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Z30G0D/Housing Prices

Last active January 8, 2018 12:34
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This notebook depicts the regression challenge in kaggle and my solution for it
Raw
Housing Prices
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Housing Prices from Kaggle\n",
"# My second competition from kaggle\n",
"\n",
"Hey, this will be my second kaggle competition after the titanic challenge.\n",
"This one is a regression problem (not a classifying problem) unlike the titanic so it could be a little bit more challenging.\n",
"If any one has any comments about my code (I am still a beginner) please contact me through tomer@nahshon.net.\n",
"\n",
"The competition page is __[Here](https://www.kaggle.com/c/house-prices-advanced-regression-techniques)__\n",
"\n",
"The objective is to predict the housing prices according to the features given in our training set. Later on we will examine our algorithm on the test set."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"((1459, 80), (1460, 81))"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from sklearn import feature_selection\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn import linear_model\n",
"from sklearn.metrics import mean_squared_error\n",
"\n",
"train = pd.read_csv('Housing/train.csv')\n",
"test = pd.read_csv('Housing/test.csv')\n",
"\n",
"test.shape, train.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that our training set is made of 1460 samples and 80 features ( the last column is actual price, and our target variable), and in the test set we have 1459 samples and 80 features (no price column of course, because we need to submit the result.\n",
"\n",
"Let's examine the training set."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" text-align: left;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>MSSubClass</th>\n",
" <th>LotFrontage</th>\n",
" <th>LotArea</th>\n",
" <th>OverallQual</th>\n",
" <th>OverallCond</th>\n",
" <th>YearBuilt</th>\n",
" <th>YearRemodAdd</th>\n",
" <th>MasVnrArea</th>\n",
" <th>BsmtFinSF1</th>\n",
" <th>...</th>\n",
" <th>WoodDeckSF</th>\n",
" <th>OpenPorchSF</th>\n",
" <th>EnclosedPorch</th>\n",
" <th>3SsnPorch</th>\n",
" <th>ScreenPorch</th>\n",
" <th>PoolArea</th>\n",
" <th>MiscVal</th>\n",
" <th>MoSold</th>\n",
" <th>YrSold</th>\n",
" <th>SalePrice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1452.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>...</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>730.500000</td>\n",
" <td>56.897260</td>\n",
" <td>70.049958</td>\n",
" <td>10516.828082</td>\n",
" <td>6.099315</td>\n",
" <td>5.575342</td>\n",
" <td>1971.267808</td>\n",
" <td>1984.865753</td>\n",
" <td>103.685262</td>\n",
" <td>443.639726</td>\n",
" <td>...</td>\n",
" <td>94.244521</td>\n",
" <td>46.660274</td>\n",
" <td>21.954110</td>\n",
" <td>3.409589</td>\n",
" <td>15.060959</td>\n",
" <td>2.758904</td>\n",
" <td>43.489041</td>\n",
" <td>6.321918</td>\n",
" <td>2007.815753</td>\n",
" <td>180921.195890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>421.610009</td>\n",
" <td>42.300571</td>\n",
" <td>24.284752</td>\n",
" <td>9981.264932</td>\n",
" <td>1.382997</td>\n",
" <td>1.112799</td>\n",
" <td>30.202904</td>\n",
" <td>20.645407</td>\n",
" <td>181.066207</td>\n",
" <td>456.098091</td>\n",
" <td>...</td>\n",
" <td>125.338794</td>\n",
" <td>66.256028</td>\n",
" <td>61.119149</td>\n",
" <td>29.317331</td>\n",
" <td>55.757415</td>\n",
" <td>40.177307</td>\n",
" <td>496.123024</td>\n",
" <td>2.703626</td>\n",
" <td>1.328095</td>\n",
" <td>79442.502883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>20.000000</td>\n",
" <td>21.000000</td>\n",
" <td>1300.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1872.000000</td>\n",
" <td>1950.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2006.000000</td>\n",
" <td>34900.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>365.750000</td>\n",
" <td>20.000000</td>\n",
" <td>59.000000</td>\n",
" <td>7553.500000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1954.000000</td>\n",
" <td>1967.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>2007.000000</td>\n",
" <td>129975.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>730.500000</td>\n",
" <td>50.000000</td>\n",
" <td>69.000000</td>\n",
" <td>9478.500000</td>\n",
" <td>6.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1973.000000</td>\n",
" <td>1994.000000</td>\n",
" <td>0.000000</td>\n",
" <td>383.500000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>25.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2008.000000</td>\n",
" <td>163000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1095.250000</td>\n",
" <td>70.000000</td>\n",
" <td>80.000000</td>\n",
" <td>11601.500000</td>\n",
" <td>7.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2000.000000</td>\n",
" <td>2004.000000</td>\n",
" <td>166.000000</td>\n",
" <td>712.250000</td>\n",
" <td>...</td>\n",
" <td>168.000000</td>\n",
" <td>68.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.000000</td>\n",
" <td>2009.000000</td>\n",
" <td>214000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1460.000000</td>\n",
" <td>190.000000</td>\n",
" <td>313.000000</td>\n",
" <td>215245.000000</td>\n",
" <td>10.000000</td>\n",
" <td>9.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>1600.000000</td>\n",
" <td>5644.000000</td>\n",
" <td>...</td>\n",
" <td>857.000000</td>\n",
" <td>547.000000</td>\n",
" <td>552.000000</td>\n",
" <td>508.000000</td>\n",
" <td>480.000000</td>\n",
" <td>738.000000</td>\n",
" <td>15500.000000</td>\n",
" <td>12.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>755000.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Id MSSubClass LotFrontage LotArea OverallQual \\\n",
"count 1460.000000 1460.000000 1201.000000 1460.000000 1460.000000 \n",
"mean 730.500000 56.897260 70.049958 10516.828082 6.099315 \n",
"std 421.610009 42.300571 24.284752 9981.264932 1.382997 \n",
"min 1.000000 20.000000 21.000000 1300.000000 1.000000 \n",
"25% 365.750000 20.000000 59.000000 7553.500000 5.000000 \n",
"50% 730.500000 50.000000 69.000000 9478.500000 6.000000 \n",
"75% 1095.250000 70.000000 80.000000 11601.500000 7.000000 \n",
"max 1460.000000 190.000000 313.000000 215245.000000 10.000000 \n",
"\n",
" OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 \\\n",
"count 1460.000000 1460.000000 1460.000000 1452.000000 1460.000000 \n",
"mean 5.575342 1971.267808 1984.865753 103.685262 443.639726 \n",
"std 1.112799 30.202904 20.645407 181.066207 456.098091 \n",
"min 1.000000 1872.000000 1950.000000 0.000000 0.000000 \n",
"25% 5.000000 1954.000000 1967.000000 0.000000 0.000000 \n",
"50% 5.000000 1973.000000 1994.000000 0.000000 383.500000 \n",
"75% 6.000000 2000.000000 2004.000000 166.000000 712.250000 \n",
"max 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 \n",
"\n",
" ... WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n",
"count ... 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean ... 94.244521 46.660274 21.954110 3.409589 \n",
"std ... 125.338794 66.256028 61.119149 29.317331 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 0.000000 0.000000 0.000000 0.000000 \n",
"50% ... 0.000000 25.000000 0.000000 0.000000 \n",
"75% ... 168.000000 68.000000 0.000000 0.000000 \n",
"max ... 857.000000 547.000000 552.000000 508.000000 \n",
"\n",
" ScreenPorch PoolArea MiscVal MoSold YrSold \\\n",
"count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean 15.060959 2.758904 43.489041 6.321918 2007.815753 \n",
"std 55.757415 40.177307 496.123024 2.703626 1.328095 \n",
"min 0.000000 0.000000 0.000000 1.000000 2006.000000 \n",
"25% 0.000000 0.000000 0.000000 5.000000 2007.000000 \n",
"50% 0.000000 0.000000 0.000000 6.000000 2008.000000 \n",
"75% 0.000000 0.000000 0.000000 8.000000 2009.000000 \n",
"max 480.000000 738.000000 15500.000000 12.000000 2010.000000 \n",
"\n",
" SalePrice \n",
"count 1460.000000 \n",
"mean 180921.195890 \n",
"std 79442.502883 \n",
"min 34900.000000 \n",
"25% 129975.000000 \n",
"50% 163000.000000 \n",
"75% 214000.000000 \n",
"max 755000.000000 \n",
"\n",
"[8 rows x 38 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see we have a few missing values here, e.g. MasVnrArea has 1452 instead of 1460, we will have to deal with them later."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>MSSubClass</th>\n",
" <th>MSZoning</th>\n",
" <th>LotFrontage</th>\n",
" <th>LotArea</th>\n",
" <th>Street</th>\n",
" <th>Alley</th>\n",
" <th>LotShape</th>\n",
" <th>LandContour</th>\n",
" <th>Utilities</th>\n",
" <th>...</th>\n",
" <th>PoolArea</th>\n",
" <th>PoolQC</th>\n",
" <th>Fence</th>\n",
" <th>MiscFeature</th>\n",
" <th>MiscVal</th>\n",
" <th>MoSold</th>\n",
" <th>YrSold</th>\n",
" <th>SaleType</th>\n",
" <th>SaleCondition</th>\n",
" <th>SalePrice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>60</td>\n",
" <td>RL</td>\n",
" <td>65.0</td>\n",
" <td>8450</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>Reg</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2008</td>\n",
" <td>WD</td>\n",
" <td>Normal</td>\n",
" <td>208500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>RL</td>\n",
" <td>80.0</td>\n",
" <td>9600</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>Reg</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>WD</td>\n",
" <td>Normal</td>\n",
" <td>181500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>60</td>\n",
" <td>RL</td>\n",
" <td>68.0</td>\n",
" <td>11250</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>IR1</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2008</td>\n",
" <td>WD</td>\n",
" <td>Normal</td>\n",
" <td>223500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>70</td>\n",
" <td>RL</td>\n",
" <td>60.0</td>\n",
" <td>9550</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>IR1</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2006</td>\n",
" <td>WD</td>\n",
" <td>Abnorml</td>\n",
" <td>140000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>60</td>\n",
" <td>RL</td>\n",
" <td>84.0</td>\n",
" <td>14260</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>IR1</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2008</td>\n",
" <td>WD</td>\n",
" <td>Normal</td>\n",
" <td>250000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 81 columns</p>\n",
"</div>"
],
"text/plain": [
" Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
"0 1 60 RL 65.0 8450 Pave NaN Reg \n",
"1 2 20 RL 80.0 9600 Pave NaN Reg \n",
"2 3 60 RL 68.0 11250 Pave NaN IR1 \n",
"3 4 70 RL 60.0 9550 Pave NaN IR1 \n",
"4 5 60 RL 84.0 14260 Pave NaN IR1 \n",
"\n",
" LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal \\\n",
"0 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"1 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"2 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"3 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"4 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"\n",
" MoSold YrSold SaleType SaleCondition SalePrice \n",
"0 2 2008 WD Normal 208500 \n",
"1 5 2007 WD Normal 181500 \n",
"2 9 2008 WD Normal 223500 \n",
"3 2 2006 WD Abnorml 140000 \n",
"4 12 2008 WD Normal 250000 \n",
"\n",
"[5 rows x 81 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see we have many different values for each feature and they are not binary.\n",
"\n",
"Let's try visualize our data to see which feature is the most significant and affects sale price."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def lot_visualization(data):\n",
" data['LotFrontage'] = data['LotFrontage'].fillna(data['LotFrontage'].mean())\n",
" df = data[['LotFrontage', 'SalePrice']]\n",
" df = df.sort_values(['LotFrontage'])\n",
" df.plot(x='LotFrontage', y='SalePrice')\n",
" plt.xlabel(\"Linear feet of street connected to property\")\n",
" plt.ylabel(\"Selling Price\")\n",
" plt.show()\n",
" return 0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xbbd8208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = lot_visualization(train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that we couldn't distinguish much of this feature unfortunately.\n",
"Let's try something else. Let's try to change correct the skewness of the selling prices, because we can see many values are concetrated in the left side of the plot."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def show_skew(data):\n",
" print(train.SalePrice.skew())\n",
