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Tugas 2_06211640000065_06211640000088.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TUGAS 2 DATA MINING\n",
"Adam Fahmi Fandisyah (06211640000065)\n",
"Ronny Sugiarto Putra (06211640000088)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IMPORT DATA"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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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>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>0</th>\n",
" <td>1</td>\n",
" <td>60</td>\n",
" <td>65.0</td>\n",
" <td>8450</td>\n",
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" <td>5</td>\n",
" <td>2003</td>\n",
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" <td>196.0</td>\n",
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" <td>2008</td>\n",
" <td>208500</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>80.0</td>\n",
" <td>9600</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>1976</td>\n",
" <td>1976</td>\n",
" <td>0.0</td>\n",
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" <td>0</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>181500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>60</td>\n",
" <td>68.0</td>\n",
" <td>11250</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2001</td>\n",
" <td>2002</td>\n",
" <td>162.0</td>\n",
" <td>486</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2008</td>\n",
" <td>223500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>70</td>\n",
" <td>60.0</td>\n",
" <td>9550</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>1915</td>\n",
" <td>1970</td>\n",
" <td>0.0</td>\n",
" <td>216</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>35</td>\n",
" <td>272</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2006</td>\n",
" <td>140000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>60</td>\n",
" <td>84.0</td>\n",
" <td>14260</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>350.0</td>\n",
" <td>655</td>\n",
" <td>...</td>\n",
" <td>192</td>\n",
" <td>84</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2008</td>\n",
" <td>250000</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>50</td>\n",
" <td>85.0</td>\n",
" <td>14115</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1993</td>\n",
" <td>1995</td>\n",
" <td>0.0</td>\n",
" <td>732</td>\n",
" <td>...</td>\n",
" <td>40</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>320</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>700</td>\n",
" <td>10</td>\n",
" <td>2009</td>\n",
" <td>143000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>20</td>\n",
" <td>75.0</td>\n",
" <td>10084</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2004</td>\n",
" <td>2005</td>\n",
" <td>186.0</td>\n",
" <td>1369</td>\n",
" <td>...</td>\n",
" <td>255</td>\n",
" <td>57</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2007</td>\n",
" <td>307000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>60</td>\n",
" <td>NaN</td>\n",
" <td>10382</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>1973</td>\n",
" <td>1973</td>\n",
" <td>240.0</td>\n",
" <td>859</td>\n",
" <td>...</td>\n",
" <td>235</td>\n",
" <td>204</td>\n",
" <td>228</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>2009</td>\n",
" <td>200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>50</td>\n",
" <td>51.0</td>\n",
" <td>6120</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>1931</td>\n",
" <td>1950</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>90</td>\n",
" <td>0</td>\n",
" <td>205</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2008</td>\n",
" <td>129900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>190</td>\n",
" <td>50.0</td>\n",
" <td>7420</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1939</td>\n",
" <td>1950</td>\n",
" <td>0.0</td>\n",
" <td>851</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2008</td>\n",
" <td>118000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>20</td>\n",
" <td>70.0</td>\n",
" <td>11200</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1965</td>\n",
" <td>1965</td>\n",
" <td>0.0</td>\n",
" <td>906</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2008</td>\n",
" <td>129500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>60</td>\n",
" <td>85.0</td>\n",
" <td>11924</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2006</td>\n",
" <td>286.0</td>\n",
" <td>998</td>\n",
" <td>...</td>\n",
" <td>147</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2006</td>\n",
" <td>345000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>20</td>\n",
" <td>NaN</td>\n",
" <td>12968</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1962</td>\n",
" <td>1962</td>\n",
" <td>0.0</td>\n",
" <td>737</td>\n",
" <td>...</td>\n",
" <td>140</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>176</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2008</td>\n",
" <td>144000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>20</td>\n",
" <td>91.0</td>\n",
" <td>10652</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>2007</td>\n",
" <td>306.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>160</td>\n",
" <td>33</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2007</td>\n",
" <td>279500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>20</td>\n",
" <td>NaN</td>\n",
" <td>10920</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1960</td>\n",
" <td>1960</td>\n",
" <td>212.0</td>\n",
" <td>733</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>213</td>\n",
" <td>176</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2008</td>\n",
" <td>157000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>45</td>\n",
" <td>51.0</td>\n",
" <td>6120</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>1929</td>\n",
" <td>2001</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>48</td>\n",
" <td>112</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2007</td>\n",
" <td>132000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>20</td>\n",
" <td>NaN</td>\n",
" <td>11241</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1970</td>\n",
" <td>1970</td>\n",
" <td>180.0</td>\n",
" <td>578</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>700</td>\n",
" <td>3</td>\n",
" <td>2010</td>\n",
" <td>149000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>90</td>\n",
" <td>72.0</td>\n",
" <td>10791</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1967</td>\n",
" <td>1967</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>500</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>90000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>20</td>\n",
" <td>66.0</td>\n",
" <td>13695</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2004</td>\n",
" <td>2004</td>\n",
" <td>0.0</td>\n",
" <td>646</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>102</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2008</td>\n",
" <td>159000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>20</td>\n",
" <td>70.0</td>\n",
" <td>7560</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1958</td>\n",
" <td>1965</td>\n",
" <td>0.0</td>\n",
" <td>504</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>139000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>60</td>\n",
" <td>101.0</td>\n",
" <td>14215</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2006</td>\n",
" <td>380.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>240</td>\n",
" <td>154</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2006</td>\n",
" <td>325300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>45</td>\n",
" <td>57.0</td>\n",
" <td>7449</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>1930</td>\n",
" <td>1950</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>205</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2007</td>\n",
" <td>139400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>20</td>\n",
" <td>75.0</td>\n",
" <td>9742</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2002</td>\n",
" <td>2002</td>\n",
" <td>281.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>171</td>\n",
" <td>159</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2008</td>\n",
" <td>230000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>120</td>\n",
" <td>44.0</td>\n",
" <td>4224</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1976</td>\n",
" <td>1976</td>\n",
" <td>0.0</td>\n",
" <td>840</td>\n",
" <td>...</td>\n",
" <td>100</td>\n",
" <td>110</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2007</td>\n",
" <td>129900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>20</td>\n",
" <td>NaN</td>\n",
" <td>8246</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>1968</td>\n",
" <td>2001</td>\n",
" <td>0.0</td>\n",
" <td>188</td>\n",
" <td>...</td>\n",
" <td>406</td>\n",
" <td>90</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>154000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>20</td>\n",
" <td>110.0</td>\n",
" <td>14230</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>2007</td>\n",
" <td>640.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>56</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2009</td>\n",
" <td>256300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>20</td>\n",
" <td>60.0</td>\n",
" <td>7200</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1951</td>\n",
" <td>2000</td>\n",
" <td>0.0</td>\n",
" <td>234</td>\n",
" <td>...</td>\n",
" <td>222</td>\n",
" <td>32</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>134800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>20</td>\n",
" <td>98.0</td>\n",
" <td>11478</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>2008</td>\n",
" <td>200.0</td>\n",
" <td>1218</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>306000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>20</td>\n",
" <td>47.0</td>\n",
" <td>16321</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1957</td>\n",
" <td>1997</td>\n",
" <td>0.0</td>\n",
" <td>1277</td>\n",
" <td>...</td>\n",
" <td>288</td>\n",
" <td>258</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2006</td>\n",
" <td>207500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>30</td>\n",
" <td>60.0</td>\n",
" <td>6324</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>1927</td>\n",
" <td>1950</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>49</td>\n",
" <td>0</td>\n",
" <td>87</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2008</td>\n",
" <td>68500</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",
" <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",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1430</th>\n",
" <td>1431</td>\n",
" <td>60</td>\n",
" <td>60.0</td>\n",
" <td>21930</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2005</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>100</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2006</td>\n",
" <td>192140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1431</th>\n",
" <td>1432</td>\n",
" <td>120</td>\n",
" <td>NaN</td>\n",
" <td>4928</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>1976</td>\n",
" <td>1976</td>\n",
" <td>0.0</td>\n",
" <td>958</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2009</td>\n",
" <td>143750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1432</th>\n",
" <td>1433</td>\n",
" <td>30</td>\n",
" <td>60.0</td>\n",
" <td>10800</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>1927</td>\n",
" <td>2007</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2007</td>\n",
" <td>64500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1433</th>\n",
" <td>1434</td>\n",
" <td>60</td>\n",
" <td>93.0</td>\n",
" <td>10261</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>318.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2008</td>\n",
" <td>186500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1434</th>\n",
" <td>1435</td>\n",
" <td>20</td>\n",
" <td>80.0</td>\n",
" <td>17400</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1977</td>\n",
" <td>1977</td>\n",
" <td>0.0</td>\n",
" <td>936</td>\n",
" <td>...</td>\n",
" <td>295</td>\n",
" <td>41</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>160000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1435</th>\n",
" <td>1436</td>\n",
" <td>20</td>\n",
" <td>80.0</td>\n",
" <td>8400</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>1962</td>\n",
" <td>2005</td>\n",
" <td>237.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>36</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2008</td>\n",
" <td>174000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1436</th>\n",
" <td>1437</td>\n",
" <td>20</td>\n",
" <td>60.0</td>\n",
" <td>9000</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>1971</td>\n",
" <td>1971</td>\n",
" <td>0.0</td>\n",
" <td>616</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>120500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1437</th>\n",
" <td>1438</td>\n",
" <td>20</td>\n",
" <td>96.0</td>\n",
" <td>12444</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2008</td>\n",
" <td>2008</td>\n",
" <td>426.0</td>\n",
" <td>1336</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>66</td>\n",
" <td>0</td>\n",
" <td>304</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2008</td>\n",
" <td>394617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1438</th>\n",
" <td>1439</td>\n",
" <td>20</td>\n",
" <td>90.0</td>\n",
" <td>7407</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1957</td>\n",
" <td>1996</td>\n",
" <td>0.0</td>\n",
" <td>600</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>158</td>\n",
" <td>158</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>149700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1439</th>\n",
" <td>1440</td>\n",
" <td>60</td>\n",
" <td>80.0</td>\n",
" <td>11584</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>1979</td>\n",
" <td>1979</td>\n",
" <td>96.0</td>\n",
" <td>315</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>88</td>\n",
" <td>216</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2007</td>\n",
" <td>197000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1440</th>\n",
" <td>1441</td>\n",
" <td>70</td>\n",
" <td>79.0</td>\n",
" <td>11526</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1922</td>\n",
" <td>1994</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>431</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2008</td>\n",
" <td>191000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1441</th>\n",
" <td>1442</td>\n",
" <td>120</td>\n",
" <td>NaN</td>\n",
" <td>4426</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2004</td>\n",
" <td>2004</td>\n",
" <td>147.0</td>\n",
" <td>697</td>\n",
" <td>...</td>\n",
" <td>149</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2008</td>\n",
" <td>149300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1442</th>\n",
" <td>1443</td>\n",
" <td>60</td>\n",
" <td>85.0</td>\n",
" <td>11003</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>2008</td>\n",
" <td>2008</td>\n",
" <td>160.0</td>\n",
" <td>765</td>\n",
" <td>...</td>\n",
" <td>168</td>\n",
" <td>52</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2009</td>\n",
" <td>310000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1443</th>\n",
" <td>1444</td>\n",
" <td>30</td>\n",
" <td>NaN</td>\n",
" <td>8854</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>1916</td>\n",
" <td>1950</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>98</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>121000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444</th>\n",
" <td>1445</td>\n",
" <td>20</td>\n",
" <td>63.0</td>\n",
" <td>8500</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2004</td>\n",
" <td>2004</td>\n",
" <td>106.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>192</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2007</td>\n",
" <td>179600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1445</th>\n",
" <td>1446</td>\n",
" <td>85</td>\n",
" <td>70.0</td>\n",
" <td>8400</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1966</td>\n",
" <td>1966</td>\n",
" <td>0.0</td>\n",
" <td>187</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>252</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>129000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1446</th>\n",
" <td>1447</td>\n",
" <td>20</td>\n",
" <td>NaN</td>\n",
" <td>26142</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1962</td>\n",
" <td>1962</td>\n",
" <td>189.0</td>\n",
" <td>593</td>\n",
" <td>...</td>\n",
" <td>261</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>157900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1447</th>\n",
" <td>1448</td>\n",
" <td>60</td>\n",
" <td>80.0</td>\n",
" <td>10000</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>1995</td>\n",
" <td>1996</td>\n",
" <td>438.0</td>\n",
" <td>1079</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2007</td>\n",
" <td>240000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1448</th>\n",
" <td>1449</td>\n",
" <td>50</td>\n",
" <td>70.0</td>\n",
" <td>11767</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>1910</td>\n",
" <td>2000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>168</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>112000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1449</th>\n",
" <td>1450</td>\n",
" <td>180</td>\n",
" <td>21.0</td>\n",
" <td>1533</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1970</td>\n",
" <td>1970</td>\n",
" <td>0.0</td>\n",
" <td>553</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2006</td>\n",
" <td>92000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1450</th>\n",
" <td>1451</td>\n",
" <td>90</td>\n",
" <td>60.0</td>\n",
" <td>9000</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1974</td>\n",
" <td>1974</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>32</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2009</td>\n",
" <td>136000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1451</th>\n",
" <td>1452</td>\n",
" <td>20</td>\n",
" <td>78.0</td>\n",
" <td>9262</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2008</td>\n",
" <td>2009</td>\n",
" <td>194.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>36</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>287090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1452</th>\n",
