Housing Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Housing Price Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.1 Download House Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the data\n",
    "import os\n",
    "import tarfile\n",
    "from six.moves import urllib\n",
    "\n",
    "# dataset이 존재하는 url 경로\n",
    "DOWNLOAD_ROOT = \"https://raw.githubusercontent.com/ageron/handson-ml/master/\"\n",
    "HOUSING_PATH = os.path.join(\"datasets\", \"housing\")\n",
    "HOUSING_URL = DOWNLOAD_ROOT + \"datasets/housing/housing.tgz\"\n",
    "\n",
    "#data를 다운하는 함수 작성\n",
    "def fetch_housing_data(housing_url=HOUSING_URL,housing_path=HOUSING_PATH):\n",
    "    if not os.path.isdir(housing_path):\t#dataset/housing 이라는 폴더가 없으면\n",
    "        os.makedirs(housing_path)\t# dir 생성\n",
    "    tgz_path = os.path.join(housing_path,\"housing.tgz\")\n",
    "    urllib.request.urlretrieve(housing_url,tgz_path)\t#url주소에 가서 housing.tgz를 찾기\n",
    "    housing_tgz = tarfile.open(tgz_path)\t#파일을 열어서\n",
    "    housing_tgz.extractall(path=housing_path)\t#csv파일 생성\n",
    "    housing_tgz.close()\n",
    "\n",
    "fetch_housing_data()\t#서버에서 URL주소의 tgz파일을 읽어와서 csv파일을 dataset으로 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>919.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2535.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>1094.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>3.6591</td>\n",
       "      <td>299200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3104.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>3.1200</td>\n",
       "      <td>241400.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2555.0</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1206.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2.0804</td>\n",
       "      <td>226700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3549.0</td>\n",
       "      <td>707.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>714.0</td>\n",
       "      <td>3.6912</td>\n",
       "      <td>261100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2202.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>3.2031</td>\n",
       "      <td>281500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>752.0</td>\n",
       "      <td>1504.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>3.2705</td>\n",
       "      <td>241800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2491.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1098.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>3.0750</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>345.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>2.6736</td>\n",
       "      <td>191300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2643.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>1.9167</td>\n",
       "      <td>159200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.85</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>2.1250</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>2.7750</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1228.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.1202</td>\n",
       "      <td>155500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.84</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2239.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>1.9911</td>\n",
       "      <td>158700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1503.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>690.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2.6033</td>\n",
       "      <td>162900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>40.0</td>\n",
       "      <td>751.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>1.3578</td>\n",
       "      <td>147500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.85</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1639.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>1.7135</td>\n",
       "      <td>159800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2436.0</td>\n",
       "      <td>541.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>478.0</td>\n",
       "      <td>1.7250</td>\n",
       "      <td>113900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>853.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2.1806</td>\n",
       "      <td>99700.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2224.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>2.6000</td>\n",
       "      <td>132600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>41.0</td>\n",
       "      <td>535.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.4038</td>\n",
       "      <td>107500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>2.4597</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1898.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>397.0</td>\n",
       "      <td>1.8080</td>\n",
       "      <td>105500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.84</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>473.0</td>\n",
       "      <td>1.6424</td>\n",
       "      <td>108900.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>1.6875</td>\n",
       "      <td>132000.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20610</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.10</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>1.3631</td>\n",
       "      <td>45500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20611</th>\n",
       "      <td>-121.55</td>\n",
