold]\n",
    "\n",
    "# feature 重要性\n",
    "importances = features\n",
    "# 由小到大排序並回傳對應的 index，再將之反向，也就是改為由大到小的排序\n",
    "indices = np.argsort(importances)[::-1]\n",
    "# 個數\n",
    "n = len(features)\n",
    "# 畫直方圖\n",
    "plt.barh(range(n), importances[indices[:n]], align=\"center\")\n",
    "plt.yticks(range(n), names[indices[:n]])\n",
    "plt.show()\n",
    "\n",
    "# 印出排序後的 feature 重要性\n",
    "print(\"Feature ranking:\")\n",
    "for f in range(n):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1, names[indices[f]], importances[indices[f]]))\n",
    "    \n",
    "linearLasso_important_index = names[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "657119be-3adc-474c-946d-a668ddd083bc",
    "_uuid": "075113070c48abb62b6ee78997893fcaeec347dc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0tqT/J2l0ZdoVgSOAr1eH5YPptjzvTEk/kjSpmw2T9D5J10paIGlaB9PvIemuvM6fSxrX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d20d0470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "clf = SVR(kernel='linear')\n",
    "linearSVR_clf = evaluate_model(clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "e285bda2-bd6f-443f-9427-69f7c2d00e80",
    "_uuid": "ed1163d3c1008c89c691b98755e52ec3804a31d8",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qAI7jbum7hXm/bphFnhuXLeM+WMa31vupWhlhwV83vKUs7yHy+NykNl9t3mEq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\nwczMzKocJJiZmVnV/wdyJVK3eV+OGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4cc2822e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "clf = SVR(kernel='rbf')\n",
    "rbfSVR_clf = evaluate_model(clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "2b5214f2-8e84-4519-835c-918660930db7",
    "_uuid": "a416232523a559d9da560c82925a15e5affefc89",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wd/If4KOy0oPIQHxB0HTEsVsDK+DlfAtf8FcvJwDXRP1/3/xTxYPxWb0JeD02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\nHQA6INABoAMCHQA6INABoAP/B1TDpnwQYVeEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4cc13fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "clf = RandomForestRegressor()\n",
    "RandomForest_clf = evaluate_model(clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "3db045d6-ff22-4ae1-921f-ce87626974ab",
    "_uuid": "ad88729ee828cef861d19106774c94ca96a6a28d",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ivrzdcYXLhF9opZ1VgUciYotcOMcCz0i6RtJBkop/s47aMjOzGmmkgjYDuFrS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4cc28add8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature OverallQual (0.552875)\n",
      "2. feature GrLivArea (0.099953)\n",
      "3. feature GarageCars (0.058969)\n",
      "4. feature TotalBsmtSF (0.050982)\n",
      "5. feature 1stFlrSF (0.020584)\n",
      "6. feature BsmtFinSF1 (0.020299)\n",
      "7. feature GarageArea (0.018159)\n",
      "8. feature YearBuilt (0.016106)\n",
      "9. feature LotArea (0.012027)\n",
      "10. feature OverallCond (0.010452)\n",
      "11. feature YearRemodAdd (0.009928)\n",
      "12. feature BsmtUnfSF (0.006493)\n",
      "13. feature CentralAir_N (0.006151)\n",
      "14. feature 2ndFlrSF (0.005934)\n",
      "15. feature LotFrontage (0.005490)\n"
     ]
    }
   ],
   "source": [
    "# feature 重要性\n",
    "importances = RandomForest_clf.feature_importances_\n",
    "# 由小到大排序並回傳對應的 index，再將之反向，也就是改為由大到小的排序\n",
    "indices = np.argsort(importances)[::-1]\n",
    "# 印出的個數\n",
    "n = sum(importances > 0.005)\n",
    " \n",
    "# 畫直方圖\n",
    "plt.barh(range(n), importances[indices[:n]], align=\"center\")\n",
    "plt.yticks(range(n), X.columns[indices[:n]])\n",
    "plt.show()\n",
    "\n",
    "# 印出排序後的 feature 重要性\n",
    "print(\"Feature ranking:\")\n",
    "for f in range(n):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1, X.columns[indices[f]], importances[indices[f]]))\n",
    "    \n",
    "RandomForest_important_index = X.columns[indices[:n]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "0cdbc3ab-33f7-485a-ba64-ce70376398e0",
    "_uuid": "59ecec9005abb2b80be9e3dfd46148a57571d689",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OyMjIyMhoItqsiCUNlPRQcr+BpP8Gyfn3Oka8jLkBkn4s6dfxfyVJmQS7AyDpEUn7NFuO\nEiSdJuniDoyvS/QnvToqzoyOhaTBkq6YSXEfJunvMyPu2Q0dOSM+HbjYzLqb2bCip6Thkj6JF6t0\n3V4rUkn9JZmkzm0VrJDml5I+Tu73bmu8bZDjIElfRLpTJD0ladtZlX4zIKkLcCLw65mcTudoJ/3r\nDP+2pE1npkx1ylGS+8OkTX7QbLnaAjM71cyOAJC0mqTP2xnfp9GfjOkYCWsjBjfrz8L0ukr6ZFal\nN7uiI9pLlbgfifdrmcRte0kvz4z02oKOVMT9gBdqhDkiXqzStUNHJFxLSadpAm8BOyRu1zYaXzvx\nYMixCHAFMFRSj45OZCbnoZ70O0nqBHwfeNbMxjZTnkbRhPJbM2mTS8zitNuNji6vZrffjLkOn+AT\ngtkSNRWxpD6SbpU0TtL4cktPkl4HVgBujxF9l0aEkHS8pEdLL5+kwyW9IKkrMCKCTYq4vxXL4g9L\nukDSeGCQpBUl3R8yfiDpWkmL1Jn+mZJulHS9pKnAPqFITpT0esR3g6RFk2c2iJHWJElPS9o48TtQ\n0khJUyW9IWnPYppm9iVwDdAdWKnOeFeU9FDEe6+k30u6KvxWipnV/pLeAu5tq5ySVpE0QtLkyPt1\nyTMbSnoi/B6TNCDxe0jSGZL+DXwI9AW2BR4oU+YHSxoT188S966SLpL0rqR3JJ0vaYHE/zBJr0U9\nD5O0bHiV2skL0U52kbSUpLsi7xMkjYg4rgd6AXdH2J+XK79oAzdLGhtxDJe0eiLLEEmXSPpHlOE/\nJfUp5rWtkLSkpLvj3Zsg6S9JfothV4vynxzhr0781op3Y6Kkl9TA1pGkdZJnx0o6OtwHS7ou3pup\nwJ5qvUw5AphPLbP8r8Zzh0p6JfJzp6Tlwr1rlP/h8v7k+cStd4RZLNIcJ+lNScdJUvgdFvVwUdTV\n65K2aLzUW+V9sLwfuTHy8LSk5SWdGu/FSEnfScIfXHifdkv8yua7QXlWk/S5/L19J96BAyR9W9Lz\nke/zC+GHR5rjJP1ZMegPvwmS1or7fhHft2rIsJK8750q6W5g0YL/RvK+fJKk/0jaIPF7RN4/PBnt\n9BZJC4d32fbij3VInf4G2F9S3wr5+oqkByOdZ5WsVMr7/t9I+lvk+2FJ/RL/Nr9f02FmFS9gPuAZ\n4AJgIaArsGH4DQQeSsKOBLZI7k8A7kjuhwMHVUinE14Rg4CVgYnAV8OvP2BA5yT8QOBz4CdAZ6Ab\nrsy2BLoAS0Z8vymTVis5w+1M4H/ADiFLN+Bo4GFgucj3FcA1Eb4PMB7YOsJvA3wALA70BCYDK0fY\nZYE14v9BwPD43xn4KfApsESteMP/ceCXwALAxsBU4KrwWynK6UpgwchDW+W8CTg+nukKbBDuS8Qz\nPwj59434Fw3/h6J8VwfmjzBPATsnZV2S85qQc52IY9PwPxv4V9ThUsCjwKnhtxXwPrBuyPU74P6k\nPA3on6T1K+DikGUBYOPE7+1SmlXKrxPe1npEehcDTyTPDIny2ABvd5eU6jf8XwAmVbguqiR38vzS\nwE4hy8LAX4AbEv9HgH3i/23AMYAifKnOegLvAnvj7/N6wARgpWrvfjy7KDAOOCLy1xNYL/wG4213\nO1remcHAFeG/GvB5Ib49gJeAVaJOzgT+GX5doxzuxFeLuiVuvSPMULxtlgavbwJ7h99hwGfADyOf\nPwNGJmn/sUpdPFYh/4OBj4DvRD3dGGkeE/c/AV5KymoSsGLc9wJWr5XvRq4oUwMujPrYER/w3oK/\nm33xvnNAEn4zvO0vE+1lcBLfT/D+vRveP59ZI33h7/M5EefmUT6lOu+Pv8tbRJvYLtpPqX94BBgV\ncnUHbq/RXjqkTiPdffD+opTe9sDLSdt7C+/z58f7y2nA8uF/A97vfC38b6al323z+9UqrzUK/ltR\nkJ3L+A2kiiIuE354VFpaWGck/v0jAy8B/1dwL6eI36oh+/eAp8q4zyAn/mLcX3D7L7BJct8HX97o\nBPwCuLIQ/h9RGT0jbzsDXQthDsIHEJOigX0E7JL4V4t3Bbzj65b43cCMirhvnfFVk/M64PfAcgX3\n/YF/Fdwep0UZPAScUvB/k9YDtJKcKyVu5wOXxf9RwFaJ33eB1+L/n4GzE7+ewBdAb8or4rOBW4nO\nsSBXJUXctxg2CbNEhFko7ocAQxL/hYEvgWXrfgFb5J5Cy3txfoWw6wPvJvepIh6KDxSWLTyzH3Bf\nwe3PwPF1yLY/8O8KfoOBe8u4VetY/0kozrifH38PlqZF6X478Z+uiHHF8wWwQuL/U+Ce+H8Y8Hzi\nt1g8u0i9dVEhj7cn97vhikZxv2Sk0Y0WRbwTM75PFfPdoDwlRbx44vYhsFNyfydwWIXn90zrE1es\nfwOeA54A5q+R/ip4H9g1cbs1qfNTgcsLzzwA7JG010GJ39eAD6u0lw6pU1oUcS988rIyrRXxlni/\no+SZ24AT4v8N+Pmnkt/3gafb+36lV62l6T7AKDPrqE30I81skeQ6ueRhZiPxBtsfn1nUwuj0RtLS\nsYTwjqQpeCfZyF7b6MJ9X3ypfZKkSXhjBZ+l9QN+UPIL//WBXmY2BZ8x/hgYK+kOSask8T5kZovg\njeouYMPEr2K8cY03s4+ryFx0a6ucpZHhE5Kek7RfuPfCG2yKUfiqQSWZJuIzympyjoq4y6WRxt/K\nL/IwsZB+isER/h+xrHVshXBl5ZI0n6RzY5lxCvBaeC1RLryZTcZnyG055bt28l78PNLvIelPkt6K\n9O+lcpv+GT6TfyqW1kqnqfsBGxfawC74Ckgt9AFer+Jfrv1VQz/g0kSOcfjAtHcdcS6DD4LfStyK\nbS89h/BR/HZvUMYi3kv+fwyMs+ht4x58YDYRH+Aeib9Pf5VU2nKqJ9/14gszG1+QqShjdwBJvSTd\nlPSJV5C0n8jHFcBawIVm9lmNtHvh+U8Pl6Xvaj98Wy9ta9+g9ftQfO8XTJany6HD6tT80N8f8JXX\nFL3wiZ0lbrXaVkmG9rxf01FLEY8G+moWHJyQ9F18Bv4PfEmxBCv/xAzuZ4fbV8ysJz4CUgMiFON7\nG9iyMHDoan7oaDQ+00z9FjKzXwGY2d1mtgVeGa8Bl82QmNlU4HDgQElrh3O1eN8FFpfvm5cww35k\noTG1SU4ze9fMDjKzZXFF/QdJywNj8IaXoi/wTpVyfBYfSReRyt434qZMGmn8rfxiv2vR8J+hnZjZ\nFDP7mZn1x1dIjpe0SQU5S8+k7j/El9c2w2e7pY41bVfT8xEdysKlvMj3BKdVuOr5zOcEvLNeL9r0\nVlRo02b2jpkdgNflkcCfYj9sND5zTdtAdzM7qo70RwMrVvGv9G5W8hsNDCzI0s3MnqwjzrH4akO6\nx1dsexUh6aoqdfFk7Rhqw8zuNLPNiY4dX1WC+vI9M/ArfMa8VrSfg0jaj/wMzXn4Eu+ZNRQieB+0\nRKEPSutjND47LvY3FyRhiu/9RzGArdaWyqKNdToYnw2vlbiNKeSjJFs9bas979d01FLEj+GFP1jS\nQvLDExvUeKZhSFoCH5kdhE/1d5C0XXiPw1/AFWpE0wNf158sPwhRz+ynGi4Fzi5t7ssP/uwYftcA\nO0vaMmZNXSV9J0agy0raQdKC+L7zhyH/DDCzccCfgJNrxWtmr+Oz8lMlLSBpQ3zZthraJKek3dVy\nmGQS/pJ8AdwBrClpD/lnN3vhyunOKjLcBWxSxv1kSd0kfQWv8xvD/XrgFElLSFoyymZI4negpLXl\nBwLPwU+hv21mX+DLhtPbSeRvRUnCZ6pf0FIX71Ffm/o04l0QOKtMmB3kBwi74FscD5rZuwBmtqq1\n/kogvY6okXYp/Y/wg4pLACdVChh10isGEpPC+QtgGPDV8J8/2s76itUP+SGnSp9xDANWkh+gWkBS\nT0nr1SE3+J7afGp9OOZS4CRJq0bai0rapZ7IzOxTfLnw7OiLVsSXpodUf3L68wOr1MXX68xTRUha\nTtJ34336FO+LSm2t7nzLD4jd0155AqU+cUrUw88L/pfge9UH4UvIv60R36vAK/i7u4D8oNo2if+f\ngd0kbR79Tbf4v0wSZqD8MGh3fGZaeu/LtZeqaEudRp97Ea31w4NAJ0lHRb+2JT7oHVqHGO15v6aj\nqiKOzm0HvLN9C58l7lGHcMhPHN9dcL64wqjlD8BfzOyuWHY5ELhC0uJm9hHeAT4sn/pX+sbvNHzP\nYTKuGG6tR84qOB+4B1/WnIofIFoPpi+j74wriXG0bPR3wjfsj8UHMOOBb+Ozykq4ANhR0ho14gVf\nSt444j0Vb8SfVoq4HXIOAB6X9CFejj82s7eiEe+IH+Qajy+Hbh/LcpUwDFi78DKC7ye/gS+3nmNm\n94f7afgBkufx2fSjuMLFzO7Bv1e/LeTuiy8HlnAqcF20k+8DqwL3453Rw/jy24MR9mzgtAhbafR6\nJT5aHoMfvPpXmTBDcAX8AbA2PovuKPwaX0ocj5fXXVXCfgt4UtI0/EDTITFLnogfPtkfL7MxIe/8\n8VwfvGxmQDy7Jb63+D7eCW9YLmyFZ88NmSZJWtfMrsf3sW+VL5U+HfHXi0PjdxRer1cAM3x+2CTM\nh69gjMXraz38kBsN5rtifbQBp+D1NRl/Z24peUjaPfyODKefAJtUGxjFIG93/PDaBOA4koGQmb2B\nL8uehr8Po/DBUqpnrsEH1O/gA5Wj49kZ2ktbM10Hfk2yMmC+1L49sCted+fj+9pv1IqoPe9XitKh\ng4w5EJJuwQ8NnNFsWapB0o/wQzbHNFuWjoSkIfhBskHNlqWtkDQcODBWXDKaDEnP4yfeJzdblo6G\npEfwQ091rWLMDaj3/cofzc9BkPRNfGY7Cl8S2p4ZDx7MdjCz3zVbhozyMLNNmy1DRgvMbK3aoTLm\nFNT7fmVFPGehF768tBi+TXCwmT1X/ZGMjIyM+hBnHcZX8P6OmT0+K+WZV5CXpjMyMjIyMpqITIOY\nkZGRkZHRRGRFnJGRkZGR0URkRZwxSyG3gDZCbjz9vGbLkzF7QE6W0C6Shoinm6Tb5aQCN3WEbHWk\nuamkt2dFWhlzJ7IizpjVOAT/xrCnmR3dnojklnXO7BixZg4kDZI0qAPiaYiXO8KPbG+6cyB2xW1X\nL25mu9UKPKchaQd3FdyHdEQ7y2gOsiLOmNXoB7xos8EpwXqVWsYchX7Aq9Zx9vFnVwyQ9O1mC5HR\nMciKeB5CMpreX9JoOX/mYZLWkxMFTFJiA1lVOJ7Db4Kkr8V9Lznn6aZV0r8KN2d5XFhW20LO+XuC\nnJRhvKShkhZLnrlJzoM7OZa01wz3Q3CrWqW4bg93U4ux/Vaz5tISopz/eixuOQtJ28u5ZidJ+pda\nbH+XuLLfiaX0VyRt3o7yX1ROrjEuyv4OBddu+Ldano3ZdMn4QTle7k6STpI0StL7kq5WbXvB5eQq\nlctxEc+7kr4naTtJr0Y9n5iE/6akf0d5vSvpYgVntJwb9wMFL7Ocz3iipNUakKetbeI03JrUHlFG\nB1ZJY6Ccv/nXId+bas1B20tO3DBBzn99cOLXLdrVREkvEhb3Cs/eohbe5CMTv2/K+bynSHpPCX9w\ngziX8iZXS+kcHHJPiHz0SvxM/t7/N+rwEkmpDeoD5Ly6E+UcvEX78hkdDWsjRVi+5ryLFkrJS3GK\nua1wWrNhOKvUcrgpw00ifFWOZ+Bg4EXcDvPfgF/XIcNVJLynuAm8R2ihursMuD7xPwC3mdsFJ/d+\nulJc4VakWJweBtgUZ735ZcTXDfhq5HkAbqZwP5wqswtuInM0zlZVKr8S3+xeVOZCnUQZOkWcB3qX\nKK8euCnKYYn/SFpTRg4iaBYpTwd6AE7WsQLOBnMrwZndYLsolcspuGm+g3HDMdeFnGvirD4lftav\n4yxenUOul4CjkvjOwk1QdsPtox9RhwzT897ONjG9zGqkNxCnIjw46v1w3Dxh6ZPOETh/bVec/3oc\nsFn4DcbtEy+GmzB8Hng7/DoBT0ZZLhB18wawdfj/G9g3/ncH1k9kqtaeSpR8pXbQAzcTWSqzIQTF\nIE5S8gFu8rcLbkN6ROEduQPnfe4bedsm/HaKNrV61O9JFKhP89XxV9MFyNcsrOyWl3i5xG08wRca\n97eknWrh+Rk4noG/Rmf7LNClDhmuorUifgnYPLlfNjrIchzYi4T8C5eLK9xqKeL/0ZpP9fckvNjh\n9gpOVLESrqS3oAZXaxvrY12dm+yiAAAgAElEQVRgYnI/ksYU8T+AHyX3q1YquxpybIor2vnivkek\nNSAJ8yTwvQrPHwXcltzPH+Gfw+21qw4Zpue9nW1iepnVSG8gwXMd9wtGPMvgyvULoEfifw4t3N9v\nEIor7g+hRREPoMCVDvwfwQuOK/jTgCXa2GamtwPgR8Aj4Z4q4j8C5ybPdI/y65+8Ixsm/kNpUfR3\n4yYZS36dcOKRfh3d/vPVcuWl6XkTRf7SSnym9XA8X45Tiv3WnCGnUfQDblMLl+dLeCe4tJzBZXAs\nUU7BO2vKyNAIinyq/YCj1ZpPtA8+C34NVzKDgPejLNrCNQyApAUlXRZLyVPwTnkRSfO1Mcpy3M2d\n8cNKjWK8OckLtPDsVmoXq8Sy+tjIx9m05rn9DB8ArQWcZ9GjN4BZ1Samc8yak8uA57EXMMGcqrSE\nIi92kVc3lb1XoT2dSEudHIjTgr4s6XFJ27dB7hKuwMtkh4J7kbd7Gj7grpdf98JE9gk4QUIlzu+M\nDkBWxBnVUJXjWU5l9ht8BD4o3cdrAKOBbW1G3ud38OXfnfAZ6cL4bIBEhnId/Ef47KaEIutT8ZnR\nwFmF9Bc0Z8zBzK4zsw3xDsrwZW0k7a3KXKjTVJ7O7Wh81jogynPjQn4+rCJ7ubyW427+nNYKdGbg\n98DLwMqRjxNp3S6Ww5mwrgTOk5tNbATtaRMdgTHAYnK+6xJSftp3mZFXN5X9zYLsPcxsOwAz+6+Z\n/QDfCvolcLOkhQBqtKcTKcDM/ofPrs+gdf6LvN0L4dsi9fLrHmozcieXYx7L6CBkRZxRDbU4ni8E\nnjDnM70T33tuFJcCZ5UOhEhaUtJOSfopH/DZhWfLcQo/DewVM6dtKM+FnOJy4DBJA+RYSM4r20PS\nqpI2C0XyCT4r/BLAzK61ylyo3c3srTJp9Yg4JsWg5dQysu8p5zX9Bv4pTgnleLmvB34mafkYFJ0N\n3GhlTgzH4aKrapRFvegBTAGmyQ9hHZ6kI3w2/Ed89vcurigaQXvaRLthZqNxystz5Bzea+N5KR2c\nGwr8n/zwXW+cQrCEx4Cp8kN+3aIdrqXgcZa0j6QlzexLWnijS22qWnuqlM9r8H3slBf4emB/SetG\n2z0beNScFrUWLo28lQ7ALSxp+mdgkoYrfybV4ciKOKMaKnI8R8e4DS2d8M+Br0nauxhJDVyI7zPf\nK+d9fgTfZwO4Gl9iewc/FPZI4dk/AmvEMtqwcPspzqE9CT9VPYwqMLMn8AM7FwMT8YMqA8O7C34w\n5wN8KW8pfL+vrfgNfoDpg8hLkQD+ZGDFkOM0/LBUSc5yvNx/wjviEcCb+GDhJ5RHR/LcHoPPTKfi\nA5kbE78j8XI6OZak98eVwkYNxN+eNtFR+AE+2x6Dc/meamZ/D7/TQoY3cT7ta0oPxfL+9vj+/5t4\nXV+Bz97B35kX5LzRFwJ7mllpK6BhRHqn4AfHSm5/x9vSLfhAaEWcU7qe+G7DZ+o3xNL/88C2SZCO\nbEcZgUz6kJExl0P+adEzwNqxf5uR0TBi9j/UzPL3yx2MrIgzMjIyMjKaiLw0ndHhqHLgpJHlyYy5\nCJL6NniwrSPSvLRCem05y5CRMdOQZ8QZGRkZGRlNRJ4Rz0GIT2bubXYcHYmYoRRPPnd0GqmpyIwm\nQtKJkq5odhwdhWSm39ZvwTsUSsykzk7llFEdWRHPQYhPZrZqdhwdifg04w2YM9iU5hTEp1i/lNtq\nHh//K35rK2kvuaGRDyUNU9u+Ca8JMzs7PndrahwdBTN7K9rwFzD9857ZRbbp5aQG2btSyO1ym6Tj\nCu5vq4pt+Yz6kRVxRlPQlg5hdsIcIP8huEnSdYC18U+6Di0XML4ZvQzYF7cA9RFuZzmjCuaANtCR\nmIATrPSoGTKjYWRF3EQko9T9VR8b0kBJDyX3VVlUKqRZLo4fRRxTJZ0hZ1b6l5whZqhamHVKLD0n\nyhl2RqbfDRdnAxXS+rGk/wL/TdxWUhk2JUnHSrqlIP9Fki6skcflJT0Q+bmPgvlDSetH/iZJeiYd\n1UtaTNKVksZEfQwr5L0R5qYSg9BUSS9K2jnxWylknBxleWPit5qk++TMOa9I2r1afitgP9y85Nth\nkeo8Wr6PLmJv4HYzGxHmEE8Gvl9Pp6vGmZumbxMk7X8/SW9FOfyijjTLxdHIO/SwnDFqsqSXlTBq\nqQoDVpLWgZLeAu5P3DpLOgvYCLg42vDF8nfyvIL8f5X0sxp5LMv6FfLcLOnG8PuPpHVqlRNl2Ltq\nlXMBL+GEFT+vkFYXSb+J92ZM/O8SfqU2cnTSRvYvPPvraAPvyQ/ZdWtQvjkbs9Kwdb5aXzTOhjQQ\neCh5viKLSpU0y8XxF6AnzrLzKU4msAJuhOBFYL8IuyluQvF83NjFJrhZxlXDfzhwUI207sOND3RL\n3FaK/1fRmhBi2Yh/kbjvHOXx9Rp5/Hci48a44YkSecJyuFWm7fCB6JZxv2T434kbqFgUJy/YpJD3\nupib4pndcLu/nYA9Ii/Lht/1wC/CrythhB9YCDczuH/k96u4UYg1wv8EqrD0JGUwmdakDd8AplYo\nr78AxxfcptUq50K51MvcNIgZiSwuj/JcB29/q9dIs1wcjbxDnwM/C3n3iLJaLPxHUpt44+qop24U\nyDiY8R34Jm4UpFPcL4GvOCxdJX/VWL8G4QQOu4b8x+CGQ+Yvyl9B9pQ0ZEOqMz5tmJTZQwRJSVJW\nbwObxv/TceMqS+FMbf8iyFRoaSOnh8zbRRksGv4X4AZcFsPbzO3AOc3ql5tx5Rnx7IEzzOwTM7sX\n76yvN7P3zWcyD+KdcSUMNrNJ5iYV/4m/LI3iXDObYmYv4JZ07jWzN8xsMs7GUkz/ZDP71MwewBVX\nIzO2c8xsgtVhTcjM3sVH8iUTe9sAH5jZk5WekX8Ks14i4wj8xS5hH+AuM7vLzL40s/uAJ4DtJC2L\nWxE6zMwmmtlnkccSvsQtLH0a8h8CXGZmj5rZF2b2Z1yRrB/y32RmYyKdG/FVgG9GXJ8RBAFR96WV\ng+2BkWZ2pZl9bmZP4RaSdos4B1trO8CtrkTW7riCKWEy0F0qu2JSDFsKX+8y5Ge4ve7PgBtwZXOh\nmU2NNvUirmQr4TQz+9jMnsENj1QLWwmNvEPv43Sen0W9vAJ8t4G0BpnZh3W24cfwsizNuvcEhptZ\nNXvgX+CDvTUkzW9mI83s9cT/STO7Ocr7fHwAsn4D8pdke6haW0raZCn80/hA+vgy0e0NnB5lPg63\nPrZv4v9Z+H9mZnfhA71Voz0eAvws+oWpuEnOuiyBzS3Iinj2QF1sSBVQiUVlZqU/0cw+TO5H4bO+\nejG6dpBW+DOuPInfa6qEJWQpJ2MJ/YDd1JodZ0N89t0HZ92ZWCHuupmbACT9MFm2noSzEZWWyY/D\nDfU/JukFSQckcQ4oxLk3M5JX1MI0fJWjhJ7ANDMr971iMWwp/NQyYcuhbuamCpjVbfidQjnMVm3Y\narN+jU7CfonPTNvMCtYgTgEOl1Rk+CrHBJbKNN5a20Av1fOSuM3wJ5P2fk+4zzPIijijUSyqYIsJ\n9MWX3qA6e1AJ1T5cL+c3DFhb0lr4bPHaGvK9W0HGEkYD1xRG/guZ2eDwW0xSOrOsJl9F5iY5YcHl\nwBHA4jFbfZ5gyTGzsWZ2sJn1wg9R/U7SShHnA4U4u5vZ4TD9k5SKLD2JbC/Qema5TriVQ6uw8s/J\nugCvVgg/p2O5wsrAzG7DQ4CdYi93dWrYP4fKrF+B6cxPkjoBvRP565ZL0kbV2pLKGOAxs5dxm/PF\nvfxyTGC1ZALfdvkYWDNp7wubWVsGY3MssiLOaAtOk7RAvKjbAzeF+9P4IZ8FQ6kc2GC8M7ApxQz0\nZnzP8TErz2qUhh+FLzWXZNwQPzFcwhBgB0lby5lxusZhkt6xFH43rhQXlbMgbVwmmRIqMjfhe4iG\n75cSh1PWKj0oaTe57V7wfTfDl77vAFaRtG+kP7/84NHqkb+zrQpLTyLb1cDPJS0Xs6mj8T34crg2\nymSjGMCcDtway4Qdzdw0O2Ap4Mgo291w5XhX+FVjwKoH5drw28Dj+Ez4llpL2qrC+hX4uqTvy09t\nH4Vvh9Qiv5iBvcvMHqzWlszswQpxnYafYUgHrNcDJ8mZspbAZ841v92PGf3lwAWSlor8Lydp66Q8\nTHP5Z1JZEWc0irG44hiDd+CHxSgZ/NDF//DO6M/Unr0WUY5NiYjrK9Reli5hL/wA1QScavDqkoc5\nxd1OOIfuOHwGeiwt78K++H7Wy/he4lGVErEqzE1m9iJ+UvnfeHl8hdasNesBj8Ys9q/AT8335afi\nB472xMt4LC0HxBrBZfje+HP4TPzOcAOmG1LZKGR9ATgMr6/38b3hHyVxzW2MO48CK+OzsbOAXc1s\nfPhVZMCqExcCu8pPb1+UuDfShmuxfv0FP2Q2EW+v37caZB5Wnr2rTTCzN/F8pKtOZ+ID4GfxNvef\ncKsHx+PvziNyxqe/4wfWkNQH3yJ5rq3yzgnIJi4z6kaMSoeYWe9aYTs43b64YlzGzKbMyrTndWgu\nY26SNBA/1bzhLE53Y3yG2K/CPn298QzCvzLYp1bYuQGS9sGXrdtDPzrbY176ID1jDkTsgf0cuCEr\n4VkPM/sfvnSb0UZImh/nyb6iPUp4XoSZzROmafPS9FwIzSWsM7FfOQX/1vfUgl9meJqLIenuCvV7\nYu2nZx/E3v4k/FT+bxL3Wc5GlTH7Ii9NZ2RkZGRkNBF5RpyRkZGRkdFEZEWc0TA0m1EpthVqByNN\nnfFbfMbV3niWljRCblv4vNpPzHpoLqHci0/Z3m62HCmK7TSW7fdrtlwZHYesiDMahs1mVIrzAA7B\nP2XpaWZH1/uQpLUk/U1OpjBT96BsNqImnNthZtuam1OdgVilEchJVZ6PAd6bko7tWEkz6kVWxBkZ\nsz/6AS+24cTtZ8BQGjeskjFvQMAPcYKTbYAjJM1TNp5nF2RFnNEsOsZqNIAXhhxTJD2ZnoSWU7vd\nJGlIjOSfk7SKpP+TU6yNlrRVEn64pHMkPRbx/UUVSO8lLSzpj3KatncknSlpvlry1lnGFane5Fa8\n7pA0Lsr+DoXVLblFq/1ooYfcokoyrWBmr5jZH6ls2rKavM2gN/ympCeint6TdH7id5OksVH+I+Qc\nyiW/qyT9Ti0nrR+WtIycim+inOrwq0n4kdFeXgz/KyV1rSBTL0m3RN28KenIeuStEFfXaLfj4z15\nXGGzucF2OlzSQfIT2ZcC34p8T6pVxinM7Fwz+485ucgruKGQDRqJI6NjkBVxRooBuMWhPfBPLX4B\nbIFT2e0uaZMqz26PW4taG2dj2rpKWIAzgHvx0Xhv4LeJ3+M4i9RiuGWjmwod5Q64ZZ9FgaeAv+Ft\neTncPONltMYPgQPwT0g+By6iPK4K/5Vwtp6tgNJyazV568FgYJXI10oh6ynh1wnnN+6H2+j9GLfW\nhZkNxC1enRtmB/8uaS8lpBBlro76/GUZnNmnJOvlOGnB13He3ZMlLV/l+Q1xC0mbA6eE4qiGC3HW\npp64dauhid/deNtcCrfaVLTatjtwEk6q8Slu0ew/cX8zzlKUYm+8ja6I18tJRWHk37Dfjhs0WS7y\ncZRazC9Wk7cc9sOpRfsAi+PWzFJzl/W2UwDM7KWI49/RNhYJuU+o1j7KxSVJeJ02PGjL6ADYbMDF\nmK/mXrRwlS6XuI0H9kjubwGOiv8DmZFneMPkfihwQo00rwb+APSuQ76JwDrxfxBwX+K3A84eNF/c\n9wh5ShzGw3GqyFL4NXAznPMl+e4MLI134N2SsD8A/tmovIVyWQlfAvyQ4JQNv28Bb1Z4bl2cQap0\nfxUJT3Mb6nclf9UbemZTXEkUyzXlOH4S+F5SL0Xu295J2MeAPWukOQI3K7lEjXCLRPwLJ+VzeeL/\nE+Cl5P4rtOZqHombZi3dbwe8nuT77fg/AHirkPb/AVc2Im/y7AE4T+/aZfzqaqdJ2IOszLvYjjZy\nGj7g6NLeuPLV+JVnxBkpZiUdYyUaQCQdI+mlWIachM8ilkieLcr1gc1Iw5emn9LWjcLJydP4wGej\n8wPvJjOHy/AZWFV560BVqjc5ScZlkkbJbe2OABZRLIs3EbOa3vBAfHb6cizbbg8gJ+cYLOn1KJ+R\nEb5am6glZ7FNlKMR7Af0KswmT8QHbRXlrYJr8NWbGySNkXSu3OpWJZnKtdMOh6Qj8Nn4d83s05md\nXsaMyCYuM5oCMxuLEyYgZ0j6u6QR+LLccfgy4Atm9qWkiQR9YBvRJ/nfFz/E9EHBfTQ+I17CWvOm\nVpXXnDu2FlKqt3fK+B+NL+EOMLOxktbFl9zL5lnS3sy4/J5iDavBUjU7wsz+C/wgloS/D9wsafH4\nvxO+TTISH5h1dJsoR9k3Gl+1WLkRea01F3Ya/jN85nmapP4449MrONlJOZnKtdMZoi06yPfuK1og\ns4SlKwaUJwAbm7NEZTQBeUac0RSoMg1gD3x/bBzQWdIpzEha3yj2kbSGpAXxPeSbk5keAOYUiPcC\n50nqKamTpBVL++JV5K0Jq0311gNX1JPigM6p5WOaHt+1Vp2+7q1IQ7G3vkDcd5VT6xH3sxW9oaR9\nJC0Z5VXayyy1iU/x7ZIFgbM7ILkfS+od5f0LoNzhu8eAqZKOl9QtZuZrSVqvhryV8vcdSV+JlY4p\nuKJNw9dsp2XwHtBbTs4B1E+VGQO6s4EtzeyNMvIOl5NMZMxkZEWc0SyUpQHEl+7uwUnpR+F8rKMr\nxlIfrsH3Ecfih4+OrBDuh7jSehFXtjfjM/Rq8taLilRv+MG4bvjs5xE8/x2BfriCLx3A+RifgZUw\nu9EbbgO8EGV8Ib6n/DG+Pz8KeAevm1rcu/XgOnzg9QbwOmUo+0IJbo/v2b+J188V+Iy8mryVsAze\npqYALwEP0JoWsd52muJ+vH7HSvqgjvApzsQPjT2u8vboZ7f2Mdci25rOmKshaTh+iGiOt/rUkdBc\nRm/YCCSNxA87/b3ZspQwu7XTWP0ZambfbrYs8wLyHnFGxjwIy/SGGVUQ+8VZCc8i5KXpjJkGzSV0\njJUgaaMK+ZvWbNlmV2guoTesBLkd9nL5y9/nZlREXprOyMjIyMhoIvKMOCMjIyMjo4nIirhJ0GxC\nJSipbyyd1TQeoQ6kDdQspvbTTKaOU2JruQPiOlxuu3hafEebkUBzCeXinIIG+4gOew/mJWRF3CTY\nbEIlaGZvxfeFtb5XrIkGX8I2Ufu1VQ5LqONmZ8gtLZ0PbBX1Mr6BZ/tL+qekj+REBxUJIuQkFH+S\nEwyMlfTzjpC/o6Ey/MA2EykX5YQQdRNrzCQZqtJXSlpM0m2SPpRbY9trZsrTkX1Ee1GrbOZUZEWc\n0SxUpfbriFn3HIql8W9I23K453rcItfiuJGKmyUtWSHsIJxEoR/wHZzdaZs2pJkR6MA2W4u+8hLc\nDvXSOHnF75WwUc3lmDupPZtt7HpuuWgxzL4/boBiIs6Msh7wLG555+Ik/EBmJE44DPhvhL2EOExX\nJc1RwNfj/94Rx5pxfyAwLP53ws3YvY5bJxoKLFaQu2RQfnnc1vFU3OjEJcxozH8/4C18RvuL8NsG\n7xw+w0kYnqki91UR7n8RdgtcMdwMDMENHhwEfBNn0ZkEvIszEi2QxLMmcB8wAbcwdGIlOWhtKL8T\nzrYzCngfNxixcK081qiLQaVyivv1cQP/k/DvdTdN/PbHDTpMxQ1KHBruq+DkEBay399A+1sFtz7V\nI3F7kITcoBB+DD7rLt2fAdxQZ1pXRbu4M/LwKAmhRZXnVkvq6xVg98RvO9xYx1TccMcxwEK4EZIv\nozym4Tahp5c1jb93K+JGMMZH3V5LC0HINZHWx5HWceG+Iz4wmhTtaPUkvpG4sZZno/w7x/07kZdX\ngM3b2KfMQNYRZfI/YJXE7RoSwogacQ6Pun445LuX2iQbpTIu9RG9cKM2E3AjNQcX3oObcUtlU3EG\nrHUS/5lWNnPy1XQB5pYraayX4jOarXCrUMNw4oDl8E5/kwg/kBkV8R04s0xf3MTjNjXSvBo4Ov7/\nAVe0hyd+P4v/P8WtEfUGuuB2iq8vyF16yf4N/Bq3MLUhrhSLnd7luCWodaLzWT38B5EooxqyX0XC\nKBTPfgZ8D1eU3XC6vfXxzq0/rrxKDFA9cOV8dJR3D4IZqJwctFbEB0QHsgJOBnArcE09eaySn+lp\nRl2Px5VLJ2DLuF8y/L+LKwQBm+CECF8rVx/hVlIo5a7fRZidSRiHwu1i4LdlZF000lg6cdsVeK6B\nuhuPD5Q648qsqhLHFchoXGF2xmkmP8DtYhN1uVEiX6k8NiXYkCqUdam86n3vVor66IKTbowAfpPE\nPRLYIrkvDY62xEkYjou2s0AS/mncClU33FraaKBXIt+K8X+vKvU4CehbyGc5RfxV4KOC2zHA7XXW\n3XC8n1gl5B1ODSXOjH3ECOB3Ud7r4n3VZoX3eNcor2Nwq2Tzz+yymZOvvDTd8TjDzD4xs3vxF/h6\nM3vf3Nj/g/iLVAmDzWySua3gf+KNvBoewDtycC7Rc5L7TcIffIbwCzN725xdZRCwa3EpTc5jux5w\nipn9z8wewke+RZxmZh+b2TP4bG+dGnLWi3+b2TAz+zLif9LMHjEnLh+JDyBK+dseGGtm50V5TzWz\nR+tMZ2/gfDN7w8ym4dR2exbKoz153Ae4y8zuirzcBzyBK2bM7E4ze90cD+Czko0qRWZma5vZIhWu\nH0Ww7sDkwqOT8QFKEd0T/1phK+E2M3vMnCDjWmq31e2BkWZ2ZdTnUzi15m7h/xmwhqSeZjbRzP7T\ngCxQ53tnZq+Z2X1m9qmZjcP34zepHC17AHfGM5/hg9RutDZ2cZGZjTY3b/kFruTXkDS/mY00s9cj\n7euq1OMiVh9RR3d8cJyi0bq70sxeDXmHUrvupkNSH2AD4Pgo76dxs58/TII9aWY3R3mdjyvs9Zn5\nZTPHIivijsespBJ8ANhI0rI4b+lQYAM5s8vC+EgdfB/wNrVQub2EvxRLF+LrBUwws48St3J2nhuV\ns160SkvSKpLuiMNEU3AD9SVauD74yL4t6IUvS5cwihZO4hLak8d+wG5qTZ+3IWG3WtK2kh6RNCH8\ntqP9dHfTmJEcoye+BFgubMm/VthKaLR8+gEDCmWyN25/GWAXvBxGSXpA0rcakAXqfO/itP4Nkt6J\nNjWE6mXfqq2YEzyMxmfaJYxO/F8DjsIHu+9HWuUoFtuKRuq5EtrTtkt9RJreKCqXx5fA2/gseGaX\nzRyLrIjnYETD/ggnQh9hZlPwl+wQfNm7xOwyGti2MMLsajNS8r0LLCZnfymhGgXbDCK1LScVn/89\n8DKwspn1xPeAS9R3o/Gl5bbIMQZXDCX0xRmf3isfvGGMxpe60/JeyMwGy9mPbsFnVkub2SI4HV5F\nSj85/3FZC15qsVL2ArCCpHRmtA5lDn2Z2US8rtepFbYDMRp4oFAm3c3s8JDpcTPbCV9OHoYPKqH9\nbaqIsyPOr0Sb2ofWZV9Mr1VbkST8nXin0jMxu9swnjPgl/FsJatbpatvHfK/irOSpdSMM7vuUozB\n+4i0nfWldXlM7zPkFJG947mZXTZzLLIinvPxAHAELcvQwwv34PtnZ0nqByBpSUk7FSMys1H4Euog\nSQvErGSHBmR5D+gfL19HoAe+DDdN0mrA4YnfHcCyko6KT3F6SBpQpxzXAz+TtLyk7njnfKOV4SFu\nI4YAO0jaWk6d1zU+w+mN7713wffVPpe0Lb6vWRFmtqZVprU7LMK8iq+AnBrp7QysjSv9crgaOEnS\nolG2B+N7vwDIvxfftD2FUMAdwCqS9pU0f1zrSVo92trekhaO5cwptNADvgcsLmnhijE3hh74rHKy\npOWAYwv+79F6gDcU+K6kzeWflh2Nnxn4V7nIJa0qabMYcH1Cy2EzrAPoK825jm8FTpe0kKQNcK7m\nayJs6Vv//u0rpvIws9GR93NCrrXxg6Hp54Jfl/T92Oo5Ci+vR2Z22cT9bEXtWS+yIp7z8QDeuYyo\ncA9O0fZX4F5JU/GDWwMoj72Bb+GHcc7ETz9+WqcsN8XveEmN7vGVwzH4IY6p+OGp6ZyxsTS2JT5Q\nGIufNv9OnXL8Ce+4RuAHST7BVxU6BNFZ7YTP4Mfhs8FjgU4h95F4Bz8x8lduH74t2BP4RsQ7GNg1\n9kFLM4501nQqvrQ/Cm8zvzKzeyJsH7zMn+sguUr1tVXIOAavs1/igxKAfYGRsVx8GN4OMbOX8YHT\nG7Gk3d6lzNOAr+H7qnfiSi3FOfgAZZKkY8zsFXzW/Fv8cNkOwA7mpBnl0AUv+w8ij0vhZxAaQT+q\n01f+CN+nfh8vm8PNrBS2Dy2UkTMLP8APWo0BbgNOtdZMVn/B99Yn4vX6/RhgzYqymSOpG7Ot6Yyq\nkHQj8LKZndpsWTJmDSTtg38G12gnmdFkSDoJGGdml3VgnCvgS+Lz22ysMDQHU3tmRZzRCpLWw78P\nfBOfwQwDvmV+yjUjI2Meg6QdgQvMbMVmyzK3Ii9Nz+bQrKcSXAbfZ54GXIQve7VJCVc5eFHxU53Z\nGZrLKfzaC2VayDkWVQ5LfYzbKDih2TLOzcgz4oyMjIyMjCYiz4gzMjIyMjKaiKyIMzIyMjIymoh5\nUhHHt2ZnNluO2Q2StpA0stlytAeSVtJcRI+W0RgkPSRpYLPlyJizIWkbSa+1M47ecmM889cKO08q\n4rkdcj7ezwqHLo5rtlzzGiQdJGl4A+FnyiBCUucw8vC0JCXugyVd0dHpZZSHpK9J+o+cL/rxMIYx\nV0PSme01sNGeCUKkX+wLZwn3tpm9jTOTDawVNivi2QTqeP7dGwuWac7t4Pg7BDMh3xmV0YcWkoW5\nCpI6qeMsunU4wvrTX4ArcXap64Fh9cyWZqJM