" <td>1453</td>\n",
" <td>180</td>\n",
" <td>35.0</td>\n",
" <td>3675</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2005</td>\n",
" <td>80.0</td>\n",
" <td>547</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>145000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1453</th>\n",
" <td>1454</td>\n",
" <td>20</td>\n",
" <td>90.0</td>\n",
" <td>17217</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>2006</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>36</td>\n",
" <td>56</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2006</td>\n",
" <td>84500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1454</th>\n",
" <td>1455</td>\n",
" <td>20</td>\n",
" <td>62.0</td>\n",
" <td>7500</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2004</td>\n",
" <td>2005</td>\n",
" <td>0.0</td>\n",
" <td>410</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>113</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2009</td>\n",
" <td>185000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1455</th>\n",
" <td>1456</td>\n",
" <td>60</td>\n",
" <td>62.0</td>\n",
" <td>7917</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1999</td>\n",
" <td>2000</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2007</td>\n",
" <td>175000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1456</th>\n",
" <td>1457</td>\n",
" <td>20</td>\n",
" <td>85.0</td>\n",
" <td>13175</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>1978</td>\n",
" <td>1988</td>\n",
" <td>119.0</td>\n",
" <td>790</td>\n",
" <td>...</td>\n",
" <td>349</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2010</td>\n",
" <td>210000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1457</th>\n",
" <td>1458</td>\n",
" <td>70</td>\n",
" <td>66.0</td>\n",
" <td>9042</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" <td>1941</td>\n",
" <td>2006</td>\n",
" <td>0.0</td>\n",
" <td>275</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2500</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>266500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1458</th>\n",
" <td>1459</td>\n",
" <td>20</td>\n",
" <td>68.0</td>\n",
" <td>9717</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1950</td>\n",
" <td>1996</td>\n",
" <td>0.0</td>\n",
" <td>49</td>\n",
" <td>...</td>\n",
" <td>366</td>\n",
" <td>0</td>\n",
" <td>112</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>142125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1459</th>\n",
" <td>1460</td>\n",
" <td>20</td>\n",
" <td>75.0</td>\n",
" <td>9937</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1965</td>\n",
" <td>1965</td>\n",
" <td>0.0</td>\n",
" <td>830</td>\n",
" <td>...</td>\n",
" <td>736</td>\n",
" <td>68</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2008</td>\n",
" <td>147500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1460 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Id MSSubClass LotFrontage LotArea OverallQual OverallCond \\\n",
"0 1 60 65.0 8450 7 5 \n",
"1 2 20 80.0 9600 6 8 \n",
"2 3 60 68.0 11250 7 5 \n",
"3 4 70 60.0 9550 7 5 \n",
"4 5 60 84.0 14260 8 5 \n",
"5 6 50 85.0 14115 5 5 \n",
"6 7 20 75.0 10084 8 5 \n",
"7 8 60 NaN 10382 7 6 \n",
"8 9 50 51.0 6120 7 5 \n",
"9 10 190 50.0 7420 5 6 \n",
"10 11 20 70.0 11200 5 5 \n",
"11 12 60 85.0 11924 9 5 \n",
"12 13 20 NaN 12968 5 6 \n",
"13 14 20 91.0 10652 7 5 \n",
"14 15 20 NaN 10920 6 5 \n",
"15 16 45 51.0 6120 7 8 \n",
"16 17 20 NaN 11241 6 7 \n",
"17 18 90 72.0 10791 4 5 \n",
"18 19 20 66.0 13695 5 5 \n",
"19 20 20 70.0 7560 5 6 \n",
"20 21 60 101.0 14215 8 5 \n",
"21 22 45 57.0 7449 7 7 \n",
"22 23 20 75.0 9742 8 5 \n",
"23 24 120 44.0 4224 5 7 \n",
"24 25 20 NaN 8246 5 8 \n",
"25 26 20 110.0 14230 8 5 \n",
"26 27 20 60.0 7200 5 7 \n",
"27 28 20 98.0 11478 8 5 \n",
"28 29 20 47.0 16321 5 6 \n",
"29 30 30 60.0 6324 4 6 \n",
"... ... ... ... ... ... ... \n",
"1430 1431 60 60.0 21930 5 5 \n",
"1431 1432 120 NaN 4928 6 6 \n",
"1432 1433 30 60.0 10800 4 6 \n",
"1433 1434 60 93.0 10261 6 5 \n",
"1434 1435 20 80.0 17400 5 5 \n",
"1435 1436 20 80.0 8400 6 9 \n",
"1436 1437 20 60.0 9000 4 6 \n",
"1437 1438 20 96.0 12444 8 5 \n",
"1438 1439 20 90.0 7407 6 7 \n",
"1439 1440 60 80.0 11584 7 6 \n",
"1440 1441 70 79.0 11526 6 7 \n",
"1441 1442 120 NaN 4426 6 5 \n",
"1442 1443 60 85.0 11003 10 5 \n",
"1443 1444 30 NaN 8854 6 6 \n",
"1444 1445 20 63.0 8500 7 5 \n",
"1445 1446 85 70.0 8400 6 5 \n",
"1446 1447 20 NaN 26142 5 7 \n",
"1447 1448 60 80.0 10000 8 5 \n",
"1448 1449 50 70.0 11767 4 7 \n",
"1449 1450 180 21.0 1533 5 7 \n",
"1450 1451 90 60.0 9000 5 5 \n",
"1451 1452 20 78.0 9262 8 5 \n",
"1452 1453 180 35.0 3675 5 5 \n",
"1453 1454 20 90.0 17217 5 5 \n",
"1454 1455 20 62.0 7500 7 5 \n",
"1455 1456 60 62.0 7917 6 5 \n",
"1456 1457 20 85.0 13175 6 6 \n",
"1457 1458 70 66.0 9042 7 9 \n",
"1458 1459 20 68.0 9717 5 6 \n",
"1459 1460 20 75.0 9937 5 6 \n",
"\n",
" YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 ... WoodDeckSF \\\n",
"0 2003 2003 196.0 706 ... 0 \n",
"1 1976 1976 0.0 978 ... 298 \n",
"2 2001 2002 162.0 486 ... 0 \n",
"3 1915 1970 0.0 216 ... 0 \n",
"4 2000 2000 350.0 655 ... 192 \n",
"5 1993 1995 0.0 732 ... 40 \n",
"6 2004 2005 186.0 1369 ... 255 \n",
"7 1973 1973 240.0 859 ... 235 \n",
"8 1931 1950 0.0 0 ... 90 \n",
"9 1939 1950 0.0 851 ... 0 \n",
"10 1965 1965 0.0 906 ... 0 \n",
"11 2005 2006 286.0 998 ... 147 \n",
"12 1962 1962 0.0 737 ... 140 \n",
"13 2006 2007 306.0 0 ... 160 \n",
"14 1960 1960 212.0 733 ... 0 \n",
"15 1929 2001 0.0 0 ... 48 \n",
"16 1970 1970 180.0 578 ... 0 \n",
"17 1967 1967 0.0 0 ... 0 \n",
"18 2004 2004 0.0 646 ... 0 \n",
"19 1958 1965 0.0 504 ... 0 \n",
"20 2005 2006 380.0 0 ... 240 \n",
"21 1930 1950 0.0 0 ... 0 \n",
"22 2002 2002 281.0 0 ... 171 \n",
"23 1976 1976 0.0 840 ... 100 \n",
"24 1968 2001 0.0 188 ... 406 \n",
"25 2007 2007 640.0 0 ... 0 \n",
"26 1951 2000 0.0 234 ... 222 \n",
"27 2007 2008 200.0 1218 ... 0 \n",
"28 1957 1997 0.0 1277 ... 288 \n",
"29 1927 1950 0.0 0 ... 49 \n",
"... ... ... ... ... ... ... \n",
"1430 2005 2005 0.0 0 ... 100 \n",
"1431 1976 1976 0.0 958 ... 0 \n",
"1432 1927 2007 0.0 0 ... 0 \n",
"1433 2000 2000 318.0 0 ... 0 \n",
"1434 1977 1977 0.0 936 ... 295 \n",
"1435 1962 2005 237.0 0 ... 0 \n",
"1436 1971 1971 0.0 616 ... 0 \n",
"1437 2008 2008 426.0 1336 ... 0 \n",
"1438 1957 1996 0.0 600 ... 0 \n",
"1439 1979 1979 96.0 315 ... 0 \n",
"1440 1922 1994 0.0 0 ... 431 \n",
"1441 2004 2004 147.0 697 ... 149 \n",
"1442 2008 2008 160.0 765 ... 168 \n",
"1443 1916 1950 0.0 0 ... 0 \n",
"1444 2004 2004 106.0 0 ... 192 \n",
"1445 1966 1966 0.0 187 ... 0 \n",
"1446 1962 1962 189.0 593 ... 261 \n",
"1447 1995 1996 438.0 1079 ... 0 \n",
"1448 1910 2000 0.0 0 ... 168 \n",
"1449 1970 1970 0.0 553 ... 0 \n",
"1450 1974 1974 0.0 0 ... 32 \n",
"1451 2008 2009 194.0 0 ... 0 \n",
"1452 2005 2005 80.0 547 ... 0 \n",
"1453 2006 2006 0.0 0 ... 36 \n",
"1454 2004 2005 0.0 410 ... 0 \n",
"1455 1999 2000 0.0 0 ... 0 \n",
"1456 1978 1988 119.0 790 ... 349 \n",
"1457 1941 2006 0.0 275 ... 0 \n",
"1458 1950 1996 0.0 49 ... 366 \n",
"1459 1965 1965 0.0 830 ... 736 \n",
"\n",
" OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal \\\n",
"0 61 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 \n",
"2 42 0 0 0 0 0 \n",
"3 35 272 0 0 0 0 \n",
"4 84 0 0 0 0 0 \n",
"5 30 0 320 0 0 700 \n",
"6 57 0 0 0 0 0 \n",
"7 204 228 0 0 0 350 \n",
"8 0 205 0 0 0 0 \n",
"9 4 0 0 0 0 0 \n",
"10 0 0 0 0 0 0 \n",
"11 21 0 0 0 0 0 \n",
"12 0 0 0 176 0 0 \n",
"13 33 0 0 0 0 0 \n",
"14 213 176 0 0 0 0 \n",
"15 112 0 0 0 0 0 \n",
"16 0 0 0 0 0 700 \n",
"17 0 0 0 0 0 500 \n",
"18 102 0 0 0 0 0 \n",
"19 0 0 0 0 0 0 \n",
"20 154 0 0 0 0 0 \n",
"21 0 205 0 0 0 0 \n",
"22 159 0 0 0 0 0 \n",
"23 110 0 0 0 0 0 \n",
"24 90 0 0 0 0 0 \n",
"25 56 0 0 0 0 0 \n",
"26 32 0 0 0 0 0 \n",
"27 50 0 0 0 0 0 \n",
"28 258 0 0 0 0 0 \n",
"29 0 87 0 0 0 0 \n",
"... ... ... ... ... ... ... \n",
"1430 40 0 0 0 0 0 \n",
"1431 60 0 0 0 0 0 \n",
"1432 0 0 0 0 0 0 \n",
"1433 0 0 0 0 0 0 \n",
"1434 41 0 0 0 0 0 \n",
"1435 36 0 0 0 0 0 \n",
"1436 0 0 0 0 0 0 \n",
"1437 66 0 304 0 0 0 \n",
"1438 158 158 0 0 0 0 \n",
"1439 88 216 0 0 0 0 \n",
"1440 0 0 0 0 0 0 \n",
"1441 0 0 0 0 0 0 \n",
"1442 52 0 0 0 0 0 \n",
"1443 98 0 0 40 0 0 \n",
"1444 60 0 0 0 0 0 \n",
"1445 0 252 0 0 0 0 \n",
"1446 39 0 0 0 0 0 \n",
"1447 65 0 0 0 0 0 \n",
"1448 24 0 0 0 0 0 \n",
"1449 0 0 0 0 0 0 \n",
"1450 45 0 0 0 0 0 \n",
"1451 36 0 0 0 0 0 \n",
"1452 28 0 0 0 0 0 \n",
"1453 56 0 0 0 0 0 \n",
"1454 113 0 0 0 0 0 \n",
"1455 40 0 0 0 0 0 \n",
"1456 0 0 0 0 0 0 \n",
"1457 60 0 0 0 0 2500 \n",
"1458 0 112 0 0 0 0 \n",
"1459 68 0 0 0 0 0 \n",
"\n",
" MoSold YrSold SalePrice \n",
"0 2 2008 208500 \n",
"1 5 2007 181500 \n",
"2 9 2008 223500 \n",
"3 2 2006 140000 \n",
"4 12 2008 250000 \n",
"5 10 2009 143000 \n",
"6 8 2007 307000 \n",
"7 11 2009 200000 \n",
"8 4 2008 129900 \n",
"9 1 2008 118000 \n",
"10 2 2008 129500 \n",
"11 7 2006 345000 \n",
"12 9 2008 144000 \n",
"13 8 2007 279500 \n",
"14 5 2008 157000 \n",
"15 7 2007 132000 \n",
"16 3 2010 149000 \n",
"17 10 2006 90000 \n",
"18 6 2008 159000 \n",
"19 5 2009 139000 \n",
"20 11 2006 325300 \n",
"21 6 2007 139400 \n",
"22 9 2008 230000 \n",
"23 6 2007 129900 \n",
"24 5 2010 154000 \n",
"25 7 2009 256300 \n",
"26 5 2010 134800 \n",
"27 5 2010 306000 \n",
"28 12 2006 207500 \n",
"29 5 2008 68500 \n",
"... ... ... ... \n",
"1430 7 2006 192140 \n",
"1431 10 2009 143750 \n",
"1432 8 2007 64500 \n",
"1433 5 2008 186500 \n",
"1434 5 2006 160000 \n",
"1435 7 2008 174000 \n",
"1436 5 2007 120500 \n",
"1437 11 2008 394617 \n",
"1438 4 2010 149700 \n",
"1439 11 2007 197000 \n",
"1440 9 2008 191000 \n",
"1441 5 2008 149300 \n",
"1442 4 2009 310000 \n",
"1443 5 2009 121000 \n",
"1444 11 2007 179600 \n",
"1445 5 2007 129000 \n",
"1446 4 2010 157900 \n",
"1447 12 2007 240000 \n",
"1448 5 2007 112000 \n",
"1449 8 2006 92000 \n",
"1450 9 2009 136000 \n",
"1451 5 2009 287090 \n",
"1452 5 2006 145000 \n",
"1453 7 2006 84500 \n",
"1454 10 2009 185000 \n",
"1455 8 2007 175000 \n",
"1456 2 2010 210000 \n",
"1457 5 2010 266500 \n",
"1458 4 2010 142125 \n",
"1459 6 2008 147500 \n",
"\n",
"[1460 rows x 38 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Import data houseprice\n",
"import pandas as pd\n",
"\n",
"houseprice = pd.read_excel ('E:\\DATA MINING\\houseprice.xlsx') #(use \"r\" before the path string to address special character, such as '\\'). Don't forget to put the file name at the end of the path + '.xlsx'\n",
"houseprice = pd.DataFrame(houseprice)\n",
"houseprice"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1460, 38)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Dimensi Data Houseprice\n",
"houseprice.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Id int64\n",
"MSSubClass int64\n",
"LotFrontage float64\n",
"LotArea int64\n",
"OverallQual int64\n",
"OverallCond int64\n",
"YearBuilt int64\n",
"YearRemodAdd int64\n",
"MasVnrArea float64\n",
"BsmtFinSF1 int64\n",
"BsmtFinSF2 int64\n",
"BsmtUnfSF int64\n",
"TotalBsmtSF int64\n",
"1stFlrSF int64\n",
"2ndFlrSF int64\n",
"LowQualFinSF int64\n",
"GrLivArea int64\n",
"BsmtFullBath int64\n",
"BsmtHalfBath int64\n",
"FullBath int64\n",
"HalfBath int64\n",
"BedroomAbvGr int64\n",
"KitchenAbvGr int64\n",
"TotRmsAbvGrd int64\n",
"Fireplaces int64\n",
"GarageYrBlt float64\n",
"GarageCars int64\n",
"GarageArea int64\n",
"WoodDeckSF int64\n",
"OpenPorchSF int64\n",
"EnclosedPorch int64\n",
"3SsnPorch int64\n",
"ScreenPorch int64\n",
"PoolArea int64\n",
"MiscVal int64\n",
"MoSold int64\n",
"YrSold int64\n",
"SalePrice int64\n",
"dtype: object"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Tipe Variabel data houseprice\n",
"houseprice.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# STATISTIKA DESKRIPTIF"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"#Statistika deskriptif data housprice\n",
"import pandas as pd \n",
"from sklearn import linear_model \n",
"import random \n",
"import numpy as np "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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 ScreenPorch \\\n",
"count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean 94.244521 46.660274 21.954110 3.409589 15.060959 \n",
"std 125.338794 66.256028 61.119149 29.317331 55.757415 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 0.000000 25.000000 0.000000 0.000000 0.000000 \n",
"75% 168.000000 68.000000 0.000000 0.000000 0.000000 \n",
"max 857.000000 547.000000 552.000000 508.000000 480.000000 \n",
"\n",
" PoolArea MiscVal MoSold YrSold SalePrice \n",
"count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean 2.758904 43.489041 6.321918 2007.815753 180921.195890 \n",
"std 40.177307 496.123024 2.703626 1.328095 79442.502883 \n",
"min 0.000000 0.000000 1.000000 2006.000000 34900.000000 \n",
"25% 0.000000 0.000000 5.000000 2007.000000 129975.000000 \n",
"50% 0.000000 0.000000 6.000000 2008.000000 163000.000000 \n",
"75% 0.000000 0.000000 8.000000 2009.000000 214000.000000 \n",
"max 738.000000 15500.000000 12.000000 2010.000000 755000.000000 \n",
"\n",
"[8 rows x 38 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"houseprice.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IMPUTASI MISSING VALUE"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Id 0.000000\n",
"MSSubClass 0.000000\n",
"LotFrontage 17.739726\n",
"LotArea 0.000000\n",
"OverallQual 0.000000\n",
"OverallCond 0.000000\n",
"YearBuilt 0.000000\n",
"YearRemodAdd 0.000000\n",
"MasVnrArea 0.547945\n",
"BsmtFinSF1 0.000000\n",
"BsmtFinSF2 0.000000\n",
"BsmtUnfSF 0.000000\n",
"TotalBsmtSF 0.000000\n",
"1stFlrSF 0.000000\n",
"2ndFlrSF 0.000000\n",
"LowQualFinSF 0.000000\n",
"GrLivArea 0.000000\n",
"BsmtFullBath 0.000000\n",
"BsmtHalfBath 0.000000\n",
"FullBath 0.000000\n",
"HalfBath 0.000000\n",
"BedroomAbvGr 0.000000\n",
"KitchenAbvGr 0.000000\n",
"TotRmsAbvGrd 0.000000\n",
"Fireplaces 0.000000\n",
"GarageYrBlt 5.547945\n",
"GarageCars 0.000000\n",
"GarageArea 0.000000\n",
"WoodDeckSF 0.000000\n",
"OpenPorchSF 0.000000\n",
"EnclosedPorch 0.000000\n",
"3SsnPorch 0.000000\n",
"ScreenPorch 0.000000\n",
"PoolArea 0.000000\n",
"MiscVal 0.000000\n",
"MoSold 0.000000\n",
"YrSold 0.000000\n",
"SalePrice 0.000000\n",
"dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Melihat berapa persen missing value pada setiap variabel\n",
"houseprice.isnull().sum()\n",
"houseprice.isnull().sum()/np.count_nonzero(houseprice['Id'])*100"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"#Dilakukan imputasi dengan mean, pada variabel yg memiliki missing value\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.preprocessing import Imputer"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>4</th>\n",
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" <th>32</th>\n",
" <th>33</th>\n",
" <th>34</th>\n",
" <th>35</th>\n",
" <th>36</th>\n",
" <th>37</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>60.0</td>\n",
" <td>65.000000</td>\n",
" <td>8450.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>2003.0</td>\n",
" <td>2003.0</td>\n",
" <td>196.0</td>\n",
" <td>706.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2008.0</td>\n",
" <td>208500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.0</td>\n",
" <td>20.0</td>\n",
" <td>80.000000</td>\n",
" <td>9600.0</td>\n",
" <td>6.0</td>\n",
" <td>8.0</td>\n",
" <td>1976.0</td>\n",
" <td>1976.0</td>\n",
" <td>0.0</td>\n",
" <td>978.0</td>\n",
" <td>...</td>\n",
" <td>298.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2007.0</td>\n",
" <td>181500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>60.0</td>\n",
" <td>68.000000</td>\n",
" <td>11250.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>2001.0</td>\n",
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" <td>162.0</td>\n",
" <td>486.0</td>\n",
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" <td>0.0</td>\n",
" <td>42.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>2008.0</td>\n",
" <td>223500.0</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>4.0</td>\n",
" <td>70.0</td>\n",
" <td>60.000000</td>\n",
" <td>9550.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>1915.0</td>\n",
" <td>1970.0</td>\n",
" <td>0.0</td>\n",
" <td>216.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>35.0</td>\n",
" <td>272.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2006.0</td>\n",
" <td>140000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>60.0</td>\n",
" <td>84.000000</td>\n",
" <td>14260.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>2000.0</td>\n",
" <td>2000.0</td>\n",
" <td>350.0</td>\n",
" <td>655.0</td>\n",
" <td>...</td>\n",
" <td>192.0</td>\n",
" <td>84.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>2008.0</td>\n",
" <td>250000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6.0</td>\n",
" <td>50.0</td>\n",
" <td>85.000000</td>\n",
" <td>14115.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>1993.0</td>\n",
" <td>1995.0</td>\n",
" <td>0.0</td>\n",
" <td>732.0</td>\n",
" <td>...</td>\n",
" <td>40.0</td>\n",
" <td>30.0</td>\n",
" <td>0.0</td>\n",
" <td>320.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>700.0</td>\n",
" <td>10.0</td>\n",
" <td>2009.0</td>\n",
" <td>143000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7.0</td>\n",
" <td>20.0</td>\n",
" <td>75.000000</td>\n",
" <td>10084.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>2004.0</td>\n",
" <td>2005.0</td>\n",
" <td>186.0</td>\n",
" <td>1369.0</td>\n",
" <td>...</td>\n",
" <td>255.0</td>\n",
" <td>57.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>2007.0</td>\n",
" <td>307000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8.0</td>\n",
" <td>60.0</td>\n",
" <td>70.049958</td>\n",
" <td>10382.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>1973.0</td>\n",
" <td>1973.0</td>\n",
" <td>240.0</td>\n",
" <td>859.0</td>\n",
" <td>...</td>\n",
" <td>235.0</td>\n",
" <td>204.0</td>\n",
" <td>228.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>350.0</td>\n",
" <td>11.0</td>\n",
" <td>2009.0</td>\n",
" <td>200000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9.0</td>\n",
" <td>50.0</td>\n",
" <td>51.000000</td>\n",
" <td>6120.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>1931.0</td>\n",
" <td>1950.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>90.0</td>\n",
" <td>0.0</td>\n",
" <td>205.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>2008.0</td>\n",