       "      <td>39.10</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1783.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1.2857</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20612</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.08</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1377.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>761.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>1.4934</td>\n",
       "      <td>48300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20613</th>\n",
       "      <td>-121.55</td>\n",
       "      <td>39.09</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1.4958</td>\n",
       "      <td>53400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20614</th>\n",
       "      <td>-121.54</td>\n",
       "      <td>39.08</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2276.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1455.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>2.4695</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20615</th>\n",
       "      <td>-121.54</td>\n",
       "      <td>39.08</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>2.3598</td>\n",
       "      <td>57500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20616</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.08</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>2.0469</td>\n",
       "      <td>55100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20617</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>3.3021</td>\n",
       "      <td>70800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20618</th>\n",
       "      <td>-121.55</td>\n",
       "      <td>39.06</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1332.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>2.2500</td>\n",
       "      <td>63400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20619</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.01</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1891.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>2.7303</td>\n",
       "      <td>99100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20620</th>\n",
       "      <td>-121.48</td>\n",
       "      <td>39.05</td>\n",
       "      <td>40.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.5625</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20621</th>\n",
       "      <td>-121.47</td>\n",
       "      <td>39.01</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2.3661</td>\n",
       "      <td>77500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20622</th>\n",
       "      <td>-121.44</td>\n",
       "      <td>39.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>457.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>2.4167</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20623</th>\n",
       "      <td>-121.37</td>\n",
       "      <td>39.03</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>2.8235</td>\n",
       "      <td>65500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20624</th>\n",
       "      <td>-121.41</td>\n",
       "      <td>39.04</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>3.0739</td>\n",
       "      <td>87200.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20625</th>\n",
       "      <td>-121.52</td>\n",
       "      <td>39.12</td>\n",
       "      <td>37.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.1250</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20626</th>\n",
       "      <td>-121.43</td>\n",
       "      <td>39.18</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>504.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>2.1667</td>\n",
       "      <td>93800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20627</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.13</td>\n",
       "      <td>5.0</td>\n",
       "      <td>358.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>162500.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20628</th>\n",
       "      <td>-121.48</td>\n",
       "      <td>39.10</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2043.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>2.5952</td>\n",
       "      <td>92400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20629</th>\n",
       "      <td>-121.39</td>\n",
       "      <td>39.12</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10035.0</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>6912.0</td>\n",
       "      <td>1818.0</td>\n",
       "      <td>2.0943</td>\n",
       "      <td>108300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20630</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.29</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2640.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>1257.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>3.5673</td>\n",
       "      <td>112000.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20631</th>\n",
       "      <td>-121.40</td>\n",
       "      <td>39.33</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2655.0</td>\n",
       "      <td>493.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>3.5179</td>\n",
       "      <td>107200.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20632</th>\n",
       "      <td>-121.45</td>\n",
       "      <td>39.26</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2319.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>3.1250</td>\n",
       "      <td>115600.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20633</th>\n",
       "      <td>-121.53</td>\n",
       "      <td>39.19</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>2.5495</td>\n",
       "      <td>98300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20634</th>\n",
       "      <td>-121.56</td>\n",
       "      <td>39.27</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2332.0</td>\n",
       "      <td>395.0</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>3.7125</td>\n",
       "      <td>116800.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18.0</td>\n",
       "      <td>697.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1860.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2785.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400.0</td>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                41.0        880.0           129.0   \n",
       "1        -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2        -122.24     37.85                52.0       1467.0           190.0   \n",
       "3        -122.25     37.85                52.0       1274.0           235.0   \n",
       "4        -122.25     37.85                52.0       1627.0           280.0   \n",
       "5        -122.25     37.85                52.0        919.0           213.0   \n",
       "6        -122.25     37.84                52.0       2535.0           489.0   \n",
       "7        -122.25     37.84                52.0       3104.0           687.0   \n",
       "8        -122.26     37.84                42.0       2555.0           665.0   \n",
       "9        -122.25     37.84                52.0       3549.0           707.0   \n",
       "10       -122.26     37.85                52.0       2202.0           434.0   \n",
       "11       -122.26     37.85                52.0       3503.0           752.0   \n",
       "12       -122.26     37.85                52.0       2491.0           474.0   \n",
       "13       -122.26     37.84                52.0        696.0           191.0   \n",
       "14       -122.26     37.85                52.0       2643.0           626.0   \n",
       "15       -122.26     37.85                50.0       1120.0           283.0   \n",
       "16       -122.27     37.85                52.0       1966.0           347.0   \n",
       "17       -122.27     37.85                52.0       1228.0           293.0   \n",
       "18       -122.26     37.84                50.0       2239.0           455.0   \n",
       "19       -122.27     37.84                52.0       1503.0           298.0   \n",
       "20       -122.27     37.85                40.0        751.0           184.0   \n",
       "21       -122.27     37.85                42.0       1639.0           367.0   \n",
       "22       -122.27     37.84                52.0       2436.0           541.0   \n",
       "23       -122.27     37.84                52.0       1688.0           337.0   \n",
       "24       -122.27     37.84                52.0       2224.0           437.0   \n",
       "25       -122.28     37.85                41.0        535.0           123.0   \n",
       "26       -122.28     37.85                49.0       1130.0           244.0   \n",
       "27       -122.28     37.85                52.0       1898.0           421.0   \n",
       "28       -122.28     37.84                50.0       2082.0           492.0   \n",
       "29       -122.28     37.84                52.0        729.0           160.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20610    -121.56     39.10                28.0       2130.0           484.0   \n",
       "20611    -121.55     39.10                27.0       1783.0           441.0   \n",
       "20612    -121.56     39.08                26.0       1377.0           289.0   \n",
       "20613    -121.55     39.09                31.0       1728.0           365.0   \n",
       "20614    -121.54     39.08                26.0       2276.0           460.0   \n",
       "20615    -121.54     39.08                23.0       1076.0           216.0   \n",
       "20616    -121.53     39.08                15.0       1810.0           441.0   \n",
       "20617    -121.53     39.06                20.0        561.0           109.0   \n",
       "20618    -121.55     39.06                25.0       1332.0           247.0   \n",
       "20619    -121.56     39.01                22.0       1891.0           340.0   \n",
       "20620    -121.48     39.05                40.0        198.0            41.0   \n",
       "20621    -121.47     39.01                37.0       1244.0           247.0   \n",
       "20622    -121.44     39.00                20.0        755.0           147.0   \n",
       "20623    -121.37     39.03                32.0       1158.0           244.0   \n",
       "20624    -121.41     39.04                16.0       1698.0           300.0   \n",
       "20625    -121.52     39.12                37.0        102.0            17.0   \n",
       "20626    -121.43     39.18                36.0       1124.0           184.0   \n",
       "20627    -121.32     39.13                 5.0        358.0            65.0   \n",
       "20628    -121.48     39.10                19.0       2043.0           421.0   \n",
       "20629    -121.39     39.12                28.0      10035.0          1856.0   \n",
       "20630    -121.32     39.29                11.0       2640.0           505.0   \n",
       "20631    -121.40     39.33                15.0       2655.0           493.0   \n",
       "20632    -121.45     39.26                15.0       2319.0           416.0   \n",
       "20633    -121.53     39.19                27.0       2080.0           412.0   \n",
       "20634    -121.56     39.27                28.0       2332.0           395.0   \n",
       "20635    -121.09     39.48                25.0       1665.0           374.0   \n",
       "20636    -121.21     39.49                18.0        697.0           150.0   \n",
