Proyecto_5_Ricardo_Fuentes(Previo).ipynb

{
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     "end_time": "2021-01-26T10:54:06.268742Z",
     "start_time": "2021-01-26T10:53:52.545076Z"
    }
   },
   "outputs": [],
   "source": [
    "# Importamos librerias\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Limpieza Inicial de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:09.844148Z",
     "start_time": "2021-01-26T10:54:06.279748Z"
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
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       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
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       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>Reg</td>\n",
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       "      <td>MnPrv</td>\n",
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       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
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       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
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       "      <td>Normal</td>\n",
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       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
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       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>1460 rows × 81 columns</p>\n",
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      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "0            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "3            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "4            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "...          ...       ...  ...      ...    ...    ...         ...     ...   \n",
       "1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1456         Lvl    AllPub  ...        0    NaN  MnPrv         NaN       0   \n",
       "1457         Lvl    AllPub  ...        0    NaN  GdPrv        Shed    2500   \n",
       "1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1459         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         2   2008        WD         Normal     208500  \n",
       "1         5   2007        WD         Normal     181500  \n",
       "2         9   2008        WD         Normal     223500  \n",
       "3         2   2006        WD        Abnorml     140000  \n",
       "4        12   2008        WD         Normal     250000  \n",
       "...     ...    ...       ...            ...        ...  \n",
       "1455      8   2007        WD         Normal     175000  \n",
       "1456      2   2010        WD         Normal     210000  \n",
       "1457      5   2010        WD         Normal     266500  \n",
       "1458      4   2010        WD         Normal     142125  \n",
       "1459      6   2008        WD         Normal     147500  \n",
       "\n",
       "[1460 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Leemos la info\n",
    "df = pd.read_csv('train.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:09.899163Z",
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   },
   "outputs": [],
   "source": [
    "# Iniciamos por averiguar en que columnas tenemos datos nulos para poder analizar que es lo que aremos con ellos. \n",
    "# Hay columnas en las que por el tipo de dato podemos cambiar el nulo por un 0 y no nos afectaria en las predicciones, \n",
    "#hay otros que si nos pueden afectar. \n",
    "\n",
    "# ya que es una funcion que estaremos corriendo constantemente, la guardaremos como tal.\n",
    "\n",
    "def nulos(data):\n",
    "    null = df.isnull().sum()\n",
    "    df_null = pd.DataFrame(null[null>0],columns=['Value'])\n",
    "    return df_null.sort_values(by='Value',ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:10.051204Z",
     "start_time": "2021-01-26T10:54:09.907165Z"
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   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
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       "      <th>197</th>\n",
       "      <td>198</td>\n",
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       "      <td>Fa</td>\n",
       "      <td>GdPrv</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1170</th>\n",
       "      <td>1171</td>\n",
       "      <td>80</td>\n",
       "      <td>RL</td>\n",
       "      <td>76.0</td>\n",
       "      <td>9880</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>576</td>\n",
       "      <td>Gd</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>171000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>1183</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>160.0</td>\n",
       "      <td>15623</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
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       "      <td>...</td>\n",
       "      <td>555</td>\n",
       "      <td>Ex</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>745000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>1299</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>313.0</td>\n",
       "      <td>63887</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR3</td>\n",
       "      <td>Bnk</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>480</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>1387</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>16692</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>519</td>\n",
       "      <td>Fa</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>TenC</td>\n",
       "      <td>2000</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1423</th>\n",
       "      <td>1424</td>\n",
       "      <td>80</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19690</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>738</td>\n",
       "      <td>Gd</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Alloca</td>\n",
       "      <td>274970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "197    198          75       RL        174.0    25419   Pave   NaN      Reg   \n",
       "810    811          20       RL         78.0    10140   Pave   NaN      Reg   \n",
       "1170  1171          80       RL         76.0     9880   Pave   NaN      Reg   \n",
       "1182  1183          60       RL        160.0    15623   Pave   NaN      IR1   \n",
       "1298  1299          60       RL        313.0    63887   Pave   NaN      IR3   \n",
       "1386  1387          60       RL         80.0    16692   Pave   NaN      IR1   \n",
       "1423  1424          80       RL          NaN    19690   Pave   NaN      IR1   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "197          Lvl    AllPub  ...      512     Ex  GdPrv         NaN       0   \n",
       "810          Lvl    AllPub  ...      648     Fa  GdPrv         NaN       0   \n",
       "1170         Lvl    AllPub  ...      576     Gd  GdPrv         NaN       0   \n",
       "1182         Lvl    AllPub  ...      555     Ex  MnPrv         NaN       0   \n",
       "1298         Bnk    AllPub  ...      480     Gd    NaN         NaN       0   \n",
       "1386         Lvl    AllPub  ...      519     Fa  MnPrv        TenC    2000   \n",
       "1423         Lvl    AllPub  ...      738     Gd  GdPrv         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "197       3   2006        WD        Abnorml     235000  \n",
       "810       1   2006        WD         Normal     181000  \n",
       "1170      7   2008        WD         Normal     171000  \n",
       "1182      7   2007        WD        Abnorml     745000  \n",
       "1298      1   2008       New        Partial     160000  \n",
       "1386      7   2006        WD         Normal     250000  \n",
       "1423      8   2006        WD         Alloca     274970  \n",
       "\n",
       "[7 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iniciamos con los que tienen una mayor cantidad de nulos. \n",
    "# La columna 'PoolQC', nos indica la calidad de la alberca, y la documentacion nos dice que si tiene \"NA\" es que no tiene alberca. \n",
    "# Requerimos transformar los datos para poder evaluarla, por lo que los null los dejamos como '0'\n",
    "\n",
    "df['PoolQC'] = df.PoolQC.fillna(0)\n",
    "\n",
    "# Validamos que se hayan transformado correctamente los datos.\n",
    "df[df['PoolQC']!=0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:10.281303Z",
     "start_time": "2021-01-26T10:54:10.058203Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Id</th>\n",
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       "  <tbody>\n",
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       "      <th>5</th>\n",
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       "      <td>8</td>\n",
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       "      <td>10382</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "      <td>350</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "5   6          50       RL         85.0    14115   Pave   NaN      IR1   \n",
       "7   8          60       RL          NaN    10382   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "5         Lvl    AllPub  ...        0      0  MnPrv        Shed     700   \n",
       "7         Lvl    AllPub  ...        0      0    NaN        Shed     350   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "5     10   2009        WD         Normal     143000  \n",
       "7     11   2009        WD         Normal     200000  \n",
       "\n",
       "[2 rows x 81 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# La columna 'MiscFeature' nos indica si la casa tiene otros miscelaneos no cubiertos en alguna otra categoria. \n",
    "# ya que el NaN significa que no tiene nnguno, podemos aprovechar para sustituirlos por '0'\n",
    "\n",
    "df['MiscFeature'] = df.MiscFeature.fillna(0)\n",
    "\n",
    "# Validamos que se hayan transformado correctamente los datos.\n",
    "df[df['MiscFeature']!=0].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:10.453347Z",
     "start_time": "2021-01-26T10:54:10.287306Z"
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   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>RM</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7449</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Grvl</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>6</td>\n",
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       "      <td>139400</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>70</td>\n",
       "      <td>C (all)</td>\n",
       "      <td>50.0</td>\n",
       "      <td>8500</td>\n",
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       "      <td>Pave</td>\n",
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       "      <td>Lvl</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>40000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "21  22          45       RM         57.0     7449   Pave  Grvl      Reg   \n",
       "30  31          70  C (all)         50.0     8500   Pave  Pave      Reg   \n",
       "\n",
       "   LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "21         Bnk    AllPub  ...        0      0  GdPrv           0       0   \n",
       "30         Lvl    AllPub  ...        0      0  MnPrv           0       0   \n",
       "\n",
       "   MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "21      6   2007        WD         Normal     139400  \n",
       "30      7   2008        WD         Normal      40000  \n",
       "\n",
       "[2 rows x 81 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# la columna 'Alley' indica el tipo de camino que se tiene como acceso al domicilio.\n",
    "# ya que el NaN significa que no tiene nnguno, podemos aprovechar para sustituirlos por '0'\n",
    "\n",
    "df['Alley'] = df.Alley.fillna(0)\n",
    "\n",
    "# Validamos que se hayan transformado correctamente los datos.\n",
    "df[df['Alley']!=0].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:10.590905Z",
     "start_time": "2021-01-26T10:54:10.458349Z"
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   },
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>GdWo</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>157000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "5    6          50       RL         85.0    14115   Pave     0      IR1   \n",
       "14  15          20       RL          NaN    10920   Pave     0      IR1   \n",
       "\n",
       "   LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "5          Lvl    AllPub  ...        0      0  MnPrv        Shed     700   \n",
       "14         Lvl    AllPub  ...        0      0   GdWo           0       0   \n",
       "\n",
       "   MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "5      10   2009        WD         Normal     143000  \n",
       "14      5   2008        WD         Normal     157000  \n",
       "\n",
       "[2 rows x 81 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# la columna 'Fence' nos indica la calidad de la cerca. \n",
    "# ya que el NaN significa que no tiene nnguno, podemos aprovechar para sustituirlos por '0'\n",
    "\n",
    "df['Fence'] = df.Fence.fillna(0)\n",
    "\n",
    "# Validamos que se hayan transformado correctamente los datos.\n",
    "df[df['Fence']!=0].head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:10.762953Z",
     "start_time": "2021-01-26T10:54:10.600903Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "1   2          20       RL         80.0     9600   Pave     0      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave     0      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "1         Lvl    AllPub  ...        0      0     0           0       0      5   \n",
       "2         Lvl    AllPub  ...        0      0     0           0       0      9   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "\n",
       "[2 rows x 81 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# la columna 'FireplaceQu' nos indica la calidad de la chimenea.\n",
    "# ya que el NaN significa que no tiene nnguno, podemos aprovechar para sustituirlos por '0'\n",
    "\n",
    "df['FireplaceQu'] = df.FireplaceQu.fillna(0)\n",
    "\n",
    "# Validamos que se hayan transformado correctamente los datos.\n",
    "df[df['FireplaceQu']!=0].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:10.944991Z",
     "start_time": "2021-01-26T10:54:10.769948Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave     0      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave     0      Reg   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0      0     0           0       0      2   \n",
       "1         Lvl    AllPub  ...        0      0     0           0       0      5   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "\n",
       "[2 rows x 81 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# La columna 'LotFrontage' nos indica los pies lineales de calle conectados a la casa, podemos intuir que si tiene un valor \n",
    "# nulo es que no tiene coneccion a la calle. por lo tanto, podemos pasar a sustituirlos\n",
    "\n",
    "\n",
    "df['LotFrontage'] = df.LotFrontage.fillna(0)\n",
    "\n",
    "# Validamos que se hayan transformado correctamente los datos.\n",
    "df[df['LotFrontage']!=0].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:11.041014Z",
     "start_time": "2021-01-26T10:54:10.950992Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Value\n",
       "BsmtExposure     38\n",
       "BsmtFinType2     38\n",
       "BsmtQual         37\n",
       "BsmtCond         37\n",
       "BsmtFinType1     37\n",
       "MasVnrType        8\n",
       "MasVnrArea        8\n",
       "Electrical        1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Las siguientes 5 columnas se refieren al garage de la casa: 'GarageType','GarageYrBlt','GarageFinish','GarageQual','GarageCond'\n",
    "# podemos ver que en tods tenemos el mismo numero de datos nulos (81 cada una), lo que nos indica que no tiene garage el domicilio. \n",
    "# Procedemos a sustituirlos por 0\n",
    "\n",
    "df['GarageType'] = df.GarageType.fillna(0)\n",
    "df['GarageYrBlt'] = df.GarageYrBlt.fillna(0)\n",
    "df['GarageFinish'] = df.GarageFinish.fillna(0)\n",
    "df['GarageQual'] = df.GarageQual.fillna(0)\n",
    "df['GarageCond'] = df.GarageCond.fillna(0)\n",
    "\n",
    "# Validamos que se hayan transformado correctamente los datos.\n",
    "nulos(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:11.145042Z",
     "start_time": "2021-01-26T10:54:11.049018Z"
    }
   },
   "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>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value\n",
       "MasVnrType      8\n",
       "MasVnrArea      8\n",
       "Electrical      1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# de igual manera, las siguientes columnas nos indican si la casa tiene \"sotano\". el NaN indica que no hay, por lo que\n",
    "# podemos proceder a sustituirlos por 0\n",
    "# 'BsmtExposure', 'BsmtFinType2', 'BsmtQual', 'BsmtCond', 'BsmtFinType1'\n",
    "\n",
    "df['BsmtExposure'] = df.BsmtExposure.fillna(0)\n",
    "df['BsmtFinType2'] = df.BsmtFinType2.fillna(0)\n",
    "df['BsmtQual'] = df.BsmtQual.fillna(0)\n",
    "df['BsmtCond'] = df.BsmtCond.fillna(0)\n",
    "df['BsmtFinType1'] = df.BsmtFinType1.fillna(0)\n",
    "\n",
    "# Validamos que se hayan transformado correctamente los datos.\n",
    "nulos(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:11.207060Z",
     "start_time": "2021-01-26T10:54:11.170047Z"
    }
   },
   "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>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value\n",
       "Electrical      1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# las siguientes columnas nos indican el tipo de recubrimiento de la mamposteria, si no tiene mamposteria viene como nulo, \n",
    "# por lo que podemos proceder a sustituirlo por 0\n",
    "\n",
    "df['MasVnrType'] = df.MasVnrType.fillna(0)\n",
    "df['MasVnrArea'] = df.MasVnrArea.fillna(0)\n",
    "\n",
    "# Validamos que se hayan transformado correctamente los datos.\n",
    "nulos(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:11.300078Z",
     "start_time": "2021-01-26T10:54:11.215058Z"
    }
   },
   "outputs": [],
   "source": [
    "# en la columna de 'Electrical', indica el tipo de coneccion electrica con la que cuenta, pero como todas las casas deben de tener\n",
    "# coneccion electrica, podemos intuir que tenemos un fallo en la captura de los datos, por lo que podemos eliminar este dato \n",
    "# para que no nos haga ruido. \n",
    "\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:11.470122Z",
     "start_time": "2021-01-26T10:54:11.306080Z"
    }
   },
   "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>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Value]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# refactorizamos todo el codigo para que quede en una sola celda y mas eficiente:\n",
    "df[df['Electrical'].isnull()]\n",
    "df = df.dropna(subset=['Electrical'])\n",
    "df = df.fillna(0)\n",
    "\n",
    "# validamos que ya no tengamos nulos\n",
    "nulos(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:11.500132Z",
     "start_time": "2021-01-26T10:54:11.479122Z"
    }
   },
   "outputs": [],
   "source": [
    "# Generamos un CSV de respaldo con esta info\n",
    "#df.to_csv('Data_no_nulls.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:16.730919Z",
     "start_time": "2021-01-26T10:54:16.588899Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13175</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave     0      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave     0      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave     0      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave     0      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave     0      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1455  1456          60       RL         62.0     7917   Pave     0      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave     0      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave     0      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave     0      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave     0      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "0            Lvl    AllPub  ...        0      0      0           0       0   \n",
       "1            Lvl    AllPub  ...        0      0      0           0       0   \n",
       "2            Lvl    AllPub  ...        0      0      0           0       0   \n",
       "3            Lvl    AllPub  ...        0      0      0           0       0   \n",
       "4            Lvl    AllPub  ...        0      0      0           0       0   \n",
       "...          ...       ...  ...      ...    ...    ...         ...     ...   \n",
       "1455         Lvl    AllPub  ...        0      0      0           0       0   \n",
       "1456         Lvl    AllPub  ...        0      0  MnPrv           0       0   \n",
       "1457         Lvl    AllPub  ...        0      0  GdPrv        Shed    2500   \n",
       "1458         Lvl    AllPub  ...        0      0      0           0       0   \n",
       "1459         Lvl    AllPub  ...        0      0      0           0       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         2   2008        WD         Normal     208500  \n",