" plt.hist(train.SalePrice, color='blue')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.88287575977\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xbc95d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = show_skew(train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we suspcted, there is a positive skewness of 1.882(lean to the left of the histogram). Let's correct the skewness using the log transform, we have to remember that in the end of our predicted results we have to use a reverse transformation (The exponential transformation) in order to predict the true values."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.121335062205\n"
]
},
{
"data": {
"text/plain": [
"(array([ 5., 12., 54., 184., 470., 400., 220., 90., 19., 6.]),\n",
" array([ 10.46024211, 10.7676652 , 11.07508829, 11.38251138,\n",
" 11.68993448, 11.99735757, 12.30478066, 12.61220375,\n",
" 12.91962684, 13.22704994, 13.53447303]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xcd03518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y = np.log(train.SalePrice)\n",
"print(y.skew())\n",
"plt.hist(y, color='red')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now it is less skewed than before. It is a common practice in regression problems to perform this technique. more details __[here](https://stats.stackexchange.com/questions/12262/what-if-residuals-are-normally-distributed-but-y-is-not)__.\n",
"Now next, we will find the most important features that have to strongest link to the target variable (Sales Price).\n",
"\n",
"Now, let's try to find the best features to choose for our machine learning algorithm. Let's examine the type of features that we have."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Id MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt \\\n",
"0 1 60 65 8450 7 5 2003 \n",
"1 2 20 80 9600 6 8 1976 \n",
"2 3 60 68 11250 7 5 2001 \n",
"3 4 70 60 9550 7 5 1915 \n",
"4 5 60 84 14260 8 5 2000 \n",
"5 6 50 85 14115 5 5 1993 \n",
"6 7 20 75 10084 8 5 2004 \n",
"7 8 60 70.05 10382 7 6 1973 \n",
"8 9 50 51 6120 7 5 1931 \n",
"9 10 190 50 7420 5 6 1939 \n",
"10 11 20 70 11200 5 5 1965 \n",
"11 12 60 85 11924 9 5 2005 \n",
"12 13 20 70.05 12968 5 6 1962 \n",
"13 14 20 91 10652 7 5 2006 \n",
"14 15 20 70.05 10920 6 5 1960 \n",
"15 16 45 51 6120 7 8 1929 \n",
"16 17 20 70.05 11241 6 7 1970 \n",
"17 18 90 72 10791 4 5 1967 \n",
"18 19 20 66 13695 5 5 2004 \n",
"19 20 20 70 7560 5 6 1958 \n",
"20 21 60 101 14215 8 5 2005 \n",
"21 22 45 57 7449 7 7 1930 \n",
"22 23 20 75 9742 8 5 2002 \n",
"23 24 120 44 4224 5 7 1976 \n",
"24 25 20 70.05 8246 5 8 1968 \n",
"25 26 20 110 14230 8 5 2007 \n",
"26 27 20 60 7200 5 7 1951 \n",
"27 28 20 98 11478 8 5 2007 \n",
"28 29 20 47 16321 5 6 1957 \n",
"29 30 30 60 6324 4 6 1927 \n",
"... ... ... ... ... ... ... ... \n",
"1430 1431 60 60 21930 5 5 2005 \n",
"1431 1432 120 70.05 4928 6 6 1976 \n",
"1432 1433 30 60 10800 4 6 1927 \n",
"1433 1434 60 93 10261 6 5 2000 \n",
"1434 1435 20 80 17400 5 5 1977 \n",
"1435 1436 20 80 8400 6 9 1962 \n",
"1436 1437 20 60 9000 4 6 1971 \n",
"1437 1438 20 96 12444 8 5 2008 \n",
"1438 1439 20 90 7407 6 7 1957 \n",
"1439 1440 60 80 11584 7 6 1979 \n",
"1440 1441 70 79 11526 6 7 1922 \n",
"1441 1442 120 70.05 4426 6 5 2004 \n",
"1442 1443 60 85 11003 10 5 2008 \n",
"1443 1444 30 70.05 8854 6 6 1916 \n",
"1444 1445 20 63 8500 7 5 2004 \n",
"1445 1446 85 70 8400 6 5 1966 \n",
"1446 1447 20 70.05 26142 5 7 1962 \n",
"1447 1448 60 80 10000 8 5 1995 \n",
"1448 1449 50 70 11767 4 7 1910 \n",
"1449 1450 180 21 1533 5 7 1970 \n",
"1450 1451 90 60 9000 5 5 1974 \n",
"1451 1452 20 78 9262 8 5 2008 \n",
"1452 1453 180 35 3675 5 5 2005 \n",
"1453 1454 20 90 17217 5 5 2006 \n",
"1454 1455 20 62 7500 7 5 2004 \n",
"1455 1456 60 62 7917 6 5 1999 \n",
"1456 1457 20 85 13175 6 6 1978 \n",
"1457 1458 70 66 9042 7 9 1941 \n",
"1458 1459 20 68 9717 5 6 1950 \n",
"1459 1460 20 75 9937 5 6 1965 \n",
"\n",
" YearRemodAdd MasVnrArea BsmtFinSF1 ... WoodDeckSF OpenPorchSF \\\n",
"0 2003 196 706 ... 0 61 \n",
"1 1976 0 978 ... 298 0 \n",
"2 2002 162 486 ... 0 42 \n",
"3 1970 0 216 ... 0 35 \n",
"4 2000 350 655 ... 192 84 \n",
"5 1995 0 732 ... 40 30 \n",
"6 2005 186 1369 ... 255 57 \n",
"7 1973 240 859 ... 235 204 \n",
"8 1950 0 0 ... 90 0 \n",
"9 1950 0 851 ... 0 4 \n",
"10 1965 0 906 ... 0 0 \n",
"11 2006 286 998 ... 147 21 \n",
"12 1962 0 737 ... 140 0 \n",
"13 2007 306 0 ... 160 33 \n",
"14 1960 212 733 ... 0 213 \n",
"15 2001 0 0 ... 48 112 \n",
"16 1970 180 578 ... 0 0 \n",
"17 1967 0 0 ... 0 0 \n",
"18 2004 0 646 ... 0 102 \n",
"19 1965 0 504 ... 0 0 \n",
"20 2006 380 0 ... 240 154 \n",
"21 1950 0 0 ... 0 0 \n",
"22 2002 281 0 ... 171 159 \n",
"23 1976 0 840 ... 100 110 \n",
"24 2001 0 188 ... 406 90 \n",
"25 2007 640 0 ... 0 56 \n",
"26 2000 0 234 ... 222 32 \n",
"27 2008 200 1218 ... 0 50 \n",
"28 1997 0 1277 ... 288 258 \n",
"29 1950 0 0 ... 49 0 \n",
"... ... ... ... ... ... ... \n",
"1430 2005 0 0 ... 100 40 \n",
"1431 1976 0 958 ... 0 60 \n",
"1432 2007 0 0 ... 0 0 \n",
"1433 2000 318 0 ... 0 0 \n",
"1434 1977 0 936 ... 295 41 \n",
"1435 2005 237 0 ... 0 36 \n",
"1436 1971 0 616 ... 0 0 \n",
"1437 2008 426 1336 ... 0 66 \n",
"1438 1996 0 600 ... 0 158 \n",
"1439 1979 96 315 ... 0 88 \n",
"1440 1994 0 0 ... 431 0 \n",
"1441 2004 147 697 ... 149 0 \n",
"1442 2008 160 765 ... 168 52 \n",
"1443 1950 0 0 ... 0 98 \n",
"1444 2004 106 0 ... 192 60 \n",
"1445 1966 0 187 ... 0 0 \n",
"1446 1962 189 593 ... 261 39 \n",
"1447 1996 438 1079 ... 0 65 \n",
"1448 2000 0 0 ... 168 24 \n",
"1449 1970 0 553 ... 0 0 \n",
"1450 1974 0 0 ... 32 45 \n",
"1451 2009 194 0 ... 0 36 \n",
"1452 2005 80 547 ... 0 28 \n",
"1453 2006 0 0 ... 36 56 \n",
"1454 2005 0 410 ... 0 113 \n",
"1455 2000 0 0 ... 0 40 \n",
"1456 1988 119 790 ... 349 0 \n",
"1457 2006 0 275 ... 0 60 \n",
"1458 1996 0 49 ... 366 0 \n",
"1459 1965 0 830 ... 736 68 \n",
"\n",
" EnclosedPorch 3SsnPorch ScreenPorch PoolArea MiscVal MoSold YrSold \\\n",
"0 0 0 0 0 0 2 2008 \n",
"1 0 0 0 0 0 5 2007 \n",
"2 0 0 0 0 0 9 2008 \n",
"3 272 0 0 0 0 2 2006 \n",
"4 0 0 0 0 0 12 2008 \n",
"5 0 320 0 0 700 10 2009 \n",
"6 0 0 0 0 0 8 2007 \n",
"7 228 0 0 0 350 11 2009 \n",
"8 205 0 0 0 0 4 2008 \n",
"9 0 0 0 0 0 1 2008 \n",
"10 0 0 0 0 0 2 2008 \n",
"11 0 0 0 0 0 7 2006 \n",
"12 0 0 176 0 0 9 2008 \n",
"13 0 0 0 0 0 8 2007 \n",
"14 176 0 0 0 0 5 2008 \n",
"15 0 0 0 0 0 7 2007 \n",
"16 0 0 0 0 700 3 2010 \n",
"17 0 0 0 0 500 10 2006 \n",
"18 0 0 0 0 0 6 2008 \n",
"19 0 0 0 0 0 5 2009 \n",
"20 0 0 0 0 0 11 2006 \n",
"21 205 0 0 0 0 6 2007 \n",
"22 0 0 0 0 0 9 2008 \n",
"23 0 0 0 0 0 6 2007 \n",
"24 0 0 0 0 0 5 2010 \n",
"25 0 0 0 0 0 7 2009 \n",
"26 0 0 0 0 0 5 2010 \n",
"27 0 0 0 0 0 5 2010 \n",
"28 0 0 0 0 0 12 2006 \n",
"29 87 0 0 0 0 5 2008 \n",
"... ... ... ... ... ... ... ... \n",
"1430 0 0 0 0 0 7 2006 \n",
"1431 0 0 0 0 0 10 2009 \n",
"1432 0 0 0 0 0 8 2007 \n",
"1433 0 0 0 0 0 5 2008 \n",
"1434 0 0 0 0 0 5 2006 \n",
"1435 0 0 0 0 0 7 2008 \n",
"1436 0 0 0 0 0 5 2007 \n",
"1437 0 304 0 0 0 11 2008 \n",
"1438 158 0 0 0 0 4 2010 \n",
"1439 216 0 0 0 0 11 2007 \n",
"1440 0 0 0 0 0 9 2008 \n",
"1441 0 0 0 0 0 5 2008 \n",
"1442 0 0 0 0 0 4 2009 \n",
"1443 0 0 40 0 0 5 2009 \n",
"1444 0 0 0 0 0 11 2007 \n",
"1445 252 0 0 0 0 5 2007 \n",
"1446 0 0 0 0 0 4 2010 \n",
"1447 0 0 0 0 0 12 2007 \n",
"1448 0 0 0 0 0 5 2007 \n",
"1449 0 0 0 0 0 8 2006 \n",
"1450 0 0 0 0 0 9 2009 \n",
"1451 0 0 0 0 0 5 2009 \n",
"1452 0 0 0 0 0 5 2006 \n",
"1453 0 0 0 0 0 7 2006 \n",
"1454 0 0 0 0 0 10 2009 \n",
"1455 0 0 0 0 0 8 2007 \n",
"1456 0 0 0 0 0 2 2010 \n",
"1457 0 0 0 0 2500 5 2010 \n",
"1458 112 0 0 0 0 4 2010 \n",
"1459 0 0 0 0 0 6 2008 \n",
"\n",
" SalePrice \n",
"0 208500 \n",
"1 181500 \n",
"2 223500 \n",
"3 140000 \n",
"4 250000 \n",
"5 143000 \n",
"6 307000 \n",
"7 200000 \n",
"8 129900 \n",
"9 118000 \n",
"10 129500 \n",
"11 345000 \n",
"12 144000 \n",
"13 279500 \n",
"14 157000 \n",
"15 132000 \n",
"16 149000 \n",
"17 90000 \n",
"18 159000 \n",
"19 139000 \n",
"20 325300 \n",
"21 139400 \n",
"22 230000 \n",
"23 129900 \n",
"24 154000 \n",
"25 256300 \n",
"26 134800 \n",
"27 306000 \n",
"28 207500 \n",
"29 68500 \n",
"... ... \n",
"1430 192140 \n",
"1431 143750 \n",
"1432 64500 \n",
"1433 186500 \n",
"1434 160000 \n",
"1435 174000 \n",
"1436 120500 \n",
"1437 394617 \n",
"1438 149700 \n",
"1439 197000 \n",
"1440 191000 \n",
"1441 149300 \n",
"1442 310000 \n",
"1443 121000 \n",
"1444 179600 \n",
"1445 129000 \n",
"1446 157900 \n",
"1447 240000 \n",
"1448 112000 \n",
"1449 92000 \n",
"1450 136000 \n",
"1451 287090 \n",
"1452 145000 \n",
"1453 84500 \n",
"1454 185000 \n",
"1455 175000 \n",
"1456 210000 \n",
"1457 266500 \n",
"1458 142125 \n",
"1459 147500 \n",
"\n",
"[1460 rows x 38 columns]\n"
]
}
],
"source": [
"numeric_features = train.select_dtypes(include=[np.number])\n",
"print(numeric_features.astype(np.dtype))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that we have 38 numeric features (i.e. no words). Let's check out their variable types"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"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": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numeric_features.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try to correlate between the different columns and the sales price"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"KitchenAbvGr -0.135907\n",
"EnclosedPorch -0.128578\n",
"MSSubClass -0.084284\n",
"OverallCond -0.077856\n",
"YrSold -0.028923\n",
"LowQualFinSF -0.025606\n",
"Id -0.021917\n",
"MiscVal -0.021190\n",
"BsmtHalfBath -0.016844\n",
"BsmtFinSF2 -0.011378\n",
"3SsnPorch 0.044584\n",
"MoSold 0.046432\n",
"PoolArea 0.092404\n",
"ScreenPorch 0.111447\n",
"BedroomAbvGr 0.168213\n",
"BsmtUnfSF 0.214479\n",
"BsmtFullBath 0.227122\n",
"LotArea 0.263843\n",
"HalfBath 0.284108\n",
"OpenPorchSF 0.315856\n",
"2ndFlrSF 0.319334\n",
"WoodDeckSF 0.324413\n",
"LotFrontage 0.334901\n",
"BsmtFinSF1 0.386420\n",
"Fireplaces 0.466929\n",
"MasVnrArea 0.477493\n",
"GarageYrBlt 0.486362\n",
"YearRemodAdd 0.507101\n",
"YearBuilt 0.522897\n",
"TotRmsAbvGrd 0.533723\n",
"FullBath 0.560664\n",
"1stFlrSF 0.605852\n",
"TotalBsmtSF 0.613581\n",
"GarageArea 0.623431\n",
"GarageCars 0.640409\n",
"GrLivArea 0.708624\n",
"OverallQual 0.790982\n",
"SalePrice 1.000000\n",
"Name: SalePrice, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"correlations = numeric_features[numeric_features.columns[0:]].corr()['SalePrice']\n",
"correlations.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see negative correlation (feature goes up so sales price goes down and vice versa) and also a positive correlation.\n",
"For example, Above grade (ground) living area square feet feature (GrLivArea) has a very high positive value which means that if this area is high then the sale price is higher.\n",
"Let's take best negative and positive correlations, as it indicates the sale price. Let's take the best ones."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Negative Correlation\n"
]
},
{
"data": {
"text/plain": [
"KitchenAbvGr -0.135907\n",
"EnclosedPorch -0.128578\n",
"MSSubClass -0.084284\n",
"OverallCond -0.077856\n",
"YrSold -0.028923\n",
"Name: SalePrice, dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Negative Correlation')\n",
"correlations.sort_values().head(5)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Positive Correlation\n"
]
},
{
"data": {
"text/plain": [
"FullBath 0.560664\n",
"1stFlrSF 0.605852\n",
"TotalBsmtSF 0.613581\n",
"GarageArea 0.623431\n",
"GarageCars 0.640409\n",
"GrLivArea 0.708624\n",
"OverallQual 0.790982\n",
"SalePrice 1.000000\n",
"Name: SalePrice, dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print('Positive Correlation')\n",
"correlations.sort_values().tail(8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"obviously, the highest correlation is SalePrice(Variable with itself, like duh?) and that is why I took the 6 best correlators with positive correlation. Let's try a \"distance correlation\", we will try this distance correlation since it could be that there is also a non linear connection between our features and the pearson correlation is only effective for linear connections between variables. In order to understand this implementation please refer to __[Here](https://stats.stackexchange.com/questions/183572/understanding-distance-correlation-computations)__"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from scipy.spatial.distance import pdist, squareform\n",
"import numpy as np\n",
"\n",
"def distcorr(column):\n",
" X= column\n",
" Y = numeric_features.SalePrice\n",
" X = np.atleast_1d(X)\n",
" Y = np.atleast_1d(Y)\n",
" if np.prod(X.shape) == len(X):\n",
" X = X[:, None]\n",
" if np.prod(Y.shape) == len(Y):\n",
