Proyecto_5_Ricardo_Fuentes_RL.ipynb

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   "cell_type": "code",
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     "end_time": "2021-01-28T05:39:03.835649Z",
     "start_time": "2021-01-28T05:38:57.630041Z"
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   "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",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:03.993562Z",
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   "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>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "df = pd.read_csv('train.csv')\n",
    "df_original = df.copy()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T01:34:26.052000Z",
     "start_time": "2021-01-28T01:34:25.939972Z"
    }
   },
   "source": [
    "# Tratamiento de los datos inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:04.106593Z",
     "start_time": "2021-01-28T05:39:04.000563Z"
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   },
   "outputs": [],
   "source": [
    "# Eliminamos el NaN encontrado\n",
    "df = df.dropna(subset=['Electrical'])\n",
    "\n",
    "# Los demás NaN los convertimos a 0\n",
    "df = df.fillna(0)\n",
    "\n",
    "# Ponemos el Id como indice para que no nos de problemas mas adelante\n",
    "df = df.set_index('Id',drop=True)\n",
    "\n",
    "# Obtenemos una columna binaria que nos indique si la casa ah sido remodelada\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",
    "\n",
    "# Obtenemos una columna binaria que nos indique si tiene o no Aire Acondicionado\n",
    "df['CentralAir'] = df['CentralAir'].replace({'N':0,'Y':1})\n",
    "\n",
    "df_col_binarias = ['Remodelacion','CentralAir']\n",
    "\n",
    "# Obtenemos una columna con los años de haber sido construida la casa\n",
    "df['AñosConstruidos'] = 2021 - df['YearBuilt']\n",
    "# Obtenemos una columna con los años de haber sido remodelada la casa\n",
    "df['AñosRemodelacion'] = 2021 - df['YearRemodAdd']\n",
    "df['AñosRemodelacion'] = df['AñosRemodelacion'].where(df['Remodelacion']==1,0)\n",
    "df['AñosRemodelacion']\n",
    "\n",
    "# Obtenemos en una columna los años de construccion del garaje\n",
    "df['AñosConstGar']= 2021 - df['GarageYrBlt']\n",
    "df['AñosConstGar'] = df['AñosConstGar'].replace({2021:0})\n",
    "\n",
    "# Unificamos datos\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",
    "# Sumaremos el total de pies cuadrados de terminacion del sotano\n",
    "df['TotalBsmFinisSF'] = df['BsmtFinSF1']+df['BsmtFinSF2']\n",
    "\n",
    "# Sumaremos el total de pies cuadrados de los pisos de la casa\n",
    "df['TotalFlrSF'] = df['1stFlrSF']+df['2ndFlrSF']\n",
    "\n",
    "# Eliminamos las columnas sobre las cuales ya ejecutamos alguna transformacion\n",
    "df.drop(['BsmtFinSF1','BsmtFinSF2','1stFlrSF','2ndFlrSF','GarageYrBlt','BsmtFullBath','FullBath','BsmtHalfBath','HalfBath','MSSubClass','MoSold','YrSold','YearRemodAdd','YearBuilt'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformacion Log del Precio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:04.121593Z",
     "start_time": "2021-01-28T05:39:04.111593Z"
    }
   },
   "outputs": [],
   "source": [
    "# aplicamos una trasnformacion logaritmica sobre el precio ya que en el ejercicio anterior habiamos observado que \n",
    "# las columnas de 'Años Construccion Casa' y 'Años Construccion Garaje' no tenian una relacion lineal con el precio. \n",
    "precio_log = np.log(df['SalePrice'])\n",
    "df['Log_SalePrice'] = precio_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:08.701770Z",
     "start_time": "2021-01-28T05:39:04.125598Z"
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   "outputs": [
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N5WB9LO/AS2+aQcqLvfg8OocaYm2mHQNeHb9XpyGcTGfzHlKmjWkJEvHODjMFiqI63S8hD0G/mx99+Zw2m2i668vOZNwZP2xVVbCFkn1OeSApkQwNAy7UkUiEK664gv/93/9l0qRJbNiwgR/84Ackk0kWL17MjTfeCMDWrVu59dZbiUQizJkzh+985zvoes/D+/bv/kVDS4KJeT6m5x6iCSGIxJ12O69bz/YyP/vaXuIpMzv0UtecYFt1E0GvhqppJFPO4V7SKExZA1qNmjRVYcbkYnYcaMGyBaYlaAg7mW5JyJvt4DAtO11nF6iKkq1H2wJcKqQMi/rmBAfrY1x881OMLfGycv7RzJxW3qEM1N6VL3MQaZqttXhVcQ5g5YGkRDJ0DOhh4ubNm/n4xz/Orl27AEgkEnzzm9/k7rvv5plnnmHLli384x//AODrX/86t912G//3f/+HEIKHHnqoV9dKmRaxpJk1Zso9IMs9REsZFvXhJEnDRlGcksHjL+/i8Zd3kcgR6VwiCYtwNEU4miqoSOeS6f4wTRthp4d3bLBsp5sknjTT93PKLplD0VyHvpRpYwta6+NCcKAhzu+f3krVjjpmTivnyvOnUxJwE0s4fedXnj89K7KZg0hdV7OfFmzhHMDKA0mJZOgYUKF+6KGH+Pa3v01lZSUAVVVVHHHEEUyePBld11m+fDnr169n3759JBIJZs+eDcDFF1/M+vXre3UtVVVQgHjKamNcVLWjjl8/+S71zQlqGmKEc7oybOFkshm6q1gUcsAlL4rzs+R8mc2u40kTTVXRVCU9zt614YiqqiiKYyqVa5F60ydOYfV1Z3DTJ05pkwlnPD58bg2B8/MKYePz6J0eSA6mUZREcjgzoKWP22+/vc3XNTU1VFRUZL+urKzk0KFDHW6vqKjg0KFDvb6egtMfnfmYnsn+4nky5fa3dWdLOhgIAblD7ILW3u+mliRejw5CoKoKpSGPU47IE7dl2dhCYArBe3ua+NId/0TTlGx5CDrW4688fzrrN1Vj5XR9jC319fhAUiKRFJ5BPUwUnShhppOis9t7g3N/BbdLwRaC8RVBnn9zPx63hoKCQGQN/fNdp7PvDzadxoiT/VtCcOPHTwHg0Rc/yPqAONvKwbTtNo+1bJHVcCEgaVooJtQ2x7n3r9tQFIWAT6c46CaSMPjL8x9w7cUz+dGXz8kb3/iKII3hON6cA8lEymR8RZCKilC/fvb+Pn6wGClxwsiJdaTECUMT66AK9dixY6mra61l1tTUUFlZ2eH22trabLmkp1iWjY3A49JJpiwWnDyhNftLj/x1JsJmTivdUIs0OId3OV13aCpoqoplCzy6xhHlfgBuXDkTgNUPvMH2vc1oilPGMa222Xjr8yhoivMmFombiHQZpyjgdjbIqCqmYvHgs9uy1+iMBSdPYM1z2zEt0eZAcsHJE9p0mPSWiopQvx4/WIyUOGHkxDpS4oSBjbWrN4BBFepZs2bx4Ycfsnv3biZNmsS6deu45JJLmDhxIh6Ph3//+9+ceuqpPP7445x99tm9em63ruH36Iwr82c/ppcXO2117vQAii1Ep+UOlwaG1XGEezDRVAWvW8O0BJbd2qZni8z/OGWI3I4Lr0slnrKwbYGNM+CiqUqbWnrG6c+yRbpTJHd8vu1P25MShrRAlUgGn0EVao/Hww9/+EOuv/56kskk55xzDosWLQLgxz/+MbfeeivRaJTjjz+eVatW9eq5v/OZ0zqYMmXa0XweHcNMoioKqBDyudLaJ/D7XDSGEyhW5/amA41bV1ly+hRe31bDocYEds47SaYMYgtBwKdTFHBnnfHA6egAgc+tEk85Pda65tSudU1BURTqm+Mo6TNHIZxs3aUr6aUIbctLKdPG69ZY/cAbXYqwtECVSAYXRXRWIB6B1NdHOnXPy2SguUtiJ5YHiMRSmMIZo95zqKVgywB6S0ZQxxR7cevOII1h2igqqIqSrU+risLYUm825oMNsaxplK4phPwumiOOKdS0icUsmjuFx17+kL2HIoAgd1hSUyHgdYGi4Pfq2RJGLGFm37xyyxq5LXwDxUj5+DtS4oSRE+tIiRMOk9LHUNA++6vaUcfaF3ewty4GgEtTspl0Z6WPQi0JyJCbwzodHQJVJTstaNlOR4eqgKqpmKYz4OJ2qSQMO9txkRlKUdIDKX6vC59HJ5YwuekTzoHjA89/QGnITUvcRBjpRbwCVEXlmqXHAW1LGLpC9o0A5PZxiWS4MOqFOpeqHXX8/pltROOp7G1GnsM3ICuYwu5Y2+4rnT2NbQv2HGrB5dJQFOdr0wZVOMMnPrfGuDLngO9gQ4x4ynJqzoCiCNxpYW2/pmtsmZ/axhjjytzZ25KGRUnAnRXeXAG+6Z4NsvVOIhmGjHqb01zWb6omkTRRFBVd67r9TwF01Tmc612jYN+wBZhmWoDTNeXMwV84lmLGlBJmTCkhHEthmjaa6tzHFo6YdraG7OJzj+7xktqqHXXEEiZ7a6McbIgRSziDQXL7uEQy9Iz6jDq3S6I5ksS0BS5Nxant0qadLRcBbXydVVXptAbeX3LLLXZOC6GutbbaqZrC6+/VEvS5KA54iCdNTMvGpTvtdvGk1akV6ZzjxmYHWLo6HMwMBnncGknDxDRtGlqSGKaNS1fl9nGJZIgZ1ULdfl9iZgOLZTv+0qrStirdZXteurUt469RyFKIpiptWgczMziapjp91bZgf12UoM9FSXrdV2tYok1duj096dDIjIX7vRouXSUcTWGYFinD5urFM2R9WiIZYkZ16aO9L0Vx0J0VvkxfcQYl+z9tyb2pssyP36NTVuTNusvlK4t0V1rJxUrXwFUFtOzjMtvORbYcEombHTbIFKI0kbvZxufRGVvmZ2JFEL9XlyItkQwDRnVG3X5/oN/rQghBY0sqLbQKY0Iu6ltS2Y6Izsi0yDliJmhqSWY7QTrrGOmNSLfHSpc7zHbLBzKi3RxNAhDyuzrYlPaG3JJQLOFsVM/N1FtiBsmUxU33bBiSoRZppSqRtDKqhbozo3xd1zhmUnGbUsF1P3mxS/vSzMFeZm9hSchNQziJSJdBVMXJfjMdJAptu0k6o8jvoiVuZMsdavrgUpgdB28yJv4uzekASaYsNFXps4C1LwmZlt3mDaAlZtAcTVLkd+fddj6QtI9vsK8vkQw3RrVQd2eUnyFjyK8ojiF/7qFh5r/8XsfqU9MUSoIeVEWhoSWZNkQS5G7S6s4OVVOhJOQhkjBRFcGYYh8+j87BhhiapqQHXOjg21Hkb+2VXn3dGdnv9Tb7bL87sTjoAci+ASRTFkV+d/b2we6n7slux94gs3PJSGdU16i7M8rPMKE8QFHAGbtWlbYe1aqi4PdoTKoIcuX505lYHiBpWPi9LsrSexQt28msy4s9lATdaJqabevTVGdzuFt3WgJ1TcHjcsTW69IoCnjweVqHWES6xJJbBxdAWciD3+vqUJPui5F/bk06Q1HAyZ5XX3cGfq/epgwCg9tP3Vl8fb2+XHQgGQ2M2oy6fRZ11cL8Y9C5niAtsVQ2Ix4TcvPJRR27Hv7y/AeYioXPo9McSaGpCmVFnuzmcq9HR1cgHDeJxlOYlsjWtFUVFn5kEivOOiorIknDcpbpqgqWbVMUcOPz6JSX+KhvjqOpzvb0zvqgu8o+F5x+ZKc/b1e7EzP91I0tCVy6lo1lMPupu9vt2Bt6mp3LrFsynBmVQt1ZjfP3z2yjyKeTMOwOf4gzp5Wz60CYpzbszoq0qkBT1OD3T2/lmqXHtblvcbGf3z1exYG6GEba1L+2KQEkUBQI+nQ8Lp3zTp7A0xurMWyn/q0CxQEPL7yxj9ffqyWRsvC6NRDOASU4h5ZNLUmEcDaKB3xuXCocqIsBgrFlbS1I2x+YQvfZZ76S0IwpJax5bjtul0rSVDBMm/rmePbTxowpJd0aNhWCnpasekJPXh9ZE5cMd0Zl6aN9W55tC6LxFDVNibwff7dVN6GpiuOroauoabvQcMzg10++2+GjctK0GVPi7eAFIgS0xEwawgleeHM/SrqUoipg44hwOGawtzZKSyxFNGESjjv7EMuKvYwpcurC9c0JdFXhvJMnoGoqY0q8lIQ81DTGuWttFd/67atU7ajL7jnMpbvsM19JaFt1E5qmUhz0UBbypHcnOjXrM08cxytbDg5KCaGnJaue0JPXR64Xkwx3RmVG3T6LCkdTgCO8mT/E9h9/65oTWLaNpjoHirkHebGkyS8f28Kc6eU0RlLs2B9GAYqD7rwDMrbIXLctufc3TJuWmNMaqCkKpUVePC6NgM9N0rAI+lxZ8bRtQWNLElBQVYWapgRrntueFdDeZp+dDcLkrtnye13ZdsZYwszGMViGTYWyUu1Jdt6XTyUSyWAyKjPq9lmU09EBes4BVfs/xPJib1qkO+/aME2bje/WcKgx7hj1C0FDS7JfE4qZZj7LEh2umYkvc7CWebNRFaddz7Kd0si26qZByT4LecA3mPQkO+/LpxKJZDAZlRl1+yxKVZxsusjvyt6n/R/iorlTss567cVX19JbUwQ0R5IFsz0VOVM2uZ0m7eNriqbSC26d+2TGzWsbY9Q3xQHyjpD3hq6yz/Wbqgt2wNcdhT7Y6y47L2RNXCIZCEalULdfF1VZ6iMcTaFpKkKIDn+IGWGIJ1K0GwhMt+wpmGn1LrQvU6bEomkKScMiGk85Ow0FNIYTnHJMOfXhRPbNJrMEwPGidpYO9OXgqysxzL19xpSS7OKFeMoikG7dGygxG4qDPbleTDLcGZVCDR3/+IoCbkjXW70uFd2lcf+z2/G6dxCOpkikzDZDKxlMS6AqA7/+xaWpRGKOSIPTl500nHJLML2GK7fMINIlmtJiT/bgq6fC0p0YZp4n934lIQ9azCASM7BswcR2bn2FyoILPezSU+R6MclwZtTUqFPpOnSG9oMOpi1ImjZnnTSOpGlj2gK/V6emMU5LzCBl5hfjwVjTlUhZRBNmuvNEaVN+iSZMUoaF39P6vprZgZhMWb2uFfe0y6H9/YoCbsaUeJlYHuCmT5zSQdAL0REyUmvhEslAMmqEuiWaoimSyjri5ROjZ1/b27Z1b5isjGy1OW09WMy87wjhCHnSMDs8LhwzCEdTvaoV91QMe3q/Qra3yYM9iaQjo0aoLVsQT5rUNydIGBbRhNGpyCRSZpvbdU3tdvu4qx9ueD3HuYZlO8Kce8VWa1bHJ6TtI5yMuze14p6KYU/vV8gseNHcKT3eSiORHC6MGqG+/9n32FsTwbIFzS1JKkv9BHyuNt0UKdPG69bbiE97T4v2qCpZkyRtgF4tJ0KBz916AUFrz3XI3/pzqIqS3lzufF8BfG6tV/XVnophT+9XyCy4kMMuEsloYdQcJh6sj3HP41s49dgKFp42hROPLOO51/dQWuTFMm2aoiksy2bhRya1GRBRVQWfx7EObV+L9rlVPG6dcCyFS1e7tELtDx6346mhqgr+lEkkYZI0nJp7yOfC53VlDw9tkW7lUx0H7JDfzdhSX6+u19Muh+7ulzlA3FcXJZG0CPpd/fbJzlxXCrNE0sqoEerKUh81jXFef6+WLR828NE5k1lwyiQ2bjmIjeCIsSHOPHEcJx41hqnji9qIz+nHVfLCG/tIpLd7K2mTfpfubP8+bkoJr79XR2ZzV1elEgWYWBFg5bnTAFj79w840BDPWqeqqnMop2sKDeEEoBD06c7IumVnTaByuyhKAm6uOO9odh0I8/TGaizbRtdU/F7nefoiiD0Vw3z3y+0IKQ15CKspInEDy7I73d8okUj6zqgRar9Hz2bGiZTFug27GFfmZ9kZUzlqQhGqohDwuxB0FJ/VD7yB3+eitMhLLGEQjhnYpo1pieywx5gSb7ZlzKWrVB9swbCckfNMdcU5EBQ0hJPc/+x2you9zJlRybbqJvbXRYnEzfQyWhOvW0NV1bTxUZKJ5X6uyPmI35lAzpxW3uFNJp8gVu2o4/m1VRyojQxIX3BnntZej05JwF2Q4RuJRNLKqBHqaNKkJOghaVhE4gYpw+ZgQ4zfrnuXmdPGsHjuFGwhSKYsQn5XehO5Q8broTGcIJzeSagA8aTJmue2k0hZlIY8ba5n2Xa6HGFjtlt4G0uaxFMmLbEU2/c24XPrJFJmtrSSMpw3E01VcGkKlnBMnqD7fuSeZMK5W8UHamhE+mNIJIPHqBFqv9dFYziJ1+2UETy6iqapVB+KULWjnq27G5l/8kTOmjkew3SM/4M+HQVnpdXBhlhWpKG1vNEQTmDbjmhn9iK6dC0ryu3/f/bxgmxNO5po21aXEWzLFmRmbBpbkvzikSrM9BICBcfU6VdPbGFMsY9EyupxZpzJdr1uHcO0uxwa6eugSiE9oyUSSdeMmq6PeNIg6HPhcWu4NJVYwuTzK05g5bnTCPpcGKbNs6/t4c6Hq9i2u5Fo3GDTu4f49VPv0NiSpDntdNe+Ec+yHXE2LYFhOWu6UmbHg8f+YlqCTOOEEI6YG5ZNPGVzoD7aq0GSnrbL9WdQRbbRSSSDx6jJqOubEzRHUkyqCOAJuikOuFAVhVOmV3D81FJe+Pc+Nmw5QH04wR/Xv8ekigDhWAq/10VFqQ/Tdsz7LVu02SjeHluAW1Wy28IHgsz1M1m6bZPXnrUzMtmuu5tstz/j2tIfQyIZPEaNUINCyrDYdaCFylIfVyw4Bp/HqQ173TpL5h3BqTMqWLdhFzv2hdlbGwUcEQTnILCi1EckZhCJG11cB8wuxs0LQftnz/26J3XgjBtcImWiKkredrmebj/JJ8ayjU4iGRxGjVDrqoIQAgGsPHcax08tQ1EgZepE4wZJw2JsqZ9rlhzHlg8b+Mvz7yMEROIG8aSJS1dpbklSWuTF59VpDCcx21vppRmKsfN40uzx7sKMeD7/5v4uuz66qzPLFVUSyfBg1Ah1eYkPw7IpCbizIiIEbN3VwItv7iOesvB5NGYfXc5JR41h45YDHGqME086vdNWyjnWq29OpBfLeoklzE63tAwFNY1xgj4dn0fvUR145rRyFpx+JLW1LXnv050P81A52UkkkrZ0K9TxeJz33nuP2bNn88c//pFt27Zx/fXXM2HChMGIr8ekzI6HWbkZoSNEFq9uPYTXrXHuyRN54uUP8bo1YgmLZI7HaTxpkkiZlIa8VJb6aGpJdhiRHgoicZOPnjqp3yKZW84QQhCOJjDS4/ULP9L6/LIFTyIZHnTb9fGNb3yD559/nqqqKv70pz8xYcIEbrvttsGIrVcU+VwdPCE6LrmF5pYUm949xGnHj+PS+UdTFvLg92gdnk8IpzWvOeKUQ0qCnjY2qkPF69tq+vX43E4PRYGmSBLTEpQVeSgKunlly8Fs14d0spNIhgfdCvWePXv42te+xt///ncuuugirr/+epqamgYhtN5x7YUndsg0O2tTc+kqe2qieF0apx8/jhsvP5lbrjqVgFdHgQ5inDRsDjXEAKgs9eN1dxT1wUIBDjX2L5vNffNqiRlOK6AtqGtO0tiSxDDtrD3pjCkl1Dcn2FMT4WB9jOZIEsuymTGlhNUPvMFN92xg9QNvDMgmcolE0kq3pQ/DcDogXn75ZW6++WYsyyIWiw14YIWgJ0MZfo+O161x4lFlfLC3mYZwstPnaookcWnOpnC/V9AUSWb9OwYLp5/bpmpHXZs3pd4MreSWM1JG235wy7JpidtYdVGqdtTxypaDBH0uYgkD07KIxgVzji3nlS0H5QGjRDKIdJtRn3LKKSxZsoREIsEpp5zC1VdfzRlnnDEYsfWbng5lqIrCpPIAqqowtsznmCR1UuYwLEFNYxzDtKgs9XWo3w4GAtoMpfR2aKWzckYGyxZYliAaN1n79w/QNJWigJtxYwJMqgwxpsTL5h0NBVsSIJFIeka3SnPbbbfx5ptvMmPGDFRV5TOf+Qxnn332YMTWb3ozlPHOrkYM0yZm2vjT1qLhaIpkquMixZaYQSxhUlbkxefRs4Myg4VpiWznRb7OjLV//yDbnud1ayAECcPG63KmNjujtetQsL8+RlmRp82nkczihXK91bzKNB0nv2g3vecSiaTvdCvUmqZRU1PDI488gmEYnHnmmajqyJk87+lQRl1zAl1TaIk7HSA+t0ZxwI3tF4QjqQ5ZqGULapvi+Lw6FaU+WmLGoIlVLGFQ16xk426f2ZumRU04CaqzYOBAfRRQKA25MdMjj7qqoKkqCs5YvEgbS6kK6LqGsAXhqEHA17pYIbN4IRxN0RI3UHDub1o2ti06lGQkEklh6Faof/e73/Hkk09y0UUXIYTg3nvv5eDBg1x33XWDEV/B6K6OW17sZef+cFp8FJKGTSQex+vRKS3yYpg24WiqwxBMPGGSSJqUhjz4vU4rnzHArXxJw4ZYiqoddZ3W4cNRAz1tylTXlEBRVEDQEjcZV+YIb9DnYuXF01jz3HYawglUVclulSnyuwCnpzxpWG16rBd+ZBJPv1oNwvHWtgUoiiDod7Xpry7UVnKJRNIDoX788cf585//TDAYBGDlypVcdtllI0qoezJhN2NKCduqmwBHeBRA0xQUnK6PgFdnTLGXlGHREk2Sq8VOK18St0tlTLEzKNMSS3Vw1CskScPm54+8zWkzKqgPJ9oMrZiWzZj0galp2aiKs9XcTAed6YXO/Oy/fvJdEoaFS1cp8rvwe10kDYsJ5QGCPlcHsX3hjX1Ovd8W6JpKUcCD161l+6vzvd67DoTZVt1UMPGWbwaSw4Ue1TAyIg0QCoXQ9ZE10NjdluxMh4OWXmKbca8LeHWKgl4mVQTwe3WM9FBNeanf6atud52UYXOwPoZgcFr5LFuw8d0abFugK2R3DE4oD6Cl/bZ1TcVOb6XRdZVYwsi22q1+4A0APr/ieMYUebKe2wfqotQ0xIgnOvc9mVAeoLTIy8SKIGPL/B1G2zt7vQ3T5umN1X1y6uuM/jj/SSQjjW6FeuLEifzxj3/EMAwMw+Dee+8ddlOJ3dGd7WdGWMYUedE1BV1T0TXFGS+3bFaeO40rz59OZYmzm7CuKY4tBGPH+An6XB2u1xJNUdcUJ+R3UVbkRe2shaSAtMQMkqbNVQunc9MnTmHludOwLJtEyiTkdyGEs+TArSs0hJNYtk1JyNPmk8WV509HVxXq069JwKfTFDU4UB91BmNyhLC7bprOXu940sIWdsG6Rbp785VIRhPdCvV3vvMd/va3vzF79mxmz57Ns88+y7e+9a3BiK1gdDdhlxEWn0enLC3WIr1Wq/20ozstDPGkSUNzHJeuMrbM32G60TlsTBBPmlSUeAe0lc+yRRuRymzyLi3yIQSMHxNgfJmPRNJGUxXGFPvwe11txG3mtHKCPheVZX7GlwdImc7uSEVRaYkZHe7b1abwzl5vw3K6Q3Lpzzh6Tz23JZLRQLfqMXbsWO677z7i8Ti2bRMIBAYjroLSnflQ7oGcz+MYHyUNK2vwlFtzBYFp2dn6c6oliUtXCfndhAIemiNJEql2viFJk5KQh4DXRWNLfle+vqKqSgeR6syU6aZ7NuD36ig545e5j8vtIDFNG1VxOkEy8ebet6tums5eb01V8Hvbfvrozzi63DAjOZzIK9Tf+MY3unzgD37wg4IHM1B010/dUxc5y7JpjKRQFQUr56TQMG0awgk8Lo2igJuQH5ojrUZOAmfVlq4plBV5SaSsgrryGabN3lpnLL6rFrnuxC33+7ru/LxCkM2EeyqEnb3epx9XyStbDnboIunrRpju/s0kktFEXqE+5phjAHjjjTfYv38/K1asQNM0nnnmGSZPnjxoARaKrjLA7oQ8k2keakmmu0FULLvjIEzSsJze6pyWvtxRczM92ej36lSW+miOpNq49vUH2xbEkiY/f+Rtlp9xBCvOOqrDfboTt9zvh3w6jS0pwCbk9/Z61VZnr3dPN6j39PlBbpiRHB7kFeprrrkGgOeee441a9bg8zkHaZdddhmrVq0anOgGka6EPJNpZsoB3RFPmsSTJoG0IMcTJuGcdr1Ywvl+acipXTdHkgXbwWjZgqc27Gbq+CIWVITafK87cWv//fFlPlAUEimnDNRfISz0Rhi5YUZyuNBtjbq+vh63u3U6TVEUGhsbBzSogaKvfbczppTw9KvVrVvDrZ5lwdGESSxpEvK7GVvmb7PmK2Oj6tJVykt8ROJG3tHunpLZtWjZgrUv7uD5N/eze38zpiXQNIWJ5QEWzZ3CTZ84pcNj2782Vy2cfliIoOzFlowEuhXqefPm8dnPfpZly5YhhOCJJ57gvPPOG4zYCkpf10pleqzdutqniUMhIBxNEYkbaYMjP03hBAnDeS7DtNPbW1yMKfbQHDH6fNiYm5Tvr4sST1lEEwaggAkHG2IdfuaqHXWs/fsH7K+PoWsqxUF3vx3xRor4yVVjkpFCj0yZ1qxZw3PPPYeiKCxevJgrrrhiMGIrKH1ZK1W1o45fP/kuScNECMfXoq8lCju95VxPO9IVBRUaw0mMtChH4gbRBJSGPJiW6Pdkoy2gMW3ZqmsKthDEUxZej579mTNC1RxJZcfBG1uSlBV50TSVtS/u6LXgjiTxk6vGJCOFvEIdiUQIBoNEIhEuvPBCLrzwwuz3wuEwJSUlgxFfwejtWqmM4CQMC01RMApURDYtp0PErasUh9yItKDaQmRH0V26M4oejTu17L6SWcJr2c4YuWnanQ76WLbItuLZQiEcTRHyu6hpSFAUcBNPWjS0JHl/79uUBN0oipIV7szzZMQ8EjdGjPjJVWOSkUJeof7kJz/JY489xumnn96m71YIgaIobN26dVACLBS97bvNiJgr3aZWaFKmTV1TAq9boyLnwBGcckhdU4KAT6c05KE5murXkgI77Yynpzs9cgd9/F4924qX64bXHHFWdWVc8hQElg314STlxc5U4++f3gqKgt+rZ7PnmoaY4zPSzh51OIqf7MWWjBTyCvVjjz0GwLZt2wYtmIGkt323GREr8rtoaOl860shSKQsEg0xAj4XY8t8hKMp4knnsDIaN4kpJiVBD4Zpd+q70ROEcFoDwSYWN7jivKMB8LpUDtbHHJtSAarivAlrqoJpOUMqAsdNMPcTRcaFryEtvhmPEI9LQ9dUmiOpNsMtw1X8ZC+2ZKTQbY362Wef7fT2hQsXFjyYgSRfaxrA6gfeYF9dFMtyjD4tWzhWos2DF180bhBPGAT9bkJ+d7Z+LdJ1Y5euMqbIQyRu9rn3WlWU7FLIqh11hOMmlm074qw4GTNCUBJ0E4kbJA0nyxaqaFMvz7jwOcsSnG/EkybhaArDtBGQLZ8MZ/GTvdiSkUK3Qn3fffdl/9swDN577z1OO+20fgn1qlWrqK+vz7rwffe736W6upp77rkHwzC4+uqrufLKK/v8/Plo33ebqUMbpk0saSJsUbB+5r5gpztEYgmFooCTpTa2JBDCKYfUh5MEvDo+j5twzOiyHJJp1QPQVGcZwLgyP0