" <td>129900.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10.0</td>\n",
" <td>190.0</td>\n",
" <td>50.000000</td>\n",
" <td>7420.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>1939.0</td>\n",
" <td>1950.0</td>\n",
" <td>0.0</td>\n",
" <td>851.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2008.0</td>\n",
" <td>118000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11.0</td>\n",
" <td>20.0</td>\n",
" <td>70.000000</td>\n",
" <td>11200.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>1965.0</td>\n",
" <td>1965.0</td>\n",
" <td>0.0</td>\n",
" <td>906.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2008.0</td>\n",
" <td>129500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12.0</td>\n",
" <td>60.0</td>\n",
" <td>85.000000</td>\n",
" <td>11924.0</td>\n",
" <td>9.0</td>\n",
" <td>5.0</td>\n",
" <td>2005.0</td>\n",
" <td>2006.0</td>\n",
" <td>286.0</td>\n",
" <td>998.0</td>\n",
" <td>...</td>\n",
" <td>147.0</td>\n",
" <td>21.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>2006.0</td>\n",
" <td>345000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13.0</td>\n",
" <td>20.0</td>\n",
" <td>70.049958</td>\n",
" <td>12968.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>1962.0</td>\n",
" <td>1962.0</td>\n",
" <td>0.0</td>\n",
" <td>737.0</td>\n",
" <td>...</td>\n",
" <td>140.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>176.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>2008.0</td>\n",
" <td>144000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14.0</td>\n",
" <td>20.0</td>\n",
" <td>91.000000</td>\n",
" <td>10652.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>2006.0</td>\n",
" <td>2007.0</td>\n",
" <td>306.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>160.0</td>\n",
" <td>33.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>2007.0</td>\n",
" <td>279500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15.0</td>\n",
" <td>20.0</td>\n",
" <td>70.049958</td>\n",
" <td>10920.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>1960.0</td>\n",
" <td>1960.0</td>\n",
" <td>212.0</td>\n",
" <td>733.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>213.0</td>\n",
" <td>176.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2008.0</td>\n",
" <td>157000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16.0</td>\n",
" <td>45.0</td>\n",
" <td>51.000000</td>\n",
" <td>6120.0</td>\n",
" <td>7.0</td>\n",
" <td>8.0</td>\n",
" <td>1929.0</td>\n",
" <td>2001.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>48.0</td>\n",
" <td>112.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>2007.0</td>\n",
" <td>132000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17.0</td>\n",
" <td>20.0</td>\n",
" <td>70.049958</td>\n",
" <td>11241.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>1970.0</td>\n",
" <td>1970.0</td>\n",
" <td>180.0</td>\n",
" <td>578.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>700.0</td>\n",
" <td>3.0</td>\n",
" <td>2010.0</td>\n",
" <td>149000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18.0</td>\n",
" <td>90.0</td>\n",
" <td>72.000000</td>\n",
" <td>10791.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>1967.0</td>\n",
" <td>1967.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>500.0</td>\n",
" <td>10.0</td>\n",
" <td>2006.0</td>\n",
" <td>90000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19.0</td>\n",
" <td>20.0</td>\n",
" <td>66.000000</td>\n",
" <td>13695.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>2004.0</td>\n",
" <td>2004.0</td>\n",
" <td>0.0</td>\n",
" <td>646.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>102.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>2008.0</td>\n",
" <td>159000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
" <td>70.000000</td>\n",
" <td>7560.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>1958.0</td>\n",
" <td>1965.0</td>\n",
" <td>0.0</td>\n",
" <td>504.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2009.0</td>\n",
" <td>139000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21.0</td>\n",
" <td>60.0</td>\n",
" <td>101.000000</td>\n",
" <td>14215.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>2005.0</td>\n",
" <td>2006.0</td>\n",
" <td>380.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>240.0</td>\n",
" <td>154.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>11.0</td>\n",
" <td>2006.0</td>\n",
" <td>325300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22.0</td>\n",
" <td>45.0</td>\n",
" <td>57.000000</td>\n",
" <td>7449.0</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>1930.0</td>\n",
" <td>1950.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>205.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>2007.0</td>\n",
" <td>139400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23.0</td>\n",
" <td>20.0</td>\n",
" <td>75.000000</td>\n",
" <td>9742.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>2002.0</td>\n",
" <td>2002.0</td>\n",
" <td>281.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>171.0</td>\n",
" <td>159.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>2008.0</td>\n",
" <td>230000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24.0</td>\n",
" <td>120.0</td>\n",
" <td>44.000000</td>\n",
" <td>4224.0</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" <td>1976.0</td>\n",
" <td>1976.0</td>\n",
" <td>0.0</td>\n",
" <td>840.0</td>\n",
" <td>...</td>\n",
" <td>100.0</td>\n",
" <td>110.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>2007.0</td>\n",
" <td>129900.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25.0</td>\n",
" <td>20.0</td>\n",
" <td>70.049958</td>\n",
" <td>8246.0</td>\n",
" <td>5.0</td>\n",
" <td>8.0</td>\n",
" <td>1968.0</td>\n",
" <td>2001.0</td>\n",
" <td>0.0</td>\n",
" <td>188.0</td>\n",
" <td>...</td>\n",
" <td>406.0</td>\n",
" <td>90.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2010.0</td>\n",
" <td>154000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26.0</td>\n",
" <td>20.0</td>\n",
" <td>110.000000</td>\n",
" <td>14230.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>2007.0</td>\n",
" <td>2007.0</td>\n",
" <td>640.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>56.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>2009.0</td>\n",
" <td>256300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27.0</td>\n",
" <td>20.0</td>\n",
" <td>60.000000</td>\n",
" <td>7200.0</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" <td>1951.0</td>\n",
" <td>2000.0</td>\n",
" <td>0.0</td>\n",
" <td>234.0</td>\n",
" <td>...</td>\n",
" <td>222.0</td>\n",
" <td>32.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2010.0</td>\n",
" <td>134800.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28.0</td>\n",
" <td>20.0</td>\n",
" <td>98.000000</td>\n",
" <td>11478.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>2007.0</td>\n",
" <td>2008.0</td>\n",
" <td>200.0</td>\n",
" <td>1218.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>50.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2010.0</td>\n",
" <td>306000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29.0</td>\n",
" <td>20.0</td>\n",
" <td>47.000000</td>\n",
" <td>16321.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>1957.0</td>\n",
" <td>1997.0</td>\n",
" <td>0.0</td>\n",
" <td>1277.0</td>\n",
" <td>...</td>\n",
" <td>288.0</td>\n",
" <td>258.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>2006.0</td>\n",
" <td>207500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30.0</td>\n",
" <td>30.0</td>\n",
" <td>60.000000</td>\n",
" <td>6324.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>1927.0</td>\n",
" <td>1950.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>49.0</td>\n",
" <td>0.0</td>\n",
" <td>87.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2008.0</td>\n",
" <td>68500.0</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",
" <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",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1430</th>\n",
" <td>1431.0</td>\n",
" <td>60.0</td>\n",
" <td>60.000000</td>\n",
" <td>21930.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>2005.0</td>\n",
" <td>2005.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>100.0</td>\n",
" <td>40.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>2006.0</td>\n",
" <td>192140.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1431</th>\n",
" <td>1432.0</td>\n",
" <td>120.0</td>\n",
" <td>70.049958</td>\n",
" <td>4928.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>1976.0</td>\n",
" <td>1976.0</td>\n",
" <td>0.0</td>\n",
" <td>958.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>2009.0</td>\n",
" <td>143750.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1432</th>\n",
" <td>1433.0</td>\n",
" <td>30.0</td>\n",
" <td>60.000000</td>\n",
" <td>10800.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>1927.0</td>\n",
" <td>2007.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>2007.0</td>\n",
" <td>64500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1433</th>\n",
" <td>1434.0</td>\n",
" <td>60.0</td>\n",
" <td>93.000000</td>\n",
" <td>10261.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>2000.0</td>\n",
" <td>2000.0</td>\n",
" <td>318.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2008.0</td>\n",
" <td>186500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1434</th>\n",
" <td>1435.0</td>\n",
" <td>20.0</td>\n",
" <td>80.000000</td>\n",
" <td>17400.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>1977.0</td>\n",
" <td>1977.0</td>\n",
" <td>0.0</td>\n",
" <td>936.0</td>\n",
" <td>...</td>\n",
" <td>295.0</td>\n",
" <td>41.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2006.0</td>\n",
" <td>160000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1435</th>\n",
" <td>1436.0</td>\n",
" <td>20.0</td>\n",
" <td>80.000000</td>\n",
" <td>8400.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>1962.0</td>\n",
" <td>2005.0</td>\n",
" <td>237.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>36.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>2008.0</td>\n",
" <td>174000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1436</th>\n",
" <td>1437.0</td>\n",
" <td>20.0</td>\n",
" <td>60.000000</td>\n",
" <td>9000.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>1971.0</td>\n",
" <td>1971.0</td>\n",
" <td>0.0</td>\n",
" <td>616.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2007.0</td>\n",
" <td>120500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1437</th>\n",
" <td>1438.0</td>\n",
" <td>20.0</td>\n",
" <td>96.000000</td>\n",
" <td>12444.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>426.0</td>\n",
" <td>1336.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>66.0</td>\n",
" <td>0.0</td>\n",
" <td>304.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>11.0</td>\n",
" <td>2008.0</td>\n",
" <td>394617.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1438</th>\n",
" <td>1439.0</td>\n",
" <td>20.0</td>\n",
" <td>90.000000</td>\n",
" <td>7407.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>1957.0</td>\n",
" <td>1996.0</td>\n",
" <td>0.0</td>\n",
" <td>600.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>158.0</td>\n",
" <td>158.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>2010.0</td>\n",
" <td>149700.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1439</th>\n",
" <td>1440.0</td>\n",
" <td>60.0</td>\n",
" <td>80.000000</td>\n",
" <td>11584.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>1979.0</td>\n",
" <td>1979.0</td>\n",
" <td>96.0</td>\n",
" <td>315.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>88.0</td>\n",
" <td>216.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>11.0</td>\n",
" <td>2007.0</td>\n",
" <td>197000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1440</th>\n",
" <td>1441.0</td>\n",
" <td>70.0</td>\n",
" <td>79.000000</td>\n",
" <td>11526.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>1922.0</td>\n",
" <td>1994.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>431.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>2008.0</td>\n",
" <td>191000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1441</th>\n",
" <td>1442.0</td>\n",
" <td>120.0</td>\n",
" <td>70.049958</td>\n",
" <td>4426.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>2004.0</td>\n",
" <td>2004.0</td>\n",
" <td>147.0</td>\n",
" <td>697.0</td>\n",
" <td>...</td>\n",
" <td>149.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2008.0</td>\n",
" <td>149300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1442</th>\n",
" <td>1443.0</td>\n",
" <td>60.0</td>\n",
" <td>85.000000</td>\n",
" <td>11003.0</td>\n",
" <td>10.0</td>\n",
" <td>5.0</td>\n",
" <td>2008.0</td>\n",
" <td>2008.0</td>\n",
" <td>160.0</td>\n",
" <td>765.0</td>\n",
" <td>...</td>\n",
" <td>168.0</td>\n",
" <td>52.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>2009.0</td>\n",
" <td>310000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1443</th>\n",
" <td>1444.0</td>\n",
" <td>30.0</td>\n",
" <td>70.049958</td>\n",
" <td>8854.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>1916.0</td>\n",
" <td>1950.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>98.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>40.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2009.0</td>\n",
" <td>121000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444</th>\n",
" <td>1445.0</td>\n",
" <td>20.0</td>\n",
" <td>63.000000</td>\n",
" <td>8500.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>2004.0</td>\n",
" <td>2004.0</td>\n",
" <td>106.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>192.0</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>11.0</td>\n",
" <td>2007.0</td>\n",
" <td>179600.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1445</th>\n",
" <td>1446.0</td>\n",
" <td>85.0</td>\n",
" <td>70.000000</td>\n",
" <td>8400.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>1966.0</td>\n",
" <td>1966.0</td>\n",
" <td>0.0</td>\n",
" <td>187.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>252.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2007.0</td>\n",
" <td>129000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1446</th>\n",
" <td>1447.0</td>\n",
" <td>20.0</td>\n",
" <td>70.049958</td>\n",
" <td>26142.0</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" <td>1962.0</td>\n",
" <td>1962.0</td>\n",
" <td>189.0</td>\n",
" <td>593.0</td>\n",
" <td>...</td>\n",
" <td>261.0</td>\n",
" <td>39.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>2010.0</td>\n",
" <td>157900.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1447</th>\n",
" <td>1448.0</td>\n",
" <td>60.0</td>\n",
" <td>80.000000</td>\n",
" <td>10000.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>1995.0</td>\n",
" <td>1996.0</td>\n",
" <td>438.0</td>\n",
" <td>1079.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>65.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td>2007.0</td>\n",
" <td>240000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1448</th>\n",
" <td>1449.0</td>\n",
" <td>50.0</td>\n",
" <td>70.000000</td>\n",
" <td>11767.0</td>\n",
" <td>4.0</td>\n",
" <td>7.0</td>\n",
" <td>1910.0</td>\n",
" <td>2000.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>168.0</td>\n",
" <td>24.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2007.0</td>\n",
" <td>112000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1449</th>\n",
" <td>1450.0</td>\n",
" <td>180.0</td>\n",
" <td>21.000000</td>\n",
" <td>1533.0</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" <td>1970.0</td>\n",
" <td>1970.0</td>\n",
" <td>0.0</td>\n",
" <td>553.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>2006.0</td>\n",
" <td>92000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1450</th>\n",
" <td>1451.0</td>\n",
" <td>90.0</td>\n",
" <td>60.000000</td>\n",
" <td>9000.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>1974.0</td>\n",
" <td>1974.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>32.0</td>\n",
" <td>45.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>2009.0</td>\n",
" <td>136000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1451</th>\n",
" <td>1452.0</td>\n",
" <td>20.0</td>\n",
" <td>78.000000</td>\n",
" <td>9262.0</td>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" <td>2008.0</td>\n",
" <td>2009.0</td>\n",
" <td>194.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>36.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2009.0</td>\n",
" <td>287090.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1452</th>\n",
" <td>1453.0</td>\n",
" <td>180.0</td>\n",
" <td>35.000000</td>\n",
" <td>3675.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>2005.0</td>\n",
" <td>2005.0</td>\n",
" <td>80.0</td>\n",
" <td>547.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2006.0</td>\n",
" <td>145000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1453</th>\n",
" <td>1454.0</td>\n",
" <td>20.0</td>\n",
" <td>90.000000</td>\n",
" <td>17217.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>2006.0</td>\n",
" <td>2006.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>36.0</td>\n",
" <td>56.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>7.0</td>\n",
" <td>2006.0</td>\n",
" <td>84500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1454</th>\n",
" <td>1455.0</td>\n",
" <td>20.0</td>\n",
" <td>62.000000</td>\n",
" <td>7500.0</td>\n",
" <td>7.0</td>\n",
" <td>5.0</td>\n",
" <td>2004.0</td>\n",
" <td>2005.0</td>\n",
" <td>0.0</td>\n",
" <td>410.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>113.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>2009.0</td>\n",
" <td>185000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1455</th>\n",
" <td>1456.0</td>\n",
" <td>60.0</td>\n",
" <td>62.000000</td>\n",
" <td>7917.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>1999.0</td>\n",
" <td>2000.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>40.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</td>\n",
" <td>2007.0</td>\n",
" <td>175000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1456</th>\n",
" <td>1457.0</td>\n",
" <td>20.0</td>\n",
" <td>85.000000</td>\n",
" <td>13175.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>1978.0</td>\n",
" <td>1988.0</td>\n",
" <td>119.0</td>\n",
" <td>790.0</td>\n",
" <td>...</td>\n",
" <td>349.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2010.0</td>\n",