" Y = Y[:, None]\n",
" X = np.atleast_2d(X)\n",
" Y = np.atleast_2d(Y)\n",
" n = X.shape[0]\n",
" if Y.shape[0] != X.shape[0]:\n",
" raise NameError('Not equal sample numbers')\n",
" a = squareform(pdist(X))\n",
" b = squareform(pdist(Y))\n",
" A = a - a.mean(axis=0)[None, :] - a.mean(axis=1)[:, None] + a.mean()\n",
" B = b - b.mean(axis=0)[None, :] - b.mean(axis=1)[:, None] + b.mean()\n",
" dcov2_xy = (A * B).sum()/float(n * n)\n",
" dcov2_xx = (A * A).sum()/float(n * n)\n",
" dcov2_yy = (B * B).sum()/float(n * n)\n",
" dcor = np.sqrt(dcov2_xy)/np.sqrt(np.sqrt(dcov2_xx) * np.sqrt(dcov2_yy))\n",
" return dcor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let'S examine the correlation between Sales Price and year sold"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"YrSold 0.036179\n",
"Id 0.046646\n",
"MiscVal 0.053874\n",
"LowQualFinSF 0.057627\n",
"BsmtHalfBath 0.062334\n",
"3SsnPorch 0.063333\n",
"BsmtFinSF2 0.068149\n",
"PoolArea 0.070474\n",
"MoSold 0.078024\n",
"ScreenPorch 0.108473\n",
"KitchenAbvGr 0.159370\n",
"MSSubClass 0.176071\n",
"EnclosedPorch 0.178378\n",
"BsmtUnfSF 0.215036\n",
"BedroomAbvGr 0.219351\n",
"BsmtFullBath 0.225036\n",
"OverallCond 0.250109\n",
"2ndFlrSF 0.301247\n",
"HalfBath 0.317266\n",
"WoodDeckSF 0.345903\n",
"BsmtFinSF1 0.349787\n",
"LotFrontage 0.363172\n",
"LotArea 0.409040\n",
"OpenPorchSF 0.424288\n",
"Fireplaces 0.490786\n",
"TotRmsAbvGrd 0.509000\n",
"YearRemodAdd 0.542350\n",
"1stFlrSF 0.578801\n",
"FullBath 0.596613\n",
"YearBuilt 0.604556\n",
"TotalBsmtSF 0.606260\n",
"GarageArea 0.633877\n",
"GarageCars 0.639625\n",
"GrLivArea 0.702352\n",
"OverallQual 0.783135\n",
"SalePrice 1.000000\n",
"MasVnrArea NaN\n",
"GarageYrBlt NaN\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"distance_correlations = numeric_features.apply(distcorr, axis=0)\n",
"distance_correlations.sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that we have many features similar to the pearson correlation ranking, but we also have the \"YearBuilt\" feature appearing in the distance correlation. Looking at this, seems like that the difference between the Distance Correlation and the Pearson Correlation for this feature is not so significant (0.6045 Vs. 0.522). So we can conclude that the difference between the two is not so big and the main correlations are linear in this data set.\n",
"\n",
"\n",
"Let's take a look at other features"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd21bf60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=train['GarageArea'], y=y, color='r')\n",
"plt.ylabel('price')\n",
"plt.xlabel('Garage Area')\n",
"plt.title('Garage Area Vs. Sale Price')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at this, we want to create a new feature, \"Existence of Garage\". This feature will hold \"1\" if you have a garage and 0 if you don't have a garage . This is due to the fact that we can many houses with 0 garage area, which means that they don't have a garage at all."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"a = train['GarageArea'].map(lambda s: 1 if s != 0 else 0)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xd434c88>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd4384a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that many of the houses do posses a garage. So it could be that this feature won't help much, we will see later.\n",
"\n",
"Let's move now for our missing data and handle it in order to complete it."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Id</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSSubClass</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSZoning</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotFrontage</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotArea</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Street</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alley</th>\n",
" <td>1369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotShape</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LandContour</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Utilities</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotConfig</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LandSlope</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Neighborhood</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Condition1</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Condition2</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BldgType</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HouseStyle</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OverallQual</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OverallCond</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YearBuilt</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YearRemodAdd</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RoofStyle</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RoofMatl</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exterior1st</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exterior2nd</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MasVnrType</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MasVnrArea</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExterQual</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExterCond</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Foundation</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BedroomAbvGr</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KitchenAbvGr</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KitchenQual</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TotRmsAbvGrd</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Functional</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fireplaces</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FireplaceQu</th>\n",
" <td>690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageType</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageYrBlt</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageFinish</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageCars</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageArea</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageQual</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageCond</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PavedDrive</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WoodDeckSF</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OpenPorchSF</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EnclosedPorch</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3SsnPorch</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ScreenPorch</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PoolArea</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PoolQC</th>\n",
" <td>1453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fence</th>\n",
" <td>1179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MiscFeature</th>\n",
" <td>1406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MiscVal</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MoSold</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YrSold</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SaleType</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SaleCondition</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SalePrice</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>81 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"Id 0\n",
"MSSubClass 0\n",
"MSZoning 0\n",
"LotFrontage 0\n",
"LotArea 0\n",
"Street 0\n",
"Alley 1369\n",
"LotShape 0\n",
"LandContour 0\n",
"Utilities 0\n",
"LotConfig 0\n",
"LandSlope 0\n",
"Neighborhood 0\n",
"Condition1 0\n",
"Condition2 0\n",
"BldgType 0\n",
"HouseStyle 0\n",
"OverallQual 0\n",
"OverallCond 0\n",
"YearBuilt 0\n",
"YearRemodAdd 0\n",
"RoofStyle 0\n",
"RoofMatl 0\n",
"Exterior1st 0\n",
"Exterior2nd 0\n",
"MasVnrType 8\n",
"MasVnrArea 8\n",
"ExterQual 0\n",
"ExterCond 0\n",
"Foundation 0\n",
"... ...\n",
"BedroomAbvGr 0\n",
"KitchenAbvGr 0\n",
"KitchenQual 0\n",
"TotRmsAbvGrd 0\n",
"Functional 0\n",
"Fireplaces 0\n",
"FireplaceQu 690\n",
"GarageType 81\n",
"GarageYrBlt 81\n",
"GarageFinish 81\n",
"GarageCars 0\n",
"GarageArea 0\n",
"GarageQual 81\n",
"GarageCond 81\n",
"PavedDrive 0\n",
"WoodDeckSF 0\n",
"OpenPorchSF 0\n",
"EnclosedPorch 0\n",
"3SsnPorch 0\n",
"ScreenPorch 0\n",
"PoolArea 0\n",
"PoolQC 1453\n",
"Fence 1179\n",
"MiscFeature 1406\n",
"MiscVal 0\n",
"MoSold 0\n",
"YrSold 0\n",
"SaleType 0\n",
"SaleCondition 0\n",
"SalePrice 0\n",
"\n",
"[81 rows x 1 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Missing_Features = pd.DataFrame(train.isnull().sum())\n",
"Missing_Features"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>PoolQC</th>\n",
" <td>1453</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MiscFeature</th>\n",
" <td>1406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alley</th>\n",
" <td>1369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fence</th>\n",
" <td>1179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FireplaceQu</th>\n",
" <td>690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageType</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageYrBlt</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageQual</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageCond</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageFinish</th>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtFinType2</th>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtExposure</th>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtCond</th>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtFinType1</th>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtQual</th>\n",
" <td>37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MasVnrArea</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MasVnrType</th>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electrical</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtFullBath</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Functional</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HalfBath</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BedroomAbvGr</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FullBath</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TotRmsAbvGrd</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fireplaces</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KitchenQual</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KitchenAbvGr</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtHalfBath</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Id</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LowQualFinSF</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Condition1</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Neighborhood</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LandSlope</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Utilities</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OverallCond</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LandContour</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotShape</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Street</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotArea</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotFrontage</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSZoning</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OverallQual</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YearBuilt</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1stFlrSF</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtFinSF1</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CentralAir</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSSubClass</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heating</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TotalBsmtSF</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtUnfSF</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BsmtFinSF2</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Foundation</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YearRemodAdd</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExterCond</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExterQual</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exterior2nd</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exterior1st</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RoofMatl</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RoofStyle</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SalePrice</th>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>81 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" Count\n",
