linnear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_price = pd.read_csv('./data/area_prices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sqft_living    0\n",
       "price          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_price.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4600, 2)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_price.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1340</td>\n",
       "      <td>313000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3650</td>\n",
       "      <td>2384000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1930</td>\n",
       "      <td>342000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>420000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1940</td>\n",
       "      <td>550000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sqft_living      price\n",
       "0         1340   313000.0\n",
       "1         3650  2384000.0\n",
       "2         1930   342000.0\n",
       "3         2000   420000.0\n",
       "4         1940   550000.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "area_price = area_price.sort_values('price', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4350</th>\n",
       "      <td>1180</td>\n",
       "      <td>26590000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4346</th>\n",
       "      <td>2190</td>\n",
       "      <td>12899000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>10040</td>\n",
       "      <td>7062500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>9640</td>\n",
       "      <td>4668000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2761</th>\n",
       "      <td>6430</td>\n",
       "      <td>4489000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4472</th>\n",
       "      <td>4060</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4567</th>\n",
       "      <td>4080</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4354</th>\n",
       "      <td>1490</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4454</th>\n",
       "      <td>2090</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4382</th>\n",
       "      <td>4630</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4600 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sqft_living       price\n",
       "4350         1180  26590000.0\n",
       "4346         2190  12899000.0\n",
       "2286        10040   7062500.0\n",
       "2654         9640   4668000.0\n",
       "2761         6430   4489000.0\n",
       "...           ...         ...\n",
       "4472         4060         0.0\n",
       "4567         4080         0.0\n",
       "4354         1490         0.0\n",
       "4454         2090         0.0\n",
       "4382         4630         0.0\n",
       "\n",
       "[4600 rows x 2 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_price"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ta bort extremvärdena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>10040</td>\n",
       "      <td>7062500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>9640</td>\n",
       "      <td>4668000.0</td>\n",
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       "    <tr>\n",
       "      <th>2761</th>\n",
       "      <td>6430</td>\n",
       "      <td>4489000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3729</th>\n",
       "      <td>7050</td>\n",
       "      <td>3800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1637</th>\n",
       "      <td>5550</td>\n",
       "      <td>3710000.0</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>4567</th>\n",
       "      <td>4080</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4354</th>\n",
       "      <td>1490</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4454</th>\n",
       "      <td>2090</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4382</th>\n",
       "      <td>4630</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4598 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sqft_living      price\n",
       "2286        10040  7062500.0\n",
       "2654         9640  4668000.0\n",
       "2761         6430  4489000.0\n",
       "3729         7050  3800000.0\n",
       "1637         5550  3710000.0\n",
       "...           ...        ...\n",
       "4472         4060        0.0\n",
       "4567         4080        0.0\n",
       "4354         1490        0.0\n",
       "4454         2090        0.0\n",
       "4382         4630        0.0\n",
       "\n",
       "[4598 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_price = area_price.drop([4350,4346])\n",
    "area_price"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### man kan även göra såhär för att ta bort extremvärden:\n",
    "\n",
    "### area_price = area_price[area_price['price'] < 10000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4.598000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2139.544585</td>\n",
       "      <td>5.436148e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>963.312203</td>\n",
       "      <td>3.705838e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>370.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>3.226250e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1980.000000</td>\n",
       "      <td>4.604435e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2620.000000</td>\n",
       "      <td>6.537500e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13540.000000</td>\n",
       "      <td>7.062500e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sqft_living         price\n",
       "count   4598.000000  4.598000e+03\n",
       "mean    2139.544585  5.436148e+05\n",
       "std      963.312203  3.705838e+05\n",
       "min      370.000000  0.000000e+00\n",
       "25%     1460.000000  3.226250e+05\n",
       "50%     1980.000000  4.604435e+05\n",
       "75%     2620.000000  6.537500e+05\n",
       "max    13540.000000  7.062500e+06"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'sqft_living'}>,\n",
       "        <Axes: title={'center': 'price'}>]], dtype=object)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "area_price.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = area_price['sqft_living']\n",
    "y = area_price['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.xlabel('Sqft')\n",
    "plt.ylabel('Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "511     2350\n",
       "669     1250\n",
       "689      980\n",
       "1288    2440\n",
       "4075    1810\n",
       "        ... \n",
       "1877     770\n",
       "1236    1700\n",
       "266     2420\n",
       "1284    1670\n",
       "2210    2510\n",
       "Name: sqft_living, Length: 3448, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "511     870300.0\n",
       "669     299000.0\n",
       "689     400000.0\n",
       "1288    388000.0\n",
       "4075    362000.0\n",
       "          ...   \n",
       "1877    275000.0\n",
       "1236    149000.0\n",
       "266     352000.0\n",
       "1284    585000.0\n",
       "2210    659000.0\n",
       "Name: price, Length: 3448, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.array(x_train).reshape(-1,1)\n",
