train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import joblib\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'C:\\\\Users\\\\daun5\\\\jupyter_notebook\\\\EC-Advance_daun\\\\data.xlsx'\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[['monthly_total']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(columns=['room_id', 'monthly_total'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_apartment = [[26.44,230,1,500,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0]] \n",
    "my_predict = lr.predict(my_apartment)\n",
    "my_predict #방향포함 50.38249985"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hiJhbxThqyj0lrFaybpP3gG1WipLuLG50jTAwl41fWbbJV+Mkxx0txj8nAtxTwhrXwvkzWfLzdWzf8zRP9T/DC5omMnPKZBa/qjJP96rXjhZOTuVxIki5p4Q1qghIHuwliPRzhdRb91eo3+RUz4ZNBKP1DIqIHZUPx8wqaeXa7cydOYVj5mTzdK+uXX1MmgD/vXk3u/v6mdrcxOGz9qPnqYHRF85IPSanejdSjWA1EICAQ4En0vfTSZ5YNi/z6Mxsn2R9oJ44AVat28G0/SYzrbmJvoG9rFq3g784vHY9zH2BvHzDdh+NiHkRcThwG/DmiJgVETOB04HvVytAMxu7wQP1UwN7mdbcxFPpgXpiKcNNlkD86WwRFX2ukazvzWhEpfw5vCIifjz4ISJuAf4yu5DMrFKyPlAP7IUT589gctMEdvX2M7lpAifOn8HA3goVMAYeL6l8pVws3ibpk8C3Sf6Gzgd8N4rZODB4oF677Y/s6u1naksTJ86fQX+FHi3Vnt6tfMK8Px1kd/f2M6O1dv1Q5rW1ctLhM1i+ehNb0maic/7cF4pHUsreOhf4FHAjSSL4WTrNzOpc1gfqerwHZ313D79Yt4OjXziNEw6fSU/fAL9Yt4M5M/ZzMhjGqE1DEbEjIi4FXhURx0XEZe4xZDY+ZN1MMngPzpT0YuyU5kk176bpYTXKN+ppQfqYyq8BrcChko4FLo4IP6VsnPNNN42vGjdL1ts9OO41VL5S6odfAE4FbgKIiPskvTrTqCxzvukmP+rtQJ01jx1WvpI6kUXEo0WTKnSpyWrF1WdrVO41VL5SEsGjafNQSJos6SPAQxnHZRnz0NvWqOrxukW9K6Vp6L3A/wEOBjYBt5M8vL4kkiaSPL/gsYg4PR26YjkwF9gAnB0RT5QXtu0rV5+tkeWtOWxflVIjOCIizouI9og4MCLOB44so4xLeW4N4grgzoh4EXBn+tmqzNVnMxtUSiL4UonTnkfSHOBNJL2OBp0JLE3fLwXOKmVdVll5rT6v7+5h2aqNXH3b71i2aqMf2WjGyKOPvhI4EWiT9KGCr6YCE0tc/7XA5cD+BdPaI2IzQERslnTgMOUvBhYDHHrooSUWZ+XIW/V5fXcPX/3ZOnbseZqnB/by+64nefCxXVz86sNz9XswKzZSjWAyyb0Dk0gO5IOv3cDbRluxpNOBrRGxeiyBRcSSiOiIiI62traxrMLsOX6w5jE2bNuDJKa1NCGJDdv28IM1j9U6NLOaGumZxXcBd0n6ZkRsHMO6TwLOkPRGoBmYKunbQJek2WltYDawdUyRm5VpzaM7mbpfEy1NSYW2pWkisV8Tax7dWePIzGqrlGsEX5P07FMtJB0g6bbRFoqIj0fEnIiYC7wD+El6ofkmYFE62yLgh+WHbTYWQkVP50o+13LQZLPaK6X76KyIePaUKSKeGK5dv0SfAW6QdCHJA27evg/rsgIeMmJkLz9kGnev24EkXtA0gaf697Kzt59X1vAhKmb1oJQawV5Jz16tlXQYySikJYuIFRFxevp+e0S8NiJelP70AHYVMDhkxJ6+AQ5KR5xc3rnJvWIKnLngYA5vm8Jegp1/fJq9BIe3TeHMBQfXOjSzmiqlRvAJYKWku9LPrybtzWP1w89pHd28tlYWv+pw15rMioyaCCLiVknHASeQNKZ+MCK2ZR6ZlcUjLpYmb11mzUoxbNOQpJekP48jeXj948BjJENRH1ed8KxUfk6rmY3VSDWCDwMXAdcM8V0Ap2QSkY1JPT4pyszGh5HuI7go/Xly9cKxsarGA0jMrDGNNMTEW0daMCK+X/lwbF+4/dvMxmKkpqE3pz8PJBlz6Cfp55OBFYATgZlZAxipaejdAJJuBl46OFBcOizEl6sTnpmZZa2UG8rmDiaBVBfw4oziMTOzKivlhrIV6dhC/07SW+gdwE8zjcrMzKqmlBvKLpH0FpI7igGWRMSN2YZlZmbVUkqNAOBe4MmI+C9J+0naPyKezDIwMzOrjlGvEUi6CPgP4KvppIOBH2QZlJmZVU8pF4vfT/KQmd0AEfE/JF1KzcysAZSSCJ6KiKcHP0iaRJnDUJuZWf0qJRHcJenvgBZJrwe+C/xntmGZmVm1lJIIPgZ0A78FLgZ+DHwyy6DMzKx6Ruw1JGkCcH9EHA1cV52QzMysmkasEUTEXuC+wkdVmplZYynlPoLZwIOSfgXsGZwYEWdkFpWZmVVNKYngqsyjMDOzmhnpeQTNwHuBPyO5UPz1iBgYbn4zMxufRrpGsBToIEkCb2DoR1aamdk4N1LT0Esj4mUAkr4O/Ko6IZmZWTWNVCPoH3zjJiEzs8Y1Uo3gWEm70/ciubN4d/o+ImJq5tGZmVnmRnpU5cRqBmJmZrVRyhATZmbWwJwIzMxyzonAzCznnAjMzHLOicDMLOdKfXi9WV1a393DyrXb6drVR/u0ZhbOn8m8ttZah2U2rrhGYOPW+u4elnduYk/fAAdNa2ZP3wDLOzexvrun1qGZjSuuEdi4tXLtdqa3NDG1pQng2Z8r1253raCOuRZXf1wjsHGra1cfrc3PPZdpbZ5E166+GkVko3Etrj65RmDjVvu0Znr6Bp6tCQD09A3QPq152GV8Nlpb1arFeT+XJ7MagaRmSb+SdJ+kByVdlU6/UtJjktakrzdmFYM1toXzZ7Jxxx5W/H4rtz24mRW/38rGHXtYOH/mkPP7bLT2qlGL834uX5Y1gqeAUyKiR1ITsFLSLel3X4iIqzMs23IiAggQgkg/D8PXFGpvLLW4cnk/ly+zRBARAQym4Kb0NcK/qVl5Vq7dztyZUzhmzvRnp+3u7R/2H75rVx8HFR1wWpsnscXXFKpm4fyZLO/cBCS/+56+AXb29nPqUe0VK8P7uXyZXiyWNFHSGmArcEdE3JN+dYmk+yV9Q9IBWcZgjavcZobBs9FClT4btZHNa2vlnI45TEkPzFOaJ3FOx5yKnql7P5cv00QQEc9ExAJgDnC8pKOBrwDzgQXAZoZ5BKakxZI6JXV2d3dnGaaNU+X+wy+cP5Odvf3s7u1nbwS7e/vZ2ds/7DUFy8a8tlYuOOEwPnLqEVxwwmEVb67xfi5fVbqPRsROYAVwWkR0pQliL3AdcPwwyyyJiI6I6Ghra6tGmDbOlPsPX42zUas97+fyZXaNQFIb0B8ROyW1AK8DPitpdkRsTmd7C/BAVjFYYxv8h1+5djtb0m6Cpx7VPuI//Ly2Vh8QcsD7uTxZ9hqaDSyVNJGk5nFDRNwsaZmkBSQXjjcAF2cYgzU4/8Ob7bssew3dD7x8iOkXZFWmmZmVz0NMmJnlnBOBmVnOORGYmeWcB52zXPFgZGbP5xqB5YYHIzMbmhOB5UbhYGQTJKa2NDG9pYmVa7fXOjSzmnIisNzwg2zMhuZEYLnhwcjMhuZEYLnhwcjMhuZEYLnhwcjMhubuo5YrHpvI7PlcIzAzyzknAjOznHMiMDPLOScCM7OccyIwM8s5JwIzs5xzIjAzyzknAjOznPMNZZYpj/9vVv9cI7DMePx/s/HBicAy4/H/zcYHJwLLjMf/NxsfnAgsMx7/32x8cCKwzHj8f7PxwYnAMuPx/83GB3cftUx5/H+z+ucagZlZzjkRmJnlnBOBmVnOORGYmeWcE4GZWc45EZiZ5ZwTgZlZzjkRmJnlnBOBmVnOORGYmeWcE4GZWc5lNtaQpGbgZ8AL0nL+IyI+JWkGsByYC2wAzo6IJ7KKw2rLj6o0q39Z1gieAk6JiGOBBcBpkk4ArgDujIgXAXemnytufXcPy1Zt5OrbfseyVRv9eMQa8KMqzcaHzBJBJAb/45vSVwBnAkvT6UuBsypdtg9A9cGPqjQbHzK9RiBpoqQ1wFbgjoi4B2iPiM0A6c8Dh1l2saROSZ3d3d1llesDUH3woyrNxodME0FEPBMRC4A5wPGSji5j2SUR0RERHW1tbWWV6wNQffCjKs3Gh6r0GoqIncAK4DSgS9JsgPTn1kqX5wNQffCjKs3Gh8wSgaQ2SdPT9y3A64CHgZuARelsi4AfVrpsH4Dqgx9VaTY+ZPmoytnAUkkTSRLODRFxs6S7gRskXQg8Ary90gUPHoBWrt3OlrTb4qlHtfsAVAN+VKVZ/cssEUTE/cDLh5i+HXhtVuUO8gHIzKw0vrPYzCznnAjMzHLOicDMLOecCMzMcs6JwMws5xQRtY5hVJK6gY1VKm4WsK1KZdULb3M+eJvzoXCbD4uIUYdmGBeJoJokdUZER63jqCZvcz54m/NhLNvspiEzs5xzIjAzyzkngudbUusAasDbnA/e5nwoe5t9jcDMLOdcIzAzyzknAjOznMt1IpB0iKSfSnpI0oOSLk2nXynpMUlr0tcbax1rpUhqlvQrSfel23xVOn2GpDsk/U/684Bax1oJI2xvw+7jQemjYn8j6eb0c0Pu40JDbHND72dJGyT9Nt22znRa2fs519cI0iekzY6IeyXtD6wGzgLOBnoi4uqaBpgBSQKmRESPpCZgJXAp8FZgR0R8RtIVwAER8bFaxloJI2zvaTToPh4k6UNABzA1Ik6X9DkacB8XGmKbr6SB97OkDUBHRGwrmFb2fs51jSAiNkfEven7J4GHgINrG1W2ItGTfmxKXwGcCSxNpy8lSYjj3gjb29AkzQHeBHytYHJD7uNBw2xzHpW9n3OdCApJmkvyIJ170kmXSLpf0jcarQqdVp/XkDwv+o6IuAdoj4jNkCRI4MBaxlhJw2wvNPA+Bq4FLgf2Fkxr2H2cGmqbobH3cwC3S1otaXE6rez97EQASGoFvgdcFhG7ga8A84EFwGbgmhqGV3ER8UxELADmAMdLOrrWMWVpmO1t2H0s6XRga0SsrnUs1TLCNjfsfk6dFBHHAW8A3i/p1WNZSe4TQdpu/D3g/0XE9wEiois9eOwFrgOOr2WMWYmIncAKkvbyrvSayeC1k601DC0Thdvb4Pv4JOCMtP34O8Apkr5NY+/jIbe5wfczEfF4+nMrcCPJ9pW9n3OdCNILiV8HHoqIzxdMn10w21uAB6odW1YktUmanr5vAV4HPAzcBCxKZ1sE/LA2EVbWcNvbyPs4Ij4eEXMiYi7wDuAnEXE+DbqPYfhtbuT9LGlK2skFSVOAvyLZvrL3c2YPrx8nTgIuAH6btiED/B1wrqQFJO1vG4CLaxNeJmYDSyVNJDkRuCEibpZ0N3CDpAuBR4C31zLIChpue5c18D4ezmdozH08ks818H5uB25MzmeZBFwfEbdK+jVl7udcdx81M7OcNw2ZmZkTgZlZ7jkRmJnlnBOBmVnOORGYmeWcE4E1JElvkRSSXlLCvJdJ2m8fynqXpH8ZZnp3OjLkw5I+uA9lnCXppWNd3mwkTgTWqM4lGWn0HSXMexkw5kQwiuXp8BYnAZ+QdMgY13MW4ERgmXAisIaTjh11EnAhBYkgHXzu6nT89vslfUDS3wIvBH4q6afpfD0Fy7xN0jfT92+WdE863v1/SWovNaaI2A78geQGNySdr+Q5CWskfTW94Q1JPZI+reT5CasktUs6ETgD+Od0/vn79hsyey4nAmtEZwG3RsTvgR2SjkunLwbmAS+PiGNIxpf6IvA4cHJEnDzKelcCJ0TEy0nGs7m81IAkHQo0A/dLOhI4h2TAsAXAM8B56axTgFURcSzwM+CiiPglybABH42IBRGxttRyzUqR9yEmrDGdSzIkMSQH7HOBe0nGGfq/ETEAEBE7ylzvHGB5On7NZGB9CcucI+lk4AiSg3qfpNcCfw78Oh0eoIU/DQz2NHBz+n418PoyYzQrmxOBNRRJM4FTgKMlBTARCEmXA6K0h9IUztNc8P5LwOcj4iZJrwGuLGFdyyPiEkmvBH4k6ZY0jqUR8fEh5u+PP4378gz+H7UqcNOQNZq3Ad+KiMMiYm5EHEJy5r4QuB14r6RJkDzbNV3mSWD/gnV0STpS0gSSESsHTQMeS98vogwRcTewjOQxmXcCb5N04GAckg4bZRXFMZpVjBOBNZpzScZlL/Q94K9JHmH4CEk7/X3pNIAlwC2DF4uBK0iaZ35C8jCTQVcC35X0c2Ab5fss8G7gUeCTJE+Wuh+4g/Qi8gi+A3w0vVDti8VWUR591Mws50AKs34AAAAtSURBVFwjMDPLOScCM7OccyIwM8s5JwIzs5xzIjAzyzknAjOznHMiMDPLuf8PLKlquPIGSy4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_predict, alpha=0.4)\n",
    "plt.xlabel(\"Actual Rent\")\n",
    "plt.ylabel(\"Predicted Rent\")\n",
    "plt.title(\"MULTIPLE LINEAR REGRESSION\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8.98179538e-01 -3.15119245e-02  1.03498682e+01 -3.96082253e-03\n",
      "  -1.12645845e+01 -7.10542736e-15 -5.32907052e-15  6.32328574e+00\n",
      "  -2.29567238e-01 -5.32907052e-15 -1.33995819e+01  1.73100410e+00\n",
      "  -1.16200180e+00  1.41355943e+00  2.66453526e-15  8.31813754e-01\n",
      "   3.21534780e-01  9.63672335e+00  1.55797281e+00  1.87113266e+00\n",
      "   9.62394302e-02 -1.18181204e+00 -4.71941251e-01]]\n"
     ]
    }
   ],
   "source": [
    "print(lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.687362294159248\n"
     ]
    }
   ],
   "source": [
    "print(lr.score(x_train, y_train)) #0.7372618220379018 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./lr_model.pkl']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(lr, './lr_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>room_size</th>\n",