8zUr7XrRQf1D0WrX+R0QZ91pA4fWCjTbNtx6IGnH\nmPpPkjRc0uqJ3+rhNszZRJcAABcmSURBVCnC7Fh4fAlJ90maKjcy348qkOMCSe9LmiLpOUlrhV83\nSedJGiVpciyPdatDxpGSjpf0LPBhzFx6SbpF0jhJb0o6suNKbPos7aXI9+uSDqoS9kRJYyK/LytM\nHkand2I8/4HcePuidaZ/pqQbJV0vt/K1j6RvyUkQJkl6V9JFpQ4qmc0dKuk1SRMlXZTEN1/Uy3hJ\nb+B8xGl6veXEERMk/VfSAQVZbghZpkl6RtKKkk6K8n9L0hZ15OnAqMupkt6QtKekr+A0hKVPej6I\nsDvKZ6ZTIv6Tk6hGRJjSyH29uD8oyn+i/BOqRux/pzgXOK1SByxp56St3i9p1cTvbUk/j3Y/Ocos\nNS24Y5TfpGj/a9UjUNTJNsn9AlFXa8f9BknbeFrSxknYhySdIenfOONSyR7xypKeCDlvS9tmjTx2\neHtPsDnwpZn91pwB7QLc0lQ15qeSXGdJuiD+d5H0saRz4r67pE8kLRxy3iwnSSnX3wyRdImkeyR9\niLfNIZJ+K+lv0eZGyIkxfhtxvCSpJvNYubKTtD1OG1n6NOrJCFuxD1LMfiO+sbhFvduBvsl7sVT9\nxV5V5kND1qnyvuWAKmFPlvdNU0L2jcJ9vvB7I9rGtZIWSR59GFhbUpFgpzVsNuBibMtFFZ7QuH8N\nNzG4ALAZbrJv1Xj2qrjfGH8ZLiThBq6Q3tbAkzhfsIDVgWXD7xL829vlcBakb0e8jXKZdoo0Tgm5\nV8CJ47e2Bjg7qcILjJvoWyHysBluIm7t8NsCp6oDWBM3lbdM3C8PrBD/j44GthxOcXYFwedbR72d\niROb70AL7/B6uMnNziHbq8AREb4zbhz+LzijVH/c4MgW4X8Ebu6uN7A4rswsSe9h3DxhV9y04Qe0\ncNOeGfnfItK5DjdkckLcHw78t0Z+euLmEleO+2Vp4dg9CBheCL9ZlG0n3Fj/B8D24VeOf3YX3ITf\nqiHTIODBxL9EWF/uuqhQhsvj7W1guA8Groj/q+Pfjm+Gt9UTI935w/9t3DTqMlHOr9LC77webqN5\nPbz9H4Cbz1ygjvZwOvDn5H4n4Pn43wc3tbp1lNc2UV6Lh/9D+Du0esjcOdxGA2vgHMjDgKtq5ZE2\ntvfIb7V38pgIdywFzmDgHuCndZTRVsBT8X/jKNuHE78n438nfBm0R8h5MfBEEs8Q3OzktyJsl3B7\nH6eJ7IqbO30T72vmizZyXw35qpXdmaXyb6AP+hy3/74A3j9M75eSOPatUe69KqWfxLFjyKpI42Pc\nohx4W3st/q+D98NLR9gVgOXD73icZrNXlN9VOM1kms6rwFZVy7CeznN2vICTgaHJfSfcvuqmOLfr\nWNy2b8n/emBQ/L+KhMgcpwH7AuhTJb3NokDXL8TbKSpwnUZkjPuRwAGJ/wDgrUIc/1es2DrKZhCu\n7GZomGXC3gH8OHkJRsb/VfHOdXMSkvrw+y+hzOK+D26vuVMdsp0J3F8jzDHATfG/pETWT/xvpaWD\nG0EohLjfjlBm8ZJ9BiyU+P+KFuVzJnB34rczrlQ7xf2ikXb3KrL2jPLdGeha8JtBEZd5/mLczjOU\nV8T3Afsl951x29/LNdAeSmXYH+983sSVT6qITwOuK7TVscCGcf82sGfifz5wcfy/HLc3nKb5OrBB\nHbKtFmXeNe5vBE6M/78otn3gH8De8f8hnDs79X8IODO5XzvapqrlcWa19+SZ0ygMjiOvJ9XxbPeo\n80WAk/DOfwywIHAWzq1d7rklot4XivshwJ8KYYYAv0/ufwY8l9x/FfighnzVyq6iIkzCFPugT0gG\ncZRRxA2Ue2ngn/aFS1UIew9waPxPFfGaOFvZd8rk7820neN9zkeEjY5wexLYvZqcc/LSdDWe0F7A\naGuhAYTqnJnT8FlWRYPyZnY/3mlegnNp/kFST7yxd6U8N25DXKa4QfNeas3ZeiIz8gbXg6HWmnJu\nDICk7SU9qhYu3K0ow8dqbuz+aHzG8n4sRZa4Y/sCtycylsgB6l0yKvIOrybpTrXwDp9eRqZKHKq9\nCvGlPMO98E7kw4J/Wv5F3tpxSbv5OH4r8rWaU0/+APgxMFa+DL5KpfDyZfjh8qXvybiyrsaH2w+4\nJCnrD3DGmt5VnqkIM/srPgMqbkmUa6tv07qsKtVBP+D4QrtdtvBsJXlext+d78qZsLbHVyZK8f6g\nEO/6tH5Py/FlF9tDF2Cxanmcye0d2sEjHP3TU/hseGN89e0RfGa7CcG0Fsuk58Yy6RR89Q1at69y\n5dUeDvVafcUMqKMPes8qk2q0BdcV+sL3Q44dJT2WyLEZ5fvCF/BVsrMif9fG8n2JEvOupG08hQ/w\nFk+i6IEPACpiTlbE1XhCxwB91PrwRjXOzO74izqmWoJmdpGZfR1f9loFX276AB/BlbPD2iiX6Wjg\nzUKj6WFm28Xz7eLslO9b34wzzJS4cO+lAheumQ0xsw3wUd588Rx457WlzchvPLZcPOWiLtxfBjwP\nrGTOEXtKJZnK4F1acyanZTAGPwuwUMG/Q5lpzOxuM9sCVz6v4fmB8ly6N+DUhH3MbGF8mVNVwo8G\nDiyUdTczexRA0itV2sPFFUT+BT6z6pq4FdtqiUe2nrIaDZxWkHFBMxta80nH9fhgZmfgaTMbmcR7\npc3I6/yr5NlyZVZsD5/iA+2qeWxLew/lV+2dLH2t8AIJ/3P0BV+hfh7hB/CZ4VfwGdYDwLY429aD\nEeaH+IrQZvg2zkql5JJ4ypVXu1Gl7FqlV2cfVJRxBpkl7Vej3KuydEWfcBNwBj5DXgS4n8p94Z/N\n7Nv4snRXfNXF8LazWZm2UToT0hVfiXqmmjxzsiKuxhP6KD5iP07Oe7opvi9xQ/L8dpI2lDN2nAE8\nYk5fVxZy7tQBkdaHuPL9MkbVfwLOlx+0mi9mPV1qyFgOjwFT5Qe4ukVcaykO7VidnJ1V0AXfdxkH\nfCE/TLF5hfyuLuk7kY+PSbhDcX7js0uKX9JSSg7DyQ/27FNDlhQ98OXJD+WHS2qeMkwwFDhK0nKS\nFseX7QAwszdxfuWz5Ydc1gX2pzV3arsgaVlJO0haEF8C+5DWXLq91fpkbA9ggpl9Iml9nBawhPcB\nk7PdlHAp8IsoFyQtImnXJI+rVmkPR5ST2Zyy7lV8n62EocCO8kM28+ODzKn4u1QLlwM/jndE8gNE\nO5QGQPIDQdU+k7oeVyqH0DIbBqeq3FnSlmrhdf5OrU4W+KF8lWUhfEl4aHSaFfPY1vZuZl/UeCdL\nXyvcD8wn6ceRxk/xbZPSbPYgVf9u9QF8//dZc97s4VFer5jZhAjTA+9fxtOybD3TUaPsStzgJQVX\ndx+U4D18QN2j5BCKsVq5V51U4XvP8+Pv3JdRn5tWyN8akjap0jYGKw5QRttIOdy/jZ95eI8qmGMV\nsVXhCY1ljR3wl/sD4HfAD2MZrITrcE7WCf/f3v3HSH7XdRx/fXob6TWk2l6x1ivxgqfhz0bvDzX+\ngdjCiRFQSSRRe4iNJsbrUeUPIjFpIqbShMT2TIqVaO6IkqY1hornNXeFJhpFuJLSa9Om3ZBDOEtt\nt1Ag10L2+vGP71wdjr293budfc/sPh7J5HZ3Zr/z+cx3dp77/bE3SX56tKzlXJ7hBefrGXZvLWQ4\n3pgMxzSPJ/n8aHkfznD8aFXvZdp7P51h19x1GY49PJ9hi2lN3hS99/6NDMeA/mk0zndlOD6zlNdk\nOMv2zHuHXpFhSyoZjg8eTvJgG858/o8MJ+qc+Q3wiqzsBfyMP06yJ8OL4l9n7H2HV+CuDMcNzzz+\n9511/W8k+YnRHO7LcPzxoVUs/3y2ZHhBfybDc+LnMuymTobju08nebYNZ4Amwwlgt40etz/JEIck\nr75n720ZwvCN1tqu3vu9GR7ve9uwu/HRDCcvXawPZtgLdOa+H8+wDu7K8CK5O8nb+wreTq73/tnR\nvO7K8PPxVL7352nZ94jtw99bHsuw23n88TiRYSv5T0dj+u8Mz5XzvW59PMMvW89kWD/vW8EcL+j5\nvlK995cznIh2U4bdlL+V5B1jj+/53kf33zPE9cz7jB/PcFLT+PuO/12Grf7/ybClfa5f+Nfaco/d\nPRnC+0Jr7XOrfA1KkvTeH8uwF+nE6Ofios+aHm2xvj/DGdkLSd6Z5NA5br41yUcyzO+ZDLvqz/y1\nw+1Jjib59Nhz46fGvvc3M8R6Wd70gTU12vvwu7333z7fbdn4Rr+YfSHDWbGL1eOZVq21BzO809lT\n1WNhbbTWtmfY7X7d+X6hFWIAKOR/NRrThj/S/telruu9L3vmIBtXG/4DjBfPcfUNvff/XM/xwHob\nnbfw6Dmu/skVHJNlGbaIAaCQLeIVuuqqq/qOHTuqhwEwUx5++OHne++vqx7HNBPiFdqxY0eOHTtW\nPQyAmdJa+/L5b7W5zeyfLwHARiDEAFBIiAGgkBADQCEhBoBCQgwAhYQYAAoJMQAUEmIAKCTEAFBI\niAGgkBADQCEhBoBCQgwAhYQYAAoJMQAUEmIAKCTEAFBIiAGg0Fz1AIDpsH///szPz1cPo9zJkyeT\nJNu3by8dx86dO7N3797SMbA+hBhIkszPz+eRx57I6cuurB5KqS2nXkySfO07dS+PW069UHbfrD8h\nBl51+rIr89Ib31Y9jFJbnzyUJKWPw5kxsDk4RgwAhYQYAAoJMQAUEmIAKCTEAFBIiAGgkBADQCEh\nBoBCQgwAhYQYAAoJMQAUEmIAKCTEAFBIiAGgkBADQCEhBoBCQgwAhYQYAAoJMQAUEmIAKCTEAFBI\niAGgkBADQCEhBoBCQgwAhYQYAAoJMQAUEmIAKCTEAFBIiAGgkBADQCEhBoBCQgwAhYQYAAoJMQAU\nEmIAKCTEAFBIiAGgkBADQCEhBoBCQgwAhYSYmbR///7s37+/ehiw4flZm7y56gHAhZifn68eAmwK\nftYmzxYxABQSYgAoJMQAUEiIAaCQEANAISEGgEJCDACFhBgACgkxABQSYgAoJMQAUEiIAaCQEANA\nISEGgEJCDACFhBgACgkxABQSYgAoJMQAUEiIAaCQEANAISEGgEJCDACFhBgACgkxABQSYgAoJMQA\nUEiIAaCQEANAISEGgEJCDACFhBgACgkxABQSYgAoJMQAUEiIAaCQEANAISEGgEJCPGELCwu5+eab\ns7CwUD0UAKaQEE/YgQMHcvz48Rw8eLB6KABMISGeoIWFhRw+fDi99xw+fNhWMQDfZ656ABvZgQMH\n8sorryRJTp8+nYMHD+aWW24pHtXGcPLkybz00kvZt29f9VA2jPn5+Vzy3V49DJJc8vI3Mz//ral4\nfs/Pz2fr1q3Vw9jQbBEvo7X2e621Y621Y88999yqv//o0aNZXFxMkiwuLubIkSNrPUQAZpwt4mX0\n3u9OcneS7Nq1a9WbCtdff30OHTqUxcXFzM3N5YYbbljzMW5W27dvT5LccccdxSPZOPbt25eHv/Rs\n9TBI8sqll2fnG66eiuf3NGyVb3S2iCdoz549ueSS4SHesmVLbrzxxuIRATBthHiCtm3blt27d6e1\nlt27d2fbtm3VQwJgytg1PWF79uzJiRMnbA0DsCQhnrBt27blzjvvrB4GAFPKrmkAKCTEAFBIiAGg\nkBADQCEhBoBCQgwAhYQYAAoJMQAUEmIAKCTEAFBIiAGgkBADQCEhBoBCQgwAhYQYAAoJMQAUEmIA\nKCTEAFBIiAGgkBADQCEhBoBCQgwAhYQYAAoJMQAUEmIAKCTEAFBIiAGgkBADQCEhBoBCQgwAhYQY\nAAoJMQAUEmIAKCTEAFBIiAGgkBADQCEhBoBCQgwAheaqBwAXYufOndVDgE3Bz9rkCTEzae/evdVD\ngE3Bz9rk2TUNAIWEGAAKCTEAFBJiACgkxABQSIgBoJAQA0AhIQaAQkIMAIWEGAAKCTEAFBJiACgk\nxABQSIgBoJAQA0AhIQaAQkIMAIWEGAAKCTEAFBJiACgkxABQSIgBoJAQA0AhIQaAQkIMAIWEGAAK\nCTEAFBJiACgkxABQSIgBoJAQA0AhIQaAQkIMAIWEGAAKCTEAFBJiACgkxABQSIgBoJAQA0AhIQaA\nQnPVAwCmx5ZTL2Trk4eqh1Fqy6mFJCl9HLaceiHJ1WX3z/oSYiBJsnPnzuohTIWTJxeTJNu3V4bw\nautjExFiIEmyd+/e6iHApuQYMQAUEmIAKCTEAFBIiAGgkBADQCEhBoBCQgwAhYQYAAoJMQAUEmIA\nKCTEAFBIiAGgkBADQCEhBoBCQgwAhYQYAAoJMQAUEmIAKCTEAFBIiAGgUOu9V49hJrTWnkvy5epx\nJLkqyfPVg5gg85t9G32OG31+ydrO8cd6769bo2VtSEI8Y1prx3rvu6rHMSnmN/s2+hw3+vySzTHH\naWLXNAAUEmIAKCTEs+fu6gFMmPnNvo0+x40+v2RzzHFqOEYMAIVsEQNAISEGgEJCPCVaa1e21o60\n1p4e/XvFOW63Z3Sbp1tre5a4/v7W2mOrXe6kXez8WmuHW2tfbK093lr7aGtty+jrt7bWTrbWHhld\n3rZeczpr3JOa31Ssv9WMZak5ttYua639S2vtydEc/2Ls9u9prT03tg5vWq85nTXuSc3vNa21e1pr\n8621/2qt7VifGX3fuC/2OfrnrbWvtNa+fdbtp2L9zbTeu8sUXJLcnuQDo48/kOTDS9zmyiRfGv17\nxejjK8au/7Uk/5DksdUsdxbml+Ty0b8tyT8meffo81uTvH/W198y85uK9Xexc0xyWZJfGN3mB5L8\nW5JfGn3+niR/Ncvr8Dzz+4MkHx19/O4k98za/EbX/UySa5J8+6zvmYr1N8sXW8TT4x1JDow+PpDk\nnUvc5q1JjvTeX+i9fz3JkSS7k6S19tokf5TkQxew3PVwUfPrvX9zdJu5DC9003aW4aTmNy3rb6Vj\nWXKOvfdTvffPJEnv/btJvpDk2nUY82pMan7jy70vyS+21tqE5rCci32Ofrb3/sy6jHSTEeLpcfXY\nk/xrSa5e4jbbk3xl7POvjr6WJH+W5CNJTl3ActfDxc4vrbUHkvxvkm9leEE74w9ba4+21v62cNft\npOY3LetvpWNZdo5J0lr7oSS/kuTBsS//+mgd3tdae/0ajnk1JjW/V7+n976Y5MUk29Zu2Cu2JvM7\nh2lYfzNrrnoAm0lr7WiSH1niqg+Of9J77621FW/xtdauS/Ljvfdbljv+tNrlrtak5jf2fW9trV2a\n5O+TvDnDb+t3ZfglpOf/fxl572qXvRJF87vo5a7GpOfYWptL8okkd/bevzT68j8n+UTv/Tuttd/P\nsLX25tUue4X3XzG/dTPp+Z3Duq2/jUqI11Hv/fpzXddae7a1dk3v/ZnW2jUZtozOdjLJm8Y+vzbJ\nQ0l+Nsmu1tqJDOv0h1trD/Xe35RkJctdExOc3/h9vNxa+2SG3WxHeu/Pjt3H3yT51IXPYHkV88s6\nrr/R/U96jncnebr3/pdj97kwdv3HMhzLnIiK+Y2+5/VJvjoK9Q8mGZ/zmlmP5+gS97lu62+jsmt6\netyf5MwZinuSfHKJ2zyQ5C2ttStGu2DfkuSB3vtdvfcf7b3vSPLzSZ4aRXily10PFzy/1tprRy8c\nZ7Y4fjnJk6PPrxn7/l9N8lhqTGR+K1zuerngOSZJa+1DGSL0vvFvOGsdvj3JE2s87pWayPzOWu67\nkny6915xjsNFze9cpmj9za7qs8VchkuGY0YPJnk6ydEkV46+vivJx8Zu994k86PL7yyxnB353rOm\nl1zuLM0vw7Gszyd5NENo9yeZG1338STHR9fdn+SaDTa/qVh/azDHazMcPngiySOjy02j625L8niS\nLyb5TJI3brD5XZrk3tHtP5fkDbM2v9HXb89wzPiV0b+3TtP6m+WL/+ISAArZNQ0AhYQYAAoJMQAU\nEmIAKCTEAFBIiAGgkBADQKH/AzMJYbNWmJIYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4cc2e97b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "\n",