    "x_test = np.array(x_test).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-23628.419921490015"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([266.76850674])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = regressor.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_train,y_train)\n",
    "plt.plot(x_train, y_pred_train, color='red')\n",
    "plt.xlabel('Sqft')\n",
    "plt.ylabel('Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test,y_test)\n",
    "plt.plot(x_test, y_pred_test, color='red')\n",
    "plt.xlabel('Sqft')\n",
    "plt.ylabel('Price')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Robert kör med regressor.score! Verkar vara att man testar både x och y, r2_score är för att testa predicten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4599904330760545"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4004115284036369"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4599904330760545"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4004115284036369"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VG del"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_price = pd.read_csv('./data/house_prices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0       0\n",
       "price            0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "view             0\n",
       "condition        0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "city             0\n",
       "statezip         0\n",
       "country          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_price.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_price = house_price.drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4598, 16)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_price.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>city</th>\n",
       "      <th>statezip</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>313000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1340</td>\n",
       "      <td>7912</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1340</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>2005</td>\n",
       "      <td>Shoreline</td>\n",
       "      <td>WA 98133</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2384000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3650</td>\n",
       "      <td>9050</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3370</td>\n",
       "      <td>280</td>\n",
       "      <td>1921</td>\n",
       "      <td>0</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA 98119</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>342000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1930</td>\n",
       "      <td>11947</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1930</td>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "      <td>Kent</td>\n",
       "      <td>WA 98042</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>420000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2000</td>\n",
       "      <td>8030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>WA 98008</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>550000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1940</td>\n",
       "      <td>10500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1140</td>\n",
       "      <td>800</td>\n",
       "      <td>1976</td>\n",
       "      <td>1992</td>\n",
       "      <td>Redmond</td>\n",
       "      <td>WA 98052</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront   \n",
       "0   313000.0       3.0       1.50         1340      7912     1.5           0  \\\n",
       "1  2384000.0       5.0       2.50         3650      9050     2.0           0   \n",
       "2   342000.0       3.0       2.00         1930     11947     1.0           0   \n",
       "3   420000.0       3.0       2.25         2000      8030     1.0           0   \n",
       "4   550000.0       4.0       2.50         1940     10500     1.0           0   \n",
       "\n",
       "   view  condition  sqft_above  sqft_basement  yr_built  yr_renovated   \n",
       "0     0          3        1340              0      1955          2005  \\\n",
       "1     4          5        3370            280      1921             0   \n",
       "2     0          4        1930              0      1966             0   \n",
       "3     0          4        1000           1000      1963             0   \n",
       "4     0          4        1140            800      1976          1992   \n",
       "\n",
       "        city  statezip country  \n",
       "0  Shoreline  WA 98133     USA  \n",
       "1    Seattle  WA 98119     USA  \n",
       "2       Kent  WA 98042     USA  \n",
       "3   Bellevue  WA 98008     USA  \n",
       "4    Redmond  WA 98052     USA  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.598000e+03</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4.598000e+03</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4598.000000</td>\n",
       "      <td>4598.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.436148e+05</td>\n",
       "      <td>3.401044</td>\n",
       "      <td>2.160776</td>\n",
       "      <td>2139.544585</td>\n",
       "      <td>1.485480e+04</td>\n",
       "      <td>1.512288</td>\n",
       "      <td>0.007177</td>\n",
       "      <td>0.240757</td>\n",
       "      <td>3.451718</td>\n",
       "      <td>1827.466507</td>\n",
       "      <td>312.078077</td>\n",
       "      <td>1970.784906</td>\n",
       "      <td>808.524793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.705838e+05</td>\n",
       "      <td>0.909007</td>\n",
       "      <td>0.783932</td>\n",
       "      <td>963.312203</td>\n",
       "      <td>3.589205e+04</td>\n",
       "      <td>0.538299</td>\n",
       "      <td>0.084422</td>\n",
       "      <td>0.778558</td>\n",
       "      <td>0.677296</td>\n",
       "      <td>862.293944</td>\n",
       "      <td>464.190168</td>\n",
       "      <td>29.735870</td>\n",
       "      <td>979.397082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>6.380000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>370.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.226250e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>5.000250e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.604435e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>7.683000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1590.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.537500e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2620.000000</td>\n",
       "      <td>1.100000e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2300.000000</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.062500e+06</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.074218e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price     bedrooms    bathrooms   sqft_living      sqft_lot   \n",
       "count  4.598000e+03  4598.000000  4598.000000   4598.000000  4.598000e+03  \\\n",
       "mean   5.436148e+05     3.401044     2.160776   2139.544585  1.485480e+04   \n",
       "std    3.705838e+05     0.909007     0.783932    963.312203  3.589205e+04   \n",
       "min    0.000000e+00     0.000000     0.000000    370.000000  6.380000e+02   \n",
       "25%    3.226250e+05     3.000000     1.750000   1460.000000  5.000250e+03   \n",