nDynqC+L3OvsiMz7ZbVygKuFEU0lanyXS7X+vzOs+lpv/bKYrEkyYN4QSgoKV9UiJxA8uymZBuCeyus+b5tVUcqI0MuljKXmzJSKBXQg1QXV3NT37ykz5fUAjBzp07efHFF7NCfejQIW688UYeffRR3G43V1xxBXPnzuXoo4/u83V6wvpN1ZiWIBxNDalAt8e0hDOS7tYoL/YSS1rZ7THRhImiQHHATcq086/VSv9/VSF7kAht68UZ3w+fx/l3EEJwoC7KmBIffq+GEILmSAqBI/yZ/wn5HC8Ur1sDRaGpJZneECMQkO0aKQm4O+3ZziXzZulxa/3uEhkpbYESSW/p9U6tKVOmsHPnzj5fcOfOnSiKwuc+9zlWrFjB/fffz4YNGzj99NMpKSnB7/dzwQUXsH79+j5fo6fsr4sSjhZuKrDQJFMWtU2OqFaW+XC7nH8uIaAp4pQZStvtNWyPLZzuj0wWnKkX53MUBCXrSlcc9FBR6sPjUlEUmFjuuPCBQknAzTVLj+OaJTOyZRFNUykLefB7XT0+QMwc2nrder/sSqU/tWQ006satRCCLVu29GtxQDgcZt68efzXf/0XiUSCVatWsXjxYioqKrL3qayspKqqqlfPO2ZMsPs7tcMSgKI4meAwFWtorV8XBTwUBRxPadsWGKZNYziJ36tT0kU5xLKhIujGsm0QcPnCGQD86tEqLNvxiE4aFgiYUBnAsmy8afFPmSpOSUOltNjHxecezZzjxrZ5/uff3E9jOI7X3fp7kUiZjK8IUtFujL09DZEUIZ/zOJeeWXag0BhJdfvYNjGsrcLj1rIxuF0aiZTJ82/uZ8HpR/b4eXpCb+IaakZKrCMlThiaWHtV+lAUhbKyMn74wx/2+YInn3wyJ598MgB+v5+VK1fygx/8gC984Qtt7pfbEtgT6usjvW5hy/h25NZzhyu2gKaIMzBTGnJjmCLrwBdLmMSBoqAb23bqw+3feGIJM1svPqLcD8AVC47u9HB1zXPbicaddWJOtwiE/Dq1jTHuXvtWB4/uBSdPYM1z2zEt0aZ7YsHJE9rYrGbILVHEEiaplEl5iS87+Zk0LEqD7k4fm48DtZH0Fp7WTwmqonCgNtKr5+mOiopQQZ9vIBkpsY6UOGFgY+3qDaBboV60aFFBD/Zef/11DMNg3rx5gJOlT5w4kbq61o+oNTU1VFZWFuya+ZhYHuBgQ4x4yiKZKoyL3UBjWjb1zUl8HsfwKRxNkUhZCKA5kkLTFEpDXkzLIpmys8MuE8oDHerF+Q7Sdh0I8/Sr1VmRVhWIJS28brvN8oDc54H8HTWdvRFkJhctW9AcTaJpCgFv37tEZE+0ZDTTbY36z3/+c0Ev2NLSwurVq0kmk0QiER577DF+9KMfsXHjRhoaGojH4zz77LOcffbZBb1uZyyaOwWXrlIa8lBR0vM/aAWypYGhIp40qW2K43ZpVJQ4W2kArPRBZMqwCfldaJqKEILte5pY/cAbVO2oo2pHHasfeIOb7tmQvS2XbdVNjCl2ntOtq+iaigKEY0be2vPMaeXc9IlTWH3dGdk3hNya8cGGGL98bAs/f+RtmiPOAI+iKLh0FUVxyjkH6mLoCh0y9p7Q3XowiWQk021GfeSRR3LrrbcyZ84c/H5/9va+tufNnz+fzZs387GPfQzbtvnEJz7Bqaeeyo033siqVaswDIOVK1cyc+bMPj1/b2ifCXZVAlEUsn4fJSFP1kdjKMkYPuma085n23a2SyORskikLEJ+Fy6vTjJl5Z0mbN9lkZ1Y1FRMKz1ejtM/3dMsNddHI5YwaIkbIDK9145zYNDncoZ4hNPqN6bES7IPxle5scuuD8loRBGi62O0T37ykx0fpCj86U9/GrCg+kJfatTtueFn/yCS6FgCUXHqv+FYiuKAx1mvNQxLJV63s2EmHEuRSLbGpypQWuRBTbfSGZbIblDXdZWgz8XYUh83feKUNkZUqprxAXHewlRFoTjo7jLjzdSft+9pwqVrFAVchGNOT7WqKBimnd72rmDbNqrqrDdz6SqVpf7sCrTu2vqGCllPLTwjJU4YxjXqr3/96x2y2w0bNvQ/qmFG1Y46Ysk84qvAuDI/586ewLbqJrbvbXYWv9J3N72BIJGySBpxZ3+j301jOIFpOUM89c3OdGNJkZd4wsyOo2e20FiWnW1xc7tUkqaCbTsLfoUCwhYUB52FuPc/u53y4uoOGWuuc55LVx0DqpYkwhZoaXc+XVNwmgCduFSc3uuSoDPgM1x9QSSSoSSvUL/77rsIIbj55pv5yU9+QibxNk2TW2+9lRdeeGHQghwM1m+qRtfUtOeFc5stnCzyqAlF2WxzW3WT44GR9c8YXmTLIQmVkpAH0xQ0RZwyjWHa1DbGsweRLTGDeNLM/iyZcoXf68Klq4RjBoZp49E1Fn5kEq9sOYhpi2zJ5PfPbKPIp5MwnHJIJJbKljuKgx4aws5UpSBjDCUoLfJmB2mUnCw94HNh9KK0IpEcTuQV6j//+c+88sor1NTU8KUvfan1AbrOBRdcMCjBDSZ1zQmKg24aW5LYQkFN16RN2zmQys0WxxR7qW2MDXXIXWJajgOf36sztszZz5gp12RWhRUH3AT9PsLpbpFc20+/14Xf60IIQSxhOm9QOfalti2IxlMkkqazDCHtnFeWHsDxeXTKirw0R5IkDYGqKgT9LrxujZRpUxx0c+aJ43hly8Hsgac8AJRIOievUH/ve98D4I477uDGG28ctICGikx7V1mRN+t9oakKlaV+Zk4rZ/UDb2SFyqWr2LaPmsY44Axq9GX7y2AQS5gkkiZFQQ8hv5v6cCJby2+OplDjCmOKPEwdFyKWNKkPJzttcWvv3RyOOosNUqbN/rqo0xmiKoSjBgGfs7rN59FRVSW7b7Gzg77MwtvGSIrSYP/3Mkoko5Fua9TXXnstb731FrNnz+aPf/wj27Zt4/rrr2fChAmDEd+gkbG81DSVylJftp935bnTgI4m8z6PjselYVoWE8oD7D44fA9DbAFNLY4AV5T4sk534GTGtU0JDNOmpMiDadqYpo3fq7fpaV6/qbpNn3LKdKxRHQ9rxamF2wIbJzN26yrhaMrZGxk3WL+pY00bWnu5h8uBkvQLkQxHuu2j/uY3v8nzzz9PVVUVf/rTn5gwYQK33XbbYMQ2qMycVs6V50+nJOAmljApCbTtbujMG8Pn0VAVtc+2owONkv6/DEnDorYxhhCCsWWtHh7geIfsORhJj5SrmKZNaU6HR/s+ZZHOytX0b1DG/MmtO2ZMjS1JonGToM9FScgzIrw3pF+IZLjSbUa9Z88efvazn3HnnXdy0UUX8aUvfYlLLrlkMGIbdLqyvMw1mdc1haRh4dJVls6bwrbqJg7Vxwo+hu51qxhmxiO76/tqavrQLn0/j0slmO78GFPkQVEUwjHD6YU2bDTN4hc3nsMvH6vi/T3NRBMmli1obEkRjZscOT7El1fOyhpBte9TdnYsOkMrQjidGyiOx8ZNnziF1Q+80SYD78tKrsHObuUORclwpVuhNgynjevll1/m5ptvxrIsYrHhfZA2EOQKVft66gqcUek9NRGieWxHe4KiOIMfqqJQWerju5+ZmxW8uqZ4up+ZDpm9rinZVsGSoIuU4ZQuxlcE8blUTOGITmbrSqZXWVMV6puTHDO5hMaWJAfrYyQNi5Rp896eZh7423YuOG0KY4qd7Dv3jWz1A29kx+9N00bXVXxux/Ma+r+PcCiW5ModipLhSrdCfcopp7BkyRI0TeOUU07hU5/6FGecccZgxDbs6KqeumjuFGfqrx84k4+O+T5CcNM9G/C6VGIJE1VRsNJ9ze2xLCejVRSn5vz5FccDjqtdc8xwPKsV5wKaquL16Nl1XAGvzqHGGGOKvBQFijlYH6M+7c73z80HeOv9OhaffgRzjx9L0O9CT9c6Mp8wvB690zVW/fXeGIrsVvqFSIYr3Qr1bbfdxptvvsmxxx6Lqqp85jOf4ZxzzhmM2EYEmY/n++ui/cqmM1hCYKRsIgmTovRyAISgKODOm9nlLglAUdh1IMwrWw7icWt4XCqRmMjex7JtTCvFrgPhbO15zXPbqWtOEPK5qCj14/VojAl52fJhA+GYwYMvfMCmdw+x4qwjmTaxGL9H73Jku2pHHZGY066nayrFQbez7bwXrXdDkd3KHYqS4Uq3I+QA//rXv2hubm6TzQ23nYmFGCHvKZmMOvfjeWYK0OpjDArgcTuZnJEuJWTKCM2RJCnDJpEyu5yEVFUFXVVQFCgt8hL0udhTE8E0LSw73aGhtq7WmlQRyHa1ZAS3osTL0nlTmTw2xL7aCE9v3M07HzY4MSow97ixLDxtMhUlPqf9rp0dbe5rYpoW4aiBmV7JtfLcaR2y4cwbXUMkRVlOOal9jRsYlPHy7uriw6U7pSeMlFhHSpwwjEfIb7rpJl599VWOOOKI7G2Kogw7oR4Kcj+eW+kxacsW2bVVvVlGIICigJuGcAJVaV0g22po1P0+R9sWmOn7maYFuDBNOyvM7fcf1jTGWfPcdq48f3oH8dvyYT2vbDlIJJZifJmPWNKiOZri1XcPUbWjngtOm8zc48YSDLjxe1p/jXJfE49LI+BzkzQsgj5XpyKdEfWQr20deqiyW7lDUTIc6VaoX3/9df76178SCAQGI54RRe7H84zTHDiC6NbUNl0RliWcoRDF2VXodjlLZZNpL2mXruLzOI51mYwaHGtRBGmDpO6HapxRbQhHDUpCXnRdxerEQEpRnBH5zvylMwLqLCnwEPC7sZtjHDOpgrd3NhBLmjz20of8a2sNy8+cylETigj4XHjdOvXhRHYHY4Z8JYtcUc+s4crUoTNvHLKnWSLpgVBPmDBBinQecg+fcrNhO2PnqUDQq+PS1Ww/ckYEVVXJGv+HYymCPmdc2+fRSZlJNIVsFwbQu5JKemJwz6EWx2Ap1fEuTjlboTGcoKYhxrd+twmEIGE4C3MzbXk79odRFYXSkJuWWJIbL5/F/22q5s3369hXF+V/n3iHU6ZXcMFpkykr8jBtYhE1TQmiMYPm9IRnpoulPd3VoWV2K5E49Kjr48Ybb2T+/Pl4va2n37L00fbjudetEfK7icQMPBooioqmKYwr87fJBNsfwuW68tU1Jxhb6mPG5GJef68OW/RtLF2QMZZSiMZNPC6VpNH2uex0HcRWFBRFYW9tFABddXZJJlLO82iq43ZX0xgnEneRSFpcvuAYTjtuLE++8iEH6mO8sb2Wdz5s4KNzJjF9Ugm7DuxFUZ09i4mUhao4a8OqdtS1Ed7+dlnIKULJ4UK3Qv3mm28C8PDDD2dvkzVqh/aiG/DqBDxa1k0un3B0limuyPnv1Q+8wZgSLx6Xxv66aJ98RDRNZfLYIJG4QTiSoiSoE4kbbRz/Mjajucm6mT50dKxIyR4WCgHRuLN4YNwYHwGfi/M/Mont1c38a+shkobF0xt343VpJAwLXVMpCrjTywFSqKrSocSSeaNrTvt7p0zHX+X041rXsOUT46Hos5ZIhooeL7c1TRMhBC6Xa8CDGk50JhQLck5nM6JbKOGo2lHHjn3N2ELg0p1DSlVtnTjsCY7zn6O+bl0lkTIpLwlSHPQQSxiEY0Z2R2RnFZXMNhuBk+FmNt+oKkQTKRIpN6Zl88+3DlDXFM9uvrEFJNKlGtOyaQgncOkqxQE3HrdGS8xoc52Z08qd/Ywbq52fV1Pwe128suUgU8cXAeR9TeUUoeRwoluvj/r6ej772c8ye/ZsZs6cyapVqzh06NBgxDbk5PN+eH1rx5+/s4OxzEFdb6+npMsRpuV4avRUpDOdckI4h5vgCK3XrWenGf1eF+PK/Lh1FSXP87QXb5HzHy7d+fkicZO9tRF8Xhdjy3y49M5/lQzTpq45QX04wfgx/g619m3VTYwp8XLkhCLGjQlQFHBnX7euXtO65gTudteUU4SS0Uq3GfV3v/tdZs+ezU9/+lMsy+K+++7jv/7rv7jnnnsGI74hJV/W9uiLH3DjyrZbb/oyoNE+W88Y7xcH29qR9pRMO6AAQn4XiZSJZdlZ0//cVjdNhU7OGDslk2HbwmkhBNJWsILapjglQTflJV6aI0Z2c0x7EkmLPTURvv27TUweG+Q/Zk7g+Kll3b5u+b43EqYIZQ1dUii6Fepdu3Zx5513Zr++4YYbWLp06YAGNVzIJyI1DR29TnojHFU76lj79w/YX986uXeoMU5jSxKF9Lqqftpb1zYlmDq+iJXnT8v6Pq/9+wfsq432eign8wagQLb1zsxpyG6KpGiJGRQHPVR4dWcvYyd19YaWJCVBNzv3h9lXF2XlOUdzxLggB+rjuNMVtXjSpKkliRBk13nllmwM08br0phxUkn2zSd3sEZXlQ6HlkOBrKFLCkm3Qm2aJslkEo/H2WkXj8dRlHwfmkcX+cS3sszf4b65HSBWuj5r2VDbGOdbv9uUnQBc++IO9tdFs+WFlGlT25RoU4Yo1IqvXQfC/PrJdwAFwzTprRtr+63sAthb0+KsLGsn9pYtaEj3UI8p9hJLmLTEnOUCuqpg2sJZBdaUyNbcf/XEFhbMmURjxKCpJUlji/OaqQqUFXmwbGiOJkkZFknTxk57miRSJk+/Ws2c6eXsqYlQE06ipzfvmLYYMEGs2lHH82urOFAb6TZDljV0SU/Jnc793X+e3+l9uhXqJUuWcPXVV3PxxRcD8Oijj47KVVydkW867uJzj+5w38wf39oXd3Cw3vnYni4Tc6A+yq+efAeXrpFI7yhsj2j3/wtFLGllD/p6Qu62ms4eomsahmXnfb540iSZcjbKVJb6SZkWyaRJyKXRHE1hi9aD0XjKYv2m3bg0hYDPTUWpn2jcKZ8oikJRwEmzmyOprJWqpjqdKLYteP29OsaWeqks87d5Mw2nLH795Lv4vXrBSg6ZDNkWEImlaGxJsH1PE2VFziem9teRTnyFZbSWkdpP5+ajW6H+4he/yLhx43jppZewbZuLL76YlStXFjTY4Uo+46E5x43tdN5/5rRy1m+qxq2r2cW4ALZQSKQsLEtgC1FwMe6OnlxP15y+Z6OLtFtVYNwY59PE3poWbNH5mHxmo4zXrTG5MkhLzMDK0xNuWo77X0Wpi4P1UUqCHkJ+V3YjfMjvojmSxO3SsGw7+5pqimMwdagxwfjy1k84zvaaJAKF8hJvwUoO6zdVY1qiTeeKLcj6o7S/zkiooY8URnMZqf2BeT66FGrDMEilUlxyySVccsklvPfeexx11FGHTekDej8dV9eccFrqcl4iVQFTOMLi0p31XV3RvuTQX3riOSKAIr+Luub8sdkCDjXEKAq40bWOQzTtSRkWe2paKAt5KQl5icScpbqdXbshnESAs7NRVwgGPHjdGvtqowjhGDJpqtPf7di9prfXqIKUaWcF0VkxpuDS1Q5j6f35o65rThBLGCiKc93cH70lZjC2zN/mOtKJr3CMtjJS7qeD5kiK4qCrzRt6Z+Rtzzt48CDLli3jxRdfzN52zz33sHz58sOmPa8vlBd70VSljdBm/Dc0Vc12TXTFYGfc4Ih5MmV1K+qm5dSie/JmbQtIGjZFATdL5h5ByO+irMiLqnZ8bGYYxxaCeMqp8e+tiRD0u5gyNohLV7Fs5/rZw83008TiRnZFWDJlYdkCw7A42BAjljAKUnIoL/amx+Gdr3NjyBysth9/72q1m6TnjKZWzPYtv4oCjS0pYonOu6Uy5M2oV69ezSWXXNKmw+NnP/sZd999Nz/60Y/48Y9/XLjoRxGZBQLRhImNSLe1OdMjhmXT1JIc6hA7xbaFYwDVDUp6kjHRidFTZwgBuw6GeebV3TQ0x/H73FSW+miOpNpk1+m9BllMS2BaJrGESXHQzYQxflriZs6G+MwbmlOOiMRN53VOo6mKc6jbkiRk2lnL2L6yaO4UduwLY9oCldZPPaqitOlZzy1tSK+SwjCaykjtPx2UhDzUN8dpjqS6TOLyZtTvv/8+n//85zvc/oUvfIF33323ACGPTmZOK+eapccxvsyHku7lUBWF4qCbMUVO58xIrhzZgmxWqXWSGXeGYQkONsRwpR0D65oSBHOy69nTyjhyQnGHrMm5hlNa2FsbIWlYlJd4GVPkwe3SIH0wmdluA63LfJ3ykwICogmz3yWHmdPKWTpvCpqiYInMyjQAQcjvImlYsrQxQLRfrDySX+v2nw58Hp2yIi9CCCLx/ItH8mbU+UbFVVXNtupJOqf9bsHcbCDjz9yQ/tjWfv/hcEUBNE1hYkWQA3VRFEU49dpufLczNd14yqI05EHXDKJxk9rGOEV+FwtOmciCOZMpKfHT0BBl98EW/vDM1uzrYtqgKs4FonGDRMqgOOChOOghkbJoiaaw7NaFDZnOkMwnGV1z6tSFyGxXnHUUs2aM48Fnt1HXnMDrUkFxDopLAu5R04kw3Ohqm9BIo7NPB5qmMm1iMT/6cv7NWXmFOhAIsGfPHiZPntzm9urqajSt68K3pJV8bVpet4bHrWGYNk2Rns4IDh0Cpxyx+2Brt0vm0LTLtm+RKWNYxOIG0bhBZXqEPWXabPmwnllHl1NZ7vinHDEuhK4pmFZrS2FuK6BlOQePigIhv5uKUh/xpElLuvUvg6I4byqZrTCFYs5xYzmivH9lFEnvGS1lpL4eMuctfVxzzTVcd911bNy4kWQySTweZ+PGjXzxi1/k6quvLnD4o5fyYm+HrDllOquprjx/er9rp0OJyNOe1+Y+Of8djhnYAmoaYjRHknhcGgKFdRt2UV7ipSTkYef+ZlLplorOCiu61urmF446exmFgMoyP0UBN4ribLFxulJG7kdkcA6eVj/wBjfds4HVD7xB1Y66oQ5J0k/6esicN6OeP38+kUiEW2+9lX379qEoClOnTuWLX/wiy5YtK/gPMFrp6h00kyV85a6XiCaMNmuyRgp9WRFpC2iOOgeXxUEPdc1OF4nXpVG1o55x5X4O1sfbjKlnyExtZg7z7LRgR+MGoYCbsWV+WmIGqsKILkeM5t7hw52+fDroso96+fLlLF++nKamJlRVpaioqF8BHo70pL42oTzAocY4zZFkn4RvOKCp9PqNJhw18HldbU7vt+5uBAUqS720xIzsgIlbh+KAl7pwImvBmotlC5pakvjcGkeMC/KVy07G41J7tbdyODHaeocl/aPbycS6ujo2b97MggUL+P73v8+2bdv45je/yYwZMwYjvhFPprl9X10UyxJ8GAtz19q3s5OLXrdKWZGXSDw1YkUaei/SkF62m/508frWQzz47DYiMQOB09NdGvQQ8rmoa06gKgpL5h3BEy/vJJqwOpg+KQoEfS50TWHu8eNobEngdesEfDrbdjfyzMbdnS4fyLeUoKcHVwM12jyUI+ijdVx7JNOtUN9yyy2cddZZbNy4kVdffZWrr76a//7v/+b+++8fjPhGNFU76rj7sS15OztsIYglLWLpNViHI1eePx2Aux58k3Aslc2UU4bNoca4076UNnnaVxvB49KIxJw2ptyOEyEgEjOoKPGwrzbCS5v3c6gxjqqCz+NCVZz6dqaEsOtAmFe2HOxQWsh3O3QsOQxkeWKoeodlyWV40q1QNzU1cfXVV/M///M/LFu2jIsvvpg1a9YMRmwjnt89vXXEtN8NBbrq/PF/63eb8pZ94knT6dwIeqja2UBTSwozfUeXpnDslFLe+bDB8R0BapqS/O3f+9o8R0vMJODVKQq6CXh04imLZ151Fjo4G3QcxbeF4MlXdlMUcOH3Ou2pXZUcOitPNCfNTg2hepulDtUIek9KLjLjHny6FWrDMDAMg5deeokf/vCHxONxYrGOfsyjhUL+ErZfPSVpi8Dxjj7UEOuy7GPnWKiWFnlJpJwJxZQp2LKzwfEA6aZsFE2YRBMmQZ+LkqALv89FPGEAAsNsa+baFElh24LSIq/jgx01qGmIsfqBN7h84Yxse16mPJHxyk6lrOwnAssWNLQk2bF/C3Oml/PB/jCxhEksaVLXnOC96iZOP76Sz604MW/MHl3lUGMCEIwt83PFeQM/gt5dyUVm3ENDt6u4FixYwLx58ygtLeXEE0/k0ksvHbVdH/lWb8m2qIFBUxXWPLe9x4sM4kmT2sYYiqJQWerH49aynR89JRI32FcXw7Jsyoq8hPyeTr1HwjGDxnCChpYkpmXj0lWaoil+9WhV9vehvNhLOJqioSWJ1a5Ib1oWmuK8ybz6bo3TmZIw22zh2fhuDU++vLPDtTO/h6aA8eV+xpT4SPbWTLyP5GsnzZRcCrFyTtJ7uhXqG264gXXr1mWX3P74xz/mi1/84oAHNhQU+pdwJI+KDwamZVPXlOiV0GYsVJtakpQEPZSEej8lm6lnO5m8oLLUR1HATXu9DscMLMuZenR8tAW6rmR/HxbNnUI0YYJwbAJyfwzLdkbnM7a2yZze8Mz/ATz72t4O8Q2lGHY3rj2aDJJGEt2WPmzb5qmnnuKf//wnpmly5plncvTRR6Pr3T50xFHok/bTj6tk47s1hQhtVNKfLpekYVHTGCPkd1NZ5u9g8tQdgtahmUjcIOR3UVnWurigfVufYdrUNDr7IRNpr+yZ08rxuTVHzNJTmm2mI8k/EJS5OZY0+dId/0TXFCaUB1g0d0qPfw+7KtP1ZhtNLt21k3rdGgfrY+nxfMcNUlWVvIecsp5dGLpV25/85Cds27aNT33qU9i2zYMPPsjq1av55je/ORjxDSqFPml36o9b2LS1to2zm6QwZIQ2njQpCXnweXSaIsk2a8J0zVlO0BW2LWiOpIjEcoZm0qWK9jRFUrh1lZvu2UB5sZcivwtTOH7CsYRBbVOrmGai6GylWS5JwyJpCA41xlnz3Ha8bq2NxzZ0/D3sqlYMsOa57XjcWp/qyPkGMqp21BFOe6soivPmVd8cJ+Bzc8V5Hbce9aWeLYW9c7oV6pdeeolHHnkka9J07rnnsmLFilEp1ANx0v65FSfyuRWtX1ftqOPnj7zd6wWzkvwYpk1tY9zJikt9hKMpYgmTj546kfNOncx71Y088o8dXbqTQevQTCZTDPrd2TeCXEzLzgpPLGFm0+bM4t9cNJVsH6FldxRpVXFq9bZQsm84COGUH8j/e9hVdwY4Rj9et46RFvxCDMus31SN3+fC49YIxwxM03Y81n16p8/b26EdeVCZn25r1EKINk56brc7r7PeSGcwzN5nTivnmEmOpaeqyDp2f2hf722JGdQ1JSgOuDnuiBLOO9UxFDt2SimXnDMNTencP6Q9Zno5cWM4QcCnU1nqayPCziIIp3bs9+oUBdzZ3xm/Ryfg1fG4NTRVQdc1Qj4Xx0wq4WNnTcWffh6FzDKJjBWuc123rpIw7G5/D7uqFQ9UHTnzvH6vi3FlfiZVBhk3xk8iz6af3sYhDyrz021GPWPGDL7//e9z1VVXAbBmzRqmT58+4IENFYPh0tW6XMCpazoJmciORmfWTI30nFvXFCyrdUekrikIQb8/Tbg0UBQVy7YJ+d1E4gaZVywjeEtOP4LioJtIzMCyBcdOKeWIcSHCcYOG5p4dYKZM57DT49Yo8rsJ+l20RFNtlia4dZVYwuS7n5kLtM0KO/N2WXHWUVnr28Z0t4iCI/66pmZLHN39HnZXpmuKphzP7k6+11d6Wxrs7f3lQuD8dJtRf/vb36a5uZkrrriCyy67jPr6em677bbBiG3U0n65gKrA+DEBVpw5lZDf3UakVdURuO4yQV1TcOvDJz0v8rvS208UxoTcznqytD+01936h9tZxGoXma/HpVAc9FJZ4iXgc+P16JSGPM5Bni2oLPFy5fnTOeHIMfjcjim716OhAP8xawKWZaOp+V/PzpYhJFMWtU1xWqIpQgE3FSU+3K78W126y4YznRW+dHuhswPSxufRe1xq66o7I/O9RMosqNF+bw38e3v/7loDD2cUIUbHKVd9faTNIdJAUlER6nQLeSGo2lHH2hd3sL8umq6VujAtQTiWojjgbOhOmXa2Nur3udpkbmeeOC47Ah3w6tQ3JwjHUvg9OomkSebvwO/Rspu+26MAqqoQ8Oqcd8pEXt9Ww6HGhNNqJkS6XKOk9wWKDssDQn6dgM+NZdlZkWp/SDRjSgnbqpuyX2cGSQbGZ0OQMGwi0RTv7Gpg/aZqatLj5cJ2FubmUuR3cdHZR/HESztpinYcWvJ7NKZNKnb+XaIpLj13Wq8/hbX3gMnt+ujpc3Xb9fHm/l53ffTnmn29f+bvKd+nkeG0a3Ig//YrKkJ5v5dXqN9++21OOukk/vWvf/Hzn/+c5uZmcu/61FNPFT7SfjBahDpDd8KWyUq6MhVqjKQoDbrbPDZ3K4nXrdHUkiCacCbqWuumKmNLvaycf3Sn/hb54vK6NRCOKPZWHAbjNbWFs18xkTLZtruRlzbvp7ElSWnIw9zjx1JdE2HjloPZssikigD1zQlMy8ayBZqqoKkKxUEPKcNiXJmfj542mZlHjUFTu/1wOugMxmtaCHLjHO5dH8NOqK+66iruv/9+li5dysqVKzn++OPbbJ4+7bTTCh9pPxhtQl0IRkqcMHixKoqzFiwSbfUMyeVQQ4ynNuxi5/4w4HyyKPK7QAjKirwsOesoJpT62jxGVRR8Xh2/R+/xHsnBYKT8+4+UOGHohDrvYWLGz8PtdvPpT3+68FFJJEOAEOB1abiLHb/rRNJsc2g7tszPZ5Yex5YPG3hm426aoymaIimKA27mnjCOE44aQ2NjW68bWwiicYN40sTnGX6CLRn55P285nY7e+aOOeYY3nvvvUELSCIZDFRFoSTopjjkQW8nqoqicNJRY7jxslnMP3kiuqbQHE3x57+9z8/+8iaHGjs3JbNtR7Drwwla4oYccpIUjLwZ9de+9jUA9uzZwyWXXMKECROy28cVReHJJ58cnAglkgEiN7uOpDPiXG11uzTO/8hkTjm2gqc37GZbdSPv7W7k/eom5p04lgWnTsLr7vgnlBHseNLEny6JqLJhXtIP8gr1Rz7yEQBuvPHG7G2maVJfX88f//jHgY9MIhkkVEWhOODG49Jo6aR2PabIy6pFx/JedSPPbKqmtjHOK28f5K0P6lk8dwqzjynvVIhtWxCJGc4gjBRsST/oduDltNNOo7m5mQcffJA1a9YQi8X45Cc/ORixSSSDhhDOiLMrT3YNzoTjnBMnsO6fH/D3N/YRjRusfXEHm949xIozpzKxItjpc+cKdsCr45OCLeklXQr1zp07uffee3nqqaeYOHEiiUSCF154gVAo/+mkRDKSyWTXXpdOOJbMbj3P4NJVzpk9kdlHl/PXTdVU7ahnT02Eux/bwpwZlSw8bTIBb+cWC7bttAfGEiZ+nwu/R6P7USaJpIvDxM997nNcddVVuN1u/vSnP7Fu3ToCgYAUacmoRwhwu1TGFHsJ+Fyd+rEUBz1cseAYPrvseMaV+RHAa9tq+OmDb7HxnYNdjslbtqAlmqK+KUEs1XNrVsnhS16h3rp1K8cffzzHHHMMU6dOBWjTRz0QPPXUUyxZsoTzzz9f7mWUDDkKCiGfi9KQF1ee8fyjJhTxxYtPYtkZU/G6NeJJi6de2cXdj73NhwfCXT6/aQvCkRR1zXHiKXPEe7tIBo68Qv3iiy9yySWXsG7dOs466yxuuOEGksnkgAVy6NAh7rjjDh544AGeeOIJHnzwQT744IMBu55E0lPcukpZUTq77uT7mqpwxonj+Orls5lzbAUKcKA+xm+eepcHX3ifcDTV5fObluOH3RCOkxiklVuSkUVeodZ1ncWLF3PffffxyCOPUFlZSSKRYOHChfz5z38ueCAbNmzg9NNPp6SkBL/fzwUXXMD69esLfh2JpC9ks+siDy698z+boM/FxedM4wsfO5FJFQEANn9Qz08feot/vrUf0+p6I71hOn7YDS1JUobNyPdPlBSKXpkyxeNxnnzySf7yl7/w2GOPFTSQX/3qV8RisWw74MMPP0xVVRXf+973CnodiaS/ZGrMsYSRV0ptIdhYdYDH//FBdhv92DI/l310OiccNaZH13FpKqGAC59ndPq/S3pOrxYf+nw+Lr/8ci6//PKCB9LZ+0VvauLS66MjIyVOGDmxVlSEaKiPOF9YNpGYQSpPueK4ycVMvXQWf/v3Xja9c5BDDTF+/tBbHHdEKUvnHUFZUff2nTV1Tttg0OfGpSt5dzDmi3WkvKYjIU4YOq+PYWP5NXbsWOrq6rJf19TUUFlZOYQRSSRd49JUSoOO9Wy+vmifR2f5GVP50iUzOXJ8EQBbdzfys4c387fX95DqZqGjEJBIWdSH4zRHU1h21+UTyehk2Aj1GWecwcaNG2loaCAej/Pss89y9tlnD3VYEkmXKAoEvC7KirxtNqq0Z1yZn88uO44rFhxNUcCNaQleeGMfP3toM1s+bOj0E2UuQkAsYVLfnKQlbsidm4cZvSp9DCRjx47lxhtvZNWqVRiGwcqVK5k5c+ZQhyWR9AhdUygNeYglTaKxzg2ZFEVh5rRyjp1Syotv7uPlqgM0RVI88Nx2jp5YzLIzplLZzkK1PblOfXLK8fBBbnjpAyOlpjZS4oSRE2tP4jTTh43Jblrt6psTrNu4i/eqmwBnKvKME8dx3qkTOzV76gxNVQj4XNmlub2NdTgwUuIEWaOWSEYNuqpQEvJQFHCjduFLPabYy6cWzWDVBcdSVuTBFoKX3z7AHQ9u5o3ttT2ySbVsZx2YHJoZ3UihlkgGAAXwe3TGFHnwdlG7BphxRClfXjmL8+dMxqWptKTNnn795Dvsq4v26Hq5QzPdZfKSkYcUaolkANFUlZKQm6Jg19m1S1eZf8pEbrx8FiceVQZA9aEIdz/6No+/tJNYouOS3c4wTEFjemjGeYzMsUcDUqglkgFHwe/WKS/yOAuAu6Ak6OETH53OZ5YeR2WpDwH8a2sNP3lwM6++e7DH5zApw6KxJUlTS4qUaXdqLCUZOUihlkgGCVVVKQm6KQl5ut2pOG1iMddfchJL5x2Bx6URT5o8+fIufvnY2+w+2PPDrIRh0diSoDHS0bJVMnKQQi2RDCoKXpdGWZEXn0fvMtPVVJUzTxrPVy+fxSnTKwDH7OlXT77DQy98QDjWtdlTBiEgkXSGZsKxFLYcmhlxSKGWSIYATXWW65YEOy7XbU/I72bludP4woUnMDFt9vTWB3X89MG3eGlz92ZPGTJDM3VyaGbEIYVaIhkiMuu/yoq9+L1dZ9cAU8aGuO5jJ3LR2Ufh9+qkDJu/bqrmrrVVvL+3qcfXzQzN1IcTRBJyW/pIQAq1RDLEZNZ/lQa96HkWFOTe9yMzKvna5bM5/YSxKArUNSf4wzPbuP/Z92hsSfT4upldjnXNCaJSsIc1UqglkmFAdv1XUf71X7n4PDorzjySL118ElPHORNt7+5q5I6HNvP8v/dimD2vQ2d2OTY0J4glTYRs6Rt2SKGWSIYRbdd/df/nOX5MgM8tP57LzzuaIr8L0xI8/++9/Ozhzby1vbZbs6dczPSUo9zlOPyQQi2RDEPcupNdB/3dZ9eKojDr6HJuvHw2Z8+agKYqNLYk+d9Hq7j3r9uobYr36tq5uxwThiXz62GAFGqJZBgT9LoYU+Tr0kI1g8elsWjuFL68cibTJxcD8P7eZu5aW8VfX91NMtW70XLTSq8GC0vBHmqkUEskw5yMhWqoG5OnDOUlPj61aAbXXTKT0pAHyxa8VHWAnz70Fm+9X9ercgjk7HIMJ9I+IlKyB5th40ctkUjyowABj47XpdISNbrdVq4oCrOOqWBcsZeXqvbzjzf30xIzeOjvH7Bp6yGWnzGVCeWBXsVgmDZNLUlcLo2g14XbpaQjkww0MqOWSEYQGZOn4qC72zF0cMyezjtlEl+5bBYnHumYPe0+2MIvH3ubJ17+kFiid4eGgrSPSCRBo/QRGTSkUEskIw4Fn1unrMiL16P1KKctDXn4xPnTuWbpcVSU+BACNr17iJ8++Bab3j3U66UbQkAy4yPSksSwpGAPJFKoJZIRiqYqlATcFIe6H0PPcPTEYm5YeRJLTnfMnmJJkyde/pC7H99C9aHeby7JLN9tCCdoiqQwpY/IgCCFWiIZ0aRNnno4hg5O+eSsmRmzp3IA9tdF+d8n3mHtix/Q0kOzp1yEgHjSpL45IY2fBgAp1BLJKKB1DN3T7Rh6Bsfs6Wi+cOEJ2YPFN7bX8dMHN/Ny1QGsPoitNH4aGKRQSySjBGcMXcuOofeUKWND/H8fO5GP/ceR+D06ScPimVd3c9fat/lgX3OfYsk1fpI+Iv1HCrVEMsrIjKGXFXl6NIYOoKoKpx03lq9ePpu5xztmT7VNcX7/9FbWPLedxpZkn2LJ+IjUNyeISh+RPiOFWiIZpfg8rh6PoWfwe3UuPOtIvnjRSRwx1jF7eufDBn720GZeeKN3Zk+5WLagJcdHRCbYvUMKtUQyygl6XZQVeXs0hp5hQnmAz684nsvmH03I78KwbP72+l7ufHgzW3c19Hq6MUPGR6S+RY6l9wYp1BLJYYBLUykNegj5Xag9TK8VRWH2MeV89bLZ/MfM8WiqQkNLkvue3c4f12+jrpdmT7mYOWPpUbktvVukUEskhwmKAoE+ZNcet8bi04/ghpUzOWaSY/a0fU8zd66tYv2m6rT/R9/IjKXLKceukUItkRxm9NbkKUNFiY+rF8/gqoXTs2ZP/9y8nzsefIu3Pui92VMuuVOOpiWkYLdDCrVEchiSMXkaU+TB04vsWlEUjp9axlcuncWCUyehawrhmMFDL3zAb9a9y4H6aJ9jykw51ofjNEdTferjHq1IoZZIDmM0VaU05KEo2Lvs2qWrLDh1EjdeNosTpjpmT7sOtPCLR9/myVc+JJ7s+4aYzNBMvRyaySKFWiKR4HfrlBd58Lp7nl0DlIa8XLlwOp9eMoOKEi9CwKvvHOInD77Fa9tq+jXoIreltyKFWiKRAKCqKiVBNyUhT48sVHM5ZlIJ118yk8Vzp+B2qcQSJo/9cyf3PL6FPTW9N3vKJbMt/XAempFCLZFIckibPPXCQjWDrqn8x6wJfPXy2Zx8jGP2tK82yj2Pv8MjL+7ok9lTLm2GZvpRWhmJSKGWSCQd6IuFaoYiv5tL5x/NtStOYPwYPwD/3l7LTx/czCtv983sKZfMtvS65jjxw2TKUQq1RCLJQ6uFqs/TMwvVXI4YF+KLF53EhWcdiS9t9vT0xt38/JG32bG/b2ZPuZiWoPkwmXKUQi2RSLpEVRSndh3sfXatqgpzjx/L1y6fxWnHVaIANY1xfrduKw/8bTtNkb6ZPeVidli+O/qQQi2RSLpFCPD0ckFBLn6vi4/9x1H8fxefxJSxQQC27Gzgjoc288yGD/ts9pSLYdo0tiRpaEmSMmxG01i6FGqJRNJjWhcUeHu8oCCXieUBrl1xApeeO42Qz4Vh2jz5z53cuXYz23Y3FiTGVHbKcfSMpUuhlkgkvcJZUKBmFxT0VggVReHk6RXcePks/mPmeFRVoSGc5E//9x5//Os26pr7bvaUjZHRtXxXCrVEIukTmQUFpSEvrj5k1163zuLTj+C2a+Zy9ETH7Om9PU3c+XAV//evalIFqDePluW7UqglEkm/cOsqY4p8vVpQkMv48gCfXjKDK8+fTknQjWUL/vHWfu54aDNVO/pn9pRhpC/flUItkUgKQnZBQQ/Xf+WiKAonHFnGjZfN5rxTJqJrCs3RFH95/gN+u24rBxtiBYkxu3w37PiIjJSxdCnUEomkYLg0ldKQt1cLCto8Xlf56JzJfOXSWRw/tRSADw+E+cUjVTy1YVe/zJ5ysW3HR6SueWQs35VCLZFICkpfFxTkUlbk5aqFx3L14hmUF3uxBWzccpCfPvgWr/fT7CmX3OW7sWHsIyKFWiKRDAhtFhT0seVi+uQSblg5k0Vps6dowuTRf+7kfx/fwt6aSMFitdJj6fVNiWE5li6FWiKRDBiZBQVlxb1bUJCLrqmcPWsCX71sNrOPdsye9tZGuefxLTz6jx1E4kbB4jXt4TmWLoVaIpEMOHofFxTkUhRwc9l5R/P5FcczfowfAbz+Xi0/ffAtNmw5UNAFA7lj6U6b4NBKthRqiUQyaPR1QUEuU8cV8cWLTmLFmVPxeTQSKYt1G3bzi0eq2FkAs6dcMmPpmSnHoUIKtUQiGVQyCwqKg+5eLyhofQ6F008Yx1cvn81HZjhmT4ca4/x23Vb+/Lf3aS6A2VOG3CnH2sb4kEw5SqGWSCRDgILPrVNW5MXj6t2CglwCXhcXnX0U/99FJzK50jF7entnPT99aDMvvrkP0ypcFiwEpMzcKcfBK4dIoZZIJEOGpiqMKfb2aUFBLhMrglx74QmsPHcawbTZ07Ov7eHOh6t4r7owZk8ZWqcc44M25SiFWiKRDCmK0r8FBRlUReGU6RV89fJZnHnSOFQF6sMJ/rj+Pf60fhv14URB485OOQ7CtnQp1BKJZFjQnwUFuXjdOkvnTeX6lTOZNrEIgG3VTfzsoc08+9qegpg95ZK7LX2gphylUEskkmFDfxcU5DK21M81S47j4x89huKAY/b04pv70mZP9QUxe8old8qx0NvSpVBLJJJhR38XFGRQFIWTjhrDjZfPYn4bs6f3+d3ThTN7yqXNtvQCTTkOulA//vjjnHXWWVx44YVceOGF3HHHHQDs37+fK6+8kkWLFnHdddcRjUYHOzSJRDKM6O+Cglzcusb5abOn445wzJ527nfMnp7esItEqjBmT7mYtiBcoClHRRQ6/++G733ve5x88sksW7asze3XXnstK1asYOnSpfzyl78kFovx9a9/vcfPW18fwR6kdpmKihC1tS2Dcq3+MFLihJET60iJE0ZOrD2NM2XatMSSGGb//87fq25k3cbd1Dc7B4wBn4tFp03m5OkVXfqSlJUFaGjoWxLp0lWCPhcelwqdNCRWVITyPnbQM+q3336bxx9/nBUrVvD//t//o7m5GcMweO2117jgggsAuPjii1m/fv1ghyaRSIYxmQUF/c2uAY6dUsqXV87kgtMm49ZVonGDR/6xk1898U5BzZ5yMUzbGUtvSZEyejc0M+hCXVFRwfXXX88TTzzB+PHj+e53v0tjYyPBYBBd17P3OXTo0GCHJpFIRgAhX98XFOSiayrnzJ7IjZfPZua0MQDsqYk4Zk//3FlQs6cMgvTy3UiCxnCyx8t3B6z08de//pUf/OAHbW476qijuPfee7NfNzc389GPfpR169Zx6aWX8s9//hMA0zQ5+eSTefvttwciNIlEMgrIHNrFEkZB+iu2Vzfy4HPb2VfrZNR+j87ys4/i7JMnoqkDl9N6XBohnwuPR897n/zf6SeLFy9m8eLFbW5raWnh3nvv5eqrrwZACIGu65SVlRGJRLAsC03TqK2tpbKyslfXkzXqjoyUOGHkxDpS4oSRE2u/47RsIjGj3/3R5UE3X7jwBP717iGee30PsaTJg89t5x//3svyM6dy5PiiftWou+KgApPHl1Ac8nT6/UEtffj9fn7729+yefNmAO6//37OP/98XC4Xc+bM4ZlnngGczpCzzz57MEOTSCQjFJemUhr0EOrjct1cNFVh3oltzZ4ONsT4zVPv8pfn36expbDTjRmEAKMLX5JB7/p4/fXXuf3220kkEkydOpXVq1cTCoXYt28ft9xyC/X19YwfP56f/vSnFBcX9/h5ZUbdkZESJ4ycWEdKnDByYi1knKYlCMdSBZs+3FsT4akNu9iTPmD0uDTOPXkCZ540Hl0rbJ47fmwR5SW+Tr836EI9UEih7shIiRNGTqwjJU4YObEWOk4hIJY0iMbNgoxz20Lw5vZa1v9rD9H0AeOYYi/Lz5jK9Mkl/X7+DF0JtZxMlEgko4pCLNfNRVUUTj22kq9eNovz5kx2zJ6aE9z7123c93/v0VBgs6fOGLDDRIlEIhlKMst1Y4nCZNc+j85lH53OiVNLWbdhFzv3h9m6u5H39zbxH7MmcM7sCbj1/r8xdIbMqCUSyahFobDZNcC4Mj+fWXocVyxwzJ5MS/D3N/bxs4c28/bOwps9gRRqiURyGJDJrkMBd5cj4j1FURRmThvDjZfN4tzZE9BUhaZIij//7X1+/8xWDjUW1uxJCrVEIjksUICARy9odu12aSw8bQpfuXQWM6aUALBjX5ifr32bZzbuLpjZkxRqiURyWFHo7BqcLpBVi2awatGxlBV5sIXg5bcP8NMHN/PG9tp+18elUEskksOObHZdXLjsGmDGlFK+cuksFn5kMi5dJRI3WPviDn795DvZ0fS+IIVaIpEctuhq4bNrXVM59+SJ3HjZLE46yjF7qj4U4e7HtvD4SzuJJXpv9iSFWiKRHNbkZteeAmbXJUEPH//oMXx22XGMLfUhgH9treEnD77Fq+8e7NWAnhRqiUQiwcmuS0IeigqYXQMcNaGYL10yk2VnHIHXrRFPWjz58i5++djb7DoY7tFzSKGWSCSSNAqOvWlZsaeg2bWmKpxx4ni+evlsTj22AoAD9TF+/eS7PPTCB4RjqS4fL4VaIpFI2qGramt2rRYuuw76XFxyzjSu+9gJTKoIAPDWB3X89MG3unycFGqJRCLphEx2PaaosNk1wOTKEF/42IlcfPZR+L06KSO/xSlIrw+JRCLpEk1VHc+QpNlvv+tcVEVhzoxKTjiyjL+/sa/r+xbushKJRDJ68Xt0xhT7Cp5d+zw6S+Yd0eV9pFBLJBJJD/G4NEpDHoqCha1dd4cUaolEIuklfrdOeZEHr3tgbE3bI4VaIpFI+oCqqpQE3ZSEPGgDnF1LoZZIJJI+o+B1aZQVefF5dAZKrqVQSyQSST/RVIWSoJvikAd9ALJrKdQSiURSAITAya6Lvfi9emFb+Qr3VBKJRCJRFYXigJvSoAddK4xaS6GWSCSSAiOEs/2lrKgw2bUUaolEIhkgVEWhyO+mNOTFpfddraVQSyQSyQDj1lXKirwEfK4+ZddSqCUSiWQQUFAI+Vzp7Lp30iuFWiKRSAYRt64ypshL0N/z7FoKtUQikQwBQa+LsiIv7h5k19LmVCKRSIYIl6ZSGvISTRp0NdYoM2qJRCIZQhTFya6LA+6895FCLZFIJMMAl57fiU8KtUQikQxzpFBLJBLJMEcKtUQikQxzpFBLJBLJMEcKtUQikQxzpFBLJBLJMEcKtUQikQxzpFBLJBLJMEcKtUQikQxzpFBLJBLJMEcKtUQikQxzRo17njoAK9qH0/X6ykiJE0ZOrCMlThg5sY6UOGFoYlWEEGLQryqRSCSSHiNLHxKJRDLMkUItkUgkwxwp1BKJRDLMkUItkUgkwxwp1BKJRDLMkUItkUgkwxwp1BKJRDLMkUItkUgkwxwp1BKJRDLMOSyFOhKJsGzZMvbu3QvAhg0bWL58OQsXLuSOO+7I3m/r1q1ccsklXHDBBfznf/4npmkCsH//fq688koWLVrEddddRzQaBSAcDvP5z3+exYsXc+WVV1JbWwtAKpXi61//OosXL+aiiy5ix44d3cb4i1/8gqVLl7J06VJWr149bOMEuPPOO1myZAlLly7lD3/4w7COFeB//ud/uOWWWwYlHiEE//M//8OiRYtYsmQJ//73v3sU46pVq1i6dCkXXnghF154IZs3b+app55iyZIlnH/++axZsyZ734F+rbvihRde4OKLL2bRokX893//96DE05d/+4cffjj7Wl544YWceuqpfPe73x2WsXaKOMx46623xLJly8QJJ5wg9uzZI+LxuDjnnHNEdXW1MAxDXHPNNeLFF18UQgixdOlS8eabbwohhPjGN74h1qxZI4QQ4vOf/7xYt26dEEKIX/ziF2L16tVCCCG+853viF/96ldCCCEee+wx8eUvf1kIIcRvf/tbcdtttwkhhPjXv/4lVq5c2WWMr7zyirj88stFMpkUqVRKrFq1Sjz11FPDLk4hhNi0aZO44oorhGEYIh6Pi/nz54utW7cOy1iFEGLDhg1i7ty54uabbx6UeP7617+Kz33uc8KyLLFz507x0Y9+VBiG0WWMtm2LM888s839Dh48KObPny8aGxtFNBoVy5cvF++///6g/P7mo7q6Wpx11lniwIEDIpVKiY9//OPixRdfHLb/9hm2b98uzj//fLF///5hH2uGw06ov/nNb4rXXntNzJ8/X+zZs0ds2rRJrFq1Kvv9xx57TNxyyy1i7969YsGCBdnbX3vtNfHJT35SpFIpcfLJJ2f/iPbv3y/OO+88IYQQ8+fPF/v37xdCCGEYhjj55JNFKpUSV111lXjttdeyz7VgwQKxb9++vDFu3749+0sihPNL8POf/3zYxZkhlUoJIYTYu3evOPvss4flayqEEI2NjeLSSy8Vf/jDH8TNN988KPHccsst4rHHHsvevmrVKvGvf/2ryzg/+OADcdZZZ4mrr75aLF++XNx3333i0UcfFd/4xjey9/nFL34hfv7znw/Ka52P3/3ud+L73/9+9uuDBw8O23/7XD7xiU+IZ555ZkTEmuGwK33cfvvtzJkzJ/t1TU0NFRUV2a8rKys5dOhQh9srKio4dOgQjY2NBINBdF1vc3v759J1nWAwSENDQ6fPdfDgwbwxHnPMMcyePRuAXbt28cwzz6AoyrCLM4PL5eKuu+5i6dKlzJs3b1i+pgDf+ta3uPHGGykqKurw3AMVT01NDZWVlb2KMxwOM2/ePH75y19y77338pe//IX9+/f36DUdiNc6H7t378ayLD7zmc+wYsUKHnjggWH7b59hw4YNJBIJFi9ePOxjzeWwE+r2iE7MAxVF6fXt+VDVzl/ifLfn8v7773PNNddw8803M2XKlGEbJ8ANN9zAxo0bOXDgALt27Rp2sT788MOMHz+eefPmZW8bjHg6e67uXtOTTz6Z1atX4/f7KSsrY+XKldx11129inWgfzYAy7LYuHEjP/rRj3jooYd4++23s+c+gx1PT39P//KXv/DpT38aGN5/+x0e0+tHjDLGjh1LXV1d9utMBtT+9traWiorKykrKyMSiWBZVpvbwXlHzjzGNE0ikQglJSVUVla2OZjJfUw+/v3vf3P11Vfzta99jYsuumjYxrljxw62bt0KgM/nY+HChWzatGnYxfrMM8/wyiuvcOGFF3LXXXfxwgsv8PDDDw94PGPHju31a/r666+zcePG7NdCCCZOnNij13QgXut8lJeXM2/ePMrKyvB6vSxYsIBXXnll2P3bZ0ilUrz22mucd955wPD92++Mw16oZ82axYcffpj9GLdu3TrOPvtsJk6ciMfjyZ7SP/7445x99tm4XC7mzJnDM8880+Z2gHPOOYfHH38ccIRhzpw5uFwuzjnnHJ544gnA+SP0eDxMmDAhb0wHDhzgi1/8Ij/+8Y9ZunTpsI0TYO/evdx6662kUilSqRTPP/88V1xxxbCL9Q9/+APr1q3jiSee4IYbbuC8887jBz/4wYDHc/bZZ/PUU09hWRa7d+9m165dnHTSSV2+pi0tLaxevZpkMkkkEuGxxx7jRz/6ERs3bqShoYF4PM6zzz7L2WefPSi/F/mYP38+L7/8MuFwGMuyeOmll1i0aNGw+7fP8N577zF16lT8fj8wfP+mOqXXVe1RQuYwUQinE2D58uVi4cKF4vbbbxe2bQshhNi6dau45JJLxKJFi8RXv/pVkUwmhRDOodlVV10lFi9eLK655hrR1NQkhHAOq6699lqxZMkScfnll2efP5FIiJtuukksWbJEfOxjHxNbtmzpMrbvfe97Yvbs2WLFihXZ/3vggQeGXZwZ7rzzTrF48WKxbNkycddddw3L1zSXRx55JNv1MdDx2LYtfvjDH4olS5aIJUuWiJdeeqlHMd5xxx1i0aJFYuHCheLee+8VQgjx5JNPiqVLl4qFCxeKX//619n7DvRr3RUPP/xwNqbvfOc7wrKsYftv//TTT4uvfOUrbW4brrG2R254kUgkkmHOYV/6kEgkkuGOFGqJRCIZ5kihlkgkkmGOFGqJRCIZ5kihlkgkkmGOPtQBSCSF4K233uInP/kJTU1NCCEYN24cN998M8ccc0zex9xyyy0cc8wxfOYzn8l7n71793L++eczffr07G1CCFatWsXKlSs73P/5559n48aN3Hrrrf37gSSSHKRQS0Y8qVSKa6+9lt///veccMIJADzxxBN87nOf4/nnn0fTtH49v9frzQ4tABw6dIhly5Zx4oknMmPGjDb3XbBgAQsWLOjX9SSS9kihlox44vE4LS0txGKx7G0rVqwgGAxiWRY/+MEP2Lx5M9FoFCEE//3f/82pp57a5jl27NjB7bffTlNTE5Zl8clPfrLTjBmc0eMjjjiCXbt28e6777J27Vri8TjBYJCLLrqI//u//+NXv/oVtbW1fPvb32bnzp2oqsoVV1zBqlWraGlp4fbbb2f79u0YhsG8efO46aabsmY/Ekl75G+GZMRTXFzM17/+dT772c9SXl7OKaecwty5c1m6dCnvvPMONTU1PPjgg6iqyq9//Wt+85vftBFq0zS54YYbWL16NSeccAItLS1cfvnlHH300ZSXl3e43ptvvkl1dTWzZs1i48aNfPDBB7zwwgsEg0EeffTR7P2+853vMHXqVO6++25aWlr4+Mc/zjnnnMP//u//csIJJ/DDH/4Qy7K45ZZb+MMf/sDnPve5QXm9JCMPKdSSUcGnP/1pLr30Ul577TVee+01fvOb3/Cb3/yGtWvX8pWvfIW//OUv7Nmzh02bNhEIBNo8dteuXVRXV/PNb34ze1sikeDdd9/l7LPPJpFIcOGFFwKOY1xpaSk/+tGPGD9+PADHHnsswWCwQ0wbNmzg61//OgChUIh169YB8OKLL/L222+zdu3a7LUkkq6QQi0Z8fz73//mzTff5LOf/Szz589n/vz5fPWrX2X58uX87W9/4+677+bTn/40CxYs4KijjuLJJ59s83jLsigqKmpTh66rqyMUClFbW9uhRt2ejMlPe3Rdb2ODuWfPHkpLS7FtmzvvvJNp06YBjv90V3aZEolsz5OMeMrKyrjnnnt4/fXXs7fV1tYSj8d5+umnmT9/Pp/4xCc46aST+Nvf/pa1qcxw5JFH4vF4smJ84MABli1bxpYtW/oV17x583jkkUcAxxHvU5/6FLt27eKss87i3nvvRQhBKpXiuuuu4/777+/XtSSjG5lRS0Y8Rx55JL/85S+54447OHjwIB6Ph1AoxHe/+10mTpzI//t//4/ly5ejaRpz5szh2Wefxbbt7OPdbjd33303t99+O7/97W8xTZMvf/nLnHrqqZ0a4feUb33rW/zXf/0Xy5cvRwjBtddey4knnsh//ud/cvvtt7N8+XIMw+CMM87gs5/9bCFeCskoRbrnSSQSyTBHlj4kEolkmCOFWiKRSIY5UqglEolkmCOFWiKRSIY5UqglEolkmCOFWiKRSIY5UqglEolkmCOFWiKRSIY5/z8szwMgAIHSAAAAAABJRU5ErkJggg==\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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=df,x='SalePrice',y='AñosConstruidos')\n",
    "sns.lmplot(data=df,x='SalePrice',y='AñosRemodelacion')\n",
    "sns.lmplot(data=df,x='SalePrice',y='AñosConstGar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:13.447557Z",
     "start_time": "2021-01-28T05:39:08.712761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1101d298>"
      ]
     },
     "execution_count": 6,
     "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Asi queda la distribucion posterior a la transformacion logaitmica del precio\n",
    "sns.lmplot(data=df,x='Log_SalePrice',y='AñosConstruidos')\n",
    "sns.lmplot(data=df,x='Log_SalePrice',y='AñosRemodelacion')\n",
    "sns.lmplot(data=df,x='Log_SalePrice',y='AñosConstGar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:13.484568Z",
     "start_time": "2021-01-28T05:39:13.459565Z"
    }
   },
   "outputs": [],
   "source": [
    "# Podemos eliminar la columna de precio original, ya que de momento no nos sirve.\n",
    "df.drop(['SalePrice'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T01:38:37.828687Z",
     "start_time": "2021-01-28T01:38:37.815684Z"
    }
   },
   "source": [
    "# Tratamiento de datos numericos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:13.841658Z",
     "start_time": "2021-01-28T05:39:13.503572Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Analizaremos todos los datos numericos, excepto por el precio (log), y las dos columnas de las cuales sabemos que\n",
    "# corresponden a variables binarias y tambien aquellas que no consideramos para una normalizacion\n",
    "no_numeric_columns = ['Log_SalePrice','Remodelacion','CentralAir']\n",
    "\n",
    "# Queremos solo normalizar las columnas cuyo dato corresponde a una medida de metros cuadrados.\n",
    "col_to_normalize = ['LotFrontage','LotArea','MasVnrArea','BsmtUnfSF','TotalBsmFinisSF','TotalFlrSF','TotalBsmtSF','LowQualFinSF','GrLivArea','GarageArea','WoodDeckSF','OpenPorchSF','EnclosedPorch','3SsnPorch','ScreenPorch','PoolArea']\n",
    "\n",
    "# Tenemos varias columnas con calificaciones, las cuales podemos cambiar de un valor categorico a un numero\n",
    "columnas_calificacion = ['LotShape','Utilities','LandSlope','ExterQual','ExterCond','BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2','HeatingQC','KitchenQual','Functional','FireplaceQu','GarageFinish','GarageQual','GarageCond','PoolQC','Fence']\n",
    "\n",
    "for column in columnas_calificacion:\n",
    "    df[column] = df[column].replace({'Reg':3,'IR1':2,'IR2':1,'IR3':0,'AllPub':4,'NoSewr':3,'NoSeWa':2,'ELO':1,'Gtl':2,'Mod':1,'Sev':0,'Ex':4,'Gd':3,'TA':2,'Fa':1,'Po':0,'Gd':4,'Av':3,'Mn':2,'No':1,'GLQ':6,'ALQ':5,'BLQ':4,'Rec':3,'LwQ':2,'Unf':1,'Typ':8,'Min1':7,'Min2':6,'Mod':5,'Maj1':4,'Maj2':3,'Sev':2,'Sal':1,'Fin':3,'RFn':2,'Unf':1,'NA':0,'GdPrv':4,'MnPrv':3,'GdWo':2,'MnWw':1})\n",
    "\n",
    "# Obtenemos los datos numericos en un solo DF\n",
    "df_numericos = df._get_numeric_data()\n",
    "df_numericos.drop(no_numeric_columns,axis=1,inplace=True)\n",
    "df_numericos.head()\n",
    "columns_numeric = list(df_numericos.columns)\n",
    "\n",
    "# Iremos obteniendo de una vez los nombres de las columnas que no contienen datos numericos, para poder usarlo mas adelande\n",
    "# en la obtencion de los dummies\n",
    "df_no_numericos = df.drop(columns_numeric,axis=1)\n",
    "df_no_numericos.drop(no_numeric_columns,axis=1,inplace=True)\n",
    "columns_dummies = list(df_no_numericos.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:15.920144Z",
     "start_time": "2021-01-28T05:39:13.848658Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\rikrdinii\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\statsmodels\\stats\\outliers_influence.py:193: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  vif = 1. / (1. - r_squared_i)\n"
     ]
    },
    {
     "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>VIF</th>\n",
       "      <th>Variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.603086</td>\n",
       "      <td>LotFrontage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.864155</td>\n",
       "      <td>LotArea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.700366</td>\n",
       "      <td>LotShape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>315.950522</td>\n",
       "      <td>Utilities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.253460</td>\n",
       "      <td>LandSlope</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          VIF    Variables\n",
       "0    4.603086  LotFrontage\n",
       "1    2.864155      LotArea\n",
       "2   26.700366     LotShape\n",
       "3  315.950522    Utilities\n",
       "4   14.253460    LandSlope"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# para poder realizar una RL buena, tenemos que saber si los datos cumplen con las siguientes caracteristicas:\n",
    "# 1. Normalidad --> se cumple ya que realizamos una transformacion log en uno de los datos\n",