       "20637    -121.22     39.43                17.0       2254.0           485.0   \n",
       "20638    -121.32     39.43                18.0       1860.0           409.0   \n",
       "20639    -121.24     39.37                16.0       2785.0           616.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "0           322.0       126.0         8.3252            452600.0   \n",
       "1          2401.0      1138.0         8.3014            358500.0   \n",
       "2           496.0       177.0         7.2574            352100.0   \n",
       "3           558.0       219.0         5.6431            341300.0   \n",
       "4           565.0       259.0         3.8462            342200.0   \n",
       "5           413.0       193.0         4.0368            269700.0   \n",
       "6          1094.0       514.0         3.6591            299200.0   \n",
       "7          1157.0       647.0         3.1200            241400.0   \n",
       "8          1206.0       595.0         2.0804            226700.0   \n",
       "9          1551.0       714.0         3.6912            261100.0   \n",
       "10          910.0       402.0         3.2031            281500.0   \n",
       "11         1504.0       734.0         3.2705            241800.0   \n",
       "12         1098.0       468.0         3.0750            213500.0   \n",
       "13          345.0       174.0         2.6736            191300.0   \n",
       "14         1212.0       620.0         1.9167            159200.0   \n",
       "15          697.0       264.0         2.1250            140000.0   \n",
       "16          793.0       331.0         2.7750            152500.0   \n",
       "17          648.0       303.0         2.1202            155500.0   \n",
       "18          990.0       419.0         1.9911            158700.0   \n",
       "19          690.0       275.0         2.6033            162900.0   \n",
       "20          409.0       166.0         1.3578            147500.0   \n",
       "21          929.0       366.0         1.7135            159800.0   \n",
       "22         1015.0       478.0         1.7250            113900.0   \n",
       "23          853.0       325.0         2.1806             99700.0   \n",
       "24         1006.0       422.0         2.6000            132600.0   \n",
       "25          317.0       119.0         2.4038            107500.0   \n",
       "26          607.0       239.0         2.4597             93800.0   \n",
       "27         1102.0       397.0         1.8080            105500.0   \n",
       "28         1131.0       473.0         1.6424            108900.0   \n",
       "29          395.0       155.0         1.6875            132000.0   \n",
       "...           ...         ...            ...                 ...   \n",
       "20610      1195.0       439.0         1.3631             45500.0   \n",
       "20611      1163.0       409.0         1.2857             47000.0   \n",
       "20612       761.0       267.0         1.4934             48300.0   \n",
       "20613      1167.0       384.0         1.4958             53400.0   \n",
       "20614      1455.0       474.0         2.4695             58000.0   \n",
       "20615       724.0       197.0         2.3598             57500.0   \n",
       "20616      1157.0       375.0         2.0469             55100.0   \n",
       "20617       308.0       114.0         3.3021             70800.0   \n",
       "20618       726.0       226.0         2.2500             63400.0   \n",
       "20619      1023.0       296.0         2.7303             99100.0   \n",
       "20620       151.0        48.0         4.5625            100000.0   \n",
       "20621       484.0       157.0         2.3661             77500.0   \n",
       "20622       457.0       157.0         2.4167             67000.0   \n",
       "20623       598.0       227.0         2.8235             65500.0   \n",
       "20624       731.0       291.0         3.0739             87200.0   \n",
       "20625        29.0        14.0         4.1250             72000.0   \n",
       "20626       504.0       171.0         2.1667             93800.0   \n",
       "20627       169.0        59.0         3.0000            162500.0   \n",
       "20628      1018.0       390.0         2.5952             92400.0   \n",
       "20629      6912.0      1818.0         2.0943            108300.0   \n",
       "20630      1257.0       445.0         3.5673            112000.0   \n",
       "20631      1200.0       432.0         3.5179            107200.0   \n",
       "20632      1047.0       385.0         3.1250            115600.0   \n",
       "20633      1082.0       382.0         2.5495             98300.0   \n",
       "20634      1041.0       344.0         3.7125            116800.0   \n",
       "20635       845.0       330.0         1.5603             78100.0   \n",
       "20636       356.0       114.0         2.5568             77100.0   \n",
       "20637      1007.0       433.0         1.7000             92300.0   \n",
       "20638       741.0       349.0         1.8672             84700.0   \n",
       "20639      1387.0       530.0         2.3886             89400.0   \n",
       "\n",
       "      ocean_proximity  \n",
       "0            NEAR BAY  \n",
       "1            NEAR BAY  \n",
       "2            NEAR BAY  \n",
       "3            NEAR BAY  \n",
       "4            NEAR BAY  \n",
       "5            NEAR BAY  \n",
       "6            NEAR BAY  \n",
       "7            NEAR BAY  \n",
       "8            NEAR BAY  \n",
       "9            NEAR BAY  \n",
       "10           NEAR BAY  \n",
       "11           NEAR BAY  \n",
       "12           NEAR BAY  \n",
       "13           NEAR BAY  \n",