       "1         5   2007        WD         Normal     181500  \n",
       "2         9   2008        WD         Normal     223500  \n",
       "3         2   2006        WD        Abnorml     140000  \n",
       "4        12   2008        WD         Normal     250000  \n",
       "...     ...    ...       ...            ...        ...  \n",
       "1455      8   2007        WD         Normal     175000  \n",
       "1456      2   2010        WD         Normal     210000  \n",
       "1457      5   2010        WD         Normal     266500  \n",
       "1458      4   2010        WD         Normal     142125  \n",
       "1459      6   2008        WD         Normal     147500  \n",
       "\n",
       "[1459 rows x 81 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisis de las columnas de manera individual vs el precio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:29.067842Z",
     "start_time": "2021-01-26T10:54:25.670699Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass      -0.084230\n",
      "LotFrontage      0.209692\n",
      "LotArea          0.263837\n",
      "OverallQual      0.791069\n",
      "OverallCond     -0.077924\n",
      "YearBuilt        0.523273\n",
      "YearRemodAdd     0.507430\n",
      "MasVnrArea       0.472606\n",
      "BsmtFinSF1       0.386436\n",
      "BsmtFinSF2      -0.011412\n",
      "BsmtUnfSF        0.214446\n",
      "TotalBsmtSF      0.613905\n",
      "1stFlrSF         0.605968\n",
      "2ndFlrSF         0.319464\n",
      "LowQualFinSF    -0.025620\n",
      "GrLivArea        0.708618\n",
      "BsmtFullBath     0.227082\n",
      "BsmtHalfBath    -0.016873\n",
      "FullBath         0.560881\n",
      "HalfBath         0.284400\n",
      "BedroomAbvGr     0.168235\n",
      "KitchenAbvGr    -0.135935\n",
      "TotRmsAbvGrd     0.533779\n",
      "Fireplaces       0.466968\n",
      "GarageYrBlt      0.261413\n",
      "GarageCars       0.640473\n",
      "GarageArea       0.623423\n",
      "WoodDeckSF       0.324422\n",
      "OpenPorchSF      0.315831\n",
      "EnclosedPorch   -0.128627\n",
      "3SsnPorch        0.044571\n",
      "ScreenPorch      0.111419\n",
      "PoolArea         0.092397\n",
      "MiscVal         -0.021200\n",
      "MoSold           0.046380\n",
      "YrSold          -0.028907\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Primero veamos cual es la correlacion de cada una de las variables vs el precio\n",
    "tabla_correlacion = df.corr()\n",
    "# podemos eliminar el ID ya que sabemos que es unicamente el numero de identificacion de cada vivienda y no tiene relacion \n",
    "# alguna con los datos.\n",
    "tabla_correlacion.drop('Id',inplace=True)\n",
    "# Quitamos el dato de 'SalePrice' ya que tendrá correlacion de 1 consigo mismo\n",
    "# obtenemos el total de columnas de las cuales podemos tener una correlacion\n",
    "len(tabla_correlacion['SalePrice'][:-1])\n",
    "print(tabla_correlacion['SalePrice'][:-1])\n",
    "sns.heatmap(tabla_correlacion);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:41.813200Z",
     "start_time": "2021-01-26T10:54:41.805194Z"
    }
   },
   "outputs": [],
   "source": [
    "# Podemos ver que realmente no podemos sacar nada en este momento y con todos los datos, la matriz de correlacion graficada es \n",
    "# imposible de leer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:43.155535Z",
     "start_time": "2021-01-26T10:54:43.135531Z"
    }
   },
   "outputs": [],
   "source": [
    "# obtenemos que columnas no son numeros\n",
    "columnas = df.columns\n",
    "col_objeto = []\n",
    "for columna in columnas:\n",
    "    if df[columna].dtypes == object:\n",
    "        col_objeto.append(columna)\n",
    "\n",
    "# obtenemos que columnas son numeros\n",
    "col_numero =[]\n",
    "for columna in columnas:\n",
    "    if columna not in col_objeto:\n",
    "        col_numero.append(columna)\n",
    "\n",
    "# Vamos a proceder de la siguiente manera:\n",
    "# dividiremos nuestro DF general en dos partes:\n",
    "# 1. todos los datos numericos\n",
    "# 2. todos los datos no numericos. \n",
    "# posteriormente procederemos a realizar calculos y transformaciones en cada uno de ellos para ver con que datos podemos obtener \n",
    "# una prediccion mas acertada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:45.458762Z",
     "start_time": "2021-01-26T10:54:45.297200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1999</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457</td>\n",
       "      <td>20</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13175</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1978</td>\n",
       "      <td>1988</td>\n",
       "      <td>119.0</td>\n",
       "      <td>790</td>\n",
       "      <td>...</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1941</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>275</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>1996</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>...</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1965</td>\n",
       "      <td>1965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>830</td>\n",
       "      <td>...</td>\n",
       "      <td>736</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  \\\n",
       "0        1          60         65.0     8450            7            5   \n",
       "1        2          20         80.0     9600            6            8   \n",
       "2        3          60         68.0    11250            7            5   \n",
       "3        4          70         60.0     9550            7            5   \n",
       "4        5          60         84.0    14260            8            5   \n",
       "...    ...         ...          ...      ...          ...          ...   \n",
       "1455  1456          60         62.0     7917            6            5   \n",
       "1456  1457          20         85.0    13175            6            6   \n",
       "1457  1458          70         66.0     9042            7            9   \n",
       "1458  1459          20         68.0     9717            5            6   \n",
       "1459  1460          20         75.0     9937            5            6   \n",
       "\n",
       "      YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  WoodDeckSF  \\\n",
       "0          2003          2003       196.0         706  ...           0   \n",
       "1          1976          1976         0.0         978  ...         298   \n",
       "2          2001          2002       162.0         486  ...           0   \n",
       "3          1915          1970         0.0         216  ...           0   \n",
       "4          2000          2000       350.0         655  ...         192   \n",
       "...         ...           ...         ...         ...  ...         ...   \n",
       "1455       1999          2000         0.0           0  ...           0   \n",
       "1456       1978          1988       119.0         790  ...         349   \n",
       "1457       1941          2006         0.0         275  ...           0   \n",
       "1458       1950          1996         0.0          49  ...         366   \n",
       "1459       1965          1965         0.0         830  ...         736   \n",
       "\n",
       "      OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  \\\n",
       "0              61              0          0            0         0        0   \n",
       "1               0              0          0            0         0        0   \n",
       "2              42              0          0            0         0        0   \n",
       "3              35            272          0            0         0        0   \n",
       "4              84              0          0            0         0        0   \n",
       "...           ...            ...        ...          ...       ...      ...   \n",
       "1455           40              0          0            0         0        0   \n",
       "1456            0              0          0            0         0        0   \n",
       "1457           60              0          0            0         0     2500   \n",
       "1458            0            112          0            0         0        0   \n",
       "1459           68              0          0            0         0        0   \n",
       "\n",
       "      MoSold  YrSold  SalePrice  \n",
       "0          2    2008     208500  \n",
       "1          5    2007     181500  \n",
       "2          9    2008     223500  \n",
       "3          2    2006     140000  \n",
       "4         12    2008     250000  \n",
       "...      ...     ...        ...  \n",
       "1455       8    2007     175000  \n",
       "1456       2    2010     210000  \n",
       "1457       5    2010     266500  \n",
       "1458       4    2010     142125  \n",
       "1459       6    2008     147500  \n",
       "\n",
       "[1459 rows x 38 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos el df solo con las columnas que tienen datos numericos\n",
    "df_numericos = df[col_numero]\n",
    "df_numericos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:48.499178Z",
     "start_time": "2021-01-26T10:54:48.359144Z"
    }
   },
   "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>MSZoning</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>...</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NWAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Edwards</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>Fin</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope  \\\n",
       "0          RL   Pave     0      Reg         Lvl    AllPub    Inside       Gtl   \n",
       "1          RL   Pave     0      Reg         Lvl    AllPub       FR2       Gtl   \n",
       "2          RL   Pave     0      IR1         Lvl    AllPub    Inside       Gtl   \n",
       "3          RL   Pave     0      IR1         Lvl    AllPub    Corner       Gtl   \n",
       "4          RL   Pave     0      IR1         Lvl    AllPub       FR2       Gtl   \n",
       "...       ...    ...   ...      ...         ...       ...       ...       ...   \n",
       "1455       RL   Pave     0      Reg         Lvl    AllPub    Inside       Gtl   \n",
       "1456       RL   Pave     0      Reg         Lvl    AllPub    Inside       Gtl   \n",
       "1457       RL   Pave     0      Reg         Lvl    AllPub    Inside       Gtl   \n",
       "1458       RL   Pave     0      Reg         Lvl    AllPub    Inside       Gtl   \n",
       "1459       RL   Pave     0      Reg         Lvl    AllPub    Inside       Gtl   \n",
       "\n",
       "     Neighborhood Condition1  ... GarageFinish GarageQual GarageCond  \\\n",
       "0         CollgCr       Norm  ...          RFn         TA         TA   \n",
       "1         Veenker      Feedr  ...          RFn         TA         TA   \n",
       "2         CollgCr       Norm  ...          RFn         TA         TA   \n",
       "3         Crawfor       Norm  ...          Unf         TA         TA   \n",
       "4         NoRidge       Norm  ...          RFn         TA         TA   \n",
       "...           ...        ...  ...          ...        ...        ...   \n",
       "1455      Gilbert       Norm  ...          RFn         TA         TA   \n",
       "1456       NWAmes       Norm  ...          Unf         TA         TA   \n",
       "1457      Crawfor       Norm  ...          RFn         TA         TA   \n",
       "1458        NAmes       Norm  ...          Unf         TA         TA   \n",
       "1459      Edwards       Norm  ...          Fin         TA         TA   \n",
       "\n",
       "     PavedDrive PoolQC  Fence MiscFeature SaleType SaleCondition SalePrice  \n",
       "0             Y      0      0           0       WD        Normal    208500  \n",
       "1             Y      0      0           0       WD        Normal    181500  \n",
       "2             Y      0      0           0       WD        Normal    223500  \n",
       "3             Y      0      0           0       WD       Abnorml    140000  \n",
       "4             Y      0      0           0       WD        Normal    250000  \n",
       "...         ...    ...    ...         ...      ...           ...       ...  \n",
       "1455          Y      0      0           0       WD        Normal    175000  \n",
       "1456          Y      0  MnPrv           0       WD        Normal    210000  \n",
       "1457          Y      0  GdPrv        Shed       WD        Normal    266500  \n",
       "1458          Y      0      0           0       WD        Normal    142125  \n",
       "1459          Y      0      0           0       WD        Normal    147500  \n",
       "\n",
       "[1459 rows x 44 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos el df sin datos numericos (excepto el precio)\n",
    "df_no_numericos = df.drop(col_numero[:-1],axis=1)\n",
    "df_no_numericos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-21T21:02:22.062306Z",
     "start_time": "2021-01-21T21:02:22.043275Z"
    }
   },
   "source": [
    "## Graficas para analisis previo. --> Datos Numericos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:54:55.072498Z",
     "start_time": "2021-01-26T10:54:55.004479Z"
    }
   },
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0   1          60         65.0     8450            7            5       2003   \n",
       "1   2          20         80.0     9600            6            8       1976   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  WoodDeckSF  OpenPorchSF  \\\n",
       "0          2003       196.0         706  ...           0           61   \n",
       "1          1976         0.0         978  ...         298            0   \n",
       "\n",
       "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
       "0              0          0            0         0        0       2    2008   \n",
       "1              0          0            0         0        0       5    2007   \n",
       "\n",
       "   SalePrice  \n",
       "0     208500  \n",
       "1     181500  \n",
       "\n",
       "[2 rows x 38 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numericos.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:30:50.086656Z",
     "start_time": "2021-01-26T10:29:01.051490Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# para graficar, eliminamos dos columnas: la de Id y la de precio, la primera porque no tiene relacion con los datos y\n",
    "# la segunda porque no tiene caso graficarla\n",
    "for columna in df_numericos.columns[1:-1]:\n",
    "    y=str(columna)\n",
    "    sns.displot(data=df_numericos,y=y,x='SalePrice')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-21T23:07:42.473987Z",
     "start_time": "2021-01-21T23:07:42.456981Z"
    }
   },
   "source": [
    "Con estas graficas podemos ver que no podemos hacer una aproximacion correcta de los datos. Algunos de ellos tendremos que \n",
    "normalizalos y otros tendremos que limpiarlos y otros mas tendremos que normalizarlos ya que son medidas de terreno y caracteristicas. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graficas para analisis previo. --> Datos Categoricos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:55:02.586285Z",
     "start_time": "2021-01-26T10:55:02.504266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>MSZoning</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>...</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>...</td>\n",
       "      <td>RFn</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  MSZoning Street Alley LotShape LandContour Utilities LotConfig LandSlope  \\\n",
       "0       RL   Pave     0      Reg         Lvl    AllPub    Inside       Gtl   \n",
       "1       RL   Pave     0      Reg         Lvl    AllPub       FR2       Gtl   \n",
       "\n",
       "  Neighborhood Condition1  ... GarageFinish GarageQual GarageCond PavedDrive  \\\n",
       "0      CollgCr       Norm  ...          RFn         TA         TA          Y   \n",
       "1      Veenker      Feedr  ...          RFn         TA         TA          Y   \n",
       "\n",
       "  PoolQC Fence MiscFeature SaleType SaleCondition SalePrice  \n",
       "0      0     0           0       WD        Normal    208500  \n",
       "1      0     0           0       WD        Normal    181500  \n",
       "\n",
       "[2 rows x 44 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_numericos.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:32:58.920974Z",
     "start_time": "2021-01-26T10:30:50.288707Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Para las graficas de datos no numericos, lo que tenemos que hacer es tomar cada una de las columnas y graficarlas \n",
    "# contra el precio. \n",
    "for columna in df_no_numericos.columns[:-1]:\n",
    "    y=str(columna)\n",
    "    sns.catplot(data=df_no_numericos,y=y,x='SalePrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:55:07.630785Z",
     "start_time": "2021-01-26T10:55:07.617782Z"
    }
   },
   "outputs": [],
   "source": [
    "# Viendo las graficas, nos podemos dar cuenta del peso que tienen los datos categoricos en el precio de las casas.\n",
    "# si bien, en este momento no los podemos analizar com tal, podemos ver que caracteristicas pesan mas en el precio de las \n",
    "# casas y cuales son los mas comunes. \n",
    "# de aqui, podemos inferir cuales son los mas escenciales para el analisis de la prediccion.\n",
    "\n",
    "# con esto en mente, lo que haremos será consultar el diccionario del df para ver que significa cada uno y ver de \n",
    "# que manera los podemos convertir para que nos sea mas util en en analisis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-21T23:53:59.288091Z",
     "start_time": "2021-01-21T23:53:50.580896Z"
    }
   },
   "source": [
    "### Tratamiento de las variables categoricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:55:11.155126Z",
     "start_time": "2021-01-26T10:55:11.144123Z"
    }
   },
   "outputs": [],
   "source": [
    "# Analizando el diccionario del DataSet, tenemos varias de las columnas categoricas que pueden ser tratadas como \n",
    "# dummies, y otras que se pueden modificar los datos para convertirlos en numeros y poder tratarlos sin problemas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:55:22.185332Z",
     "start_time": "2021-01-26T10:55:22.165328Z"
    }
   },
   "outputs": [],
   "source": [
    "# Primero vemos que hay una columna que (desde mi punto de vista) no nos sirve, por lo que la eliminaremos\n",
    "df.drop(['MSSubClass','MoSold','YrSold','Id'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:55:38.154786Z",
     "start_time": "2021-01-26T10:55:38.132785Z"
    }
   },
   "outputs": [],
   "source": [
    "# La columna 'YearRemodAdd' indica la fecha en la cual se realizo remodelacion en el domicilio.\n",
    "# En caso de no haber sido remodelada, mantiene la fecha de construccion por lo que vamos a comvertirla en una variable binaria\n",
    "# para saber si la casa ah sido remodelada o no, ya que no nos interesa la fecha.\n",
    "# una vez creada esta columna binaria, eliminaremos la columna original.\n",
    "df['Remodelacion'] = df.YearRemodAdd == df.YearBuilt\n",
    "df['Remodelacion'] = df['Remodelacion'].astype('str')\n",
    "df['Remodelacion'] = df['Remodelacion'].replace({'True':0,'False':1})\n",
    "df.drop('YearRemodAdd',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:55:41.856705Z",
     "start_time": "2021-01-26T10:55:41.698664Z"
    }
   },
   "outputs": [
    {
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       "      <th>LotFrontage</th>\n",
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       "      <th>PoolArea</th>\n",
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       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>Remodelacion</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  MSZoning  LotFrontage  LotArea Street Alley LotShape LandContour Utilities  \\\n",
       "0       RL         65.0     8450   Pave     0      Reg         Lvl    AllPub   \n",
       "1       RL         80.0     9600   Pave     0      Reg         Lvl    AllPub   \n",
       "2       RL         68.0    11250   Pave     0      IR1         Lvl    AllPub   \n",
       "3       RL         60.0     9550   Pave     0      IR1         Lvl    AllPub   \n",
       "4       RL         84.0    14260   Pave     0      IR1         Lvl    AllPub   \n",
       "\n",
       "  LotConfig LandSlope  ... ScreenPorch PoolArea PoolQC Fence MiscFeature  \\\n",
       "0    Inside       Gtl  ...           0        0      0     0           0   \n",
       "1       FR2       Gtl  ...           0        0      0     0           0   \n",
       "2    Inside       Gtl  ...           0        0      0     0           0   \n",
       "3    Corner       Gtl  ...           0        0      0     0           0   \n",
       "4       FR2       Gtl  ...           0        0      0     0           0   \n",
       "\n",
       "   MiscVal  SaleType  SaleCondition SalePrice Remodelacion  \n",
       "0        0        WD         Normal    208500            0  \n",
       "1        0        WD         Normal    181500            0  \n",
       "2        0        WD         Normal    223500            1  \n",
       "3        0        WD        Abnorml    140000            1  \n",