    "# 2. Multicolinearidad --> Vamos a revisarlo con el metodo Factor Inflacion de Variacion (VIF)\n",
    "\n",
    "variables = df_numericos\n",
    "VIF = pd.DataFrame()\n",
    "VIF['VIF'] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "VIF['Variables'] = variables.columns\n",
    "\n",
    "VIF.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:15.952151Z",
     "start_time": "2021-01-28T05:39:15.927146Z"
    }
   },
   "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>VIF</th>\n",
       "      <th>Variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.700366</td>\n",
       "      <td>LotShape</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315.950522</td>\n",
       "      <td>Utilities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.253460</td>\n",
       "      <td>LandSlope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>76.359696</td>\n",
       "      <td>OverallQual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43.744661</td>\n",
       "      <td>OverallCond</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          VIF    Variables\n",
       "0   26.700366     LotShape\n",
       "1  315.950522    Utilities\n",
       "2   14.253460    LandSlope\n",
       "3   76.359696  OverallQual\n",
       "4   43.744661  OverallCond"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Este metodo nos indica cuales de las columnas están muy relacionadas unas con otras. \n",
    "# Las reglas son las siguientes:\n",
    "# VIF = 1, no hay multicolinearidad\n",
    "# 1 < VIF < 5 = se considera buena la multicolinearidad\n",
    "# 5 a 10 < VIF = hay una multicolinearidad muy grande, por lo que podemos eliminar dicha variable.\n",
    "\n",
    "vif_to_drop = VIF[VIF['VIF']>5].reset_index(drop=True)\n",
    "vif_to_drop.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:16.095187Z",
     "start_time": "2021-01-28T05:39:15.960154Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\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>LandContour</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>Remodelacion</th>\n",
       "      <th>AñosRemodelacion</th>\n",
       "      <th>TotMedBaño</th>\n",
       "      <th>Log_SalePrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.247694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.109011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</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>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>12.317167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</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>1</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>11.849398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>0</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>FR2</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12.429216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSZoning  LotFrontage  LotArea Street Alley LandContour LotConfig  \\\n",
       "Id                                                                     \n",
       "1        RL         65.0     8450   Pave     0         Lvl    Inside   \n",
       "2        RL         80.0     9600   Pave     0         Lvl       FR2   \n",
       "3        RL         68.0    11250   Pave     0         Lvl    Inside   \n",
       "4        RL         60.0     9550   Pave     0         Lvl    Corner   \n",
       "5        RL         84.0    14260   Pave     0         Lvl       FR2   \n",
       "\n",
       "   Neighborhood Condition1 Condition2  ... ScreenPorch Fence MiscFeature  \\\n",
       "Id                                     ...                                 \n",
       "1       CollgCr       Norm       Norm  ...           0     0           0   \n",
       "2       Veenker      Feedr       Norm  ...           0     0           0   \n",
       "3       CollgCr       Norm       Norm  ...           0     0           0   \n",
       "4       Crawfor       Norm       Norm  ...           0     0           0   \n",
       "5       NoRidge       Norm       Norm  ...           0     0           0   \n",
       "\n",
       "   MiscVal SaleType SaleCondition Remodelacion  AñosRemodelacion TotMedBaño  \\\n",
       "Id                                                                            \n",
       "1        0       WD        Normal            0                 0          1   \n",
       "2        0       WD        Normal            0                 0          1   \n",
       "3        0       WD        Normal            1                19          1   \n",
       "4        0       WD       Abnorml            1                51          0   \n",
       "5        0       WD        Normal            0                 0          1   \n",
       "\n",
       "    Log_SalePrice  \n",
       "Id                 \n",
       "1       12.247694  \n",
       "2       12.109011  \n",
       "3       12.317167  \n",
       "4       11.849398  \n",
       "5       12.429216  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtuvimos que mas del 50% de las columnas de datos numerico tienen una alta Multicolinearidad, por lo que procedemos a \n",
    "# eliminarlas\n",
    "vif_to_drop = list(vif_to_drop['Variables'])\n",
    "df = df.drop(vif_to_drop,axis=1)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T03:18:58.009511Z",
     "start_time": "2021-01-28T03:18:57.880479Z"
    }
   },
   "source": [
    "# Tratamiento de variables Categoricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:16.316248Z",
     "start_time": "2021-01-28T05:39:16.101189Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
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       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
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       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
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       "      <td>65.0</td>\n",
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       "      <td>196.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>5</th>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>350.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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 × 165 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LotFrontage  LotArea  MasVnrArea  BsmtExposure  BsmtFinType2  CentralAir  \\\n",
       "Id                                                                             \n",
       "1          65.0     8450       196.0             1             1           1   \n",
       "2          80.0     9600         0.0             4             1           1   \n",
       "3          68.0    11250       162.0             2             1           1   \n",
       "4          60.0     9550         0.0             1             1           1   \n",
       "5          84.0    14260       350.0             3             1           1   \n",
       "\n",
       "    WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ...  SaleType_ConLI  \\\n",
       "Id                                                     ...                   \n",
       "1            0           61              0          0  ...               0   \n",
       "2          298            0              0          0  ...               0   \n",
       "3            0           42              0          0  ...               0   \n",
       "4            0           35            272          0  ...               0   \n",
       "5          192           84              0          0  ...               0   \n",
       "\n",
       "    SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "Id                                                            \n",
       "1                0             0             0            1   \n",
       "2                0             0             0            1   \n",
       "3                0             0             0            1   \n",
       "4                0             0             0            1   \n",
       "5                0             0             0            1   \n",
       "\n",
       "    SaleCondition_AdjLand  SaleCondition_Alloca  SaleCondition_Family  \\\n",
       "Id                                                                      \n",
       "1                       0                     0                     0   \n",
       "2                       0                     0                     0   \n",
       "3                       0                     0                     0   \n",
       "4                       0                     0                     0   \n",
       "5                       0                     0                     0   \n",
       "\n",
       "    SaleCondition_Normal  SaleCondition_Partial  \n",
       "Id                                               \n",
       "1                      1                      0  \n",
       "2                      1                      0  \n",
       "3                      1                      0  \n",
       "4                      0                      0  \n",
       "5                      1                      0  \n",
       "\n",
       "[5 rows x 165 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anteriormente habiamos generado el nombre de las columnas que se estarían transformando en Dummies\n",
    "# Así Que vamos a utilizarla para generar las dimmies y tener nuestro data ya completo\n",
    "\n",
    "df = pd.get_dummies(data=df,columns=columns_dummies,drop_first=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T03:33:29.697227Z",
     "start_time": "2021-01-28T03:33:29.684224Z"
    }
   },
   "source": [
    "# Prueba de Hipotesis. \n",
    "#### Serán nescesarias todas las variables para la prediccion del precio de una casa?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:16.332249Z",
     "start_time": "2021-01-28T05:39:16.322245Z"
    }
   },
   "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": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:16.684343Z",
     "start_time": "2021-01-28T05:39:16.338249Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero de Variables utiles para RL: 102\n",
      "Numero de Variable NO utiles para RL: 63\n",
      "Total de variables: 165\n",
      "Porcentaje no util:38.18%,sobrepasa el 30% planteado para la H0\n",
      "\n",
      "\n",
      "Variebles utiles para RL:\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "</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>Condition1_PosA</td>\n",
       "      <td>0.044803</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RoofMatl_WdShake</td>\n",
       "      <td>0.043087</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Exterior2nd_Wd Shng</td>\n",
       "      <td>0.039323</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Condition2_RRNn</td>\n",
       "      <td>0.037025</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3SsnPorch</td>\n",
       "      <td>0.036007</td>\n",
       "      <td>Valida para Reg Lin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Variable   P_Value                   H0\n",
       "0      Condition1_PosA  0.044803  Valida para Reg Lin\n",
       "1     RoofMatl_WdShake  0.043087  Valida para Reg Lin\n",
       "2  Exterior2nd_Wd Shng  0.039323  Valida para Reg Lin\n",
       "3      Condition2_RRNn  0.037025  Valida para Reg Lin\n",
       "4            3SsnPorch  0.036007  Valida para Reg Lin"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para póder hacer esta prueba de hipotesis, lo que haremos es realizar una regresion linear a cada variable contra el precio\n",
    "# y obtendremos el p_value, posteriormente, si mas del 10% de las variables determinamos que no nos sirven para la prediccion, \n",
    "# procederemos a eliminarlas del modelo de regresion lineal\n",
    "from scipy.stats import linregress\n",
    "p_hipotesis_1 = df.copy()\n",
    "\n",
    "def p_value(data,int_confianza):\n",
    "      \n",
    "    y = data['Log_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",
    "# Obtenemos las Variables Utiles para la RL\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",
    "\n",
    "# Obtenemos las variables (Columnas) a sacar de nuesto df\n",
    "variables_sacar = variables_df[variables_df['H0']=='No Valida Reg Lin']\n",
    "variables_sacar= variables_sacar['Variable'].to_list()\n",
    "\n",
    "\n",
    "print('Numero de Variables utiles para RL:',len(p_value_rl))\n",
    "print('Numero de Variable NO utiles para RL:',len(variables_sacar))\n",
    "print('Total de variables:',len(p_hipotesis_1.columns))\n",
    "porcentaje_var_no_utiles = round((len(variables_sacar)/len(p_hipotesis_1.columns))*100,2)\n",
    "porcentaje_impuesto = 30\n",
    "if porcentaje_var_no_utiles > porcentaje_impuesto:\n",
    "    print(f'Porcentaje no util:{porcentaje_var_no_utiles}%,sobrepasa el {porcentaje_impuesto}% planteado para la H0')\n",
    "else:\n",
    "    print(f'Porcentaje no util:{porcentaje_var_no_utiles}%,por debajo de {porcentaje_impuesto}% planteado para la H0')\n",
    "\n",
    "print('')\n",
    "print('')\n",
    "print('Variebles utiles para RL:')\n",
    "p_value_rl[:-1].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:16.785364Z",
     "start_time": "2021-01-28T05:39:16.699341Z"
    }
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotFrontage</th>\n",
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      ],
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       "    LotFrontage  LotArea  MasVnrArea  BsmtExposure  CentralAir  WoodDeckSF  \\\n",
       "Id                                                                           \n",
       "1          65.0     8450       196.0             1           1           0   \n",
       "2          80.0     9600         0.0             4           1         298   \n",
       "3          68.0    11250       162.0             2           1           0   \n",
       "4          60.0     9550         0.0             1           1           0   \n",
       "\n",
       "    OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  ...  \\\n",
       "Id                                                      ...   \n",
       "1            61              0          0            0  ...   \n",
       "2             0              0          0            0  ...   \n",
       "3            42              0          0            0  ...   \n",
       "4            35            272          0            0  ...   \n",
       "\n",
       "    GarageType_Detchd  PavedDrive_P  PavedDrive_Y  MiscFeature_Othr  \\\n",
       "Id                                                                    \n",
       "1                   0             0             1                 0   \n",
       "2                   0             0             1                 0   \n",
       "3                   0             0             1                 0   \n",
       "4                   1             0             1                 0   \n",
       "\n",
       "    MiscFeature_Shed  SaleType_New  SaleType_WD  SaleCondition_AdjLand  \\\n",
       "Id                                                                       \n",
       "1                  0             0            1                      0   \n",