"PoolQC 1453\n",
"MiscFeature 1406\n",
"Alley 1369\n",
"Fence 1179\n",
"FireplaceQu 690\n",
"GarageType 81\n",
"GarageYrBlt 81\n",
"GarageQual 81\n",
"GarageCond 81\n",
"GarageFinish 81\n",
"BsmtFinType2 38\n",
"BsmtExposure 38\n",
"BsmtCond 37\n",
"BsmtFinType1 37\n",
"BsmtQual 37\n",
"MasVnrArea 8\n",
"MasVnrType 8\n",
"Electrical 1\n",
"BsmtFullBath 0\n",
"Functional 0\n",
"HalfBath 0\n",
"BedroomAbvGr 0\n",
"FullBath 0\n",
"TotRmsAbvGrd 0\n",
"Fireplaces 0\n",
"KitchenQual 0\n",
"KitchenAbvGr 0\n",
"BsmtHalfBath 0\n",
"Id 0\n",
"LowQualFinSF 0\n",
"... ...\n",
"Condition1 0\n",
"Neighborhood 0\n",
"LandSlope 0\n",
"Utilities 0\n",
"OverallCond 0\n",
"LandContour 0\n",
"LotShape 0\n",
"Street 0\n",
"LotArea 0\n",
"LotFrontage 0\n",
"MSZoning 0\n",
"OverallQual 0\n",
"YearBuilt 0\n",
"1stFlrSF 0\n",
"BsmtFinSF1 0\n",
"CentralAir 0\n",
"MSSubClass 0\n",
"Heating 0\n",
"TotalBsmtSF 0\n",
"BsmtUnfSF 0\n",
"BsmtFinSF2 0\n",
"Foundation 0\n",
"YearRemodAdd 0\n",
"ExterCond 0\n",
"ExterQual 0\n",
"Exterior2nd 0\n",
"Exterior1st 0\n",
"RoofMatl 0\n",
"RoofStyle 0\n",
"SalePrice 0\n",
"\n",
"[81 rows x 1 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Missing_Features.columns = ['Count']\n",
"Missing_Features.sort_values('Count', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see tons of missing values in PoolQC (stands for pool quality according to the description), let's handle the missing values in these 18 categories (we will do it later)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([nan, 'TA', 'Gd', 'Fa', 'Ex', 'Po'], dtype=object)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.FireplaceQu.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use get dummies for these features, the get dummies will give us a column for each category (total of 6 columns) and will present a \"1\" in the line of the sample if this category fits the sample. You can see in the next example this kind of procedure."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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" </tr>\n",
" <tr>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
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" </tr>\n",
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" <th>18</th>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
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" <th>19</th>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
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" <th>20</th>\n",
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" <td>0</td>\n",
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" <th>23</th>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" </tr>\n",
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" </tr>\n",
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" <th>29</th>\n",
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" </tr>\n",
" <tr>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1430</th>\n",
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" <tr>\n",
" <th>1445</th>\n",
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" <tr>\n",
" <th>1447</th>\n",
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" <tr>\n",
" <th>1449</th>\n",
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" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1450</th>\n",
" <td>0</td>\n",
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" <tr>\n",
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" <tr>\n",
" <th>1455</th>\n",
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" <tr>\n",
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"</table>\n",
"<p>1460 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Ex Fa Gd NA Po TA\n",
"0 0 0 0 1 0 0\n",
"1 0 0 0 0 0 1\n",
"2 0 0 0 0 0 1\n",
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"29 0 0 0 1 0 0\n",
"... .. .. .. .. .. ..\n",
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"1458 0 0 0 1 0 0\n",
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"\n",
"[1460 rows x 6 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.FireplaceQu = train.FireplaceQu.fillna('NA') # filling missing values (nan) with NA (Not Applicable) Values for get dummies\n",
"Fire = pd.get_dummies(train.FireplaceQu)\n",
"Fire"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will complete the missing values of the numerical features with the median (it is possible to complete it with the mean as well). This method is not the best one since it is possible that our missing values are not exactly distribuded univariently. There are many different methods to handle missing data (this process is called data amputation), and I chose a simple way since data amputation is not the focus of this exercise. Maybe I will try to improve it later."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"data = train.select_dtypes(include=[np.number]).interpolate().dropna()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Filled all the missing values, now let's remove the Sale price column and the Id."
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th>OverallCond</th>\n",
" <th>YearBuilt</th>\n",
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" <th>BsmtFinSF1</th>\n",
" <th>BsmtFinSF2</th>\n",
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" <td>2009</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>...</td>\n",
" <td>736</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",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>20</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>352</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",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>20</td>\n",
" <td>91.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>840</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",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>20</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>352</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",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>45</td>\n",
" <td>51.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>576</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",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>20</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>480</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",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>90</td>\n",
" <td>72.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>516</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",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20</td>\n",
" <td>66.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>576</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",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>70.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>294</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",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>60</td>\n",
" <td>101.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>853</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",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>45</td>\n",
" <td>57.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>280</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",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>20</td>\n",
" <td>75.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>534</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",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>120</td>\n",
" <td>44.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>572</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",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>20</td>\n",
" <td>70.049958</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>668</td>\n",
" <td>...</td>\n",
" <td>270</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",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>20</td>\n",
" <td>110.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>890</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",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>20</td>\n",
" <td>60.000000</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>486</td>\n",
" <td>...</td>\n",
" <td>576</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",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>20</td>\n",
" <td>98.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>772</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",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>20</td>\n",
" <td>47.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>319</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",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>240</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",
" </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>60</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>372</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",
" </tr>\n",
" <tr>\n",
" <th>1431</th>\n",
" <td>120</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>440</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",
" </tr>\n",
" <tr>\n",
" <th>1432</th>\n",
" <td>30</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>216</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",
" </tr>\n",
" <tr>\n",
" <th>1433</th>\n",
" <td>60</td>\n",
" <td>93.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>451</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",
" </tr>\n",
" <tr>\n",
" <th>1434</th>\n",
" <td>20</td>\n",
" <td>80.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>484</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",
" </tr>\n",
" <tr>\n",
" <th>1435</th>\n",
" <td>20</td>\n",
" <td>80.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>462</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",
" </tr>\n",
" <tr>\n",
" <th>1436</th>\n",
" <td>20</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>528</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",
" </tr>\n",
" <tr>\n",
" <th>1437</th>\n",
" <td>20</td>\n",
" <td>96.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>774</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",
" </tr>\n",
" <tr>\n",
" <th>1438</th>\n",
" <td>20</td>\n",
" <td>90.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>923</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",
" </tr>\n",
" <tr>\n",
" <th>1439</th>\n",
" <td>60</td>\n",
" <td>80.000000</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>110</td>\n",
" <td>...</td>\n",
" <td>550</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",
" </tr>\n",
" <tr>\n",
" <th>1440</th>\n",
" <td>70</td>\n",
" <td>79.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>672</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",
" </tr>\n",
" <tr>\n",
" <th>1441</th>\n",
" <td>120</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>420</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",
" </tr>\n",
" <tr>\n",
" <th>1442</th>\n",
" <td>60</td>\n",
" <td>85.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>812</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",
" </tr>\n",
" <tr>\n",
" <th>1443</th>\n",
" <td>30</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>192</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",
" </tr>\n",
" <tr>\n",
" <th>1444</th>\n",
" <td>20</td>\n",
" <td>63.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>626</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",
" </tr>\n",
" <tr>\n",
" <th>1445</th>\n",
" <td>85</td>\n",
" <td>70.000000</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>627</td>\n",
" <td>...</td>\n",
" <td>240</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",
" </tr>\n",
" <tr>\n",
" <th>1446</th>\n",
" <td>20</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>312</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",
" </tr>\n",
" <tr>\n",
" <th>1447</th>\n",
" <td>60</td>\n",
" <td>80.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>556</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",
" </tr>\n",
" <tr>\n",
" <th>1448</th>\n",
" <td>50</td>\n",
" <td>70.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>384</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",
" </tr>\n",
" <tr>\n",
" <th>1449</th>\n",
" <td>180</td>\n",