       "14           NEAR BAY  \n",
       "15           NEAR BAY  \n",
       "16           NEAR BAY  \n",
       "17           NEAR BAY  \n",
       "18           NEAR BAY  \n",
       "19           NEAR BAY  \n",
       "20           NEAR BAY  \n",
       "21           NEAR BAY  \n",
       "22           NEAR BAY  \n",
       "23           NEAR BAY  \n",
       "24           NEAR BAY  \n",
       "25           NEAR BAY  \n",
       "26           NEAR BAY  \n",
       "27           NEAR BAY  \n",
       "28           NEAR BAY  \n",
       "29           NEAR BAY  \n",
       "...               ...  \n",
       "20610          INLAND  \n",
       "20611          INLAND  \n",
       "20612          INLAND  \n",
       "20613          INLAND  \n",
       "20614          INLAND  \n",
       "20615          INLAND  \n",
       "20616          INLAND  \n",
       "20617          INLAND  \n",
       "20618          INLAND  \n",
       "20619          INLAND  \n",
       "20620          INLAND  \n",
       "20621          INLAND  \n",
       "20622          INLAND  \n",
       "20623          INLAND  \n",
       "20624          INLAND  \n",
       "20625          INLAND  \n",
       "20626          INLAND  \n",
       "20627          INLAND  \n",
       "20628          INLAND  \n",
       "20629          INLAND  \n",
       "20630          INLAND  \n",
       "20631          INLAND  \n",
       "20632          INLAND  \n",
       "20633          INLAND  \n",
       "20634          INLAND  \n",
       "20635          INLAND  \n",
       "20636          INLAND  \n",
       "20637          INLAND  \n",
       "20638          INLAND  \n",
       "20639          INLAND  \n",
       "\n",
       "[20640 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the data\n",
    "import pandas as pd\n",
    "\n",
    "#dataset을 불러오는 함수작성\n",
    "def load_housing_data(housing_path = HOUSING_PATH):\n",
    "    csv_path = os.path.join(housing_path,\"housing.csv\")\t#csv파일의 위치\n",
    "    return pd.read_csv(csv_path)\t#csv파일을 read\n",
    "\n",
    "housing = load_housing_data()\t#read하여 변수에 저장"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.2 Make Training Set and Testing Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#전처리 후에 set을 나누는 것을 시도하다가 pandas문법에 익숙치 않아서\n",
    "#책순서로 set을 먼저 나누고 training set으로 진행하였습니다!\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "\n",
    "#sklearn의 train_test_split() method를 통해 train, test set을 얻는다 (random state는 일반적으로 42)\n",
    "train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)\n",
    "housing[\"income_cat\"] = np.ceil(housing[\"median_income\"] / 1.5)\n",
    "housing[\"income_cat\"].where(housing[\"income_cat\"] < 5, 5.0, inplace=True)\n",
    "\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "#sklearn의 StratifiedShuffleSplit() method를 통해\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "\n",
    "for train_index, test_index in split.split(housing, housing[\"income_cat\"]):\n",
    "    strat_train_set = housing.loc[train_index]\t#training set 생성\n",
    "    strat_test_set = housing.loc[test_index]\n",
    "\n",
    "housing = strat_train_set.drop(\"median_house_value\", axis=1) #training dataset\n",
    "housing_labels = strat_train_set[\"median_house_value\"].copy() #labels data\n",
    "housing_num = housing.drop(\"ocean_proximity\", axis=1)  #text제외한 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "#속성을 추가한다 : rooms_per_househole , population_per_household , bedrooms_per_room\n",
    "rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self, X, y=None):\n",
    "        return self  # nothing else to do\n",
    "    def transform(self, X, y=None):\n",
    "        rooms_per_household = X[:, rooms_ix] / X[:, household_ix]\n",
    "        population_per_household = X[:, population_ix] / X[:, household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]\n",
    "            return np.c_[X, rooms_per_household, population_per_household,\n",
    "                         bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X, rooms_per_household, population_per_household]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "# Create a class to select numerical or categorical columns \n",
    "class OldDataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Python\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Imputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "num_attribs = list(housing_num)\n",
    "cat_attribs = [\"ocean_proximity\"]\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "        ('selector', OldDataFrameSelector(num_attribs)),\n",
    "        ('imputer', Imputer(strategy=\"median\")),\n",
    "        ('attribs_adder', CombinedAttributesAdder()),\n",
    "        ('std_scaler', StandardScaler()),\n",
    "    ])\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "        ('selector', OldDataFrameSelector(cat_attribs)),\n",
    "        ('onehot_encoder', OneHotEncoder(sparse=False)),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "        (\"num_pipeline\", num_pipeline),\n",
    "        (\"cat_pipeline\", cat_pipeline),\n",
    "    ])\n",
    "housing_prepared = full_pipeline.fit_transform(housing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.3 Regression Model Generating and Evaluating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LinearRegression model make\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared, housing_labels)\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "some_data_prepared = full_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#LinearRegression model의 RMSE값 계산\n",