       "50%    4.604435e+05     3.000000     2.250000   1980.000000  7.683000e+03   \n",
       "75%    6.537500e+05     4.000000     2.500000   2620.000000  1.100000e+04   \n",
       "max    7.062500e+06     9.000000     8.000000  13540.000000  1.074218e+06   \n",
       "\n",
       "            floors   waterfront         view    condition   sqft_above   \n",
       "count  4598.000000  4598.000000  4598.000000  4598.000000  4598.000000  \\\n",
       "mean      1.512288     0.007177     0.240757     3.451718  1827.466507   \n",
       "std       0.538299     0.084422     0.778558     0.677296   862.293944   \n",
       "min       1.000000     0.000000     0.000000     1.000000   370.000000   \n",
       "25%       1.000000     0.000000     0.000000     3.000000  1190.000000   \n",
       "50%       1.500000     0.000000     0.000000     3.000000  1590.000000   \n",
       "75%       2.000000     0.000000     0.000000     4.000000  2300.000000   \n",
       "max       3.500000     1.000000     4.000000     5.000000  9410.000000   \n",
       "\n",
       "       sqft_basement     yr_built  yr_renovated  \n",
       "count    4598.000000  4598.000000   4598.000000  \n",
       "mean      312.078077  1970.784906    808.524793  \n",
       "std       464.190168    29.735870    979.397082  \n",
       "min         0.000000  1900.000000      0.000000  \n",
       "25%         0.000000  1951.000000      0.000000  \n",
       "50%         0.000000  1976.000000      0.000000  \n",
       "75%       610.000000  1997.000000   1999.000000  \n",
       "max      4820.000000  2014.000000   2014.000000  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_price.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'price'}>,\n",
       "        <Axes: title={'center': 'bedrooms'}>,\n",
       "        <Axes: title={'center': 'bathrooms'}>,\n",
       "        <Axes: title={'center': 'sqft_living'}>],\n",
       "       [<Axes: title={'center': 'sqft_lot'}>,\n",
       "        <Axes: title={'center': 'floors'}>,\n",
       "        <Axes: title={'center': 'waterfront'}>,\n",
       "        <Axes: title={'center': 'view'}>],\n",
       "       [<Axes: title={'center': 'condition'}>,\n",
       "        <Axes: title={'center': 'sqft_above'}>,\n",
       "        <Axes: title={'center': 'sqft_basement'}>,\n",
       "        <Axes: title={'center': 'yr_built'}>],\n",
       "       [<Axes: title={'center': 'yr_renovated'}>, <Axes: >, <Axes: >,\n",
       "        <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "house_price.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_price['city'] = house_price['city'].astype('category')\n",
    "house_price['city'] = house_price['city'].cat.codes\n",
    "house_price['country'] = house_price['country'].astype('category')\n",
    "house_price['country'] = house_price['country'].cat.codes\n",
    "house_price['statezip'] = house_price['statezip'].str.replace('WA ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
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      ],
      "text/plain": [
       "             price  bedrooms  bathrooms  sqft_living  sqft_lot  floors   \n",
       "0     3.130000e+05       3.0       1.50         1340      7912     1.5  \\\n",
       "1     2.384000e+06       5.0       2.50         3650      9050     2.0   \n",
       "2     3.420000e+05       3.0       2.00         1930     11947     1.0   \n",
       "3     4.200000e+05       3.0       2.25         2000      8030     1.0   \n",
       "4     5.500000e+05       4.0       2.50         1940     10500     1.0   \n",
       "...            ...       ...        ...          ...       ...     ...   \n",
       "4593  3.081667e+05       3.0       1.75         1510      6360     1.0   \n",
       "4594  5.343333e+05       3.0       2.50         1460      7573     2.0   \n",
       "4595  4.169042e+05       3.0       2.50         3010      7014     2.0   \n",
       "4596  2.034000e+05       4.0       2.00         2090      6630     1.0   \n",
       "4597  2.206000e+05       3.0       2.50         1490      8102     2.0   \n",
       "\n",
       "      waterfront  view  condition  sqft_above  sqft_basement  yr_built   \n",
       "0              0     0          3        1340              0      1955  \\\n",
       "1              0     4          5        3370            280      1921   \n",
       "2              0     0          4        1930              0      1966   \n",
       "3              0     0          4        1000           1000      1963   \n",
       "4              0     0          4        1140            800      1976   \n",
       "...          ...   ...        ...         ...            ...       ...   \n",
       "4593           0     0          4        1510              0      1954   \n",
       "4594           0     0          3        1460              0      1983   \n",
       "4595           0     0          3        3010              0      2009   \n",
       "4596           0     0          3        1070           1020      1974   \n",
       "4597           0     0          4        1490              0      1990   \n",
       "\n",
       "      yr_renovated  city statezip  country  \n",
       "0             2005    36    98133        0  \n",
       "1                0    35    98119        0  \n",
       "2                0    18    98042        0  \n",
       "3                0     3    98008        0  \n",
       "4             1992    31    98052        0  \n",
       "...            ...   ...      ...      ...  \n",
       "4593          1979    35    98133        0  \n",
       "4594          2009     3    98007        0  \n",
       "4595             0    32    98059        0  \n",
       "4596             0    35    98178        0  \n",
       "4597             0     9    98042        0  \n",
       "\n",
       "[4598 rows x 16 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_price"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "När Robert körde x så droppar han price, city, statezip och country då dom är textdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4598 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view   \n",
       "0          3.0       1.50         1340      7912     1.5           0     0  \\\n",
       "1          5.0       2.50         3650      9050     2.0           0     4   \n",
       "2          3.0       2.00         1930     11947     1.0           0     0   \n",
       "3          3.0       2.25         2000      8030     1.0           0     0   \n",
       "4          4.0       2.50         1940     10500     1.0           0     0   \n",
       "...        ...        ...          ...       ...     ...         ...   ...   \n",
       "4593       3.0       1.75         1510      6360     1.0           0     0   \n",
       "4594       3.0       2.50         1460      7573     2.0           0     0   \n",
       "4595       3.0       2.50         3010      7014     2.0           0     0   \n",
       "4596       4.0       2.00         2090      6630     1.0           0     0   \n",
       "4597       3.0       2.50         1490      8102     2.0           0     0   \n",
       "\n",
       "      condition  sqft_above  sqft_basement  yr_built  yr_renovated  city   \n",