" <td>21.000000</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>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>0</td>\n",
" <td>8</td>\n",
" <td>2006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1450</th>\n",
" <td>90</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</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",
" </tr>\n",
" <tr>\n",
" <th>1451</th>\n",
" <td>20</td>\n",
" <td>78.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>840</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",
" </tr>\n",
" <tr>\n",
" <th>1452</th>\n",
" <td>180</td>\n",
" <td>35.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>525</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",
" </tr>\n",
" <tr>\n",
" <th>1453</th>\n",
" <td>20</td>\n",
" <td>90.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</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",
" </tr>\n",
" <tr>\n",
" <th>1454</th>\n",
" <td>20</td>\n",
" <td>62.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>400</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",
" </tr>\n",
" <tr>\n",
" <th>1455</th>\n",
" <td>60</td>\n",
" <td>62.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>460</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",
" </tr>\n",
" <tr>\n",
" <th>1456</th>\n",
" <td>20</td>\n",
" <td>85.000000</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>163</td>\n",
" <td>...</td>\n",
" <td>500</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",
" </tr>\n",
" <tr>\n",
" <th>1457</th>\n",
" <td>70</td>\n",
" <td>66.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>252</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",
" </tr>\n",
" <tr>\n",
" <th>1458</th>\n",
" <td>20</td>\n",
" <td>68.000000</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>1029</td>\n",
" <td>...</td>\n",
" <td>240</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",
" </tr>\n",
" <tr>\n",
" <th>1459</th>\n",
" <td>20</td>\n",
" <td>75.000000</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>290</td>\n",
" <td>...</td>\n",
" <td>276</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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1460 rows × 36 columns</p>\n",
"</div>"
],
"text/plain": [
" MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt \\\n",
"0 60 65.000000 8450 7 5 2003 \n",
"1 20 80.000000 9600 6 8 1976 \n",
"2 60 68.000000 11250 7 5 2001 \n",
"3 70 60.000000 9550 7 5 1915 \n",
"4 60 84.000000 14260 8 5 2000 \n",
"5 50 85.000000 14115 5 5 1993 \n",
"6 20 75.000000 10084 8 5 2004 \n",
"7 60 70.049958 10382 7 6 1973 \n",
"8 50 51.000000 6120 7 5 1931 \n",
"9 190 50.000000 7420 5 6 1939 \n",
"10 20 70.000000 11200 5 5 1965 \n",
"11 60 85.000000 11924 9 5 2005 \n",
"12 20 70.049958 12968 5 6 1962 \n",
"13 20 91.000000 10652 7 5 2006 \n",
"14 20 70.049958 10920 6 5 1960 \n",
"15 45 51.000000 6120 7 8 1929 \n",
"16 20 70.049958 11241 6 7 1970 \n",
"17 90 72.000000 10791 4 5 1967 \n",
"18 20 66.000000 13695 5 5 2004 \n",
"19 20 70.000000 7560 5 6 1958 \n",
"20 60 101.000000 14215 8 5 2005 \n",
"21 45 57.000000 7449 7 7 1930 \n",
"22 20 75.000000 9742 8 5 2002 \n",
"23 120 44.000000 4224 5 7 1976 \n",
"24 20 70.049958 8246 5 8 1968 \n",
"25 20 110.000000 14230 8 5 2007 \n",
"26 20 60.000000 7200 5 7 1951 \n",
"27 20 98.000000 11478 8 5 2007 \n",
"28 20 47.000000 16321 5 6 1957 \n",
"29 30 60.000000 6324 4 6 1927 \n",
"... ... ... ... ... ... ... \n",
"1430 60 60.000000 21930 5 5 2005 \n",
"1431 120 70.049958 4928 6 6 1976 \n",
"1432 30 60.000000 10800 4 6 1927 \n",
"1433 60 93.000000 10261 6 5 2000 \n",
"1434 20 80.000000 17400 5 5 1977 \n",
"1435 20 80.000000 8400 6 9 1962 \n",
"1436 20 60.000000 9000 4 6 1971 \n",
"1437 20 96.000000 12444 8 5 2008 \n",
"1438 20 90.000000 7407 6 7 1957 \n",
"1439 60 80.000000 11584 7 6 1979 \n",
"1440 70 79.000000 11526 6 7 1922 \n",
"1441 120 70.049958 4426 6 5 2004 \n",
"1442 60 85.000000 11003 10 5 2008 \n",
"1443 30 70.049958 8854 6 6 1916 \n",
"1444 20 63.000000 8500 7 5 2004 \n",
"1445 85 70.000000 8400 6 5 1966 \n",
"1446 20 70.049958 26142 5 7 1962 \n",
"1447 60 80.000000 10000 8 5 1995 \n",
"1448 50 70.000000 11767 4 7 1910 \n",
"1449 180 21.000000 1533 5 7 1970 \n",
"1450 90 60.000000 9000 5 5 1974 \n",
"1451 20 78.000000 9262 8 5 2008 \n",
"1452 180 35.000000 3675 5 5 2005 \n",
"1453 20 90.000000 17217 5 5 2006 \n",
"1454 20 62.000000 7500 7 5 2004 \n",
"1455 60 62.000000 7917 6 5 1999 \n",
"1456 20 85.000000 13175 6 6 1978 \n",
"1457 70 66.000000 9042 7 9 1941 \n",
"1458 20 68.000000 9717 5 6 1950 \n",
"1459 20 75.000000 9937 5 6 1965 \n",
"\n",
" YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... GarageArea \\\n",
"0 2003 196.0 706 0 ... 548 \n",
"1 1976 0.0 978 0 ... 460 \n",
"2 2002 162.0 486 0 ... 608 \n",
"3 1970 0.0 216 0 ... 642 \n",
"4 2000 350.0 655 0 ... 836 \n",
"5 1995 0.0 732 0 ... 480 \n",
"6 2005 186.0 1369 0 ... 636 \n",
"7 1973 240.0 859 32 ... 484 \n",
"8 1950 0.0 0 0 ... 468 \n",
"9 1950 0.0 851 0 ... 205 \n",
"10 1965 0.0 906 0 ... 384 \n",
"11 2006 286.0 998 0 ... 736 \n",
"12 1962 0.0 737 0 ... 352 \n",
"13 2007 306.0 0 0 ... 840 \n",
"14 1960 212.0 733 0 ... 352 \n",
"15 2001 0.0 0 0 ... 576 \n",
"16 1970 180.0 578 0 ... 480 \n",
"17 1967 0.0 0 0 ... 516 \n",
"18 2004 0.0 646 0 ... 576 \n",
"19 1965 0.0 504 0 ... 294 \n",
"20 2006 380.0 0 0 ... 853 \n",
"21 1950 0.0 0 0 ... 280 \n",
"22 2002 281.0 0 0 ... 534 \n",
"23 1976 0.0 840 0 ... 572 \n",
"24 2001 0.0 188 668 ... 270 \n",
"25 2007 640.0 0 0 ... 890 \n",
"26 2000 0.0 234 486 ... 576 \n",
"27 2008 200.0 1218 0 ... 772 \n",
"28 1997 0.0 1277 0 ... 319 \n",
"29 1950 0.0 0 0 ... 240 \n",
"... ... ... ... ... ... ... \n",
"1430 2005 0.0 0 0 ... 372 \n",
"1431 1976 0.0 958 0 ... 440 \n",
"1432 2007 0.0 0 0 ... 216 \n",
"1433 2000 318.0 0 0 ... 451 \n",
"1434 1977 0.0 936 0 ... 484 \n",
"1435 2005 237.0 0 0 ... 462 \n",
"1436 1971 0.0 616 0 ... 528 \n",
"1437 2008 426.0 1336 0 ... 774 \n",
"1438 1996 0.0 600 0 ... 923 \n",
"1439 1979 96.0 315 110 ... 550 \n",
"1440 1994 0.0 0 0 ... 672 \n",
"1441 2004 147.0 697 0 ... 420 \n",
"1442 2008 160.0 765 0 ... 812 \n",
"1443 1950 0.0 0 0 ... 192 \n",
"1444 2004 106.0 0 0 ... 626 \n",
"1445 1966 0.0 187 627 ... 240 \n",
"1446 1962 189.0 593 0 ... 312 \n",
"1447 1996 438.0 1079 0 ... 556 \n",
"1448 2000 0.0 0 0 ... 384 \n",
"1449 1970 0.0 553 0 ... 0 \n",
"1450 1974 0.0 0 0 ... 0 \n",
"1451 2009 194.0 0 0 ... 840 \n",
"1452 2005 80.0 547 0 ... 525 \n",
"1453 2006 0.0 0 0 ... 0 \n",
"1454 2005 0.0 410 0 ... 400 \n",
"1455 2000 0.0 0 0 ... 460 \n",
"1456 1988 119.0 790 163 ... 500 \n",
"1457 2006 0.0 275 0 ... 252 \n",
"1458 1996 0.0 49 1029 ... 240 \n",
"1459 1965 0.0 830 290 ... 276 \n",
"\n",
" WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch ScreenPorch \\\n",
"0 0 61 0 0 0 \n",
"1 298 0 0 0 0 \n",
"2 0 42 0 0 0 \n",
"3 0 35 272 0 0 \n",
"4 192 84 0 0 0 \n",
"5 40 30 0 320 0 \n",
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"... ... ... ... ... ... \n",
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"\n",
" PoolArea MiscVal MoSold YrSold \n",
"0 0 0 2 2008 \n",
"1 0 0 5 2007 \n",
"2 0 0 9 2008 \n",
"3 0 0 2 2006 \n",
"4 0 0 12 2008 \n",
"5 0 700 10 2009 \n",
"6 0 0 8 2007 \n",
"7 0 350 11 2009 \n",
"8 0 0 4 2008 \n",
"9 0 0 1 2008 \n",
"10 0 0 2 2008 \n",
"11 0 0 7 2006 \n",
"12 0 0 9 2008 \n",
"13 0 0 8 2007 \n",
"14 0 0 5 2008 \n",
"15 0 0 7 2007 \n",
"16 0 700 3 2010 \n",
"17 0 500 10 2006 \n",
"18 0 0 6 2008 \n",
"19 0 0 5 2009 \n",
"20 0 0 11 2006 \n",
"21 0 0 6 2007 \n",
"22 0 0 9 2008 \n",
"23 0 0 6 2007 \n",
"24 0 0 5 2010 \n",
"25 0 0 7 2009 \n",
"26 0 0 5 2010 \n",
"27 0 0 5 2010 \n",
"28 0 0 12 2006 \n",
"29 0 0 5 2008 \n",
"... ... ... ... ... \n",
"1430 0 0 7 2006 \n",
"1431 0 0 10 2009 \n",
"1432 0 0 8 2007 \n",
"1433 0 0 5 2008 \n",
"1434 0 0 5 2006 \n",
"1435 0 0 7 2008 \n",
"1436 0 0 5 2007 \n",
"1437 0 0 11 2008 \n",
"1438 0 0 4 2010 \n",
"1439 0 0 11 2007 \n",
"1440 0 0 9 2008 \n",
"1441 0 0 5 2008 \n",
"1442 0 0 4 2009 \n",
"1443 0 0 5 2009 \n",
"1444 0 0 11 2007 \n",
"1445 0 0 5 2007 \n",
"1446 0 0 4 2010 \n",
"1447 0 0 12 2007 \n",
"1448 0 0 5 2007 \n",
"1449 0 0 8 2006 \n",
"1450 0 0 9 2009 \n",
"1451 0 0 5 2009 \n",
"1452 0 0 5 2006 \n",
"1453 0 0 7 2006 \n",
"1454 0 0 10 2009 \n",
"1455 0 0 8 2007 \n",
"1456 0 0 2 2010 \n",
"1457 0 2500 5 2010 \n",
"1458 0 0 4 2010 \n",
"1459 0 0 6 2008 \n",
"\n",
"[1460 rows x 36 columns]"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data =data.drop(['SalePrice','Id'], axis=1)\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's add the feature we engineered concerning the garage one hot encoder."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MSSubClass</th>\n",
" <th>LotFrontage</th>\n",
" <th>LotArea</th>\n",
" <th>OverallQual</th>\n",
" <th>OverallCond</th>\n",
" <th>YearBuilt</th>\n",
" <th>YearRemodAdd</th>\n",
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" <th>BsmtFinSF1</th>\n",
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" <th>0</th>\n",
" <td>60</td>\n",
" <td>65.000000</td>\n",
" <td>8450</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2003</td>\n",
" <td>2003</td>\n",
" <td>196.0</td>\n",
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" <td>20</td>\n",
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" <td>8</td>\n",
" <td>1976</td>\n",
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" <td>0.0</td>\n",
" <td>216</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <th>4</th>\n",
" <td>60</td>\n",
" <td>84.000000</td>\n",
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" <td>5</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>350.0</td>\n",
" <td>655</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <td>14115</td>\n",
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" <td>5</td>\n",
" <td>1993</td>\n",
" <td>1995</td>\n",
" <td>0.0</td>\n",
" <td>732</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
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" <td>10</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <th>6</th>\n",
" <td>20</td>\n",
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" <td>5</td>\n",
" <td>2004</td>\n",
" <td>2005</td>\n",
" <td>186.0</td>\n",
" <td>1369</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2007</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>60</td>\n",
" <td>70.049958</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>32</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>350</td>\n",
" <td>11</td>\n",
" <td>2009</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",
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" <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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2008</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",
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" <th>9</th>\n",
" <td>190</td>\n",
" <td>50.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2008</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",
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" <th>10</th>\n",
" <td>20</td>\n",
" <td>70.000000</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>0</td>\n",
" <td>...</td>\n",
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" <td>0</td>\n",
" <td>2</td>\n",
" <td>2008</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <th>11</th>\n",
" <td>60</td>\n",
" <td>85.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <td>20</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
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" <td>0</td>\n",
" <td>9</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>20</td>\n",
" <td>91.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2007</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <th>14</th>\n",
" <td>20</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <th>15</th>\n",
" <td>45</td>\n",
" <td>51.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2007</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>16</th>\n",
" <td>20</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>700</td>\n",
" <td>3</td>\n",
" <td>2010</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",
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" <tr>\n",
" <th>17</th>\n",
" <td>90</td>\n",
" <td>72.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>500</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20</td>\n",
" <td>66.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>70.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>20</th>\n",
" <td>60</td>\n",
" <td>101.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>45</td>\n",
" <td>57.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2007</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>20</td>\n",
" <td>75.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>120</td>\n",
" <td>44.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2007</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",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>20</td>\n",
" <td>70.049958</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>668</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2010</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",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>20</td>\n",
" <td>110.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>20</td>\n",
" <td>60.000000</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>486</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>20</td>\n",
" <td>98.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>20</td>\n",
" <td>47.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </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>60</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1431</th>\n",
" <td>120</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1432</th>\n",
" <td>30</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2007</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1433</th>\n",
" <td>60</td>\n",
" <td>93.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2008</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",
" </tr>\n",
" <tr>\n",
" <th>1434</th>\n",
" <td>20</td>\n",
" <td>80.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1435</th>\n",
" <td>20</td>\n",
" <td>80.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1436</th>\n",
" <td>20</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1437</th>\n",
" <td>20</td>\n",
" <td>96.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1438</th>\n",
" <td>20</td>\n",
" <td>90.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1439</th>\n",
" <td>60</td>\n",
" <td>80.000000</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>110</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2007</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",
" </tr>\n",
" <tr>\n",
" <th>1440</th>\n",
" <td>70</td>\n",
" <td>79.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1441</th>\n",
" <td>120</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2008</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",
" </tr>\n",
" <tr>\n",
" <th>1442</th>\n",
" <td>60</td>\n",
" <td>85.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2009</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1443</th>\n",
" <td>30</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444</th>\n",
" <td>20</td>\n",
" <td>63.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2007</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1445</th>\n",
" <td>85</td>\n",
" <td>70.000000</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>627</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1446</th>\n",
" <td>20</td>\n",
" <td>70.049958</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1447</th>\n",
" <td>60</td>\n",
" <td>80.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2007</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",
" </tr>\n",
" <tr>\n",
" <th>1448</th>\n",
" <td>50</td>\n",
" <td>70.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1449</th>\n",
" <td>180</td>\n",
" <td>21.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1450</th>\n",
" <td>90</td>\n",
" <td>60.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1451</th>\n",
" <td>20</td>\n",
" <td>78.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1452</th>\n",
" <td>180</td>\n",
" <td>35.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1453</th>\n",
" <td>20</td>\n",
" <td>90.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1454</th>\n",
" <td>20</td>\n",
" <td>62.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1455</th>\n",
" <td>60</td>\n",
" <td>62.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>2007</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",
" </tr>\n",
" <tr>\n",
" <th>1456</th>\n",
" <td>20</td>\n",
" <td>85.000000</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>163</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2010</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",
" </tr>\n",
" <tr>\n",
" <th>1457</th>\n",
" <td>70</td>\n",
" <td>66.000000</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>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>2500</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1458</th>\n",
" <td>20</td>\n",
" <td>68.000000</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>1029</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1459</th>\n",
" <td>20</td>\n",
" <td>75.000000</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>290</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1460 rows × 42 columns</p>\n",
"</div>"
],
"text/plain": [
" MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt \\\n",
"0 60 65.000000 8450 7 5 2003 \n",
"1 20 80.000000 9600 6 8 1976 \n",
"2 60 68.000000 11250 7 5 2001 \n",
"3 70 60.000000 9550 7 5 1915 \n",
"4 60 84.000000 14260 8 5 2000 \n",
"5 50 85.000000 14115 5 5 1993 \n",
"6 20 75.000000 10084 8 5 2004 \n",
"7 60 70.049958 10382 7 6 1973 \n",
"8 50 51.000000 6120 7 5 1931 \n",
"9 190 50.000000 7420 5 6 1939 \n",
"10 20 70.000000 11200 5 5 1965 \n",
"11 60 85.000000 11924 9 5 2005 \n",
"12 20 70.049958 12968 5 6 1962 \n",
"13 20 91.000000 10652 7 5 2006 \n",
"14 20 70.049958 10920 6 5 1960 \n",
"15 45 51.000000 6120 7 8 1929 \n",
"16 20 70.049958 11241 6 7 1970 \n",
"17 90 72.000000 10791 4 5 1967 \n",
"18 20 66.000000 13695 5 5 2004 \n",
"19 20 70.000000 7560 5 6 1958 \n",
"20 60 101.000000 14215 8 5 2005 \n",
"21 45 57.000000 7449 7 7 1930 \n",
"22 20 75.000000 9742 8 5 2002 \n",
"23 120 44.000000 4224 5 7 1976 \n",
"24 20 70.049958 8246 5 8 1968 \n",
"25 20 110.000000 14230 8 5 2007 \n",
"26 20 60.000000 7200 5 7 1951 \n",
"27 20 98.000000 11478 8 5 2007 \n",
"28 20 47.000000 16321 5 6 1957 \n",
"29 30 60.000000 6324 4 6 1927 \n",
"... ... ... ... ... ... ... \n",
"1430 60 60.000000 21930 5 5 2005 \n",
"1431 120 70.049958 4928 6 6 1976 \n",
"1432 30 60.000000 10800 4 6 1927 \n",
"1433 60 93.000000 10261 6 5 2000 \n",
"1434 20 80.000000 17400 5 5 1977 \n",
"1435 20 80.000000 8400 6 9 1962 \n",
"1436 20 60.000000 9000 4 6 1971 \n",
"1437 20 96.000000 12444 8 5 2008 \n",
"1438 20 90.000000 7407 6 7 1957 \n",
"1439 60 80.000000 11584 7 6 1979 \n",
"1440 70 79.000000 11526 6 7 1922 \n",
"1441 120 70.049958 4426 6 5 2004 \n",
"1442 60 85.000000 11003 10 5 2008 \n",
"1443 30 70.049958 8854 6 6 1916 \n",
"1444 20 63.000000 8500 7 5 2004 \n",
"1445 85 70.000000 8400 6 5 1966 \n",
"1446 20 70.049958 26142 5 7 1962 \n",
"1447 60 80.000000 10000 8 5 1995 \n",
"1448 50 70.000000 11767 4 7 1910 \n",
"1449 180 21.000000 1533 5 7 1970 \n",
"1450 90 60.000000 9000 5 5 1974 \n",
"1451 20 78.000000 9262 8 5 2008 \n",
"1452 180 35.000000 3675 5 5 2005 \n",
"1453 20 90.000000 17217 5 5 2006 \n",
"1454 20 62.000000 7500 7 5 2004 \n",
"1455 60 62.000000 7917 6 5 1999 \n",
"1456 20 85.000000 13175 6 6 1978 \n",
"1457 70 66.000000 9042 7 9 1941 \n",
"1458 20 68.000000 9717 5 6 1950 \n",
"1459 20 75.000000 9937 5 6 1965 \n",
"\n",
" YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... PoolArea MiscVal \\\n",
"0 2003 196.0 706 0 ... 0 0 \n",
"1 1976 0.0 978 0 ... 0 0 \n",
"2 2002 162.0 486 0 ... 0 0 \n",
"3 1970 0.0 216 0 ... 0 0 \n",
"4 2000 350.0 655 0 ... 0 0 \n",
"5 1995 0.0 732 0 ... 0 700 \n",
"6 2005 186.0 1369 0 ... 0 0 \n",
"7 1973 240.0 859 32 ... 0 350 \n",
"8 1950 0.0 0 0 ... 0 0 \n",
"9 1950 0.0 851 0 ... 0 0 \n",
"10 1965 0.0 906 0 ... 0 0 \n",
"11 2006 286.0 998 0 ... 0 0 \n",
"12 1962 0.0 737 0 ... 0 0 \n",
"13 2007 306.0 0 0 ... 0 0 \n",
"14 1960 212.0 733 0 ... 0 0 \n",
"15 2001 0.0 0 0 ... 0 0 \n",
"16 1970 180.0 578 0 ... 0 700 \n",
"17 1967 0.0 0 0 ... 0 500 \n",
"18 2004 0.0 646 0 ... 0 0 \n",
"19 1965 0.0 504 0 ... 0 0 \n",
"20 2006 380.0 0 0 ... 0 0 \n",
"21 1950 0.0 0 0 ... 0 0 \n",
"22 2002 281.0 0 0 ... 0 0 \n",
"23 1976 0.0 840 0 ... 0 0 \n",
"24 2001 0.0 188 668 ... 0 0 \n",
"25 2007 640.0 0 0 ... 0 0 \n",
"26 2000 0.0 234 486 ... 0 0 \n",
"27 2008 200.0 1218 0 ... 0 0 \n",
"28 1997 0.0 1277 0 ... 0 0 \n",
"29 1950 0.0 0 0 ... 0 0 \n",
"... ... ... ... ... ... ... ... \n",
"1430 2005 0.0 0 0 ... 0 0 \n",
"1431 1976 0.0 958 0 ... 0 0 \n",
"1432 2007 0.0 0 0 ... 0 0 \n",
"1433 2000 318.0 0 0 ... 0 0 \n",
"1434 1977 0.0 936 0 ... 0 0 \n",
"1435 2005 237.0 0 0 ... 0 0 \n",
"1436 1971 0.0 616 0 ... 0 0 \n",
"1437 2008 426.0 1336 0 ... 0 0 \n",
"1438 1996 0.0 600 0 ... 0 0 \n",
"1439 1979 96.0 315 110 ... 0 0 \n",
"1440 1994 0.0 0 0 ... 0 0 \n",
"1441 2004 147.0 697 0 ... 0 0 \n",
"1442 2008 160.0 765 0 ... 0 0 \n",
"1443 1950 0.0 0 0 ... 0 0 \n",
"1444 2004 106.0 0 0 ... 0 0 \n",
"1445 1966 0.0 187 627 ... 0 0 \n",
"1446 1962 189.0 593 0 ... 0 0 \n",
"1447 1996 438.0 1079 0 ... 0 0 \n",
"1448 2000 0.0 0 0 ... 0 0 \n",
"1449 1970 0.0 553 0 ... 0 0 \n",
"1450 1974 0.0 0 0 ... 0 0 \n",
"1451 2009 194.0 0 0 ... 0 0 \n",
"1452 2005 80.0 547 0 ... 0 0 \n",
"1453 2006 0.0 0 0 ... 0 0 \n",
"1454 2005 0.0 410 0 ... 0 0 \n",
"1455 2000 0.0 0 0 ... 0 0 \n",
"1456 1988 119.0 790 163 ... 0 0 \n",
"1457 2006 0.0 275 0 ... 0 2500 \n",
"1458 1996 0.0 49 1029 ... 0 0 \n",
"1459 1965 0.0 830 290 ... 0 0 \n",
"\n",
" MoSold YrSold Ex Fa Gd NA Po TA \n",
"0 2 2008 0 0 0 1 0 0 \n",
"1 5 2007 0 0 0 0 0 1 \n",
"2 9 2008 0 0 0 0 0 1 \n",
"3 2 2006 0 0 1 0 0 0 \n",
"4 12 2008 0 0 0 0 0 1 \n",
"5 10 2009 0 0 0 1 0 0 \n",
"6 8 2007 0 0 1 0 0 0 \n",
"7 11 2009 0 0 0 0 0 1 \n",
"8 4 2008 0 0 0 0 0 1 \n",
"9 1 2008 0 0 0 0 0 1 \n",
"10 2 2008 0 0 0 1 0 0 \n",
"11 7 2006 0 0 1 0 0 0 \n",
"12 9 2008 0 0 0 1 0 0 \n",
"13 8 2007 0 0 1 0 0 0 \n",
"14 5 2008 0 1 0 0 0 0 \n",
"15 7 2007 0 0 0 1 0 0 \n",
"16 3 2010 0 0 0 0 0 1 \n",
"17 10 2006 0 0 0 1 0 0 \n",
"18 6 2008 0 0 0 1 0 0 \n",
"19 5 2009 0 0 0 1 0 0 \n",
"20 11 2006 0 0 1 0 0 0 \n",
"21 6 2007 0 0 1 0 0 0 \n",
"22 9 2008 0 0 1 0 0 0 \n",
"23 6 2007 0 0 0 0 0 1 \n",
"24 5 2010 0 0 0 0 0 1 \n",
"25 7 2009 0 0 1 0 0 0 \n",
"26 5 2010 0 0 0 1 0 0 \n",
"27 5 2010 0 0 1 0 0 0 \n",
"28 12 2006 0 0 1 0 0 0 \n",
"29 5 2008 0 0 0 1 0 0 \n",
"... ... ... .. .. .. .. .. .. \n",
"1430 7 2006 0 0 1 0 0 0 \n",
"1431 10 2009 0 0 0 1 0 0 \n",
"1432 8 2007 0 0 0 1 0 0 \n",
"1433 5 2008 0 0 0 0 0 1 \n",
"1434 5 2006 0 0 1 0 0 0 \n",
"1435 7 2008 0 0 1 0 0 0 \n",
"1436 5 2007 0 0 0 1 0 0 \n",
"1437 11 2008 0 0 1 0 0 0 \n",
"1438 4 2010 0 0 0 1 0 0 \n",
"1439 11 2007 0 0 0 0 0 1 \n",
"1440 9 2008 0 0 1 0 0 0 \n",
"1441 5 2008 0 0 0 0 0 1 \n",
"1442 4 2009 1 0 0 0 0 0 \n",
"1443 5 2009 0 0 1 0 0 0 \n",
"1444 11 2007 0 0 0 1 0 0 \n",
"1445 5 2007 0 0 0 1 0 0 \n",
"1446 4 2010 0 0 0 1 0 0 \n",
"1447 12 2007 0 0 0 0 0 1 \n",
"1448 5 2007 0 0 0 1 0 0 \n",
"1449 8 2006 0 0 0 1 0 0 \n",
"1450 9 2009 0 0 0 1 0 0 \n",
"1451 5 2009 0 0 1 0 0 0 \n",
"1452 5 2006 0 0 0 1 0 0 \n",
"1453 7 2006 0 0 0 1 0 0 \n",
"1454 10 2009 0 0 0 1 0 0 \n",
"1455 8 2007 0 0 0 0 0 1 \n",
"1456 2 2010 0 0 0 0 0 1 \n",
"1457 5 2010 0 0 1 0 0 0 \n",
"1458 4 2010 0 0 0 1 0 0 \n",
"1459 6 2008 0 0 0 1 0 0 \n",
"\n",
"[1460 rows x 42 columns]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new = pd.concat([data, Fire], axis=1)\n",
"new"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, Let's train our linear regression model with the data we processed. Since we have a test data that we have to submit, I will split our training set to a testing set as well (or cross validation set for that matter...)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"Xtrain, Xcross, ytrain, ycross = train_test_split(new, y, random_state=42, test_size=0.3)\n",
"model = linear_model.LinearRegression()# importing linear regression model\n",
"model = lr.fit(Xtrain, ytrain) # training model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we want to examine our model on the cross validation set we just split. Tweaking our model according to this data will give us better results, but I will be satisified with whatever comes first because all I want is just submitting the first results. Let's get our results:"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.87052769514481809"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.score(Xcross, ycross)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Let's visualize our results."