    "from sklearn.metrics import mean_squared_error\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "lin_rmse = np.sqrt(lin_mse) #RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DecisionTreeRegressor model make\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse) #RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.4 Get Accuracy using cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Step2.4 Get accuracy using cross validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(tree_reg, housing_prepared, housing_labels,\n",
    "                         scoring=\"neg_mean_squared_error\", cv=10)\n",
    "tree_rmse_scores = np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [68931.66753402 67513.29517113 70472.87303005 69907.36560794\n",
      " 71812.46641221 75610.17837927 69680.71352324 71517.78840901\n",
      " 77401.69340381 70464.12289546]\n",
      "Mean: 71331.21643661351\n",
      "Standard deviation: 2863.583243826886\n"
     ]
    }
   ],
   "source": [
    "#accuracy 평가하는 함수\n",
    "def display_scores(scores):\n",
    "    print(\"Scores:\", scores)\n",
    "    print(\"Mean:\", scores.mean())\n",
    "    print(\"Standard deviation:\", scores.std())\n",
    "\n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [66877.52325028 66608.120256   70575.91118868 74179.94799352\n",
      " 67683.32205678 71103.16843468 64782.65896552 67711.29940352\n",
      " 71080.40484136 67687.6384546 ]\n",
      "Mean: 68828.99948449328\n",
      "Standard deviation: 2662.7615706103393\n"
     ]
    }
   ],
   "source": [
    "lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels,\n",
    "                             scoring=\"neg_mean_squared_error\", cv=10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10, random_state=42)\n",
    "forest_reg.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22107.0888400092"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_predictions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels, housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: [51481.61843757 48867.18698326 53592.93924497 54917.35753217\n",
      " 50463.39403515 56776.52132037 51940.08691385 50521.84102811\n",
      " 55729.5024898  53136.5656865 ]\n",
      "Mean: 52742.701367175265\n",
      "Standard deviation: 2412.0829538615826\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "forest_scores = cross_val_score(forest_reg, housing_prepared, housing_labels,\n",
    "                                scoring=\"neg_mean_squared_error\", cv=10)\n",
    "forest_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.5 Find Hyper-parameter using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'n_estimators': [10, 30], 'max_features': [2, 4, 6, 8, 10]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = [\n",
    "    {'n_estimators': [10, 30], 'max_features': [2, 4, 6, 8, 10]}\n",
    "  ]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "# train across 5 folds, that's a total of (12+6)*5=90 rounds of training \n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error', return_train_score=True)\n",
    "grid_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55869.738265789805 {'max_features': 2, 'n_estimators': 10}\n",
      "53472.049050362606 {'max_features': 2, 'n_estimators': 30}\n",
      "53846.28747553631 {'max_features': 4, 'n_estimators': 10}\n",
      "51270.16050804186 {'max_features': 4, 'n_estimators': 30}\n",
      "53114.388232043595 {'max_features': 6, 'n_estimators': 10}\n",
      "50811.40364168747 {'max_features': 6, 'n_estimators': 30}\n",
      "52884.76688140635 {'max_features': 8, 'n_estimators': 10}\n",
      "50944.352464380354 {'max_features': 8, 'n_estimators': 30}\n",
      "52723.44011504588 {'max_features': 10, 'n_estimators': 10}\n",
      "50749.58545502611 {'max_features': 10, 'n_estimators': 30}\n"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.6 Find Hyper-parameter using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=None,\n",
       "          param_distributions={'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001EA3C01CBE0>, 'max_features': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000001EA3C01CD68>},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring='neg_mean_squared_error',\n",
       "          verbose=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs = {\n",
    "        'n_estimators': randint(low=1, high=200),\n",
    "        'max_features': randint(low=1, high=8),\n",
    "    }\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "rnd_search = RandomizedSearchCV(forest_reg, param_distributions=param_distribs,\n",
    "                                n_iter=10, cv=5, scoring='neg_mean_squared_error', random_state=42)\n",
    "rnd_search.fit(housing_prepared, housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49931.83370450304 {'max_features': 7, 'n_estimators': 180}\n",
      "52122.26073885765 {'max_features': 5, 'n_estimators': 15}\n",
      "51463.906813209505 {'max_features': 3, 'n_estimators': 72}\n",
      "51381.385870076425 {'max_features': 5, 'n_estimators': 21}\n",
      "50040.63743808166 {'max_features': 7, 'n_estimators': 122}\n",
      "51436.763292635806 {'max_features': 3, 'n_estimators': 75}\n",
      "51286.345451957626 {'max_features': 3, 'n_estimators': 88}\n",
      "50312.57595748435 {'max_features': 5, 'n_estimators': 100}\n",
      "50995.415279184126 {'max_features': 3, 'n_estimators': 150}\n",
      "65455.38356204712 {'max_features': 5, 'n_estimators': 2}\n"
     ]
    }
   ],
   "source": [
    "cvres = rnd_search.cv_results_\n",
    "for mean_score, params in zip(cvres[\"mean_test_score\"], cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score), params)"
   ]
  }
 ],
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