       "2                  0             0            1                      0   \n",
       "3                  0             0            1                      0   \n",
       "4                  0             0            1                      0   \n",
       "\n",
       "    SaleCondition_Normal  SaleCondition_Partial  \n",
       "Id                                               \n",
       "1                      1                      0  \n",
       "2                      1                      0  \n",
       "3                      1                      0  \n",
       "4                      0                      0  \n",
       "\n",
       "[4 rows x 102 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminamos las columnas a sacar del df\n",
    "df.drop(variables_sacar,axis=1,inplace=True)\n",
    "df.head(4)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T03:48:40.396109Z",
     "start_time": "2021-01-28T03:48:40.377103Z"
    }
   },
   "source": [
    "Hasta este punto, se ah elaborado un analisis de los datos, su transformacion, eliminacion de columnas que no son reelevantes para realizar la prediccion de los precios, por lo que ahora, procederemos con el modelo de Regresion Lineal para ver que tan bien se ah realizado nuestro trabajo y si todo lo anterior nos sirve de algo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T03:52:26.097210Z",
     "start_time": "2021-01-28T03:52:26.084190Z"
    }
   },
   "source": [
    "# Regresion Lineal con SKLearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:16.876390Z",
     "start_time": "2021-01-28T05:39:16.794366Z"
    }
   },
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>350.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LotFrontage  LotArea  MasVnrArea  BsmtExposure  CentralAir  WoodDeckSF  \\\n",
       "Id                                                                           \n",
       "1          65.0     8450       196.0             1           1           0   \n",
       "2          80.0     9600         0.0             4           1         298   \n",
       "3          68.0    11250       162.0             2           1           0   \n",
       "4          60.0     9550         0.0             1           1           0   \n",
       "5          84.0    14260       350.0             3           1         192   \n",
       "\n",
       "    OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  ...  \\\n",
       "Id                                                      ...   \n",
       "1            61              0          0            0  ...   \n",
       "2             0              0          0            0  ...   \n",
       "3            42              0          0            0  ...   \n",
       "4            35            272          0            0  ...   \n",
       "5            84              0          0            0  ...   \n",
       "\n",
       "    GarageType_Detchd  PavedDrive_P  PavedDrive_Y  MiscFeature_Othr  \\\n",
       "Id                                                                    \n",
       "1                   0             0             1                 0   \n",
       "2                   0             0             1                 0   \n",
       "3                   0             0             1                 0   \n",
       "4                   1             0             1                 0   \n",
       "5                   0             0             1                 0   \n",
       "\n",
       "    MiscFeature_Shed  SaleType_New  SaleType_WD  SaleCondition_AdjLand  \\\n",
       "Id                                                                       \n",
       "1                  0             0            1                      0   \n",
       "2                  0             0            1                      0   \n",
       "3                  0             0            1                      0   \n",
       "4                  0             0            1                      0   \n",
       "5                  0             0            1                      0   \n",
       "\n",
       "    SaleCondition_Normal  SaleCondition_Partial  \n",
       "Id                                               \n",
       "1                      1                      0  \n",
       "2                      1                      0  \n",
       "3                      1                      0  \n",
       "4                      0                      0  \n",
       "5                      1                      0  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# para no alterar nuestro df ya con los cambios realizados, lo que haremos es una copia del mismo\n",
    "df_rl = df.copy()\n",
    "df_rl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:16.892401Z",
     "start_time": "2021-01-28T05:39:16.883388Z"
    }
   },
   "outputs": [],
   "source": [
    "objetivo = df_rl['Log_SalePrice']\n",
    "inputs = df_rl.drop('Log_SalePrice',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:16.988415Z",
     "start_time": "2021-01-28T05:39:16.899392Z"
    }
   },
   "outputs": [],
   "source": [
    "# Escalamos los datos para tenerlos todos dentro de una sola dimencion (normalizamos). El precio no lo hacemos ya que\n",
    "# este lo tenemos en una escala logaritmica\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(inputs)\n",
    "inputs_scaled = scaler.transform(inputs)\n",
    "\n",
    "# Separamos los datos en prueba y testeo considerando para prueba un 80% de los datos y un 20% para las pruebas\n",
    "# y le colocamos un random de 77 para que siempre nos salgan los mismos datos.\n",
    "x_train, x_test, y_train, y_test = train_test_split(inputs_scaled,objetivo, test_size = 0.2, random_state = 77)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:17.772613Z",
     "start_time": "2021-01-28T05:39:16.993415Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Para el modelo de Regresion Lineal, usaremos la libreria de sklearn\n",
    "reg_lineal = LinearRegression()\n",
    "reg_lineal.fit(x_train,y_train)\n",
    "\n",
    "predicciones = reg_lineal.predict(x_train)\n",
    "\n",
    "plt.scatter(y_train,predicciones,alpha=.5,color='orange')\n",
    "plt.xlabel('Objetivos (y_train)',size=15)\n",
    "plt.ylabel('Predicciones',size=15)\n",
    "plt.title('Objetivos vs Predicciones',size=18);\n",
    "# Por la grafica podemos ver que nuestras predicciones quedaron bastante ajustadas a los datos que ya teniamos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:18.918905Z",
     "start_time": "2021-01-28T05:39:17.784617Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\rikrdinii\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vamos a graficar los residuos\n",
    "sns.distplot(y_train-predicciones,color='purple')\n",
    "plt.title('Residuales',size=18);\n",
    "# Vemos que tenemos una distribucion uniforme de los residuos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:18.966917Z",
     "start_time": "2021-01-28T05:39:18.943912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " % de datos explicados con nuestro modelo: 81.46 %\n"
     ]
    }
   ],
   "source": [
    "# Obtenemos el R2 de nuestro modelo. \n",
    "r_2 = reg_lineal.score(x_train,y_train)\n",
    "print(\" % de datos explicados con nuestro modelo:\",round(r_2*100,2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:19.079952Z",
     "start_time": "2021-01-28T05:39:18.972919Z"
    }
   },
   "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>Factores</th>\n",
       "      <th>Valores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LotFrontage</td>\n",
       "      <td>0.023865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LotArea</td>\n",
       "      <td>0.041675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>0.025869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BsmtExposure</td>\n",
       "      <td>0.039225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CentralAir</td>\n",
       "      <td>0.038037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Factores   Valores\n",
       "0   LotFrontage  0.023865\n",
       "1       LotArea  0.041675\n",
       "2    MasVnrArea  0.025869\n",
       "3  BsmtExposure  0.039225\n",
       "4    CentralAir  0.038037"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vamos a obtener el resumen\n",
    "reg_summary = pd.DataFrame(inputs.columns.values,columns=['Factores'])\n",
    "reg_summary['Valores'] = reg_lineal.coef_\n",
    "reg_summary.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:19.890152Z",
     "start_time": "2021-01-28T05:39:19.089947Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vamos a ver las predicciones que podemos hacer\n",
    "predicciones_test = reg_lineal.predict(x_test)\n",
    "plt.scatter(y_test,predicciones_test,alpha=0.5,color='green');\n",
    "# Por la concentracion de los datos, podemos inferir que nuestro modelo es bueno para predecir aquellos precios medios\n",
    "# mientras que los mas altos y los mas bajos no se tendrá tanta certeza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:19.972175Z",
     "start_time": "2021-01-28T05:39:19.897152Z"
    }
   },
   "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>Prediccion</th>\n",
       "      <th>Objetivo</th>\n",
       "      <th>Residual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>207648.543518</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>5351.456482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118094.497407</td>\n",
       "      <td>109000.0</td>\n",
       "      <td>-9094.497407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114707.520323</td>\n",
       "      <td>179000.0</td>\n",
       "      <td>64292.479677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160961.591895</td>\n",
       "      <td>169500.0</td>\n",
       "      <td>8538.408105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>152193.517740</td>\n",
       "      <td>151500.0</td>\n",
       "      <td>-693.517740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Prediccion  Objetivo      Residual\n",
       "0  207648.543518  213000.0   5351.456482\n",
       "1  118094.497407  109000.0  -9094.497407\n",
       "2  114707.520323  179000.0  64292.479677\n",
       "3  160961.591895  169500.0   8538.408105\n",
       "4  152193.517740  151500.0   -693.517740"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crearemos un DF con los datos objetivo y los datos predichos para poder visualizarlos. \n",
    "y_test = y_test.reset_index(drop=True) \n",
    "df_predicciones = pd.DataFrame(np.exp(predicciones_test),columns=['Prediccion'])\n",
    "df_predicciones['Objetivo'] = np.exp(y_test)\n",
    "df_predicciones['Residual'] = df_predicciones['Objetivo']-df_predicciones['Prediccion'] \n",
    "df_predicciones.head(5)\n",
    "\n",
    "# Podemos observar que hay algunos de los datos en los cuales tuvimos un sobrante y otros algun faltante. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:20.001180Z",
     "start_time": "2021-01-28T05:39:19.978172Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediccion    178789.313365\n",
       "Objetivo      179465.729452\n",
       "Residual         676.416087\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predicciones.mean()\n",
    "# Con esto, podemos observar que en promedio tenemos un precio de venta de: 179,465.72 (Objetivo)\n",
    "# un promedio de prediccion de: 178,789.31\n",
    "# una diferencia promedio de: 676.41\n",
    "\n",
    "# Podemos inferir que nuestro modelo es suficientemente acertado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:20.065196Z",
     "start_time": "2021-01-28T05:39:20.006179Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prediccion</th>\n",
       "      <th>Objetivo</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Dif %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>207648.543518</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>5351.456482</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118094.497407</td>\n",
       "      <td>109000.0</td>\n",
       "      <td>-9094.497407</td>\n",
       "      <td>-8.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114707.520323</td>\n",
       "      <td>179000.0</td>\n",
       "      <td>64292.479677</td>\n",
       "      <td>35.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160961.591895</td>\n",
       "      <td>169500.0</td>\n",
       "      <td>8538.408105</td>\n",
       "      <td>5.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>152193.517740</td>\n",
       "      <td>151500.0</td>\n",
       "      <td>-693.517740</td>\n",
       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Prediccion  Objetivo      Residual  Dif %\n",
       "0  207648.543518  213000.0   5351.456482   2.51\n",
       "1  118094.497407  109000.0  -9094.497407  -8.34\n",
       "2  114707.520323  179000.0  64292.479677  35.92\n",
       "3  160961.591895  169500.0   8538.408105   5.04\n",
       "4  152193.517740  151500.0   -693.517740  -0.46"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predicciones['Dif %'] = round((df_predicciones['Residual']/df_predicciones['Objetivo'])*100,2)\n",
    "df_predicciones.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresion Lineal OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:20.146215Z",
     "start_time": "2021-01-28T05:39:20.070194Z"
    }
   },
   "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>MasVnrArea</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageType_Detchd</th>\n",
       "      <th>PavedDrive_P</th>\n",
       "      <th>PavedDrive_Y</th>\n",
       "      <th>MiscFeature_Othr</th>\n",
       "      <th>MiscFeature_Shed</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>162.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>350.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    LotFrontage  LotArea  MasVnrArea  BsmtExposure  CentralAir  WoodDeckSF  \\\n",
       "Id                                                                           \n",
       "1          65.0     8450       196.0             1           1           0   \n",
       "2          80.0     9600         0.0             4           1         298   \n",
       "3          68.0    11250       162.0             2           1           0   \n",
       "4          60.0     9550         0.0             1           1           0   \n",
       "5          84.0    14260       350.0             3           1         192   \n",
       "\n",
       "    OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  ...  \\\n",
       "Id                                                      ...   \n",
       "1            61              0          0            0  ...   \n",
       "2             0              0          0            0  ...   \n",
       "3            42              0          0            0  ...   \n",
       "4            35            272          0            0  ...   \n",
       "5            84              0          0            0  ...   \n",
       "\n",
       "    GarageType_Detchd  PavedDrive_P  PavedDrive_Y  MiscFeature_Othr  \\\n",