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,u'Linear Regression model for housing prices for kaggle competition by Tomer Nahshon')"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd37f278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y_pred = model.predict(Xcross)\n",
"\n",
"plt.scatter(y_pred, ycross)\n",
"plt.axis([11,13.5,11, 13.5])\n",
"plt.xlabel('Predicted results')\n",
"plt.ylabel('Actual results')\n",
"plt.title('Linear Regression model for housing prices for kaggle competition by Tomer Nahshon')"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Root Mean Square Error: 0.0219644799916\n"
]
}
],
"source": [
"print('Root Mean Square Error:'),mean_squared_error(ycross, y_pred)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's remember we did a log transform to our data, so basically our predicted data needs to go through exponential transform , but we will skip this part."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MSSubClass</th>\n",
" <th>LotFrontage</th>\n",
" <th>LotArea</th>\n",
" <th>OverallQual</th>\n",
" <th>OverallCond</th>\n",
" <th>YearBuilt</th>\n",
" <th>YearRemodAdd</th>\n",
" <th>MasVnrArea</th>\n",
" <th>BsmtFinSF1</th>\n",
" <th>BsmtFinSF2</th>\n",
" <th>...</th>\n",
" <th>PoolArea</th>\n",
" <th>MiscVal</th>\n",
" <th>MoSold</th>\n",
" <th>YrSold</th>\n",
" <th>Ex</th>\n",
" <th>Fa</th>\n",
" <th>Gd</th>\n",
" <th>NA</th>\n",
" <th>Po</th>\n",
" <th>TA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20</td>\n",
" <td>80.0</td>\n",
" <td>11622</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1961</td>\n",
" <td>1961</td>\n",
" <td>0.0</td>\n",
" <td>468.0</td>\n",
" <td>144.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20</td>\n",
" <td>81.0</td>\n",
" <td>14267</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>1958</td>\n",
" <td>1958</td>\n",
" <td>108.0</td>\n",
" <td>923.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>12500</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>60</td>\n",
" <td>74.0</td>\n",
" <td>13830</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1997</td>\n",
" <td>1998</td>\n",
" <td>0.0</td>\n",
" <td>791.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2010</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",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60</td>\n",
" <td>78.0</td>\n",
" <td>9978</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>1998</td>\n",
" <td>1998</td>\n",
" <td>20.0</td>\n",
" <td>602.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>120</td>\n",
" <td>43.0</td>\n",
" <td>5005</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>1992</td>\n",
" <td>1992</td>\n",
" <td>0.0</td>\n",
" <td>263.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>60</td>\n",
" <td>75.0</td>\n",
" <td>10000</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1993</td>\n",
" <td>1994</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</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",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>20</td>\n",
" <td>69.0</td>\n",
" <td>7980</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1992</td>\n",
" <td>2007</td>\n",
" <td>0.0</td>\n",
" <td>935.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>500</td>\n",
" <td>3</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>60</td>\n",
" <td>63.0</td>\n",
" <td>8402</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1998</td>\n",
" <td>1998</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>20</td>\n",
" <td>85.0</td>\n",
" <td>10176</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>1990</td>\n",
" <td>1990</td>\n",
" <td>0.0</td>\n",
" <td>637.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>20</td>\n",
" <td>70.0</td>\n",
" <td>8400</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1970</td>\n",
" <td>1970</td>\n",
" <td>0.0</td>\n",
" <td>804.0</td>\n",
" <td>78.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>120</td>\n",
" <td>26.0</td>\n",
" <td>5858</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>1999</td>\n",
" <td>1999</td>\n",
" <td>0.0</td>\n",
" <td>1051.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>160</td>\n",
" <td>21.0</td>\n",
" <td>1680</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1971</td>\n",
" <td>1971</td>\n",
" <td>504.0</td>\n",
" <td>156.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>160</td>\n",
" <td>21.0</td>\n",
" <td>1680</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1971</td>\n",
" <td>1971</td>\n",
" <td>492.0</td>\n",
" <td>300.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>160</td>\n",
" <td>24.0</td>\n",
" <td>2280</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>1975</td>\n",
" <td>1975</td>\n",
" <td>0.0</td>\n",
" <td>514.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</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",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>120</td>\n",
" <td>24.0</td>\n",
" <td>2280</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>1975</td>\n",
" <td>1975</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>60</td>\n",
" <td>102.0</td>\n",
" <td>12858</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>2010</td>\n",
" <td>162.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>20</td>\n",
" <td>94.0</td>\n",
" <td>12883</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>2010</td>\n",
" <td>256.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>20</td>\n",
" <td>90.0</td>\n",
" <td>11520</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2005</td>\n",
" <td>615.0</td>\n",
" <td>110.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>20</td>\n",
" <td>79.0</td>\n",
" <td>14122</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2006</td>\n",
" <td>240.0</td>\n",
" <td>28.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>110.0</td>\n",
" <td>14300</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>2003</td>\n",
" <td>2004</td>\n",
" <td>1095.0</td>\n",
" <td>1373.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>60</td>\n",
" <td>105.0</td>\n",
" <td>13650</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2002</td>\n",
" <td>2002</td>\n",
" <td>232.0</td>\n",
" <td>578.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>120</td>\n",
" <td>41.0</td>\n",
" <td>7132</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>2006</td>\n",
" <td>178.0</td>\n",
" <td>24.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>20</td>\n",
" <td>100.0</td>\n",
" <td>18494</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2005</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>120</td>\n",
" <td>43.0</td>\n",
" <td>3203</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>2006</td>\n",
" <td>14.0</td>\n",
" <td>16.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>80</td>\n",
" <td>67.0</td>\n",
" <td>13300</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2004</td>\n",
" <td>2004</td>\n",
" <td>0.0</td>\n",
" <td>326.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>60</td>\n",
" <td>63.0</td>\n",
" <td>8577</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2004</td>\n",
" <td>2004</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>60</td>\n",
" <td>60.0</td>\n",
" <td>17433</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>1998</td>\n",
" <td>1998</td>\n",
" <td>114.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2010</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",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>20</td>\n",
" <td>73.0</td>\n",
" <td>8987</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2006</td>\n",
" <td>226.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>20</td>\n",
" <td>92.0</td>\n",
" <td>9215</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>2009</td>\n",
" <td>2010</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>20</td>\n",
" <td>84.0</td>\n",
" <td>10440</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2005</td>\n",
" <td>0.0</td>\n",
" <td>1414.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2010</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </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>1429</th>\n",
" <td>30</td>\n",
" <td>50.0</td>\n",
" <td>7030</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>1925</td>\n",
" <td>1950</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1430</th>\n",
" <td>50</td>\n",
" <td>75.0</td>\n",
" <td>9060</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1957</td>\n",
" <td>1957</td>\n",
" <td>327.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1431</th>\n",
" <td>30</td>\n",
" <td>69.0</td>\n",
" <td>12366</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1945</td>\n",
" <td>1950</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1432</th>\n",
" <td>190</td>\n",
" <td>50.0</td>\n",
" <td>9000</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1951</td>\n",
" <td>1951</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1433</th>\n",
" <td>50</td>\n",
" <td>60.0</td>\n",
" <td>8520</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1916</td>\n",
" <td>1950</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1434</th>\n",
" <td>120</td>\n",
" <td>41.0</td>\n",
" <td>5748</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2006</td>\n",
" <td>473.0</td>\n",
" <td>1573.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1435</th>\n",
" <td>120</td>\n",
" <td>44.0</td>\n",
" <td>3842</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2004</td>\n",
" <td>2005</td>\n",
" <td>186.0</td>\n",
" <td>1564.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1436</th>\n",
" <td>20</td>\n",
" <td>69.0</td>\n",
" <td>23580</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>1979</td>\n",
" <td>1979</td>\n",
" <td>0.0</td>\n",
" <td>776.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2006</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",
" </tr>\n",
" <tr>\n",
" <th>1437</th>\n",
" <td>90</td>\n",
" <td>65.0</td>\n",
" <td>8385</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1978</td>\n",
" <td>1978</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1438</th>\n",
" <td>20</td>\n",
" <td>70.0</td>\n",
" <td>9116</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2001</td>\n",
" <td>2001</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1439</th>\n",
" <td>80</td>\n",
" <td>140.0</td>\n",
" <td>11080</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>1975</td>\n",
" <td>1975</td>\n",
" <td>257.0</td>\n",
" <td>576.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1440</th>\n",
" <td>20</td>\n",
" <td>125.0</td>\n",
" <td>50102</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>1958</td>\n",
" <td>1958</td>\n",
" <td>0.0</td>\n",
" <td>909.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1441</th>\n",
" <td>20</td>\n",
" <td>110.0</td>\n",
" <td>8098</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>2000</td>\n",
" <td>2000</td>\n",
" <td>0.0</td>\n",
" <td>1136.0</td>\n",
" <td>116.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1442</th>\n",
" <td>20</td>\n",
" <td>95.0</td>\n",
" <td>13618</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2006</td>\n",
" <td>198.0</td>\n",
" <td>1350.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1443</th>\n",
" <td>20</td>\n",
" <td>88.0</td>\n",
" <td>11577</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>2005</td>\n",
" <td>2006</td>\n",
" <td>382.0</td>\n",
" <td>1455.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1444</th>\n",
" <td>20</td>\n",
" <td>125.0</td>\n",