       "0             3        1340              0      1955          2005    36  \\\n",
       "1             5        3370            280      1921             0    35   \n",
       "2             4        1930              0      1966             0    18   \n",
       "3             4        1000           1000      1963             0     3   \n",
       "4             4        1140            800      1976          1992    31   \n",
       "...         ...         ...            ...       ...           ...   ...   \n",
       "4593          4        1510              0      1954          1979    35   \n",
       "4594          3        1460              0      1983          2009     3   \n",
       "4595          3        3010              0      2009             0    32   \n",
       "4596          3        1070           1020      1974             0    35   \n",
       "4597          4        1490              0      1990             0     9   \n",
       "\n",
       "     statezip  country  \n",
       "0       98133        0  \n",
       "1       98119        0  \n",
       "2       98042        0  \n",
       "3       98008        0  \n",
       "4       98052        0  \n",
       "...       ...      ...  \n",
       "4593    98133        0  \n",
       "4594    98007        0  \n",
       "4595    98059        0  \n",
       "4596    98178        0  \n",
       "4597    98042        0  \n",
       "\n",
       "[4598 rows x 15 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = house_price.drop(columns='price')\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = house_price['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size= 0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44213573.4449149"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.57469550e+04,  3.34119303e+04,  1.79911071e+02, -6.08390819e-01,\n",
       "        6.46084549e+04,  3.65635392e+05,  5.06522752e+04,  2.42242903e+04,\n",
       "        8.57766220e+01,  9.41344491e+01, -2.52592283e+03,  7.72887831e+00,\n",
       "        2.21499164e+03, -4.01648649e+02,  0.00000000e+00])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = regressor.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 798437.21922725, 1212200.0841656 ,  449027.98730854, ...,\n",
       "        451471.83345017,  951935.77980715,  380042.3747706 ])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train,y_pred_train)\n",
    "plt.xlabel(\"Actual Charges\")\n",
    "plt.ylabel(\"Predicted Charges\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5246455884162471"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5534139827557769"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5246455884162471"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,y_pred_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5534139827557769"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.57469550e+04,  3.34119303e+04,  1.79911071e+02, -6.08390819e-01,\n",
       "        6.46084549e+04,  3.65635392e+05,  5.06522752e+04,  2.42242903e+04,\n",
       "        8.57766220e+01,  9.41344491e+01, -2.52592283e+03,  7.72887831e+00,\n",
       "        2.21499164e+03, -4.01648649e+02,  0.00000000e+00])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',\n",
       "       'waterfront', 'view', 'condition', 'sqft_above', 'sqft_basement',\n",
       "       'yr_built', 'yr_renovated', 'city', 'statezip', 'country'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.feature_names_in_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = x.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Kodprojekt\\Jupyter\\env\\lib\\site-packages\\pandas\\io\\formats\\style.py:3618: RuntimeWarning: All-NaN slice encountered\n",
      "  smin = np.nanmin(gmap) if vmin is None else vmin\n",
      "c:\\Kodprojekt\\Jupyter\\env\\lib\\site-packages\\pandas\\io\\formats\\style.py:3619: RuntimeWarning: All-NaN slice encountered\n",
      "  smax = np.nanmax(gmap) if vmax is None else vmax\n"
     ]
    },
    {
     "data": {
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       "  background-color: #90b2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row1_col7, #T_8cc54_row12_col9 {\n",
       "  background-color: #7b9ff9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row1_col8 {\n",
       "  background-color: #f39778;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row1_col9 {\n",
       "  background-color: #ccd9ed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row1_col10 {\n",
       "  background-color: #f3c7b1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row1_col11, #T_8cc54_row7_col1, #T_8cc54_row11_col0 {\n",
       "  background-color: #536edd;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row1_col13 {\n",
       "  background-color: #5d7ce6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row2_col0 {\n",
       "  background-color: #f6bda2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row2_col4 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row2_col5 {\n",
       "  background-color: #6687ed;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row2_col7, #T_8cc54_row4_col12, #T_8cc54_row5_col1, #T_8cc54_row6_col4, #T_8cc54_row6_col10 {\n",
       "  background-color: #89acfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row2_col8 {\n",
       "  background-color: #d55042;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row2_col9, #T_8cc54_row10_col1 {\n",
       "  background-color: #ebd3c6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row2_col10 {\n",
       "  background-color: #dadce0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row2_col12, #T_8cc54_row11_col6, #T_8cc54_row12_col2 {\n",
       "  background-color: #5470de;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row2_col13, #T_8cc54_row3_col12 {\n",
       "  background-color: #5b7ae5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row3_col0 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row3_col1, #T_8cc54_row5_col10, #T_8cc54_row5_col13 {\n",
       "  background-color: #93b5fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row3_col2, #T_8cc54_row6_col8 {\n",
       "  background-color: #afcafc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row3_col4, #T_8cc54_row4_col13 {\n",
       "  background-color: #81a4fb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row3_col5 {\n",
       "  background-color: #465ecf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row3_col6, #T_8cc54_row5_col0 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row3_col7, #T_8cc54_row4_col0, #T_8cc54_row5_col7 {\n",
       "  background-color: #9abbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row3_col8 {\n",
       "  background-color: #bbd1f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row3_col9, #T_8cc54_row5_col4 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row3_col10 {\n",
       "  background-color: #a6c4fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row3_col11, #T_8cc54_row8_col6 {\n",
       "  background-color: #84a7fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row4_col1, #T_8cc54_row8_col10 {\n",
       "  background-color: #eed0c0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row4_col2, #T_8cc54_row6_col9 {\n",