    "clf = ExtraTreesRegressor()\n",
    "ExtraTrees_clf = evaluate_model(clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "3546a87a-1df4-4569-90b1-0e60d324c186",
    "_uuid": "d07a3d8e7629090f3a76efd2ceca596ca0886093",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SpOuY2YJmPxXvSyiykx1sT5S0DnCApAnAeFrK1DXD3cBBMe4fOzm/VYGTJX1G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\nmZXkpGlmZlbSfwH4y/MPUHBsEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4d160eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "1. feature ExterQual_TA (0.227428)\n",
      "2. feature OverallQual (0.191663)\n",
      "3. feature GrLivArea (0.065842)\n",
      "4. feature GarageCars (0.051435)\n",
      "5. feature YearBuilt (0.042292)\n",
      "6. feature FireplaceQu_NA (0.034517)\n",
      "7. feature BsmtQual_Ex (0.027484)\n",
      "8. feature CentralAir_N (0.023659)\n",
      "9. feature ExterQual_Fa (0.019977)\n",
      "10. feature Fireplaces (0.018254)\n",
      "11. feature 1stFlrSF (0.015657)\n",
      "12. feature FullBath (0.015231)\n",
      "13. feature BedroomAbvGr (0.010050)\n",
      "14. feature KitchenQual_Ex (0.009846)\n",
      "15. feature GarageType_Detchd (0.009376)\n",
      "16. feature BsmtFinSF1 (0.009200)\n",
      "17. feature YearRemodAdd (0.008357)\n",
      "18. feature TotalBsmtSF (0.007985)\n",
      "19. feature MSZoning_C (all) (0.007935)\n",
      "20. feature MSZoning_RM (0.007027)\n",
      "21. feature GarageType_Attchd (0.006930)\n",
      "22. feature LotArea (0.006607)\n",
      "23. feature TotRmsAbvGrd (0.005963)\n",
      "24. feature HalfBath (0.005448)\n",
      "25. feature BsmtFullBath (0.005111)\n"
     ]
    }
   ],
   "source": [
    "# feature 重要性\n",
    "importances = ExtraTrees_clf.feature_importances_\n",
    "# 由小到大排序並回傳對應的 index，再將之反向，也就是改為由大到小的排序\n",
    "indices = np.argsort(importances)[::-1]\n",
    "# 印出的個數\n",
    "n = sum(importances > 0.005)\n",
    "\n",
    "# 畫直方圖\n",
    "plt.barh(range(n), importances[indices[:n]], align=\"center\")\n",
    "plt.yticks(range(n), X.columns[indices[:n]])\n",
    "plt.show()\n",
    "\n",
    "# 印出排序後的 feature 重要性\n",
    "print(\"Feature ranking:\")\n",
    "for f in range(n):\n",
    "    print(\"%d. feature %s (%f)\" % (f + 1, X.columns[indices[f]], importances[indices[f]]))\n",
    "    \n",
    "ExtraTrees_important_index = X.columns[indices[:n]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "8205c79e-a5e6-4ac6-a8d9-55b3fefb75f9",
    "_uuid": "325ae68d6c0110c35c95014ba5c90f5c698e5be4",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+4LyUY5gm6VpJ7+jLOLpN0qrAvsAvux1Lb8nb6FHVbhGxQUSM64N5LbCTmzyvkHRSXfdr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4b61256a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "clf = AdaBoostRegressor()\n",
    "AdaBoost_clf = evaluate_model(clf, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "8ad34c9d-e80a-47ef-a5b0-1045e74425e5",
    "_uuid": "1f65dd06e6efc3573c514e0971555415e4c92ae0",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QS49Fc1CutbuVaVcgaRFJf5M0VVLZV0CZHkoexHsY1g3lDufkQ018fzlOLrV5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\nAEUJcQAo6v8BZds4TIYljrcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4b6032b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0.13455\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "clf = GradientBoostingRegressor()\n",
    "GradientBoosting_clf = evaluate_model(clf, X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1d261d71-7fa0-49ea-84ed-1a52f99ef3e5",
    "_uuid": "4da4083b4ba47d7951940204602b22b4ffa30a1c"
   },
   "source": [
    "挑選重要 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "4c807258-8ae9-45de-9e41-1cf890733223",
    "_uuid": "55497887a1edd44ecdc02ec410a726791fa81501",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1stFlrSF', '2ndFlrSF', 'BedroomAbvGr', 'BsmtFinSF1', 'BsmtFullBath',\n",
       "       'BsmtQual_Ex', 'BsmtUnfSF', 'CentralAir_N', 'Condition2_PosA',\n",
       "       'Condition2_PosN', 'ExterQual_Fa', 'ExterQual_TA', 'FireplaceQu_NA',\n",
       "       'Fireplaces', 'FullBath', 'Functional_Maj2', 'GarageArea', 'GarageCars',\n",
       "       'GarageCond_Ex', 'GarageQual_Ex', 'GarageType_Attchd',\n",
       "       'GarageType_Detchd', 'GrLivArea', 'HalfBath', 'Heating_Grav',\n",
       "       'KitchenAbvGr', 'KitchenQual_Ex', 'LotArea', 'LotFrontage',\n",
       "       'MSZoning_C (all)', 'MSZoning_RM', 'Neighborhood_Edwards',\n",
       "       'Neighborhood_MeadowV', 'Neighborhood_NoRidge', 'Neighborhood_NridgHt',\n",
       "       'Neighborhood_StoneBr', 'OverallCond', 'OverallQual', 'PoolArea',\n",
       "       'PoolQC_Ex', 'PoolQC_Fa', 'PoolQC_Gd', 'PoolQC_NA', 'RoofMatl_ClyTile',\n",
       "       'RoofMatl_Membran', 'RoofMatl_WdShngl', 'TotRmsAbvGrd', 'TotalBsmtSF',\n",
       "       'YearBuilt', 'YearRemodAdd', 'YrSold'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = pd.Index([])\n",
    "temp = temp.union(linear_important_index)\n",
    "temp = temp.union(linearLasso_important_index)\n",
    "temp = temp.union(linearRidge_important_index)\n",
    "temp = temp.union(RandomForest_important_index)\n",
    "index = temp.union(ExtraTrees_important_index)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "514dc918-9197-44fb-ba65-a2e14d4eea54",
    "_uuid": "a8da6cd14a5cfe9d8f33f1ce151c388a41d9e04f",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I7ogzMxW5B7Rhcufpv2u2PZlZA7lYwnSJNEQ63STdIRcVuLEjbKsjz00lvTsz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\nkBADQCEhBoBCQgwAhYQYAAr9P+gKjFVL8b17AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4b5fc9518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "\n",
    "clf = ExtraTreesRegressor()\n",
    "ExtraTrees_clf_reduce = evaluate_model(clf, X[index], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "5af50721-cba4-4350-b7dc-cb9a113dc134",