       "4        0        WD         Normal    250000            0  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:55:48.253500Z",
     "start_time": "2021-01-26T10:55:48.230499Z"
    }
   },
   "outputs": [],
   "source": [
    "# Tenemos varias columnas con años, por lo que podemos tratarlas para agrupar las casas o su caracteristica por antiguedad\n",
    "# en lugar de tener como tal la fecha\n",
    "# Estas dos columnas indican el año en el cual fue construida la casa y el garage, como lo que nos importa es\n",
    "# el tiempo, vamos a obtener el año actual y restaremos los años correspondientes para tener una cantidad en lugar de fecha.\n",
    "\n",
    "df['AñosConstCasa'] = 2021 - df['YearBuilt'] \n",
    "df['AñosConstGar']= 2021 - df['GarageYrBlt']\n",
    "\n",
    "# Podemos eliminar las columnas que ya transformarmos\n",
    "df.drop(['YearBuilt','GarageYrBlt'],axis=1,inplace=True)\n",
    "\n",
    "# Veamos un poco los datos\n",
    "\"\"\"sns.displot(data=df,x='AñosConstCasa')\n",
    "sns.displot(data=df,x='AñosConstGar')\n",
    "\"\"\"\n",
    "# Vemos que en la columna \"AñosConstGar\" que corresponde a los años de construccion del garage, tenemos unos extremos\n",
    "# muy raros. Podemos intuir que fue algun error de dedo en la captura, por lo que se procede a eliminarlos convirtiendolos\n",
    "# en 0.\n",
    "df['AñosConstGar'] = df['AñosConstGar'].replace({2021:0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:55:52.443218Z",
     "start_time": "2021-01-26T10:55:52.418209Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"sns.displot(data=df,x='AñosConstGar')\""
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"sns.displot(data=df,x='AñosConstGar')\"\"\"\n",
    "# Después de haber transformado los datos, podemos ver de una mejor manera la distribucion de los datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:56:02.758257Z",
     "start_time": "2021-01-26T10:55:55.200644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x53107f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analizaremos si los años de construccion de la casa y garage tienen alguna relacion con el precio de venta\n",
    "sns.lmplot(data=df,x='SalePrice',y='AñosConstGar')\n",
    "sns.lmplot(data=df,x='SalePrice',y='AñosConstCasa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:56:02.793261Z",
     "start_time": "2021-01-26T10:56:02.765254Z"
    }
   },
   "outputs": [],
   "source": [
    "# No se puede ver una relacion completamente bien, hasta que hagamos una prueba de hipotesis no sabremos completamente \n",
    "# si es que son variable validas o no."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:56:03.063855Z",
     "start_time": "2021-01-26T10:56:02.808266Z"
    }
   },
   "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>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>Remodelacion</th>\n",
       "      <th>AñosConstCasa</th>\n",
       "      <th>AñosConstGar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  MSZoning  LotFrontage  LotArea Street Alley LotShape LandContour Utilities  \\\n",
       "0       RL         65.0     8450   Pave     0      Reg         Lvl    AllPub   \n",
       "1       RL         80.0     9600   Pave     0      Reg         Lvl    AllPub   \n",
       "2       RL         68.0    11250   Pave     0      IR1         Lvl    AllPub   \n",
       "3       RL         60.0     9550   Pave     0      IR1         Lvl    AllPub   \n",
       "4       RL         84.0    14260   Pave     0      IR1         Lvl    AllPub   \n",
       "\n",
       "  LotConfig LandSlope  ... PoolQC Fence MiscFeature MiscVal SaleType  \\\n",
       "0    Inside       Gtl  ...      0     0           0       0       WD   \n",
       "1       FR2       Gtl  ...      0     0           0       0       WD   \n",
       "2    Inside       Gtl  ...      0     0           0       0       WD   \n",
       "3    Corner       Gtl  ...      0     0           0       0       WD   \n",
       "4       FR2       Gtl  ...      0     0           0       0       WD   \n",
       "\n",
       "   SaleCondition  SalePrice Remodelacion AñosConstCasa AñosConstGar  \n",
       "0         Normal     208500            0            18         18.0  \n",
       "1         Normal     181500            0            45         45.0  \n",
       "2         Normal     223500            1            20         20.0  \n",
       "3        Abnorml     140000            1           106         23.0  \n",
       "4         Normal     250000            0            21         21.0  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creacion de Dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:56:26.523902Z",
     "start_time": "2021-01-26T10:56:26.122805Z"
    }
   },
   "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>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Reg</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Reg</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>IR1</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>IR1</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>IR1</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 226 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotFrontage  LotArea LotShape Utilities LandSlope  OverallQual  \\\n",
       "0         65.0     8450      Reg    AllPub       Gtl            7   \n",
       "1         80.0     9600      Reg    AllPub       Gtl            6   \n",
       "2         68.0    11250      IR1    AllPub       Gtl            7   \n",
       "3         60.0     9550      IR1    AllPub       Gtl            7   \n",
       "4         84.0    14260      IR1    AllPub       Gtl            8   \n",
       "\n",
       "   OverallCond  MasVnrArea ExterQual ExterCond  ... SaleType_ConLw  \\\n",
       "0            5       196.0        Gd        TA  ...              0   \n",
       "1            8         0.0        TA        TA  ...              0   \n",
       "2            5       162.0        Gd        TA  ...              0   \n",
       "3            5         0.0        TA        TA  ...              0   \n",
       "4            5       350.0        Gd        TA  ...              0   \n",
       "\n",
       "  SaleType_New SaleType_Oth SaleType_WD  SaleCondition_Abnorml  \\\n",
       "0            0            0           1                      0   \n",
       "1            0            0           1                      0   \n",
       "2            0            0           1                      0   \n",
       "3            0            0           1                      1   \n",
       "4            0            0           1                      0   \n",
       "\n",
       "  SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  \\\n",
       "0                     0                     0                     0   \n",
       "1                     0                     0                     0   \n",
       "2                     0                     0                     0   \n",
       "3                     0                     0                     0   \n",
       "4                     0                     0                     0   \n",
       "\n",
       "   SaleCondition_Normal SaleCondition_Partial  \n",
       "0                     1                     0  \n",
       "1                     1                     0  \n",
       "2                     1                     0  \n",
       "3                     0                     0  \n",
       "4                     1                     0  \n",
       "\n",
       "[5 rows x 226 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# varias de las variables categoricas podemos convertirlas en dummies para poder tratarlas\n",
    "# Viendo el diccionario del Data, es como se llega a estas conclusiones.\n",
    "\n",
    "dummies = ['MSZoning','Street','Alley','LandContour','LotConfig','Neighborhood','Condition1','Condition2','BldgType','HouseStyle','RoofStyle','RoofMatl','Exterior1st','Exterior2nd','MasVnrType','Foundation','Heating','CentralAir','Electrical','GarageType','PavedDrive','MiscFeature','SaleType','SaleCondition']\n",
    "\n",
    "df = pd.get_dummies(data=df,columns=dummies)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T06:31:48.293408Z",
     "start_time": "2021-01-22T06:31:48.285405Z"
    }
   },
   "source": [
    "### Trabajando con los datos que pueden ser convertidos a variables numericas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:56:31.744927Z",
     "start_time": "2021-01-26T10:56:31.463285Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
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       "<p>5 rows × 226 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotFrontage  LotArea  LotShape  Utilities  LandSlope  OverallQual  \\\n",
       "0         65.0     8450         3          4          2            7   \n",
       "1         80.0     9600         3          4          2            6   \n",
       "2         68.0    11250         2          4          2            7   \n",
       "3         60.0     9550         2          4          2            7   \n",
       "4         84.0    14260         2          4          2            8   \n",
       "\n",
       "   OverallCond  MasVnrArea  ExterQual  ExterCond  ...  SaleType_ConLw  \\\n",
       "0            5       196.0          3          2  ...               0   \n",
       "1            8         0.0          2          2  ...               0   \n",
       "2            5       162.0          3          2  ...               0   \n",
       "3            5         0.0          2          2  ...               0   \n",
       "4            5       350.0          3          2  ...               0   \n",
       "\n",
       "   SaleType_New  SaleType_Oth  SaleType_WD  SaleCondition_Abnorml  \\\n",
       "0             0             0            1                      0   \n",
       "1             0             0            1                      0   \n",
       "2             0             0            1                      0   \n",
       "3             0             0            1                      1   \n",
       "4             0             0            1                      0   \n",
       "\n",
       "   SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  \\\n",
       "0                      0                     0                     0   \n",
       "1                      0                     0                     0   \n",
       "2                      0                     0                     0   \n",
       "3                      0                     0                     0   \n",
       "4                      0                     0                     0   \n",
       "\n",
       "   SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                     1                      0  \n",
       "1                     1                      0  \n",
       "2                     1                      0  \n",
       "3                     0                      0  \n",
       "4                     1                      0  \n",
       "\n",
       "[5 rows x 226 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisando las descripciones en el diccionario, vemos que para todas estas variables califican alguna de las caracteristicas de la casa\n",
    "# con lo que nos permite asignarle una calificacion de menor a mayor, dependiendo de cada una de las variables\n",
    "df['LotShape']=df['LotShape'].replace({'Reg':3,'IR1':2,'IR2':1,'IR3':0})\n",
    "df['Utilities']=df['Utilities'].replace({'AllPub':4,'NoSewr':'3','NoSeWa':2,'ELO':1})\n",
    "df['LandSlope']=df['LandSlope'].replace({'Gtl':2,'Mod':1,'Sev':0})\n",
    "df['ExterQual']=df['ExterQual'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['ExterCond'] =df['ExterCond'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['BsmtQual'] = df['BsmtQual'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['BsmtCond']=df['BsmtCond'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['BsmtExposure']=df['BsmtExposure'].replace({'Gd':4,'Av':3,'Mn':2,'No':1})\n",
    "df['BsmtFinType1'] =df['BsmtFinType1'].replace({'GLQ':6,'ALQ':5,'BLQ':4,'Rec':3,'LwQ':2,'Unf':1})\n",
    "df['BsmtFinType2'] =df['BsmtFinType2'].replace({'GLQ':6,'ALQ':5,'BLQ':4,'Rec':3,'LwQ':2,'Unf':1})\n",
    "df['HeatingQC'] =df['HeatingQC'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['KitchenQual'] =df['KitchenQual'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['Functional'] = df['Functional'].replace({'Typ':8,'Min1':7,'Min2':6,'Mod':5,'Maj1':4,'Maj2':3,'Sev':2,'Sal':1})\n",
    "df['FireplaceQu']=df['FireplaceQu'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['GarageFinish']=df['GarageFinish'].replace({'Fin':3,'RFn':2,'Unf':1,'NA':0})\n",
    "df['GarageQual']=df['GarageQual'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['GarageCond']=df['GarageCond'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['PoolQC']=df['PoolQC'].replace({'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0})\n",
    "df['Fence']=df['Fence'].replace({'GdPrv':4,'MnPrv':3,'GdWo':2,'MnWw':1})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T07:01:40.394628Z",
     "start_time": "2021-01-22T07:01:39.974521Z"
    }
   },
   "source": [
    "# Analisis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:56:38.251911Z",
     "start_time": "2021-01-26T10:56:38.237903Z"
    }
   },
   "outputs": [],
   "source": [
    "# Tenemos ya nuestro DF listo, sin variables categoricas (convertidas en dummies o con un valor agregado) \n",
    "# y con algunas variables mas manipuladas a info que nos pueda servir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:56:43.696884Z",
     "start_time": "2021-01-26T10:56:40.286088Z"
    }
   },
   "outputs": [],
   "source": [
    "# Veamos primero las estadisticas descriptivas de la data\n",
    "df.describe()\n",
    "# nuevamente al ser tantos datos no nos da mucha informacion\n",
    "for i in df.columns:\n",
    "    df[i].astype('int64')\n",
    "    \n",
    "# Debemos de tener todos los datos en numero, por lo que pasaremos todos a 'int64'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:57:12.206197Z",
     "start_time": "2021-01-26T10:57:12.018151Z"
    }
   },
   "outputs": [],
   "source": [
    "# para reducir un poco el numero de columnas, haremos algunas agrupaciones\n",
    "\n",
    "# Sumaremos el numero total de baños completos\n",
    "df['TotBaños'] = df['BsmtFullBath']+df['FullBath']\n",
    "\n",
    "# Sumaremos el numero total de medios baños\n",
    "df['TotMedBaño']=df['BsmtHalfBath']+df['HalfBath']\n",
    "\n",
    "# El sotano tiene varias medidas, por lo que trataremos de crear una sola variable que las pueda explicar todas\n",
    "\n",
    "# vamos a multiplicar la calificion del sotano por la de tamaño correspondiente\n",
    "df['Bsmt1Cal'] = df['BsmtFinType1']*df['BsmtFinSF1']\n",
    "df['Bsmt2Cal'] = df['BsmtFinType2']*df['BsmtFinSF2']\n",
    "\n",
    "# por ultimo, vamos a sacar un promedio de calificacion del sotano, dividiendolo entre el total de tamaño del sotano\n",
    "df['BsmtCalTot'] = ((df['Bsmt1Cal']+df['Bsmt2Cal']+df['BsmtQual']+df['BsmtCond']+df['BsmtExposure'])/5)/(df['TotalBsmtSF'])\n",
    "\n",
    "# por ultimo eliminamos las columnas del sotano que ya no requerimos.  --> eliminar datos\n",
    "col_sotano = ['BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinSF1','BsmtFinType2','BsmtFinSF2','TotalBsmtSF']\n",
    "# Eliminamos las columnas que ya no requerimos\n",
    "df.drop(['BsmtFullBath','FullBath','BsmtHalfBath','HalfBath','TotRmsAbvGrd','BsmtUnfSF','LowQualFinSF','GrLivArea'],axis=1,inplace=True)\n",
    "df.drop(col_sotano,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:57:16.553121Z",
     "start_time": "2021-01-26T10:57:16.522582Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 212)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ahora Trabajamos con la de Garage\n",
    "df['GaragCal'] = ((df['GarageFinish']+df['GarageQual']+df['GarageCond'])/3)/df['GarageArea']\n",
    "\n",
    "# Eliminamos las columnas que ya no requerimos\n",
    "df.drop(['GarageFinish','GarageQual','GarageCond','GarageArea'],axis=1,inplace=True)\n",
    "df.fillna(0)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:57:21.706379Z",
     "start_time": "2021-01-26T10:57:21.668368Z"
    }
   },
   "outputs": [],
   "source": [
    "# Creamos la calificacion de la cocina\n",
    "df['KitchCal'] = df['KitchenAbvGr']*df['KitchenQual']\n",
    "\n",
    "# Creamos la calificacion de Chimenea\n",
    "df['FireplCal'] = df['Fireplaces']+df['FireplaceQu']\n",
    "\n",
    "# Creamos la calificacion de la Alberca\n",
    "df['PoolCal'] = (df['PoolArea']*df['PoolQC'])/2\n",
    "\n",
    "df['BsmtCalTot'] = df['BsmtCalTot'].replace(np.nan,0)\n",
    "df['GaragCal']=df['GaragCal'].replace(np.nan,0)\n",
    "df.drop(['KitchenAbvGr','PoolArea','PoolQC','KitchenQual','Fireplaces','FireplaceQu'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:57:23.557268Z",
     "start_time": "2021-01-26T10:57:23.541262Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 209)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T08:46:14.181226Z",
     "start_time": "2021-01-22T08:46:14.162221Z"
    }
   },
   "source": [
    "## Correlacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:57:45.602965Z",
     "start_time": "2021-01-26T10:57:44.700699Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotArea        0.263837\n",
       "LotShape      -0.267726\n",
       "Utilities      0.014317\n",
       "LandSlope     -0.051127\n",
       "OverallQual    0.791069\n",
       "                 ...   \n",
       "BsmtCalTot     0.251313\n",
       "GaragCal      -0.169859\n",
       "KitchCal       0.512832\n",
       "FireplCal      0.527316\n",
       "PoolCal        0.115831\n",
       "Name: SalePrice, Length: 207, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analizaremos la correlacion de los datos con el precio de venta. \n",
    "# siguien siendo muchas columnas, pero podremos analizar cuales son las que tengan una mejor correlacion con el precio y \n",
    "# trabajar solamente con ellas.\n",
    "\n",
    "tabla_correlacion = df.corr()\n",
    "tabla_correlacion[tabla_correlacion['SalePrice']!=1]['SalePrice'][1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:57:49.734265Z",
     "start_time": "2021-01-26T10:57:49.716242Z"
    }
   },
   "outputs": [],
   "source": [
    "# Seguimos teniendo muchas variables, pero haremos un primer analisis realizando una regresion lineal y viendo que tal sale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Primera Regresion Lineal Inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:42:57.891995Z",
     "start_time": "2021-01-26T10:42:57.868965Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:58:19.136922Z",