       "      <th>age_month</th>\n",
       "      <th>room_floor_str</th>\n",
       "      <th>deposit</th>\n",
       "      <th>gasrange</th>\n",
       "      <th>frige</th>\n",
       "      <th>wash</th>\n",
       "      <th>shoes</th>\n",
       "      <th>doorlock</th>\n",
       "      <th>air</th>\n",
       "      <th>induction</th>\n",
       "      <th>closet</th>\n",
       "      <th>microwave</th>\n",
       "      <th>desk</th>\n",
       "      <th>bed</th>\n",
       "      <th>north_west</th>\n",
       "      <th>north</th>\n",
       "      <th>north_east</th>\n",
       "      <th>east</th>\n",
       "      <th>south_east</th>\n",
       "      <th>south</th>\n",
       "      <th>south_west</th>\n",
       "      <th>west</th>\n",
       "      <th>videophone</th>\n",
       "      <th>robby</th>\n",
       "      <th>man</th>\n",
       "      <th>cctv</th>\n",
       "      <th>window</th>\n",
       "      <th>intercom</th>\n",
       "      <th>cardkey</th>\n",
       "      <th>firealarm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>room_size</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.321580</td>\n",
       "      <td>0.040720</td>\n",
       "      <td>0.260656</td>\n",
       "      <td>0.343640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.321440</td>\n",
       "      <td>-0.192815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.421753</td>\n",
       "      <td>-0.331739</td>\n",
       "      <td>-0.355756</td>\n",
       "      <td>-0.353773</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.090041</td>\n",
       "      <td>-0.105466</td>\n",
       "      <td>-0.030946</td>\n",
       "      <td>0.106214</td>\n",
       "      <td>-0.063162</td>\n",
       "      <td>0.130079</td>\n",
       "      <td>-0.090041</td>\n",
       "      <td>-0.103865</td>\n",
       "      <td>-0.073267</td>\n",
       "      <td>-0.231286</td>\n",
       "      <td>-0.033442</td>\n",
       "      <td>-0.081122</td>\n",
       "      <td>-0.057100</td>\n",
       "      <td>-0.049625</td>\n",
       "      <td>0.037267</td>\n",
       "      <td>-0.061943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_month</th>\n",
       "      <td>0.321580</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097585</td>\n",
       "      <td>0.258406</td>\n",
       "      <td>0.416357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.258517</td>\n",
       "      <td>-0.184700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.390675</td>\n",
       "      <td>-0.327845</td>\n",
       "      <td>-0.374283</td>\n",
       "      <td>-0.363666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011879</td>\n",
       "      <td>-0.066118</td>\n",
       "      <td>-0.036033</td>\n",
       "      <td>0.045659</td>\n",
       "      <td>-0.034351</td>\n",
       "      <td>-0.100772</td>\n",
       "      <td>-0.043730</td>\n",
       "      <td>0.151497</td>\n",
       "      <td>-0.077648</td>\n",
       "      <td>-0.230285</td>\n",
       "      <td>-0.082285</td>\n",
       "      <td>-0.218036</td>\n",
       "      <td>-0.087756</td>\n",
       "      <td>-0.218994</td>\n",
       "      <td>-0.007358</td>\n",
       "      <td>-0.022205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>room_floor_str</th>\n",
       "      <td>0.040720</td>\n",
       "      <td>0.097585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.164600</td>\n",
       "      <td>0.101021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033528</td>\n",
       "      <td>-0.024438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.128295</td>\n",
       "      <td>-0.056977</td>\n",
       "      <td>-0.114945</td>\n",
       "      <td>-0.182300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028933</td>\n",
       "      <td>-0.082097</td>\n",
       "      <td>0.020385</td>\n",
       "      <td>0.045825</td>\n",
       "      <td>-0.137448</td>\n",
       "      <td>0.019461</td>\n",
       "      <td>-0.114700</td>\n",
       "      <td>0.074340</td>\n",
       "      <td>0.063007</td>\n",
       "      <td>-0.033484</td>\n",
       "      <td>0.172566</td>\n",
       "      <td>-0.015401</td>\n",
       "      <td>-0.130473</td>\n",
       "      <td>-0.078560</td>\n",
       "      <td>0.135026</td>\n",
       "      <td>0.122955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deposit</th>\n",
       "      <td>0.260656</td>\n",
       "      <td>0.258406</td>\n",
       "      <td>0.164600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.081846</td>\n",
       "      <td>0.008739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.060691</td>\n",
       "      <td>-0.065941</td>\n",
       "      <td>-0.174228</td>\n",
       "      <td>-0.164738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.057225</td>\n",
       "      <td>-0.042609</td>\n",
       "      <td>-0.040317</td>\n",
       "      <td>0.091539</td>\n",
       "      <td>0.135102</td>\n",
       "      <td>-0.051637</td>\n",
       "      <td>-0.093381</td>\n",
       "      <td>-0.021406</td>\n",
       "      <td>0.144834</td>\n",
       "      <td>-0.065096</td>\n",
       "      <td>-0.021069</td>\n",
       "      <td>-0.154292</td>\n",
       "      <td>-0.061460</td>\n",
       "      <td>-0.127003</td>\n",
       "      <td>0.026521</td>\n",
       "      <td>0.009011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasrange</th>\n",
       "      <td>0.343640</td>\n",
       "      <td>0.416357</td>\n",
       "      <td>0.101021</td>\n",
       "      <td>0.014202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.059066</td>\n",
       "      <td>-0.071588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.787408</td>\n",
       "      <td>-0.202333</td>\n",
       "      <td>-0.221221</td>\n",
       "      <td>-0.387423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.117719</td>\n",
       "      <td>-0.017234</td>\n",
       "      <td>-0.035911</td>\n",
       "      <td>-0.058855</td>\n",
       "      <td>0.026387</td>\n",
       "      <td>0.094515</td>\n",
       "      <td>-0.050971</td>\n",
       "      <td>-0.054796</td>\n",
       "      <td>-0.029365</td>\n",
       "      <td>-0.151252</td>\n",
       "      <td>-0.050971</td>\n",
       "      <td>-0.117122</td>\n",
       "      <td>0.037220</td>\n",
       "      <td>-0.065819</td>\n",
       "      <td>0.108091</td>\n",
       "      <td>-0.068290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frige</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wash</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shoes</th>\n",
       "      <td>-0.321440</td>\n",
       "      <td>-0.258517</td>\n",
       "      <td>0.033528</td>\n",
       "      <td>-0.081846</td>\n",
       "      <td>-0.059066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402379</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.509805</td>\n",
       "      <td>0.271535</td>\n",
       "      <td>0.290812</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040436</td>\n",
       "      <td>0.116398</td>\n",
       "      <td>0.028488</td>\n",
       "      <td>-0.092131</td>\n",
       "      <td>0.064646</td>\n",
       "      <td>-0.126068</td>\n",
       "      <td>0.040436</td>\n",
       "      <td>0.103893</td>\n",
       "      <td>0.088056</td>\n",
       "      <td>0.403522</td>\n",
       "      <td>0.040436</td>\n",
       "      <td>0.321788</td>\n",
       "      <td>0.145120</td>\n",
       "      <td>0.133218</td>\n",
       "      <td>0.071082</td>\n",
       "      <td>0.113005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doorlock</th>\n",
       "      <td>-0.192815</td>\n",
       "      <td>-0.184700</td>\n",
       "      <td>-0.024438</td>\n",