    "_uuid": "260f38883c2a49ff6a02822e671ef8fc283a477f",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NkrTTrAYmaXNJz83qdGzOk3S0pG/l7/PkdpP0kKTNux3HvErSMElndmnep0r6UTfmPS+T\nNFLSVt2Oo6b+2NTb9unZeWV9JHByRCwREZc2G0jS6ZKmSlp+ds04nzi8nU8UJkl6StL+s2v6Teb5\nrqSUDzhv5RgmSLpV0kfnZBzdJmk5YE/gtG7HMrvkMnpUtVtErBURw+fAvObayU2eV0g6pa77LZL2\nnhsxlCgi9ouIn3Y7jqo5dfIiaXlJl0t6PpeFoW2GHyrpBkmTJT1aUvLtRF6Pwxr1m1P7dCckRU/H\nmZ3JeiXgoVYDSFoc2Bl4FfjybJw3wG35RGGJPI9fSPrQbJ5HJ87LMSwL3ABcMCdmIqnPnJjuTMx/\nb+CqiJjSxXDmWzNRDl4H9mh3kJ5XdHs/aaTLMb0DXEM6RnbiHOAeYBngB8CF+QTdZsKsbPu2yVrS\nYEkXSxorabykkxsM8ySwMnBFvqpcpMnkdgYmkK7C96qbRt98RfOKpIeBDev6HybpSUkTJT0s6bPN\nYo6Ie4BHgDUq438mV3tMkDRcUrXfGrnbhDzMZyr9ts/zmyhptKTv5JOOq4EVKlfzK9TFMBU4CxhU\nLdySPi3p3sqV9zqVfutLuifP6wJJ59Wu8GpXYJK+J2kM8OcOpve9HPNESY9J2jJ330jSXZJek/Si\npF91uJ5G5mneD7yeC952wI3120DS4ZLG5XF2r3T/VF7G1ySNqp71SlpU0pm5nE2Q9G9JA3O/JSX9\nUdILeZmOkrRgszJQmea+kh7J5epaSSvl7pL0a0kv5VgekLS2pK8DuwOH5u16RWXZt8rfh+Xtc2Ze\ntw9IWk3S9/P0RknauhLDPjmGiUq1Pt/I3RuWI0mLSDpB6ern+fx9kVbloAcmAKcDRzRZXwtI+qGk\nZ/Ky/FXSkrnfUKWrsb0kPZu37w/qxq3tp+MlnS9p6R7GV5vWR3J5niDpPlWqK5utz9zvXeun0u2Q\nvEwvSNqnMs60mpQOhl1G0hW5zPw7l8NptwNbLE9I+l9JTwBP5G4n5rLymqS7JW2au28LHA58MZeJ\n+3L3mdoHqiLixYg4Bfh3BzGvBqwPHBERUyLiIuABOk/01Wm1KtPLSroyb+uXJd0saYHcr+ExbHZo\nsE+fn8v7RKVj4AaVYVeQdJFSHnxa0jcr/TaSdFuO/wVJJ0tauNL/Xdt+pkRE0w+wIHAf8GtgcWBR\nYJPcb2/glsqwI4GtKr8PA66sm94/gF8AA4GpwIcr/Y4BbgaWBgYDDwLPVfp/AViBdILxRdIVwvJN\nYtmQdFBaLf9eLQ//SWAh4FBgBLBw/j2CtHMsDGwBTARWz+O+AGyavy8FrJ+/b16NL3cbBpyZvy+c\nl2kc0Cd3+xDwErBxXrd75fW2SB7+GeDgHNPngDeBoyrzmwocm4fv22Z6qwOjgBXy+EOBVfL324A9\n8vclgI+0W0+VbXxv3j59c7exwIaVdVCL81c5js3yNFev9P9g3o7rAC8CO+V+3wCuABbLy/NhoH/u\ndwmpqn1x4D3AncA32pTfHXP8awB9gB8Ct+Z+2wB3AwMA5WFq5en02npvVL7zdn4jT6MP8FfgadKV\nx0LA14CnK+N+Clglz2czYDKty9GRwO15OZcDbgV+2qIcDCGV92af3arzAt4LvFbZJrcAe+fv++Z1\ntjKpbFwMnFEpQwH8Ps93XeC/wBq5/8E57hVzbKcB57TaRk32nUHAeGD7XE4+mX8v1+H6rF8/tW5H\n5u2zfR5nqfrt3cGw5+bPYsCapH3slg6WL4DrSce32r7zZdIVax/gEGAMsGj9+qhMo+k+AGzSpgxs\nUjetPjmmoS1i/izwSF23k4GTOtymI5m+z7Qq00cDp+b1vRCwad62rY5hu7VZ3iEN4tmcGXNKNb5h\npH16e9Kx52jg9txvAdKx4sek4/TKwFPANrn/h4GP5HU6lHSh+K1W235mPu1W9kdJB+M+DfrtTYtk\n3WD4IaQqmPXy72uBEyv9nwK2rfz+OnUHsbrp3QvsWIllat5IE/PKOQlQ7v8j4PzKuAsAo/PG25S0\nkyxQ6X8OMCx/f5aURPq32vCVDf5mjuNt0gFm80r//yMX0Eq3x0gHnI/nmFTpdwszHkTeJO/MHUxv\nVVIi3wpYqG6Ym4CfAMvWdW+6nirbeN+6cd4CPlC3XqYCi1e6nQ/8qMl2PAH4df6+L2knXqdumIGk\npNC30u1LwA1tyu/VwFfqlmcy6ZbNFsDjpJ1sgbrxTqd9sr6+0m8HYBKwYP7dj1QGBzSJ61Lg4Bbl\n6Elg+8rvbYCRzcpBxzt7ZV6kk+bzKuVs7/z9H8ABlXFWz9u4diAKYMVK/zuBXfP3R4AtK/2Wr43b\nQWzDmJ6sv0c+Qaj0vxbYq8P1Wb+fbA5MqcZB2jdqJ6nTtnerYUkH8bfIJzm531F0nqy3aDPMK8C6\n9etjVvaBFvPqJFnvQU5YlW4/A07vcB4jmb7PtCrTRwKXAavWjd/0GDaTyzyt/DeIbxjw90q/NYEp\n+fvGwLN10/o+8Ocm8/kWcElPtn0nn3bV4IOBZyJV6c6qPUhnaffm32cBu0laKP9egXQWVfNMdWRJ\ne2p6de8EYG3SfeGa2yNiQET0I105rAX8vDLtadOLiHfyvAbV5pu7Vec9KH/fmXS29YykG9X+gbHz\nI2IAaed6kHTWVbMScEhtGfJyDM4xrACMjrx1s+r6ABgbEW90Mr2IGEEqNMOAlySdq+lV9V8hXUU/\nmqvyPt3BemoW0yuk5DRDt4h4vfL7mTxtJG2s9MDKWEmvAvsxfTueQToon5uryn6Ry8dKpDPuFyrL\neRrpLL2VlYATK+O8TDpjHxQR/yRdJfw2r5/fSerfZnpVL1a+TwHGRcTbld+QrkyRtJ2k23MV3wRS\neaqW3XozbAcq6y+rLwcz41hgG0nrdjDvPqTyXDOm8n0yeTlJ6/uSyvp+hHTSWh23EysBX6gr15uQ\nkn8n67PR+hlfdxyrxl2v2bDLkdZFdR+o3x9amWFYpVtqj0h6NS/HkjQvFzO7D8yKSUD9PtGfdEHU\nU63K9HGk2pzrlG5rHAbQ5hg2J9SX60WVbvWtRLpVVS2Ph5PLtdItsCsljZH0Ginv1G/HnpSThtol\n61HAEM2eByL2BFbOCzSGVE26LGlHg1TdPLgy/JDaF6X7jL8HDgSWycnwQdKB910i4kXgItIVD8Dz\npBVem57yvEbnfoNr90gq8x6dp/XviNiRtFNcSrpKhHS21FREjCPVDgzT9CffRwE/yycVtc9iEXFO\nXv5BObaawfWTrfvdanpExNkRsUle9iAdoImIJyLiS3mZjiU9NLJ4m/XULIb7SYm/aqk8vZohedoA\nZwOXA4MjYklS9ZdyXG9FxE8iYk3gf4BPk8rNKNJVxbKV5ewfEWvR2ihSNWF1/fSNiFvz/H4TER8m\nnUWvBny3yTLOtHxf7iLgl8DAXHavYnrZbTSvGbYDM66/d40jaYim3/Nu9NmdOhExnlSrUf8UdKN5\nT2XGk5NmRgHb1a3vRSNidNsx3z2dM+qms3hEHNPB+oTZuP3qjCWtixUr3er30VamxaV0f/pQYBdS\nFfsA0oO3zcpFy31A0qZtysCmPVrS5CHSMbt6Mr4ubR4kbqJpmY6IiRFxSESsDHwG+H+1e9PNjmGS\ndm+zvEOYfUaRbmtVy2O/iKjlrv8DHgXeHxH9SYm8PjfNcplsl6zvJCWRYyQtrvQA0Md6OpN8NboK\nsBGwXv6sTTpw75kHOx/4vqSlJK0IHFSZxOKkhR2bp7dPHr/Z/JYh3W+pFarzgU9J2jJfqR1CKvi3\nAneQzqIOlbSQ0oMsO5Cu7hbOhWLJiHiLdJ+vdgX+IrCM8sM3jUTEY6QrxUNzp98D++WrS+V1+qm8\nM9xGugo5UFIfSTvm9dVK0+lJWl3SFvng9gbpau+dvH6+LGm5fOU8IU/rnTbrqZmrSNXu9X6S19+m\npKRbeyq+H/ByRLwhaSPSvSdyXJ+Q9EGlh2ZeI1U5vhMRLwDXAcdL6q/0INMqkhrNt+pUUpmqHdCW\nlPSF/H3DvN4WIt1Tf4MZt+3KbabdqYVJ907HAlMlbQdsXenfqBydA/xQ0nKSliXdK2v6bzwR8Wzk\n/4Ro8jmryai/Ip0UrVHpdg7wbUnvk7QE6SrhvA5r104FfqbpD/Etl8sx+fdIdfYvYmcCO0jaRtKC\n+bizeT4utFufc0yuObmYdAK+mKQPMP341VP9SIl/LNBH0o+Z8Sr2RWBo7SKi3T4QETe3KQM31yYs\naVHSOgRYJP9utLyPk243HpG3wWdJz5lclKezuTr/F6SmZVrpIdlVJYl0wvI28E6rY1hEnNVmeZ/t\nMK5O3AlMVHrYrW8uk2tLqj0E3Y90vJqUy0SP/m1Y6eG24e2Ga5msc+HcgXTv4FnSwylf7DCAwyVd\nnX/uBVwWEQ9ExJjaBzgR+LTSE6M/IVWNPE0qlGdU4ngYOJ6U0F4kPaD0r7pZfrR2VkWqfhtLTvg5\naX6ZdB97XF6mHSLizYh4M//eLvc7BdgzIh7N090DGKlUvbEf6Ulhcv9zgKeUqkaaVc8cB3xd0nsi\n4i7Sw0cnk6qPR5Dut5Pj+BypinpCjvdKUrJsqNX0SDtj7QG3MaSr6O/nftsCD+V1dSLpnuOUVuup\nWQykh6u2l9S30m1Mjud50u2O/Srr8wDgSEkTSTvs+ZXx3gtcSCr4j5CeMq+Vgz1JB+qH87QvJFeL\ntlg/l5DOxM/N2+9B0naGdGD8fZ7WM6TnC47L/f4IrJm3a9N3BnQiIiYC3yQt5yukk5PLK/0blaOj\ngLtItRYPAP/J3WariHiNdO+6+sT2n0jr/CbSvvgGM544t3Iiadmuy9v3dtL9PpSejl0md2sX1yjS\nw4GHk/bjUaRajwXarc+54EBSdfUY0no6hxb7aAvXkv6F6nFS+XuDGatKaye34yX9J3/v8T7QxBRS\nFTekK8Jp/3ap9IKYUyvD7gpskOd3DPD5iBib+w2m9Yl8Vasy/X7g7zmm24BTIuIGWh/D5pqcBz9N\nush8OsfzB1I5APgOqRxOJB1TzuvhLAbz7nz2LrUHsKxAku4ATo2IP3c7llYk/Rx4KSJO6HYsViZJ\nmwD/m2+/zDMkHQu8NyL26nYsc5ukPwAXRMS13Y6lN5N0L+nBzPEth3OyLkeu0nqMdOa2O6laceVc\nBWZmXZarORcmXR1uSLoN9NVo8dZGs9nBDXmUZXXS/7VPIN0v/rwTdWu52q7RAyanth/brMf6ke5b\nv06q7jweuEwtHvDqarQ2z/CVtZmZWeF8ZW1mZlY4J2szM7PCOVlbr6Ze1ibt/ESFtWc8v5N0vOZw\n08E25zhZW6+hebSd6TZxDJf0RuWBpcfq+u+m1ELW65Iu1Uy2cjUv0/TWwjp+E+OcOtGobM/BlW5b\nSRo5u+fVwC+Bw1VpEcp6DydrszmgJ4mhAwdW3sy0emUea5HeD70H6T3Fk0kv9emx2RyvtfY6qdGc\nuSr/Z8mjpFd6Wi/jZG2zJF+x7CfpifwGrt/m1wa2G2+eb2daLdrpnU12B66IiJsiYhIpAXxOM77L\nudn6bxTvUjnesXm7XKn0is/aOMMl/VTSv/I6u07p1ZG1/nvkq/zxqrRznft1su4O1fQ2pHdSakv+\n8bzuDu9gmZq1035T/jshb8uPKr2q85851nGSzpI0IE/nDNK7q6/Iwx+auzdtY7uHfgN8SdIqTZZj\njbyuJyjd5vlMpd/peR/7W94Gd1SnI+kDkq7P6+wxSbvUTX44qYlR621iFpvt8mf+/pDe2X4lqV3o\nIaTXQ27bZpz5pZ3phu305uGvpHlbvFdW5jk8r9NxpFcSbl7pdxnwvboYJ1FpJ77FNmgU7zKkVuYW\nI/0/8QXApXWxPElq9KRv/n1M7rdmnvfH8/R+laffSXvGtVh+XNlOY0ltB/QjtaA3BXhfm2Vq1k77\nUFI5rTZ7uSqpnexFcjw3ASc0Klf5d7s2tk9psT3vr1uHX83rp9Yk6FZMby5yIdK+cTjp5StbkF5j\nWWt7/PQ8341IZfws4Nzcb3HSK0v3yf0+RCo3a1bm/zngP90+bvjT80/XA/Cnd3/yQXCTyu/zgcPa\njDNftDNNk3Z6e7h+N85xL0J6x/5EYJXc7x+k965Xh5/W/nib6b4r3gbDrEdq7rT2ezjww8rvA4Br\n8vcf15JG/r14nn4n7RlvTkrG9dtp48rwdwM7tVmmZu20D6UuWTcYdyfgnkblKv/uURvbLeYznJSs\nlyM1WrEWMybrTUnvwV6gMs5UbqbJAAAO4klEQVQ5wLDKPvCHSr/tgUfz9y8CN9fN7zTgiMrvTwJP\nzWx59Kd7H1eD2+zQrH3jZlZi/mhnumE7vT0REXdEakLwvxHxF9LVda1pvlltb3iGeJVakjotV2W/\nRkp+A5RaQatptq1naI8+Unvm1Xcdt1t34xtsp/pt2a5cNWun/V0kDVRqH3l0XtYzab3tW7ax3VOR\nGsM4mXRCV7UCMCpSi3g1zzBjm/Kt2hPfuC7G3UkN5NT0Y3pLe9aLOFlbN8wX7UxHi3Z6JV2t5m3x\nXk1zUYnxIVL7wrVlWpl0Bf54y4VvEi/pFberk65o+5OqtKnMr5UZ2qOXtBipWr2m3bqbZdG8nfZG\n2/LnufsH87J+mdZtYjdtYxtavvZ2kqRm7T8fB3wC+HCl2/PA4LpnG4YwY5vyzYwCbqyLcYmIqP67\n1hqkVxpbL+Nkbd0wX7QzrSbt9AJExHbRvC3e7fL4A5TadF5UqY3z3UkJ9Jo8i7NI7T5vmpPSkcDF\nkZqRrD2MdHoP1kk/0hXsBKV/ATuiB+NeSGrudhOlfw06khmPLz1adzNDzdtpH5v/VstOP1LNxKuS\nBjH9hLCmvqy1amObiNivxfZcq1G8ETGB9G7xQyud7yBdLR8qaaH8ENsOwLkdrIIrgdWUHvRbKH82\nlFRtr3wz0m0o62WcrG2ui/mnnelm7fR2aqE8/doDZgeR7ts+nmN8iNTG+lnAS6QEdEBl/I7aya04\ngfTg2DjSw2DXtB58uhzL/5IeCnuBtE6r/6c+N9robtZO+2TgZ8C/8rb8COne9vqkk6i/kRrnqDqa\ndHIxQdJ3okUb27MY84mkkzhgWrv2O5D2h3GkB9f2jOntwTeVy/TWpDaonydVl9ceIETS8qTaKrcQ\n1gu5IQ+zeVC+ur0PWCci3up2PNZ9ko4HnoyImfpffOsuJ2szM7PCuRrc5ogWD9y4nWmbaS0ezGv7\n0hSz3sxX1mZmZoXz+4Bn0rLLLhtDhw7tdhhmZr3K3XffPS4ilut2HL2Nk/VMGjp0KHfddVe3wzAz\n61UkPdN+KKvne9ZmZmaFc7I2MzMrnJO1mZlZ4ZyszczMCudkbWZmVjgnazMzs8I5WZuZmRXOydrM\nzKxwTtZmZmaFc7I2MzMrnJO1mZlZ4ZyszczMCudkbWZmVjgnazMzs8I5WZuZmRXOydrMzKxwTtZm\nZmaFc7I2MzMrnJO1mZlZ4fp0OwCzWXHSSScxYsSIbodRpNGjRwMwaNCgLkfSHauuuioHHXRQt8Mw\nmy2crK1XGzFiBPc++AhvL7Z0t0MpzoKTXwVgzH/nv918wckvdzsEs9lq/tuLbZ7z9mJLM+UD23c7\njOL0ffQqgPly3dSW3Wxe4XvWZmZmhXOyNjMzK5yTtZmZWeGcrM3MzArnZG1mZlY4J2szM7PCOVmb\nmZkVzsnazMyscE7WZmZmhXOyNjMzK5yTtZmZWeGcrM3MzArnZG1mZlY4J2szM7PCOVmbmZkVzsna\nzMyscE7WZmZmhXOyNjMzK5yTtZmZWeGcrM3MzArnZG1mZlY4J2szM7PCOVmbmZkVzsnazMyscE7W\nZmZmhXOyNjMzK5yTtZmZWeGcrM3MzArnZG1mZlY4J2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOy\nNjMzK5yTtZmZWeGcrM3MzArnZG1mZlY4J2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOynstOOukk\nTjrppG6HYWY2U3wM644+3Q5gfjNixIhuh2BmNtN8DOsOX1mbmZkVzsnazMyscE7WZmZmhXOyNjMz\nK5yTtZmZWeGcrM3MzArnZG1mZlY4J2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOyNjMzK5yTtZmZ\nWeGcrM3MzArnZG1mZlY4J2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOyNjMzK5yTtZmZWeGcrM3M\nzArnZG1mZlY4J2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOyNjMzK5yTtZmZWeGcrM3MzArnZG1m\nZlY4J2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOyNjMzK5yTtZmZWeGcrM3MzArnZG1mZlY4J2sz\nM7PCOVmbmZkVrk+3A5jfjB49milTpnDwwQd3O5R5wogRI1jgzeh2GFaYBd54jREjJno/mwNGjBhB\n3759ux3GfMdX1j0g6euS7pJ019ixY7sdjpmZzSd8Zd0DEfE74HcAG2ywwUxdzg0aNAiAE088cfYF\nNh87+OCDufupF7sdhhXmnUX7s+rKA72fzQGuregOX1mbmZkVzsnazMyscE7WZmZmhXOyNjMzK5yT\ntZmZWeGcrM3MzArnZG1mZlY4J2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOyNjMzK5yTtZmZWeGc\nrM3MzArnZG1mZlY4J2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOyNjMzK5yTtZmZWeGcrM3MzArn\nZG1mZlY4J2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOyNjMzK5yTtZmZWeGcrM3MzArnZG1mZlY4\nJ2szM7PCOVmbmZkVzsnazMyscE7WZmZmhXOyNjMzK5yTtZmZWeGcrM3MzArnZG1mZlY4J2szM7PC\nOVmbmZkVzsnazMyscH26HcD8ZtVVV+12CGZmM83HsO5wsp7LDjrooG6HYGY203wM6w5Xg5uZmRXO\nydrMzKxwTtZmZmaFc7I2MzMrnJO1mZlZ4ZyszczMCudkbWZmVjgnazMzs8I5WZuZmRXOydrMzKxw\nTtZmZmaFc7I2MzMrnJO1mZlZ4ZyszczMCudkbWZmVjgnazMzs8I5WZuZmRXOydrMzKxwTtZmZmaF\nc7I2MzMrnJO1mZlZ4ZyszczMCudkbWZmVjgnazMzs8I5WZuZmRXOydrMzKxwTtZmZmaFc7I2MzMr\nnJO1mZlZ4ZyszczMCudkbWZmVjgnazMzs8I5WZuZmRXOydrMzKxwTtZmZmaFc7I2MzMrnJO1mZlZ\n4ZyszczMCudkbWZmVjgnazMzs8L16XYAZrNqwckv0/fRq7odRnEWnDweYL5cNwtOfhkY2O0wzGYb\nJ2vr1VZdddVuh1Cs0aOnAjBo0PyYtAa6bNg8xcnaerWDDjqo2yGYmc1xvmdtZmZWOCdrMzOzwjlZ\nm5mZFc7J2szMrHBO1mZmZoVzsjYzMyuck7WZmVnhnKzNzMwK52RtZmZWOCdrMzOzwjlZm5mZFc7J\n2szMrHBO1mZmZoVzsjYzMyuck7WZmVnhnKzNzMwK52RtZmZWOCdrMzOzwjlZm5mZFc7J2szMrHCK\niG7H0CtJGgs80+046iwLjOt2EG30hhjBcc5uvSHO3hAj9P44V4qI5eZ2ML2dk/U8RNJdEbFBt+No\npTfECI5zdusNcfaGGMFxzq9cDW5mZlY4J2szM7PCOVnPW37X7QA60BtiBMc5u/WGOHtDjOA450u+\nZ21mZlY4X1mbmZkVzsnazMyscE7WhZO0tKTrJT2R/y7VZLi98jBPSNqr0v0aSfdJekjSqZIWzN3X\nk3S7pHsl3SVpoxLjzP0OkvRo7veLUuPM/Q+RFJKWLTFOScfldXm/pEskDSgwxo6mOzfilLSYpL9V\nyt8xleGHSLpB0j15fW5fYpy5/y6SHs79zi41zjzMznkf8r99VUWEPwV/gF8Ah+XvhwHHNhhmaeCp\n/Hep/H2p3K9//ivgImDX/Ps6YLv8fXtgeKFxfgL4O7BI/v2eEuPM3QYD15JelrNsiXECWwN98vdj\nG023gBjbTnduxQksBnwiD7MwcHNlv/kdsH/+viYwstA43w/cU1nvXduHWsWZu/UDbgJuBzaYlTjn\ntY+vrMu3I/CX/P0vwE4NhtkGuD4iXo6IV4DrgW0BIuK1PEwf0s5Re6IwgP75+5LA84XGuT9wTET8\nNw/3UqFxAvwaOLSuW1FxRsR1ETE197sdWLG0GDuc7lyJMyImR8QNOd43gf8wfZ0Vsw+1ifNrwG/z\n8F3dh9rECfBT0knkG7MY4zzHybp8AyPihfx9DDCwwTCDgFGV38/lbgBIuhZ4CZgIXJg7fws4TtIo\n4JfA9wuNczVgU0l3SLpR0oYlxilpR2B0RNw3i/HN0Tjr7AtcXWCMnUx3rsaZYx0A7AD8I3caBnxZ\n0nPAVcBBhca5GrCapH8p3fratsQ4Ja0PDI6Iv81ifPOkPt0OwEDS34H3Nuj1g+qPiAhJPb5qi4ht\nJC0KnAVsQTrL3R/4dkRcJGkX4I/AVgXG2YdUlfYRYEPgfEkrR64zKyFOSf8CDidVMXesS+uzNu8f\nAFNzvyJj7Ml053SckvoA5wC/iYincucvAadHxPGSPgqcIWntiHinsDj7kKrCNyddxd4k6YMRMaGU\nOCUtAPwK2Lun05pfOFkXICKaJklJL0paPiJekLQ86Sqk3mjSjlizIjC8bh5vSLqMVIV1PbAXcHDu\nfQHwh0LjfA64OCfnOyW9Q2ogYGxBcY4B3gfcJ6k2/H8kbRQRYwqK8/o87b2BTwNbtjrp6WKMnUx3\nbsf5O+CJiDih0u0rTK/Svy2fdCzbKt4uxfkccEdEvAU8LelxUvL+d0Fx9gPWBobnfei9wOWSPhMR\ndzWLZX7iavDyXU5KrOS/lzUY5lpga0lL5ScztwaulbRE3plqZ7KfAh7N4zwPbJa/bwE8UWicl5Ie\nMkPSaqR7m7PS4tBsjzMiHoiI90TE0IgYSjo4rt8qUXcjzvx7W9J99c9ExORZiG+OxdjhdOdKnDm+\no0j3pL9VN86zwJZ5mDWARWlxEtnFOC8lJ06l/1JYjfTAVzFxRsSrEbFsZR+6nVRGnahrGj115k85\nH2AZ0j2dJ0hPRS+du28A/KEy3L7AiPzZJ3cbSDp7vh94EDiJ6U8CbwLcDdwH3AF8uNA4FwbOzN3/\nA2xRYpx18xjJrD8NPqfW5wjSvcR78+fUAmNsON0uxbki6UGyRyrr7Ku535rAv0j70L3A1oXGKVIV\n88PAA1T+g6GkOOvmMRw/DT7Dx68bNTMzK5yrwc3MzArnZG1mZlY4J2szM7PCOVmbmZkVzsnazMys\ncE7WZmZmhXOyNjMzK9z/B3QxJpqkzzAOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4b601ee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "\n",
    "clf = AdaBoostRegressor()\n",
    "AdaBoost_clf_reduce = evaluate_model(clf, X[index], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "9bd3aa08-745c-436c-8bd5-cf5a7da76831",
    "_uuid": "f28a93d533d793751eba993775dd13081091878d",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dgaT5JP1d0nuSyr4CynRT8iDezbAuKHc4Mx9q4vvLsXKpzesk1fysSdJfJT0n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4b606e240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "clf = GradientBoostingRegressor()\n",
    "GradientBoosting_clf_reduce = evaluate_model(clf, X[index], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "296c9d10-c0ef-4f81-b953-7daaad4f336f",
    "_uuid": "4fcadf3807e98390320600ed7f6ce8899291dd15",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3YXMdfBOYKmlAPah0a4kKeFrycl19sKlHg+HoO3KxlJ17idqPRrA5ZA83kzpO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\n4JmZWS044JmZWS044JmZWS0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd4b6099748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 0.14136\n",
    "from sklearn import linear_model\n",
    "\n",
    "clf = linear_model.Ridge()\n",
    "linearRidge_clf_reduce = evaluate_model(clf, X[index], y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "93bccf62-c14b-4630-991d-c9cd164cb7e6",
    "_uuid": "f0183a7172ca456a69c32b73d0a83491fd88e01d"
   },
   "source": [
    "結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "03f5fdc7-214f-4ebb-afeb-d20ab16e9198",
    "_uuid": "e33b863308f3f84a52e260460056771960b0317d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf_final = linearRidge_clf\n",
    "#X_test = X_test[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "ba6a4e6d-4c7a-4b5c-a070-499aa6aef0a9",
    "_uuid": "6ea62d62d387f683fe826803b49bed3a90616d5b",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>SaleType_NA</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>9.360741</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.581720</td>\n",
       "      <td>7.581720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.150603</td>\n",
       "      <td>4.976734</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>4.406719</td>\n",
       "      <td>9.565775</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.580189</td>\n",
       "      <td>7.580189</td>\n",
       "      <td>4.691348</td>\n",
       "      <td>6.828712</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1462</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>9.534668</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.599902</td>\n",