     "start_time": "2021-01-26T10:58:18.031102Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>SalePrice</td>    <th>  R-squared:         </th> <td>   0.917</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.903</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   62.48</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 26 Jan 2021</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>04:58:18</td>     <th>  Log-Likelihood:    </th> <td> -13377.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1167</td>      <th>  AIC:               </th> <td>2.711e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   990</td>      <th>  BIC:               </th> <td>2.800e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   176</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotFrontage</th>           <td>   29.1847</td> <td>   27.314</td> <td>    1.068</td> <td> 0.286</td> <td>  -24.415</td> <td>   82.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotArea</th>               <td>    0.5896</td> <td>    0.111</td> <td>    5.299</td> <td> 0.000</td> <td>    0.371</td> <td>    0.808</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotShape</th>              <td> -179.5828</td> <td> 1691.808</td> <td>   -0.106</td> <td> 0.915</td> <td>-3499.525</td> <td> 3140.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Utilities</th>             <td>-3.253e+04</td> <td> 6355.430</td> <td>   -5.119</td> <td> 0.000</td> <td> -4.5e+04</td> <td>-2.01e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LandSlope</th>             <td>-2734.9538</td> <td> 4026.224</td> <td>   -0.679</td> <td> 0.497</td> <td>-1.06e+04</td> <td> 5165.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OverallQual</th>           <td> 8634.6943</td> <td> 1224.595</td> <td>    7.051</td> <td> 0.000</td> <td> 6231.595</td> <td>  1.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OverallCond</th>           <td> 5347.0268</td> <td>  986.952</td> <td>    5.418</td> <td> 0.000</td> <td> 3410.269</td> <td> 7283.785</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrArea</th>            <td>   35.4026</td> <td>    6.940</td> <td>    5.101</td> <td> 0.000</td> <td>   21.784</td> <td>   49.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ExterQual</th>             <td> 5586.9567</td> <td> 2564.574</td> <td>    2.179</td> <td> 0.030</td> <td>  554.331</td> <td> 1.06e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ExterCond</th>             <td>-1884.7055</td> <td> 2604.224</td> <td>   -0.724</td> <td> 0.469</td> <td>-6995.139</td> <td> 3225.728</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HeatingQC</th>             <td> 2044.9664</td> <td> 1168.856</td> <td>    1.750</td> <td> 0.081</td> <td> -248.752</td> <td> 4338.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1stFlrSF</th>              <td>   61.4090</td> <td>    4.768</td> <td>   12.879</td> <td> 0.000</td> <td>   52.052</td> <td>   70.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2ndFlrSF</th>              <td>   65.7918</td> <td>    6.055</td> <td>   10.866</td> <td> 0.000</td> <td>   53.911</td> <td>   77.673</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BedroomAbvGr</th>          <td>-3342.4958</td> <td> 1474.282</td> <td>   -2.267</td> <td> 0.024</td> <td>-6235.573</td> <td> -449.419</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Functional</th>            <td> 5907.1194</td> <td> 1364.361</td> <td>    4.330</td> <td> 0.000</td> <td> 3229.747</td> <td> 8584.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageCars</th>            <td>  1.18e+04</td> <td> 2515.735</td> <td>    4.690</td> <td> 0.000</td> <td> 6862.131</td> <td> 1.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WoodDeckSF</th>            <td>   14.3149</td> <td>    7.173</td> <td>    1.996</td> <td> 0.046</td> <td>    0.238</td> <td>   28.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OpenPorchSF</th>           <td>    4.3916</td> <td>   14.138</td> <td>    0.311</td> <td> 0.756</td> <td>  -23.353</td> <td>   32.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EnclosedPorch</th>         <td>   16.2058</td> <td>   15.359</td> <td>    1.055</td> <td> 0.292</td> <td>  -13.934</td> <td>   46.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3SsnPorch</th>             <td>   64.4813</td> <td>   26.570</td> <td>    2.427</td> <td> 0.015</td> <td>   12.342</td> <td>  116.621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ScreenPorch</th>           <td>   42.0246</td> <td>   16.327</td> <td>    2.574</td> <td> 0.010</td> <td>    9.985</td> <td>   74.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fence</th>                 <td>-1068.4132</td> <td>  745.097</td> <td>   -1.434</td> <td> 0.152</td> <td>-2530.564</td> <td>  393.737</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscVal</th>               <td>    5.6372</td> <td>   10.676</td> <td>    0.528</td> <td> 0.598</td> <td>  -15.313</td> <td>   26.588</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Remodelacion</th>          <td> 3353.4416</td> <td> 1989.756</td> <td>    1.685</td> <td> 0.092</td> <td> -551.183</td> <td> 7258.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AñosConstCasa</th>         <td> -319.1784</td> <td>   91.328</td> <td>   -3.495</td> <td> 0.000</td> <td> -498.398</td> <td> -139.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AñosConstGar</th>          <td>   19.2098</td> <td>   69.232</td> <td>    0.277</td> <td> 0.781</td> <td> -116.649</td> <td>  155.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_C (all)</th>      <td>-2.156e+04</td> <td> 1.07e+04</td> <td>   -2.023</td> <td> 0.043</td> <td>-4.25e+04</td> <td> -643.541</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_FV</th>           <td> 7534.9749</td> <td> 7041.363</td> <td>    1.070</td> <td> 0.285</td> <td>-6282.737</td> <td> 2.14e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_RH</th>           <td>-1207.6911</td> <td> 8136.496</td> <td>   -0.148</td> <td> 0.882</td> <td>-1.72e+04</td> <td> 1.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_RL</th>           <td> 4958.4177</td> <td> 3944.000</td> <td>    1.257</td> <td> 0.209</td> <td>-2781.142</td> <td> 1.27e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_RM</th>           <td> 2141.4684</td> <td> 4510.414</td> <td>    0.475</td> <td> 0.635</td> <td>-6709.602</td> <td>  1.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Street_Grvl</th>           <td>-1461.4467</td> <td> 7226.327</td> <td>   -0.202</td> <td> 0.840</td> <td>-1.56e+04</td> <td> 1.27e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Street_Pave</th>           <td>-6671.2832</td> <td> 7085.171</td> <td>   -0.942</td> <td> 0.347</td> <td>-2.06e+04</td> <td> 7232.396</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Alley_0</th>               <td>-2339.6253</td> <td> 2722.876</td> <td>   -0.859</td> <td> 0.390</td> <td>-7682.898</td> <td> 3003.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Alley_Grvl</th>            <td>-3884.3681</td> <td> 3801.615</td> <td>   -1.022</td> <td> 0.307</td> <td>-1.13e+04</td> <td> 3575.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Alley_Pave</th>            <td>-1908.7365</td> <td> 4166.773</td> <td>   -0.458</td> <td> 0.647</td> <td>-1.01e+04</td> <td> 6267.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LandContour_Bnk</th>       <td>-6758.3469</td> <td> 3630.882</td> <td>   -1.861</td> <td> 0.063</td> <td>-1.39e+04</td> <td>  366.762</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LandContour_HLS</th>       <td> 6325.5840</td> <td> 4061.237</td> <td>    1.558</td> <td> 0.120</td> <td>-1644.038</td> <td> 1.43e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LandContour_Low</th>       <td>-6123.1974</td> <td> 4983.430</td> <td>   -1.229</td> <td> 0.219</td> <td>-1.59e+04</td> <td> 3656.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LandContour_Lvl</th>       <td>-1576.7696</td> <td> 2740.808</td> <td>   -0.575</td> <td> 0.565</td> <td>-6955.230</td> <td> 3801.691</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotConfig_Corner</th>      <td> 2579.2219</td> <td> 3389.014</td> <td>    0.761</td> <td> 0.447</td> <td>-4071.254</td> <td> 9229.698</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotConfig_CulDSac</th>     <td> 7822.6470</td> <td> 4081.659</td> <td>    1.917</td> <td> 0.056</td> <td> -187.051</td> <td> 1.58e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotConfig_FR2</th>         <td>-4633.8755</td> <td> 4571.447</td> <td>   -1.014</td> <td> 0.311</td> <td>-1.36e+04</td> <td> 4336.964</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotConfig_FR3</th>         <td>-1.419e+04</td> <td> 1.11e+04</td> <td>   -1.276</td> <td> 0.202</td> <td> -3.6e+04</td> <td> 7626.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotConfig_Inside</th>      <td>  291.0906</td> <td> 3135.622</td> <td>    0.093</td> <td> 0.926</td> <td>-5862.139</td> <td> 6444.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Blmngtn</th>  <td> 7052.5519</td> <td> 8358.309</td> <td>    0.844</td> <td> 0.399</td> <td>-9349.486</td> <td> 2.35e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Blueste</th>  <td> 8966.0769</td> <td> 1.84e+04</td> <td>    0.487</td> <td> 0.626</td> <td>-2.71e+04</td> <td> 4.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_BrDale</th>   <td> 4266.2659</td> <td> 9468.379</td> <td>    0.451</td> <td> 0.652</td> <td>-1.43e+04</td> <td> 2.28e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_BrkSide</th>  <td>-1279.1404</td> <td> 5664.745</td> <td>   -0.226</td> <td> 0.821</td> <td>-1.24e+04</td> <td> 9837.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_ClearCr</th>  <td>-1.136e+04</td> <td> 6779.455</td> <td>   -1.676</td> <td> 0.094</td> <td>-2.47e+04</td> <td> 1939.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_CollgCr</th>  <td>-9433.4977</td> <td> 3536.539</td> <td>   -2.667</td> <td> 0.008</td> <td>-1.64e+04</td> <td>-2493.525</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Crawfor</th>  <td> 7137.6472</td> <td> 4893.845</td> <td>    1.458</td> <td> 0.145</td> <td>-2465.855</td> <td> 1.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Edwards</th>  <td>-1.888e+04</td> <td> 3818.089</td> <td>   -4.946</td> <td> 0.000</td> <td>-2.64e+04</td> <td>-1.14e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Gilbert</th>  <td> -1.26e+04</td> <td> 4480.171</td> <td>   -2.812</td> <td> 0.005</td> <td>-2.14e+04</td> <td>-3804.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_IDOTRR</th>   <td>-6632.3322</td> <td> 7804.047</td> <td>   -0.850</td> <td> 0.396</td> <td>-2.19e+04</td> <td> 8682.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_MeadowV</th>  <td> 1068.6665</td> <td> 9440.761</td> <td>    0.113</td> <td> 0.910</td> <td>-1.75e+04</td> <td> 1.96e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Mitchel</th>  <td>-1.632e+04</td> <td> 4750.218</td> <td>   -3.436</td> <td> 0.001</td> <td>-2.56e+04</td> <td>-7001.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NAmes</th>    <td>-1.445e+04</td> <td> 3156.286</td> <td>   -4.579</td> <td> 0.000</td> <td>-2.06e+04</td> <td>-8258.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NPkVill</th>  <td> 1.932e+04</td> <td> 1.33e+04</td> <td>    1.449</td> <td> 0.148</td> <td>-6844.546</td> <td> 4.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NWAmes</th>   <td>-1.669e+04</td> <td> 4165.040</td> <td>   -4.008</td> <td> 0.000</td> <td>-2.49e+04</td> <td>-8518.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NoRidge</th>  <td>  2.35e+04</td> <td> 6075.579</td> <td>    3.869</td> <td> 0.000</td> <td> 1.16e+04</td> <td> 3.54e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NridgHt</th>  <td>   3.7e+04</td> <td> 4881.728</td> <td>    7.580</td> <td> 0.000</td> <td> 2.74e+04</td> <td> 4.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_OldTown</th>  <td>-1.665e+04</td> <td> 5754.489</td> <td>   -2.893</td> <td> 0.004</td> <td>-2.79e+04</td> <td>-5356.742</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_SWISU</th>    <td>-8882.7873</td> <td> 6836.027</td> <td>   -1.299</td> <td> 0.194</td> <td>-2.23e+04</td> <td> 4531.981</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Sawyer</th>   <td>-7566.6107</td> <td> 4207.093</td> <td>   -1.799</td> <td> 0.072</td> <td>-1.58e+04</td> <td>  689.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_SawyerW</th>  <td>-5868.5688</td> <td> 4467.962</td> <td>   -1.313</td> <td> 0.189</td> <td>-1.46e+04</td> <td> 2899.195</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Somerst</th>  <td> 2306.4109</td> <td> 6976.221</td> <td>    0.331</td> <td> 0.741</td> <td>-1.14e+04</td> <td>  1.6e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_StoneBr</th>  <td> 4.336e+04</td> <td> 6947.324</td> <td>    6.242</td> <td> 0.000</td> <td> 2.97e+04</td> <td>  5.7e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Timber</th>   <td>-1.483e+04</td> <td> 5862.277</td> <td>   -2.530</td> <td> 0.012</td> <td>-2.63e+04</td> <td>-3326.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Veenker</th>  <td> -665.4658</td> <td> 9336.146</td> <td>   -0.071</td> <td> 0.943</td> <td> -1.9e+04</td> <td> 1.77e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_Artery</th>     <td>-7310.8859</td> <td> 5439.623</td> <td>   -1.344</td> <td> 0.179</td> <td> -1.8e+04</td> <td> 3363.630</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_Feedr</th>      <td>-3875.5730</td> <td> 4446.692</td> <td>   -0.872</td> <td> 0.384</td> <td>-1.26e+04</td> <td> 4850.450</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_Norm</th>       <td> 7484.4599</td> <td> 3053.818</td> <td>    2.451</td> <td> 0.014</td> <td> 1491.760</td> <td> 1.35e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_PosA</th>       <td> 2954.6303</td> <td> 8990.098</td> <td>    0.329</td> <td> 0.742</td> <td>-1.47e+04</td> <td> 2.06e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_PosN</th>       <td> 3905.4063</td> <td> 6599.576</td> <td>    0.592</td> <td> 0.554</td> <td>-9045.358</td> <td> 1.69e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_RRAe</th>       <td>-1.922e+04</td> <td> 8318.966</td> <td>   -2.311</td> <td> 0.021</td> <td>-3.55e+04</td> <td>-2896.226</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_RRAn</th>       <td> 7152.9545</td> <td> 6174.819</td> <td>    1.158</td> <td> 0.247</td> <td>-4964.283</td> <td> 1.93e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_RRNe</th>       <td>-1.506e-08</td> <td> 9.06e-09</td> <td>   -1.663</td> <td> 0.097</td> <td>-3.28e-08</td> <td> 2.72e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_RRNn</th>       <td>  777.3370</td> <td> 1.31e+04</td> <td>    0.059</td> <td> 0.953</td> <td>-2.49e+04</td> <td> 2.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_Artery</th>     <td> 3.228e+04</td> <td>  2.8e+04</td> <td>    1.151</td> <td> 0.250</td> <td>-2.27e+04</td> <td> 8.73e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_Feedr</th>      <td> 9543.8633</td> <td> 1.62e+04</td> <td>    0.590</td> <td> 0.555</td> <td>-2.22e+04</td> <td> 4.13e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_Norm</th>       <td> 2.633e+04</td> <td> 9669.257</td> <td>    2.723</td> <td> 0.007</td> <td> 7356.631</td> <td> 4.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_PosA</th>       <td> 9.742e+04</td> <td> 2.71e+04</td> <td>    3.599</td> <td> 0.000</td> <td> 4.43e+04</td> <td> 1.51e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_PosN</th>       <td>-2.053e+05</td> <td> 2.02e+04</td> <td>  -10.149</td> <td> 0.000</td> <td>-2.45e+05</td> <td>-1.66e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_RRAe</th>       <td>-3.329e+04</td> <td> 3.93e+04</td> <td>   -0.848</td> <td> 0.397</td> <td> -1.1e+05</td> <td> 4.38e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_RRAn</th>       <td> 3.201e+04</td> <td> 2.45e+04</td> <td>    1.305</td> <td> 0.192</td> <td>-1.61e+04</td> <td> 8.02e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_RRNn</th>       <td> 3.282e+04</td> <td> 1.89e+04</td> <td>    1.739</td> <td> 0.082</td> <td>-4208.116</td> <td> 6.98e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_1Fam</th>         <td> 1.645e+04</td> <td> 2785.931</td> <td>    5.905</td> <td> 0.000</td> <td>  1.1e+04</td> <td> 2.19e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_2fmCon</th>       <td> 1.005e+04</td> <td> 5814.033</td> <td>    1.729</td> <td> 0.084</td> <td>-1355.753</td> <td> 2.15e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_Duplex</th>       <td>-3421.2403</td> <td> 5227.190</td> <td>   -0.655</td> <td> 0.513</td> <td>-1.37e+04</td> <td> 6836.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_Twnhs</th>        <td>-1.935e+04</td> <td> 5083.507</td> <td>   -3.806</td> <td> 0.000</td> <td>-2.93e+04</td> <td>-9369.809</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_TwnhsE</th>       <td>-1.187e+04</td> <td> 3862.473</td> <td>   -3.073</td> <td> 0.002</td> <td>-1.95e+04</td> <td>-4291.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_1.5Fin</th>     <td>-2264.7901</td> <td> 3497.320</td> <td>   -0.648</td> <td> 0.517</td> <td>-9127.802</td> <td> 4598.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_1.5Unf</th>     <td> 5838.6857</td> <td> 8747.682</td> <td>    0.667</td> <td> 0.505</td> <td>-1.13e+04</td> <td>  2.3e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_1Story</th>     <td> 7257.3522</td> <td> 3873.286</td> <td>    1.874</td> <td> 0.061</td> <td> -343.442</td> <td> 1.49e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_2.5Fin</th>     <td>-1.602e+04</td> <td> 1.28e+04</td> <td>   -1.247</td> <td> 0.213</td> <td>-4.12e+04</td> <td> 9185.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_2.5Unf</th>     <td>-7806.3196</td> <td> 9335.152</td> <td>   -0.836</td> <td> 0.403</td> <td>-2.61e+04</td> <td> 1.05e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_2Story</th>     <td>-5985.0419</td> <td> 3854.614</td> <td>   -1.553</td> <td> 0.121</td> <td>-1.35e+04</td> <td> 1579.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_SFoyer</th>     <td> 6327.9573</td> <td> 5815.289</td> <td>    1.088</td> <td> 0.277</td> <td>-5083.751</td> <td> 1.77e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_SLvl</th>       <td> 4518.2180</td> <td> 4862.770</td> <td>    0.929</td> <td> 0.353</td> <td>-5024.302</td> <td> 1.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Flat</th>        <td>-9520.7425</td> <td> 1.77e+04</td> <td>   -0.537</td> <td> 0.592</td> <td>-4.43e+04</td> <td> 2.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Gable</th>       <td>-1.012e+04</td> <td> 7339.414</td> <td>   -1.379</td> <td> 0.168</td> <td>-2.45e+04</td> <td> 4278.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Gambrel</th>     <td>-8569.8360</td> <td> 1.06e+04</td> <td>   -0.812</td> <td> 0.417</td> <td>-2.93e+04</td> <td> 1.22e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Hip</th>         <td>-8078.9879</td> <td> 7489.540</td> <td>   -1.079</td> <td> 0.281</td> <td>-2.28e+04</td> <td> 6618.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Mansard</th>     <td>-5388.2954</td> <td> 1.15e+04</td> <td>   -0.468</td> <td> 0.640</td> <td> -2.8e+04</td> <td> 1.72e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Shed</th>        <td> 3.355e+04</td> <td> 2.61e+04</td> <td>    1.287</td> <td> 0.198</td> <td>-1.76e+04</td> <td> 8.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_ClyTile</th>      <td>-6.577e-09</td> <td> 3.86e-09</td> <td>   -1.705</td> <td> 0.089</td> <td>-1.41e-08</td> <td> 9.95e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_CompShg</th>      <td>-7518.4186</td> <td> 9658.121</td> <td>   -0.778</td> <td> 0.436</td> <td>-2.65e+04</td> <td> 1.14e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_Membran</th>      <td> 2.319e+04</td> <td> 2.66e+04</td> <td>    0.872</td> <td> 0.384</td> <td> -2.9e+04</td> <td> 7.54e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_Metal</th>        <td>-4.499e-12</td> <td> 2.42e-11</td> <td>   -0.186</td> <td> 0.853</td> <td>-5.21e-11</td> <td> 4.31e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_Roll</th>         <td>-3.457e+04</td> <td> 2.46e+04</td> <td>   -1.402</td> <td> 0.161</td> <td>-8.29e+04</td> <td> 1.38e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_Tar&Grv</th>      <td>-1.732e+04</td> <td> 1.57e+04</td> <td>   -1.103</td> <td> 0.270</td> <td>-4.81e+04</td> <td> 1.35e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_WdShake</th>      <td> -2.76e+04</td> <td> 1.66e+04</td> <td>   -1.664</td> <td> 0.096</td> <td>-6.02e+04</td> <td> 4951.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_WdShngl</th>      <td> 5.568e+04</td> <td> 1.39e+04</td> <td>    4.017</td> <td> 0.000</td> <td> 2.85e+04</td> <td> 8.29e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_AsbShng</th>   <td> 1.177e+04</td> <td> 1.61e+04</td> <td>    0.732</td> <td> 0.464</td> <td>-1.98e+04</td> <td> 4.33e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_AsphShn</th>   <td>-4.486e+04</td> <td> 3.69e+04</td> <td>   -1.216</td> <td> 0.224</td> <td>-1.17e+05</td> <td> 2.75e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_BrkComm</th>   <td> 3410.3323</td> <td> 2.69e+04</td> <td>    0.127</td> <td> 0.899</td> <td>-4.94e+04</td> <td> 5.62e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_BrkFace</th>   <td>   1.6e+04</td> <td> 7848.051</td> <td>    2.039</td> <td> 0.042</td> <td>  598.774</td> <td> 3.14e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_CBlock</th>    <td> 1.634e-08</td> <td> 9.76e-09</td> <td>    1.675</td> <td> 0.094</td> <td>-2.81e-09</td> <td> 3.55e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_CemntBd</th>   <td> 7.662e+04</td> <td>  2.7e+04</td> <td>    2.834</td> <td> 0.005</td> <td> 2.36e+04</td> <td>  1.3e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_HdBoard</th>   <td> -546.9486</td> <td> 6818.374</td> <td>   -0.080</td> <td> 0.936</td> <td>-1.39e+04</td> <td> 1.28e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_ImStucc</th>   <td>-6.067e+04</td> <td> 2.63e+04</td> <td>   -2.309</td> <td> 0.021</td> <td>-1.12e+05</td> <td>-9106.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_MetalSd</th>   <td> 6084.9933</td> <td> 9221.893</td> <td>    0.660</td> <td> 0.510</td> <td> -1.2e+04</td> <td> 2.42e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_Plywood</th>   <td>-5584.2901</td> <td> 7071.468</td> <td>   -0.790</td> <td> 0.430</td> <td>-1.95e+04</td> <td> 8292.498</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_Stone</th>     <td>-1905.9816</td> <td> 2.18e+04</td> <td>   -0.088</td> <td> 0.930</td> <td>-4.46e+04</td> <td> 4.08e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_Stucco</th>    <td>-1397.6534</td> <td> 1.04e+04</td> <td>   -0.134</td> <td> 0.893</td> <td>-2.18e+04</td> <td>  1.9e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_VinylSd</th>   <td>-1.068e+04</td> <td> 9324.755</td> <td>   -1.146</td> <td> 0.252</td> <td> -2.9e+04</td> <td> 7613.754</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_Wd Sdng</th>   <td>-2615.4412</td> <td> 6649.854</td> <td>   -0.393</td> <td> 0.694</td> <td>-1.57e+04</td> <td> 1.04e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_WdShing</th>   <td> 6251.8096</td> <td> 8018.523</td> <td>    0.780</td> <td> 0.436</td> <td>-9483.444</td> <td>  2.2e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_AsbShng</th>   <td>-8767.7851</td> <td> 1.47e+04</td> <td>   -0.595</td> <td> 0.552</td> <td>-3.77e+04</td> <td> 2.02e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_AsphShn</th>   <td>  2.21e+04</td> <td> 2.72e+04</td> <td>    0.812</td> <td> 0.417</td> <td>-3.13e+04</td> <td> 7.55e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Brk Cmn</th>   <td> 2964.5382</td> <td> 1.92e+04</td> <td>    0.155</td> <td> 0.877</td> <td>-3.46e+04</td> <td> 4.06e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_BrkFace</th>   <td>-8265.3450</td> <td> 8992.470</td> <td>   -0.919</td> <td> 0.358</td> <td>-2.59e+04</td> <td> 9381.