       "      <td>0.008739</td>\n",
       "      <td>-0.071588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.402379</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.314469</td>\n",
       "      <td>0.463520</td>\n",
       "      <td>0.286458</td>\n",
       "      <td>0.231407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071431</td>\n",
       "      <td>0.046836</td>\n",
       "      <td>0.050326</td>\n",
       "      <td>0.067152</td>\n",
       "      <td>0.026012</td>\n",
       "      <td>-0.012120</td>\n",
       "      <td>-0.066471</td>\n",
       "      <td>-0.124829</td>\n",
       "      <td>0.155554</td>\n",
       "      <td>0.457491</td>\n",
       "      <td>0.071431</td>\n",
       "      <td>0.404800</td>\n",
       "      <td>0.166375</td>\n",
       "      <td>0.325961</td>\n",
       "      <td>0.125569</td>\n",
       "      <td>0.101475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>induction</th>\n",
       "      <td>-0.421753</td>\n",
       "      <td>-0.390675</td>\n",
       "      <td>-0.128295</td>\n",
       "      <td>-0.060691</td>\n",
       "      <td>-0.787408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.314469</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.477926</td>\n",
       "      <td>0.380141</td>\n",
       "      <td>0.524467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.080394</td>\n",
       "      <td>0.074936</td>\n",
       "      <td>0.045607</td>\n",
       "      <td>0.051446</td>\n",
       "      <td>0.010683</td>\n",
       "      <td>-0.183352</td>\n",
       "      <td>0.064733</td>\n",
       "      <td>0.088313</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.328173</td>\n",
       "      <td>0.064733</td>\n",
       "      <td>0.282134</td>\n",
       "      <td>0.042921</td>\n",
       "      <td>0.165579</td>\n",
       "      <td>-0.056285</td>\n",
       "      <td>0.062423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>closet</th>\n",
       "      <td>-0.331739</td>\n",
       "      <td>-0.327845</td>\n",
       "      <td>-0.056977</td>\n",
       "      <td>-0.065941</td>\n",
       "      <td>-0.202333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.509805</td>\n",
       "      <td>0.463520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.477926</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.397083</td>\n",
       "      <td>0.528710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.052393</td>\n",
       "      <td>0.023765</td>\n",
       "      <td>0.036913</td>\n",
       "      <td>-0.058055</td>\n",
       "      <td>-0.127929</td>\n",
       "      <td>0.005152</td>\n",
       "      <td>0.052393</td>\n",
       "      <td>0.063442</td>\n",
       "      <td>0.033998</td>\n",
       "      <td>0.216373</td>\n",
       "      <td>-0.113121</td>\n",
       "      <td>0.260965</td>\n",
       "      <td>0.080031</td>\n",
       "      <td>0.361534</td>\n",
       "      <td>-0.004884</td>\n",
       "      <td>0.077125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>microwave</th>\n",
       "      <td>-0.355756</td>\n",
       "      <td>-0.374283</td>\n",
       "      <td>-0.114945</td>\n",
       "      <td>-0.174228</td>\n",
       "      <td>-0.221221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.271535</td>\n",
       "      <td>0.286458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.380141</td>\n",
       "      <td>0.397083</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.386360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.110006</td>\n",
       "      <td>-0.102539</td>\n",
       "      <td>0.077504</td>\n",
       "      <td>-0.124757</td>\n",
       "      <td>0.020658</td>\n",
       "      <td>0.135767</td>\n",
       "      <td>-0.011350</td>\n",
       "      <td>0.008673</td>\n",
       "      <td>-0.054080</td>\n",
       "      <td>0.235685</td>\n",
       "      <td>-0.011350</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.196832</td>\n",
       "      <td>0.318349</td>\n",
       "      <td>0.051159</td>\n",
       "      <td>0.040684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>desk</th>\n",
       "      <td>-0.353773</td>\n",
       "      <td>-0.363666</td>\n",
       "      <td>-0.182300</td>\n",
       "      <td>-0.164738</td>\n",
       "      <td>-0.387423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.290812</td>\n",
       "      <td>0.231407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.524467</td>\n",
       "      <td>0.528710</td>\n",
       "      <td>0.386360</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.158712</td>\n",
       "      <td>0.024228</td>\n",
       "      <td>-0.111819</td>\n",
       "      <td>0.078344</td>\n",
       "      <td>-0.093422</td>\n",
       "      <td>-0.047509</td>\n",
       "      <td>0.091981</td>\n",
       "      <td>0.034202</td>\n",
       "      <td>0.018329</td>\n",
       "      <td>0.188139</td>\n",
       "      <td>-0.033366</td>\n",
       "      <td>0.097605</td>\n",
       "      <td>0.043838</td>\n",
       "      <td>0.209823</td>\n",
       "      <td>-0.132103</td>\n",
       "      <td>-0.086687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bed</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>north_west</th>\n",
       "      <td>-0.090041</td>\n",
       "      <td>0.011879</td>\n",
       "      <td>0.028933</td>\n",
       "      <td>-0.057225</td>\n",
       "      <td>0.117719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040436</td>\n",
       "      <td>0.071431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.080394</td>\n",
       "      <td>0.052393</td>\n",
       "      <td>0.110006</td>\n",
       "      <td>-0.158712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042023</td>\n",
       "      <td>-0.010285</td>\n",
       "      <td>-0.079505</td>\n",
       "      <td>-0.023339</td>\n",
       "      <td>-0.083598</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>-0.059324</td>\n",
       "      <td>-0.031791</td>\n",
       "      <td>0.089152</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>0.096304</td>\n",
       "      <td>-0.052393</td>\n",
       "      <td>0.144919</td>\n",
       "      <td>-0.025663</td>\n",
       "      <td>-0.062038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>north</th>\n",
       "      <td>-0.105466</td>\n",
       "      <td>-0.066118</td>\n",
       "      <td>-0.082097</td>\n",
       "      <td>-0.042609</td>\n",
       "      <td>-0.017234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116398</td>\n",
       "      <td>0.046836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.074936</td>\n",
       "      <td>0.023765</td>\n",
       "      <td>-0.102539</td>\n",
       "      <td>0.024228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.042023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.029607</td>\n",
       "      <td>-0.228862</td>\n",
       "      <td>-0.067184</td>\n",
       "      <td>-0.240645</td>\n",
       "      <td>-0.042023</td>\n",
       "      <td>-0.170768</td>\n",
       "      <td>0.096936</td>\n",
       "      <td>-0.034567</td>\n",
       "      <td>0.152684</td>\n",
       "      <td>-0.055786</td>\n",
       "      <td>-0.150818</td>\n",
       "      <td>-0.054265</td>\n",
       "      <td>0.040220</td>\n",
       "      <td>-0.121518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>north_east</th>\n",
       "      <td>-0.030946</td>\n",
       "      <td>-0.036033</td>\n",
       "      <td>0.020385</td>\n",
       "      <td>-0.040317</td>\n",
       "      <td>-0.035911</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.028488</td>\n",
       "      <td>0.050326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.045607</td>\n",
       "      <td>0.036913</td>\n",