       "      <td>7.600402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.674561</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1463</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>9.208238</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.600402</td>\n",
       "      <td>7.600402</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>6.401917</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>4.795791</td>\n",
       "      <td>3.784190</td>\n",
       "      <td>8.518392</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.597396</td>\n",
       "      <td>7.597396</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.575949</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.330733</td>\n",
       "      <td>9.210440</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.597898</td>\n",
       "      <td>7.598399</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>4.252854</td>\n",
       "      <td>8.984819</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>7.597396</td>\n",
       "      <td>7.604894</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.841615</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>4.158883</td>\n",
       "      <td>9.036344</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.600402</td>\n",
       "      <td>7.600402</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>4.454347</td>\n",
       "      <td>9.227886</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.596392</td>\n",
       "      <td>7.596392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.458338</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>4.262680</td>\n",
       "      <td>9.036106</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.586296</td>\n",
       "      <td>7.586296</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.690842</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 309 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "1460    3.044522     4.394449  9.360741     1.791759     1.945910   7.581720   \n",
       "1461    3.044522     4.406719  9.565775     1.945910     1.945910   7.580189   \n",
       "1462    4.110874     4.317488  9.534668     1.791759     1.791759   7.599902   \n",
       "1463    4.110874     4.369448  9.208238     1.945910     1.945910   7.600402   \n",
       "1464    4.795791     3.784190  8.518392     2.197225     1.791759   7.597396   \n",
       "1465    4.110874     4.330733  9.210440     1.945910     1.791759   7.597898   \n",
       "1466    3.044522     4.252854  8.984819     1.945910     2.079442   7.597396   \n",
       "1467    4.110874     4.158883  9.036344     1.945910     1.791759   7.600402   \n",
       "1468    3.044522     4.454347  9.227886     2.079442     1.791759   7.596392   \n",
       "1469    3.044522     4.262680  9.036106     1.609438     1.791759   7.586296   \n",
       "\n",
       "      YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2          ...            \\\n",
       "1460      7.581720    0.000000    6.150603    4.976734          ...             \n",
       "1461      7.580189    4.691348    6.828712    0.000000          ...             \n",
       "1462      7.600402    0.000000    6.674561    0.000000          ...             \n",
       "1463      7.600402    3.044522    6.401917    0.000000          ...             \n",
       "1464      7.597396    0.000000    5.575949    0.000000          ...             \n",
       "1465      7.598399    0.000000    0.000000    0.000000          ...             \n",
       "1466      7.604894    0.000000    6.841615    0.000000          ...             \n",
       "1467      7.600402    0.000000    0.000000    0.000000          ...             \n",
       "1468      7.596392    0.000000    6.458338    0.000000          ...             \n",
       "1469      7.586296    0.000000    6.690842    4.369448          ...             \n",
       "\n",
       "      SaleType_NA  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "1460            0             0             0            1   \n",
       "1461            0             0             0            1   \n",
       "1462            0             0             0            1   \n",
       "1463            0             0             0            1   \n",
       "1464            0             0             0            1   \n",
       "1465            0             0             0            1   \n",
       "1466            0             0             0            1   \n",
       "1467            0             0             0            1   \n",
       "1468            0             0             0            1   \n",
       "1469            0             0             0            1   \n",
       "\n",
       "      SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "1460                      0                      0                     0   \n",
       "1461                      0                      0                     0   \n",
       "1462                      0                      0                     0   \n",
       "1463                      0                      0                     0   \n",
       "1464                      0                      0                     0   \n",
       "1465                      0                      0                     0   \n",
       "1466                      0                      0                     0   \n",
       "1467                      0                      0                     0   \n",
       "1468                      0                      0                     0   \n",
       "1469                      0                      0                     0   \n",
       "\n",
       "      SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "1460                     0                     1                      0  \n",
       "1461                     0                     1                      0  \n",
       "1462                     0                     1                      0  \n",
       "1463                     0                     1                      0  \n",
       "1464                     0                     1                      0  \n",
       "1465                     0                     1                      0  \n",
       "1466                     0                     1                      0  \n",
       "1467                     0                     1                      0  \n",
       "1468                     0                     1                      0  \n",
       "1469                     0                     1                      0  \n",
       "\n",
       "[10 rows x 309 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "0cedf60a-6d0d-470a-a036-3376f9034dea",
    "_uuid": "54e88f022f339349252eac9a4a0ff472217a7c2d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predict = clf_final.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "67ab0a26-b416-446a-b679-66173bcc6ea4",
    "_uuid": "4e9c3bcabcefbda8467e744c7b1b7f578d717070",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = pd.DataFrame({\n",
    "    'Id' : test_data['Id'],\n",
    "    'SalePrice' : np.expm1(y_predict)\n",
    " })\n",
    "results.to_csv(\"output.csv\", index=False)"
   ]
  }
 ],
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