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_CBlock</th>    <td>-6.808e-10</td> <td>    4e-10</td> <td>   -1.703</td> <td> 0.089</td> <td>-1.47e-09</td> <td> 1.04e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_CmentBd</th>   <td>-6.317e+04</td> <td> 2.68e+04</td> <td>   -2.355</td> <td> 0.019</td> <td>-1.16e+05</td> <td>-1.05e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_HdBoard</th>   <td> 3376.7082</td> <td> 6053.802</td> <td>    0.558</td> <td> 0.577</td> <td>-8503.050</td> <td> 1.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_ImStucc</th>   <td> 2.079e+04</td> <td> 1.02e+04</td> <td>    2.039</td> <td> 0.042</td> <td>  786.051</td> <td> 4.08e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_MetalSd</th>   <td>  745.4736</td> <td> 8863.927</td> <td>    0.084</td> <td> 0.933</td> <td>-1.66e+04</td> <td> 1.81e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Other</th>     <td> -7.37e-11</td> <td> 3.46e-11</td> <td>   -2.131</td> <td> 0.033</td> <td>-1.42e-10</td> <td>-5.84e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Plywood</th>   <td> 1550.6605</td> <td> 5909.091</td> <td>    0.262</td> <td> 0.793</td> <td>   -1e+04</td> <td> 1.31e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Stone</th>     <td>-3417.1868</td> <td> 1.46e+04</td> <td>   -0.234</td> <td> 0.815</td> <td>-3.21e+04</td> <td> 2.53e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Stucco</th>    <td> 1.194e+04</td> <td> 9975.581</td> <td>    1.197</td> <td> 0.232</td> <td>-7635.046</td> <td> 3.15e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_VinylSd</th>   <td> 1.416e+04</td> <td> 8678.500</td> <td>    1.631</td> <td> 0.103</td> <td>-2874.062</td> <td> 3.12e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Wd Sdng</th>   <td> 5279.4378</td> <td> 5835.168</td> <td>    0.905</td> <td> 0.366</td> <td>-6171.280</td> <td> 1.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Wd Shng</th>   <td>-7407.9291</td> <td> 6404.762</td> <td>   -1.157</td> <td> 0.248</td> <td>   -2e+04</td> <td> 5160.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_0</th>          <td>-1.784e+04</td> <td> 9833.302</td> <td>   -1.814</td> <td> 0.070</td> <td>-3.71e+04</td> <td> 1454.675</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_BrkCmn</th>     <td>-6597.6112</td> <td> 6528.944</td> <td>   -1.011</td> <td> 0.312</td> <td>-1.94e+04</td> <td> 6214.548</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_BrkFace</th>    <td>  899.9806</td> <td> 3302.896</td> <td>    0.272</td> <td> 0.785</td> <td>-5581.501</td> <td> 7381.462</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_None</th>       <td> 6817.6420</td> <td> 3241.404</td> <td>    2.103</td> <td> 0.036</td> <td>  456.831</td> <td> 1.32e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_Stone</th>      <td> 8589.0917</td> <td> 3844.590</td> <td>    2.234</td> <td> 0.026</td> <td> 1044.611</td> <td> 1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_BrkTil</th>     <td> 3790.4191</td> <td> 4949.215</td> <td>    0.766</td> <td> 0.444</td> <td>-5921.738</td> <td> 1.35e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_CBlock</th>     <td> 7675.5951</td> <td> 4463.937</td> <td>    1.719</td> <td> 0.086</td> <td>-1084.270</td> <td> 1.64e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_PConc</th>      <td> 7508.2436</td> <td> 4616.390</td> <td>    1.626</td> <td> 0.104</td> <td>-1550.789</td> <td> 1.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_Slab</th>       <td> 7655.6925</td> <td> 7867.239</td> <td>    0.973</td> <td> 0.331</td> <td>-7782.687</td> <td> 2.31e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_Stone</th>      <td> 7863.9507</td> <td>  1.2e+04</td> <td>    0.658</td> <td> 0.511</td> <td>-1.56e+04</td> <td> 3.13e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_Wood</th>       <td>-4.263e+04</td> <td> 1.68e+04</td> <td>   -2.533</td> <td> 0.011</td> <td>-7.56e+04</td> <td>-9607.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_Floor</th>         <td>-2839.1042</td> <td> 2.31e+04</td> <td>   -0.123</td> <td> 0.902</td> <td>-4.81e+04</td> <td> 4.25e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_GasA</th>          <td> 2544.2407</td> <td> 7912.666</td> <td>    0.322</td> <td> 0.748</td> <td> -1.3e+04</td> <td> 1.81e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_GasW</th>          <td> -210.2103</td> <td> 9660.717</td> <td>   -0.022</td> <td> 0.983</td> <td>-1.92e+04</td> <td> 1.87e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_Grav</th>          <td> 9014.8927</td> <td> 1.18e+04</td> <td>    0.763</td> <td> 0.446</td> <td>-1.42e+04</td> <td> 3.22e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_OthW</th>          <td>-3.118e+04</td> <td> 2.41e+04</td> <td>   -1.295</td> <td> 0.196</td> <td>-7.84e+04</td> <td> 1.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_Wall</th>          <td> 1.453e+04</td> <td> 1.84e+04</td> <td>    0.788</td> <td> 0.431</td> <td>-2.16e+04</td> <td> 5.07e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CentralAir_N</th>          <td>-4415.2608</td> <td> 2427.123</td> <td>   -1.819</td> <td> 0.069</td> <td>-9178.157</td> <td>  347.636</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CentralAir_Y</th>          <td>-3717.4691</td> <td> 2545.776</td> <td>   -1.460</td> <td> 0.145</td> <td>-8713.206</td> <td> 1278.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_FuseA</th>      <td> 1050.5275</td> <td> 7860.479</td> <td>    0.134</td> <td> 0.894</td> <td>-1.44e+04</td> <td> 1.65e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_FuseF</th>      <td>-1684.7166</td> <td> 8946.402</td> <td>   -0.188</td> <td> 0.851</td> <td>-1.92e+04</td> <td> 1.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_FuseP</th>      <td>-1.752e+04</td> <td> 2.04e+04</td> <td>   -0.859</td> <td> 0.390</td> <td>-5.75e+04</td> <td> 2.25e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_Mix</th>        <td> 1.323e+04</td> <td>  2.3e+04</td> <td>    0.575</td> <td> 0.566</td> <td>-3.19e+04</td> <td> 5.84e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_SBrkr</th>      <td>-3204.0867</td> <td> 7673.061</td> <td>   -0.418</td> <td> 0.676</td> <td>-1.83e+04</td> <td> 1.19e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_0</th>          <td> 2.333e+04</td> <td> 8219.770</td> <td>    2.839</td> <td> 0.005</td> <td> 7203.238</td> <td> 3.95e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_2Types</th>     <td>-2.633e+04</td> <td> 1.17e+04</td> <td>   -2.253</td> <td> 0.025</td> <td>-4.93e+04</td> <td>-3392.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Attchd</th>     <td>-3505.1138</td> <td> 3423.251</td> <td>   -1.024</td> <td> 0.306</td> <td>-1.02e+04</td> <td> 3212.549</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Basment</th>    <td> 6670.9519</td> <td> 6857.835</td> <td>    0.973</td> <td> 0.331</td> <td>-6786.610</td> <td> 2.01e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_BuiltIn</th>    <td>-2651.5990</td> <td> 4688.889</td> <td>   -0.566</td> <td> 0.572</td> <td>-1.19e+04</td> <td> 6549.704</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_CarPort</th>    <td>-6353.6547</td> <td> 9964.907</td> <td>   -0.638</td> <td> 0.524</td> <td>-2.59e+04</td> <td> 1.32e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Detchd</th>     <td>  699.6009</td> <td> 3360.035</td> <td>    0.208</td> <td> 0.835</td> <td>-5894.007</td> <td> 7293.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PavedDrive_N</th>          <td>-4881.6328</td> <td> 3095.612</td> <td>   -1.577</td> <td> 0.115</td> <td> -1.1e+04</td> <td> 1193.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PavedDrive_P</th>          <td>-1471.0089</td> <td> 3699.736</td> <td>   -0.398</td> <td> 0.691</td> <td>-8731.234</td> <td> 5789.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PavedDrive_Y</th>          <td>-1780.0881</td> <td> 2443.125</td> <td>   -0.729</td> <td> 0.466</td> <td>-6574.386</td> <td> 3014.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscFeature_0</th>         <td> 1.938e+04</td> <td> 1.79e+04</td> <td>    1.084</td> <td> 0.278</td> <td>-1.57e+04</td> <td> 5.45e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscFeature_Gar2</th>      <td>-3.329e+04</td> <td> 3.93e+04</td> <td>   -0.848</td> <td> 0.397</td> <td> -1.1e+05</td> <td> 4.38e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscFeature_Othr</th>      <td> 2.796e+04</td> <td> 2.64e+04</td> <td>    1.061</td> <td> 0.289</td> <td>-2.38e+04</td> <td> 7.97e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscFeature_Shed</th>      <td> 1.623e+04</td> <td> 1.31e+04</td> <td>    1.238</td> <td> 0.216</td> <td>-9500.426</td> <td>  4.2e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscFeature_TenC</th>      <td>-3.841e+04</td> <td> 2.33e+04</td> <td>   -1.649</td> <td> 0.100</td> <td>-8.41e+04</td> <td> 7306.797</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_COD</th>          <td>-1.371e+04</td> <td> 6566.426</td> <td>   -2.088</td> <td> 0.037</td> <td>-2.66e+04</td> <td> -825.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_CWD</th>          <td>  1.34e+04</td> <td> 1.45e+04</td> <td>    0.926</td> <td> 0.354</td> <td> -1.5e+04</td> <td> 4.18e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_Con</th>          <td> 9620.0869</td> <td> 2.31e+04</td> <td>    0.416</td> <td> 0.678</td> <td>-3.58e+04</td> <td>  5.5e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_ConLD</th>        <td>-7649.9542</td> <td> 1.04e+04</td> <td>   -0.733</td> <td> 0.464</td> <td>-2.81e+04</td> <td> 1.28e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_ConLI</th>        <td>-6099.0060</td> <td> 1.16e+04</td> <td>   -0.528</td> <td> 0.598</td> <td>-2.88e+04</td> <td> 1.66e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_ConLw</th>        <td>-1.267e+04</td> <td> 1.18e+04</td> <td>   -1.077</td> <td> 0.282</td> <td>-3.57e+04</td> <td> 1.04e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_New</th>          <td> 2.269e+04</td> <td> 1.47e+04</td> <td>    1.549</td> <td> 0.122</td> <td>-6060.655</td> <td> 5.14e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_Oth</th>          <td>-1952.2812</td> <td> 1.46e+04</td> <td>   -0.134</td> <td> 0.893</td> <td>-3.06e+04</td> <td> 2.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_WD</th>           <td>-1.177e+04</td> <td> 4946.589</td> <td>   -2.379</td> <td> 0.018</td> <td>-2.15e+04</td> <td>-2059.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Abnorml</th> <td>-2314.0888</td> <td> 5059.775</td> <td>   -0.457</td> <td> 0.648</td> <td>-1.22e+04</td> <td> 7615.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_AdjLand</th> <td> 1.674e+04</td> <td> 1.36e+04</td> <td>    1.232</td> <td> 0.218</td> <td>-9919.407</td> <td> 4.34e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Alloca</th>  <td>-1.062e+04</td> <td> 8712.512</td> <td>   -1.218</td> <td> 0.223</td> <td>-2.77e+04</td> <td> 6481.655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Family</th>  <td>-7120.2347</td> <td> 6850.312</td> <td>   -1.039</td> <td> 0.299</td> <td>-2.06e+04</td> <td> 6322.564</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Normal</th>  <td> 4279.2353</td> <td> 4309.320</td> <td>    0.993</td> <td> 0.321</td> <td>-4177.215</td> <td> 1.27e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Partial</th> <td>-9097.2030</td> <td> 1.36e+04</td> <td>   -0.669</td> <td> 0.504</td> <td>-3.58e+04</td> <td> 1.76e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotBaños</th>              <td> 1560.0686</td> <td> 1955.464</td> <td>    0.798</td> <td> 0.425</td> <td>-2277.262</td> <td> 5397.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotMedBaño</th>            <td> 2303.0228</td> <td> 2019.434</td> <td>    1.140</td> <td> 0.254</td> <td>-1659.839</td> <td> 6265.885</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bsmt1Cal</th>              <td>    8.1129</td> <td>    1.045</td> <td>    7.767</td> <td> 0.000</td> <td>    6.063</td> <td>   10.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bsmt2Cal</th>              <td>    8.1320</td> <td>    1.578</td> <td>    5.155</td> <td> 0.000</td> <td>    5.036</td> <td>   11.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BsmtCalTot</th>            <td>-1.971e+04</td> <td> 6463.692</td> <td>   -3.050</td> <td> 0.002</td> <td>-3.24e+04</td> <td>-7026.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GaragCal</th>              <td> 1.501e+06</td> <td> 8.97e+05</td> <td>    1.674</td> <td> 0.094</td> <td>-2.59e+05</td> <td> 3.26e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>KitchCal</th>              <td> 1872.4092</td> <td> 1684.968</td> <td>    1.111</td> <td> 0.267</td> <td>-1434.109</td> <td> 5178.928</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FireplCal</th>             <td>   -5.3390</td> <td>  566.346</td> <td>   -0.009</td> <td> 0.992</td> <td>-1116.715</td> <td> 1106.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PoolCal</th>               <td>  111.2473</td> <td>   16.102</td> <td>    6.909</td> <td> 0.000</td> <td>   79.648</td> <td>  142.846</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>409.511</td> <th>  Durbin-Watson:     </th> <td>   1.952</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>10074.136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.048</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>17.240</td>  <th>  Cond. No.          </th> <td>1.36e+16</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 1.36e-21. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              SalePrice   R-squared:                       0.917\n",
       "Model:                            OLS   Adj. R-squared:                  0.903\n",
       "Method:                 Least Squares   F-statistic:                     62.48\n",
       "Date:                Tue, 26 Jan 2021   Prob (F-statistic):               0.00\n",
       "Time:                        04:58:18   Log-Likelihood:                -13377.\n",
       "No. Observations:                1167   AIC:                         2.711e+04\n",
       "Df Residuals:                     990   BIC:                         2.800e+04\n",
       "Df Model:                         176                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "LotFrontage              29.1847     27.314      1.068      0.286     -24.415      82.784\n",
       "LotArea                   0.5896      0.111      5.299      0.000       0.371       0.808\n",
       "LotShape               -179.5828   1691.808     -0.106      0.915   -3499.525    3140.360\n",
       "Utilities             -3.253e+04   6355.430     -5.119      0.000    -4.5e+04   -2.01e+04\n",
       "LandSlope             -2734.9538   4026.224     -0.679      0.497   -1.06e+04    5165.959\n",
       "OverallQual            8634.6943   1224.595      7.051      0.000    6231.595     1.1e+04\n",
       "OverallCond            5347.0268    986.952      5.418      0.000    3410.269    7283.785\n",
       "MasVnrArea               35.4026      6.940      5.101      0.000      21.784      49.021\n",
       "ExterQual              5586.9567   2564.574      2.179      0.030     554.331    1.06e+04\n",
       "ExterCond             -1884.7055   2604.224     -0.724      0.469   -6995.139    3225.728\n",
       "HeatingQC              2044.9664   1168.856      1.750      0.081    -248.752    4338.685\n",
       "1stFlrSF                 61.4090      4.768     12.879      0.000      52.052      70.766\n",
       "2ndFlrSF                 65.7918      6.055     10.866      0.000      53.911      77.673\n",
       "BedroomAbvGr          -3342.4958   1474.282     -2.267      0.024   -6235.573    -449.419\n",
       "Functional             5907.1194   1364.361      4.330      0.000    3229.747    8584.492\n",
       "GarageCars              1.18e+04   2515.735      4.690      0.000    6862.131    1.67e+04\n",
       "WoodDeckSF               14.3149      7.173      1.996      0.046       0.238      28.392\n",
       "OpenPorchSF               4.3916     14.138      0.311      0.756     -23.353      32.136\n",
       "EnclosedPorch            16.2058     15.359      1.055      0.292     -13.934      46.346\n",
       "3SsnPorch                64.4813     26.570      2.427      0.015      12.342     116.621\n",
       "ScreenPorch              42.0246     16.327      2.574      0.010       9.985      74.064\n",
       "Fence                 -1068.4132    745.097     -1.434      0.152   -2530.564     393.737\n",
       "MiscVal                   5.6372     10.676      0.528      0.598     -15.313      26.588\n",
       "Remodelacion           3353.4416   1989.756      1.685      0.092    -551.183    7258.066\n",
       "AñosConstCasa          -319.1784     91.328     -3.495      0.000    -498.398    -139.959\n",
       "AñosConstGar             19.2098     69.232      0.277      0.781    -116.649     155.069\n",
       "MSZoning_C (all)      -2.156e+04   1.07e+04     -2.023      0.043   -4.25e+04    -643.541\n",
       "MSZoning_FV            7534.9749   7041.363      1.070      0.285   -6282.737    2.14e+04\n",
       "MSZoning_RH           -1207.6911   8136.496     -0.148      0.882   -1.72e+04    1.48e+04\n",
       "MSZoning_RL            4958.4177   3944.000      1.257      0.209   -2781.142    1.27e+04\n",
       "MSZoning_RM            2141.4684   4510.414      0.475      0.635   -6709.602     1.1e+04\n",
       "Street_Grvl           -1461.4467   7226.327     -0.202      0.840   -1.56e+04    1.27e+04\n",
       "Street_Pave           -6671.2832   7085.171     -0.942      0.347   -2.06e+04    7232.396\n",
       "Alley_0               -2339.6253   2722.876     -0.859      0.390   -7682.898    3003.647\n",
       "Alley_Grvl            -3884.3681   3801.615     -1.022      0.307   -1.13e+04    3575.782\n",
       "Alley_Pave            -1908.7365   4166.773     -0.458      0.647   -1.01e+04    6267.985\n",
       "LandContour_Bnk       -6758.3469   3630.882     -1.861      0.063   -1.39e+04     366.762\n",
       "LandContour_HLS        6325.5840   4061.237      1.558      0.120   -1644.038    1.43e+04\n",
       "LandContour_Low       -6123.1974   4983.430     -1.229      0.219   -1.59e+04    3656.102\n",
       "LandContour_Lvl       -1576.7696   2740.808     -0.575      0.565   -6955.230    3801.691\n",
       "LotConfig_Corner       2579.2219   3389.014      0.761      0.447   -4071.254    9229.698\n",
       "LotConfig_CulDSac      7822.6470   4081.659      1.917      0.056    -187.051    1.58e+04\n",
       "LotConfig_FR2         -4633.8755   4571.447     -1.014      0.311   -1.36e+04    4336.964\n",
       "LotConfig_FR3         -1.419e+04   1.11e+04     -1.276      0.202    -3.6e+04    7626.000\n",
       "LotConfig_Inside        291.0906   3135.622      0.093      0.926   -5862.139    6444.320\n",
       "Neighborhood_Blmngtn   7052.5519   8358.309      0.844      0.399   -9349.486    2.35e+04\n",
       "Neighborhood_Blueste   8966.0769   1.84e+04      0.487      0.626   -2.71e+04    4.51e+04\n",
       "Neighborhood_BrDale    4266.2659   9468.379      0.451      0.652   -1.43e+04    2.28e+04\n",
       "Neighborhood_BrkSide  -1279.1404   5664.745     -0.226      0.821   -1.24e+04    9837.147\n",
       "Neighborhood_ClearCr  -1.136e+04   6779.455     -1.676      0.094   -2.47e+04    1939.327\n",
       "Neighborhood_CollgCr  -9433.4977   3536.539     -2.667      0.008   -1.64e+04   -2493.525\n",
       "Neighborhood_Crawfor   7137.6472   4893.845      1.458      0.145   -2465.855    1.67e+04\n",
       "Neighborhood_Edwards  -1.888e+04   3818.089     -4.946      0.000   -2.64e+04   -1.14e+04\n",
       "Neighborhood_Gilbert   -1.26e+04   4480.171     -2.812      0.005   -2.14e+04   -3804.669\n",
       "Neighborhood_IDOTRR   -6632.3322   7804.047     -0.850      0.396   -2.19e+04    8682.042\n",
       "Neighborhood_MeadowV   1068.6665   9440.761      0.113      0.910   -1.75e+04    1.96e+04\n",
       "Neighborhood_Mitchel  -1.632e+04   4750.218     -3.436      0.001   -2.56e+04   -7001.069\n",
       "Neighborhood_NAmes    -1.445e+04   3156.286     -4.579      0.000   -2.06e+04   -8258.735\n",
       "Neighborhood_NPkVill   1.932e+04   1.33e+04      1.449      0.148   -6844.546    4.55e+04\n",
       "Neighborhood_NWAmes   -1.669e+04   4165.040     -4.008      0.000   -2.49e+04   -8518.256\n",
       "Neighborhood_NoRidge    2.35e+04   6075.579      3.869      0.000    1.16e+04    3.54e+04\n",
       "Neighborhood_NridgHt     3.7e+04   4881.728      7.580      0.000    2.74e+04    4.66e+04\n",
       "Neighborhood_OldTown  -1.665e+04   5754.489     -2.893      0.004   -2.79e+04   -5356.742\n",
       "Neighborhood_SWISU    -8882.7873   6836.027     -1.299      0.194   -2.23e+04    4531.981\n",
       "Neighborhood_Sawyer   -7566.6107   4207.093     -1.799      0.072   -1.58e+04     689.232\n",
       "Neighborhood_SawyerW  -5868.5688   4467.962     -1.313      0.189   -1.46e+04    2899.195\n",
       "Neighborhood_Somerst   2306.4109   6976.221      0.331      0.741   -1.14e+04     1.6e+04\n",
       "Neighborhood_StoneBr   4.336e+04   6947.324      6.242      0.000    2.97e+04     5.7e+04\n",
       "Neighborhood_Timber   -1.483e+04   5862.277     -2.530      0.012   -2.63e+04   -3326.474\n",
       "Neighborhood_Veenker   -665.4658   9336.146     -0.071      0.943    -1.9e+04    1.77e+04\n",