       "  background-color: #d2dbe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row4_col3, #T_8cc54_row7_col2, #T_8cc54_row8_col11 {\n",
       "  background-color: #6180e9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row4_col5 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row4_col6, #T_8cc54_row4_col7, #T_8cc54_row11_col8 {\n",
       "  background-color: #5572df;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row4_col8 {\n",
       "  background-color: #f4c5ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row4_col9, #T_8cc54_row7_col4, #T_8cc54_row7_col10, #T_8cc54_row10_col5, #T_8cc54_row10_col6, #T_8cc54_row10_col7, #T_8cc54_row10_col11, #T_8cc54_row10_col12, #T_8cc54_row10_col13, #T_8cc54_row11_col1, #T_8cc54_row13_col0, #T_8cc54_row13_col2, #T_8cc54_row13_col3, #T_8cc54_row13_col8 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row4_col10 {\n",
       "  background-color: #f4c6af;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row4_col11, #T_8cc54_row8_col13 {\n",
       "  background-color: #4e68d8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row5_col2 {\n",
       "  background-color: #94b6ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row5_col3, #T_8cc54_row12_col10 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row5_col6 {\n",
       "  background-color: #c0d4f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row5_col8, #T_8cc54_row9_col11, #T_8cc54_row12_col4 {\n",
       "  background-color: #96b7ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row5_col9, #T_8cc54_row12_col7 {\n",
       "  background-color: #97b8ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row5_col11 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row5_col12, #T_8cc54_row6_col12, #T_8cc54_row8_col9, #T_8cc54_row9_col12 {\n",
       "  background-color: #7295f4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row6_col0 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row6_col2 {\n",
       "  background-color: #cad8ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row6_col3, #T_8cc54_row9_col8 {\n",
       "  background-color: #7597f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row6_col5 {\n",
       "  background-color: #b9d0f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row6_col7 {\n",
       "  background-color: #a9c6fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row6_col13 {\n",
       "  background-color: #aac7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row7_col0, #T_8cc54_row11_col7 {\n",
       "  background-color: #6b8df0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row7_col3, #T_8cc54_row7_col6, #T_8cc54_row9_col5, #T_8cc54_row12_col8 {\n",
       "  background-color: #5f7fe8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row7_col8, #T_8cc54_row8_col12, #T_8cc54_row11_col2 {\n",
       "  background-color: #506bda;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row7_col9 {\n",
       "  background-color: #b5cdfa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row7_col11 {\n",
       "  background-color: #5a78e4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row7_col13 {\n",
       "  background-color: #9bbcff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row8_col0 {\n",
       "  background-color: #ead5c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row8_col1 {\n",
       "  background-color: #f59c7d;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row8_col2 {\n",
       "  background-color: #d75445;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row8_col3, #T_8cc54_row9_col13 {\n",
       "  background-color: #a2c1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row8_col5, #T_8cc54_row11_col3, #T_8cc54_row12_col1 {\n",
       "  background-color: #5977e3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row8_col7 {\n",
       "  background-color: #6c8ff1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row9_col0, #T_8cc54_row9_col1 {\n",
       "  background-color: #c7d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row9_col2 {\n",
       "  background-color: #e8d6cc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row9_col4 {\n",
       "  background-color: #3e51c5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row9_col6 {\n",
       "  background-color: #b3cdfb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row9_col7 {\n",
       "  background-color: #c9d7f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row9_col10 {\n",
       "  background-color: #7093f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row10_col0, #T_8cc54_row13_col9 {\n",
       "  background-color: #8fb1fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row10_col2 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row10_col3 {\n",
       "  background-color: #6e90f2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row10_col4 {\n",
       "  background-color: #efcebd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row10_col8 {\n",
       "  background-color: #e5d8d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row10_col9 {\n",
       "  background-color: #516ddb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row11_col4, #T_8cc54_row11_col5 {\n",
       "  background-color: #445acc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row11_col9, #T_8cc54_row11_col12 {\n",
       "  background-color: #88abfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row11_col10 {\n",
       "  background-color: #4b64d5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row12_col3 {\n",
       "  background-color: #4961d2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row12_col5 {\n",
       "  background-color: #4257c9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row12_col6 {\n",
       "  background-color: #4c66d6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row12_col11 {\n",
       "  background-color: #9ebeff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row12_col13 {\n",
       "  background-color: #f7b093;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row13_col1 {\n",
       "  background-color: #3c4ec2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row13_col5, #T_8cc54_row13_col10 {\n",
       "  background-color: #4358cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_8cc54_row13_col7 {\n",
       "  background-color: #a3c2fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row13_col11 {\n",
       "  background-color: #b7cff9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_8cc54_row13_col12 {\n",
       "  background-color: #f7bca1;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8cc54\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8cc54_level0_col0\" class=\"col_heading level0 col0\" >bedrooms</th>\n",
       "      <th id=\"T_8cc54_level0_col1\" class=\"col_heading level0 col1\" >bathrooms</th>\n",
       "      <th id=\"T_8cc54_level0_col2\" class=\"col_heading level0 col2\" >sqft_living</th>\n",
       "      <th id=\"T_8cc54_level0_col3\" class=\"col_heading level0 col3\" >sqft_lot</th>\n",
       "      <th id=\"T_8cc54_level0_col4\" class=\"col_heading level0 col4\" >floors</th>\n",
       "      <th id=\"T_8cc54_level0_col5\" class=\"col_heading level0 col5\" >waterfront</th>\n",