       "Id                                                                    \n",
       "1                   0             0             1                 0   \n",
       "2                   0             0             1                 0   \n",
       "3                   0             0             1                 0   \n",
       "4                   1             0             1                 0   \n",
       "5                   0             0             1                 0   \n",
       "\n",
       "    MiscFeature_Shed  SaleType_New  SaleType_WD  SaleCondition_AdjLand  \\\n",
       "Id                                                                       \n",
       "1                  0             0            1                      0   \n",
       "2                  0             0            1                      0   \n",
       "3                  0             0            1                      0   \n",
       "4                  0             0            1                      0   \n",
       "5                  0             0            1                      0   \n",
       "\n",
       "    SaleCondition_Normal  SaleCondition_Partial  \n",
       "Id                                               \n",
       "1                      1                      0  \n",
       "2                      1                      0  \n",
       "3                      1                      0  \n",
       "4                      0                      0  \n",
       "5                      1                      0  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# para no alterar nuestro df ya con los cambios realizados, lo que haremos es una copia del mismo\n",
    "df_rl_OLS = df.copy()\n",
    "df_rl_OLS.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:20.531315Z",
     "start_time": "2021-01-28T05:39:20.152215Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>Log_SalePrice</td>  <th>  R-squared:         </th> <td>   0.810</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.792</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   45.97</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 27 Jan 2021</td> <th>  Prob (F-statistic):</th> <td>9.28e-319</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:39:20</td>     <th>  Log-Likelihood:    </th> <td>  386.70</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1167</td>      <th>  AIC:               </th> <td>  -573.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1067</td>      <th>  BIC:               </th> <td>  -67.17</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    99</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>   11.0279</td> <td>    0.153</td> <td>   71.844</td> <td> 0.000</td> <td>   10.727</td> <td>   11.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotFrontage</th>           <td>    0.0007</td> <td>    0.000</td> <td>    3.689</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotArea</th>               <td> 2.693e-06</td> <td> 6.67e-07</td> <td>    4.037</td> <td> 0.000</td> <td> 1.38e-06</td> <td>    4e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrArea</th>            <td>    0.0001</td> <td> 4.44e-05</td> <td>    3.170</td> <td> 0.002</td> <td> 5.37e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BsmtExposure</th>          <td>    0.0361</td> <td>    0.006</td> <td>    5.724</td> <td> 0.000</td> <td>    0.024</td> <td>    0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CentralAir</th>            <td>    0.1947</td> <td>    0.031</td> <td>    6.375</td> <td> 0.000</td> <td>    0.135</td> <td>    0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>WoodDeckSF</th>            <td>    0.0003</td> <td> 4.94e-05</td> <td>    6.266</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>OpenPorchSF</th>           <td>    0.0006</td> <td> 8.85e-05</td> <td>    6.750</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>EnclosedPorch</th>         <td>    0.0002</td> <td> 9.79e-05</td> <td>    2.364</td> <td> 0.018</td> <td> 3.93e-05</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3SsnPorch</th>             <td>    0.0003</td> <td>    0.000</td> <td>    1.820</td> <td> 0.069</td> <td>-2.59e-05</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ScreenPorch</th>           <td>    0.0003</td> <td>    0.000</td> <td>    3.133</td> <td> 0.002</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Fence</th>                 <td>   -0.0020</td> <td>    0.005</td> <td>   -0.396</td> <td> 0.692</td> <td>   -0.012</td> <td>    0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Remodelacion</th>          <td>    0.1071</td> <td>    0.017</td> <td>    6.135</td> <td> 0.000</td> <td>    0.073</td> <td>    0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AñosRemodelacion</th>      <td>   -0.0021</td> <td>    0.000</td> <td>   -4.464</td> <td> 0.000</td> <td>   -0.003</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TotMedBaño</th>            <td>    0.0290</td> <td>    0.013</td> <td>    2.179</td> <td> 0.030</td> <td>    0.003</td> <td>    0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_FV</th>           <td>    0.4103</td> <td>    0.098</td> <td>    4.166</td> <td> 0.000</td> <td>    0.217</td> <td>    0.604</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_RH</th>           <td>    0.5017</td> <td>    0.102</td> <td>    4.933</td> <td> 0.000</td> <td>    0.302</td> <td>    0.701</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_RL</th>           <td>    0.4623</td> <td>    0.084</td> <td>    5.530</td> <td> 0.000</td> <td>    0.298</td> <td>    0.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MSZoning_RM</th>           <td>    0.4042</td> <td>    0.079</td> <td>    5.095</td> <td> 0.000</td> <td>    0.249</td> <td>    0.560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Street_Pave</th>           <td>    0.0730</td> <td>    0.086</td> <td>    0.850</td> <td> 0.395</td> <td>   -0.095</td> <td>    0.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Alley_Grvl</th>            <td>   -0.0482</td> <td>    0.034</td> <td>   -1.402</td> <td> 0.161</td> <td>   -0.116</td> <td>    0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LandContour_HLS</th>       <td>    0.0078</td> <td>    0.034</td> <td>    0.226</td> <td> 0.821</td> <td>   -0.060</td> <td>    0.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotConfig_CulDSac</th>     <td>    0.0748</td> <td>    0.026</td> <td>    2.889</td> <td> 0.004</td> <td>    0.024</td> <td>    0.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotConfig_Inside</th>      <td>   -0.0045</td> <td>    0.014</td> <td>   -0.325</td> <td> 0.745</td> <td>   -0.031</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_BrDale</th>   <td>   -0.2144</td> <td>    0.070</td> <td>   -3.051</td> <td> 0.002</td> <td>   -0.352</td> <td>   -0.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_BrkSide</th>  <td>   -0.0441</td> <td>    0.043</td> <td>   -1.017</td> <td> 0.309</td> <td>   -0.129</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_ClearCr</th>  <td>    0.0205</td> <td>    0.047</td> <td>    0.433</td> <td> 0.665</td> <td>   -0.072</td> <td>    0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_CollgCr</th>  <td>    0.0439</td> <td>    0.027</td> <td>    1.651</td> <td> 0.099</td> <td>   -0.008</td> <td>    0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Crawfor</th>  <td>    0.2009</td> <td>    0.039</td> <td>    5.186</td> <td> 0.000</td> <td>    0.125</td> <td>    0.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Edwards</th>  <td>   -0.1440</td> <td>    0.030</td> <td>   -4.760</td> <td> 0.000</td> <td>   -0.203</td> <td>   -0.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Gilbert</th>  <td>    0.0109</td> <td>    0.032</td> <td>    0.343</td> <td> 0.732</td> <td>   -0.052</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_IDOTRR</th>   <td>   -0.1122</td> <td>    0.057</td> <td>   -1.980</td> <td> 0.048</td> <td>   -0.223</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_MeadowV</th>  <td>   -0.4492</td> <td>    0.071</td> <td>   -6.301</td> <td> 0.000</td> <td>   -0.589</td> <td>   -0.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NAmes</th>    <td>   -0.0749</td> <td>    0.026</td> <td>   -2.929</td> <td> 0.003</td> <td>   -0.125</td> <td>   -0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NWAmes</th>   <td>    0.0581</td> <td>    0.033</td> <td>    1.770</td> <td> 0.077</td> <td>   -0.006</td> <td>    0.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NoRidge</th>  <td>    0.3325</td> <td>    0.040</td> <td>    8.355</td> <td> 0.000</td> <td>    0.254</td> <td>    0.411</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_NridgHt</th>  <td>    0.3037</td> <td>    0.035</td> <td>    8.778</td> <td> 0.000</td> <td>    0.236</td> <td>    0.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_OldTown</th>  <td>   -0.0946</td> <td>    0.041</td> <td>   -2.283</td> <td> 0.023</td> <td>   -0.176</td> <td>   -0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_SWISU</th>    <td>    0.0184</td> <td>    0.051</td> <td>    0.361</td> <td> 0.718</td> <td>   -0.082</td> <td>    0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Sawyer</th>   <td>   -0.0920</td> <td>    0.033</td> <td>   -2.820</td> <td> 0.005</td> <td>   -0.156</td> <td>   -0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Somerst</th>  <td>    0.1533</td> <td>    0.051</td> <td>    3.010</td> <td> 0.003</td> <td>    0.053</td> <td>    0.253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_StoneBr</th>  <td>    0.3136</td> <td>    0.049</td> <td>    6.451</td> <td> 0.000</td> <td>    0.218</td> <td>    0.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Timber</th>   <td>    0.1274</td> <td>    0.044</td> <td>    2.927</td> <td> 0.003</td> <td>    0.042</td> <td>    0.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Neighborhood_Veenker</th>  <td>    0.1580</td> <td>    0.061</td> <td>    2.579</td> <td> 0.010</td> <td>    0.038</td> <td>    0.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_Feedr</th>      <td>   -0.0282</td> <td>    0.033</td> <td>   -0.842</td> <td> 0.400</td> <td>   -0.094</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_Norm</th>       <td>    0.0326</td> <td>    0.024</td> <td>    1.369</td> <td> 0.171</td> <td>   -0.014</td> <td>    0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_PosA</th>       <td>    0.1469</td> <td>    0.072</td> <td>    2.029</td> <td> 0.043</td> <td>    0.005</td> <td>    0.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition1_PosN</th>       <td>   -0.0157</td> <td>    0.057</td> <td>   -0.277</td> <td> 0.782</td> <td>   -0.127</td> <td>    0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_Feedr</th>      <td>    0.0327</td> <td>    0.090</td> <td>    0.363</td> <td> 0.717</td> <td>   -0.144</td> <td>    0.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Condition2_RRNn</th>       <td>    0.1013</td> <td>    0.190</td> <td>    0.533</td> <td> 0.594</td> <td>   -0.272</td> <td>    0.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_2fmCon</th>       <td>    0.0159</td> <td>    0.040</td> <td>    0.394</td> <td> 0.694</td> <td>   -0.063</td> <td>    0.095</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_Duplex</th>       <td>    0.0628</td> <td>    0.036</td> <td>    1.752</td> <td> 0.080</td> <td>   -0.008</td> <td>    0.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>BldgType_Twnhs</th>        <td>   -0.1007</td> <td>    0.042</td> <td>   -2.413</td> <td> 0.016</td> <td>   -0.183</td> <td>   -0.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_1.5Unf</th>     <td>   -0.0608</td> <td>    0.054</td> <td>   -1.124</td> <td> 0.261</td> <td>   -0.167</td> <td>    0.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_1Story</th>     <td>   -0.0625</td> <td>    0.018</td> <td>   -3.445</td> <td> 0.001</td> <td>   -0.098</td> <td>   -0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_2Story</th>     <td>    0.0387</td> <td>    0.020</td> <td>    1.902</td> <td> 0.057</td> <td>   -0.001</td> <td>    0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>HouseStyle_SFoyer</th>     <td>   -0.0777</td> <td>    0.040</td> <td>   -1.929</td> <td> 0.054</td> <td>   -0.157</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Gable</th>       <td>   -0.1311</td> <td>    0.050</td> <td>   -2.641</td> <td> 0.008</td> <td>   -0.228</td> <td>   -0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofStyle_Hip</th>         <td>   -0.0509</td> <td>    0.051</td> <td>   -0.994</td> <td> 0.320</td> <td>   -0.151</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_CompShg</th>      <td>    0.1291</td> <td>    0.067</td> <td>    1.932</td> <td> 0.054</td> <td>   -0.002</td> <td>    0.260</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_WdShake</th>      <td>    0.2697</td> <td>    0.109</td> <td>    2.469</td> <td> 0.014</td> <td>    0.055</td> <td>    0.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>RoofMatl_WdShngl</th>      <td>    0.4115</td> <td>    0.108</td> <td>    3.820</td> <td> 0.000</td> <td>    0.200</td> <td>    0.623</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_BrkComm</th>   <td>   -0.4882</td> <td>    0.170</td> <td>   -2.868</td> <td> 0.004</td> <td>   -0.822</td> <td>   -0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_CemntBd</th>   <td>   -0.0166</td> <td>    0.113</td> <td>   -0.147</td> <td> 0.883</td> <td>   -0.238</td> <td>    0.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_HdBoard</th>   <td>   -0.0968</td> <td>    0.024</td> <td>   -4.101</td> <td> 0.000</td> <td>   -0.143</td> <td>   -0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_MetalSd</th>   <td>   -0.1751</td> <td>    0.069</td> <td>   -2.525</td> <td> 0.012</td> <td>   -0.311</td> <td>   -0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_VinylSd</th>   <td>   -0.1550</td> <td>    0.064</td> <td>   -2.411</td> <td> 0.016</td> <td>   -0.281</td> <td>   -0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_Wd Sdng</th>   <td>   -0.1296</td> <td>    0.036</td> <td>   -3.556</td> <td> 0.000</td> <td>   -0.201</td> <td>   -0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior1st_WdShing</th>   <td>   -0.0686</td> <td>    0.051</td> <td>   -1.353</td> <td> 0.176</td> <td>   -0.168</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Brk Cmn</th>   <td>-4.981e-05</td> <td>    0.098</td> <td>   -0.001</td> <td> 1.000</td> <td>   -0.193</td> <td>    0.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_CmentBd</th>   <td>    0.1079</td> <td>    0.114</td> <td>    0.944</td> <td> 0.345</td> <td>   -0.116</td> <td>    0.332</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_MetalSd</th>   <td>    0.1264</td> <td>    0.070</td> <td>    1.807</td> <td> 0.071</td> <td>   -0.011</td> <td>    0.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Stucco</th>    <td>   -0.0275</td> <td>    0.048</td> <td>   -0.571</td> <td> 0.568</td> <td>   -0.122</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_VinylSd</th>   <td>    0.1384</td> <td>    0.064</td> <td>    2.152</td> <td> 0.032</td> <td>    0.012</td> <td>    0.