       "Condition1_Artery     -7310.8859   5439.623     -1.344      0.179    -1.8e+04    3363.630\n",
       "Condition1_Feedr      -3875.5730   4446.692     -0.872      0.384   -1.26e+04    4850.450\n",
       "Condition1_Norm        7484.4599   3053.818      2.451      0.014    1491.760    1.35e+04\n",
       "Condition1_PosA        2954.6303   8990.098      0.329      0.742   -1.47e+04    2.06e+04\n",
       "Condition1_PosN        3905.4063   6599.576      0.592      0.554   -9045.358    1.69e+04\n",
       "Condition1_RRAe       -1.922e+04   8318.966     -2.311      0.021   -3.55e+04   -2896.226\n",
       "Condition1_RRAn        7152.9545   6174.819      1.158      0.247   -4964.283    1.93e+04\n",
       "Condition1_RRNe       -1.506e-08   9.06e-09     -1.663      0.097   -3.28e-08    2.72e-09\n",
       "Condition1_RRNn         777.3370   1.31e+04      0.059      0.953   -2.49e+04    2.64e+04\n",
       "Condition2_Artery      3.228e+04    2.8e+04      1.151      0.250   -2.27e+04    8.73e+04\n",
       "Condition2_Feedr       9543.8633   1.62e+04      0.590      0.555   -2.22e+04    4.13e+04\n",
       "Condition2_Norm        2.633e+04   9669.257      2.723      0.007    7356.631    4.53e+04\n",
       "Condition2_PosA        9.742e+04   2.71e+04      3.599      0.000    4.43e+04    1.51e+05\n",
       "Condition2_PosN       -2.053e+05   2.02e+04    -10.149      0.000   -2.45e+05   -1.66e+05\n",
       "Condition2_RRAe       -3.329e+04   3.93e+04     -0.848      0.397    -1.1e+05    4.38e+04\n",
       "Condition2_RRAn        3.201e+04   2.45e+04      1.305      0.192   -1.61e+04    8.02e+04\n",
       "Condition2_RRNn        3.282e+04   1.89e+04      1.739      0.082   -4208.116    6.98e+04\n",
       "BldgType_1Fam          1.645e+04   2785.931      5.905      0.000     1.1e+04    2.19e+04\n",
       "BldgType_2fmCon        1.005e+04   5814.033      1.729      0.084   -1355.753    2.15e+04\n",
       "BldgType_Duplex       -3421.2403   5227.190     -0.655      0.513   -1.37e+04    6836.404\n",
       "BldgType_Twnhs        -1.935e+04   5083.507     -3.806      0.000   -2.93e+04   -9369.809\n",
       "BldgType_TwnhsE       -1.187e+04   3862.473     -3.073      0.002   -1.95e+04   -4291.262\n",
       "HouseStyle_1.5Fin     -2264.7901   3497.320     -0.648      0.517   -9127.802    4598.222\n",
       "HouseStyle_1.5Unf      5838.6857   8747.682      0.667      0.505   -1.13e+04     2.3e+04\n",
       "HouseStyle_1Story      7257.3522   3873.286      1.874      0.061    -343.442    1.49e+04\n",
       "HouseStyle_2.5Fin     -1.602e+04   1.28e+04     -1.247      0.213   -4.12e+04    9185.711\n",
       "HouseStyle_2.5Unf     -7806.3196   9335.152     -0.836      0.403   -2.61e+04    1.05e+04\n",
       "HouseStyle_2Story     -5985.0419   3854.614     -1.553      0.121   -1.35e+04    1579.110\n",
       "HouseStyle_SFoyer      6327.9573   5815.289      1.088      0.277   -5083.751    1.77e+04\n",
       "HouseStyle_SLvl        4518.2180   4862.770      0.929      0.353   -5024.302    1.41e+04\n",
       "RoofStyle_Flat        -9520.7425   1.77e+04     -0.537      0.592   -4.43e+04    2.53e+04\n",
       "RoofStyle_Gable       -1.012e+04   7339.414     -1.379      0.168   -2.45e+04    4278.294\n",
       "RoofStyle_Gambrel     -8569.8360   1.06e+04     -0.812      0.417   -2.93e+04    1.22e+04\n",
       "RoofStyle_Hip         -8078.9879   7489.540     -1.079      0.281   -2.28e+04    6618.208\n",
       "RoofStyle_Mansard     -5388.2954   1.15e+04     -0.468      0.640    -2.8e+04    1.72e+04\n",
       "RoofStyle_Shed         3.355e+04   2.61e+04      1.287      0.198   -1.76e+04    8.47e+04\n",
       "RoofMatl_ClyTile      -6.577e-09   3.86e-09     -1.705      0.089   -1.41e-08    9.95e-10\n",
       "RoofMatl_CompShg      -7518.4186   9658.121     -0.778      0.436   -2.65e+04    1.14e+04\n",
       "RoofMatl_Membran       2.319e+04   2.66e+04      0.872      0.384    -2.9e+04    7.54e+04\n",
       "RoofMatl_Metal        -4.499e-12   2.42e-11     -0.186      0.853   -5.21e-11    4.31e-11\n",
       "RoofMatl_Roll         -3.457e+04   2.46e+04     -1.402      0.161   -8.29e+04    1.38e+04\n",
       "RoofMatl_Tar&Grv      -1.732e+04   1.57e+04     -1.103      0.270   -4.81e+04    1.35e+04\n",
       "RoofMatl_WdShake       -2.76e+04   1.66e+04     -1.664      0.096   -6.02e+04    4951.676\n",
       "RoofMatl_WdShngl       5.568e+04   1.39e+04      4.017      0.000    2.85e+04    8.29e+04\n",
       "Exterior1st_AsbShng    1.177e+04   1.61e+04      0.732      0.464   -1.98e+04    4.33e+04\n",
       "Exterior1st_AsphShn   -4.486e+04   3.69e+04     -1.216      0.224   -1.17e+05    2.75e+04\n",
       "Exterior1st_BrkComm    3410.3323   2.69e+04      0.127      0.899   -4.94e+04    5.62e+04\n",
       "Exterior1st_BrkFace      1.6e+04   7848.051      2.039      0.042     598.774    3.14e+04\n",
       "Exterior1st_CBlock     1.634e-08   9.76e-09      1.675      0.094   -2.81e-09    3.55e-08\n",
       "Exterior1st_CemntBd    7.662e+04    2.7e+04      2.834      0.005    2.36e+04     1.3e+05\n",
       "Exterior1st_HdBoard    -546.9486   6818.374     -0.080      0.936   -1.39e+04    1.28e+04\n",
       "Exterior1st_ImStucc   -6.067e+04   2.63e+04     -2.309      0.021   -1.12e+05   -9106.399\n",
       "Exterior1st_MetalSd    6084.9933   9221.893      0.660      0.510    -1.2e+04    2.42e+04\n",
       "Exterior1st_Plywood   -5584.2901   7071.468     -0.790      0.430   -1.95e+04    8292.498\n",
       "Exterior1st_Stone     -1905.9816   2.18e+04     -0.088      0.930   -4.46e+04    4.08e+04\n",
       "Exterior1st_Stucco    -1397.6534   1.04e+04     -0.134      0.893   -2.18e+04     1.9e+04\n",
       "Exterior1st_VinylSd   -1.068e+04   9324.755     -1.146      0.252    -2.9e+04    7613.754\n",
       "Exterior1st_Wd Sdng   -2615.4412   6649.854     -0.393      0.694   -1.57e+04    1.04e+04\n",
       "Exterior1st_WdShing    6251.8096   8018.523      0.780      0.436   -9483.444     2.2e+04\n",
       "Exterior2nd_AsbShng   -8767.7851   1.47e+04     -0.595      0.552   -3.77e+04    2.02e+04\n",
       "Exterior2nd_AsphShn     2.21e+04   2.72e+04      0.812      0.417   -3.13e+04    7.55e+04\n",
       "Exterior2nd_Brk Cmn    2964.5382   1.92e+04      0.155      0.877   -3.46e+04    4.06e+04\n",
       "Exterior2nd_BrkFace   -8265.3450   8992.470     -0.919      0.358   -2.59e+04    9381.146\n",
       "Exterior2nd_CBlock    -6.808e-10      4e-10     -1.703      0.089   -1.47e-09    1.04e-10\n",
       "Exterior2nd_CmentBd   -6.317e+04   2.68e+04     -2.355      0.019   -1.16e+05   -1.05e+04\n",
       "Exterior2nd_HdBoard    3376.7082   6053.802      0.558      0.577   -8503.050    1.53e+04\n",
       "Exterior2nd_ImStucc    2.079e+04   1.02e+04      2.039      0.042     786.051    4.08e+04\n",
       "Exterior2nd_MetalSd     745.4736   8863.927      0.084      0.933   -1.66e+04    1.81e+04\n",
       "Exterior2nd_Other      -7.37e-11   3.46e-11     -2.131      0.033   -1.42e-10   -5.84e-12\n",
       "Exterior2nd_Plywood    1550.6605   5909.091      0.262      0.793      -1e+04    1.31e+04\n",
       "Exterior2nd_Stone     -3417.1868   1.46e+04     -0.234      0.815   -3.21e+04    2.53e+04\n",
       "Exterior2nd_Stucco     1.194e+04   9975.581      1.197      0.232   -7635.046    3.15e+04\n",
       "Exterior2nd_VinylSd    1.416e+04   8678.500      1.631      0.103   -2874.062    3.12e+04\n",
       "Exterior2nd_Wd Sdng    5279.4378   5835.168      0.905      0.366   -6171.280    1.67e+04\n",
       "Exterior2nd_Wd Shng   -7407.9291   6404.762     -1.157      0.248      -2e+04    5160.540\n",
       "MasVnrType_0          -1.784e+04   9833.302     -1.814      0.070   -3.71e+04    1454.675\n",
       "MasVnrType_BrkCmn     -6597.6112   6528.944     -1.011      0.312   -1.94e+04    6214.548\n",
       "MasVnrType_BrkFace      899.9806   3302.896      0.272      0.785   -5581.501    7381.462\n",
       "MasVnrType_None        6817.6420   3241.404      2.103      0.036     456.831    1.32e+04\n",
       "MasVnrType_Stone       8589.0917   3844.590      2.234      0.026    1044.611    1.61e+04\n",
       "Foundation_BrkTil      3790.4191   4949.215      0.766      0.444   -5921.738    1.35e+04\n",
       "Foundation_CBlock      7675.5951   4463.937      1.719      0.086   -1084.270    1.64e+04\n",
       "Foundation_PConc       7508.2436   4616.390      1.626      0.104   -1550.789    1.66e+04\n",
       "Foundation_Slab        7655.6925   7867.239      0.973      0.331   -7782.687    2.31e+04\n",
       "Foundation_Stone       7863.9507    1.2e+04      0.658      0.511   -1.56e+04    3.13e+04\n",
       "Foundation_Wood       -4.263e+04   1.68e+04     -2.533      0.011   -7.56e+04   -9607.194\n",
       "Heating_Floor         -2839.1042   2.31e+04     -0.123      0.902   -4.81e+04    4.25e+04\n",
       "Heating_GasA           2544.2407   7912.666      0.322      0.748    -1.3e+04    1.81e+04\n",
       "Heating_GasW           -210.2103   9660.717     -0.022      0.983   -1.92e+04    1.87e+04\n",
       "Heating_Grav           9014.8927   1.18e+04      0.763      0.446   -1.42e+04    3.22e+04\n",
       "Heating_OthW          -3.118e+04   2.41e+04     -1.295      0.196   -7.84e+04    1.61e+04\n",
       "Heating_Wall           1.453e+04   1.84e+04      0.788      0.431   -2.16e+04    5.07e+04\n",
       "CentralAir_N          -4415.2608   2427.123     -1.819      0.069   -9178.157     347.636\n",
       "CentralAir_Y          -3717.4691   2545.776     -1.460      0.145   -8713.206    1278.268\n",
       "Electrical_FuseA       1050.5275   7860.479      0.134      0.894   -1.44e+04    1.65e+04\n",
       "Electrical_FuseF      -1684.7166   8946.402     -0.188      0.851   -1.92e+04    1.59e+04\n",
       "Electrical_FuseP      -1.752e+04   2.04e+04     -0.859      0.390   -5.75e+04    2.25e+04\n",
       "Electrical_Mix         1.323e+04    2.3e+04      0.575      0.566   -3.19e+04    5.84e+04\n",
       "Electrical_SBrkr      -3204.0867   7673.061     -0.418      0.676   -1.83e+04    1.19e+04\n",
       "GarageType_0           2.333e+04   8219.770      2.839      0.005    7203.238    3.95e+04\n",
       "GarageType_2Types     -2.633e+04   1.17e+04     -2.253      0.025   -4.93e+04   -3392.149\n",
       "GarageType_Attchd     -3505.1138   3423.251     -1.024      0.306   -1.02e+04    3212.549\n",
       "GarageType_Basment     6670.9519   6857.835      0.973      0.331   -6786.610    2.01e+04\n",
       "GarageType_BuiltIn    -2651.5990   4688.889     -0.566      0.572   -1.19e+04    6549.704\n",
       "GarageType_CarPort    -6353.6547   9964.907     -0.638      0.524   -2.59e+04    1.32e+04\n",
       "GarageType_Detchd       699.6009   3360.035      0.208      0.835   -5894.007    7293.209\n",
       "PavedDrive_N          -4881.6328   3095.612     -1.577      0.115    -1.1e+04    1193.083\n",
       "PavedDrive_P          -1471.0089   3699.736     -0.398      0.691   -8731.234    5789.216\n",
       "PavedDrive_Y          -1780.0881   2443.125     -0.729      0.466   -6574.386    3014.210\n",
       "MiscFeature_0          1.938e+04   1.79e+04      1.084      0.278   -1.57e+04    5.45e+04\n",
       "MiscFeature_Gar2      -3.329e+04   3.93e+04     -0.848      0.397    -1.1e+05    4.38e+04\n",
       "MiscFeature_Othr       2.796e+04   2.64e+04      1.061      0.289   -2.38e+04    7.97e+04\n",
       "MiscFeature_Shed       1.623e+04   1.31e+04      1.238      0.216   -9500.426     4.2e+04\n",
       "MiscFeature_TenC      -3.841e+04   2.33e+04     -1.649      0.100   -8.41e+04    7306.797\n",
       "SaleType_COD          -1.371e+04   6566.426     -2.088      0.037   -2.66e+04    -825.676\n",
       "SaleType_CWD            1.34e+04   1.45e+04      0.926      0.354    -1.5e+04    4.18e+04\n",
       "SaleType_Con           9620.0869   2.31e+04      0.416      0.678   -3.58e+04     5.5e+04\n",
       "SaleType_ConLD        -7649.9542   1.04e+04     -0.733      0.464   -2.81e+04    1.28e+04\n",
       "SaleType_ConLI        -6099.0060   1.16e+04     -0.528      0.598   -2.88e+04    1.66e+04\n",
       "SaleType_ConLw        -1.267e+04   1.18e+04     -1.077      0.282   -3.57e+04    1.04e+04\n",
       "SaleType_New           2.269e+04   1.47e+04      1.549      0.122   -6060.655    5.14e+04\n",
       "SaleType_Oth          -1952.2812   1.46e+04     -0.134      0.893   -3.06e+04    2.67e+04\n",
       "SaleType_WD           -1.177e+04   4946.589     -2.379      0.018   -2.15e+04   -2059.965\n",
       "SaleCondition_Abnorml -2314.0888   5059.775     -0.457      0.648   -1.22e+04    7615.027\n",
       "SaleCondition_AdjLand  1.674e+04   1.36e+04      1.232      0.218   -9919.407    4.34e+04\n",
       "SaleCondition_Alloca  -1.062e+04   8712.512     -1.218      0.223   -2.77e+04    6481.655\n",
       "SaleCondition_Family  -7120.2347   6850.312     -1.039      0.299   -2.06e+04    6322.564\n",
       "SaleCondition_Normal   4279.2353   4309.320      0.993      0.321   -4177.215    1.27e+04\n",
       "SaleCondition_Partial -9097.2030   1.36e+04     -0.669      0.504   -3.58e+04    1.76e+04\n",
       "TotBaños               1560.0686   1955.464      0.798      0.425   -2277.262    5397.399\n",
       "TotMedBaño             2303.0228   2019.434      1.140      0.254   -1659.839    6265.885\n",
       "Bsmt1Cal                  8.1129      1.045      7.767      0.000       6.063      10.163\n",
       "Bsmt2Cal                  8.1320      1.578      5.155      0.000       5.036      11.228\n",
       "BsmtCalTot            -1.971e+04   6463.692     -3.050      0.002   -3.24e+04   -7026.952\n",
       "GaragCal               1.501e+06   8.97e+05      1.674      0.094   -2.59e+05    3.26e+06\n",
       "KitchCal               1872.4092   1684.968      1.111      0.267   -1434.109    5178.928\n",
       "FireplCal                -5.3390    566.346     -0.009      0.992   -1116.715    1106.037\n",
       "PoolCal                 111.2473     16.102      6.909      0.000      79.648     142.846\n",
       "==============================================================================\n",
       "Omnibus:                      409.511   Durbin-Watson:                   1.952\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            10074.136\n",
       "Skew:                           1.048   Prob(JB):                         0.00\n",
       "Kurtosis:                      17.240   Cond. No.                     1.36e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.36e-21. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generamos las variables\n",
    "\n",
    "y = df['SalePrice']\n",
    "x1 = df.drop('SalePrice',axis=1)\n",
    "\n",
    "# Separamos los datos para poder hacer el entrenamiento y la prueba\n",
    "x_train, x_test, y_train,y_test=train_test_split(x1,y,test_size=.2,random_state=77)\n",
    "\n",
    "# Agregamos la constante\n",
    "X = sm.add_constant(x_train)\n",
    "\n",
    "# Generamos el modelo\n",
    "linreg_stats = sm.OLS(y_train, X).fit()\n",
    "\n",
    "linreg_stats.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:58:32.178848Z",
     "start_time": "2021-01-26T10:58:28.871342Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a continuacion se muestra el promedio por columna\n",
      "SP_test         179465.729452\n",
      "SP_predicted    183651.150068\n",
      "Diferencia       -4185.420616\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Obtuvimos una R2 muy cercana a 1, parece buena, sin embargo, tambien puede significar un overfiting.\n",
    "# Veamos que es lo que se puede hacer con esta prediccion. \n",
    "# comparamos los precios de entrenamiento vs los de prediccion\n",
    "prediccion = linreg_stats.predict(x_test)\n",
    "\n",
    "predicciones = pd.DataFrame({'SP_test':y_test,'SP_predicted':round(prediccion,2)}).reset_index(drop=True)\n",
    "predicciones['Diferencia'] = predicciones.SP_test - predicciones.SP_predicted\n",
    "print('a continuacion se muestra el promedio por columna')\n",
    "print(predicciones.mean())\n",
    "predicciones\n",
    "# esta prueba fue con todos los datos. Veamos si con algunas modificaciones mas podemos mejorar la prediccion \n",
    "# y/o usar menos variables\n",
    "\n",
    "#Graficaremos los precios\n",
    "predicciones.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisis de Hipotesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:58:37.572803Z",
     "start_time": "2021-01-26T10:58:37.560798Z"
    }
   },
   "outputs": [],
   "source": [
    "# Vamos a tratar de reducir aun mas el numero de variables que se está utilizando. \n",
    "# Para ello tenemos dos hipotesis, las cuales desarrollaremos en este momento, y con los resultados obtenidos\n",
    "# se correrá nuevamente la regresion lineal y compararemos los resultados obtenidos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Son todas las variables nescesarias para poder predecir el precio de venta de una casa?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:58:41.921007Z",
     "start_time": "2021-01-26T10:58:41.912004Z"
    }
   },
   "outputs": [],
   "source": [
    "# H0 = Todas las variables son nescesarias para predecir el valor de una casa\n",
    "# P_Value = 95%\n",
    "# Se rechazará esta hipotesis nula si nuestro P_Value resulta menor a 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T11:00:19.619924Z",
     "start_time": "2021-01-26T11:00:18.825524Z"
    }
   },
   "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>Variable</th>\n",
       "      <th>P_Value</th>\n",
       "      <th>H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Exterior1st_WdShing</td>\n",
       "      <td>4.994873e-02</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MasVnrType_0</td>\n",
       "      <td>4.736618e-02</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exterior2nd_Plywood</td>\n",
       "      <td>4.303106e-02</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Neighborhood_ClearCr</td>\n",
       "      <td>3.337780e-02</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neighborhood_Mitchel</td>\n",
       "      <td>2.708229e-02</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1stFlrSF</td>\n",
       "      <td>5.771340e-147</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>2.942120e-169</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>ExterQual</td>\n",
       "      <td>9.348924e-201</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>2.732865e-313</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>SalePrice</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>122 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Variable        P_Value                   H0\n",
       "0     Exterior1st_WdShing   4.994873e-02  Valida para Reg Lin\n",
       "1            MasVnrType_0   4.736618e-02  Valida para Reg Lin\n",
       "2     Exterior2nd_Plywood   4.303106e-02  Valida para Reg Lin\n",
       "3    Neighborhood_ClearCr   3.337780e-02  Valida para Reg Lin\n",
       "4    Neighborhood_Mitchel   2.708229e-02  Valida para Reg Lin\n",
       "..                    ...            ...                  ...\n",
       "117              1stFlrSF  5.771340e-147  Valida para Reg Lin\n",
       "118            GarageCars  2.942120e-169  Valida para Reg Lin\n",
       "119             ExterQual  9.348924e-201  Valida para Reg Lin\n",
       "120           OverallQual  2.732865e-313  Valida para Reg Lin\n",
       "121             SalePrice   0.000000e+00  Valida para Reg Lin\n",
       "\n",
       "[122 rows x 3 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import linregress\n",
    "p_hipotesis_1 = df.copy()\n",
    "\n",
    "def p_value(data,int_confianza):\n",
    "      \n",
    "    y = data['SalePrice']\n",
    "    p_value = []\n",
    "    for column in data.columns:\n",
    "        x = data[column]\n",
    "        slope, intercept, rvalue, pvalue, stderr = linregress(x, y)\n",
    "        p_value.append((column,pvalue))\n",
    "    \n",
    "    p_value_df = pd.DataFrame(p_value,columns=['Variable','P_Value'])\n",
    "    \n",
    "    p_value_df['H0'] = p_value_df['P_Value'] <= int_confianza\n",
    "            \n",
    "    p_value_df['H0'] = p_value_df['H0'].astype('str')\n",
    "    p_value_df['H0'].replace({'True':'Valida para Reg Lin','False':'No Valida Reg Lin'},inplace=True)\n",
    "    \n",
    "    return p_value_df\n",
    "\n",
    "variables_df = p_value(p_hipotesis_1,0.05)\n",
    "p_value_rl = variables_df[variables_df['H0']=='Valida para Reg Lin']\n",
    "p_value_rl=p_value_rl.sort_values(by='P_Value',ascending=False).reset_index(drop=True)\n",
    "p_value_rl\n",
    "\n",
    "variables_sacar = variables_df[variables_df['H0']=='No Valida Reg Lin']\n",
    "variables_sacar= variables_sacar['Variable'].to_list()\n",
    "\n",
    "p_value_rl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T11:00:39.731070Z",
     "start_time": "2021-01-26T11:00:39.714066Z"
    }
   },
   "outputs": [],
   "source": [
    "# En nuestro df inicial teniamos 217 variables, analizando el p_value de cada una contra el 'SalePrice' con\n",
    "# modelo de regresion lineal, unicamente 127 variables tienen un p_value mayor o igual a nuestro intervalo\n",
    "# de confianza (0.05), por lo que son las mas reprecentativas para nuestro modelo. \n",
    "\n",
    "# por lo tanto, nuestra Hipotesis Nula se descarta y no requerimos de las 217 variables para poder predecir el precio\n",
    "# de venta de una casa. \n",