       "      <th id=\"T_8cc54_level0_col6\" class=\"col_heading level0 col6\" >view</th>\n",
       "      <th id=\"T_8cc54_level0_col7\" class=\"col_heading level0 col7\" >condition</th>\n",
       "      <th id=\"T_8cc54_level0_col8\" class=\"col_heading level0 col8\" >sqft_above</th>\n",
       "      <th id=\"T_8cc54_level0_col9\" class=\"col_heading level0 col9\" >sqft_basement</th>\n",
       "      <th id=\"T_8cc54_level0_col10\" class=\"col_heading level0 col10\" >yr_built</th>\n",
       "      <th id=\"T_8cc54_level0_col11\" class=\"col_heading level0 col11\" >yr_renovated</th>\n",
       "      <th id=\"T_8cc54_level0_col12\" class=\"col_heading level0 col12\" >city</th>\n",
       "      <th id=\"T_8cc54_level0_col13\" class=\"col_heading level0 col13\" >statezip</th>\n",
       "      <th id=\"T_8cc54_level0_col14\" class=\"col_heading level0 col14\" >country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row0\" class=\"row_heading level0 row0\" >bedrooms</th>\n",
       "      <td id=\"T_8cc54_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row0_col1\" class=\"data row0 col1\" >0.545978</td>\n",
       "      <td id=\"T_8cc54_row0_col2\" class=\"data row0 col2\" >0.594883</td>\n",
       "      <td id=\"T_8cc54_row0_col3\" class=\"data row0 col3\" >0.068795</td>\n",
       "      <td id=\"T_8cc54_row0_col4\" class=\"data row0 col4\" >0.177755</td>\n",
       "      <td id=\"T_8cc54_row0_col5\" class=\"data row0 col5\" >-0.003499</td>\n",
       "      <td id=\"T_8cc54_row0_col6\" class=\"data row0 col6\" >0.110976</td>\n",
       "      <td id=\"T_8cc54_row0_col7\" class=\"data row0 col7\" >0.025098</td>\n",
       "      <td id=\"T_8cc54_row0_col8\" class=\"data row0 col8\" >0.484658</td>\n",
       "      <td id=\"T_8cc54_row0_col9\" class=\"data row0 col9\" >0.334217</td>\n",
       "      <td id=\"T_8cc54_row0_col10\" class=\"data row0 col10\" >0.142500</td>\n",
       "      <td id=\"T_8cc54_row0_col11\" class=\"data row0 col11\" >-0.061061</td>\n",
       "      <td id=\"T_8cc54_row0_col12\" class=\"data row0 col12\" >-0.130454</td>\n",
       "      <td id=\"T_8cc54_row0_col13\" class=\"data row0 col13\" >-0.153420</td>\n",
       "      <td id=\"T_8cc54_row0_col14\" class=\"data row0 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row1\" class=\"row_heading level0 row1\" >bathrooms</th>\n",
       "      <td id=\"T_8cc54_row1_col0\" class=\"data row1 col0\" >0.545978</td>\n",
       "      <td id=\"T_8cc54_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row1_col2\" class=\"data row1 col2\" >0.761206</td>\n",
       "      <td id=\"T_8cc54_row1_col3\" class=\"data row1 col3\" >0.107841</td>\n",
       "      <td id=\"T_8cc54_row1_col4\" class=\"data row1 col4\" >0.486583</td>\n",
       "      <td id=\"T_8cc54_row1_col5\" class=\"data row1 col5\" >0.076238</td>\n",
       "      <td id=\"T_8cc54_row1_col6\" class=\"data row1 col6\" >0.211985</td>\n",
       "      <td id=\"T_8cc54_row1_col7\" class=\"data row1 col7\" >-0.119913</td>\n",
       "      <td id=\"T_8cc54_row1_col8\" class=\"data row1 col8\" >0.689982</td>\n",
       "      <td id=\"T_8cc54_row1_col9\" class=\"data row1 col9\" >0.297962</td>\n",
       "      <td id=\"T_8cc54_row1_col10\" class=\"data row1 col10\" >0.463626</td>\n",
       "      <td id=\"T_8cc54_row1_col11\" class=\"data row1 col11\" >-0.216094</td>\n",
       "      <td id=\"T_8cc54_row1_col12\" class=\"data row1 col12\" >-0.097141</td>\n",
       "      <td id=\"T_8cc54_row1_col13\" class=\"data row1 col13\" >-0.206621</td>\n",
       "      <td id=\"T_8cc54_row1_col14\" class=\"data row1 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row2\" class=\"row_heading level0 row2\" >sqft_living</th>\n",
       "      <td id=\"T_8cc54_row2_col0\" class=\"data row2 col0\" >0.594883</td>\n",
       "      <td id=\"T_8cc54_row2_col1\" class=\"data row2 col1\" >0.761206</td>\n",
       "      <td id=\"T_8cc54_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row2_col3\" class=\"data row2 col3\" >0.210521</td>\n",
       "      <td id=\"T_8cc54_row2_col4\" class=\"data row2 col4\" >0.344760</td>\n",
       "      <td id=\"T_8cc54_row2_col5\" class=\"data row2 col5\" >0.117611</td>\n",
       "      <td id=\"T_8cc54_row2_col6\" class=\"data row2 col6\" >0.310986</td>\n",
       "      <td id=\"T_8cc54_row2_col7\" class=\"data row2 col7\" >-0.062657</td>\n",
       "      <td id=\"T_8cc54_row2_col8\" class=\"data row2 col8\" >0.876443</td>\n",
       "      <td id=\"T_8cc54_row2_col9\" class=\"data row2 col9\" >0.447146</td>\n",
       "      <td id=\"T_8cc54_row2_col10\" class=\"data row2 col10\" >0.287990</td>\n",
       "      <td id=\"T_8cc54_row2_col11\" class=\"data row2 col11\" >-0.123052</td>\n",
       "      <td id=\"T_8cc54_row2_col12\" class=\"data row2 col12\" >-0.109858</td>\n",
       "      <td id=\"T_8cc54_row2_col13\" class=\"data row2 col13\" >-0.211272</td>\n",
       "      <td id=\"T_8cc54_row2_col14\" class=\"data row2 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row3\" class=\"row_heading level0 row3\" >sqft_lot</th>\n",
       "      <td id=\"T_8cc54_row3_col0\" class=\"data row3 col0\" >0.068795</td>\n",
       "      <td id=\"T_8cc54_row3_col1\" class=\"data row3 col1\" >0.107841</td>\n",
       "      <td id=\"T_8cc54_row3_col2\" class=\"data row3 col2\" >0.210521</td>\n",
       "      <td id=\"T_8cc54_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row3_col4\" class=\"data row3 col4\" >0.003690</td>\n",
       "      <td id=\"T_8cc54_row3_col5\" class=\"data row3 col5\" >0.017236</td>\n",
       "      <td id=\"T_8cc54_row3_col6\" class=\"data row3 col6\" >0.073889</td>\n",
       "      <td id=\"T_8cc54_row3_col7\" class=\"data row3 col7\" >0.000579</td>\n",
       "      <td id=\"T_8cc54_row3_col8\" class=\"data row3 col8\" >0.216433</td>\n",
       "      <td id=\"T_8cc54_row3_col9\" class=\"data row3 col9\" >0.034832</td>\n",
       "      <td id=\"T_8cc54_row3_col10\" class=\"data row3 col10\" >0.050731</td>\n",
       "      <td id=\"T_8cc54_row3_col11\" class=\"data row3 col11\" >-0.022746</td>\n",
       "      <td id=\"T_8cc54_row3_col12\" class=\"data row3 col12\" >-0.079156</td>\n",
       "      <td id=\"T_8cc54_row3_col13\" class=\"data row3 col13\" >-0.133587</td>\n",
       "      <td id=\"T_8cc54_row3_col14\" class=\"data row3 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row4\" class=\"row_heading level0 row4\" >floors</th>\n",
       "      <td id=\"T_8cc54_row4_col0\" class=\"data row4 col0\" >0.177755</td>\n",
       "      <td id=\"T_8cc54_row4_col1\" class=\"data row4 col1\" >0.486583</td>\n",
       "      <td id=\"T_8cc54_row4_col2\" class=\"data row4 col2\" >0.344760</td>\n",
       "      <td id=\"T_8cc54_row4_col3\" class=\"data row4 col3\" >0.003690</td>\n",
       "      <td id=\"T_8cc54_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row4_col5\" class=\"data row4 col5\" >0.021993</td>\n",
       "      <td id=\"T_8cc54_row4_col6\" class=\"data row4 col6\" >0.031090</td>\n",
       "      <td id=\"T_8cc54_row4_col7\" class=\"data row4 col7\" >-0.275071</td>\n",
       "      <td id=\"T_8cc54_row4_col8\" class=\"data row4 col8\" >0.522733</td>\n",
       "      <td id=\"T_8cc54_row4_col9\" class=\"data row4 col9\" >-0.255580</td>\n",