" <td>31250</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1951</td>\n",
" <td>1951</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1445</th>\n",
" <td>90</td>\n",
" <td>78.0</td>\n",
" <td>7020</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>1997</td>\n",
" <td>1997</td>\n",
" <td>200.0</td>\n",
" <td>1243.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1446</th>\n",
" <td>160</td>\n",
" <td>41.0</td>\n",
" <td>2665</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1977</td>\n",
" <td>1977</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</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",
" </tr>\n",
" <tr>\n",
" <th>1447</th>\n",
" <td>20</td>\n",
" <td>58.0</td>\n",
" <td>10172</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1968</td>\n",
" <td>2003</td>\n",
" <td>0.0</td>\n",
" <td>441.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1448</th>\n",
" <td>90</td>\n",
" <td>39.5</td>\n",
" <td>11836</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1970</td>\n",
" <td>1970</td>\n",
" <td>0.0</td>\n",
" <td>149.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1449</th>\n",
" <td>180</td>\n",
" <td>21.0</td>\n",
" <td>1470</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>1970</td>\n",
" <td>1970</td>\n",
" <td>0.0</td>\n",
" <td>522.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1450</th>\n",
" <td>160</td>\n",
" <td>21.0</td>\n",
" <td>1484</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1972</td>\n",
" <td>1972</td>\n",
" <td>0.0</td>\n",
" <td>252.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1451</th>\n",
" <td>20</td>\n",
" <td>80.0</td>\n",
" <td>13384</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1969</td>\n",
" <td>1979</td>\n",
" <td>194.0</td>\n",
" <td>119.0</td>\n",
" <td>344.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2006</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",
" </tr>\n",
" <tr>\n",
" <th>1452</th>\n",
" <td>160</td>\n",
" <td>21.0</td>\n",
" <td>1533</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1970</td>\n",
" <td>1970</td>\n",
" <td>0.0</td>\n",
" <td>408.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1453</th>\n",
" <td>160</td>\n",
" <td>21.0</td>\n",
" <td>1526</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1970</td>\n",
" <td>1970</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1454</th>\n",
" <td>160</td>\n",
" <td>21.0</td>\n",
" <td>1936</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>1970</td>\n",
" <td>1970</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1455</th>\n",
" <td>160</td>\n",
" <td>21.0</td>\n",
" <td>1894</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>1970</td>\n",
" <td>1970</td>\n",
" <td>0.0</td>\n",
" <td>252.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1456</th>\n",
" <td>20</td>\n",
" <td>160.0</td>\n",
" <td>20000</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>1960</td>\n",
" <td>1996</td>\n",
" <td>0.0</td>\n",
" <td>1224.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2006</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",
" </tr>\n",
" <tr>\n",
" <th>1457</th>\n",
" <td>85</td>\n",
" <td>62.0</td>\n",
" <td>10441</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>1992</td>\n",
" <td>1992</td>\n",
" <td>0.0</td>\n",
" <td>337.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>700</td>\n",
" <td>7</td>\n",
" <td>2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1458</th>\n",
" <td>60</td>\n",
" <td>74.0</td>\n",
" <td>9627</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>1993</td>\n",
" <td>1994</td>\n",
" <td>94.0</td>\n",
" <td>758.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>2006</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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1459 rows × 42 columns</p>\n",
"</div>"
],
"text/plain": [
" MSSubClass LotFrontage LotArea OverallQual OverallCond YearBuilt \\\n",
"0 20 80.0 11622 5 6 1961 \n",
"1 20 81.0 14267 6 6 1958 \n",
"2 60 74.0 13830 5 5 1997 \n",
"3 60 78.0 9978 6 6 1998 \n",
"4 120 43.0 5005 8 5 1992 \n",
"5 60 75.0 10000 6 5 1993 \n",
"6 20 69.0 7980 6 7 1992 \n",
"7 60 63.0 8402 6 5 1998 \n",
"8 20 85.0 10176 7 5 1990 \n",
"9 20 70.0 8400 4 5 1970 \n",
"10 120 26.0 5858 7 5 1999 \n",
"11 160 21.0 1680 6 5 1971 \n",
"12 160 21.0 1680 5 5 1971 \n",
"13 160 24.0 2280 6 6 1975 \n",
"14 120 24.0 2280 7 6 1975 \n",
"15 60 102.0 12858 9 5 2009 \n",
"16 20 94.0 12883 8 5 2009 \n",
"17 20 90.0 11520 9 5 2005 \n",
"18 20 79.0 14122 8 5 2005 \n",
"19 20 110.0 14300 9 5 2003 \n",
"20 60 105.0 13650 8 5 2002 \n",
"21 120 41.0 7132 8 5 2006 \n",
"22 20 100.0 18494 6 5 2005 \n",
"23 120 43.0 3203 7 5 2006 \n",
"24 80 67.0 13300 7 5 2004 \n",
"25 60 63.0 8577 7 5 2004 \n",
"26 60 60.0 17433 8 5 1998 \n",
"27 20 73.0 8987 8 5 2005 \n",
"28 20 92.0 9215 7 5 2009 \n",
"29 20 84.0 10440 6 5 2005 \n",
"... ... ... ... ... ... ... \n",
"1429 30 50.0 7030 4 6 1925 \n",
"1430 50 75.0 9060 6 5 1957 \n",
"1431 30 69.0 12366 3 5 1945 \n",
"1432 190 50.0 9000 5 6 1951 \n",
"1433 50 60.0 8520 3 5 1916 \n",
"1434 120 41.0 5748 8 5 2005 \n",
"1435 120 44.0 3842 8 5 2004 \n",
"1436 20 69.0 23580 6 6 1979 \n",
"1437 90 65.0 8385 6 5 1978 \n",
"1438 20 70.0 9116 8 5 2001 \n",
"1439 80 140.0 11080 6 6 1975 \n",
"1440 20 125.0 50102 6 5 1958 \n",
"1441 20 110.0 8098 6 5 2000 \n",
"1442 20 95.0 13618 8 5 2005 \n",
"1443 20 88.0 11577 9 5 2005 \n",
"1444 20 125.0 31250 1 3 1951 \n",
"1445 90 78.0 7020 7 5 1997 \n",
"1446 160 41.0 2665 5 6 1977 \n",
"1447 20 58.0 10172 5 7 1968 \n",
"1448 90 39.5 11836 5 5 1970 \n",
"1449 180 21.0 1470 4 6 1970 \n",
"1450 160 21.0 1484 4 4 1972 \n",
"1451 20 80.0 13384 5 5 1969 \n",
"1452 160 21.0 1533 4 5 1970 \n",
"1453 160 21.0 1526 4 5 1970 \n",
"1454 160 21.0 1936 4 7 1970 \n",
"1455 160 21.0 1894 4 5 1970 \n",
"1456 20 160.0 20000 5 7 1960 \n",
"1457 85 62.0 10441 5 5 1992 \n",
"1458 60 74.0 9627 7 5 1993 \n",
"\n",
" YearRemodAdd MasVnrArea BsmtFinSF1 BsmtFinSF2 ... PoolArea MiscVal \\\n",
"0 1961 0.0 468.0 144.0 ... 0 0 \n",
"1 1958 108.0 923.0 0.0 ... 0 12500 \n",
"2 1998 0.0 791.0 0.0 ... 0 0 \n",
"3 1998 20.0 602.0 0.0 ... 0 0 \n",
"4 1992 0.0 263.0 0.0 ... 0 0 \n",
"5 1994 0.0 0.0 0.0 ... 0 0 \n",
"6 2007 0.0 935.0 0.0 ... 0 500 \n",
"7 1998 0.0 0.0 0.0 ... 0 0 \n",
"8 1990 0.0 637.0 0.0 ... 0 0 \n",
"9 1970 0.0 804.0 78.0 ... 0 0 \n",
"10 1999 0.0 1051.0 0.0 ... 0 0 \n",
"11 1971 504.0 156.0 0.0 ... 0 0 \n",
"12 1971 492.0 300.0 0.0 ... 0 0 \n",
"13 1975 0.0 514.0 0.0 ... 0 0 \n",
"14 1975 0.0 0.0 0.0 ... 0 0 \n",
"15 2010 162.0 0.0 0.0 ... 0 0 \n",
"16 2010 256.0 0.0 0.0 ... 0 0 \n",
"17 2005 615.0 110.0 0.0 ... 0 0 \n",
"18 2006 240.0 28.0 0.0 ... 0 0 \n",
"19 2004 1095.0 1373.0 0.0 ... 0 0 \n",
"20 2002 232.0 578.0 0.0 ... 0 0 \n",
"21 2006 178.0 24.0 0.0 ... 0 0 \n",
"22 2005 0.0 0.0 0.0 ... 0 0 \n",
"23 2006 14.0 16.0 0.0 ... 0 0 \n",
"24 2004 0.0 326.0 0.0 ... 0 0 \n",
"25 2004 0.0 0.0 0.0 ... 0 0 \n",
"26 1998 114.0 0.0 0.0 ... 0 0 \n",
"27 2006 226.0 0.0 0.0 ... 0 0 \n",
"28 2010 0.0 0.0 0.0 ... 0 0 \n",
"29 2005 0.0 1414.0 0.0 ... 0 0 \n",
"... ... ... ... ... ... ... ... \n",
"1429 1950 0.0 0.0 0.0 ... 0 0 \n",
"1430 1957 327.0 0.0 0.0 ... 0 0 \n",
"1431 1950 0.0 0.0 0.0 ... 0 0 \n",
"1432 1951 0.0 0.0 0.0 ... 0 0 \n",
"1433 1950 0.0 0.0 0.0 ... 0 0 \n",
"1434 2006 473.0 1573.0 0.0 ... 0 0 \n",
"1435 2005 186.0 1564.0 0.0 ... 0 0 \n",
"1436 1979 0.0 776.0 0.0 ... 0 0 \n",
"1437 1978 0.0 0.0 0.0 ... 0 0 \n",
"1438 2001 0.0 0.0 0.0 ... 0 0 \n",
"1439 1975 257.0 576.0 0.0 ... 0 0 \n",
"1440 1958 0.0 909.0 0.0 ... 0 0 \n",
"1441 2000 0.0 1136.0 116.0 ... 0 0 \n",
"1442 2006 198.0 1350.0 0.0 ... 0 0 \n",
"1443 2006 382.0 1455.0 0.0 ... 0 0 \n",
"1444 1951 0.0 0.0 0.0 ... 0 0 \n",
"1445 1997 200.0 1243.0 0.0 ... 0 0 \n",
"1446 1977 0.0 0.0 0.0 ... 0 0 \n",
"1447 2003 0.0 441.0 0.0 ... 0 0 \n",
"1448 1970 0.0 149.0 0.0 ... 0 0 \n",
"1449 1970 0.0 522.0 0.0 ... 0 0 \n",
"1450 1972 0.0 252.0 0.0 ... 0 0 \n",
"1451 1979 194.0 119.0 344.0 ... 0 0 \n",
"1452 1970 0.0 408.0 0.0 ... 0 0 \n",
"1453 1970 0.0 0.0 0.0 ... 0 0 \n",
"1454 1970 0.0 0.0 0.0 ... 0 0 \n",
"1455 1970 0.0 252.0 0.0 ... 0 0 \n",
"1456 1996 0.0 1224.0 0.0 ... 0 0 \n",
"1457 1992 0.0 337.0 0.0 ... 0 700 \n",
"1458 1994 94.0 758.0 0.0 ... 0 0 \n",
"\n",
" MoSold YrSold Ex Fa Gd NA Po TA \n",
"0 6 2010 0 0 0 1 0 0 \n",
"1 6 2010 0 0 0 1 0 0 \n",
"2 3 2010 0 0 0 0 0 1 \n",
"3 6 2010 0 0 1 0 0 0 \n",
"4 1 2010 0 0 0 1 0 0 \n",
"5 4 2010 0 0 0 0 0 1 \n",
"6 3 2010 0 0 0 1 0 0 \n",
"7 5 2010 0 0 1 0 0 0 \n",
"8 2 2010 0 0 0 0 1 0 \n",
"9 4 2010 0 0 0 1 0 0 \n",
"10 6 2010 0 1 0 0 0 0 \n",
"11 2 2010 0 0 0 1 0 0 \n",
"12 3 2010 0 0 0 1 0 0 \n",
"13 6 2010 0 0 0 0 0 1 \n",
"14 6 2010 0 0 0 1 0 0 \n",
"15 1 2010 0 0 1 0 0 0 \n",
"16 6 2010 0 0 0 1 0 0 \n",
"17 6 2010 0 0 1 0 0 0 \n",
"18 2 2010 0 0 1 0 0 0 \n",
"19 6 2010 0 0 1 0 0 0 \n",
"20 6 2010 1 0 0 0 0 0 \n",
"21 4 2010 0 0 1 0 0 0 \n",
"22 1 2010 0 0 0 1 0 0 \n",
"23 1 2010 0 0 0 1 0 0 \n",
"24 6 2010 0 0 1 0 0 0 \n",
"25 4 2010 0 0 1 0 0 0 \n",
"26 1 2010 0 0 0 0 0 1 \n",
"27 5 2010 0 0 1 0 0 0 \n",
"28 4 2010 0 0 0 1 0 0 \n",
"29 5 2010 0 0 1 0 0 0 \n",
"... ... ... .. .. .. .. .. .. \n",
"1429 3 2006 0 0 0 1 0 0 \n",
"1430 4 2006 0 0 0 1 0 0 \n",
"1431 10 2006 0 0 0 1 0 0 \n",
"1432 10 2006 0 0 0 1 0 0 \n",
"1433 4 2006 0 0 0 1 0 0 \n",
"1434 2 2006 0 0 1 0 0 0 \n",
"1435 12 2006 0 0 1 0 0 0 \n",
"1436 9 2006 0 0 0 0 0 1 \n",
"1437 10 2006 0 0 0 1 0 0 \n",
"1438 5 2006 0 0 0 1 0 0 \n",
"1439 5 2006 0 0 0 1 0 0 \n",
"1440 3 2006 0 0 1 0 0 0 \n",
"1441 10 2006 0 0 0 1 0 0 \n",
"1442 11 2006 0 0 1 0 0 0 \n",
"1443 9 2006 0 0 1 0 0 0 \n",
"1444 5 2006 0 0 0 1 0 0 \n",
"1445 11 2006 0 0 0 1 0 0 \n",
"1446 5 2006 0 0 0 0 0 1 \n",
"1447 10 2006 0 0 0 1 0 0 \n",
"1448 3 2006 0 0 0 1 0 0 \n",
"1449 4 2006 0 0 0 1 0 0 \n",
"1450 5 2006 0 0 0 1 0 0 \n",
"1451 5 2006 0 0 0 0 0 1 \n",
"1452 12 2006 0 0 0 1 0 0 \n",
"1453 6 2006 0 0 0 1 0 0 \n",
"1454 6 2006 0 0 0 1 0 0 \n",
"1455 4 2006 0 0 0 1 0 0 \n",
"1456 9 2006 0 0 0 0 0 1 \n",
"1457 7 2006 0 0 0 1 0 0 \n",
"1458 11 2006 0 0 0 0 0 1 \n",
"\n",
"[1459 rows x 42 columns]"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.FireplaceQu = test.FireplaceQu.fillna('NA') # filling missing values (nan) with NA (Not Applicable) Values for get dummies\n",
"Firetest = pd.get_dummies(test.FireplaceQu)\n",
"dataset = test.select_dtypes(include=[np.number]).interpolate().dropna()\n",
"dataset =dataset.drop(['Id'], axis=1)\n",
"new = pd.concat([dataset, Firetest], axis=1)\n",
"new"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 11.66892344, 11.69935818, 12.03411515, ..., 12.06284393,\n",
" 11.60493255, 12.39339157])"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_pred = model.predict(new)\n",
"test_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have the predictions, let's send them to kaggle"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"subm = pd.DataFrame()\n",
"subm['Id'] = test.Id\n",
"final_predictions = np.exp(test_pred) # transform the target variable after predicting\n",
"subm['SalePrice'] = final_predictions\n",
"submission.to_csv('submission1.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I scored 0.14949 and my position is 1799 in the public leaderboard. Nice for a first submission, no?\n",
"for any remarks please refer to my email : tomer@nahshon.net"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}
@Z30G0D
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Z30G0D commented Jan 5, 2018

My third update for this competition

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