       "      <td>0.077504</td>\n",
       "      <td>-0.111819</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.010285</td>\n",
       "      <td>-0.029607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.056014</td>\n",
       "      <td>-0.016443</td>\n",
       "      <td>-0.058898</td>\n",
       "      <td>-0.010285</td>\n",
       "      <td>-0.041796</td>\n",
       "      <td>-0.022398</td>\n",
       "      <td>0.062811</td>\n",
       "      <td>-0.010285</td>\n",
       "      <td>0.067850</td>\n",
       "      <td>-0.036913</td>\n",
       "      <td>0.102101</td>\n",
       "      <td>-0.018080</td>\n",
       "      <td>0.165790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>east</th>\n",
       "      <td>0.106214</td>\n",
       "      <td>0.045659</td>\n",
       "      <td>0.045825</td>\n",
       "      <td>0.091539</td>\n",
       "      <td>-0.058855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.092131</td>\n",
       "      <td>0.067152</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.051446</td>\n",
       "      <td>-0.058055</td>\n",
       "      <td>-0.124757</td>\n",
       "      <td>0.078344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.079505</td>\n",
       "      <td>-0.228862</td>\n",
       "      <td>-0.056014</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.127108</td>\n",
       "      <td>-0.455283</td>\n",
       "      <td>-0.079505</td>\n",
       "      <td>-0.323081</td>\n",
       "      <td>-0.109470</td>\n",
       "      <td>-0.039163</td>\n",
       "      <td>-0.079505</td>\n",
       "      <td>-0.054113</td>\n",
       "      <td>0.015131</td>\n",
       "      <td>0.088457</td>\n",
       "      <td>-0.062671</td>\n",
       "      <td>-0.067961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>south_east</th>\n",
       "      <td>-0.063162</td>\n",
       "      <td>-0.034351</td>\n",
       "      <td>-0.137448</td>\n",
       "      <td>0.135102</td>\n",
       "      <td>0.026387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.064646</td>\n",
       "      <td>0.026012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.010683</td>\n",
       "      <td>-0.127929</td>\n",
       "      <td>0.020658</td>\n",
       "      <td>-0.093422</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.023339</td>\n",
       "      <td>-0.067184</td>\n",
       "      <td>-0.016443</td>\n",
       "      <td>-0.127108</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.133652</td>\n",
       "      <td>-0.023339</td>\n",
       "      <td>-0.094843</td>\n",
       "      <td>0.106169</td>\n",
       "      <td>0.142531</td>\n",
       "      <td>-0.023339</td>\n",
       "      <td>0.153964</td>\n",
       "      <td>0.233774</td>\n",
       "      <td>-0.082503</td>\n",
       "      <td>-0.041028</td>\n",
       "      <td>-0.099183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>south</th>\n",
       "      <td>0.130079</td>\n",
       "      <td>-0.100772</td>\n",
       "      <td>0.019461</td>\n",
       "      <td>-0.051637</td>\n",
       "      <td>0.094515</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.126068</td>\n",
       "      <td>-0.012120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.183352</td>\n",
       "      <td>0.005152</td>\n",
       "      <td>0.135767</td>\n",
       "      <td>-0.047509</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.083598</td>\n",
       "      <td>-0.240645</td>\n",
       "      <td>-0.058898</td>\n",
       "      <td>-0.455283</td>\n",
       "      <td>-0.133652</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.083598</td>\n",
       "      <td>-0.339715</td>\n",
       "      <td>-0.057087</td>\n",
       "      <td>-0.036582</td>\n",
       "      <td>0.045515</td>\n",
       "      <td>0.015205</td>\n",
       "      <td>0.121223</td>\n",
       "      <td>0.110875</td>\n",
       "      <td>0.155666</td>\n",
       "      <td>0.212339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>south_west</th>\n",
       "      <td>-0.090041</td>\n",
       "      <td>-0.043730</td>\n",
       "      <td>-0.114700</td>\n",
       "      <td>-0.093381</td>\n",
       "      <td>-0.050971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040436</td>\n",
       "      <td>-0.066471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.064733</td>\n",
       "      <td>0.052393</td>\n",
       "      <td>-0.011350</td>\n",
       "      <td>0.091981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>-0.042023</td>\n",
       "      <td>-0.010285</td>\n",
       "      <td>-0.079505</td>\n",
       "      <td>-0.023339</td>\n",
       "      <td>-0.083598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.059324</td>\n",
       "      <td>0.213706</td>\n",
       "      <td>0.089152</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>-0.027643</td>\n",
       "      <td>-0.052393</td>\n",
       "      <td>-0.100736</td>\n",
       "      <td>-0.025663</td>\n",
       "      <td>-0.062038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>west</th>\n",
       "      <td>-0.103865</td>\n",
       "      <td>0.151497</td>\n",
       "      <td>0.074340</td>\n",
       "      <td>-0.021406</td>\n",
       "      <td>-0.054796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.103893</td>\n",
       "      <td>-0.124829</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088313</td>\n",
       "      <td>0.063442</td>\n",
       "      <td>0.008673</td>\n",
       "      <td>0.034202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.059324</td>\n",
       "      <td>-0.170768</td>\n",
       "      <td>-0.041796</td>\n",
       "      <td>-0.323081</td>\n",
       "      <td>-0.094843</td>\n",
       "      <td>-0.339715</td>\n",
       "      <td>-0.059324</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018607</td>\n",
       "      <td>-0.018347</td>\n",
       "      <td>-0.059324</td>\n",
       "      <td>-0.019057</td>\n",
       "      <td>-0.113264</td>\n",
       "      <td>-0.187523</td>\n",
       "      <td>-0.104285</td>\n",
       "      <td>-0.028333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>videophone</th>\n",
       "      <td>-0.073267</td>\n",
       "      <td>-0.077648</td>\n",
       "      <td>0.063007</td>\n",
       "      <td>0.144834</td>\n",
       "      <td>-0.029365</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088056</td>\n",
       "      <td>0.155554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070736</td>\n",
       "      <td>0.033998</td>\n",
       "      <td>-0.054080</td>\n",
       "      <td>0.018329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.031791</td>\n",
       "      <td>0.096936</td>\n",
       "      <td>-0.022398</td>\n",
       "      <td>-0.109470</td>\n",
       "      <td>0.106169</td>\n",
       "      <td>-0.057087</td>\n",
       "      <td>0.213706</td>\n",
       "      <td>0.018607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.194145</td>\n",
       "      <td>0.213706</td>\n",
       "      <td>0.209719</td>\n",
       "      <td>-0.033998</td>\n",
       "      <td>-0.159932</td>\n",
       "      <td>0.087968</td>\n",
       "      <td>0.008800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>robby</th>\n",
       "      <td>-0.231286</td>\n",
       "      <td>-0.230285</td>\n",
       "      <td>-0.033484</td>\n",
       "      <td>-0.065096</td>\n",
       "      <td>-0.151252</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.403522</td>\n",
       "      <td>0.457491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.328173</td>\n",
       "      <td>0.216373</td>\n",
       "      <td>0.235685</td>\n",
       "      <td>0.188139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089152</td>\n",
       "      <td>-0.034567</td>\n",