" <td>210000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1457</th>\n",
" <td>1458.0</td>\n",
" <td>70.0</td>\n",
" <td>66.000000</td>\n",
" <td>9042.0</td>\n",
" <td>7.0</td>\n",
" <td>9.0</td>\n",
" <td>1941.0</td>\n",
" <td>2006.0</td>\n",
" <td>0.0</td>\n",
" <td>275.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2500.0</td>\n",
" <td>5.0</td>\n",
" <td>2010.0</td>\n",
" <td>266500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1458</th>\n",
" <td>1459.0</td>\n",
" <td>20.0</td>\n",
" <td>68.000000</td>\n",
" <td>9717.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>1950.0</td>\n",
" <td>1996.0</td>\n",
" <td>0.0</td>\n",
" <td>49.0</td>\n",
" <td>...</td>\n",
" <td>366.0</td>\n",
" <td>0.0</td>\n",
" <td>112.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>2010.0</td>\n",
" <td>142125.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1459</th>\n",
" <td>1460.0</td>\n",
" <td>20.0</td>\n",
" <td>75.000000</td>\n",
" <td>9937.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>1965.0</td>\n",
" <td>1965.0</td>\n",
" <td>0.0</td>\n",
" <td>830.0</td>\n",
" <td>...</td>\n",
" <td>736.0</td>\n",
" <td>68.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>2008.0</td>\n",
" <td>147500.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1460 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 \\\n",
"0 1.0 60.0 65.000000 8450.0 7.0 5.0 2003.0 2003.0 196.0 \n",
"1 2.0 20.0 80.000000 9600.0 6.0 8.0 1976.0 1976.0 0.0 \n",
"2 3.0 60.0 68.000000 11250.0 7.0 5.0 2001.0 2002.0 162.0 \n",
"3 4.0 70.0 60.000000 9550.0 7.0 5.0 1915.0 1970.0 0.0 \n",
"4 5.0 60.0 84.000000 14260.0 8.0 5.0 2000.0 2000.0 350.0 \n",
"5 6.0 50.0 85.000000 14115.0 5.0 5.0 1993.0 1995.0 0.0 \n",
"6 7.0 20.0 75.000000 10084.0 8.0 5.0 2004.0 2005.0 186.0 \n",
"7 8.0 60.0 70.049958 10382.0 7.0 6.0 1973.0 1973.0 240.0 \n",
"8 9.0 50.0 51.000000 6120.0 7.0 5.0 1931.0 1950.0 0.0 \n",
"9 10.0 190.0 50.000000 7420.0 5.0 6.0 1939.0 1950.0 0.0 \n",
"10 11.0 20.0 70.000000 11200.0 5.0 5.0 1965.0 1965.0 0.0 \n",
"11 12.0 60.0 85.000000 11924.0 9.0 5.0 2005.0 2006.0 286.0 \n",
"12 13.0 20.0 70.049958 12968.0 5.0 6.0 1962.0 1962.0 0.0 \n",
"13 14.0 20.0 91.000000 10652.0 7.0 5.0 2006.0 2007.0 306.0 \n",
"14 15.0 20.0 70.049958 10920.0 6.0 5.0 1960.0 1960.0 212.0 \n",
"15 16.0 45.0 51.000000 6120.0 7.0 8.0 1929.0 2001.0 0.0 \n",
"16 17.0 20.0 70.049958 11241.0 6.0 7.0 1970.0 1970.0 180.0 \n",
"17 18.0 90.0 72.000000 10791.0 4.0 5.0 1967.0 1967.0 0.0 \n",
"18 19.0 20.0 66.000000 13695.0 5.0 5.0 2004.0 2004.0 0.0 \n",
"19 20.0 20.0 70.000000 7560.0 5.0 6.0 1958.0 1965.0 0.0 \n",
"20 21.0 60.0 101.000000 14215.0 8.0 5.0 2005.0 2006.0 380.0 \n",
"21 22.0 45.0 57.000000 7449.0 7.0 7.0 1930.0 1950.0 0.0 \n",
"22 23.0 20.0 75.000000 9742.0 8.0 5.0 2002.0 2002.0 281.0 \n",
"23 24.0 120.0 44.000000 4224.0 5.0 7.0 1976.0 1976.0 0.0 \n",
"24 25.0 20.0 70.049958 8246.0 5.0 8.0 1968.0 2001.0 0.0 \n",
"25 26.0 20.0 110.000000 14230.0 8.0 5.0 2007.0 2007.0 640.0 \n",
"26 27.0 20.0 60.000000 7200.0 5.0 7.0 1951.0 2000.0 0.0 \n",
"27 28.0 20.0 98.000000 11478.0 8.0 5.0 2007.0 2008.0 200.0 \n",
"28 29.0 20.0 47.000000 16321.0 5.0 6.0 1957.0 1997.0 0.0 \n",
"29 30.0 30.0 60.000000 6324.0 4.0 6.0 1927.0 1950.0 0.0 \n",
"... ... ... ... ... ... ... ... ... ... \n",
"1430 1431.0 60.0 60.000000 21930.0 5.0 5.0 2005.0 2005.0 0.0 \n",
"1431 1432.0 120.0 70.049958 4928.0 6.0 6.0 1976.0 1976.0 0.0 \n",
"1432 1433.0 30.0 60.000000 10800.0 4.0 6.0 1927.0 2007.0 0.0 \n",
"1433 1434.0 60.0 93.000000 10261.0 6.0 5.0 2000.0 2000.0 318.0 \n",
"1434 1435.0 20.0 80.000000 17400.0 5.0 5.0 1977.0 1977.0 0.0 \n",
"1435 1436.0 20.0 80.000000 8400.0 6.0 9.0 1962.0 2005.0 237.0 \n",
"1436 1437.0 20.0 60.000000 9000.0 4.0 6.0 1971.0 1971.0 0.0 \n",
"1437 1438.0 20.0 96.000000 12444.0 8.0 5.0 2008.0 2008.0 426.0 \n",
"1438 1439.0 20.0 90.000000 7407.0 6.0 7.0 1957.0 1996.0 0.0 \n",
"1439 1440.0 60.0 80.000000 11584.0 7.0 6.0 1979.0 1979.0 96.0 \n",
"1440 1441.0 70.0 79.000000 11526.0 6.0 7.0 1922.0 1994.0 0.0 \n",
"1441 1442.0 120.0 70.049958 4426.0 6.0 5.0 2004.0 2004.0 147.0 \n",
"1442 1443.0 60.0 85.000000 11003.0 10.0 5.0 2008.0 2008.0 160.0 \n",
"1443 1444.0 30.0 70.049958 8854.0 6.0 6.0 1916.0 1950.0 0.0 \n",
"1444 1445.0 20.0 63.000000 8500.0 7.0 5.0 2004.0 2004.0 106.0 \n",
"1445 1446.0 85.0 70.000000 8400.0 6.0 5.0 1966.0 1966.0 0.0 \n",
"1446 1447.0 20.0 70.049958 26142.0 5.0 7.0 1962.0 1962.0 189.0 \n",
"1447 1448.0 60.0 80.000000 10000.0 8.0 5.0 1995.0 1996.0 438.0 \n",
"1448 1449.0 50.0 70.000000 11767.0 4.0 7.0 1910.0 2000.0 0.0 \n",
"1449 1450.0 180.0 21.000000 1533.0 5.0 7.0 1970.0 1970.0 0.0 \n",
"1450 1451.0 90.0 60.000000 9000.0 5.0 5.0 1974.0 1974.0 0.0 \n",
"1451 1452.0 20.0 78.000000 9262.0 8.0 5.0 2008.0 2009.0 194.0 \n",
"1452 1453.0 180.0 35.000000 3675.0 5.0 5.0 2005.0 2005.0 80.0 \n",
"1453 1454.0 20.0 90.000000 17217.0 5.0 5.0 2006.0 2006.0 0.0 \n",
"1454 1455.0 20.0 62.000000 7500.0 7.0 5.0 2004.0 2005.0 0.0 \n",
"1455 1456.0 60.0 62.000000 7917.0 6.0 5.0 1999.0 2000.0 0.0 \n",
"1456 1457.0 20.0 85.000000 13175.0 6.0 6.0 1978.0 1988.0 119.0 \n",
"1457 1458.0 70.0 66.000000 9042.0 7.0 9.0 1941.0 2006.0 0.0 \n",
"1458 1459.0 20.0 68.000000 9717.0 5.0 6.0 1950.0 1996.0 0.0 \n",
"1459 1460.0 20.0 75.000000 9937.0 5.0 6.0 1965.0 1965.0 0.0 \n",
"\n",
" 9 ... 28 29 30 31 32 33 34 35 \\\n",
"0 706.0 ... 0.0 61.0 0.0 0.0 0.0 0.0 0.0 2.0 \n",
"1 978.0 ... 298.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"2 486.0 ... 0.0 42.0 0.0 0.0 0.0 0.0 0.0 9.0 \n",
"3 216.0 ... 0.0 35.0 272.0 0.0 0.0 0.0 0.0 2.0 \n",
"4 655.0 ... 192.0 84.0 0.0 0.0 0.0 0.0 0.0 12.0 \n",
"5 732.0 ... 40.0 30.0 0.0 320.0 0.0 0.0 700.0 10.0 \n",
"6 1369.0 ... 255.0 57.0 0.0 0.0 0.0 0.0 0.0 8.0 \n",
"7 859.0 ... 235.0 204.0 228.0 0.0 0.0 0.0 350.0 11.0 \n",
"8 0.0 ... 90.0 0.0 205.0 0.0 0.0 0.0 0.0 4.0 \n",
"9 851.0 ... 0.0 4.0 0.0 0.0 0.0 0.0 0.0 1.0 \n",
"10 906.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 \n",
"11 998.0 ... 147.0 21.0 0.0 0.0 0.0 0.0 0.0 7.0 \n",
"12 737.0 ... 140.0 0.0 0.0 0.0 176.0 0.0 0.0 9.0 \n",
"13 0.0 ... 160.0 33.0 0.0 0.0 0.0 0.0 0.0 8.0 \n",
"14 733.0 ... 0.0 213.0 176.0 0.0 0.0 0.0 0.0 5.0 \n",
"15 0.0 ... 48.0 112.0 0.0 0.0 0.0 0.0 0.0 7.0 \n",
"16 578.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 700.0 3.0 \n",
"17 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 500.0 10.0 \n",
"18 646.0 ... 0.0 102.0 0.0 0.0 0.0 0.0 0.0 6.0 \n",
"19 504.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"20 0.0 ... 240.0 154.0 0.0 0.0 0.0 0.0 0.0 11.0 \n",
"21 0.0 ... 0.0 0.0 205.0 0.0 0.0 0.0 0.0 6.0 \n",
"22 0.0 ... 171.0 159.0 0.0 0.0 0.0 0.0 0.0 9.0 \n",
"23 840.0 ... 100.0 110.0 0.0 0.0 0.0 0.0 0.0 6.0 \n",
"24 188.0 ... 406.0 90.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"25 0.0 ... 0.0 56.0 0.0 0.0 0.0 0.0 0.0 7.0 \n",
"26 234.0 ... 222.0 32.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"27 1218.0 ... 0.0 50.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"28 1277.0 ... 288.0 258.0 0.0 0.0 0.0 0.0 0.0 12.0 \n",
"29 0.0 ... 49.0 0.0 87.0 0.0 0.0 0.0 0.0 5.0 \n",
"... ... ... ... ... ... ... ... ... ... ... \n",
"1430 0.0 ... 100.0 40.0 0.0 0.0 0.0 0.0 0.0 7.0 \n",
"1431 958.0 ... 0.0 60.0 0.0 0.0 0.0 0.0 0.0 10.0 \n",
"1432 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.0 \n",
"1433 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"1434 936.0 ... 295.0 41.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"1435 0.0 ... 0.0 36.0 0.0 0.0 0.0 0.0 0.0 7.0 \n",
"1436 616.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"1437 1336.0 ... 0.0 66.0 0.0 304.0 0.0 0.0 0.0 11.0 \n",
"1438 600.0 ... 0.0 158.0 158.0 0.0 0.0 0.0 0.0 4.0 \n",
"1439 315.0 ... 0.0 88.0 216.0 0.0 0.0 0.0 0.0 11.0 \n",
"1440 0.0 ... 431.0 0.0 0.0 0.0 0.0 0.0 0.0 9.0 \n",
"1441 697.0 ... 149.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"1442 765.0 ... 168.0 52.0 0.0 0.0 0.0 0.0 0.0 4.0 \n",
"1443 0.0 ... 0.0 98.0 0.0 0.0 40.0 0.0 0.0 5.0 \n",
"1444 0.0 ... 192.0 60.0 0.0 0.0 0.0 0.0 0.0 11.0 \n",
"1445 187.0 ... 0.0 0.0 252.0 0.0 0.0 0.0 0.0 5.0 \n",
"1446 593.0 ... 261.0 39.0 0.0 0.0 0.0 0.0 0.0 4.0 \n",
"1447 1079.0 ... 0.0 65.0 0.0 0.0 0.0 0.0 0.0 12.0 \n",
"1448 0.0 ... 168.0 24.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"1449 553.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.0 \n",
"1450 0.0 ... 32.0 45.0 0.0 0.0 0.0 0.0 0.0 9.0 \n",
"1451 0.0 ... 0.0 36.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"1452 547.0 ... 0.0 28.0 0.0 0.0 0.0 0.0 0.0 5.0 \n",
"1453 0.0 ... 36.0 56.0 0.0 0.0 0.0 0.0 0.0 7.0 \n",
"1454 410.0 ... 0.0 113.0 0.0 0.0 0.0 0.0 0.0 10.0 \n",
"1455 0.0 ... 0.0 40.0 0.0 0.0 0.0 0.0 0.0 8.0 \n",
"1456 790.0 ... 349.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 \n",
"1457 275.0 ... 0.0 60.0 0.0 0.0 0.0 0.0 2500.0 5.0 \n",
"1458 49.0 ... 366.0 0.0 112.0 0.0 0.0 0.0 0.0 4.0 \n",
"1459 830.0 ... 736.0 68.0 0.0 0.0 0.0 0.0 0.0 6.0 \n",
"\n",
" 36 37 \n",
"0 2008.0 208500.0 \n",
"1 2007.0 181500.0 \n",
"2 2008.0 223500.0 \n",
"3 2006.0 140000.0 \n",
"4 2008.0 250000.0 \n",
"5 2009.0 143000.0 \n",
"6 2007.0 307000.0 \n",
"7 2009.0 200000.0 \n",
"8 2008.0 129900.0 \n",
"9 2008.0 118000.0 \n",
"10 2008.0 129500.0 \n",
"11 2006.0 345000.0 \n",
"12 2008.0 144000.0 \n",
"13 2007.0 279500.0 \n",
"14 2008.0 157000.0 \n",
"15 2007.0 132000.0 \n",
"16 2010.0 149000.0 \n",
"17 2006.0 90000.0 \n",
"18 2008.0 159000.0 \n",
"19 2009.0 139000.0 \n",
"20 2006.0 325300.0 \n",
"21 2007.0 139400.0 \n",
"22 2008.0 230000.0 \n",
"23 2007.0 129900.0 \n",
"24 2010.0 154000.0 \n",
"25 2009.0 256300.0 \n",
"26 2010.0 134800.0 \n",
"27 2010.0 306000.0 \n",
"28 2006.0 207500.0 \n",
"29 2008.0 68500.0 \n",
"... ... ... \n",
"1430 2006.0 192140.0 \n",
"1431 2009.0 143750.0 \n",
"1432 2007.0 64500.0 \n",
"1433 2008.0 186500.0 \n",
"1434 2006.0 160000.0 \n",
"1435 2008.0 174000.0 \n",
"1436 2007.0 120500.0 \n",
"1437 2008.0 394617.0 \n",
"1438 2010.0 149700.0 \n",
"1439 2007.0 197000.0 \n",
"1440 2008.0 191000.0 \n",
"1441 2008.0 149300.0 \n",
"1442 2009.0 310000.0 \n",
"1443 2009.0 121000.0 \n",
"1444 2007.0 179600.0 \n",
"1445 2007.0 129000.0 \n",
"1446 2010.0 157900.0 \n",
"1447 2007.0 240000.0 \n",
"1448 2007.0 112000.0 \n",
"1449 2006.0 92000.0 \n",
"1450 2009.0 136000.0 \n",
"1451 2009.0 287090.0 \n",
"1452 2006.0 145000.0 \n",
"1453 2006.0 84500.0 \n",
"1454 2009.0 185000.0 \n",
"1455 2007.0 175000.0 \n",
"1456 2010.0 210000.0 \n",
"1457 2010.0 266500.0 \n",
"1458 2010.0 142125.0 \n",
"1459 2008.0 147500.0 \n",
"\n",
"[1460 rows x 38 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_imputer = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
"mean_imputer = mean_imputer.fit(houseprice)\n",
"\n",
"imputed_df = mean_imputer.transform(houseprice.values)\n",
"\n",
"df = pd.DataFrame(imputed_df)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DETEKSI OUTLIER"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xed90a20>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Dilakukan deteksi adanya outlier dengan menggunakan visualisasi boxplot\n",
"df.boxplot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Log"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\PC\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n",
" \"\"\"Entry point for launching an IPython kernel.\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x13c6b358>"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Mengatasi outlier dengan metode rescale Log\n",
"np.log(df).boxplot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Normalization"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"#Mengatasi outlier dengan metode rescale Normalization\n",
"from sklearn.preprocessing import Normalizer \n",
"import numpy as np "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x103d6438>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"normalizer = Normalizer(norm='l2')\n",
"df_normal=normalizer.transform(df)\n",
"df_normal=pd.DataFrame(df_normal)\n",
"df_normal.boxplot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. MinMax"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"#Mengatasi outlier dengan metode rescale MinMax\n",
"from sklearn import preprocessing \n",
"import numpy as np "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11b74cc0>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 1))\n",
"x_scale = minmax_scale.fit_transform(df)\n",
"x_scale = pd.DataFrame(x_scale)\n",
"x_scale.boxplot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Standardization"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10fc45c0>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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bpPP1MV2tmenTp3PkyBFmzZrFZZddxssvv8wjjzzC9OnTs/Y+CakjF7e084tk9jxdCVwBfKq1HmueKwZ+AZRi7Hn6La31F/6rGZvp06dHfOpjxoxhyZIlLFiwwNGKtz/ZLeNUV1dHOBzOaoORa6+Pra2tzJgxg4cffpgtW7bw8MMPc/ToUR577LFMqyYEkFzc0s4vkmmxrwKWAWts5+4ANmmt71NK3WEeL/BPvcRYfvQ777yT9vZ28vPzmTlzpsO/LqMtsocrrriiy7EYdiEWubalnV8ks5n1q0qp0qjTVwMXmr9XA1tIs2EHw7jX1tbGbLnKaIvs4Zvf/CYAR44coW/fvhnWRggye/bs4dZbb2XSpEmRRt3UqVN59NFHM61axumpj32w1vpjAK31x0qpU3zQyXfSObJD6D4jRozgr3/9a+T4yJEjkfOCEM2wYcN45plnWL9+fcQVc8MNN4grBlDJvLqYLfYXbT72fVrrE2zhX2itT4zx3xnADIDBgwePX7duXSSspaWFoqKiuGn3VGbTpk088cQTka2vbrzxRiorK7uVVjr09VMmSLrEk7n44ovRWtPZ2Rk5FwqFUErx61//OnD6JiMTJF28yARJl1gy1113HZ2dnfzgBz9g1KhRfPjhh9x9992EQiGeeuqpwOmbrIxbeEVFxZta63PiRgyGT8rrB6OTtNF2/B4w1Pw9FHjPSzzjx4/XdhoaGnQigiQTJF28yARJl3gygH755ZcdMi+//LI2iml6dfFbJki6eJEJki6xZEKhkF6zZo0uLy/XoVBIl5eX6zVr1uhQKJR2XVIh4xYOvKE92NiejmN/Hphi/p4CPNfD+IQsxa95Ao2NjXGPBcGirKyMkpISx9Z4JSUl0ndGEhOUlFL1wG+BM5RSzUqpacB9wCVKqfeBS8xjISCka1KWXwuSFRcXs3DhQpYsWUJbWxtLlixh4cKFFBcXp0TvoNAb1zryg1xc48UvkhkVE2vAt7ujWsgo6ZyU5dc8gWXLlnHrrbdyxx13REbF9O/fn2XLlvmqb5DorWsdeSXeWjC5uMaLX2T9kgKCO+mcbu3XPIGqqiqmTJlCKGQUy1AoxJQpU3K6otrXOiooKGDu3LksWrSIFStWZFq1jOPlTbCqqsrhisnlspIMYtgDSk/dKOmclOXXetb19fW89NJLrF+/no0bN7J+/XpeeumlrF/XJx5e1zrqjXhpnHipJ7m2TpQXsn6tmFzEDzdKOidl+TVPoKamhhtuuMHxan3DDTek5PU6HA6zYsWKyMSW6dOnZ8T14WWto95KosaJl3qSi+tEeUEMewDxw2edzklZfvk6d+zYwa5du2hra6Ozs5M///nPLF26lJaWFl/1DZJf28taR72VRI0TLw2BXF0nKhFi2AOIH26UdHcs+bEgWSgU4sCBAwwZMoRPP/2UQYMG8cknn5CXlxeR8WNjhSCt4e9lraPeSqLGyY4dO2htbWXlypWR1vjUqVP56KOPInH01nWixMceQPzyWWdbx9LRo0dRSkVmn2qtUUpx9OhRwL9hlUHza9fW1tLW1kZDQwNtbW29xqgn8n0n2gvguOOOIxwOO3zw4XCY4447LhJHWVkZd911lyOdu+66K+fHuothDyC9eXxuv3796NevH6FQKPLbwq+RPpZf2474tdOL14d0vMbJ4cOHWbZsmaOeLFu2jMOHD0dkKioqWLRoEVOnTuWll15i6tSpLFq0yOHeyUXEFRNAevP43MOHD7N79246OzvZvXu3YxlWv16rxa+defzoKB8zZgz9+vWjsrIy8nY3fvx4+vfvH5FpaGhgwYIFrFy50rEZx7PPPpuqS+sxfrgbxbALgaKjo4MBAwZw9OhRCgoKOHDgQCTMr5E+4tfOPF7844kYPnw4r7zySpcdty699NKITFNTU+QhbnHGGWd0y8fuh8H1ksacOXMoLCwEjM1n5syZAyQ3ikcMewDprUO0APr27RsZFdPW1kbfvn0jy/f6OdIn0Rr+Qmo57rjjOP/88x0t9vPPP5+PP/7Ycxy/+c1vmDx5Mq+++iqPPvooZWVlTJ48maeffjoiM2zYMMLhMCeeaCw629raSjgcTnpp33TVyfnz59OnTx/HA2/y5MnMnz8/qXTExx5A/PIlZ+MaJAMHDmT48OGEQiGGDx/OwIEDI2Hp3lhbSB3t7e384he/cPi+f/GLXyTVgd3e3s5jjz3m8ME/9thjjjgOHjzIgQMHOHToEFprDh06xIEDBzh48GBS+qZrJndzczOrV692pLN69Wqam5uTikda7AHED19yOBzmpz/9aWR6fkdHBz/96U+B4K5Bkp+fz8SJE3n77bcBKCws5Pzzz3e0wHJtn9feSn5+Ptdee63D93399dc77rWXOGbMmMHbb78dieOss85ydILv3buXfv36sXfvXrTW7N27l/z8fPbu3ZuUvtk2bFJa7AHEj+GODz/8MFrryFDBo0ePorXm4Ycf9lVXP5k+fbprK2769OmZVq3b9Mbp7F44fPgwr732mmNUzGuvveYY0ZKIr33tazz55JNccMEFPPfcc1xwwQU8+eSTfO1rX3PIFRQUsGHDBjZu3MiGDRsoKChIWl+/hiAnoqSkhJtuuskx0uemm26ipKQkqXikxR5A/PAlW7sQhUIhOjs7I9/23YmCRq51avbmvpJEHY1jxozhmmuucfjYJ0+enNRold27d3POOeewfPlyHnnkEZRSnHPOOezevdshZx9Z5XbshXTN5F68eDFz5syJdCSPHDmSo0ePsmTJkuQi8rIbh9+fTOygtHbtWsdOK2vXru12WunQd/bs2To/P18DOj8/X8+ePTupeAAN6AcffFCvX79eP/jgg5FzqdA3XTJ+3sdUy5SXl+vNmzc7wjdv3qzLy8vTrkuyMj29R6NGjdKbN2/WGzdu1Js3b9ajRo1y3CsvMpZcrPutlNIDBgzQffv21YDu27evHjBggFZKRWQAPXDgQF1aWqqVUrq0tFQPHDiwW/UgXWUvXjp43EGpV7TYs63lZF/l0K7veeedl7S+999/P59++imnnBLIfcaTItvuY7b5Zf3Cy/osXuZqJLrfoVCIlpYWHnjggciaP7fffnukXwkM10b0WkNKqaRdG1aa6ejf8WV5Dn9VCibpXJvcD2pqajjzzDOZNGkSl1xyCZMmTeLMM8/slr6ffvopnZ2dfPrppynQNL3YJ7VYo2KsSS1BJF1+2aDh1wMtUb21jP28efOYNGkS8+bNIy8vL9KvBIZr4+jRo5HJbrt37+bo0aMsXry45xeaIvzol+kVhr2pqYnm5mZHZjU3Nwe25bRjxw5eeOEF7rnnHtavX88999zDCy+8wI4dO5KOy/Kpp9q3no5Owh07dvDYY4/R2tqK1prW1lYee+yxbuVLOuitS0N4eaBZE3Hs93LOnDmOcuPlAdHR0eHYmKWjo6OLPkqpuMdBwku+eKFXuGKGDRvGggULePLJJx2D/pOdpJBOvvzlLzs6Eb/85S8H1oCly0VitcbskzeuvfZax+qPQaK3Lg3hpaNx/vz55OXlOe7lDTfc4JiI43Wm8f333x9xxcybN88RNn/+fPr378+zzz4bM50g4SVfvOBLi10ptVMp9a5S6m2l1Bt+xJkMXlqL2oee8XShtWb79u1MnTqVF154galTp7J9+/akdM7Pz2fIkCGOc0OGDEnJQlfpcpF0dHTQt29fx7m+fft2aaUFaYhhtq2w6QdVVVWMHj2ayspKLrnkEiorKxk9erTj2pubm1mzZo3DzbJmzRrHRJzq6mquv/56Ro0aRWVlJaNGjeL666/v8sazYMECJk2a1GXpAK/pBInm5mZuvvlmR126+eabMzpBqUJr/Xcf4/OEl9binj17WLVqlaPltHjxYm6++eZ0q+uZ0tJSVq5cySOPPEJ+fj6lpaXs3LnT8//b29v55JNPHOeij72SaLehHTt2cPDgwS73IBl9vXLLLbc47uMtt9zCfffdFwnPtg7WXCQcDrN582ZHp+aCBQsIh8OOctPQ0MD3vve9yL286qqrusTV3t7Ovn37IovC2Vf7BKf7xXLLRLsdZ8yY4SiLpaWl3bqudKwVA/D4449TX18fKb/dSsPL0JlEH2AncJJXeT+HO5aXl+trrrnGMTTwmmuucQwpKy8v19XV1Y4hRNZxMmn5oa8XGcxhibNmzdIvvPCCnjVrVtJDFS35UCjk+E4mDq2NYZdKKZ2Xl6cBnZeXp5VSjuGX+fn5+sEHH3TE8+CDD+r8/Pyk0rLSizXMs6SkRA8dOtQxRG7o0KG6pKQkIhPUIYZBGq7rRaYnceTn5+vJkyc79J08ebKjPBQXF+u8vDzHcNy8vDxdXFwckSkpKdFDhgxx3O8hQ4ZE7rdVngsKChzf9jLep0+fyDn7p0+fPkldk9fhmYniSSTTp08fnZ+f7xjCmZ+fH9EXj8MdlfbBJaGU+hD4wsy0R7XWj7nIzABmAAwePHj8unXrImEtLS0UFRXFTSOWjOV/O/HEE9m3bx8nnHACX3zxBWC0CAAeeughXnjhBWbMmMFFF13E5s2beeyxx7jyyisjK6d5ScsPfb1eU1FRkWOYlnVsXVOieKx8iZ6gBHiOA+Ciiy4CjI0orLyz1jLfvHlzROb444+nX79+kaGVhw4dYv/+/REZL2kluk+bNm1i2bJlFBQURNJpa2tj9uzZVFZWAlBZWcmGDRvo06dPJJ2Ojg4mTpzIpk2bPOvit0xP4ti0aRN1dXV8//vfZ9SoUXz44Yfcf//9TJs2LXLd6dR306ZNPPHEE+zatYtTTz2VG2+80aFHRUUFxx9/PP3794/cp4MHD7J///5I2fvWt77F/v37OXr0KB0dHfTp04e8vDyOP/54fvnLX0biGT16NO+//34kbuu4oaEh7prqVjpeZLxc9y233MJtt93G2WefHZF56623WLp0KY8//rjneBLJJKq3FRUVb2qtz4kbMfjWYh9mfp8CvANcEE/ezxY7oAcMGOB4kg4YMMDx1C4vL9ejRo1yPLFHjRoV+Ba7UsrxTTda7EOGDNGhUEgPGTKkWy12QN97770OmXvvvdcRT0lJiT7hhBMck0BOOOEER0vaS1peWv6JWq7Z2GLHpUVpJxPXFK/levLJJzvu9cknnxw9iUYXFBQ4ZKzWtIVSSp900kkOmZNOOqnL5CLivHUC+txzz3W84Z177rmOdNzytjv1IBQK6cOHDztkDh8+rEOhUFLxJJKx510oFOqSd3hssfvSeaq13mN+fwo8A3zFj3i9EgqFmDp1KhMnTmTq1KmOCQoA27dv58MPP2TWrFm88MILzJo1iw8//JDt27enU82k0ebblPXdHT755BM6Ozu77V/3SkFBAStXruSVV15h5cqV3VqPo729neLiYkfHZ3FxsWO1vkSdkdk2xNA+9G706NGu54M00Wn+/PmR9VwsHQ8fPsz8+fMdcm1tbUyaNInnn3+eSZMm0dbW5gg/7rjjmDhxIoWFhSilKCwsZOLEiY5t7SziDdn9wx/+4BgW/Ic//MFV76uuuopnnnnG1Y/vhXTOSRg4cCArV65kw4YNrFy50rHCqWe8WP94H6AQGGD7vQ34erz/+N1iLywsdDz5CwsLuzy1zz77bEdL7+yzz+729Pp0tNiVUnrw4MGO72h9E0w99tRSSdQCzsvL06FQyOELDYVCOi8vLyITCoX0mjVrHPGsWbOmS2smUVp9+vTRxcXFjrev4uJihz/Uy1ILXvzRifLFTipbwPa0LZlofYLUYsd8C4z2e0fXt9GjRzveOEePHp10SzqRTCgU0kopx1upUspR7iz5nrz9am2UKcvvbX369u3ru48d0Oedd56jjJ933nlJt9j9MOxfwnC/vANsB6oT/cdPw2516lmdJNa33fBYx9EdNUE37PZCFG3Y7a/E1iub/ZXYS8Xx0iHkpfPUS+e0l7SKi4tdHyJWh9rs2bN1KBRyPPBCoVDMdXQSGVNAn3LKKd1+PfciYzUyrE9hYWEXXaz6YMUxfvz4pO+T1lqPGDHCkdaIESOS1jdROKAXL17skFm8eHEXg+xWZuwy0eXbXs7t8cQrw17XivHSwEl03ePGjdNguH1DoVDE3Ttu3Lik4kkkY5VtLJYJAAAgAElEQVSXaPeTVW7SZti78/G7xe7lyW/576zCc9JJJwXasCe6Jrtf2zLsdr+2lzi8jCjSWutLL73UkXeXXnqpI9yLwfXS6gyFQnrWrFkOfWbNmhVpgSUaMZBs/tplUmHYrUpaWlqqf/7zn+vS0tIuxt2rLoneQiyjft555+mnnnoq0sqLZdx7YtgTLarlh9H2IuOlQeGXYQf0VVdd5ZC56qqrUlZmoj+91rDbWwexCkh0oc9mww7xX4m9xqGUcrztxHsziNVZVlJSovv37+8wuP3793d0nnpx1yQy/pa+iVxUXvL3tNNOc8icdtppvldSy6jbwy3jbpexPpa7wq1sur3BRad13nnnOdKyv8Ine02xwouLi13rm32Yoh9G24uMlzcZPw37Z5995pD57LPPUlJm4unr1bAHfkkBt3UdjOtLLo7hw4fz29/+lm3btkVWd4tetznb+NKXvsSkSZMiE4fGjx+fdCepVRCif1tYnWXW5rpwrLPM6rhsbm52DMs6cuQIR48edcyWGzZsGPPnz2ft2rWOqdL2ZR28TEXXWvO3v/0NIPLdHf7yl7/EPfaLX//6112OTzvttMix1jpSxu3D+uz3IRQKobWmoKCABx54gNtvv522trYuk3Gidx96+umnU7JsRlDWXqmqqmLbtm2OOjB9+vSUTUSbNm0azz33nOM4qAR6ETB7gbnppptcz1vYdwqKRmtjr8ORI0cSCoUYOXJkZA/EZPVRSlFRURH5nUm2bdtG//79Aejfvz/btm3rVjzx8q65uZn29nbH6njt7e1dpjh3dnZSVFSEUoqioiLXEQyJDEJVVRWXX365Y1XLyy+/PGUVVSnF9ddfn9L7ePHFF8c9hmMP1IaGBteHq2XUDx06RHl5OYcOHaKgoKCL3LXXXhv32Aunnnqqo4yfeuqpjvC9e/dSUFDgWHiroKAg6a3m/MC+vPXGjRtZv349L730UkqWkBg3bhzPP/88V199Nfv27ePqq6/m+eefZ9y4cb6n5QtemvV+f7y6YkjidS1er7cfvll72vPmzev2K53f1213S9hlkomjJ+lEy9i/7TKWK8aeRrQrJpHbx4u+Xu+B1zgSxRNPxouP3Us6gP7d737nkPnd737n0NkPH7uXOCz97X07bqPQ/Cx7sWS89Nv4WWasDlTrE6vjNFE88WQS6Usu+NitCyoqKtKPPPKILioq6lYBsQreiSeeqEOhkD7xxBOTrmD2OFPZ4eb1miB+J1a6KpdXGXslHLngRa1110rodQq5H5XUL5lEevjxAPF63T0dFWMZdXt4tJ8+SOXKy6QhP8tMOpZ18MuwB9oVY9HS0sKsWbO67ITildbWVkKhEF988QWdnZ188cUXhEIhWltbk4rnZz/7WdzjdHPZZZc5JnhcdtllGdUnHvaJQ/qo+8QhP1fis7sTUolVkUYueLGLa8Setn0CT090irdw3a5duxwunV27diUdv5ufPqikc9KQX+ukpw0v1t/vT3dcMf369evxkz/Rolqx/m8Ps+vrJpfompK97lg6WYso2cPsiyglky9+ydhdXW55Y7V4UO4tHkAvXLjQ0SpauHChvbWSVOvLejPozj1IVsYtLb/KTLLX3ZPWYja12NM5KqakpMR1glKyy2YkkkmkL7nkiklHIbITr5KmunJ5TWvt2rWukzOS8Udbx/FWgLSOvQwn7anBTbTiXyYMe6I04qUF6FWrVjnSWbVqVbcM+9NPP+2Qefrpp30ve1597EEx7Fondo/4VW+t//RkxVUvMn4Z9qxwxRQVFfHII48kXC0tEdYrcKZHs/hBVVUVjz76KKeffjqoEKeffjqPPvpot0aQeNk+zz4KIl54rGMv9O/fn6KiImpra7n88supra2lqKgoMvInE+gYbhavRLtOursHgB8jXhKxa9cuRowYwbZt27juuuvYtm0bI0aMcHXpDB48GKUUgwcP9l2PZEjnRiZXXHEFDz/8MEVFRTz88MNcccUVKUurp2SFYe+pj93CqpzdraTWfy0fZqaxCvXI+c+nvFAfOXLE8R1NZ2dnZOGvgoKCbu2xumfPHmprayNj5gsLC6mtrWXPnj3d1DoYKKVYv359jxsUSim2bNmS0oaJVz/9wYMHUUpx8ODBLmHW/evTp4/j2z4XIht5/fXXHYvLvf7665lWKSaBn6BkccYZZ/Dee+9lVIexY8d2OW5sbMyQNj2nuLjYdfxxcXFxUvHMnj2bZcuWRVbws75nz56dVDxlZWUsXbqUHTt2oLVmx44dLF26NCWdYelA62OTjxYvXuw439147rrrrrjx2I1+KhsfBw4ccHzbsdYZtwYndHR0UFhY2OOGWabZu3cvEydO5MiRI122aLSTrnsQj0C32O2ZYjfqmcqsaCOezUYd4PPPP+9ixIuLi/n8888jxyNGjHD9r/18bW0ts2fPjuynmp+fz+zZsx3boHkhFArxxhtvcOWVV/LMM89w5ZVX8sYbb0TcOmvXrnX9X6zzQcDyecaafJRsPJZbKFY8PXUdJSLWnrnR51taWhy6ZLtRHzduHEeOHHG8uR45csR1glKq74EXMm7YEw1L86ti+IVSiilTpuSEnx4M424viHajDsf8rnbc/K61tbW0tbUxcsGLtLW1JW3UwXhQVlZW8sEHH/DNb36TDz74gMrKysgDtKqqirVr11JeXg4qRHl5OWvXrpW9TNPI448/7rqhuNsuQrlErLXvM7EmvhcybtiD8HTzgl0/u1ELut5+YPldrfvUnfHRXtBa8+1vf9tx7tvf/rYjj9PZr9BbibdsRlVVFatXr3Y8XFevXp3z98HaMNvr+XhEL01iJ5b7Mlm3Zlb42IPgs7KnvWXLFi688MKM6ZHLzJ07l+eeey6yUNjVV1+daZV6TFDKr1e01pTe8RI777vcNbyqqoqqqipK73iJxhgyibD6ZdzO5zpWGXDLY+tNd8WKFY6FzZJ2a/qjamqJ16qP52tMBV4WAfMy6zGeTLqvKSgUFhZy4MABnnrqKdra2njqqac4cOBAYEdTnHnXK5Te8RKld7wEGBX1zLte6SIXtLfSdM3KjYdf/TL19fWOrRQDOxM0Cfxwa2aFYU+E144lv9LyowMrkUw6rykoHDp0iIsvvpjly5dz5ZVXsnz5ci6++GIOHTqUVDyWwYXYxtYP/nHoCDvvu5yd913Oqq8XsvO+y/nHIffhoEEiKA+anhqw+vp6qqurqa2tZcOGDdTW1lJdXZ0Txr2nZNQVc+Zdr0QqglURB/bryzs/ujSTavWI6Gtyux4vMrlIousuKyvjzjvvZOPGjRF3V0NDAx9//HFS6VgG14rDKlvJMm61MeJhQBmMW30HAO9OebdbcQUBe/5D9pe9mpoa6urqqKioiNzruro6wuFwYH3+6boHvrTYlVJfV0q9p5T6i1LqDq//syqg1drJZIvH62t1IqKvye16vMjkIomu275QmDUJJHqhsHRyoOk+3p3yLrUja3l3yrscaLovI3p4ZdzqcYxbPY4BZXdEftvJ1jeMWDQ1NTFhwgTHuQkTJnQZqXLRRRdRXl5OKGR09l500UXdTtM+Cc+NRG+L6boHPW6xK6XygJ8ClwDNwOtKqee11jt6Gnc6sTIc6HFLLxFGxTOff6uNFiEk1wnl15PfrzcMP95CrFZWOBymqamJsrIyampqHK2vXHzL84sDTff58qaSiKC8lVqrO1ZUVETOua3u2NDQwAMPPMCYMWPYsWMHt99+e7fTjJ6EF40fb4t+lHE/XDFfAf6itf5vAKXUOuBqwBfD7kchGjFiBH/961+7xB1r8o1XXaB7RsVLBUx0TX49iLwURL9kvGCNuIg18ig6HSBlBixIpMt95+Z+AqcLyq/y0NNr8rKVYklJCXv37uWOO+6IzBjt169f0rOr04kfZdwPwz4csFvNZuB/+RAv4E8h2rVrF30HnkzH/r9HzvU5/qSkx2N7yXA/WuNuafUG45WLeDGUXkhXeYhudEDqHpw9vSYvb3iLFy9mzpw5FBYW8tFHHzF8+HBaW1sdSzx4oby8nO3bt7ueTwaHfYAe2Yh4+GHY3cZMdeluV0rNAGaAsTLcli1buhhBMC5yyxbn8LYtW7bQ0tLCli1bIsd23IxpdBxfXjoEGNIlXtc44sQTrUt0PAea7mPV1wsj62Xc/KvWLvp6uaZEMl71dSM6rZ7q4pdM+KOwU9gsE7Ujj42W8KvMWETfy2SvyTqOJRNdHoCYZaKnuiSS8auMe6lvfugbD0vunsP3oL6vGMOYyPE9q++JlJmhQ4dy66238sQTT0SGd956660MHTo0dtlzKXdNTU2MXeVcKwpgx9QdSeWNVR6AuDYi0T1IiJe1feN9gK8CG2zHC4GF8f5jrcdurWFtX5c4el3rsavGdvlEEx2P29rYIxe86Pj80483uMYRLx4v+nrVpacyifQFXPMuOv+85K9fMl6uO/qaEsXhFo+XdPzQ168y42cZT4e+6SrjJLGWerz1z73kb6J4vOyv6uWavNyDePUWj+ux+9Fifx0YrZQaBewGvg3c4EO8gPcOocj5Xxm+umiubP2VYzbXldOnA7nb4VZcXEzjzV0XKSsuLoYpx4695K9fMkHCcoVY+gZdF79cfEEi0TW5tZABhq0dllQ6ftxrL/58i0S2KBF+uMN6bNi11h1KqdnABiAPWKm17uqMSiFWJ2KsadDhcJjly5ezaNEi/r15JN8t+YgFCxYAJDUpIpYbILqC9fTGuqWVbEX+/PPPGTRokGNZ3uiVG5PBj2sSuk/QHpx+lIdE1/TulHeZOHEiGzdujCxdfMkll7Bhw4aeqp80Xvz5kNgWWWERYuSfPX+BpPPYlwlKWuuXgZf9iCsVrFixgkWLFjF37lyW3vESc+fOBeDOO+90GPZEGW49+ePdNC831gt+tDIsI54ojkSV1Os1JYrHr1ZnokKfrtZtujrC/MSLUUmEl/Lg9R7YlzX4px93NdiWEQ/Cm1WiEVtesOdXrPzzw4ZkfBEwL0+mRDc/Ee3t7cycOfPY1niLoLW1lXnz5kVkvGS4n/jR4im94yU+WnRse67u5I1fDyIv8fjxsEpXOhbx7pN9ZEs6DI+XMhOvrngt4z1tLYK3e7DzvsvhPh0Io51rZNSwe6mkftz8/Px8li9fjtbH4lmyZEnMTQNSjR/GNPI/qRgpw6+Hnh+GMl11xa9rFjJLxlvsfmD3JatFXX3J06dPj/jUx4wZw5IlS1iwYAEzZ87MiL5Bw/4mo3vBYmPpRAylEI39rSpVdS7rDXt0ByEYexMOGjQoYtwtP/qdd94ZGRUzc+bMbi2HaTeCkBuG0P4mIwhCarFsRirrXMaX7VVK8dGiK7q9NrTbZsxu560lQhsaGrq9xjHg2zZ9Pb1uQRCEWGTcsNsNZU/57ne/64NGsYne0qonRtnP60411rVaDyJ5GAlCaulpwy/jht0vtNZcffXVKd05yeJf//VfXc/nIo7rG3e5+3lBECL48Tbe04Zfzhj2sWPH8sknnzB2rPtsNb/QWnPdddfFzPBwOExBQQEfLbqCgoICwuGwq1xW8m78STHpci9lmxsr2/T1Qi5ek18E4W08Zwz79u3bqaqqcl2BzS+WLl0a9zgcDrNs2TLa29sBY/z8smXLst6433bbbY7Cetttt7nKpatAB6HiJEO26evF7ZZt19TbyBnDng5uu+02h4892sBZu67369cPlDK+beezlUQPNCG38GuAgJA5st6wxyp4qSyQN910U8ywvLw8Y/NlrTl06BB5eXkp0yOdKKV46KGHet2rd5BcDn7oUl9fz9ixY/lo8VWMHTtWNn7OUbLesMOxpYd72sqIHv0RizVr1sQMKygoYPPmzWzcuJHNmzfH3BsxW7Dn5bPPPut6Ppfpqcth0KBBjre8QYMGZUyX+vp6qqurDXel7mT79u1UV1eLcc9BcsKw+8Xs2bMjywzk5+d38Y3v2bPHUbn27NnTJY7W1lbeeustOjo6eOutt2htbU2L7qnErwdnb8OaPFdeXk59fT3l5eWRyXOZoKamhrq6Ose9rKurc116Vshusn7mqV/Yl/a1Nr2NXtr32muv5bXXXov859prr3WN6/bbb48sMyr0Xiyj3tjYyJYtW2hsbGTs2LEp7eCPR1NTExMmTHCcmzBhAk1NTRnRR0gd0mI3sS/tW1BQwNy5c1m0aBErVqwAjI2vt23bRr9+/aioqKBfv35s27bNsSF2SUkJoVAo0qLVWhMKhSgpKcnINQmZ5+WXX457nE7KysrYunWr49zWrVspKyvLkEZCqhDDbmIt7Wtn5syZkaGLixYtIi8vj7a2NgDa2trIy8tj0aJFEfnFixczaNAgSktLUUpRWlrKoEGDkt44V8gdLrvssrjH6cTaBaihoYGOjo7ILkDV1dUZ00lIDWLYTaylfe0sX7484nOvqamJ7ORi+Sc3btzo8E9WVVXx0EMPUVhYiFKKwsJCHnrooS67rAi9g+LiYrZv3+6YPLd9+3Zje8IMUFVVRU1NDeFwmIkTJxIOh113ARKyH/GxmyRa2terf9KPXVaE3MDantCaPAc9257QD6R89g7EsJskWtrX8k9WVFRE/iP+SSERXrYnrK+vp6amJrKXZnV1tbSihR7RI8OulPoxMB34zDx1p7n/aVZSW1tLbW2tayVMZpdyQfCKNbbcKld5eXlMmzYNQIy70G38aLH/RGv9gA/xBBqvu5QLQjJYY8srKioiDYq6ujrC4bCULaHbiCsmCcQ/KfiNjC0XUoHq4S5APwZuBvYDbwDztNZfxJCdAcwAGDx48Ph169ZFwlpaWigqKoqbVpBkgqSLF5kg6eKXTJB08SITK/yWW27htttu4+yzz47IvPXWWyxdupTHH388cPoGVSZIuvgl4xZeUVHxptb6nLgRw7Hp4rE+wK+BRpfP1cBgIA9j2GQNsDJRfFprxo8fr+00NDToRARJJki6eJEJki5+yQRJFy8yscLXrl2rR40apTdv3qw3btyoN2/erEeNGqXXrl2bMl28yAQp77zIBEkXv2TcwoE3tAcbm9AVo7W+OOHTAVBKrQBe9CIrCIKB9N0IqaCno2KGaq0/Ng+/gdGSFwQhCaTvRvCbnnaeLlZKnQVoYCdwa481EgRBEHpEjwy71vo7fikiCIIg+IOsFSMIgpBjiGEXBEHIMcSwC4Ig5Bg9mqDU7USV+gz4yHbqJODvCf4WJJkg6eJFJki6+CUTJF28yARJFy8yQdLFi0yQdPFLxi18pNb65ATxJp6glI4PHgbdB0kmSLrkor65eE1B0kX0zQ4ZL3HE+ogrRhAEIccQwy4IgpBjBMWwP5ZlMkHSxYtMkHTxSyZIuniRCZIuXmSCpIsXmSDp4peMlzhcyUjnqSAIgpA6gtJiFwRBEPyiu72ufn2ArwPvAX8B7nAJXwl8CjTGiWME0AA0AduBOVHhBcAfgHfM8LvixJUHvAW8GCN8J/Au8DYxeq2BE4Cngf8ydfpqVPgZ5v+tz37gu1Ey3zN1bQTqgQKXdOaY4dut/7vlF1AMbATeN7+fcJG5zoynEzgnRjz3m9f0J+CZGPH8mxn+NvCKqbvr/QNux1hn6LOoOH4M7LblzytucQBhs+xsN9OM1uUXtjh2Ap+7yJwF/M66n8DzLjJnAr817/tG4NXoshaVx68C/+kiY8/jy3Eps7Y8bjLz5b2ocHv+/gZ4LToOl/zd6pKOPY+3Y9SNLvHY8vg9YJdLPL+wxXEIaIsKt+fvO2YeR8dhz9+XTBlHXQVGAb838/cp4HUXmdkYdkQDw3Gp88CT5rU0AqtjxFNnnnsX2Gt+d7EbQC3QEiOdVcCHtutudJFRGMud/9m85x+5yPwnx8rwHuBZT3Y1w0Y9D/gA+BJwnHlRY6JkLgD+B/EN+1Dgf5i/B5gZNcYWroAi83dfs4CcGyOuucBa4hv2kxJc12rgf5u/jwNOSJAHn2CMT7XODTcLRT/z+JfAzVH/G2sWlv4Ya/78Ghjtll/AYsyHJnCHWbijZcowHjhbMAy7WzyXAn3M34tixHO87fdtwHNu9w/jYbzBvPaL6GrYb49XBoAK85rzzeOr4pUT4EGMChsdzyvAJPP3ZRgP9WiZ14Gvmb+/B6yILmtReVwDrHKRsefxRFzKrJXHGGV6lZnP9nB7/v4AeNqt3Nvytxm4yCWdSB4To/7Y89iUqYxTx4YCPwd+GBWHPX8nYzaGomTs+TsVuC+6rmLUgW+b55dzrCFjlzkbKMWso7jUefMeK/NTHyOe4212oxajzjjsBkYd+TmGYXdLZxVwbTz7A9wCrMHwnCigNJ6NAv4DuMmLbc20K+YrwF+01v+ttT4MrMPYwCOC1vpVjKdmTLTWH2ut/2j+PoDRIhhuC9da6xbzsK/56dK5oJQqwWhJ/ay7F6SUOh7DENWZaR/WWu+L85dK4AOt9UdR5/sA/ZRSfTCM956o8DLgd1rrg1rrDozW2zdi5NfVGA8bzO9zomW01k1a6/dsx13i0Vq/YqYFRisMF5n9tsNCU2+3+/cTYD5GC8911614ugCzMAxAuynzfIx0UEop4FvAfS4yGjje/D0Qo+xEy5yB0QoH403sfDNNe1mz5/Ey4KvRMlF5/LlbmbXyWBvLYT8HlESF2/P3KOZG8i7l3srfDowWvmvdMM/Hqj+RPDZlNsWJ5xPgQqA+Ktyev50Yxjw6Dnv+bsR4SIOzrl5k5j1mPk+KltFav6W13mm7ri51Xmv9smkPNEZL+2QXGXse9zHTj4QrpfIw3qzmx0onKn9j2Z9ZwP/VWneaMjtjxaOUGmDmwbN4INOGfTjwV9txM1GFLlmUUqUYT+7fR53PU0q9jfGavVFr/fuu/+bfMW5WZ5wkNPCKUupNc7u/aL6EUdkeV0q9pZT6mVKqME5838ZoORxLQOvdwAMYr74fA//QWr8S9b9G4AKl1CClVH+MlsiIGGkMNg0F5vcpcfTxylRgvVuAUqpGKfVXjBbaD13CrwJ2a63fiRP/bKXUn5RSK5VSJ7qEnw78s1Lq90qp3yil/mecuP4Z+JvW+n2XsO8C95v6PgAsdJFp5JixuQ4zn6PKmmsexyqPduLITAXWR4e75a9dJlb+uqTTJY+jZFzzOIa+kTyOCnfN3yiZLvlrr6sYb/X7bI2KZmB4ovocr84rpfoC38Goy11klFKPYzysvgzcEBU+G3jeut9x0qkx8/cnSql+LjL/H3C9UuoNpdR6pdQZca7pG8CmqIdObLw061P1MW/iz2zH3wFqXeRKieOKsckVAW8C/xJH5gQM3+bYqPNXAA+bvy8ktitmmPl9Cobr6IKo8HMwWkn/yzx+CPi3GHEdhzFleHDU+ROBzRitib4YT+kbXf4/DfgjRmtnOfATt/zCqBT2/30RK08xXTHx8h2oxvCxq3j3BqMS32WXwXj7+D0w0DzeiVHB7fp22XLR5ZoagaWmDl/BcF3F0vcRjP143fJmKfBN8/e3MFwP0TJfxnApvAn8CMNX7yhrMfLYtTxG5XEsGSuPY5ZpW/5GZGLk70ku+rrlcbSMWx7H0vcRYJ5LHG75Gy3TJX+j6uo/Y7zZW2mNAN6NVZ+JcpfGkFkB/HsCmTzgYQyXiRV+AUafheWSbHGLA8M1pTDcWKuBH7rItHCsXP4L8J9xdFlv5aOXT6YN+1eBDVEFdaGLXCkJDDuGAdwAzPWQ7o+w+XDNc/ditAR2YjypDwJPJIjnxy7xDAF22o7/GXgpxv+vBl5xOX8dUGc7vgnzoRNHl3uAf3XLL4zOoqHm76HmsWueksCwA1MwOrr6J7o3wEgM4xCRAcZhtEh2mp8OjE68/4oRR2l0HOb5XwEX2o4/wN2X3wf4G4ZLwy1v/sGxYb8KoyM73jWdjuETdpS1GHnsWh451o/hWmZteXx8rDii8jciEyN/d2EYiljxlEbHEyePN7voa+VxqUscbvkb75pOB/4QVVe/j9EAsoxptN1w1Gdc+sHsMubvZ4GQB7vwNcxGnhn+Iwz7YOVvJ86HjlscF2JrKFoyGB2mpba8+UcMfQdhNCa6DKCI9cm0K+Z1YLRSapRS6jgMt8TzyUZi+lDrgCat9RKX8JOVUieYv/sBF2NkagSt9UKtdYnWutTUY7PW+saoeApNXxeme+VSorYD1Fp/AvxVKXWGeaoS2BFD9Sqi3DAmu4BzlVL9zWurxPBHRl+X9bp/KsYT3y0uMPJ0ivl7CobvNmmUUl8HFgBXaa0PxpAZbTu8iq75/K7W+hStdamZ180Yb0sdtjiG2v4Sa8vFZzF8jiilTsd4+3HzsV+M8dBojnFZezAqL2Z8Xdw1tnwOYXRYHqZrWYvO4yMuMtF0KbP2PMbw1UeHR+fvALtMjPz9LfBOVDzReVzoom90Hg8B/uRyTVZ9+r8ucUTnb5d8icrff8Psq7DV1SaMB9O15l9uxWjhx6zPwCC3Oq+U+t8YHddVMWTeU0qdZp472Uzzv2zhb2qth9jy9yDGQzo6naHmOWWm9b6LvpH8xbiXH8S4puswHgxteMXrEyBVHwzf8J/Ni6p2Ca/H8DMfwSik01xkJmD4vq1hYG8Dl9nC/wljtMOfMIzEDxPodCEurhgM//k7HBuS1EVfU+4sjCFbfzJv3okuMv0xnsIDY8Rxl3ljGzF63/NdZP4T46HxDsdGLHTJL4wn/iazcG3C6F2PlvmG+bsdo/X1sYvMXzD6RKw8ft9F5j9Mnf8EvGBef8z7h/E6+klUHD/HGGL2JwyD+YxLOsdhDLdsxHBHbXJLB2N0wsw4eTMBwwXwDoYL41cuMnMwyuifzTS7lLWoPH4jhow9j/fGkLHy+H0z/POocHv+vuYWR1T+fhwjHXseb40hY8/j92KlZebx/THisOfv9hgy9vytw6WuYtS9P5j584r532iZ28z87cB4a/m7i0wHhq15G6N+fWyXwXBNvWbmzfvmfYk60sEAAABtSURBVGokht3AMOxu+m4242gEXjSvP1rmBIzhne9ybLhpFxuF8Yb39WTsqsw8FQRByDEy7YoRBEEQfEYMuyAIQo4hhl0QBCHHEMMuCIKQY4hhFwRByDHEsAuCIOQYYtgFQRByDDHsgiAIOcb/D3awczFVywgbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Mengatasi outlier dengan metode rescale Standardization\n",
"scaler = preprocessing.StandardScaler() \n",
"standardized = scaler.fit_transform(df) \n",
"standardized = pd.DataFrame(standardized)\n",
"standardized.boxplot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. PCA (Feature Extraction)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"#Dengan menggunakan preprocessing, mengatasi outlier dengan menggunakan metode MinMax\n",
"#Selanjutnya dilakukan Feature Extraction\n",
"from sklearn.preprocessing import StandardScaler \n",
"from sklearn.decomposition import PCA \n",
"from sklearn import datasets "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"X = StandardScaler().fit_transform(x_scale)\n",
"pca = PCA(n_components=0.80, whiten=True)\n",
"X_pca = pca.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Original number of features:', 38L)\n",
"('Reduced number of features:', 18L)\n"
]
}
],
"source": [
"print('Original number of features:', X.shape[1]) \n",
"print('Reduced number of features:', X_pca.shape[1]) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PCA alternatif"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"#ditentukan variabel PC sebanyak 18 dari 38\n",
"import numpy as np \n",
"from sklearn import decomposition, datasets \n",
"from sklearn.preprocessing import StandardScaler "
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"X=x_scale"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1460, 38)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"sc = StandardScaler() \n",
"X_std = sc.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"pca = decomposition.PCA(n_components=18) \n",
"X_std_pca = pca.fit_transform(X_std) "
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
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" <th>12</th>\n",
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" <th>16</th>\n",
" <th>17</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.503182</td>\n",
" <td>0.359157</td>\n",
" <td>-1.705880</td>\n",
" <td>-1.919996</td>\n",
" <td>0.475491</td>\n",
" <td>1.317957</td>\n",
" <td>-0.537867</td>\n",
" <td>-0.526460</td>\n",
" <td>-0.317798</td>\n",
" <td>-0.441389</td>\n",
" <td>-1.204446</td>\n",
" <td>0.880331</td>\n",
" <td>0.308543</td>\n",
" <td>0.291034</td>\n",
" <td>0.230563</td>\n",
" <td>-1.147820</td>\n",
" <td>-1.301392</td>\n",
" <td>0.035631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.004068</td>\n",
" <td>-1.095185</td>\n",
" <td>1.253567</td>\n",
" <td>-0.035725</td>\n",
" <td>-1.804688</td>\n",
" <td>-0.074807</td>\n",
" <td>3.755466</td>\n",
" <td>-0.303774</td>\n",
" <td>-0.128299</td>\n",
" <td>0.067504</td>\n",
" <td>-0.937441</td>\n",
" <td>-1.078768</td>\n",
" <td>1.793235</td>\n",
" <td>-0.030494</td>\n",
" <td>-0.798680</td>\n",
" <td>-1.177382</td>\n",
" <td>-0.806857</td>\n",
" <td>0.460397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.741556</td>\n",
" <td>0.234216</td>\n",
" <td>-1.481588</td>\n",
" <td>-1.367202</td>\n",
" <td>-0.186890</td>\n",
" <td>-0.218170</td>\n",
" <td>-0.141747</td>\n",
" <td>-0.459596</td>\n",
" <td>-0.323457</td>\n",
" <td>-0.534684</td>\n",
" <td>-0.975946</td>\n",
" <td>0.735206</td>\n",
" <td>-0.928036</td>\n",
" <td>0.614004</td>\n",
" <td>0.310852</td>\n",
" <td>-0.313629</td>\n",
" <td>-0.538729</td>\n",
" <td>0.860570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.499807</td>\n",
" <td>1.007560</td>\n",
" <td>0.767973</td>\n",
" <td>-0.167499</td>\n",
" <td>0.362109</td>\n",
" <td>0.478369</td>\n",
" <td>-1.429484</td>\n",
" <td>0.422885</td>\n",
" <td>-0.496519</td>\n",
" <td>-2.589422</td>\n",
" <td>-1.108853</td>\n",
" <td>-1.931205</td>\n",
" <td>-0.214752</td>\n",
" <td>1.394482</td>\n",
" <td>2.849118</td>\n",
" <td>-1.341282</td>\n",
" <td>-0.425774</td>\n",
" <td>0.051643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.459227</td>\n",
" <td>1.131053</td>\n",
" <td>-0.546911</td>\n",
" <td>-1.341411</td>\n",
" <td>-0.075457</td>\n",
" <td>-0.584217</td>\n",
" <td>0.486127</td>\n",
" <td>-0.698585</td>\n",
" <td>-0.925684</td>\n",
" <td>-0.401942</td>\n",
" <td>-0.779665</td>\n",
" <td>0.379467</td>\n",
" <td>-1.561269</td>\n",
" <td>0.599122</td>\n",
" <td>0.015822</td>\n",
" <td>-0.778584</td>\n",
" <td>-0.147841</td>\n",
" <td>1.384087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.786591</td>\n",
" <td>-1.970631</td>\n",
" <td>-1.124299</td>\n",
" <td>-1.301091</td>\n",
" <td>-0.994057</td>\n",
" <td>1.027527</td>\n",
" <td>3.139387</td>\n",
" <td>-1.989922</td>\n",
" <td>-1.968388</td>\n",
" <td>-3.757039</td>\n",
" <td>-0.955523</td>\n",
" <td>7.077242</td>\n",
" <td>-0.613106</td>\n",
" <td>-1.437053</td>\n",
" <td>4.856910</td>\n",
" <td>3.624928</td>\n",
" <td>2.031556</td>\n",
" <td>-0.406133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3.528163</td>\n",
" <td>-2.531563</td>\n",
" <td>0.407661</td>\n",
" <td>-0.222759</td>\n",
" <td>0.532914</td>\n",
" <td>-0.280499</td>\n",
" <td>0.974861</td>\n",
" <td>0.464557</td>\n",
" <td>-0.023710</td>\n",
" <td>-0.717568</td>\n",
" <td>-1.052608</td>\n",
" <td>0.081674</td>\n",
" <td>-0.754164</td>\n",
" <td>0.656083</td>\n",
" <td>-0.844053</td>\n",
" <td>-0.320223</td>\n",
" <td>-0.443510</td>\n",
" <td>0.609285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2.000422</td>\n",
" <td>1.239459</td>\n",
" <td>1.399500</td>\n",
" <td>-2.181185</td>\n",
" <td>-0.680914</td>\n",
" <td>-0.100504</td>\n",
" <td>-0.280305</td>\n",
" <td>0.385502</td>\n",
" <td>0.183573</td>\n",
" <td>-2.040443</td>\n",
" <td>-0.580881</td>\n",
" <td>-0.866647</td>\n",
" <td>-2.194117</td>\n",
" <td>1.412064</td>\n",
" <td>0.940643</td>\n",
" <td>0.618925</td>\n",
" <td>-1.330184</td>\n",
" <td>-0.161133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.059275</td>\n",
" <td>2.795188</td>\n",
" <td>1.605976</td>\n",
" <td>1.307783</td>\n",
" <td>2.405916</td>\n",
" <td>0.212986</td>\n",
" <td>0.107542</td>\n",
" <td>-0.978887</td>\n",
" <td>1.497793</td>\n",
" <td>-1.875999</td>\n",
" <td>-0.755034</td>\n",
" <td>-2.090913</td>\n",
" <td>-0.352441</td>\n",
" <td>1.314870</td>\n",
" <td>1.904752</td>\n",
" <td>0.676163</td>\n",
" <td>-0.373922</td>\n",
" <td>0.886032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-3.276945</td>\n",
" <td>-0.178214</td>\n",
" <td>1.366788</td>\n",
" <td>-1.931274</td>\n",
" <td>3.758997</td>\n",
" <td>-0.250965</td>\n",
" <td>0.716368</td>\n",
" <td>-1.067081</td>\n",
" <td>2.871377</td>\n",
" <td>-1.375700</td>\n",
" <td>-1.066569</td>\n",
" <td>-0.443518</td>\n",
" <td>0.628932</td>\n",
" <td>0.548260</td>\n",
" <td>0.074370</td>\n",
" <td>1.081301</td>\n",
" <td>-0.811329</td>\n",
" <td>0.179848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-2.459195</td>\n",
" <td>-1.816867</td>\n",
" <td>0.932250</td>\n",
" <td>-0.427412</td>\n",
" <td>0.858883</td>\n",
" <td>0.963445</td>\n",
" <td>-0.473817</td>\n",
" <td>-0.780955</td>\n",