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Wd Sdng</th>   <td>    0.0809</td> <td>    0.037</td> <td>    2.177</td> <td> 0.030</td> <td>    0.008</td> <td>    0.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Exterior2nd_Wd Shng</th>   <td>   -0.0285</td> <td>    0.046</td> <td>   -0.617</td> <td> 0.538</td> <td>   -0.119</td> <td>    0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_BrkFace</th>    <td>    0.0550</td> <td>    0.043</td> <td>    1.267</td> <td> 0.205</td> <td>   -0.030</td> <td>    0.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_None</th>       <td>    0.0355</td> <td>    0.043</td> <td>    0.832</td> <td> 0.406</td> <td>   -0.048</td> <td>    0.119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrType_Stone</th>      <td>    0.1303</td> <td>    0.047</td> <td>    2.777</td> <td> 0.006</td> <td>    0.038</td> <td>    0.222</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_CBlock</th>     <td>   -0.0318</td> <td>    0.025</td> <td>   -1.287</td> <td> 0.198</td> <td>   -0.080</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_PConc</th>      <td>    0.0624</td> <td>    0.026</td> <td>    2.401</td> <td> 0.017</td> <td>    0.011</td> <td>    0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Foundation_Slab</th>       <td>   -0.0749</td> <td>    0.052</td> <td>   -1.436</td> <td> 0.151</td> <td>   -0.177</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_GasA</th>          <td>   -0.2091</td> <td>    0.049</td> <td>   -4.252</td> <td> 0.000</td> <td>   -0.306</td> <td>   -0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_Grav</th>          <td>   -0.3494</td> <td>    0.092</td> <td>   -3.808</td> <td> 0.000</td> <td>   -0.529</td> <td>   -0.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heating_Wall</th>          <td>   -0.1856</td> <td>    0.117</td> <td>   -1.592</td> <td> 0.112</td> <td>   -0.414</td> <td>    0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_FuseF</th>      <td>    0.0622</td> <td>    0.048</td> <td>    1.309</td> <td> 0.191</td> <td>   -0.031</td> <td>    0.155</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_FuseP</th>      <td>    0.1049</td> <td>    0.119</td> <td>    0.880</td> <td> 0.379</td> <td>   -0.129</td> <td>    0.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_Mix</th>        <td>-7.539e-17</td> <td>    6e-17</td> <td>   -1.257</td> <td> 0.209</td> <td>-1.93e-16</td> <td> 4.23e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Electrical_SBrkr</th>      <td>    0.0419</td> <td>    0.025</td> <td>    1.700</td> <td> 0.089</td> <td>   -0.006</td> <td>    0.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Attchd</th>     <td>    0.1309</td> <td>    0.026</td> <td>    5.085</td> <td> 0.000</td> <td>    0.080</td> <td>    0.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_BuiltIn</th>    <td>    0.1221</td> <td>    0.036</td> <td>    3.434</td> <td> 0.001</td> <td>    0.052</td> <td>    0.192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_CarPort</th>    <td>    0.0200</td> <td>    0.078</td> <td>    0.256</td> <td> 0.798</td> <td>   -0.133</td> <td>    0.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Detchd</th>     <td>    0.0512</td> <td>    0.025</td> <td>    2.046</td> <td> 0.041</td> <td>    0.002</td> <td>    0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PavedDrive_P</th>          <td>    0.0186</td> <td>    0.046</td> <td>    0.407</td> <td> 0.684</td> <td>   -0.071</td> <td>    0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>PavedDrive_Y</th>          <td>    0.0748</td> <td>    0.028</td> <td>    2.705</td> <td> 0.007</td> <td>    0.021</td> <td>    0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscFeature_Othr</th>      <td>   -0.0687</td> <td>    0.139</td> <td>   -0.493</td> <td> 0.622</td> <td>   -0.342</td> <td>    0.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MiscFeature_Shed</th>      <td>   -0.0257</td> <td>    0.030</td> <td>   -0.847</td> <td> 0.397</td> <td>   -0.085</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_New</th>          <td>    0.0183</td> <td>    0.018</td> <td>    1.012</td> <td> 0.312</td> <td>   -0.017</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleType_WD</th>           <td>   -0.0393</td> <td>    0.026</td> <td>   -1.512</td> <td> 0.131</td> <td>   -0.090</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_AdjLand</th> <td>   -0.1047</td> <td>    0.108</td> <td>   -0.974</td> <td> 0.330</td> <td>   -0.316</td> <td>    0.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Normal</th>  <td>    0.0332</td> <td>    0.021</td> <td>    1.606</td> <td> 0.108</td> <td>   -0.007</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SaleCondition_Partial</th> <td>    0.0183</td> <td>    0.018</td> <td>    1.012</td> <td> 0.312</td> <td>   -0.017</td> <td>    0.054</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>48.840</td> <th>  Durbin-Watson:     </th> <td>   2.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 142.869</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.066</td> <th>  Prob(JB):          </th> <td>9.47e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.709</td> <th>  Cond. No.          </th> <td>1.38e+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 1.29e-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:          Log_SalePrice   R-squared:                       0.810\n",
       "Model:                            OLS   Adj. R-squared:                  0.792\n",
       "Method:                 Least Squares   F-statistic:                     45.97\n",
       "Date:                Wed, 27 Jan 2021   Prob (F-statistic):          9.28e-319\n",
       "Time:                        23:39:20   Log-Likelihood:                 386.70\n",
       "No. Observations:                1167   AIC:                            -573.4\n",
       "Df Residuals:                    1067   BIC:                            -67.17\n",
       "Df Model:                          99                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=========================================================================================\n",
       "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------------\n",
       "const                    11.0279      0.153     71.844      0.000      10.727      11.329\n",
       "LotFrontage               0.0007      0.000      3.689      0.000       0.000       0.001\n",
       "LotArea                2.693e-06   6.67e-07      4.037      0.000    1.38e-06       4e-06\n",
       "MasVnrArea                0.0001   4.44e-05      3.170      0.002    5.37e-05       0.000\n",
       "BsmtExposure              0.0361      0.006      5.724      0.000       0.024       0.048\n",
       "CentralAir                0.1947      0.031      6.375      0.000       0.135       0.255\n",
       "WoodDeckSF                0.0003   4.94e-05      6.266      0.000       0.000       0.000\n",
       "OpenPorchSF               0.0006   8.85e-05      6.750      0.000       0.000       0.001\n",
       "EnclosedPorch             0.0002   9.79e-05      2.364      0.018    3.93e-05       0.000\n",
       "3SsnPorch                 0.0003      0.000      1.820      0.069   -2.59e-05       0.001\n",
       "ScreenPorch               0.0003      0.000      3.133      0.002       0.000       0.001\n",
       "Fence                    -0.0020      0.005     -0.396      0.692      -0.012       0.008\n",
       "Remodelacion              0.1071      0.017      6.135      0.000       0.073       0.141\n",
       "AñosRemodelacion         -0.0021      0.000     -4.464      0.000      -0.003      -0.001\n",
       "TotMedBaño                0.0290      0.013      2.179      0.030       0.003       0.055\n",
       "MSZoning_FV               0.4103      0.098      4.166      0.000       0.217       0.604\n",
       "MSZoning_RH               0.5017      0.102      4.933      0.000       0.302       0.701\n",
       "MSZoning_RL               0.4623      0.084      5.530      0.000       0.298       0.626\n",
       "MSZoning_RM               0.4042      0.079      5.095      0.000       0.249       0.560\n",
       "Street_Pave               0.0730      0.086      0.850      0.395      -0.095       0.241\n",
       "Alley_Grvl               -0.0482      0.034     -1.402      0.161      -0.116       0.019\n",
       "LandContour_HLS           0.0078      0.034      0.226      0.821      -0.060       0.075\n",
       "LotConfig_CulDSac         0.0748      0.026      2.889      0.004       0.024       0.126\n",
       "LotConfig_Inside         -0.0045      0.014     -0.325      0.745      -0.031       0.022\n",
       "Neighborhood_BrDale      -0.2144      0.070     -3.051      0.002      -0.352      -0.076\n",
       "Neighborhood_BrkSide     -0.0441      0.043     -1.017      0.309      -0.129       0.041\n",
       "Neighborhood_ClearCr      0.0205      0.047      0.433      0.665      -0.072       0.113\n",
       "Neighborhood_CollgCr      0.0439      0.027      1.651      0.099      -0.008       0.096\n",
       "Neighborhood_Crawfor      0.2009      0.039      5.186      0.000       0.125       0.277\n",
       "Neighborhood_Edwards     -0.1440      0.030     -4.760      0.000      -0.203      -0.085\n",
       "Neighborhood_Gilbert      0.0109      0.032      0.343      0.732      -0.052       0.073\n",
       "Neighborhood_IDOTRR      -0.1122      0.057     -1.980      0.048      -0.223      -0.001\n",
       "Neighborhood_MeadowV     -0.4492      0.071     -6.301      0.000      -0.589      -0.309\n",
       "Neighborhood_NAmes       -0.0749      0.026     -2.929      0.003      -0.125      -0.025\n",
       "Neighborhood_NWAmes       0.0581      0.033      1.770      0.077      -0.006       0.123\n",
       "Neighborhood_NoRidge      0.3325      0.040      8.355      0.000       0.254       0.411\n",
       "Neighborhood_NridgHt      0.3037      0.035      8.778      0.000       0.236       0.372\n",
       "Neighborhood_OldTown     -0.0946      0.041     -2.283      0.023      -0.176      -0.013\n",
       "Neighborhood_SWISU        0.0184      0.051      0.361      0.718      -0.082       0.118\n",
       "Neighborhood_Sawyer      -0.0920      0.033     -2.820      0.005      -0.156      -0.028\n",
       "Neighborhood_Somerst      0.1533      0.051      3.010      0.003       0.053       0.253\n",
       "Neighborhood_StoneBr      0.3136      0.049      6.451      0.000       0.218       0.409\n",
       "Neighborhood_Timber       0.1274      0.044      2.927      0.003       0.042       0.213\n",
       "Neighborhood_Veenker      0.1580      0.061      2.579      0.010       0.038       0.278\n",
       "Condition1_Feedr         -0.0282      0.033     -0.842      0.400      -0.094       0.037\n",
       "Condition1_Norm           0.0326      0.024      1.369      0.171      -0.014       0.079\n",
       "Condition1_PosA           0.1469      0.072      2.029      0.043       0.005       0.289\n",
       "Condition1_PosN          -0.0157      0.057     -0.277      0.782      -0.127       0.096\n",
       "Condition2_Feedr          0.0327      0.090      0.363      0.717      -0.144       0.209\n",
       "Condition2_RRNn           0.1013      0.190      0.533      0.594      -0.272       0.474\n",
       "BldgType_2fmCon           0.0159      0.040      0.394      0.694      -0.063       0.095\n",
       "BldgType_Duplex           0.0628      0.036      1.752      0.080      -0.008       0.133\n",
       "BldgType_Twnhs           -0.1007      0.042     -2.413      0.016      -0.183      -0.019\n",
       "HouseStyle_1.5Unf        -0.0608      0.054     -1.124      0.261      -0.167       0.045\n",
       "HouseStyle_1Story        -0.0625      0.018     -3.445      0.001      -0.098      -0.027\n",
       "HouseStyle_2Story         0.0387      0.020      1.902      0.057      -0.001       0.079\n",
       "HouseStyle_SFoyer        -0.0777      0.040     -1.929      0.054      -0.157       0.001\n",
       "RoofStyle_Gable          -0.1311      0.050     -2.641      0.008      -0.228      -0.034\n",
       "RoofStyle_Hip            -0.0509      0.051     -0.994      0.320      -0.151       0.050\n",
       "RoofMatl_CompShg          0.1291      0.067      1.932      0.054      -0.002       0.260\n",
       "RoofMatl_WdShake          0.2697      0.109      2.469      0.014       0.055       0.484\n",
       "RoofMatl_WdShngl          0.4115      0.108      3.820      0.000       0.200       0.623\n",
       "Exterior1st_BrkComm      -0.4882      0.170     -2.868      0.004      -0.822      -0.154\n",
       "Exterior1st_CemntBd      -0.0166      0.113     -0.147      0.883      -0.238       0.205\n",
       "Exterior1st_HdBoard      -0.0968      0.024     -4.101      0.000      -0.143      -0.051\n",
       "Exterior1st_MetalSd      -0.1751      0.069     -2.525      0.012      -0.311      -0.039\n",
       "Exterior1st_VinylSd      -0.1550      0.064     -2.411      0.016      -0.281      -0.029\n",
       "Exterior1st_Wd Sdng      -0.1296      0.036     -3.556      0.000      -0.201      -0.058\n",
       "Exterior1st_WdShing      -0.0686      0.051     -1.353      0.176      -0.168       0.031\n",
       "Exterior2nd_Brk Cmn   -4.981e-05      0.098     -0.001      1.000      -0.193       0.193\n",
       "Exterior2nd_CmentBd       0.1079      0.114      0.944      0.345      -0.116       0.332\n",
       "Exterior2nd_MetalSd       0.1264      0.070      1.807      0.071      -0.011       0.264\n",
       "Exterior2nd_Stucco       -0.0275      0.048     -0.571      0.568      -0.122       0.067\n",
       "Exterior2nd_VinylSd       0.1384      0.064      2.152      0.032       0.012       0.265\n",
       "Exterior2nd_Wd Sdng       0.0809      0.037      2.177      0.030       0.008       0.154\n",
       "Exterior2nd_Wd Shng      -0.0285      0.046     -0.617      0.538      -0.119       0.062\n",
       "MasVnrType_BrkFace        0.0550      0.043      1.267      0.205      -0.030       0.140\n",
       "MasVnrType_None           0.0355      0.043      0.832      0.406      -0.048       0.119\n",
       "MasVnrType_Stone          0.1303      0.047      2.777      0.006       0.038       0.222\n",
       "Foundation_CBlock        -0.0318      0.025     -1.287      0.198      -0.080       0.017\n",
       "Foundation_PConc          0.0624      0.026      2.401      0.017       0.011       0.113\n",
       "Foundation_Slab          -0.0749      0.052     -1.436      0.151      -0.177       0.027\n",
       "Heating_GasA             -0.2091      0.049     -4.252      0.000      -0.306      -0.113\n",
       "Heating_Grav             -0.3494      0.092     -3.808      0.000      -0.529      -0.169\n",
       "Heating_Wall             -0.1856      0.117     -1.592      0.112      -0.414       0.043\n",
       "Electrical_FuseF          0.0622      0.048      1.309      0.191      -0.031       0.155\n",
       "Electrical_FuseP          0.1049      0.119      0.880      0.379      -0.129       0.339\n",
       "Electrical_Mix        -7.539e-17      6e-17     -1.257      0.209   -1.93e-16    4.23e-17\n",
       "Electrical_SBrkr          0.0419      0.025      1.700      0.089      -0.006       0.090\n",