       "      <td>0.062811</td>\n",
       "      <td>-0.039163</td>\n",
       "      <td>0.142531</td>\n",
       "      <td>-0.036582</td>\n",
       "      <td>0.089152</td>\n",
       "      <td>-0.018347</td>\n",
       "      <td>0.194145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089152</td>\n",
       "      <td>0.740370</td>\n",
       "      <td>0.237447</td>\n",
       "      <td>0.462106</td>\n",
       "      <td>0.082625</td>\n",
       "      <td>0.267682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>man</th>\n",
       "      <td>-0.033442</td>\n",
       "      <td>-0.082285</td>\n",
       "      <td>0.172566</td>\n",
       "      <td>-0.021069</td>\n",
       "      <td>-0.050971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040436</td>\n",
       "      <td>0.071431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.064733</td>\n",
       "      <td>-0.113121</td>\n",
       "      <td>-0.011350</td>\n",
       "      <td>-0.033366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>0.152684</td>\n",
       "      <td>-0.010285</td>\n",
       "      <td>-0.079505</td>\n",
       "      <td>-0.023339</td>\n",
       "      <td>0.045515</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>-0.059324</td>\n",
       "      <td>0.213706</td>\n",
       "      <td>0.089152</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.027643</td>\n",
       "      <td>-0.052393</td>\n",
       "      <td>0.022091</td>\n",
       "      <td>0.271598</td>\n",
       "      <td>0.235316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cctv</th>\n",
       "      <td>-0.081122</td>\n",
       "      <td>-0.218036</td>\n",
       "      <td>-0.015401</td>\n",
       "      <td>-0.154292</td>\n",
       "      <td>-0.117122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.321788</td>\n",
       "      <td>0.404800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.282134</td>\n",
       "      <td>0.260965</td>\n",
       "      <td>0.163824</td>\n",
       "      <td>0.097605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.096304</td>\n",
       "      <td>-0.055786</td>\n",
       "      <td>0.067850</td>\n",
       "      <td>-0.054113</td>\n",
       "      <td>0.153964</td>\n",
       "      <td>0.015205</td>\n",
       "      <td>-0.027643</td>\n",
       "      <td>-0.019057</td>\n",
       "      <td>0.209719</td>\n",
       "      <td>0.740370</td>\n",
       "      <td>-0.027643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183868</td>\n",
       "      <td>0.454466</td>\n",
       "      <td>0.169292</td>\n",
       "      <td>0.263949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>window</th>\n",
       "      <td>-0.057100</td>\n",
       "      <td>-0.087756</td>\n",
       "      <td>-0.130473</td>\n",
       "      <td>-0.061460</td>\n",
       "      <td>0.037220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.145120</td>\n",
       "      <td>0.166375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.042921</td>\n",
       "      <td>0.080031</td>\n",
       "      <td>0.196832</td>\n",
       "      <td>0.043838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.052393</td>\n",
       "      <td>-0.150818</td>\n",
       "      <td>-0.036913</td>\n",
       "      <td>0.015131</td>\n",
       "      <td>0.233774</td>\n",
       "      <td>0.121223</td>\n",
       "      <td>-0.052393</td>\n",
       "      <td>-0.113264</td>\n",
       "      <td>-0.033998</td>\n",
       "      <td>0.237447</td>\n",
       "      <td>-0.052393</td>\n",
       "      <td>0.183868</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.079284</td>\n",
       "      <td>0.392828</td>\n",
       "      <td>0.262433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intercom</th>\n",
       "      <td>-0.049625</td>\n",
       "      <td>-0.218994</td>\n",
       "      <td>-0.078560</td>\n",
       "      <td>-0.127003</td>\n",
       "      <td>-0.065819</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.133218</td>\n",
       "      <td>0.325961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.165579</td>\n",
       "      <td>0.361534</td>\n",
       "      <td>0.318349</td>\n",
       "      <td>0.209823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.144919</td>\n",
       "      <td>-0.054265</td>\n",
       "      <td>0.102101</td>\n",
       "      <td>0.088457</td>\n",
       "      <td>-0.082503</td>\n",
       "      <td>0.110875</td>\n",
       "      <td>-0.100736</td>\n",
       "      <td>-0.187523</td>\n",
       "      <td>-0.159932</td>\n",
       "      <td>0.462106</td>\n",
       "      <td>0.022091</td>\n",
       "      <td>0.454466</td>\n",
       "      <td>0.079284</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.110807</td>\n",
       "      <td>0.111878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardkey</th>\n",
       "      <td>0.037267</td>\n",
       "      <td>-0.007358</td>\n",
       "      <td>0.135026</td>\n",
       "      <td>0.026521</td>\n",
       "      <td>0.108091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071082</td>\n",
       "      <td>0.125569</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.056285</td>\n",
       "      <td>-0.004884</td>\n",
       "      <td>0.051159</td>\n",
       "      <td>-0.132103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.025663</td>\n",
       "      <td>0.040220</td>\n",
       "      <td>-0.018080</td>\n",
       "      <td>-0.062671</td>\n",
       "      <td>-0.041028</td>\n",
       "      <td>0.155666</td>\n",
       "      <td>-0.025663</td>\n",
       "      <td>-0.104285</td>\n",
       "      <td>0.087968</td>\n",
       "      <td>0.082625</td>\n",
       "      <td>0.271598</td>\n",
       "      <td>0.169292</td>\n",
       "      <td>0.392828</td>\n",
       "      <td>0.110807</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.413663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>firealarm</th>\n",
       "      <td>-0.061943</td>\n",
       "      <td>-0.022205</td>\n",
       "      <td>0.122955</td>\n",
       "      <td>0.009011</td>\n",
       "      <td>-0.068290</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.113005</td>\n",
       "      <td>0.101475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.062423</td>\n",
       "      <td>0.077125</td>\n",
       "      <td>0.040684</td>\n",
       "      <td>-0.086687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.062038</td>\n",
       "      <td>-0.121518</td>\n",
       "      <td>0.165790</td>\n",
       "      <td>-0.067961</td>\n",
       "      <td>-0.099183</td>\n",
       "      <td>0.212339</td>\n",
       "      <td>-0.062038</td>\n",
       "      <td>-0.028333</td>\n",
       "      <td>0.008800</td>\n",
       "      <td>0.267682</td>\n",
       "      <td>0.235316</td>\n",
       "      <td>0.263949</td>\n",
       "      <td>0.262433</td>\n",
       "      <td>0.111878</td>\n",
       "      <td>0.413663</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                room_size  age_month  room_floor_str   deposit  gasrange  \\\n",
       "room_size        1.000000   0.321580        0.040720  0.260656  0.343640   \n",
       "age_month        0.321580   1.000000        0.097585  0.258406  0.416357   \n",
       "room_floor_str   0.040720   0.097585        1.000000  0.164600  0.101021   \n",
       "deposit          0.260656   0.258406        0.164600  1.000000  0.014202   \n",
       "gasrange         0.343640   0.416357        0.101021  0.014202  1.000000   \n",
       "frige                 NaN        NaN             NaN       NaN       NaN   \n",
       "wash                  NaN        NaN             NaN       NaN       NaN   \n",
       "shoes           -0.321440  -0.258517        0.033528 -0.081846 -0.059066   \n",