       "      <td id=\"T_8cc54_row4_col10\" class=\"data row4 col10\" >0.467656</td>\n",
       "      <td id=\"T_8cc54_row4_col11\" class=\"data row4 col11\" >-0.234016</td>\n",
       "      <td id=\"T_8cc54_row4_col12\" class=\"data row4 col12\" >0.078533</td>\n",
       "      <td id=\"T_8cc54_row4_col13\" class=\"data row4 col13\" >-0.064775</td>\n",
       "      <td id=\"T_8cc54_row4_col14\" class=\"data row4 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row5\" class=\"row_heading level0 row5\" >waterfront</th>\n",
       "      <td id=\"T_8cc54_row5_col0\" class=\"data row5 col0\" >-0.003499</td>\n",
       "      <td id=\"T_8cc54_row5_col1\" class=\"data row5 col1\" >0.076238</td>\n",
       "      <td id=\"T_8cc54_row5_col2\" class=\"data row5 col2\" >0.117611</td>\n",
       "      <td id=\"T_8cc54_row5_col3\" class=\"data row5 col3\" >0.017236</td>\n",
       "      <td id=\"T_8cc54_row5_col4\" class=\"data row5 col4\" >0.021993</td>\n",
       "      <td id=\"T_8cc54_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row5_col6\" class=\"data row5 col6\" >0.360931</td>\n",
       "      <td id=\"T_8cc54_row5_col7\" class=\"data row5 col7\" >0.000355</td>\n",
       "      <td id=\"T_8cc54_row5_col8\" class=\"data row5 col8\" >0.078897</td>\n",
       "      <td id=\"T_8cc54_row5_col9\" class=\"data row5 col9\" >0.097512</td>\n",
       "      <td id=\"T_8cc54_row5_col10\" class=\"data row5 col10\" >-0.023561</td>\n",
       "      <td id=\"T_8cc54_row5_col11\" class=\"data row5 col11\" >0.008634</td>\n",
       "      <td id=\"T_8cc54_row5_col12\" class=\"data row5 col12\" >0.001453</td>\n",
       "      <td id=\"T_8cc54_row5_col13\" class=\"data row5 col13\" >0.005965</td>\n",
       "      <td id=\"T_8cc54_row5_col14\" class=\"data row5 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row6\" class=\"row_heading level0 row6\" >view</th>\n",
       "      <td id=\"T_8cc54_row6_col0\" class=\"data row6 col0\" >0.110976</td>\n",
       "      <td id=\"T_8cc54_row6_col1\" class=\"data row6 col1\" >0.211985</td>\n",
       "      <td id=\"T_8cc54_row6_col2\" class=\"data row6 col2\" >0.310986</td>\n",
       "      <td id=\"T_8cc54_row6_col3\" class=\"data row6 col3\" >0.073889</td>\n",
       "      <td id=\"T_8cc54_row6_col4\" class=\"data row6 col4\" >0.031090</td>\n",
       "      <td id=\"T_8cc54_row6_col5\" class=\"data row6 col5\" >0.360931</td>\n",
       "      <td id=\"T_8cc54_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row6_col7\" class=\"data row6 col7\" >0.063096</td>\n",
       "      <td id=\"T_8cc54_row6_col8\" class=\"data row6 col8\" >0.174271</td>\n",
       "      <td id=\"T_8cc54_row6_col9\" class=\"data row6 col9\" >0.321644</td>\n",
       "      <td id=\"T_8cc54_row6_col10\" class=\"data row6 col10\" >-0.064457</td>\n",
       "      <td id=\"T_8cc54_row6_col11\" class=\"data row6 col11\" >0.022999</td>\n",
       "      <td id=\"T_8cc54_row6_col12\" class=\"data row6 col12\" >0.001350</td>\n",
       "      <td id=\"T_8cc54_row6_col13\" class=\"data row6 col13\" >0.095456</td>\n",
       "      <td id=\"T_8cc54_row6_col14\" class=\"data row6 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row7\" class=\"row_heading level0 row7\" >condition</th>\n",
       "      <td id=\"T_8cc54_row7_col0\" class=\"data row7 col0\" >0.025098</td>\n",
       "      <td id=\"T_8cc54_row7_col1\" class=\"data row7 col1\" >-0.119913</td>\n",
       "      <td id=\"T_8cc54_row7_col2\" class=\"data row7 col2\" >-0.062657</td>\n",
       "      <td id=\"T_8cc54_row7_col3\" class=\"data row7 col3\" >0.000579</td>\n",
       "      <td id=\"T_8cc54_row7_col4\" class=\"data row7 col4\" >-0.275071</td>\n",
       "      <td id=\"T_8cc54_row7_col5\" class=\"data row7 col5\" >0.000355</td>\n",
       "      <td id=\"T_8cc54_row7_col6\" class=\"data row7 col6\" >0.063096</td>\n",
       "      <td id=\"T_8cc54_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row7_col8\" class=\"data row7 col8\" >-0.178145</td>\n",
       "      <td id=\"T_8cc54_row7_col9\" class=\"data row7 col9\" >0.200898</td>\n",
       "      <td id=\"T_8cc54_row7_col10\" class=\"data row7 col10\" >-0.399977</td>\n",
       "      <td id=\"T_8cc54_row7_col11\" class=\"data row7 col11\" >-0.186563</td>\n",
       "      <td id=\"T_8cc54_row7_col12\" class=\"data row7 col12\" >-0.010368</td>\n",
       "      <td id=\"T_8cc54_row7_col13\" class=\"data row7 col13\" >0.038554</td>\n",
       "      <td id=\"T_8cc54_row7_col14\" class=\"data row7 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row8\" class=\"row_heading level0 row8\" >sqft_above</th>\n",
       "      <td id=\"T_8cc54_row8_col0\" class=\"data row8 col0\" >0.484658</td>\n",
       "      <td id=\"T_8cc54_row8_col1\" class=\"data row8 col1\" >0.689982</td>\n",
       "      <td id=\"T_8cc54_row8_col2\" class=\"data row8 col2\" >0.876443</td>\n",
       "      <td id=\"T_8cc54_row8_col3\" class=\"data row8 col3\" >0.216433</td>\n",
       "      <td id=\"T_8cc54_row8_col4\" class=\"data row8 col4\" >0.522733</td>\n",
       "      <td id=\"T_8cc54_row8_col5\" class=\"data row8 col5\" >0.078897</td>\n",
       "      <td id=\"T_8cc54_row8_col6\" class=\"data row8 col6\" >0.174271</td>\n",
       "      <td id=\"T_8cc54_row8_col7\" class=\"data row8 col7\" >-0.178145</td>\n",
       "      <td id=\"T_8cc54_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row8_col9\" class=\"data row8 col9\" >-0.038790</td>\n",
       "      <td id=\"T_8cc54_row8_col10\" class=\"data row8 col10\" >0.408680</td>\n",
       "      <td id=\"T_8cc54_row8_col11\" class=\"data row8 col11\" >-0.160525</td>\n",
       "      <td id=\"T_8cc54_row8_col12\" class=\"data row8 col12\" >-0.121774</td>\n",
       "      <td id=\"T_8cc54_row8_col13\" class=\"data row8 col13\" >-0.270769</td>\n",
       "      <td id=\"T_8cc54_row8_col14\" class=\"data row8 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row9\" class=\"row_heading level0 row9\" >sqft_basement</th>\n",
       "      <td id=\"T_8cc54_row9_col0\" class=\"data row9 col0\" >0.334217</td>\n",
       "      <td id=\"T_8cc54_row9_col1\" class=\"data row9 col1\" >0.297962</td>\n",
       "      <td id=\"T_8cc54_row9_col2\" class=\"data row9 col2\" >0.447146</td>\n",
       "      <td id=\"T_8cc54_row9_col3\" class=\"data row9 col3\" >0.034832</td>\n",
       "      <td id=\"T_8cc54_row9_col4\" class=\"data row9 col4\" >-0.255580</td>\n",
       "      <td id=\"T_8cc54_row9_col5\" class=\"data row9 col5\" >0.097512</td>\n",
       "      <td id=\"T_8cc54_row9_col6\" class=\"data row9 col6\" >0.321644</td>\n",
       "      <td id=\"T_8cc54_row9_col7\" class=\"data row9 col7\" >0.200898</td>\n",
       "      <td id=\"T_8cc54_row9_col8\" class=\"data row9 col8\" >-0.038790</td>\n",
       "      <td id=\"T_8cc54_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row9_col10\" class=\"data row9 col10\" >-0.161524</td>\n",
       "      <td id=\"T_8cc54_row9_col11\" class=\"data row9 col11\" >0.042832</td>\n",
       "      <td id=\"T_8cc54_row9_col12\" class=\"data row9 col12\" >-0.001772</td>\n",
       "      <td id=\"T_8cc54_row9_col13\" class=\"data row9 col13\" >0.064546</td>\n",
       "      <td id=\"T_8cc54_row9_col14\" class=\"data row9 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row10\" class=\"row_heading level0 row10\" >yr_built</th>\n",