" <td>-0.445886</td>\n",
" <td>-0.355734</td>\n",
" <td>-1.335635</td>\n",
" <td>0.845054</td>\n",
" <td>0.380383</td>\n",
" <td>0.380002</td>\n",
" <td>-0.551042</td>\n",
" <td>-0.752242</td>\n",
" <td>-0.939405</td>\n",
" <td>0.122039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>5.704507</td>\n",
" <td>1.135514</td>\n",
" <td>-0.004093</td>\n",
" <td>-1.397921</td>\n",
" <td>0.571876</td>\n",
" <td>-0.405478</td>\n",
" <td>0.833480</td>\n",
" <td>-0.152402</td>\n",
" <td>-0.508701</td>\n",
" <td>-0.913452</td>\n",
" <td>-0.985819</td>\n",
" <td>-0.113840</td>\n",
" <td>-0.672564</td>\n",
" <td>0.779939</td>\n",
" <td>0.038843</td>\n",
" <td>-0.828510</td>\n",
" <td>0.152292</td>\n",
" <td>0.966004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>-2.799006</td>\n",
" <td>-2.146194</td>\n",
" <td>1.105268</td>\n",
" <td>-0.757819</td>\n",
" <td>-0.707265</td>\n",
" <td>-0.963245</td>\n",
" <td>-0.293222</td>\n",
" <td>-0.777226</td>\n",
" <td>1.130137</td>\n",
" <td>0.009754</td>\n",
" <td>-0.738560</td>\n",
" <td>1.217745</td>\n",
" <td>-0.602433</td>\n",
" <td>0.395287</td>\n",
" <td>-0.675727</td>\n",
" <td>-0.380627</td>\n",
" <td>0.109555</td>\n",
" <td>2.083170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>3.352163</td>\n",
" <td>-0.898496</td>\n",
" <td>-1.077251</td>\n",
" <td>3.050998</td>\n",
" <td>-0.476910</td>\n",
" <td>-0.204435</td>\n",
" <td>0.497129</td>\n",
" <td>-0.437053</td>\n",
" <td>-0.177699</td>\n",
" <td>-0.139091</td>\n",
" <td>-0.996991</td>\n",
" <td>-0.308944</td>\n",
" <td>-0.199488</td>\n",
" <td>1.038094</td>\n",
" <td>0.213564</td>\n",
" <td>-0.548759</td>\n",
" <td>0.109730</td>\n",
" <td>1.326635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>-1.017676</td>\n",
" <td>-1.109052</td>\n",
" <td>1.490766</td>\n",
" <td>-0.384485</td>\n",
" <td>-0.381484</td>\n",
" <td>-0.103378</td>\n",
" <td>-1.882487</td>\n",
" <td>-0.138264</td>\n",
" <td>0.173773</td>\n",
" <td>-1.968286</td>\n",
" <td>-1.463684</td>\n",
" <td>-0.097749</td>\n",
" <td>-0.779177</td>\n",
" <td>0.915312</td>\n",
" <td>0.526796</td>\n",
" <td>0.322514</td>\n",
" <td>-2.417918</td>\n",
" <td>-1.455287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>-1.892361</td>\n",
" <td>-0.720888</td>\n",
" <td>-1.383668</td>\n",
" <td>1.122634</td>\n",
" <td>-1.878552</td>\n",
" <td>0.123047</td>\n",
" <td>0.789392</td>\n",
" <td>1.023734</td>\n",
" <td>0.896964</td>\n",
" <td>-1.055260</td>\n",
" <td>-0.066518</td>\n",
" <td>0.064071</td>\n",
" <td>-0.769165</td>\n",
" <td>0.471503</td>\n",
" <td>0.172023</td>\n",
" <td>-0.990195</td>\n",
" <td>-1.089041</td>\n",
" <td>1.081449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-1.477463</td>\n",
" <td>-2.091270</td>\n",
" <td>0.666852</td>\n",
" <td>-0.313115</td>\n",
" <td>-0.465295</td>\n",
" <td>1.901271</td>\n",
" <td>-0.071380</td>\n",
" <td>-1.255929</td>\n",
" <td>1.021417</td>\n",
" <td>-0.947182</td>\n",
" <td>-0.095710</td>\n",
" <td>0.511001</td>\n",
" <td>0.258657</td>\n",
" <td>1.288707</td>\n",
" <td>-0.065382</td>\n",
" <td>-0.633109</td>\n",
" <td>-0.199101</td>\n",
" <td>0.584387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>-2.573991</td>\n",
" <td>0.918525</td>\n",
" <td>-0.538650</td>\n",
" <td>0.477834</td>\n",
" <td>3.277484</td>\n",
" <td>-1.826680</td>\n",
" <td>1.925275</td>\n",
" <td>-0.647005</td>\n",
" <td>0.059476</td>\n",
" <td>0.145613</td>\n",
" <td>-0.086090</td>\n",
" <td>1.147012</td>\n",
" <td>-0.416234</td>\n",
" <td>1.489527</td>\n",
" <td>1.245834</td>\n",
" <td>-1.240691</td>\n",
" <td>-0.315807</td>\n",
" <td>1.881455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.010679</td>\n",
" <td>-1.643341</td>\n",
" <td>-1.047806</td>\n",
" <td>-0.389839</td>\n",
" <td>-0.002569</td>\n",
" <td>0.428755</td>\n",
" <td>-0.475959</td>\n",
" <td>-0.211741</td>\n",
" <td>-0.717388</td>\n",
" <td>0.170309</td>\n",
" <td>-0.929869</td>\n",
" <td>1.464473</td>\n",
" <td>-0.542331</td>\n",
" <td>0.381446</td>\n",
" <td>-0.025640</td>\n",
" <td>-1.106772</td>\n",
" <td>-1.456120</td>\n",
" <td>0.196094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-2.419617</td>\n",
" <td>-0.472873</td>\n",
" <td>0.917799</td>\n",
" <td>1.097236</td>\n",
" <td>0.021196</td>\n",
" <td>0.983745</td>\n",
" <td>0.147471</td>\n",
" <td>-0.765654</td>\n",
" <td>0.232333</td>\n",
" <td>-0.315098</td>\n",
" <td>-0.887910</td>\n",
" <td>0.573837</td>\n",
" <td>0.089871</td>\n",
" <td>0.127905</td>\n",
" <td>-0.767340</td>\n",
" <td>-0.266031</td>\n",
" <td>-0.855627</td>\n",
" <td>0.660061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5.703911</td>\n",
" <td>2.737650</td>\n",
" <td>-1.498666</td>\n",
" <td>0.713163</td>\n",
" <td>-0.910461</td>\n",
" <td>-1.104017</td>\n",
" <td>0.599868</td>\n",
" <td>-0.057157</td>\n",
" <td>-1.087537</td>\n",
" <td>0.003406</td>\n",
" <td>-1.108716</td>\n",
" <td>0.240207</td>\n",
" <td>-1.222229</td>\n",
" <td>1.120402</td>\n",
" <td>0.140227</td>\n",
" <td>-0.402935</td>\n",
" <td>-0.614504</td>\n",
" <td>0.939910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-3.324897</td>\n",
" <td>1.049756</td>\n",
" <td>1.785823</td>\n",
" <td>1.156121</td>\n",
" <td>-0.979771</td>\n",
" <td>0.051787</td>\n",
" <td>-0.217761</td>\n",
" <td>0.082360</td>\n",
" <td>0.441664</td>\n",
" <td>-2.258385</td>\n",
" <td>-0.748220</td>\n",
" <td>-1.604702</td>\n",
" <td>-0.591046</td>\n",
" <td>1.091896</td>\n",
" <td>0.946252</td>\n",
" <td>0.187213</td>\n",
" <td>-0.516535</td>\n",
" <td>0.467702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>3.227921</td>\n",
" <td>-0.560121</td>\n",
" <td>-0.738624</td>\n",
" <td>3.437732</td>\n",
" <td>-0.541576</td>\n",
" <td>-0.309842</td>\n",
" <td>0.389708</td>\n",
" <td>0.375498</td>\n",
" <td>1.011672</td>\n",
" <td>-0.117884</td>\n",
" <td>-1.187719</td>\n",
" <td>0.018578</td>\n",
" <td>-0.822816</td>\n",
" <td>0.692142</td>\n",
" <td>-0.736859</td>\n",
" <td>0.835979</td>\n",
" <td>-1.213373</td>\n",
" <td>0.135398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-1.201098</td>\n",
" <td>-1.260975</td>\n",
" <td>-0.121845</td>\n",
" <td>-1.466207</td>\n",
" <td>0.265991</td>\n",
" <td>-0.596780</td>\n",
" <td>0.577933</td>\n",
" <td>0.488267</td>\n",
" <td>0.898823</td>\n",
" <td>-1.000035</td>\n",
" <td>-0.614883</td>\n",
" <td>0.031970</td>\n",
" <td>-0.570530</td>\n",
" <td>0.332224</td>\n",
" <td>-0.204037</td>\n",
" <td>-0.469032</td>\n",
" <td>-0.840719</td>\n",
" <td>0.388195</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-1.521012</td>\n",
" <td>-1.572833</td>\n",
" <td>1.515435</td>\n",
" <td>-1.166621</td>\n",
" <td>-1.507581</td>\n",
" <td>2.600164</td>\n",
" <td>1.492121</td>\n",
" <td>0.952940</td>\n",
" <td>1.260977</td>\n",
" <td>2.611519</td>\n",
" <td>-1.132831</td>\n",
" <td>-0.415694</td>\n",
" <td>-1.975916</td>\n",
" <td>0.013529</td>\n",
" <td>0.441358</td>\n",
" <td>0.798471</td>\n",
" <td>-0.971779</td>\n",
" <td>0.766564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>4.102381</td>\n",
" <td>-0.958883</td>\n",
" <td>-0.781216</td>\n",
" <td>3.351526</td>\n",
" <td>-0.386713</td>\n",
" <td>0.591560</td>\n",
" <td>-0.170959</td>\n",
" <td>-1.525983</td>\n",
" <td>0.324121</td>\n",
" <td>-0.583413</td>\n",
" <td>-0.872611</td>\n",
" <td>-0.020587</td>\n",
" <td>0.264699</td>\n",
" <td>0.551025</td>\n",
" <td>0.358167</td>\n",
" <td>-0.811786</td>\n",
" <td>-0.468778</td>\n",
" <td>1.070529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-1.863100</td>\n",
" <td>-1.418293</td>\n",
" <td>-0.102280</td>\n",
" <td>-0.234817</td>\n",
" <td>-2.038085</td>\n",
" <td>1.463289</td>\n",
" <td>3.233063</td>\n",
" <td>-0.245909</td>\n",
" <td>0.478501</td>\n",
" <td>2.499786</td>\n",
" <td>-1.123644</td>\n",
" <td>-0.913555</td>\n",
" <td>1.147065</td>\n",
" <td>-0.832825</td>\n",
" <td>1.497534</td>\n",
" <td>-1.683324</td>\n",
" <td>-1.226115</td>\n",
" <td>0.596320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>4.015499</td>\n",
" <td>-2.596841</td>\n",
" <td>0.312083</td>\n",
" <td>0.441350</td>\n",
" <td>0.699430</td>\n",
" <td>1.479999</td>\n",
" <td>-0.113488</td>\n",
" <td>-0.745799</td>\n",
" <td>0.408070</td>\n",
" <td>-0.777829</td>\n",
" <td>-0.466056</td>\n",
" <td>0.621474</td>\n",
" <td>-0.111809</td>\n",
" <td>-0.150597</td>\n",
" <td>0.157849</td>\n",
" <td>-1.221192</td>\n",
" <td>-0.877048</td>\n",
" <td>0.852310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.313504</td>\n",
" <td>-2.196533</td>\n",
" <td>2.085310</td>\n",
" <td>-1.044297</td>\n",
" <td>-0.896465</td>\n",
" <td>-2.168432</td>\n",
" <td>1.003545</td>\n",
" <td>1.572046</td>\n",
" <td>0.651263</td>\n",
" <td>-0.960301</td>\n",
" <td>-1.025751</td>\n",
" <td>0.451000</td>\n",
" <td>-2.477946</td>\n",
" <td>0.971359</td>\n",
" <td>-1.770576</td>\n",
" <td>1.223475</td>\n",
" <td>-1.611787</td>\n",
" <td>-0.623305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-5.736341</td>\n",
" <td>-0.560150</td>\n",
" <td>0.383406</td>\n",
" <td>0.754520</td>\n",
" <td>-0.724964</td>\n",
" <td>0.399839</td>\n",
" <td>-0.427290</td>\n",
" <td>-0.634554</td>\n",
" <td>-0.013699</td>\n",
" <td>-1.159241</td>\n",
" <td>-1.087908</td>\n",
" <td>-0.555923</td>\n",
" <td>-0.082980</td>\n",
" <td>1.050353</td>\n",
" <td>0.418936</td>\n",
" <td>0.091134</td>\n",
" <td>-0.421781</td>\n",
" <td>0.783819</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",
" <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>1430</th>\n",
" <td>0.897978</td>\n",
" <td>2.357886</td>\n",
" <td>-1.771060</td>\n",
" <td>-0.397046</td>\n",
" <td>-0.486595</td>\n",
" <td>-0.645040</td>\n",
" <td>-0.234131</td>\n",
" <td>0.352486</td>\n",
" <td>-1.708404</td>\n",
" <td>1.382850</td>\n",
" <td>0.597177</td>\n",
" <td>0.208528</td>\n",
" <td>-0.089789</td>\n",
" <td>0.213837</td>\n",
" <td>-0.375268</td>\n",
" <td>0.589969</td>\n",
" <td>0.785317</td>\n",
" <td>-0.777412</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1431</th>\n",
" <td>-1.466675</td>\n",
" <td>-1.150552</td>\n",
" <td>-1.081461</td>\n",
" <td>-0.645300</td>\n",
" <td>0.716990</td>\n",
" <td>-0.783282</td>\n",
" <td>0.206198</td>\n",
" <td>0.579996</td>\n",
" <td>0.081595</td>\n",
" <td>-0.207443</td>\n",
" <td>1.757779</td>\n",
" <td>0.161561</td>\n",
" <td>-0.093494</td>\n",
" <td>-1.123604</td>\n",
" <td>0.049939</td>\n",
" <td>-0.159202</td>\n",
" <td>0.299156</td>\n",
" <td>0.139409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1432</th>\n",
" <td>-3.449154</td>\n",
" <td>0.987544</td>\n",
" <td>0.245268</td>\n",
" <td>1.422742</td>\n",
" <td>-0.217484</td>\n",
" <td>-0.348845</td>\n",
" <td>0.521423</td>\n",
" <td>0.572712</td>\n",
" <td>-0.677705</td>\n",
" <td>0.620086</td>\n",
" <td>1.533987</td>\n",
" <td>0.195243</td>\n",
" <td>-0.258604</td>\n",
" <td>-0.730471</td>\n",
" <td>-0.934872</td>\n",
" <td>0.173672</td>\n",
" <td>0.231408</td>\n",
" <td>-0.084794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1433</th>\n",
" <td>1.631579</td>\n",
" <td>1.108444</td>\n",
" <td>-1.174866</td>\n",
" <td>-0.264267</td>\n",
" <td>0.242229</td>\n",
" <td>0.522892</td>\n",
" <td>-1.182379</td>\n",
" <td>-0.615730</td>\n",
" <td>-0.909748</td>\n",
" <td>0.452160</td>\n",
" <td>0.814068</td>\n",
" <td>0.132971</td>\n",
" <td>0.433669</td>\n",
" <td>-0.415922</td>\n",
" <td>-0.146054</td>\n",
" <td>0.236447</td>\n",
" <td>0.670894</td>\n",
" <td>-0.600114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1434</th>\n",
" <td>-0.102698</td>\n",
" <td>-1.889735</td>\n",
" <td>0.865516</td>\n",
" <td>-0.496762</td>\n",
" <td>0.653976</td>\n",
" <td>-0.524633</td>\n",
" <td>-0.026508</td>\n",
" <td>0.506407</td>\n",
" <td>-1.681862</td>\n",
" <td>0.799405</td>\n",
" <td>0.664814</td>\n",
" <td>-0.232384</td>\n",
" <td>-0.002174</td>\n",
" <td>0.101354</td>\n",
" <td>-0.970263</td>\n",
" <td>0.193395</td>\n",
" <td>1.008912</td>\n",
" <td>-0.678688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1435</th>\n",
" <td>0.392493</td>\n",
" <td>-0.082636</td>\n",
" <td>0.868233</td>\n",
" <td>1.018353</td>\n",
" <td>-1.732616</td>\n",
" <td>0.621552</td>\n",
" <td>-0.160167</td>\n",
" <td>0.202897</td>\n",
" <td>0.485389</td>\n",
" <td>-0.524610</td>\n",
" <td>2.258618</td>\n",
" <td>-0.328934</td>\n",
" <td>-0.484403</td>\n",
" <td>-0.901997</td>\n",
" <td>-0.890389</td>\n",
" <td>-0.003153</td>\n",
" <td>0.234040</td>\n",
" <td>-0.207977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1436</th>\n",
" <td>-2.631470</td>\n",
" <td>-1.048400</td>\n",
" <td>-0.195912</td>\n",
" <td>0.431408</td>\n",
" <td>0.026410</td>\n",
" <td>-0.269865</td>\n",
" <td>-0.451781</td>\n",
" <td>-0.195492</td>\n",
" <td>-1.283765</td>\n",
" <td>0.400497</td>\n",
" <td>1.367395</td>\n",
" <td>0.096555</td>\n",
" <td>0.715098</td>\n",
" <td>-0.560330</td>\n",
" <td>-0.231826</td>\n",
" <td>-1.018791</td>\n",
" <td>0.723595</td>\n",
" <td>-0.254655</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1437</th>\n",
" <td>5.198587</td>\n",
" <td>-3.363013</td>\n",
" <td>0.502758</td>\n",
" <td>1.192570</td>\n",
" <td>-0.179269</td>\n",
" <td>-0.386533</td>\n",
" <td>2.735824</td>\n",
" <td>-1.264222</td>\n",
" <td>-1.364915</td>\n",
" <td>-4.225437</td>\n",
" <td>1.130308</td>\n",
" <td>5.032236</td>\n",
" <td>-0.216982</td>\n",
" <td>-3.771700</td>\n",
" <td>3.986322</td>\n",
" <td>3.535238</td>\n",
" <td>2.803760</td>\n",
" <td>-1.651257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1438</th>\n",
" <td>-0.740830</td>\n",
" <td>-1.584214</td>\n",
" <td>1.105943</td>\n",
" <td>0.085738</td>\n",
" <td>-0.403349</td>\n",
" <td>1.848982</td>\n",
" <td>-1.324744</td>\n",
" <td>0.800389</td>\n",
" <td>-0.292916</td>\n",
" <td>-1.215909</td>\n",
" <td>2.482743</td>\n",
" <td>-0.495517</td>\n",
" <td>-0.405611</td>\n",
" <td>-1.231159</td>\n",
" <td>1.243773</td>\n",
" <td>-1.583964</td>\n",
" <td>-0.915113</td>\n",
" <td>-0.430744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1439</th>\n",
" <td>0.304645</td>\n",
" <td>1.679914</td>\n",
" <td>0.061394</td>\n",
" <td>-0.737441</td>\n",
" <td>-0.863404</td>\n",
" <td>-1.078333</td>\n",
" <td>-0.899108</td>\n",
" <td>0.827336</td>\n",
" <td>-1.391391</td>\n",
" <td>-0.815423</td>\n",
" <td>1.492720</td>\n",
" <td>-1.098712</td>\n",
" <td>-1.065532</td>\n",
" <td>-0.078881</td>\n",
" <td>2.004244</td>\n",
" <td>-0.202027</td>\n",
" <td>0.165607</td>\n",
" <td>-0.476461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1440</th>\n",
" <td>1.500741</td>\n",
" <td>3.405397</td>\n",
" <td>1.353358</td>\n",
" <td>0.351442</td>\n",
" <td>-0.161070</td>\n",
" <td>1.328349</td>\n",
" <td>1.097701</td>\n",
" <td>4.498576</td>\n",
" <td>0.563337</td>\n",
" <td>0.205898</td>\n",
" <td>-1.651970</td>\n",
" <td>0.338567</td>\n",
" <td>-0.218072</td>\n",
" <td>-1.532466</td>\n",
" <td>-1.654960</td>\n",
" <td>-2.330479</td>\n",
" <td>5.910636</td>\n",
" <td>-0.002478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1441</th>\n",
" <td>-1.205408</td>\n",
" <td>-3.223444</td>\n",
" <td>-2.078481</td>\n",
" <td>-1.576540</td>\n",
" <td>0.611271</td>\n",
" <td>-0.096429</td>\n",
" <td>-0.543960</td>\n",
" <td>0.461612</td>\n",
" <td>0.016084</td>\n",
" <td>-0.195526</td>\n",
" <td>0.790591</td>\n",
" <td>-0.657514</td>\n",
" <td>0.763613</td>\n",
" <td>-0.179871</td>\n",
" <td>0.093219</td>\n",
" <td>0.543359</td>\n",
" <td>1.217586</td>\n",
" <td>-0.264214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1442</th>\n",
" <td>4.616800</td>\n",
" <td>0.505609</td>\n",
" <td>-1.208632</td>\n",
" <td>-1.948038</td>\n",
" <td>0.100843</td>\n",
" <td>1.326604</td>\n",
" <td>-0.877506</td>\n",
" <td>-0.191592</td>\n",
" <td>-0.813760</td>\n",
" <td>0.146651</td>\n",
" <td>1.301268</td>\n",
" <td>-0.334883</td>\n",
" <td>0.145383</td>\n",
" <td>-0.684833</td>\n",
" <td>0.210398</td>\n",
" <td>-0.553392</td>\n",
" <td>0.888585</td>\n",
" <td>-0.287673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1443</th>\n",
" <td>-3.673905</td>\n",
" <td>-0.106484</td>\n",
" <td>1.404044</td>\n",
" <td>1.455437</td>\n",
" <td>-1.134166</td>\n",
" <td>-0.165877</td>\n",
" <td>-1.499979</td>\n",
" <td>-0.430896</td>\n",
" <td>0.919440</td>\n",
" <td>-0.066188</td>\n",
" <td>1.112453</td>\n",
" <td>-0.229567</td>\n",
" <td>0.145824</td>\n",
" <td>-0.614236</td>\n",
" <td>-0.812640</td>\n",
" <td>1.336396</td>\n",
" <td>0.112345</td>\n",
" <td>-0.910704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444</th>\n",
" <td>1.533493</td>\n",
" <td>-0.531664</td>\n",
" <td>-1.639824</td>\n",
" <td>2.893577</td>\n",
" <td>-0.006781</td>\n",
" <td>-0.995017</td>\n",
" <td>0.434119</td>\n",
" <td>1.149341</td>\n",
" <td>-0.600942</td>\n",
" <td>0.934690</td>\n",
" <td>1.264370</td>\n",
" <td>-0.310617</td>\n",
" <td>-0.517389</td>\n",
" <td>-0.326109</td>\n",
" <td>-0.561060</td>\n",
" <td>0.307428</td>\n",
" <td>0.750690</td>\n",
" <td>-0.015214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1445</th>\n",
" <td>-2.940564</td>\n",
" <td>-0.607814</td>\n",
" <td>1.054136</td>\n",
" <td>-0.746845</td>\n",
" <td>0.947838</td>\n",
" <td>0.264777</td>\n",
" <td>-1.202183</td>\n",
" <td>1.635335</td>\n",
" <td>-0.615246</td>\n",
" <td>1.196870</td>\n",
" <td>0.315161</td>\n",
" <td>-1.710754</td>\n",
" <td>-0.607282</td>\n",
" <td>-0.333753</td>\n",
" <td>3.534738</td>\n",
" <td>-0.041886</td>\n",
" <td>0.046029</td>\n",
" <td>-1.403407</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1446</th>\n",
" <td>-1.687915</td>\n",
" <td>-0.767354</td>\n",
" <td>1.496306</td>\n",
" <td>0.567116</td>\n",
" <td>-0.286047</td>\n",
" <td>1.712485</td>\n",
" <td>0.305963</td>\n",
" <td>-0.494570</td>\n",
" <td>-0.330299</td>\n",
" <td>0.829874</td>\n",
" <td>1.057015</td>\n",
" <td>-0.482425</td>\n",
" <td>0.363740</td>\n",
" <td>-0.557802</td>\n",
" <td>-1.897385</td>\n",
" <td>0.773447</td>\n",
" <td>0.582360</td>\n",
" <td>-0.672081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1447</th>\n",
" <td>3.134997</td>\n",
" <td>0.291932</td>\n",
" <td>-0.276060</td>\n",
" <td>-1.835181</td>\n",
" <td>0.321000</td>\n",
" <td>-1.796680</td>\n",
" <td>-0.500589</td>\n",
" <td>-0.046935</td>\n",
" <td>-0.989623</td>\n",
" <td>-0.655363</td>\n",
" <td>1.249279</td>\n",
" <td>0.301292</td>\n",
" <td>-0.688057</td>\n",
" <td>-0.619061</td>\n",
" <td>-0.391571</td>\n",
" <td>-0.170781</td>\n",
" <td>0.454882</td>\n",
" <td>-0.608090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1448</th>\n",