       "doorlock        -0.192815  -0.184700       -0.024438  0.008739 -0.071588   \n",
       "air                   NaN        NaN             NaN       NaN       NaN   \n",
       "induction       -0.421753  -0.390675       -0.128295 -0.060691 -0.787408   \n",
       "closet          -0.331739  -0.327845       -0.056977 -0.065941 -0.202333   \n",
       "microwave       -0.355756  -0.374283       -0.114945 -0.174228 -0.221221   \n",
       "desk            -0.353773  -0.363666       -0.182300 -0.164738 -0.387423   \n",
       "bed                   NaN        NaN             NaN       NaN       NaN   \n",
       "north_west      -0.090041   0.011879        0.028933 -0.057225  0.117719   \n",
       "north           -0.105466  -0.066118       -0.082097 -0.042609 -0.017234   \n",
       "north_east      -0.030946  -0.036033        0.020385 -0.040317 -0.035911   \n",
       "east             0.106214   0.045659        0.045825  0.091539 -0.058855   \n",
       "south_east      -0.063162  -0.034351       -0.137448  0.135102  0.026387   \n",
       "south            0.130079  -0.100772        0.019461 -0.051637  0.094515   \n",
       "south_west      -0.090041  -0.043730       -0.114700 -0.093381 -0.050971   \n",
       "west            -0.103865   0.151497        0.074340 -0.021406 -0.054796   \n",
       "videophone      -0.073267  -0.077648        0.063007  0.144834 -0.029365   \n",
       "robby           -0.231286  -0.230285       -0.033484 -0.065096 -0.151252   \n",
       "man             -0.033442  -0.082285        0.172566 -0.021069 -0.050971   \n",
       "cctv            -0.081122  -0.218036       -0.015401 -0.154292 -0.117122   \n",
       "window          -0.057100  -0.087756       -0.130473 -0.061460  0.037220   \n",
       "intercom        -0.049625  -0.218994       -0.078560 -0.127003 -0.065819   \n",
       "cardkey          0.037267  -0.007358        0.135026  0.026521  0.108091   \n",
       "firealarm       -0.061943  -0.022205        0.122955  0.009011 -0.068290   \n",
       "\n",
       "                frige  wash     shoes  doorlock  air  induction    closet  \\\n",
       "room_size         NaN   NaN -0.321440 -0.192815  NaN  -0.421753 -0.331739   \n",
       "age_month         NaN   NaN -0.258517 -0.184700  NaN  -0.390675 -0.327845   \n",
       "room_floor_str    NaN   NaN  0.033528 -0.024438  NaN  -0.128295 -0.056977   \n",
       "deposit           NaN   NaN -0.081846  0.008739  NaN  -0.060691 -0.065941   \n",
       "gasrange          NaN   NaN -0.059066 -0.071588  NaN  -0.787408 -0.202333   \n",
       "frige             NaN   NaN       NaN       NaN  NaN        NaN       NaN   \n",
       "wash              NaN   NaN       NaN       NaN  NaN        NaN       NaN   \n",
       "shoes             NaN   NaN  1.000000  0.402379  NaN   0.509804  0.509805   \n",
       "doorlock          NaN   NaN  0.402379  1.000000  NaN   0.314469  0.463520   \n",
       "air               NaN   NaN       NaN       NaN  NaN        NaN       NaN   \n",
       "induction         NaN   NaN  0.509804  0.314469  NaN   1.000000  0.477926   \n",
       "closet            NaN   NaN  0.509805  0.463520  NaN   0.477926  1.000000   \n",
       "microwave         NaN   NaN  0.271535  0.286458  NaN   0.380141  0.397083   \n",
       "desk              NaN   NaN  0.290812  0.231407  NaN   0.524467  0.528710   \n",
       "bed               NaN   NaN       NaN       NaN  NaN        NaN       NaN   \n",
       "north_west        NaN   NaN  0.040436  0.071431  NaN  -0.080394  0.052393   \n",
       "north             NaN   NaN  0.116398  0.046836  NaN   0.074936  0.023765   \n",
       "north_east        NaN   NaN  0.028488  0.050326  NaN   0.045607  0.036913   \n",
       "east              NaN   NaN -0.092131  0.067152  NaN   0.051446 -0.058055   \n",
       "south_east        NaN   NaN  0.064646  0.026012  NaN   0.010683 -0.127929   \n",
       "south             NaN   NaN -0.126068 -0.012120  NaN  -0.183352  0.005152   \n",
       "south_west        NaN   NaN  0.040436 -0.066471  NaN   0.064733  0.052393   \n",
       "west              NaN   NaN  0.103893 -0.124829  NaN   0.088313  0.063442   \n",
       "videophone        NaN   NaN  0.088056  0.155554  NaN   0.070736  0.033998   \n",
       "robby             NaN   NaN  0.403522  0.457491  NaN   0.328173  0.216373   \n",
       "man               NaN   NaN  0.040436  0.071431  NaN   0.064733 -0.113121   \n",
       "cctv              NaN   NaN  0.321788  0.404800  NaN   0.282134  0.260965   \n",
       "window            NaN   NaN  0.145120  0.166375  NaN   0.042921  0.080031   \n",
       "intercom          NaN   NaN  0.133218  0.325961  NaN   0.165579  0.361534   \n",
       "cardkey           NaN   NaN  0.071082  0.125569  NaN  -0.056285 -0.004884   \n",
       "firealarm         NaN   NaN  0.113005  0.101475  NaN   0.062423  0.077125   \n",
       "\n",
       "                microwave      desk  bed  north_west     north  north_east  \\\n",
       "room_size       -0.355756 -0.353773  NaN   -0.090041 -0.105466   -0.030946   \n",
       "age_month       -0.374283 -0.363666  NaN    0.011879 -0.066118   -0.036033   \n",
       "room_floor_str  -0.114945 -0.182300  NaN    0.028933 -0.082097    0.020385   \n",
       "deposit         -0.174228 -0.164738  NaN   -0.057225 -0.042609   -0.040317   \n",
       "gasrange        -0.221221 -0.387423  NaN    0.117719 -0.017234   -0.035911   \n",
       "frige                 NaN       NaN  NaN         NaN       NaN         NaN   \n",
       "wash                  NaN       NaN  NaN         NaN       NaN         NaN   \n",
       "shoes            0.271535  0.290812  NaN    0.040436  0.116398    0.028488   \n",
       "doorlock         0.286458  0.231407  NaN    0.071431  0.046836    0.050326   \n",
       "air                   NaN       NaN  NaN         NaN       NaN         NaN   \n",
       "induction        0.380141  0.524467  NaN   -0.080394  0.074936    0.045607   \n",
       "closet           0.397083  0.528710  NaN    0.052393  0.023765    0.036913   \n",
       "microwave        1.000000  0.386360  NaN    0.110006 -0.102539    0.077504   \n",
       "desk             0.386360  1.000000  NaN   -0.158712  0.024228   -0.111819   \n",
       "bed                   NaN       NaN  NaN         NaN       NaN         NaN   \n",
       "north_west       0.110006 -0.158712  NaN    1.000000 -0.042023   -0.010285   \n",
       "north           -0.102539  0.024228  NaN   -0.042023  1.000000   -0.029607   \n",
       "north_east       0.077504 -0.111819  NaN   -0.010285 -0.029607    1.000000   \n",
       "east            -0.124757  0.078344  NaN   -0.079505 -0.228862   -0.056014   \n",
       "south_east       0.020658 -0.093422  NaN   -0.023339 -0.067184   -0.016443   \n",
       "south            0.135767 -0.047509  NaN   -0.083598 -0.240645   -0.058898   \n",
       "south_west      -0.011350  0.091981  NaN   -0.014599 -0.042023   -0.010285   \n",
       "west             0.008673  0.034202  NaN   -0.059324 -0.170768   -0.041796   \n",
       "videophone      -0.054080  0.018329  NaN   -0.031791  0.096936   -0.022398   \n",
       "robby            0.235685  0.188139  NaN    0.089152 -0.034567    0.062811   \n",
       "man             -0.011350 -0.033366  NaN   -0.014599  0.152684   -0.010285   \n",
       "cctv             0.163824  0.097605  NaN    0.096304 -0.055786    0.067850   \n",
       "window           0.196832  0.043838  NaN   -0.052393 -0.150818   -0.036913   \n",
       "intercom         0.318349  0.209823  NaN    0.144919 -0.054265    0.102101   \n",
       "cardkey          0.051159 -0.132103  NaN   -0.025663  0.040220   -0.018080   \n",
       "firealarm        0.040684 -0.086687  NaN   -0.062038 -0.121518    0.165790   \n",
       "\n",
       "                    east  south_east     south  south_west      west  \\\n",
       "room_size       0.106214   -0.063162  0.130079   -0.090041 -0.103865   \n",