       "      <td id=\"T_8cc54_row10_col0\" class=\"data row10 col0\" >0.142500</td>\n",
       "      <td id=\"T_8cc54_row10_col1\" class=\"data row10 col1\" >0.463626</td>\n",
       "      <td id=\"T_8cc54_row10_col2\" class=\"data row10 col2\" >0.287990</td>\n",
       "      <td id=\"T_8cc54_row10_col3\" class=\"data row10 col3\" >0.050731</td>\n",
       "      <td id=\"T_8cc54_row10_col4\" class=\"data row10 col4\" >0.467656</td>\n",
       "      <td id=\"T_8cc54_row10_col5\" class=\"data row10 col5\" >-0.023561</td>\n",
       "      <td id=\"T_8cc54_row10_col6\" class=\"data row10 col6\" >-0.064457</td>\n",
       "      <td id=\"T_8cc54_row10_col7\" class=\"data row10 col7\" >-0.399977</td>\n",
       "      <td id=\"T_8cc54_row10_col8\" class=\"data row10 col8\" >0.408680</td>\n",
       "      <td id=\"T_8cc54_row10_col9\" class=\"data row10 col9\" >-0.161524</td>\n",
       "      <td id=\"T_8cc54_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row10_col11\" class=\"data row10 col11\" >-0.321185</td>\n",
       "      <td id=\"T_8cc54_row10_col12\" class=\"data row10 col12\" >-0.210967</td>\n",
       "      <td id=\"T_8cc54_row10_col13\" class=\"data row10 col13\" >-0.356794</td>\n",
       "      <td id=\"T_8cc54_row10_col14\" class=\"data row10 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row11\" class=\"row_heading level0 row11\" >yr_renovated</th>\n",
       "      <td id=\"T_8cc54_row11_col0\" class=\"data row11 col0\" >-0.061061</td>\n",
       "      <td id=\"T_8cc54_row11_col1\" class=\"data row11 col1\" >-0.216094</td>\n",
       "      <td id=\"T_8cc54_row11_col2\" class=\"data row11 col2\" >-0.123052</td>\n",
       "      <td id=\"T_8cc54_row11_col3\" class=\"data row11 col3\" >-0.022746</td>\n",
       "      <td id=\"T_8cc54_row11_col4\" class=\"data row11 col4\" >-0.234016</td>\n",
       "      <td id=\"T_8cc54_row11_col5\" class=\"data row11 col5\" >0.008634</td>\n",
       "      <td id=\"T_8cc54_row11_col6\" class=\"data row11 col6\" >0.022999</td>\n",
       "      <td id=\"T_8cc54_row11_col7\" class=\"data row11 col7\" >-0.186563</td>\n",
       "      <td id=\"T_8cc54_row11_col8\" class=\"data row11 col8\" >-0.160525</td>\n",
       "      <td id=\"T_8cc54_row11_col9\" class=\"data row11 col9\" >0.042832</td>\n",
       "      <td id=\"T_8cc54_row11_col10\" class=\"data row11 col10\" >-0.321185</td>\n",
       "      <td id=\"T_8cc54_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row11_col12\" class=\"data row11 col12\" >0.075342</td>\n",
       "      <td id=\"T_8cc54_row11_col13\" class=\"data row11 col13\" >0.169777</td>\n",
       "      <td id=\"T_8cc54_row11_col14\" class=\"data row11 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row12\" class=\"row_heading level0 row12\" >city</th>\n",
       "      <td id=\"T_8cc54_row12_col0\" class=\"data row12 col0\" >-0.130454</td>\n",
       "      <td id=\"T_8cc54_row12_col1\" class=\"data row12 col1\" >-0.097141</td>\n",
       "      <td id=\"T_8cc54_row12_col2\" class=\"data row12 col2\" >-0.109858</td>\n",
       "      <td id=\"T_8cc54_row12_col3\" class=\"data row12 col3\" >-0.079156</td>\n",
       "      <td id=\"T_8cc54_row12_col4\" class=\"data row12 col4\" >0.078533</td>\n",
       "      <td id=\"T_8cc54_row12_col5\" class=\"data row12 col5\" >0.001453</td>\n",
       "      <td id=\"T_8cc54_row12_col6\" class=\"data row12 col6\" >0.001350</td>\n",
       "      <td id=\"T_8cc54_row12_col7\" class=\"data row12 col7\" >-0.010368</td>\n",
       "      <td id=\"T_8cc54_row12_col8\" class=\"data row12 col8\" >-0.121774</td>\n",
       "      <td id=\"T_8cc54_row12_col9\" class=\"data row12 col9\" >-0.001772</td>\n",
       "      <td id=\"T_8cc54_row12_col10\" class=\"data row12 col10\" >-0.210967</td>\n",
       "      <td id=\"T_8cc54_row12_col11\" class=\"data row12 col11\" >0.075342</td>\n",
       "      <td id=\"T_8cc54_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row12_col13\" class=\"data row12 col13\" >0.579422</td>\n",
       "      <td id=\"T_8cc54_row12_col14\" class=\"data row12 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row13\" class=\"row_heading level0 row13\" >statezip</th>\n",
       "      <td id=\"T_8cc54_row13_col0\" class=\"data row13 col0\" >-0.153420</td>\n",
       "      <td id=\"T_8cc54_row13_col1\" class=\"data row13 col1\" >-0.206621</td>\n",
       "      <td id=\"T_8cc54_row13_col2\" class=\"data row13 col2\" >-0.211272</td>\n",
       "      <td id=\"T_8cc54_row13_col3\" class=\"data row13 col3\" >-0.133587</td>\n",
       "      <td id=\"T_8cc54_row13_col4\" class=\"data row13 col4\" >-0.064775</td>\n",
       "      <td id=\"T_8cc54_row13_col5\" class=\"data row13 col5\" >0.005965</td>\n",
       "      <td id=\"T_8cc54_row13_col6\" class=\"data row13 col6\" >0.095456</td>\n",
       "      <td id=\"T_8cc54_row13_col7\" class=\"data row13 col7\" >0.038554</td>\n",
       "      <td id=\"T_8cc54_row13_col8\" class=\"data row13 col8\" >-0.270769</td>\n",
       "      <td id=\"T_8cc54_row13_col9\" class=\"data row13 col9\" >0.064546</td>\n",
       "      <td id=\"T_8cc54_row13_col10\" class=\"data row13 col10\" >-0.356794</td>\n",
       "      <td id=\"T_8cc54_row13_col11\" class=\"data row13 col11\" >0.169777</td>\n",
       "      <td id=\"T_8cc54_row13_col12\" class=\"data row13 col12\" >0.579422</td>\n",
       "      <td id=\"T_8cc54_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "      <td id=\"T_8cc54_row13_col14\" class=\"data row13 col14\" >nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc54_level0_row14\" class=\"row_heading level0 row14\" >country</th>\n",
       "      <td id=\"T_8cc54_row14_col0\" class=\"data row14 col0\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col1\" class=\"data row14 col1\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col2\" class=\"data row14 col2\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col3\" class=\"data row14 col3\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col4\" class=\"data row14 col4\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col5\" class=\"data row14 col5\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col6\" class=\"data row14 col6\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col7\" class=\"data row14 col7\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col8\" class=\"data row14 col8\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col9\" class=\"data row14 col9\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col10\" class=\"data row14 col10\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col11\" class=\"data row14 col11\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col12\" class=\"data row14 col12\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col13\" class=\"data row14 col13\" >nan</td>\n",
       "      <td id=\"T_8cc54_row14_col14\" class=\"data row14 col14\" >nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f4bbf4e2f0>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
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    }
   ],
   "source": [
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  }
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