" <td>-2.995270</td>\n",
" <td>1.219623</td>\n",
" <td>-0.084334</td>\n",
" <td>-0.431336</td>\n",
" <td>-1.388231</td>\n",
" <td>0.544353</td>\n",
" <td>-0.113068</td>\n",
" <td>0.433910</td>\n",
" <td>-0.837449</td>\n",
" <td>0.265227</td>\n",
" <td>1.294666</td>\n",
" <td>-0.350264</td>\n",
" <td>0.099509</td>\n",
" <td>-0.198461</td>\n",
" <td>-0.712214</td>\n",
" <td>0.241928</td>\n",
" <td>0.609720</td>\n",
" <td>-0.324172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1449</th>\n",
" <td>-5.182754</td>\n",
" <td>-1.657431</td>\n",
" <td>-1.934226</td>\n",
" <td>-2.252267</td>\n",
" <td>0.715105</td>\n",
" <td>-1.841089</td>\n",
" <td>0.311881</td>\n",
" <td>1.823032</td>\n",
" <td>1.077651</td>\n",
" <td>-0.879499</td>\n",
" <td>0.968487</td>\n",
" <td>-0.499696</td>\n",
" <td>0.114099</td>\n",
" <td>-0.359772</td>\n",
" <td>-0.596631</td>\n",
" <td>1.317272</td>\n",
" <td>0.558575</td>\n",
" <td>-0.955830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1450</th>\n",
" <td>-1.324479</td>\n",
" <td>4.418431</td>\n",
" <td>-0.936667</td>\n",
" <td>-0.377090</td>\n",
" <td>2.758648</td>\n",
" <td>-0.313925</td>\n",
" <td>0.338167</td>\n",
" <td>-0.471347</td>\n",
" <td>0.130942</td>\n",
" <td>1.526277</td>\n",
" <td>1.143688</td>\n",
" <td>1.071101</td>\n",
" <td>-0.368004</td>\n",
" <td>-0.879051</td>\n",
" <td>-1.080818</td>\n",
" <td>1.586427</td>\n",
" <td>-0.754002</td>\n",
" <td>-0.682144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1451</th>\n",
" <td>3.378219</td>\n",
" <td>-0.871208</td>\n",
" <td>-1.349386</td>\n",
" <td>3.209416</td>\n",
" <td>-0.139112</td>\n",
" <td>0.764459</td>\n",
" <td>-0.797583</td>\n",
" <td>-0.184065</td>\n",
" <td>0.278418</td>\n",
" <td>0.398982</td>\n",
" <td>1.459796</td>\n",
" <td>-0.614984</td>\n",
" <td>0.664201</td>\n",
" <td>-0.700304</td>\n",
" <td>0.334906</td>\n",
" <td>-0.165853</td>\n",
" <td>0.789217</td>\n",
" <td>-0.253001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1452</th>\n",
" <td>-1.552560</td>\n",
" <td>-1.808536</td>\n",
" <td>-2.774719</td>\n",
" <td>-1.869207</td>\n",
" <td>1.630329</td>\n",
" <td>-1.246215</td>\n",
" <td>-0.251622</td>\n",
" <td>1.184000</td>\n",
" <td>0.153689</td>\n",
" <td>-0.218340</td>\n",
" <td>0.942586</td>\n",
" <td>-0.406343</td>\n",
" <td>0.854626</td>\n",
" <td>-0.484431</td>\n",
" <td>0.652924</td>\n",
" <td>-0.377470</td>\n",
" <td>0.749666</td>\n",
" <td>-0.796268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1453</th>\n",
" <td>-2.110802</td>\n",
" <td>-0.322511</td>\n",
" <td>-0.417135</td>\n",
" <td>2.297665</td>\n",
" <td>-0.227833</td>\n",
" <td>-0.602039</td>\n",
" <td>-0.229126</td>\n",
" <td>1.164218</td>\n",
" <td>-1.162857</td>\n",
" <td>1.249070</td>\n",
" <td>0.511594</td>\n",
" <td>0.957241</td>\n",
" <td>0.153586</td>\n",
" <td>-0.089866</td>\n",
" <td>-1.563244</td>\n",
" <td>1.090927</td>\n",
" <td>-0.497892</td>\n",
" <td>-1.333774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1454</th>\n",
" <td>0.552674</td>\n",
" <td>-1.907238</td>\n",
" <td>-1.589890</td>\n",
" <td>0.979489</td>\n",
" <td>0.400384</td>\n",
" <td>-0.231556</td>\n",
" <td>-0.389977</td>\n",
" <td>1.240229</td>\n",
" <td>-0.010903</td>\n",
" <td>0.431713</td>\n",
" <td>1.650592</td>\n",
" <td>0.680881</td>\n",
" <td>-0.918945</td>\n",
" <td>-1.049060</td>\n",
" <td>-0.266430</td>\n",
" <td>0.102958</td>\n",
" <td>-0.282776</td>\n",
" <td>-0.455825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1455</th>\n",
" <td>0.738693</td>\n",
" <td>1.404279</td>\n",
" <td>-2.024983</td>\n",
" <td>0.498799</td>\n",
" <td>-0.398388</td>\n",
" <td>-0.781910</td>\n",
" <td>-0.722621</td>\n",
" <td>0.328923</td>\n",
" <td>-0.755256</td>\n",
" <td>0.717800</td>\n",
" <td>1.024961</td>\n",
" <td>0.102038</td>\n",
" <td>-0.007866</td>\n",
" <td>-0.272632</td>\n",
" <td>0.041067</td>\n",
" <td>0.538796</td>\n",
" <td>0.635491</td>\n",
" <td>-0.596921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1456</th>\n",
" <td>2.345653</td>\n",
" <td>-1.669266</td>\n",
" <td>2.270376</td>\n",
" <td>0.149497</td>\n",
" <td>0.578132</td>\n",
" <td>2.140788</td>\n",
" <td>0.085508</td>\n",
" <td>-0.131999</td>\n",
" <td>0.494905</td>\n",
" <td>1.171112</td>\n",
" <td>0.885004</td>\n",
" <td>-1.274218</td>\n",
" <td>0.209259</td>\n",
" <td>-0.735950</td>\n",
" <td>-0.967782</td>\n",
" <td>1.324188</td>\n",
" <td>1.273428</td>\n",
" <td>-0.648434</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1457</th>\n",
" <td>0.807112</td>\n",
" <td>3.327378</td>\n",
" <td>1.691569</td>\n",
" <td>-0.174654</td>\n",
" <td>-1.559974</td>\n",
" <td>2.278323</td>\n",
" <td>1.288939</td>\n",
" <td>0.794321</td>\n",
" <td>2.646949</td>\n",
" <td>-0.371775</td>\n",
" <td>4.069685</td>\n",
" <td>1.014522</td>\n",
" <td>0.233644</td>\n",
" <td>2.249220</td>\n",
" <td>-0.925824</td>\n",
" <td>0.844603</td>\n",
" <td>0.541795</td>\n",
" <td>-1.633365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1458</th>\n",
" <td>-2.807622</td>\n",
" <td>-2.007681</td>\n",
" <td>2.001630</td>\n",
" <td>-1.078240</td>\n",
" <td>-0.441295</td>\n",
" <td>2.594946</td>\n",
" <td>0.117103</td>\n",
" <td>1.478897</td>\n",
" <td>0.432324</td>\n",
" <td>3.961691</td>\n",
" <td>0.249973</td>\n",
" <td>-2.015755</td>\n",
" <td>-1.424535</td>\n",
" <td>-0.801725</td>\n",
" <td>2.874548</td>\n",
" <td>1.067863</td>\n",
" <td>0.568758</td>\n",
" <td>-0.658048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1459</th>\n",
" <td>-0.941020</td>\n",
" <td>-1.473603</td>\n",
" <td>1.595329</td>\n",
" <td>-1.823317</td>\n",
" <td>-0.125940</td>\n",
" <td>0.929840</td>\n",
" <td>0.706528</td>\n",
" <td>0.918439</td>\n",
" <td>-1.388457</td>\n",
" <td>2.553837</td>\n",
" <td>0.220566</td>\n",
" <td>-1.004133</td>\n",
" <td>-0.889402</td>\n",
" <td>-0.109981</td>\n",
" <td>-1.673371</td>\n",
" <td>1.465350</td>\n",
" <td>1.096525</td>\n",
" <td>-0.642351</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1460 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 \\\n",
"0 1.503182 0.359157 -1.705880 -1.919996 0.475491 1.317957 -0.537867 \n",
"1 -0.004068 -1.095185 1.253567 -0.035725 -1.804688 -0.074807 3.755466 \n",
"2 1.741556 0.234216 -1.481588 -1.367202 -0.186890 -0.218170 -0.141747 \n",
"3 -0.499807 1.007560 0.767973 -0.167499 0.362109 0.478369 -1.429484 \n",
"4 4.459227 1.131053 -0.546911 -1.341411 -0.075457 -0.584217 0.486127 \n",
"5 -0.786591 -1.970631 -1.124299 -1.301091 -0.994057 1.027527 3.139387 \n",
"6 3.528163 -2.531563 0.407661 -0.222759 0.532914 -0.280499 0.974861 \n",
"7 2.000422 1.239459 1.399500 -2.181185 -0.680914 -0.100504 -0.280305 \n",
"8 -1.059275 2.795188 1.605976 1.307783 2.405916 0.212986 0.107542 \n",
"9 -3.276945 -0.178214 1.366788 -1.931274 3.758997 -0.250965 0.716368 \n",
"10 -2.459195 -1.816867 0.932250 -0.427412 0.858883 0.963445 -0.473817 \n",
"11 5.704507 1.135514 -0.004093 -1.397921 0.571876 -0.405478 0.833480 \n",
"12 -2.799006 -2.146194 1.105268 -0.757819 -0.707265 -0.963245 -0.293222 \n",
"13 3.352163 -0.898496 -1.077251 3.050998 -0.476910 -0.204435 0.497129 \n",
"14 -1.017676 -1.109052 1.490766 -0.384485 -0.381484 -0.103378 -1.882487 \n",
"15 -1.892361 -0.720888 -1.383668 1.122634 -1.878552 0.123047 0.789392 \n",
"16 -1.477463 -2.091270 0.666852 -0.313115 -0.465295 1.901271 -0.071380 \n",
"17 -2.573991 0.918525 -0.538650 0.477834 3.277484 -1.826680 1.925275 \n",
"18 0.010679 -1.643341 -1.047806 -0.389839 -0.002569 0.428755 -0.475959 \n",
"19 -2.419617 -0.472873 0.917799 1.097236 0.021196 0.983745 0.147471 \n",
"20 5.703911 2.737650 -1.498666 0.713163 -0.910461 -1.104017 0.599868 \n",
"21 -3.324897 1.049756 1.785823 1.156121 -0.979771 0.051787 -0.217761 \n",
"22 3.227921 -0.560121 -0.738624 3.437732 -0.541576 -0.309842 0.389708 \n",
"23 -1.201098 -1.260975 -0.121845 -1.466207 0.265991 -0.596780 0.577933 \n",
"24 -1.521012 -1.572833 1.515435 -1.166621 -1.507581 2.600164 1.492121 \n",
"25 4.102381 -0.958883 -0.781216 3.351526 -0.386713 0.591560 -0.170959 \n",
"26 -1.863100 -1.418293 -0.102280 -0.234817 -2.038085 1.463289 3.233063 \n",
"27 4.015499 -2.596841 0.312083 0.441350 0.699430 1.479999 -0.113488 \n",
"28 0.313504 -2.196533 2.085310 -1.044297 -0.896465 -2.168432 1.003545 \n",
"29 -5.736341 -0.560150 0.383406 0.754520 -0.724964 0.399839 -0.427290 \n",
"... ... ... ... ... ... ... ... \n",
"1430 0.897978 2.357886 -1.771060 -0.397046 -0.486595 -0.645040 -0.234131 \n",
"1431 -1.466675 -1.150552 -1.081461 -0.645300 0.716990 -0.783282 0.206198 \n",
"1432 -3.449154 0.987544 0.245268 1.422742 -0.217484 -0.348845 0.521423 \n",
"1433 1.631579 1.108444 -1.174866 -0.264267 0.242229 0.522892 -1.182379 \n",
"1434 -0.102698 -1.889735 0.865516 -0.496762 0.653976 -0.524633 -0.026508 \n",
"1435 0.392493 -0.082636 0.868233 1.018353 -1.732616 0.621552 -0.160167 \n",
"1436 -2.631470 -1.048400 -0.195912 0.431408 0.026410 -0.269865 -0.451781 \n",
"1437 5.198587 -3.363013 0.502758 1.192570 -0.179269 -0.386533 2.735824 \n",
"1438 -0.740830 -1.584214 1.105943 0.085738 -0.403349 1.848982 -1.324744 \n",
"1439 0.304645 1.679914 0.061394 -0.737441 -0.863404 -1.078333 -0.899108 \n",
"1440 1.500741 3.405397 1.353358 0.351442 -0.161070 1.328349 1.097701 \n",
"1441 -1.205408 -3.223444 -2.078481 -1.576540 0.611271 -0.096429 -0.543960 \n",
"1442 4.616800 0.505609 -1.208632 -1.948038 0.100843 1.326604 -0.877506 \n",
"1443 -3.673905 -0.106484 1.404044 1.455437 -1.134166 -0.165877 -1.499979 \n",
"1444 1.533493 -0.531664 -1.639824 2.893577 -0.006781 -0.995017 0.434119 \n",
"1445 -2.940564 -0.607814 1.054136 -0.746845 0.947838 0.264777 -1.202183 \n",
"1446 -1.687915 -0.767354 1.496306 0.567116 -0.286047 1.712485 0.305963 \n",
"1447 3.134997 0.291932 -0.276060 -1.835181 0.321000 -1.796680 -0.500589 \n",
"1448 -2.995270 1.219623 -0.084334 -0.431336 -1.388231 0.544353 -0.113068 \n",
"1449 -5.182754 -1.657431 -1.934226 -2.252267 0.715105 -1.841089 0.311881 \n",
"1450 -1.324479 4.418431 -0.936667 -0.377090 2.758648 -0.313925 0.338167 \n",
"1451 3.378219 -0.871208 -1.349386 3.209416 -0.139112 0.764459 -0.797583 \n",
"1452 -1.552560 -1.808536 -2.774719 -1.869207 1.630329 -1.246215 -0.251622 \n",
"1453 -2.110802 -0.322511 -0.417135 2.297665 -0.227833 -0.602039 -0.229126 \n",
"1454 0.552674 -1.907238 -1.589890 0.979489 0.400384 -0.231556 -0.389977 \n",
"1455 0.738693 1.404279 -2.024983 0.498799 -0.398388 -0.781910 -0.722621 \n",
"1456 2.345653 -1.669266 2.270376 0.149497 0.578132 2.140788 0.085508 \n",
"1457 0.807112 3.327378 1.691569 -0.174654 -1.559974 2.278323 1.288939 \n",
"1458 -2.807622 -2.007681 2.001630 -1.078240 -0.441295 2.594946 0.117103 \n",
"1459 -0.941020 -1.473603 1.595329 -1.823317 -0.125940 0.929840 0.706528 \n",
"\n",
" 7 8 9 10 11 12 13 \\\n",
"0 -0.526460 -0.317798 -0.441389 -1.204446 0.880331 0.308543 0.291034 \n",
"1 -0.303774 -0.128299 0.067504 -0.937441 -1.078768 1.793235 -0.030494 \n",
"2 -0.459596 -0.323457 -0.534684 -0.975946 0.735206 -0.928036 0.614004 \n",
"3 0.422885 -0.496519 -2.589422 -1.108853 -1.931205 -0.214752 1.394482 \n",
"4 -0.698585 -0.925684 -0.401942 -0.779665 0.379467 -1.561269 0.599122 \n",
"5 -1.989922 -1.968388 -3.757039 -0.955523 7.077242 -0.613106 -1.437053 \n",
"6 0.464557 -0.023710 -0.717568 -1.052608 0.081674 -0.754164 0.656083 \n",
"7 0.385502 0.183573 -2.040443 -0.580881 -0.866647 -2.194117 1.412064 \n",
"8 -0.978887 1.497793 -1.875999 -0.755034 -2.090913 -0.352441 1.314870 \n",
"9 -1.067081 2.871377 -1.375700 -1.066569 -0.443518 0.628932 0.548260 \n",
"10 -0.780955 -0.445886 -0.355734 -1.335635 0.845054 0.380383 0.380002 \n",
"11 -0.152402 -0.508701 -0.913452 -0.985819 -0.113840 -0.672564 0.779939 \n",
"12 -0.777226 1.130137 0.009754 -0.738560 1.217745 -0.602433 0.395287 \n",
"13 -0.437053 -0.177699 -0.139091 -0.996991 -0.308944 -0.199488 1.038094 \n",
"14 -0.138264 0.173773 -1.968286 -1.463684 -0.097749 -0.779177 0.915312 \n",
"15 1.023734 0.896964 -1.055260 -0.066518 0.064071 -0.769165 0.471503 \n",
"16 -1.255929 1.021417 -0.947182 -0.095710 0.511001 0.258657 1.288707 \n",
"17 -0.647005 0.059476 0.145613 -0.086090 1.147012 -0.416234 1.489527 \n",
"18 -0.211741 -0.717388 0.170309 -0.929869 1.464473 -0.542331 0.381446 \n",
"19 -0.765654 0.232333 -0.315098 -0.887910 0.573837 0.089871 0.127905 \n",
"20 -0.057157 -1.087537 0.003406 -1.108716 0.240207 -1.222229 1.120402 \n",
"21 0.082360 0.441664 -2.258385 -0.748220 -1.604702 -0.591046 1.091896 \n",
"22 0.375498 1.011672 -0.117884 -1.187719 0.018578 -0.822816 0.692142 \n",
"23 0.488267 0.898823 -1.000035 -0.614883 0.031970 -0.570530 0.332224 \n",
"24 0.952940 1.260977 2.611519 -1.132831 -0.415694 -1.975916 0.013529 \n",
"25 -1.525983 0.324121 -0.583413 -0.872611 -0.020587 0.264699 0.551025 \n",
"26 -0.245909 0.478501 2.499786 -1.123644 -0.913555 1.147065 -0.832825 \n",
"27 -0.745799 0.408070 -0.777829 -0.466056 0.621474 -0.111809 -0.150597 \n",
"28 1.572046 0.651263 -0.960301 -1.025751 0.451000 -2.477946 0.971359 \n",
"29 -0.634554 -0.013699 -1.159241 -1.087908 -0.555923 -0.082980 1.050353 \n",
"... ... ... ... ... ... ... ... \n",
"1430 0.352486 -1.708404 1.382850 0.597177 0.208528 -0.089789 0.213837 \n",
"1431 0.579996 0.081595 -0.207443 1.757779 0.161561 -0.093494 -1.123604 \n",
"1432 0.572712 -0.677705 0.620086 1.533987 0.195243 -0.258604 -0.730471 \n",
"1433 -0.615730 -0.909748 0.452160 0.814068 0.132971 0.433669 -0.415922 \n",
"1434 0.506407 -1.681862 0.799405 0.664814 -0.232384 -0.002174 0.101354 \n",
"1435 0.202897 0.485389 -0.524610 2.258618 -0.328934 -0.484403 -0.901997 \n",
"1436 -0.195492 -1.283765 0.400497 1.367395 0.096555 0.715098 -0.560330 \n",
"1437 -1.264222 -1.364915 -4.225437 1.130308 5.032236 -0.216982 -3.771700 \n",
"1438 0.800389 -0.292916 -1.215909 2.482743 -0.495517 -0.405611 -1.231159 \n",
"1439 0.827336 -1.391391 -0.815423 1.492720 -1.098712 -1.065532 -0.078881 \n",
"1440 4.498576 0.563337 0.205898 -1.651970 0.338567 -0.218072 -1.532466 \n",
"1441 0.461612 0.016084 -0.195526 0.790591 -0.657514 0.763613 -0.179871 \n",
"1442 -0.191592 -0.813760 0.146651 1.301268 -0.334883 0.145383 -0.684833 \n",
"1443 -0.430896 0.919440 -0.066188 1.112453 -0.229567 0.145824 -0.614236 \n",
"1444 1.149341 -0.600942 0.934690 1.264370 -0.310617 -0.517389 -0.326109 \n",
"1445 1.635335 -0.615246 1.196870 0.315161 -1.710754 -0.607282 -0.333753 \n",
"1446 -0.494570 -0.330299 0.829874 1.057015 -0.482425 0.363740 -0.557802 \n",
"1447 -0.046935 -0.989623 -0.655363 1.249279 0.301292 -0.688057 -0.619061 \n",
"1448 0.433910 -0.837449 0.265227 1.294666 -0.350264 0.099509 -0.198461 \n",
"1449 1.823032 1.077651 -0.879499 0.968487 -0.499696 0.114099 -0.359772 \n",
"1450 -0.471347 0.130942 1.526277 1.143688 1.071101 -0.368004 -0.879051 \n",
"1451 -0.184065 0.278418 0.398982 1.459796 -0.614984 0.664201 -0.700304 \n",
"1452 1.184000 0.153689 -0.218340 0.942586 -0.406343 0.854626 -0.484431 \n",
"1453 1.164218 -1.162857 1.249070 0.511594 0.957241 0.153586 -0.089866 \n",
"1454 1.240229 -0.010903 0.431713 1.650592 0.680881 -0.918945 -1.049060 \n",
"1455 0.328923 -0.755256 0.717800 1.024961 0.102038 -0.007866 -0.272632 \n",
"1456 -0.131999 0.494905 1.171112 0.885004 -1.274218 0.209259 -0.735950 \n",
"1457 0.794321 2.646949 -0.371775 4.069685 1.014522 0.233644 2.249220 \n",
"1458 1.478897 0.432324 3.961691 0.249973 -2.015755 -1.424535 -0.801725 \n",
"1459 0.918439 -1.388457 2.553837 0.220566 -1.004133 -0.889402 -0.109981 \n",
"\n",
" 14 15 16 17 \n",
"0 0.230563 -1.147820 -1.301392 0.035631 \n",
"1 -0.798680 -1.177382 -0.806857 0.460397 \n",
"2 0.310852 -0.313629 -0.538729 0.860570 \n",
"3 2.849118 -1.341282 -0.425774 0.051643 \n",
"4 0.015822 -0.778584 -0.147841 1.384087 \n",
"5 4.856910 3.624928 2.031556 -0.406133 \n",
"6 -0.844053 -0.320223 -0.443510 0.609285 \n",
"7 0.940643 0.618925 -1.330184 -0.161133 \n",
"8 1.904752 0.676163 -0.373922 0.886032 \n",
"9 0.074370 1.081301 -0.811329 0.179848 \n",
"10 -0.551042 -0.752242 -0.939405 0.122039 \n",
"11 0.038843 -0.828510 0.152292 0.966004 \n",
"12 -0.675727 -0.380627 0.109555 2.083170 \n",
"13 0.213564 -0.548759 0.109730 1.326635 \n",
"14 0.526796 0.322514 -2.417918 -1.455287 \n",
"15 0.172023 -0.990195 -1.089041 1.081449 \n",
"16 -0.065382 -0.633109 -0.199101 0.584387 \n",
"17 1.245834 -1.240691 -0.315807 1.881455 \n",
"18 -0.025640 -1.106772 -1.456120 0.196094 \n",
"19 -0.767340 -0.266031 -0.855627 0.660061 \n",
"20 0.140227 -0.402935 -0.614504 0.939910 \n",
"21 0.946252 0.187213 -0.516535 0.467702 \n",
"22 -0.736859 0.835979 -1.213373 0.135398 \n",
"23 -0.204037 -0.469032 -0.840719 0.388195 \n",
"24 0.441358 0.798471 -0.971779 0.766564 \n",
"25 0.358167 -0.811786 -0.468778 1.070529 \n",
"26 1.497534 -1.683324 -1.226115 0.596320 \n",
"27 0.157849 -1.221192 -0.877048 0.852310 \n",
"28 -1.770576 1.223475 -1.611787 -0.623305 \n",
"29 0.418936 0.091134 -0.421781 0.783819 \n",
"... ... ... ... ... \n",
"1430 -0.375268 0.589969 0.785317 -0.777412 \n",
"1431 0.049939 -0.159202 0.299156 0.139409 \n",
"1432 -0.934872 0.173672 0.231408 -0.084794 \n",
"1433 -0.146054 0.236447 0.670894 -0.600114 \n",
"1434 -0.970263 0.193395 1.008912 -0.678688 \n",
"1435 -0.890389 -0.003153 0.234040 -0.207977 \n",
"1436 -0.231826 -1.018791 0.723595 -0.254655 \n",
"1437 3.986322 3.535238 2.803760 -1.651257 \n",
"1438 1.243773 -1.583964 -0.915113 -0.430744 \n",
"1439 2.004244 -0.202027 0.165607 -0.476461 \n",
"1440 -1.654960 -2.330479 5.910636 -0.002478 \n",
"1441 0.093219 0.543359 1.217586 -0.264214 \n",
"1442 0.210398 -0.553392 0.888585 -0.287673 \n",
"1443 -0.812640 1.336396 0.112345 -0.910704 \n",
"1444 -0.561060 0.307428 0.750690 -0.015214 \n",
"1445 3.534738 -0.041886 0.046029 -1.403407 \n",
"1446 -1.897385 0.773447 0.582360 -0.672081 \n",
"1447 -0.391571 -0.170781 0.454882 -0.608090 \n",
"1448 -0.712214 0.241928 0.609720 -0.324172 \n",
"1449 -0.596631 1.317272 0.558575 -0.955830 \n",
"1450 -1.080818 1.586427 -0.754002 -0.682144 \n",
"1451 0.334906 -0.165853 0.789217 -0.253001 \n",
"1452 0.652924 -0.377470 0.749666 -0.796268 \n",
"1453 -1.563244 1.090927 -0.497892 -1.333774 \n",
"1454 -0.266430 0.102958 -0.282776 -0.455825 \n",
"1455 0.041067 0.538796 0.635491 -0.596921 \n",
"1456 -0.967782 1.324188 1.273428 -0.648434 \n",
"1457 -0.925824 0.844603 0.541795 -1.633365 \n",
"1458 2.874548 1.067863 0.568758 -0.658048 \n",
"1459 -1.673371 1.465350 1.096525 -0.642351 \n",
"\n",
"[1460 rows x 18 columns]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = pd.DataFrame(X_std_pca)\n",