       "age_month       0.045659   -0.034351 -0.100772   -0.043730  0.151497   \n",
       "room_floor_str  0.045825   -0.137448  0.019461   -0.114700  0.074340   \n",
       "deposit         0.091539    0.135102 -0.051637   -0.093381 -0.021406   \n",
       "gasrange       -0.058855    0.026387  0.094515   -0.050971 -0.054796   \n",
       "frige                NaN         NaN       NaN         NaN       NaN   \n",
       "wash                 NaN         NaN       NaN         NaN       NaN   \n",
       "shoes          -0.092131    0.064646 -0.126068    0.040436  0.103893   \n",
       "doorlock        0.067152    0.026012 -0.012120   -0.066471 -0.124829   \n",
       "air                  NaN         NaN       NaN         NaN       NaN   \n",
       "induction       0.051446    0.010683 -0.183352    0.064733  0.088313   \n",
       "closet         -0.058055   -0.127929  0.005152    0.052393  0.063442   \n",
       "microwave      -0.124757    0.020658  0.135767   -0.011350  0.008673   \n",
       "desk            0.078344   -0.093422 -0.047509    0.091981  0.034202   \n",
       "bed                  NaN         NaN       NaN         NaN       NaN   \n",
       "north_west     -0.079505   -0.023339 -0.083598   -0.014599 -0.059324   \n",
       "north          -0.228862   -0.067184 -0.240645   -0.042023 -0.170768   \n",
       "north_east     -0.056014   -0.016443 -0.058898   -0.010285 -0.041796   \n",
       "east            1.000000   -0.127108 -0.455283   -0.079505 -0.323081   \n",
       "south_east     -0.127108    1.000000 -0.133652   -0.023339 -0.094843   \n",
       "south          -0.455283   -0.133652  1.000000   -0.083598 -0.339715   \n",
       "south_west     -0.079505   -0.023339 -0.083598    1.000000 -0.059324   \n",
       "west           -0.323081   -0.094843 -0.339715   -0.059324  1.000000   \n",
       "videophone     -0.109470    0.106169 -0.057087    0.213706  0.018607   \n",
       "robby          -0.039163    0.142531 -0.036582    0.089152 -0.018347   \n",
       "man            -0.079505   -0.023339  0.045515   -0.014599 -0.059324   \n",
       "cctv           -0.054113    0.153964  0.015205   -0.027643 -0.019057   \n",
       "window          0.015131    0.233774  0.121223   -0.052393 -0.113264   \n",
       "intercom        0.088457   -0.082503  0.110875   -0.100736 -0.187523   \n",
       "cardkey        -0.062671   -0.041028  0.155666   -0.025663 -0.104285   \n",
       "firealarm      -0.067961   -0.099183  0.212339   -0.062038 -0.028333   \n",
       "\n",
       "                videophone     robby       man      cctv    window  intercom  \\\n",
       "room_size        -0.073267 -0.231286 -0.033442 -0.081122 -0.057100 -0.049625   \n",
       "age_month        -0.077648 -0.230285 -0.082285 -0.218036 -0.087756 -0.218994   \n",
       "room_floor_str    0.063007 -0.033484  0.172566 -0.015401 -0.130473 -0.078560   \n",
       "deposit           0.144834 -0.065096 -0.021069 -0.154292 -0.061460 -0.127003   \n",
       "gasrange         -0.029365 -0.151252 -0.050971 -0.117122  0.037220 -0.065819   \n",
       "frige                  NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "wash                   NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "shoes             0.088056  0.403522  0.040436  0.321788  0.145120  0.133218   \n",
       "doorlock          0.155554  0.457491  0.071431  0.404800  0.166375  0.325961   \n",
       "air                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "induction         0.070736  0.328173  0.064733  0.282134  0.042921  0.165579   \n",
       "closet            0.033998  0.216373 -0.113121  0.260965  0.080031  0.361534   \n",
       "microwave        -0.054080  0.235685 -0.011350  0.163824  0.196832  0.318349   \n",
       "desk              0.018329  0.188139 -0.033366  0.097605  0.043838  0.209823   \n",
       "bed                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "north_west       -0.031791  0.089152 -0.014599  0.096304 -0.052393  0.144919   \n",
       "north             0.096936 -0.034567  0.152684 -0.055786 -0.150818 -0.054265   \n",
       "north_east       -0.022398  0.062811 -0.010285  0.067850 -0.036913  0.102101   \n",
       "east             -0.109470 -0.039163 -0.079505 -0.054113  0.015131  0.088457   \n",
       "south_east        0.106169  0.142531 -0.023339  0.153964  0.233774 -0.082503   \n",
       "south            -0.057087 -0.036582  0.045515  0.015205  0.121223  0.110875   \n",
       "south_west        0.213706  0.089152 -0.014599 -0.027643 -0.052393 -0.100736   \n",
       "west              0.018607 -0.018347 -0.059324 -0.019057 -0.113264 -0.187523   \n",
       "videophone        1.000000  0.194145  0.213706  0.209719 -0.033998 -0.159932   \n",
       "robby             0.194145  1.000000  0.089152  0.740370  0.237447  0.462106   \n",
       "man               0.213706  0.089152  1.000000 -0.027643 -0.052393  0.022091   \n",
       "cctv              0.209719  0.740370 -0.027643  1.000000  0.183868  0.454466   \n",
       "window           -0.033998  0.237447 -0.052393  0.183868  1.000000  0.079284   \n",
       "intercom         -0.159932  0.462106  0.022091  0.454466  0.079284  1.000000   \n",
       "cardkey           0.087968  0.082625  0.271598  0.169292  0.392828  0.110807   \n",
       "firealarm         0.008800  0.267682  0.235316  0.263949  0.262433  0.111878   \n",
       "\n",
       "                 cardkey  firealarm  \n",
       "room_size       0.037267  -0.061943  \n",
       "age_month      -0.007358  -0.022205  \n",
       "room_floor_str  0.135026   0.122955  \n",
       "deposit         0.026521   0.009011  \n",
       "gasrange        0.108091  -0.068290  \n",
       "frige                NaN        NaN  \n",
       "wash                 NaN        NaN  \n",
       "shoes           0.071082   0.113005  \n",
       "doorlock        0.125569   0.101475  \n",
       "air                  NaN        NaN  \n",
       "induction      -0.056285   0.062423  \n",
       "closet         -0.004884   0.077125  \n",
       "microwave       0.051159   0.040684  \n",
       "desk           -0.132103  -0.086687  \n",
       "bed                  NaN        NaN  \n",
       "north_west     -0.025663  -0.062038  \n",
       "north           0.040220  -0.121518  \n",
       "north_east     -0.018080   0.165790  \n",
       "east           -0.062671  -0.067961  \n",
       "south_east     -0.041028  -0.099183  \n",
       "south           0.155666   0.212339  \n",
       "south_west     -0.025663  -0.062038  \n",
       "west           -0.104285  -0.028333  \n",
       "videophone      0.087968   0.008800  \n",
       "robby           0.082625   0.267682  \n",
       "man             0.271598   0.235316  \n",
       "cctv            0.169292   0.263949  \n",
       "window          0.392828   0.262433  \n",
       "intercom        0.110807   0.111878  \n",
       "cardkey         1.000000   0.413663  \n",
       "firealarm       0.413663   1.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\daun5\\anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1685: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return 1 - self.ssr/self.centered_tss\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF Factor</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.701254</td>\n",
       "      <td>room_size</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.793960</td>\n",
       "      <td>age_month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.258798</td>\n",
       "      <td>room_floor_str</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.492806</td>\n",
       "      <td>deposit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.029145</td>\n",