    "# Esto hace mas manejable nuestra data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T11:20:52.118024Z",
     "start_time": "2021-01-26T11:20:51.623901Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>SalePrice</td>    <th>  R-squared:         </th>  <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   1.000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>1.667e+29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 26 Jan 2021</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>05:20:51</td>     <th>  Log-Likelihood:    </th>  <td>  23161.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1167</td>      <th>  AIC:               </th> <td>-4.608e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1047</td>      <th>  BIC:               </th> <td>-4.547e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   119</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                 <td>-1.797e-10</td> <td> 4.08e-10</td> <td>   -0.440</td> <td> 0.660</td> <td>-9.81e-10</td> <td> 6.21e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotFrontage</th>           <td>-2.373e-13</td> <td> 6.38e-13</td> <td>   -0.372</td> <td> 0.710</td> <td>-1.49e-12</td> <td> 1.01e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotArea</th>               <td> 1.722e-15</td> <td> 2.43e-15</td> <td>    0.708</td> <td> 0.479</td> <td>-3.05e-15</td> <td> 6.49e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotShape</th>              <td>-4.451e-11</td> <td> 3.95e-11</td> <td>   -1.128</td> <td> 0.260</td> <td>-1.22e-10</td> <td> 3.29e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OverallQual</th>           <td> 5.542e-12</td> <td> 2.89e-11</td> <td>    0.191</td> <td> 0.848</td> <td>-5.13e-11</td> <td> 6.23e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OverallCond</th>           <td>-2.259e-12</td> <td> 2.22e-11</td> <td>   -0.102</td> <td> 0.919</td> <td>-4.57e-11</td> <td> 4.12e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrArea</th>            <td> 6.506e-14</td> <td> 1.64e-13</td> <td>    0.397</td> <td> 0.691</td> <td>-2.56e-13</td> <td> 3.87e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ExterQual</th>             <td> 2.879e-11</td> <td> 5.98e-11</td> <td>    0.481</td> <td> 0.630</td> <td>-8.86e-11</td> <td> 1.46e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HeatingQC</th>             <td> 3.436e-11</td> <td> 2.74e-11</td> <td>    1.253</td> <td> 0.211</td> <td>-1.95e-11</td> <td> 8.82e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1stFlrSF</th>              <td>-5.212e-14</td> <td> 1.14e-13</td> <td>   -0.457</td> <td> 0.648</td> <td>-2.76e-13</td> <td> 1.72e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2ndFlrSF</th>              <td>-1.058e-13</td> <td> 1.39e-13</td> <td>   -0.763</td> <td> 0.446</td> <td>-3.78e-13</td> <td> 1.66e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BedroomAbvGr</th>          <td>  5.44e-11</td> <td> 3.43e-11</td> <td>    1.587</td> <td> 0.113</td> <td>-1.28e-11</td> <td> 1.22e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Functional</th>            <td> -5.99e-12</td> <td> 3.17e-11</td> <td>   -0.189</td> <td> 0.850</td> <td>-6.83e-11</td> <td> 5.63e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageCars</th>            <td> 4.314e-11</td> <td> 5.96e-11</td> <td>    0.724</td> <td> 0.469</td> <td>-7.38e-11</td> <td>  1.6e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WoodDeckSF</th>            <td>-1.281e-13</td> <td> 1.72e-13</td> <td>   -0.746</td> <td> 0.456</td> <td>-4.65e-13</td> <td> 2.09e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OpenPorchSF</th>           <td> 2.154e-13</td> <td> 3.26e-13</td> <td>    0.662</td> <td> 0.508</td> <td>-4.23e-13</td> <td> 8.54e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EnclosedPorch</th>         <td> 5.427e-13</td> <td> 3.58e-13</td> <td>    1.514</td> <td> 0.130</td> <td>-1.61e-13</td> <td> 1.25e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ScreenPorch</th>           <td>  2.62e-14</td> <td> 3.81e-13</td> <td>    0.069</td> <td> 0.945</td> <td>-7.21e-13</td> <td> 7.73e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fence</th>                 <td> 3.944e-12</td> <td> 1.74e-11</td> <td>    0.226</td> <td> 0.821</td> <td>-3.02e-11</td> <td> 3.81e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SalePrice</th>             <td>    1.0000</td> <td> 7.03e-16</td> <td> 1.42e+15</td> <td> 0.000</td> <td>    1.000</td> <td>    1.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AñosConstCasa</th>         <td>-1.569e-12</td> <td> 2.03e-12</td> <td>   -0.772</td> <td> 0.440</td> <td>-5.55e-12</td> <td> 2.42e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AñosConstGar</th>          <td> 1.601e-12</td> <td> 1.59e-12</td> <td>    1.006</td> <td> 0.315</td> <td>-1.52e-12</td> <td> 4.72e-12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_C (all)</th>      <td>-1.501e-11</td> <td>  2.6e-10</td> <td>   -0.058</td> <td> 0.954</td> <td>-5.26e-10</td> <td> 4.96e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_FV</th>           <td>-1.498e-10</td> <td> 1.85e-10</td> <td>   -0.811</td> <td> 0.418</td> <td>-5.12e-10</td> <td> 2.13e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_RH</th>           <td> 1.251e-11</td> <td>  2.1e-10</td> <td>    0.060</td> <td> 0.953</td> <td>   -4e-10</td> <td> 4.25e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_RL</th>           <td>-6.963e-12</td> <td>  1.2e-10</td> <td>   -0.058</td> <td> 0.954</td> <td>-2.42e-10</td> <td> 2.28e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_RM</th>           <td>-1.825e-11</td> <td> 1.31e-10</td> <td>   -0.140</td> <td> 0.889</td> <td>-2.74e-10</td> <td> 2.38e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Alley_0</th>               <td>-1.195e-10</td> <td> 1.38e-10</td> <td>   -0.869</td> <td> 0.385</td> <td>-3.89e-10</td> <td>  1.5e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Alley_Grvl</th>            <td>-1.374e-10</td> <td> 1.74e-10</td> <td>   -0.792</td> <td> 0.429</td> <td>-4.78e-10</td> <td> 2.03e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LandContour_Bnk</th>       <td> -4.78e-11</td> <td> 9.81e-11</td> <td>   -0.487</td> <td> 0.626</td> <td> -2.4e-10</td> <td> 1.45e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LandContour_HLS</th>       <td> 4.468e-11</td> <td> 1.19e-10</td> <td>    0.375</td> <td> 0.708</td> <td>-1.89e-10</td> <td> 2.79e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotConfig_CulDSac</th>     <td> 1.688e-11</td> <td> 9.23e-11</td> <td>    0.183</td> <td> 0.855</td> <td>-1.64e-10</td> <td> 1.98e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotConfig_Inside</th>      <td> 8.185e-12</td> <td> 4.74e-11</td> <td>    0.173</td> <td> 0.863</td> <td>-8.48e-11</td> <td> 1.01e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_BrDale</th>   <td> 3.092e-11</td> <td> 2.46e-10</td> <td>    0.126</td> <td> 0.900</td> <td>-4.51e-10</td> <td> 5.13e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_BrkSide</th>  <td>-1.116e-10</td> <td> 1.57e-10</td> <td>   -0.712</td> <td> 0.476</td> <td>-4.19e-10</td> <td> 1.96e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_ClearCr</th>  <td> 1.284e-11</td> <td> 1.65e-10</td> <td>    0.078</td> <td> 0.938</td> <td>-3.11e-10</td> <td> 3.36e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_CollgCr</th>  <td>-4.809e-11</td> <td> 8.34e-11</td> <td>   -0.577</td> <td> 0.564</td> <td>-2.12e-10</td> <td> 1.16e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Crawfor</th>  <td>-2.785e-12</td> <td> 1.35e-10</td> <td>   -0.021</td> <td> 0.984</td> <td>-2.67e-10</td> <td> 2.62e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Edwards</th>  <td>-7.196e-11</td> <td> 1.09e-10</td> <td>   -0.660</td> <td> 0.509</td> <td>-2.86e-10</td> <td> 1.42e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_IDOTRR</th>   <td>-9.311e-11</td> <td> 2.11e-10</td> <td>   -0.440</td> <td> 0.660</td> <td>-5.08e-10</td> <td> 3.22e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_MeadowV</th>  <td> 1.301e-10</td> <td> 2.47e-10</td> <td>    0.526</td> <td> 0.599</td> <td>-3.55e-10</td> <td> 6.16e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Mitchel</th>  <td>-1.278e-10</td> <td> 1.22e-10</td> <td>   -1.047</td> <td> 0.295</td> <td>-3.67e-10</td> <td> 1.12e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NAmes</th>    <td>-1.143e-10</td> <td> 8.72e-11</td> <td>   -1.310</td> <td> 0.190</td> <td>-2.85e-10</td> <td> 5.69e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NoRidge</th>  <td>-6.349e-11</td> <td> 1.49e-10</td> <td>   -0.426</td> <td> 0.670</td> <td>-3.56e-10</td> <td> 2.29e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NridgHt</th>  <td> 3.206e-11</td> <td> 1.21e-10</td> <td>    0.265</td> <td> 0.791</td> <td>-2.06e-10</td> <td>  2.7e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_OldTown</th>  <td>-1.995e-11</td> <td> 1.65e-10</td> <td>   -0.121</td> <td> 0.904</td> <td>-3.43e-10</td> <td> 3.03e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_SWISU</th>    <td> -7.66e-11</td> <td>  1.8e-10</td> <td>   -0.425</td> <td> 0.671</td> <td> -4.3e-10</td> <td> 2.77e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Sawyer</th>   <td>-6.141e-11</td> <td>  1.1e-10</td> <td>   -0.558</td> <td> 0.577</td> <td>-2.77e-10</td> <td> 1.55e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Somerst</th>  <td> -2.41e-11</td> <td> 1.71e-10</td> <td>   -0.141</td> <td> 0.888</td> <td>-3.59e-10</td> <td> 3.11e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_StoneBr</th>  <td>-5.748e-12</td> <td> 1.77e-10</td> <td>   -0.032</td> <td> 0.974</td> <td>-3.53e-10</td> <td> 3.42e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Timber</th>   <td> 1.887e-11</td> <td> 1.42e-10</td> <td>    0.133</td> <td> 0.894</td> <td> -2.6e-10</td> <td> 2.98e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Veenker</th>  <td> 2.544e-11</td> <td> 2.34e-10</td> <td>    0.109</td> <td> 0.913</td> <td>-4.34e-10</td> <td> 4.84e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_Artery</th>     <td> 1.615e-11</td> <td>  1.5e-10</td> <td>    0.107</td> <td> 0.915</td> <td>-2.79e-10</td> <td> 3.11e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_Feedr</th>      <td> 6.224e-12</td> <td> 1.23e-10</td> <td>    0.051</td> <td> 0.960</td> <td>-2.35e-10</td> <td> 2.48e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_Norm</th>       <td>-1.219e-11</td> <td> 8.99e-11</td> <td>   -0.136</td> <td> 0.892</td> <td>-1.89e-10</td> <td> 1.64e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_1Fam</th>         <td> 1.205e-11</td> <td> 9.62e-11</td> <td>    0.125</td> <td> 0.900</td> <td>-1.77e-10</td> <td> 2.01e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_2fmCon</th>       <td>-1.442e-10</td> <td> 1.86e-10</td> <td>   -0.774</td> <td> 0.439</td> <td> -5.1e-10</td> <td> 2.22e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_Duplex</th>       <td>-1.116e-10</td> <td> 1.68e-10</td> <td>   -0.666</td> <td> 0.505</td> <td> -4.4e-10</td> <td> 2.17e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_Twnhs</th>        <td> 4.388e-11</td> <td> 1.41e-10</td> <td>    0.311</td> <td> 0.756</td> <td>-2.33e-10</td> <td> 3.21e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_1.5Fin</th>     <td> 8.486e-12</td> <td> 1.14e-10</td> <td>    0.074</td> <td> 0.941</td> <td>-2.15e-10</td> <td> 2.32e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_1.5Unf</th>     <td> 3.777e-11</td> <td> 2.45e-10</td> <td>    0.154</td> <td> 0.878</td> <td>-4.43e-10</td> <td> 5.19e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_1Story</th>     <td> 7.844e-12</td> <td> 9.84e-11</td> <td>    0.080</td> <td> 0.936</td> <td>-1.85e-10</td> <td> 2.01e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_2Story</th>     <td> 1.467e-11</td> <td> 1.16e-10</td> <td>    0.127</td> <td> 0.899</td> <td>-2.13e-10</td> <td> 2.42e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_SFoyer</th>     <td>-4.081e-11</td> <td> 1.52e-10</td> <td>   -0.269</td> <td> 0.788</td> <td>-3.39e-10</td> <td> 2.57e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Gable</th>       <td> 5.441e-11</td> <td>  1.6e-10</td> <td>    0.341</td> <td> 0.733</td> <td>-2.59e-10</td> <td> 3.67e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Hip</th>         <td> 4.187e-11</td> <td> 1.66e-10</td> <td>    0.253</td> <td> 0.800</td> <td>-2.83e-10</td> <td> 3.67e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_CompShg</th>      <td>-3.195e-11</td> <td> 2.06e-10</td> <td>   -0.155</td> <td> 0.877</td> <td>-4.36e-10</td> <td> 3.72e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_WdShngl</th>      <td>-2.466e-10</td> <td> 3.61e-10</td> <td>   -0.682</td> <td> 0.495</td> <td>-9.56e-10</td> <td> 4.63e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_AsbShng</th>   <td>-8.038e-11</td> <td> 3.71e-10</td> <td>   -0.216</td> <td> 0.829</td> <td>-8.09e-10</td> <td> 6.48e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_CemntBd</th>   <td> 5.002e-11</td> <td> 6.87e-10</td> <td>    0.073</td> <td> 0.942</td> <td> -1.3e-09</td> <td>  1.4e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_HdBoard</th>   <td>  1.62e-11</td> <td> 1.27e-10</td> <td>    0.128</td> <td> 0.899</td> <td>-2.33e-10</td> <td> 2.65e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_MetalSd</th>   <td> 2.154e-11</td> <td> 2.22e-10</td> <td>    0.097</td> <td> 0.923</td> <td>-4.15e-10</td> <td> 4.58e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_VinylSd</th>   <td>-2.433e-11</td> <td> 2.18e-10</td> <td>   -0.112</td> <td> 0.911</td> <td>-4.52e-10</td> <td> 4.03e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_Wd Sdng</th>   <td>-4.157e-11</td> <td> 1.27e-10</td> <td>   -0.326</td> <td> 0.744</td> <td>-2.92e-10</td> <td> 2.09e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_WdShing</th>   <td>  1.62e-12</td> <td> 1.57e-10</td> <td>    0.010</td> <td> 0.992</td> <td>-3.07e-10</td> <td>  3.1e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_AsbShng</th>   <td> 6.793e-11</td> <td> 3.58e-10</td> <td>    0.190</td> <td> 0.849</td> <td>-6.34e-10</td> <td>  7.7e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_CmentBd</th>   <td>-1.614e-10</td> <td> 6.82e-10</td> <td>   -0.237</td> <td> 0.813</td> <td> -1.5e-09</td> <td> 1.18e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_HdBoard</th>   <td>  2.64e-11</td> <td> 1.45e-10</td> <td>    0.182</td> <td> 0.855</td> <td>-2.58e-10</td> <td> 3.11e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_ImStucc</th>   <td> 2.121e-10</td> <td> 2.41e-10</td> <td>    0.879</td> <td> 0.379</td> <td>-2.61e-10</td> <td> 6.85e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_MetalSd</th>   <td>-1.141e-11</td> <td> 2.28e-10</td> <td>   -0.050</td> <td> 0.960</td> <td>-4.59e-10</td> <td> 4.36e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Plywood</th>   <td> 3.752e-12</td> <td> 1.12e-10</td> <td>    0.033</td> <td> 0.973</td> <td>-2.17e-10</td> <td> 2.24e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_VinylSd</th>   <td>-4.206e-11</td> <td> 2.13e-10</td> <td>   -0.198</td> <td> 0.843</td> <td> -4.6e-10</td> <td> 3.75e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Wd Sdng</th>   <td>-3.183e-12</td> <td> 1.33e-10</td> <td>   -0.024</td> <td> 0.981</td> <td>-2.65e-10</td> <td> 2.59e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_0</th>          <td>-1.318e-10</td> <td> 3.39e-10</td> <td>   -0.389</td> <td> 0.697</td> <td>-7.97e-10</td> <td> 5.33e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_BrkFace</th>    <td>-9.322e-11</td> <td> 1.83e-10</td> <td>   -0.511</td> <td> 0.610</td> <td>-4.51e-10</td> <td> 2.65e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_None</th>       <td>-8.697e-11</td> <td> 1.84e-10</td> <td>   -0.473</td> <td> 0.637</td> <td>-4.48e-10</td> <td> 2.74e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_Stone</th>      <td>-5.002e-11</td> <td> 1.96e-10</td> <td>   -0.255</td> <td> 0.799</td> <td>-4.35e-10</td> <td> 3.35e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_BrkTil</th>     <td> 1.904e-10</td> <td> 2.63e-10</td> <td>    0.724</td> <td> 0.469</td> <td>-3.25e-10</td> <td> 7.06e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_CBlock</th>     <td>  2.43e-10</td> <td> 2.63e-10</td> <td>    0.925</td> <td> 0.355</td> <td>-2.72e-10</td> <td> 7.58e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_PConc</th>      <td> 1.639e-10</td> <td> 2.65e-10</td> <td>    0.618</td> <td> 0.536</td> <td>-3.56e-10</td> <td> 6.84e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_Slab</th>       <td>  2.46e-10</td> <td> 3.21e-10</td> <td>    0.765</td> <td> 0.444</td> <td>-3.85e-10</td> <td> 8.77e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_GasA</th>          <td>-2.305e-11</td> <td> 1.76e-10</td> <td>   -0.131</td> <td> 0.896</td> <td>-3.68e-10</td> <td> 3.22e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_Grav</th>          <td> 8.816e-11</td> <td> 3.16e-10</td> <td>    0.279</td> <td> 0.780</td> <td>-5.31e-10</td> <td> 7.07e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_Wall</th>          <td> 3.222e-12</td> <td> 5.09e-10</td> <td>    0.006</td> <td> 0.995</td> <td>-9.96e-10</td> <td>    1e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CentralAir_N</th>          <td>-6.872e-11</td> <td> 2.07e-10</td> <td>   -0.332</td> <td> 0.740</td> <td>-4.75e-10</td> <td> 3.38e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CentralAir_Y</th>          <td>-2.657e-11</td> <td> 2.15e-10</td> <td>   -0.123</td> <td> 0.902</td> <td>-4.49e-10</td> <td> 3.96e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_FuseA</th>      <td>-8.413e-11</td> <td> 4.32e-10</td> <td>   -0.195</td> <td> 0.846</td> <td>-9.31e-10</td> <td> 7.63e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_FuseF</th>      <td>-9.999e-11</td> <td> 4.52e-10</td> <td>   -0.221</td> <td> 0.825</td> <td>-9.88e-10</td> <td> 7.88e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_SBrkr</th>      <td>-1.372e-10</td> <td> 4.28e-10</td> <td>   -0.320</td> <td> 0.749</td> <td>-9.78e-10</td> <td> 7.03e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_0</th>          <td>-5.593e-11</td> <td>  2.6e-10</td> <td>   -0.215</td> <td> 0.830</td> <td>-5.67e-10</td> <td> 4.55e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Attchd</th>     <td>-3.089e-11</td> <td>  1.5e-10</td> <td>   -0.207</td> <td> 0.836</td> <td>-3.24e-10</td> <td> 2.63e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_BuiltIn</th>    <td> -9.14e-11</td> <td> 1.75e-10</td> <td>   -0.523</td> <td> 0.601</td> <td>-4.35e-10</td> <td> 2.52e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_CarPort</th>    <td>-6.025e-12</td> <td> 2.84e-10</td> <td>   -0.021</td> <td> 0.983</td> <td>-5.63e-10</td> <td> 5.51e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Detchd</th>     <td>-2.086e-11</td> <td> 1.52e-10</td> <td>   -0.137</td> <td> 0.891</td> <td> -3.2e-10</td> <td> 2.78e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PavedDrive_N</th>          <td>-4.491e-11</td> <td>  1.5e-10</td> <td>   -0.299</td> <td> 0.765</td> <td> -3.4e-10</td> <td>  2.5e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PavedDrive_P</th>          <td>-1.162e-11</td> <td> 1.69e-10</td> <td>   -0.069</td> <td> 0.945</td> <td>-3.43e-10</td> <td>  3.2e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PavedDrive_Y</th>          <td>-4.755e-11</td> <td> 1.44e-10</td> <td>   -0.329</td> <td> 0.742</td> <td>-3.31e-10</td> <td> 2.36e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscFeature_0</th>         <td>-1.459e-10</td> <td> 3.75e-10</td> <td>   -0.388</td> <td> 0.698</td> <td>-8.83e-10</td> <td> 5.91e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscFeature_Shed</th>      <td> 2.138e-10</td> <td> 3.88e-10</td> <td>    0.551</td> <td> 0.582</td> <td>-5.48e-10</td> <td> 9.76e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_COD</th>          <td> 1.171e-10</td> <td> 1.78e-10</td> <td>    0.656</td> <td> 0.512</td> <td>-2.33e-10</td> <td> 4.67e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_New</th>          <td>-4.474e-10</td> <td> 3.86e-10</td> <td>   -1.159</td> <td> 0.247</td> <td> -1.2e-09</td> <td>  3.1e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_WD</th>           <td> 5.797e-11</td> <td> 1.36e-10</td> <td>    0.425</td> <td> 0.671</td> <td> -2.1e-10</td> <td> 3.26e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Abnorml</th> <td> 1.063e-11</td> <td> 1.44e-10</td> <td>    0.074</td> <td> 0.941</td> <td>-2.73e-10</td> <td> 2.94e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Normal</th>  <td> 4.239e-11</td> <td> 1.23e-10</td> <td>    0.346</td> <td> 0.730</td> <td>-1.98e-10</td> <td> 2.83e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Partial</th> <td> 3.844e-10</td> <td> 3.93e-10</td> <td>    0.977</td> <td> 0.329</td> <td>-3.87e-10</td> <td> 1.16e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotBaños</th>              <td>-1.751e-11</td> <td>  4.6e-11</td> <td>   -0.381</td> <td> 0.703</td> <td>-1.08e-10</td> <td> 7.27e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotMedBaño</th>            <td>  4.69e-12</td> <td> 4.73e-11</td> <td>    0.099</td> <td> 0.921</td> <td>-8.81e-11</td> <td> 9.75e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Bsmt1Cal</th>              <td> -6.04e-15</td> <td> 1.92e-14</td> <td>   -0.314</td> <td> 0.753</td> <td>-4.37e-14</td> <td> 3.17e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BsmtCalTot</th>            <td> 6.092e-11</td> <td>  1.2e-10</td> <td>    0.507</td> <td> 0.612</td> <td>-1.75e-10</td> <td> 2.97e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GaragCal</th>              <td> 9.313e-10</td> <td>  2.1e-08</td> <td>    0.044</td> <td> 0.965</td> <td>-4.03e-08</td> <td> 4.22e-08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>KitchCal</th>              <td> 1.749e-11</td> <td> 3.94e-11</td> <td>    0.444</td> <td> 0.657</td> <td>-5.98e-11</td> <td> 9.48e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FireplCal</th>             <td> -1.17e-11</td> <td> 1.34e-11</td> <td>   -0.871</td> <td> 0.384</td> <td> -3.8e-11</td> <td> 1.46e-11</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PoolCal</th>               <td> 5.592e-13</td> <td> 3.81e-13</td> <td>    1.466</td> <td> 0.143</td> <td>-1.89e-13</td> <td> 1.31e-12</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>41.778</td> <th>  Durbin-Watson:     </th> <td>   0.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 110.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.057</td> <th>  Prob(JB):          </th> <td>1.05e-24</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.503</td> <th>  Cond. No.          </th> <td>4.88e+16</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 1.93e-20. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              SalePrice   R-squared:                       1.000\n",