"df1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. High Correlation (Feature Selection)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"#Dilakukan feature selection menggunakan metode High Correlation\n",
"import pandas as pd \n",
"import numpy as np "
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"x_scale=pd.DataFrame(x_scale)\n",
"corr_matrix = x_scale.corr().abs() "
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool)) \n",
"to_drop = [column for column in upper.columns if any(upper[column] > 0.80)] "
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"scrolled": true
},
"outputs": [
{
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.909091</td>\n",
" <td>0.00</td>\n",
" <td>0.403277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.014393</td>\n",
" <td>0.147059</td>\n",
" <td>0.123288</td>\n",
" <td>0.028741</td>\n",
" <td>0.666667</td>\n",
" <td>0.750</td>\n",
" <td>0.420290</td>\n",
" <td>0.000000</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>0.371377</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.454545</td>\n",
" <td>0.25</td>\n",
" <td>0.145119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.015079</td>\n",
" <td>0.000000</td>\n",
" <td>0.184932</td>\n",
" <td>0.039459</td>\n",
" <td>0.777778</td>\n",
" <td>0.500</td>\n",
" <td>0.942029</td>\n",
" <td>0.866667</td>\n",
" <td>0.175625</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.199533</td>\n",
" <td>0.290676</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.727273</td>\n",
" <td>0.50</td>\n",
" <td>0.270935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.015764</td>\n",
" <td>0.588235</td>\n",
" <td>0.078767</td>\n",
" <td>0.013667</td>\n",
" <td>0.444444</td>\n",
" <td>0.750</td>\n",
" <td>0.753623</td>\n",
" <td>0.433333</td>\n",
" <td>0.000000</td>\n",
" <td>0.148831</td>\n",
" <td>...</td>\n",
" <td>0.116686</td>\n",
" <td>0.201097</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.454545</td>\n",
" <td>0.25</td>\n",
" <td>0.131926</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.016450</td>\n",
" <td>0.000000</td>\n",
" <td>0.167979</td>\n",
" <td>0.032466</td>\n",
" <td>0.444444</td>\n",
" <td>0.875</td>\n",
" <td>0.695652</td>\n",
" <td>0.850000</td>\n",
" <td>0.000000</td>\n",
" <td>0.033310</td>\n",
" <td>...</td>\n",
" <td>0.473746</td>\n",
" <td>0.164534</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.363636</td>\n",
" <td>1.00</td>\n",
" <td>0.165394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.017135</td>\n",
" <td>0.000000</td>\n",
" <td>0.304795</td>\n",
" <td>0.060436</td>\n",
" <td>0.777778</td>\n",
" <td>0.500</td>\n",
" <td>0.978261</td>\n",
" <td>0.950000</td>\n",
" <td>0.400000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.102377</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.545455</td>\n",
" <td>0.75</td>\n",
" <td>0.307457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.017820</td>\n",
" <td>0.000000</td>\n",
" <td>0.133562</td>\n",
" <td>0.027577</td>\n",
" <td>0.444444</td>\n",
" <td>0.750</td>\n",
" <td>0.572464</td>\n",
" <td>0.833333</td>\n",
" <td>0.000000</td>\n",
" <td>0.041460</td>\n",
" <td>...</td>\n",
" <td>0.259043</td>\n",
" <td>0.058501</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.363636</td>\n",
" <td>1.00</td>\n",
" <td>0.138731</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.018506</td>\n",
" <td>0.000000</td>\n",
" <td>0.263699</td>\n",
" <td>0.047573</td>\n",
" <td>0.777778</td>\n",
" <td>0.500</td>\n",
" <td>0.978261</td>\n",
" <td>0.966667</td>\n",
" <td>0.125000</td>\n",
" <td>0.215804</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.091408</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.363636</td>\n",
" <td>1.00</td>\n",
" <td>0.376475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.019191</td>\n",
" <td>0.000000</td>\n",
" <td>0.089041</td>\n",
" <td>0.070210</td>\n",
" <td>0.444444</td>\n",
" <td>0.625</td>\n",
" <td>0.615942</td>\n",
" <td>0.783333</td>\n",
" <td>0.000000</td>\n",
" <td>0.226258</td>\n",
" <td>...</td>\n",
" <td>0.336056</td>\n",
" <td>0.471664</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.00</td>\n",
" <td>0.239689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.019877</td>\n",
" <td>0.058824</td>\n",
" <td>0.133562</td>\n",
" <td>0.023483</td>\n",
" <td>0.333333</td>\n",
" <td>0.625</td>\n",
" <td>0.398551</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.057176</td>\n",
" <td>0.000000</td>\n",
" <td>0.157609</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.363636</td>\n",
" <td>0.50</td>\n",
" <td>0.046660</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",
" <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",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1430</th>\n",
" <td>0.980123</td>\n",
" <td>0.235294</td>\n",
" <td>0.133562</td>\n",
" <td>0.096427</td>\n",
" <td>0.444444</td>\n",
" <td>0.500</td>\n",
" <td>0.963768</td>\n",
" <td>0.916667</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.116686</td>\n",
" <td>0.073126</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.545455</td>\n",
" <td>0.00</td>\n",
" <td>0.218359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1431</th>\n",
" <td>0.980809</td>\n",
" <td>0.588235</td>\n",
" <td>0.167979</td>\n",
" <td>0.016958</td>\n",
" <td>0.555556</td>\n",
" <td>0.625</td>\n",
" <td>0.753623</td>\n",
" <td>0.433333</td>\n",
" <td>0.000000</td>\n",
" <td>0.169738</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.109689</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.818182</td>\n",
" <td>0.75</td>\n",
" <td>0.151160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1432</th>\n",
" <td>0.981494</td>\n",
" <td>0.058824</td>\n",
" <td>0.133562</td>\n",
" <td>0.044404</td>\n",
" <td>0.333333</td>\n",
" <td>0.625</td>\n",
" <td>0.398551</td>\n",
" <td>0.950000</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>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.636364</td>\n",
" <td>0.25</td>\n",
" <td>0.041105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1433</th>\n",
" <td>0.982180</td>\n",
" <td>0.235294</td>\n",
" <td>0.246575</td>\n",
" <td>0.041885</td>\n",
" <td>0.555556</td>\n",
" <td>0.500</td>\n",
" <td>0.927536</td>\n",
" <td>0.833333</td>\n",
" <td>0.198750</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>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.363636</td>\n",
" <td>0.50</td>\n",
" <td>0.210526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1434</th>\n",
" <td>0.982865</td>\n",
" <td>0.000000</td>\n",
" <td>0.202055</td>\n",
" <td>0.075253</td>\n",
" <td>0.444444</td>\n",
" <td>0.500</td>\n",
" <td>0.760870</td>\n",
" <td>0.450000</td>\n",
" <td>0.000000</td>\n",
" <td>0.165840</td>\n",
" <td>...</td>\n",
" <td>0.344224</td>\n",
" <td>0.074954</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.363636</td>\n",
" <td>0.00</td>\n",
" <td>0.173726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1435</th>\n",
" <td>0.983550</td>\n",
" <td>0.000000</td>\n",
" <td>0.202055</td>\n",
" <td>0.033186</td>\n",
" <td>0.555556</td>\n",
" <td>1.000</td>\n",
" <td>0.652174</td>\n",
" <td>0.916667</td>\n",
" <td>0.148125</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.065814</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.545455</td>\n",
" <td>0.50</td>\n",
" <td>0.193168</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1436</th>\n",
" <td>0.984236</td>\n",
" <td>0.000000</td>\n",
" <td>0.133562</td>\n",
" <td>0.035991</td>\n",
" <td>0.333333</td>\n",
" <td>0.625</td>\n",
" <td>0.717391</td>\n",
" <td>0.350000</td>\n",
" <td>0.000000</td>\n",
" <td>0.109142</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>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.363636</td>\n",
" <td>0.25</td>\n",
" <td>0.118872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1437</th>\n",
" <td>0.984921</td>\n",
" <td>0.000000</td>\n",
" <td>0.256849</td>\n",
" <td>0.052088</td>\n",
" <td>0.777778</td>\n",
" <td>0.500</td>\n",
" <td>0.985507</td>\n",
" <td>0.966667</td>\n",
" <td>0.266250</td>\n",
" <td>0.236712</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.120658</td>\n",
" <td>0.000000</td>\n",
" <td>0.598425</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.909091</td>\n",
" <td>0.50</td>\n",
" <td>0.499538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1438</th>\n",
" <td>0.985607</td>\n",
" <td>0.000000</td>\n",
" <td>0.236301</td>\n",
" <td>0.028545</td>\n",
" <td>0.555556</td>\n",
" <td>0.750</td>\n",
" <td>0.615942</td>\n",
" <td>0.766667</td>\n",
" <td>0.000000</td>\n",
" <td>0.106308</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.288848</td>\n",
" <td>0.286232</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.272727</td>\n",
" <td>1.00</td>\n",
" <td>0.159422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1439</th>\n",
" <td>0.986292</td>\n",
" <td>0.235294</td>\n",
" <td>0.202055</td>\n",
" <td>0.048068</td>\n",
" <td>0.666667</td>\n",
" <td>0.625</td>\n",
" <td>0.775362</td>\n",
" <td>0.483333</td>\n",
" <td>0.060000</td>\n",
" <td>0.055811</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.160878</td>\n",
" <td>0.391304</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.909091</td>\n",
" <td>0.25</td>\n",
" <td>0.225108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1440</th>\n",
" <td>0.986977</td>\n",
" <td>0.294118</td>\n",
" <td>0.198630</td>\n",
" <td>0.047797</td>\n",
" <td>0.555556</td>\n",
" <td>0.750</td>\n",
" <td>0.362319</td>\n",
" <td>0.733333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.502917</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.727273</td>\n",
" <td>0.50</td>\n",
" <td>0.216775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1441</th>\n",
" <td>0.987663</td>\n",
" <td>0.588235</td>\n",
" <td>0.167979</td>\n",
" <td>0.014611</td>\n",
" <td>0.555556</td>\n",
" <td>0.500</td>\n",
" <td>0.956522</td>\n",
" <td>0.900000</td>\n",
" <td>0.091875</td>\n",
" <td>0.123494</td>\n",
" <td>...</td>\n",
" <td>0.173862</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.363636</td>\n",
" <td>0.50</td>\n",
" <td>0.158867</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1442</th>\n",
" <td>0.988348</td>\n",
" <td>0.235294</td>\n",
" <td>0.219178</td>\n",
" <td>0.045353</td>\n",
" <td>1.000000</td>\n",
" <td>0.500</td>\n",
" <td>0.985507</td>\n",
" <td>0.966667</td>\n",
" <td>0.100000</td>\n",
" <td>0.135542</td>\n",
" <td>...</td>\n",
" <td>0.196033</td>\n",
" <td>0.095064</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.272727</td>\n",
" <td>0.75</td>\n",
" <td>0.382030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1443</th>\n",
" <td>0.989034</td>\n",
" <td>0.058824</td>\n",
" <td>0.167979</td>\n",
" <td>0.035308</td>\n",
" <td>0.555556</td>\n",
" <td>0.625</td>\n",
" <td>0.318841</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.179159</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.083333</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.363636</td>\n",
" <td>0.75</td>\n",
" <td>0.119567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444</th>\n",
" <td>0.989719</td>\n",
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" <th>1445</th>\n",
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" <th>1446</th>\n",
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" <td>0.991775</td>\n",
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" <th>1448</th>\n",
" <td>0.992461</td>\n",
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" <th>1449</th>\n",
" <td>0.993146</td>\n",
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" <th>1450</th>\n",
" <td>0.993831</td>\n",
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" <tr>\n",
" <th>1451</th>\n",
" <td>0.994517</td>\n",
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" <tr>\n",
" <th>1452</th>\n",
" <td>0.995202</td>\n",
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" <tr>\n",
" <th>1453</th>\n",
" <td>0.995888</td>\n",
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" <th>1454</th>\n",
" <td>0.996573</td>\n",
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" <tr>\n",
" <th>1455</th>\n",
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" <tr>\n",
" <th>1456</th>\n",
" <td>0.997944</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1457</th>\n",
" <td>0.998629</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1458</th>\n",
" <td>0.999315</td>\n",
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" <tr>\n",
" <th>1459</th>\n",
" <td>1.000000</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1460 rows × 35 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 \\\n",
"0 0.000000 0.235294 0.150685 0.033420 0.666667 0.500 0.949275 \n",
"1 0.000685 0.000000 0.202055 0.038795 0.555556 0.875 0.753623 \n",
"2 0.001371 0.235294 0.160959 0.046507 0.666667 0.500 0.934783 \n",
"3 0.002056 0.294118 0.133562 0.038561 0.666667 0.500 0.311594 \n",
"4 0.002742 0.235294 0.215753 0.060576 0.777778 0.500 0.927536 \n",
"5 0.003427 0.176471 0.219178 0.059899 0.444444 0.500 0.876812 \n",
"6 0.004112 0.000000 0.184932 0.041057 0.777778 0.500 0.956522 \n",
"7 0.004798 0.235294 0.167979 0.042450 0.666667 0.625 0.731884 \n",
"8 0.005483 0.176471 0.102740 0.022529 0.666667 0.500 0.427536 \n",
"9 0.006169 1.000000 0.099315 0.028605 0.444444 0.625 0.485507 \n",
"10 0.006854 0.000000 0.167808 0.046274 0.444444 0.500 0.673913 \n",
"11 0.007539 0.235294 0.219178 0.049658 0.888889 0.500 0.963768 \n",
"12 0.008225 0.000000 0.167979 0.054537 0.444444 0.625 0.652174 \n",
"13 0.008910 0.000000 0.239726 0.043712 0.666667 0.500 0.971014 \n",
"14 0.009596 0.000000 0.167979 0.044965 0.555556 0.500 0.637681 \n",
"15 0.010281 0.147059 0.102740 0.022529 0.666667 0.875 0.413043 \n",
"16 0.010966 0.000000 0.167979 0.046465 0.555556 0.750 0.710145 \n",
"17 0.011652 0.411765 0.174658 0.044362 0.333333 0.500 0.688406 \n",
"18 0.012337 0.000000 0.154110 0.057935 0.444444 0.500 0.956522 \n",
"19 0.013023 0.000000 0.167808 0.029260 0.444444 0.625 0.623188 \n",
"20 0.013708 0.235294 0.273973 0.060366 0.777778 0.500 0.963768 \n",
"21 0.014393 0.147059 0.123288 0.028741 0.666667 0.750 0.420290 \n",
"22 0.015079 0.000000 0.184932 0.039459 0.777778 0.500 0.942029 \n",
"23 0.015764 0.588235 0.078767 0.013667 0.444444 0.750 0.753623 \n",
"24 0.016450 0.000000 0.167979 0.032466 0.444444 0.875 0.695652 \n",
"25 0.017135 0.000000 0.304795 0.060436 0.777778 0.500 0.978261 \n",
"26 0.017820 0.000000 0.133562 0.027577 0.444444 0.750 0.572464 \n",
"27 0.018506 0.000000 0.263699 0.047573 0.777778 0.500 0.978261 \n",
"28 0.019191 0.000000 0.089041 0.070210 0.444444 0.625 0.615942 \n",
"29 0.019877 0.058824 0.133562 0.023483 0.333333 0.625 0.398551 \n",
"... ... ... ... ... ... ... ... \n",
"1430 0.980123 0.235294 0.133562 0.096427 0.444444 0.500 0.963768 \n",
"1431 0.980809 0.588235 0.167979 0.016958 0.555556 0.625 0.753623 \n",
"1432 0.981494 0.058824 0.133562 0.044404 0.333333 0.625 0.398551 \n",
"1433 0.982180 0.235294 0.246575 0.041885 0.555556 0.500 0.927536 \n",
"1434 0.982865 0.000000 0.202055 0.075253 0.444444 0.500 0.760870 \n",
"1435 0.983550 0.000000 0.202055 0.033186 0.555556 1.000 0.652174 \n",
"1436 0.984236 0.000000 0.133562 0.035991 0.333333 0.625 0.717391 \n",
"1437 0.984921 0.000000 0.256849 0.052088 0.777778 0.500 0.985507 \n",
"1438 0.985607 0.000000 0.236301 0.028545 0.555556 0.750 0.615942 \n",
"1439 0.986292 0.235294 0.202055 0.048068 0.666667 0.625 0.775362 \n",
"1440 0.986977 0.294118 0.198630 0.047797 0.555556 0.750 0.362319 \n",
"1441 0.987663 0.588235 0.167979 0.014611 0.555556 0.500 0.956522 \n",
"1442 0.988348 0.235294 0.219178 0.045353 1.000000 0.500 0.985507 \n",
"1443 0.989034 0.058824 0.167979 0.035308 0.555556 0.625 0.318841 \n",
"1444 0.989719 0.000000 0.143836 0.033654 0.666667 0.500 0.956522 \n",
"1445 0.990404 0.382353 0.167808 0.033186 0.555556 0.500 0.681159 \n",
"1446 0.991090 0.000000 0.167979 0.116114 0.444444 0.750 0.652174 \n",
"1447 0.991775 0.235294 0.202055 0.040665 0.777778 0.500 0.891304 \n",
"1448 0.992461 0.176471 0.167808 0.048924 0.333333 0.750 0.275362 \n",
"1449 0.993146 0.941176 0.000000 0.001089 0.444444 0.750 0.710145 \n",
"1450 0.993831 0.411765 0.133562 0.035991 0.444444 0.500 0.739130 \n",
"1451 0.994517 0.000000 0.195205 0.037215 0.777778 0.500 0.985507 \n",
"1452 0.995202 0.941176 0.047945 0.011101 0.444444 0.500 0.963768 \n",
"1453 0.995888 0.000000 0.236301 0.074398 0.444444 0.500 0.971014 \n",
"1454 0.996573 0.000000 0.140411 0.028979 0.666667 0.500 0.956522 \n",
"1455 0.997258 0.235294 0.140411 0.030929 0.555556 0.500 0.920290 \n",
"1456 0.997944 0.000000 0.219178 0.055505 0.555556 0.625 0.768116 \n",
"1457 0.998629 0.294118 0.154110 0.036187 0.666667 1.000 0.500000 \n",
"1458 0.999315 0.000000 0.160959 0.039342 0.444444 0.625 0.565217 \n",
"1459 1.000000 0.000000 0.184932 0.040370 0.444444 0.625 0.673913 \n",
"\n",
" 7 8 9 ... 28 29 30 \\\n",
"0 0.883333 0.122500 0.125089 ... 0.000000 0.111517 0.000000 \n",
"1 0.433333 0.000000 0.173281 ... 0.347725 0.000000 0.000000 \n",
"2 0.866667 0.101250 0.086109 ... 0.000000 0.076782 0.000000 \n",
"3 0.333333 0.000000 0.038271 ... 0.000000 0.063985 0.492754 \n",
"4 0.833333 0.218750 0.116052 ... 0.224037 0.153565 0.000000 \n",
"5 0.750000 0.000000 0.129695 ... 0.046674 0.054845 0.000000 \n",
"6 0.916667 0.116250 0.242558 ... 0.297550 0.104205 0.000000 \n",
"7 0.383333 0.150000 0.152197 ... 0.274212 0.372943 0.413043 \n",
"8 0.000000 0.000000 0.000000 ... 0.105018 0.000000 0.371377 \n",
"9 0.000000 0.000000 0.150780 ... 0.000000 0.007313 0.000000 \n",
"10 0.250000 0.000000 0.160524 ... 0.000000 0.000000 0.000000 \n",
"11 0.933333 0.178750 0.176825 ... 0.171529 0.038391 0.000000 \n",
"12 0.200000 0.000000 0.130581 ... 0.163361 0.000000 0.000000 \n",
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"14 0.166667 0.132500 0.129872 ... 0.000000 0.389397 0.318841 \n",
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"... ... ... ... ... ... ... ... \n",
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"\n",
" 31 32 33 34 35 36 37 \n",
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"29 0.000000 0.000000 0.0 0.000000 0.363636 0.50 0.046660 \n",
"... ... ... ... ... ... ... ... \n",
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"\n",
"[1460 rows x 35 columns]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_scale.drop(x_scale.columns[to_drop], axis=1) "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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