       "      <td>gasrange</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>frige</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>wash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.068436</td>\n",
       "      <td>shoes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.774407</td>\n",
       "      <td>doorlock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.229402</td>\n",
       "      <td>induction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.732629</td>\n",
       "      <td>closet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.732690</td>\n",
       "      <td>microwave</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.075627</td>\n",
       "      <td>desk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>bed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.901461</td>\n",
       "      <td>north_west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.448327</td>\n",
       "      <td>north</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.575677</td>\n",
       "      <td>north_east</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.924367</td>\n",
       "      <td>east</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.103052</td>\n",
       "      <td>south_east</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.904161</td>\n",
       "      <td>south</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2.086883</td>\n",
       "      <td>south_west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14.013902</td>\n",
       "      <td>west</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.437759</td>\n",
       "      <td>videophone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.630929</td>\n",
       "      <td>robby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.453343</td>\n",
       "      <td>man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.282132</td>\n",
       "      <td>cctv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.639810</td>\n",
       "      <td>window</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.095737</td>\n",
       "      <td>intercom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.722863</td>\n",
       "      <td>cardkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1.679294</td>\n",
       "      <td>firealarm</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    VIF Factor        features\n",
       "0     1.701254       room_size\n",
       "1     1.793960       age_month\n",
       "2     1.258798  room_floor_str\n",
       "3     1.492806         deposit\n",
       "4     6.029145        gasrange\n",
       "5          NaN           frige\n",
       "6          NaN            wash\n",
       "7     3.068436           shoes\n",
       "8     1.774407        doorlock\n",
       "9          NaN             air\n",
       "10    8.229402       induction\n",
       "11    2.732629          closet\n",
       "12    1.732690       microwave\n",
       "13    2.075627            desk\n",
       "14         NaN             bed\n",
       "15    1.901461      north_west\n",
       "16    7.448327           north\n",
       "17    1.575677      north_east\n",
       "18   20.924367            east\n",
       "19    3.103052      south_east\n",
       "20   21.904161           south\n",
       "21    2.086883      south_west\n",
       "22   14.013902            west\n",
       "23    1.437759      videophone\n",
       "24    3.630929           robby\n",
       "25    1.453343             man\n",
       "26    3.282132            cctv\n",
       "27    1.639810          window\n",
       "28    2.095737        intercom\n",
       "29    1.722863         cardkey\n",
       "30    1.679294       firealarm"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif = pd.DataFrame()\n",
    "vif[\"VIF Factor\"] = [variance_inflation_factor(\n",
    "    x.values, i) for i in range(x.shape[1])]\n",
    "vif[\"features\"] = x.columns\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교차 검증 평균 점수 : 0.30\n",
      "교차 검증 점수 :  [ 0.39587815  0.64121957  0.1093681  -0.02840779  0.36617849]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "basic_scores = cross_val_score(lr ,x ,y)\n",
    "print(\"교차 검증 평균 점수 : {:.2f}\".format(basic_scores.mean()))\n",
    "print(\"교차 검증 점수 : \", basic_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:          monthly_total   R-squared (uncentered):                   0.978\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.975\n",
      "Method:                 Least Squares   F-statistic:                              286.2\n",
      "Date:                Tue, 28 Sep 2021   Prob (F-statistic):                    1.95e-90\n",
      "Time:                        17:02:51   Log-Likelihood:                         -446.87\n",
      "No. Observations:                 139   AIC:                                      931.7\n",
      "Df Residuals:                     120   BIC:                                      987.5\n",
      "Df Model:                          19                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "room_size          1.6628      0.103     16.188      0.000       1.459       1.866\n",
      "age_month         -0.0198      0.009     -2.320      0.022      -0.037      -0.003\n",
      "room_floor_str    12.0265      1.347      8.925      0.000       9.359      14.694\n",
      "deposit           -0.0045      0.001     -5.979      0.000      -0.006      -0.003\n",
      "gasrange         -10.3512      3.735     -2.772      0.006     -17.746      -2.957\n",
      "frige          -3.862e-15    1.8e-15     -2.143      0.034   -7.43e-15   -2.93e-16\n",
      "wash           -7.399e-15   3.84e-15     -1.927      0.056    -1.5e-14    2.02e-16\n",
      "shoes              9.9481      3.130      3.178      0.002       3.751      16.145\n",
      "doorlock           1.1425      1.612      0.709      0.480      -2.049       4.334\n",
      "air            -8.383e-15    2.6e-15     -3.227      0.002   -1.35e-14   -3.24e-15\n",
      "induction        -10.7567      3.669     -2.932      0.004     -18.021      -3.492\n",
      "closet             2.2796      2.354      0.969      0.335      -2.380       6.940\n",
      "microwave          0.5180      1.354      0.382      0.703      -2.163       3.199\n",
      "desk               2.1216      1.543      1.375      0.172      -0.934       5.177\n",
      "bed             1.119e-15   1.37e-15      0.819      0.414   -1.58e-15    3.82e-15\n",
      "videophone         1.5600      2.608      0.598      0.551      -3.603       6.723\n",
      "robby              1.5184      2.086      0.728      0.468      -2.612       5.649\n",
      "man               10.6526      5.467      1.948      0.054      -0.172      21.477\n",
      "cctv               0.4587      1.938      0.237      0.813      -3.379       4.296\n",
      "window             2.3630      1.806      1.308      0.193      -1.213       5.939\n",
      "intercom          -0.1813      1.544     -0.117      0.907      -3.239       2.876\n",
      "cardkey           -5.6971      3.448     -1.652      0.101     -12.525       1.130\n",
      "firealarm          0.3737      1.645      0.227      0.821      -2.884       3.631\n",
      "==============================================================================\n",
      "Omnibus:                        2.402   Durbin-Watson:                   2.045\n",
      "Prob(Omnibus):                  0.301   Jarque-Bera (JB):                1.982\n",
      "Skew:                          -0.170   Prob(JB):                        0.371\n",
      "Kurtosis:                       3.477   Cond. No.                     6.55e+19\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 4.89e-32. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "model = sm.OLS(y, x)\n",
    "result = model.fit()\n",
    "\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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