       "Model:                            OLS   Adj. R-squared:                  1.000\n",
       "Method:                 Least Squares   F-statistic:                 1.667e+29\n",
       "Date:                Tue, 26 Jan 2021   Prob (F-statistic):               0.00\n",
       "Time:                        05:20:51   Log-Likelihood:                 23161.\n",
       "No. Observations:                1167   AIC:                        -4.608e+04\n",
       "Df Residuals:                    1047   BIC:                        -4.547e+04\n",
       "Df Model:                         119                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "const                 -1.797e-10   4.08e-10     -0.440      0.660   -9.81e-10    6.21e-10\n",
       "LotFrontage           -2.373e-13   6.38e-13     -0.372      0.710   -1.49e-12    1.01e-12\n",
       "LotArea                1.722e-15   2.43e-15      0.708      0.479   -3.05e-15    6.49e-15\n",
       "LotShape              -4.451e-11   3.95e-11     -1.128      0.260   -1.22e-10    3.29e-11\n",
       "OverallQual            5.542e-12   2.89e-11      0.191      0.848   -5.13e-11    6.23e-11\n",
       "OverallCond           -2.259e-12   2.22e-11     -0.102      0.919   -4.57e-11    4.12e-11\n",
       "MasVnrArea             6.506e-14   1.64e-13      0.397      0.691   -2.56e-13    3.87e-13\n",
       "ExterQual              2.879e-11   5.98e-11      0.481      0.630   -8.86e-11    1.46e-10\n",
       "HeatingQC              3.436e-11   2.74e-11      1.253      0.211   -1.95e-11    8.82e-11\n",
       "1stFlrSF              -5.212e-14   1.14e-13     -0.457      0.648   -2.76e-13    1.72e-13\n",
       "2ndFlrSF              -1.058e-13   1.39e-13     -0.763      0.446   -3.78e-13    1.66e-13\n",
       "BedroomAbvGr            5.44e-11   3.43e-11      1.587      0.113   -1.28e-11    1.22e-10\n",
       "Functional             -5.99e-12   3.17e-11     -0.189      0.850   -6.83e-11    5.63e-11\n",
       "GarageCars             4.314e-11   5.96e-11      0.724      0.469   -7.38e-11     1.6e-10\n",
       "WoodDeckSF            -1.281e-13   1.72e-13     -0.746      0.456   -4.65e-13    2.09e-13\n",
       "OpenPorchSF            2.154e-13   3.26e-13      0.662      0.508   -4.23e-13    8.54e-13\n",
       "EnclosedPorch          5.427e-13   3.58e-13      1.514      0.130   -1.61e-13    1.25e-12\n",
       "ScreenPorch             2.62e-14   3.81e-13      0.069      0.945   -7.21e-13    7.73e-13\n",
       "Fence                  3.944e-12   1.74e-11      0.226      0.821   -3.02e-11    3.81e-11\n",
       "SalePrice                 1.0000   7.03e-16   1.42e+15      0.000       1.000       1.000\n",
       "AñosConstCasa         -1.569e-12   2.03e-12     -0.772      0.440   -5.55e-12    2.42e-12\n",
       "AñosConstGar           1.601e-12   1.59e-12      1.006      0.315   -1.52e-12    4.72e-12\n",
       "MSZoning_C (all)      -1.501e-11    2.6e-10     -0.058      0.954   -5.26e-10    4.96e-10\n",
       "MSZoning_FV           -1.498e-10   1.85e-10     -0.811      0.418   -5.12e-10    2.13e-10\n",
       "MSZoning_RH            1.251e-11    2.1e-10      0.060      0.953      -4e-10    4.25e-10\n",
       "MSZoning_RL           -6.963e-12    1.2e-10     -0.058      0.954   -2.42e-10    2.28e-10\n",
       "MSZoning_RM           -1.825e-11   1.31e-10     -0.140      0.889   -2.74e-10    2.38e-10\n",
       "Alley_0               -1.195e-10   1.38e-10     -0.869      0.385   -3.89e-10     1.5e-10\n",
       "Alley_Grvl            -1.374e-10   1.74e-10     -0.792      0.429   -4.78e-10    2.03e-10\n",
       "LandContour_Bnk        -4.78e-11   9.81e-11     -0.487      0.626    -2.4e-10    1.45e-10\n",
       "LandContour_HLS        4.468e-11   1.19e-10      0.375      0.708   -1.89e-10    2.79e-10\n",
       "LotConfig_CulDSac      1.688e-11   9.23e-11      0.183      0.855   -1.64e-10    1.98e-10\n",
       "LotConfig_Inside       8.185e-12   4.74e-11      0.173      0.863   -8.48e-11    1.01e-10\n",
       "Neighborhood_BrDale    3.092e-11   2.46e-10      0.126      0.900   -4.51e-10    5.13e-10\n",
       "Neighborhood_BrkSide  -1.116e-10   1.57e-10     -0.712      0.476   -4.19e-10    1.96e-10\n",
       "Neighborhood_ClearCr   1.284e-11   1.65e-10      0.078      0.938   -3.11e-10    3.36e-10\n",
       "Neighborhood_CollgCr  -4.809e-11   8.34e-11     -0.577      0.564   -2.12e-10    1.16e-10\n",
       "Neighborhood_Crawfor  -2.785e-12   1.35e-10     -0.021      0.984   -2.67e-10    2.62e-10\n",
       "Neighborhood_Edwards  -7.196e-11   1.09e-10     -0.660      0.509   -2.86e-10    1.42e-10\n",
       "Neighborhood_IDOTRR   -9.311e-11   2.11e-10     -0.440      0.660   -5.08e-10    3.22e-10\n",
       "Neighborhood_MeadowV   1.301e-10   2.47e-10      0.526      0.599   -3.55e-10    6.16e-10\n",
       "Neighborhood_Mitchel  -1.278e-10   1.22e-10     -1.047      0.295   -3.67e-10    1.12e-10\n",
       "Neighborhood_NAmes    -1.143e-10   8.72e-11     -1.310      0.190   -2.85e-10    5.69e-11\n",
       "Neighborhood_NoRidge  -6.349e-11   1.49e-10     -0.426      0.670   -3.56e-10    2.29e-10\n",
       "Neighborhood_NridgHt   3.206e-11   1.21e-10      0.265      0.791   -2.06e-10     2.7e-10\n",
       "Neighborhood_OldTown  -1.995e-11   1.65e-10     -0.121      0.904   -3.43e-10    3.03e-10\n",
       "Neighborhood_SWISU     -7.66e-11    1.8e-10     -0.425      0.671    -4.3e-10    2.77e-10\n",
       "Neighborhood_Sawyer   -6.141e-11    1.1e-10     -0.558      0.577   -2.77e-10    1.55e-10\n",
       "Neighborhood_Somerst   -2.41e-11   1.71e-10     -0.141      0.888   -3.59e-10    3.11e-10\n",
       "Neighborhood_StoneBr  -5.748e-12   1.77e-10     -0.032      0.974   -3.53e-10    3.42e-10\n",
       "Neighborhood_Timber    1.887e-11   1.42e-10      0.133      0.894    -2.6e-10    2.98e-10\n",
       "Neighborhood_Veenker   2.544e-11   2.34e-10      0.109      0.913   -4.34e-10    4.84e-10\n",
       "Condition1_Artery      1.615e-11    1.5e-10      0.107      0.915   -2.79e-10    3.11e-10\n",
       "Condition1_Feedr       6.224e-12   1.23e-10      0.051      0.960   -2.35e-10    2.48e-10\n",
       "Condition1_Norm       -1.219e-11   8.99e-11     -0.136      0.892   -1.89e-10    1.64e-10\n",
       "BldgType_1Fam          1.205e-11   9.62e-11      0.125      0.900   -1.77e-10    2.01e-10\n",
       "BldgType_2fmCon       -1.442e-10   1.86e-10     -0.774      0.439    -5.1e-10    2.22e-10\n",
       "BldgType_Duplex       -1.116e-10   1.68e-10     -0.666      0.505    -4.4e-10    2.17e-10\n",
       "BldgType_Twnhs         4.388e-11   1.41e-10      0.311      0.756   -2.33e-10    3.21e-10\n",
       "HouseStyle_1.5Fin      8.486e-12   1.14e-10      0.074      0.941   -2.15e-10    2.32e-10\n",
       "HouseStyle_1.5Unf      3.777e-11   2.45e-10      0.154      0.878   -4.43e-10    5.19e-10\n",
       "HouseStyle_1Story      7.844e-12   9.84e-11      0.080      0.936   -1.85e-10    2.01e-10\n",
       "HouseStyle_2Story      1.467e-11   1.16e-10      0.127      0.899   -2.13e-10    2.42e-10\n",
       "HouseStyle_SFoyer     -4.081e-11   1.52e-10     -0.269      0.788   -3.39e-10    2.57e-10\n",
       "RoofStyle_Gable        5.441e-11    1.6e-10      0.341      0.733   -2.59e-10    3.67e-10\n",
       "RoofStyle_Hip          4.187e-11   1.66e-10      0.253      0.800   -2.83e-10    3.67e-10\n",
       "RoofMatl_CompShg      -3.195e-11   2.06e-10     -0.155      0.877   -4.36e-10    3.72e-10\n",
       "RoofMatl_WdShngl      -2.466e-10   3.61e-10     -0.682      0.495   -9.56e-10    4.63e-10\n",
       "Exterior1st_AsbShng   -8.038e-11   3.71e-10     -0.216      0.829   -8.09e-10    6.48e-10\n",
       "Exterior1st_CemntBd    5.002e-11   6.87e-10      0.073      0.942    -1.3e-09     1.4e-09\n",
       "Exterior1st_HdBoard     1.62e-11   1.27e-10      0.128      0.899   -2.33e-10    2.65e-10\n",
       "Exterior1st_MetalSd    2.154e-11   2.22e-10      0.097      0.923   -4.15e-10    4.58e-10\n",
       "Exterior1st_VinylSd   -2.433e-11   2.18e-10     -0.112      0.911   -4.52e-10    4.03e-10\n",
       "Exterior1st_Wd Sdng   -4.157e-11   1.27e-10     -0.326      0.744   -2.92e-10    2.09e-10\n",
       "Exterior1st_WdShing     1.62e-12   1.57e-10      0.010      0.992   -3.07e-10     3.1e-10\n",
       "Exterior2nd_AsbShng    6.793e-11   3.58e-10      0.190      0.849   -6.34e-10     7.7e-10\n",
       "Exterior2nd_CmentBd   -1.614e-10   6.82e-10     -0.237      0.813    -1.5e-09    1.18e-09\n",
       "Exterior2nd_HdBoard     2.64e-11   1.45e-10      0.182      0.855   -2.58e-10    3.11e-10\n",
       "Exterior2nd_ImStucc    2.121e-10   2.41e-10      0.879      0.379   -2.61e-10    6.85e-10\n",
       "Exterior2nd_MetalSd   -1.141e-11   2.28e-10     -0.050      0.960   -4.59e-10    4.36e-10\n",
       "Exterior2nd_Plywood    3.752e-12   1.12e-10      0.033      0.973   -2.17e-10    2.24e-10\n",
       "Exterior2nd_VinylSd   -4.206e-11   2.13e-10     -0.198      0.843    -4.6e-10    3.75e-10\n",
       "Exterior2nd_Wd Sdng   -3.183e-12   1.33e-10     -0.024      0.981   -2.65e-10    2.59e-10\n",
       "MasVnrType_0          -1.318e-10   3.39e-10     -0.389      0.697   -7.97e-10    5.33e-10\n",
       "MasVnrType_BrkFace    -9.322e-11   1.83e-10     -0.511      0.610   -4.51e-10    2.65e-10\n",
       "MasVnrType_None       -8.697e-11   1.84e-10     -0.473      0.637   -4.48e-10    2.74e-10\n",
       "MasVnrType_Stone      -5.002e-11   1.96e-10     -0.255      0.799   -4.35e-10    3.35e-10\n",
       "Foundation_BrkTil      1.904e-10   2.63e-10      0.724      0.469   -3.25e-10    7.06e-10\n",
       "Foundation_CBlock       2.43e-10   2.63e-10      0.925      0.355   -2.72e-10    7.58e-10\n",
       "Foundation_PConc       1.639e-10   2.65e-10      0.618      0.536   -3.56e-10    6.84e-10\n",
       "Foundation_Slab         2.46e-10   3.21e-10      0.765      0.444   -3.85e-10    8.77e-10\n",
       "Heating_GasA          -2.305e-11   1.76e-10     -0.131      0.896   -3.68e-10    3.22e-10\n",
       "Heating_Grav           8.816e-11   3.16e-10      0.279      0.780   -5.31e-10    7.07e-10\n",
       "Heating_Wall           3.222e-12   5.09e-10      0.006      0.995   -9.96e-10       1e-09\n",
       "CentralAir_N          -6.872e-11   2.07e-10     -0.332      0.740   -4.75e-10    3.38e-10\n",
       "CentralAir_Y          -2.657e-11   2.15e-10     -0.123      0.902   -4.49e-10    3.96e-10\n",
       "Electrical_FuseA      -8.413e-11   4.32e-10     -0.195      0.846   -9.31e-10    7.63e-10\n",
       "Electrical_FuseF      -9.999e-11   4.52e-10     -0.221      0.825   -9.88e-10    7.88e-10\n",
       "Electrical_SBrkr      -1.372e-10   4.28e-10     -0.320      0.749   -9.78e-10    7.03e-10\n",
       "GarageType_0          -5.593e-11    2.6e-10     -0.215      0.830   -5.67e-10    4.55e-10\n",
       "GarageType_Attchd     -3.089e-11    1.5e-10     -0.207      0.836   -3.24e-10    2.63e-10\n",
       "GarageType_BuiltIn     -9.14e-11   1.75e-10     -0.523      0.601   -4.35e-10    2.52e-10\n",
       "GarageType_CarPort    -6.025e-12   2.84e-10     -0.021      0.983   -5.63e-10    5.51e-10\n",
       "GarageType_Detchd     -2.086e-11   1.52e-10     -0.137      0.891    -3.2e-10    2.78e-10\n",
       "PavedDrive_N          -4.491e-11    1.5e-10     -0.299      0.765    -3.4e-10     2.5e-10\n",
       "PavedDrive_P          -1.162e-11   1.69e-10     -0.069      0.945   -3.43e-10     3.2e-10\n",
       "PavedDrive_Y          -4.755e-11   1.44e-10     -0.329      0.742   -3.31e-10    2.36e-10\n",
       "MiscFeature_0         -1.459e-10   3.75e-10     -0.388      0.698   -8.83e-10    5.91e-10\n",
       "MiscFeature_Shed       2.138e-10   3.88e-10      0.551      0.582   -5.48e-10    9.76e-10\n",
       "SaleType_COD           1.171e-10   1.78e-10      0.656      0.512   -2.33e-10    4.67e-10\n",
       "SaleType_New          -4.474e-10   3.86e-10     -1.159      0.247    -1.2e-09     3.1e-10\n",
       "SaleType_WD            5.797e-11   1.36e-10      0.425      0.671    -2.1e-10    3.26e-10\n",
       "SaleCondition_Abnorml  1.063e-11   1.44e-10      0.074      0.941   -2.73e-10    2.94e-10\n",
       "SaleCondition_Normal   4.239e-11   1.23e-10      0.346      0.730   -1.98e-10    2.83e-10\n",
       "SaleCondition_Partial  3.844e-10   3.93e-10      0.977      0.329   -3.87e-10    1.16e-09\n",
       "TotBaños              -1.751e-11    4.6e-11     -0.381      0.703   -1.08e-10    7.27e-11\n",
       "TotMedBaño              4.69e-12   4.73e-11      0.099      0.921   -8.81e-11    9.75e-11\n",
       "Bsmt1Cal               -6.04e-15   1.92e-14     -0.314      0.753   -4.37e-14    3.17e-14\n",
       "BsmtCalTot             6.092e-11    1.2e-10      0.507      0.612   -1.75e-10    2.97e-10\n",
       "GaragCal               9.313e-10    2.1e-08      0.044      0.965   -4.03e-08    4.22e-08\n",
       "KitchCal               1.749e-11   3.94e-11      0.444      0.657   -5.98e-11    9.48e-11\n",
       "FireplCal              -1.17e-11   1.34e-11     -0.871      0.384    -3.8e-11    1.46e-11\n",
       "PoolCal                5.592e-13   3.81e-13      1.466      0.143   -1.89e-13    1.31e-12\n",
       "==============================================================================\n",
       "Omnibus:                       41.778   Durbin-Watson:                   0.179\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              110.422\n",
       "Skew:                          -0.057   Prob(JB):                     1.05e-24\n",
       "Kurtosis:                       4.503   Cond. No.                     4.88e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 1.93e-20. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vamos a aplicar un modelo de regresion lineal con estos datos y corrobaremos nuestra R2 para ver que tal queda\n",
    "\n",
    "y2 = df['SalePrice']\n",
    "x2 = df.drop(variables_sacar,axis=1)\n",
    "\n",
    "# Separamos los datos para poder hacer el entrenamiento y la prueba\n",
    "x2_train, x2_test, y2_train,y2_test=train_test_split(x2,y2,test_size=.2,random_state=77)\n",
    "\n",
    "# Agregamos la constante\n",
    "X2 = sm.add_constant(x2_train)\n",
    "\n",
    "# Generamos el modelo\n",
    "linreg_stats2 = sm.OLS(y2_train, X2).fit()\n",
    "\n",
    "linreg_stats2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T11:20:59.912783Z",
     "start_time": "2021-01-26T11:20:59.892778Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1167 292\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['const', 'LotFrontage', 'LotArea', 'LotShape', 'OverallQual',\n",
       "       'OverallCond', 'MasVnrArea', 'ExterQual', 'HeatingQC', '1stFlrSF',\n",
       "       ...\n",
       "       'SaleCondition_Normal', 'SaleCondition_Partial', 'TotBaños',\n",
       "       'TotMedBaño', 'Bsmt1Cal', 'BsmtCalTot', 'GaragCal', 'KitchCal',\n",
       "       'FireplCal', 'PoolCal'],\n",
       "      dtype='object', length=123)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(X2),len(x2_test))\n",
    "#prediccion2 = linreg_stats2.predict(x2_test)\n",
    "X2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T11:21:05.396601Z",
     "start_time": "2021-01-26T11:21:05.346597Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shapes (292,122) and (123,) not aligned: 122 (dim 1) != 123 (dim 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-75-8cba8078ac89>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprediccion2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinreg_stats2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx2_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mpredicciones2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'SP_test'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0my2_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'SP_predicted'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprediccion2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mpredicciones2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Diferencia'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpredicciones2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSP_test\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mpredicciones2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSP_predicted\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'a continuacion se muestra el promedio por columna'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\rikrdinii\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, exog, transform, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1097\u001b[0m             \u001b[0mexog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexog\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# needed in count model shape[1]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1098\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1099\u001b[1;33m         predict_results = self.model.predict(self.params, exog, *args,\n\u001b[0m\u001b[0;32m   1100\u001b[0m                                              **kwargs)\n\u001b[0;32m   1101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\rikrdinii\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\statsmodels\\regression\\linear_model.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, params, exog)\u001b[0m\n\u001b[0;32m    378\u001b[0m             \u001b[0mexog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    379\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 380\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    381\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    382\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_distribution\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdist_class\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: shapes (292,122) and (123,) not aligned: 122 (dim 1) != 123 (dim 0)"
     ]
    }
   ],
   "source": [
    "prediccion2 = linreg_stats2.predict(x2_test)\n",
    "\n",
    "predicciones2 = pd.DataFrame({'SP_test':y2_test,'SP_predicted':round(prediccion2,2)}).reset_index(drop=True)\n",
    "predicciones2['Diferencia'] = predicciones2.SP_test - predicciones2.SP_predicted\n",
    "print('a continuacion se muestra el promedio por columna')\n",
    "print(predicciones2.mean())\n",
    "predicciones2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ¿Cuales son las variables que mas influyen para que el precio de venta de una casa sea mas elevado?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:27:22.634539Z",
     "start_time": "2021-01-26T10:26:19.834Z"
    }
   },
   "outputs": [],
   "source": [
    "# H0 = Solo el 10% de las variables hacen que incremente el precio de venta de una casa\n",
    "# P_Value = 95%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T09:43:26.879142Z",
     "start_time": "2021-01-22T09:43:26.870140Z"
    }
   },
   "source": [
    "# Transformacion con PCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:27:22.637541Z",
     "start_time": "2021-01-26T10:26:19.914Z"
    }
   },
   "outputs": [],
   "source": [
    "df_pca = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T20:22:36.463978Z",
     "start_time": "2021-01-22T20:22:36.453976Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediccion con MSE (Mean Squared Error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T19:47:53.120512Z",
     "start_time": "2021-01-22T19:47:52.646391Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-26T10:27:22.647542Z",
     "start_time": "2021-01-26T10:26:19.986Z"
    }
   },
   "outputs": [],
   "source": [
    "# Pendiente hacer pruebas de hipotesis -  hacerlo en un jupyter notebook a parte\n",
    "- casas con alberca\n",
    "- remodelacion de las casas\n",
    "- construccion de las casas\n",
    "- cuales son las casas que mas rapido se venden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T20:01:28.261289Z",
     "start_time": "2021-01-22T20:01:28.158260Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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