       "GarageType_Attchd         0.1309      0.026      5.085      0.000       0.080       0.181\n",
       "GarageType_BuiltIn        0.1221      0.036      3.434      0.001       0.052       0.192\n",
       "GarageType_CarPort        0.0200      0.078      0.256      0.798      -0.133       0.173\n",
       "GarageType_Detchd         0.0512      0.025      2.046      0.041       0.002       0.100\n",
       "PavedDrive_P              0.0186      0.046      0.407      0.684      -0.071       0.109\n",
       "PavedDrive_Y              0.0748      0.028      2.705      0.007       0.021       0.129\n",
       "MiscFeature_Othr         -0.0687      0.139     -0.493      0.622      -0.342       0.205\n",
       "MiscFeature_Shed         -0.0257      0.030     -0.847      0.397      -0.085       0.034\n",
       "SaleType_New              0.0183      0.018      1.012      0.312      -0.017       0.054\n",
       "SaleType_WD              -0.0393      0.026     -1.512      0.131      -0.090       0.012\n",
       "SaleCondition_AdjLand    -0.1047      0.108     -0.974      0.330      -0.316       0.106\n",
       "SaleCondition_Normal      0.0332      0.021      1.606      0.108      -0.007       0.074\n",
       "SaleCondition_Partial     0.0183      0.018      1.012      0.312      -0.017       0.054\n",
       "==============================================================================\n",
       "Omnibus:                       48.840   Durbin-Watson:                   2.003\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              142.869\n",
       "Skew:                           0.066   Prob(JB):                     9.47e-32\n",
       "Kurtosis:                       4.709   Cond. No.                     1.38e+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 1.29e-21. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generamos las variables\n",
    "y = df_rl_OLS['Log_SalePrice']\n",
    "x1 = df_rl_OLS.drop('Log_SalePrice',axis=1)\n",
    "\n",
    "# Agregamos la constante\n",
    "X = sm.add_constant(x1)\n",
    "\n",
    "# Separamos los datos para poder hacer el entrenamiento y la prueba\n",
    "x_train_OLS, x_test_OLS, y_train_OLS,y_test_OLS=train_test_split(X,y,test_size=.2,random_state=100)\n",
    "\n",
    "\n",
    "# Generamos el modelo\n",
    "linreg_stats = sm.OLS(y_train_OLS, x_train_OLS).fit()\n",
    "\n",
    "linreg_stats.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:20.549317Z",
     "start_time": "2021-01-28T05:39:20.539314Z"
    }
   },
   "outputs": [],
   "source": [
    "# Podemos Observar que obtenemos una R2 casi identica a la anterior, \n",
    "# por lo que asumismos que el trabajo está bien realizado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:21.691608Z",
     "start_time": "2021-01-28T05:39:20.589329Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hacemos una prediccion con los datos de prueba y los graficamos\n",
    "\n",
    "prediccion_OLS_train = linreg_stats.predict(x_train_OLS)\n",
    "plt.scatter(y_train_OLS,prediccion_OLS_train,alpha=.5,color='orange')\n",
    "plt.xlabel('Objetivos (y_test)',size=15)\n",
    "plt.ylabel('Predicciones',size=15)\n",
    "plt.title('Objetivos vs Predicciones',size=18);\n",
    "\n",
    "\n",
    "# Nuevamente podemos ver que nuestro modelo predice bastante bien los datos con precios no tan altos o bajos. \n",
    "# entre mas extremo sea nuestro precio menos eficiente será el modelo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:22.829899Z",
     "start_time": "2021-01-28T05:39:21.704612Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\rikrdinii\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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tXx9Lfw1Nza/Vaur8Im2yQ0ZDhw5l37595ObmUlxczNatWxk+fHjl9K5du3Lp0iVSU1MB2L59O6GhoaaKI4SqykvKSY1OxrlzO7qN6NYi67Sys6LLaD/S96aR/su5FlmnsGwm3UNYsGABs2bNQq/XM336dMLCwpg3bx7z588nNDSUZcuW8fjjj6MoCm5ubvz97383VRwhVHV222nKrpYRfF8fNNqWu4vYJ8KXi/vSOPiP3/Ad1qXF1issk0nvVI6KiiIqKqrKcytXrqz8ecSIEYwYMcKUEYRQXWFGIem7z+E92Id2Xdq36Lq11jrC7u/Lr6/t5dKBC3gPMt6hI9H6yJ3KQpiQoiikfJuIzlaHX2T9HdaZQtBtvbFzs+fQ27+psn5hOaQgCGFCOSeyyU/Opdt4f2ycbFTJYO1gTeicvpzddpq8lFxVMgjLIAVBCBNRDAqnt6Rg7+GAT4SvqlmCZ4WhtdER9/FRVXMI8yYFQQgTyTxyiaKMQrqN90ejU/et5uDpSI8pgZz8Ip7SK6WqZhHmSwqCECZQUVbBmR9ScfJxwiPMuDeFNVXovH7oC/Wc/CJe7SjCTElBEMIEEr9JoCS3mG4TAlr0MtO6ePbxwnuwD8c/OoKiwg2dwvxJQRDCyPSFeg7/6xDt/VxwDXJTO04VYfP6ceXMZc5uP612FGGGpCAIYWTxnx2jOLsIv8gAsxvK0i8yAMeOTsSuOKJ2FGGGpCAIYUTlJeUc/eAQPuG+tPdzUTtONTprHcGzw0jbdZbLp/PVjiPMjBQEIYzo5No4ijIL6ffgALWj1CrozmA0Wo2cXBbVSEEQwkgq9BUcfv8A3oN86DjYfLuIcOroTOdR3Tj5RTyGChlmU/xBCoIQRpL0dQIFaVcZsGCw2Z07+F+97gqh8GIB5386o3YUYUakIAhhBIYKA4ff2Y97qKdJR0Izlm7jumPvbk/Cmji1owgzIgVBCCM4FZ3M5dR8Bjxu/nsHADobHT1v682ZH1IpyipSO44wE1IQhGgmRVE4+sFBXPw70H1iD7XjNFivmSEYyg0kfXVC7SjCTEhBEKKZLv6aTtbRDPo8NMBs7kpuCNeebngN7EjC2rgax0AXbY8UBCGa6egHB7Fzsyfw9l5qR2m0XncGk5eUS1ZsptpRhBmQgiBEM+Sl5HLmh1RC7u2Dlb212nEazX9yT7Q2OjlsJAApCEI0y7F/H0ZnqyPk/r5qR2kS2/Z2dBvXneT1iRjK5Z6Ets6kYyoLYXGKy9EX6hs2a24xievi6TG5J9YaDfrs4j8m6i3nw7Xn9F6kbk4m7eezdBll/pfMCtORgiDEn+gL9SRtSW7QvGe2plJRWoGzn0u1ZfxvspwP1q6ju2HrYkviVwlSENo4OWQkRBNU6Cu4sOc8rr3ccfB0VDtOs+hsrQi4JZDT36egLyhTO45QkRQEIZog89Al9IV6Oo3oonYUo+g5vRflReWkbklRO4pQkRQEIRpJMSik/XwWJ19nXPw7qB3HKLwH++DcpR1JXyeoHUWoSM4hCNFIuSdzKMosIuiuYIvopkILVU941yIgsgdHVxzmckI2Dh7VD4NZO1qDvXxktGby2xWikc7vOotte1s8+nipHaVB9EXlnNpZ/5CZVu1sUAwKv761j07Du1ab3jOyB9ZSEFo1OWQkRCNcTbvC5VN5+N7YBa2udb19HDwdce7UjoxDl9SOIlTSuv6ihTCxtF3n0Nnq6DjEV+0oJuE5wJuC9KsUXipQO4pQgRQEIRqoJL+EzGMZeA/xxaqVHjrx7OsFWg0Zh2UvoS2SgiBEA6XvPgdAp2GdVU5iOjbOtnTo4Urm4UsoBukBta2RgiBEA5QXl3Px13Q8wjyxc7VXO45JefX3pjS/hMtn8tWOIlqYFAQhGuDib+lUlFbQeUT1q29aG/cQD7TWWjLlsFGbIwVBiHoYKgyk/3KO9t1dcO7cTu04JqeztcI9xIOs2EzpAbWNkYIgRD2yjmVSml9Kpzawd3CdZ/+OlBfpyU3MUTuKaEFSEISog6IopO06i72HA2693NWO02I69HTF2tFaDhu1MSYtCNHR0URGRjJ27FjWrFlTbXpqair33HMPkydPZs6cOVy+fNmUcYRotPxTeRSkX6XTiC4WNV5yc2l1Wjz6eJETn0V5SbnacUQLMVlByMjIYPny5axdu5aNGzeybt06UlL+6ElRURT+8pe/MG/ePDZt2kSvXr1YsWKFqeII0SRpu85h7WiN14COakdpcZ79vTGUG8g+LuMttxUmKwh79+4lPDwcFxcXHBwcGD9+PDExMZXT4+PjcXBwYPjw4QA89NBDzJw501RxhGi0wksF5CZk43NDZ3TWOrXjtLh2Xdtj52YvN6m1ISa73TIzMxMPD4/Kx56ensTGxlY+PnfuHO7u7jzzzDOcOHGCnj178vzzzzdqHW5uTkbL21geHs6qrdsYJH/N8osqcHa2AyDlywR0tjoCxwdg42TTqHasrbWV7dSkrmkNbcNYWerSeUgnkr9PxtoADg42uFzb7pb+9wOW/xpMkd9kBUFRqt/l+OeugsvLy9m/fz+rV68mNDSUt99+m1dffZVXX321wevIySnAoMLdlB4ezmRlXW3x9RqL5K+dvqiMq1dLKM4uIv1AOp2Gd6FUMVB6taRx7egNXK1lGWdnu1qnNbQNY2Wpj0uIO2xJ5vQvZ/Gb1AN91lWL//uBtvse0Go1dX6RNtkhIy8vL7KzsysfZ2Zm4unpWfnYw8ODrl27EhoaCsCkSZOq7EEIoabzP51Fo9O2qUtNa+Lg6YhTJ2c5bNRGmKwgDB06lH379pGbm0txcTFbt26tPF8A0K9fP3Jzczl58iQAO3bsIDg42FRxhGiw0sslXDp4gY6DfLBtZ6t2HNV59femIO0q+afz1I4iTMykewgLFixg1qxZTJkyhUmTJhEWFsa8efM4fvw4dnZ2/POf/2Tx4sVMnDiR3377jWeffdZUcYRosPM7z6Io0Hlk2947uM6jrzdoIGVzstpRhIk16BzCY489xp133snQoUMb1XhUVBRRUVFVnlu5cmXlz3369OHrr79uVJtCmFJxThEXf03Hq593q+/ErqFs29niEuBKyuZkwpcMr38BYbEatIcwbtw4PvjgA8aPH8/HH39Mfn6+iWMJoY6jK49gKDfQZXQ3taOYFa/+3lw9f4WMQxfVjiJMqEEFISoqitWrV/PBBx+Qk5PDbbfdxlNPPSUngUWrcjXtCic+j8N7kA8OntUHmW/L3EM90dnqSPo6Qe0owoQafA7BYDBw9uxZzpw5Q3l5OW5ubixZsoQ33njDlPmEaDEH//ErAF3Hdlc5ifmxsrOi68hupGxMokJfoXYcYSINOoewfPly1q9fT+fOnbnrrrt45513sLa2pqioiJEjR/LUU0+ZOqcQJpV/Ko+Tn8cTfFcIdh2afzNYaxQwqQepMadI3ZaKyyBvteMIE2hQQcjNzWXlypUEBQVVed7BwYG33nrLJMGEaEn7X9+LztaKPvP6k7Y/Xe04ZqnTsC7YdrDj+Jrj3CgFoVVq0CGjioqKasXgscceA2DYsGHGTyVEC8qKzSBlQyJ9HuyPg7uD2nHMls5Gh39UT05+exJ9QZnacYQJ1LmH8Le//Y2MjAwOHTpEbm5u5fPl5eWkpqaaPJwQpqYoCruf+wl7d3v6PjwA9DKwfF0Cp/fixH9jOfVdCkF39FY7jjCyOgvC9OnTSU5OJjExkfHjx1c+r9Pp6Nevn8nDCWFqSV8ncOnABUa+PQ7b9nbos4vVjmTWvIf44Brgyskv4qQgtEJ1FoTQ0FBCQ0O54YYb8PLyaqlMQrSIsoIy9r20G89+XgTNkG5TGkKj0dDn3j78tPgnLp/Jp303F7UjCSOqsyD83//9H++88w5z586tcXp0dLRJQgnREg7941eKMgqZ8OnkNjUaWnP1mdWHn57/icR1Jxj8TON6LxDmrc6CMG/ePIBGj1MghLnLS87l2IeHCbozuE2OhtYc7Tu3p/OIriR+eYJBT0VIMW1F6rzKKCQkBIDBgwfTsWNHBg8eTH5+Pvv376dXr14tElC0csXl6LOLG/Uv/2x+tecobvi4v4ZyAzvmx2DtaE34IrlKrimC7gzm6vkrpP9yXu0owogadB/CCy+8AMDs2bN56aWXuPHGG1m0aBHvvvuuScOJ1k9fqCdpS+N60axpgJmekT2wtm/YeE9HPzhIxqFLjP13pHRR0UR+EwKwbW9LwudxdBreRe04wkgadB9CXFwcS5YsYdu2bUydOpVly5aRni437wjLk5OQzf7X99F9Ug8CpgaqHcdiWdlZETA1iNNbUii9Uqp2HGEkDSoIiqKg1WrZs2cP4eHhABQXy+V5wrJU6CvYMf8HbNvZMOL10VWGdBWNFzSjN+XF5ZzamKh2FGEkDSoIXbp0Yd68eaSlpTF48GD++te/Ehgo366EZdm/bA9ZxzIY/tpo7OWO5Gbz7OdNh0A3Ej6PVzuKMJIGHXRdtmwZ27ZtY8CAAVhbWzNw4ECmTJli4mhCGE/qd8kcef8gwbPD8I/qqXacVkGj0RA0I5h9L/5MXnIuHXq4qh1JNFOD9hAcHBwYOHAgV65cIT4+nrCwMOm6QliM/FN5bH/sBzz7eTHslZvUjtOqBE7vhUan4eQXspfQGjRoD+GNN95g9erVuLm5VT6n0WjYvn27yYIJYQz6Qj0x929CZ6Nl/MdR6GwbdiWSaBgHL0e6ju3++01qzw5FZ61TO5Johga9O77//nu2bt0q3VcIi1Khr2DrA5vJPZnDpC9uxblTO7UjtUq97w7lTMwpzsScksNxFq5Bh4w6duwoxUBYFMWgsHPBNs5uO83wV0fTZWQ3tSO1Wl1Gd8PJ15n4/x5XO4popgbtIURERPD6668zevRo7Oz+GE0qOFg6BBPmad9LP//etcLTEYTc10ftOK2aVqel992h7H9tL5dP59Pez0XtSKKJGlQQ1q9fD0BMTEzlc3IOQZirI+8d4OgHhwid05eBfw1XO06roYXfuw4pqkBfVHWAnIAJARx4cx9xK48w+Im6t7m1ozU08K5y0bIa9FvZsWOHqXMIYRQJa+PY9/JuAqYGMmzpSLn5zIj0ReWc2nm6xq5DAFx7uXPi8zjaBXRAa1X70ejGdDMiWlaDziEUFhby0ksvMXv2bPLz83nhhRcoLCw0dTYhGuV0zCl2PrGNzjd1ZfR7N0svnC3MJ9wXfaGe7LhMtaOIJmpQQXjllVdwdnYmJycHW1tbCgoKKju8E8IcXDx4ga0PbMajrxc3/ycKnY1c/tjSOvR0w66DHRf2ST9nlqpBBSEhIYEFCxZgZWWFvb09b775JgkJCabOJkSDFFy4yg8Pf49zp3ZMXDMVaycbtSO1SRqtho4Rnbh8Ko/CSwVqxxFN0KCCoNVWna2ioqLac0KooTiniNiVR7Bxsibqy2nYu9mrHalN6zjEB42VVsZJsFAN+lQfNGgQb7zxBiUlJezevZtHH32UIUOGmDqbEHUqu1JK7IojKBUKE1ZMkhvPzIC1ow1e/b3JOHQRfZFe7TiikRpUEJ588kkcHBxwdnbm7bffJigoiKefftrU2YSoVUVpOcc/PkrZlVJC5/SlQ4B0rGYufG/ojEFv4NL+C2pHEY1U77Vf27Zt4+OPPyYxMRE7OzsCAwPp378/tra2LZFPiGoMFQZOrDpOwYWrhNzXl3Zd26sdSfyJk68z7f1cuLA3jU7Du8jVXhakzoLw7bff8sEHHzB//nyCgoLQaDQcP36cpUuXUlpayrhx41oqpxDA74M1HV97nNyTOfSYHoRbb3e1I4ka+A7rzIlVx8lJyMY92EPtOKKB6iwIq1at4tNPP8XHx6fyOX9/f/r06cPChQulIIgWd/6ns5z/5TxdRnfDJ7yT2nFELdxCPLBtb0v6L+elIFiQOs8h6PX6KsXgOj8/P0pLZRxV0bJyErI5/X0KPoN86Hazv9pxRB20Oi0+QzuRn5xLwYWrascRDVRnQdDpar+5R1GUehuPjo4mMjKSsWPHsmbNmlrn27lzJ6NGjaq3PdF2FWcXcXJtHI4dnegzq490SWEBOkZ0QmujI23XWbWjiAYy2c0EGRkZLF++nLVr17Jx40bWrVtHSkpKtfmys7N57bXXTBVDtAIVpRXEfxYLGg3Bs/vIXcgWwtrBmo5DfMg8kkFpfvW+j4T5qbMgJCYm0r9//2r/+vXrR1JSUp0N7927l/DwcFxcXHBwcGD8+PFVeku9bvHixTz66KPNexWiVUv6JoHCSwX0mhkiN55ZmE43dkEB0nbLjWqWoM6Tytu2bWtyw5mZmXh4/HEyydPTk9jY2Crz/Pe//6V379706SP91YuaZRy+SObhS3Qd1x3XQLf6FxBmxc7VHo8wTy7+mkbXMX5YSS+nZq3O346vr2+TG67pHMOfj/smJSWxdetWPv30Uy5dutSkdbi5OTU5X3N5eDirtm5jMJf8+UUVODvb1TitKLuIlPWJuAa4EjK1V5Xr2f93GQcHG1yM8JrqytMY1tbaOttpyDrqa8NYWZrSTmPaC4zswS9HM8g9moH/OH+j/a6ay1zeA01livwmK9deXl4cPHiw8nFmZiaenp6Vj2NiYsjKymLatGno9XoyMzO56667WLt2bYPXkZNTgMFQ/8ltY/PwcCYry3KvnDCn/Pqishr71lcqDBxdeQgFhR6396Kg8I+r2mrqj7+oqAy9EV5TbXka3Y7eUGs7tY0n0Jg2jJWlKe00NP91Olc7XPw7cOrHVNwHdTTa76o5zOk90BRNza/Vaur8Im2yk8pDhw5l37595ObmUlxczNatWxk+fHjl9Pnz5/PDDz+wceNGVqxYgaenZ6OKgWjdzm4/w5Uzl+k5rRd2rnLewNJ1HtWNssulZBy8qHYUUQeTFQQvLy8WLFjArFmzmDJlCpMmTSIsLIx58+Zx/LgMxi1qV3DhKud+PI1nP288+3mrHUcYQYeerjh3bse5HWcw6CvUjiNqYdIzPFFRUURFRVV5buXKldXm69SpkwzTKYDfDxUlfZmAlb0VAVN6qh1HGIlGo6HLGD/iPzlGypYUguf0VTuSqIEMaiDMStru81xNu0LA1CCsHWWgm9bErbc7jj5OHP3wEIYKg9pxRA2kIAizUZxdxJmYU7gFe+DRx7P+BYRF0Wg0dB3tx+UzlzkVnax2HFEDKQjCLCiKQtJXCWistPS4NUi6pmil3EM9cenegUPLf0VR4QpBUTcpCMIsZB3NIP9UHt0jA7BtL2NttFYarYZ+D/UnNyGHlE1193YgWp4UBKG68pJyTkUn4+TrTMfwpt8MKSxD9wkBuAa5ceC1vRjK5VyCOZH7yIXqzv14mrIrpfSeFdrk0bW0gD67uPlh9PIBZWpanZbBzwwl5r5oEr86Qa87Q9SOJK6RgiBUlZ+aR9rP5/Ae1JH23Vya3I6+qJxTO083O4//TX7NbkPUzy8yAI++Xhx881d63hqEzlY+isyBHDISqlEUhb1Lf0Fro8MvsofacUQL0mg0DHnuBq6ev8KJ1XFqxxHXSEEQqkndnEz6vjT8bu6OjbPcc9DWdL6pKz4Rvhxa/hv6Qr3acQRSEIRK9IV69rywC9dAN3wiZGzktkij0TBk0Y0UZRZy9F8H619AmJwUBKGKw+/upyD9KkMXDUOjkz/DtqrjYB/8o3pw5P0DFF4qUDtOmyfvRNHi8lPzOPLPg/Sc3ouOA33UjiNUFr74Rgx6A7+9ukftKG2eFATRohRF4ZdFP6Gz0RHxtxvVjiPMQHs/F0Ln9OPk5/Fkx2WpHadNk4IgWtSZH1I5t/0Mg56KwNFLvRHvhHkZ8MQQbF3s2Pu3XTWOtihahhQE0WLKi/X8svgnOgS6ETq3r9pxhBmxc7Fj0JPhpO0+x9kfm38/iWgaKQiixRx+7wBXz11h+LKR6Kx1ascRZiZ4dh/ad3dh34s/S5cWKpGCIFrE5dP5HHnvAAFTA/Ed1kXtOMIM6Wx0RLwwnLykXE78N1btOG2SFARhcoqisHvhDrTWOm54cYTacYQZ85vgj88Nnfjttb2U5BqhbyrRKFIQhMmd3pLCue1nGPx0BI7eciJZ1E6j0XDj0pGUXSnl17/LZagtTQqCMCl9oZ5fFu/EtZc7oXP7qR1HWAC33h6EzunLiVWxZB7LUDtOmyIFQZjUweW/UpB+lRGvjUZrJX9uomEGPT0UezcHdj+7XUZWa0HyDhUmk5ecy7F/HSJoRrAMfCMaxbadLREv3EjGoUucXBevdpw2QwqCMAlFUfj52e1YO1gT/rzckSwaL/D23ngP8uHXl3dTkl+idpw2QQqCMInEdSdI332eIQuH4eDhoHYcYYE0Wg3DXx1FSW4JB17bq3acNkEKgjC6wowC9jy/k47hvgTPDlM7jrBg7qGeBM8OI+6TY9LPUQuQceuEUSmKws/P7KC8tJyRy8c1eYxk0Xo1dvzrfvP6k/JtIrue3EbUqiloNL//TVk7WoO9fIQZk2xNYVSnopM5vSWFiOdvxMW/g9pxhBlqyvjXXcb4kfRVAr+8shuvAR0B6BnZA2spCEYlh4yE0RRnF7H72R149PGiz18GqB1HtCLeg3xw7tyOU5uTKS8uVztOqyUFQRiFoij89MQ2Sq+UMuqdcXLPgTAqjVZDj1uD0BeUcWbrKbXjtFryrhVGcWLVcc7EnCJi8TDcenuoHUe0Qs6d29FxiC/pe9IouCjDbZqCFATRbPmn8tjzwk46De9C2AP91Y4jWjG/Cf5Y2VmRsuGkDKRjAlIQRLNUlFWw7S9b0NlaMeq98XJVkTApa0cb/Cb4czk1n1PfJasdp9WRgiCaZd9LP5N1NIOb3hyDU0dnteOINqDjEF+cOjnz2xv7KLtaqnacVkUKgmiylI2JxK44QtgD/fCP6ql2HNFGaLQaekwNoiiriANv/qp2nFZFCoJokrzkXH56fCteAzsS8cJwteOINqZd1/YETgvi+Moj5CbmqB2n1TBpQYiOjiYyMpKxY8eyZs2aatN//PFHbrnlFiZPnszDDz/M5cuXTRlHGIm+oIwf5kSjs7Ni/EeT0NnI+Mii5Q1aEI61ozW7n9shJ5iNxGQFISMjg+XLl7N27Vo2btzIunXrSElJqZxeUFDAkiVLWLFiBZs2bSIwMJD33nvPVHGEkSgGhR8f/p68pFxGvTYGWxsr9NnFTf6HXgZTF01j72rPkOduIP2X86RsTFI7Tqtgsvu+9+7dS3h4OC4uLgCMHz+emJgYHn30UQD0ej1LlizBy8sLgMDAQKKjo00VRxjJr0t/4XTMKSIWDqMwv5ikLc270sP/Jj8jJRNtUe/ZYZxYE8fev+2i2xg/rJ1s1I5k0Uy2h5CZmYmHxx83KHl6epKR8cdweB06dGDMmDEAlJSUsGLFisrHwjyd/CKeI+8dIHh2GMEzQ9SOIwRanZbhr46i8GIBB/8hJ5iby2R7CDUd07veS+GfXb16lYcffpigoCCmTp3aqHW4uak3YLuHh2VfYtnY/Gd2nWHnX7fhN9qPqSsnc/XCVZyd7Zqdw9pa26R2/neZprZjrDyNbach6zDn19Sc9oyVx8HBBhcPZzwiA0m9ty/H/n2YoY8MwT3IvUHLt7X3cEOYrCB4eXlx8ODByseZmZl4enpWmSczM5M5c+YQHh7OwoULG72OnJwCDCqMt+rh4UxW1tUWX6+xNDZ/dnwW305eR3s/F0b+awK5+UXoi8q4erX5o1jp9YZGt+PsbFdtmaa0Y6w8jW2npvxqZWlKOw3Nb+o8RUVl6K/9Hfd9cggn1iew8cFoor6aVuOXzz9ra+/h67RaTZ1fpE12yGjo0KHs27eP3NxciouL2bp1K8OH/3F5YkVFBQ899BATJkxg0aJF9f4ChTqunLvM5hnrsXayYdK6adi5NP+bnRDGcH1cBX12MdZaLQMeGUjaz+dIWhtf78UM+Wfz/7iwQXpPrWTSPYQFCxYwa9Ys9Ho906dPJywsjHnz5jF//nwuXbrEiRMnqKio4IcffgAgJCSEpUuXmiqSaKTinGI2z1hPRUk5U6PvwNnXsnexRevyv+MqWLvY4tjRiV9e3EVpkR6dbe2XQ/95L0fGVfiDSbdCVFQUUVFRVZ5buXIlAKGhoZw8edKUqxfNUHq5hOjbv+Fq2hWivpyGawOPywqhFo1OS49bgzj6z4Oc3X6a7pEBakeyOHKnsqhGX1DG5js3kHsym5s/mYxPeCe1IwnRIO39XPAa4E3arrMUZRWqHcfiSEEQVZQX69kyayOZRy4x9sOJdB0t9wkIy9J9Yg+01lpSNiTKHcyNJAfO2qDivOIaBzmvKKtg2/wY0vecZ+Sro+kS3qn2wdDlDmNhpmza2dJtvD+nNiaRHZeFR6hn/QsJQApCm1R6pbTaHcaGCgMJq+PIPp5Jz+m9MOg0dd6FLHcYC3PmO7QTl/Zf4NTGRFx7utV5gln8QQ4ZiSrFwP+WnnQM91U7khDNotFpCZgaSGl+Ked2nK5/AQFIQWjzqhSDyT3pdGMXtSMJYRQu3TvgNcCb8zvlBHNDSUFow34vBscr9ww6DZdiIFqXyhPM3ybJCeYGkILQRhnKDSSsOk728azfi4HsGYhW6PoJ5rzEHHListSOY/akILRBFWUVnFh9nOy4LAKmSDEQrZvv0E44dnQiZWMSFWUVascxa1IQ2piKsgq2PLKFnLgsAqYE4jtMioFo3f44wVzCue1ygrkuUhDakIqyCrbO3UzqttRrxaCz2pGEaBEu3Tvg2f/6CeYiteOYLSkIbUR5STkx90dzOuYUN710kxQD0eb4T/r9BHPy+pNygrkWUhDaAH1BGd/N3MDZbamMeGMMfWb1UTuSEC3Opp0tfhMCyE/OJW1fmtpxzJIUhFau9Eop0Xes58KeNEa/dzPBs8PUjiSEanwiOtHOz4UTX52g7Gqp2nHMjhSEVqw4p5hNt35F5tFLjFs5kcDbe6sdSQhVabQaAm/rRUVZBSkbEtWOY3akILRShRkFbJz6JblJOUz4bDL+UT3VjiSEWXDwdKTHxB5kxWaSHZepdhyzIgWhFbqadoVvJ3/JlXNXmLR2Kl3HdFc7khBmxX+8P44+TiR9c5Li3Fp69G2DpCC0MjkJ2ayf+AXFOcVM/mqa3GcgRA20Oi1Bd4ZQXlzO7ud3ylVH10j315akuBx9ob7WyRcPXmDro99jZWvFpE8n4+bXocbxDDQa+R4ghFNHJ7pHBnBqUxIJq4/T+x654EIKggXRF+prHaMgKzaDhLXx2LvaETq3HzmpeeSk5tU4b9BYf1PGFMJi+A7rTHFmIb88vxOfGzrj0r2D2pFUJV8VW4H0X85zYtVxnDs50/eRgdi52qsdSQiLoNFqGPH3UehsdGx74DvKi2vfA28LpCBYMKXCQMrGRFK+TcSttwdhD/bH2tFG7VhCWBQnbydGv3czWbGZ/PzMjjZ9PkEKgoXSF+mJ/ego6bvP43tjZ4JnhaKzlmEChWiKbuP9GfjXcE5+EU/8J8fUjqMaOYdggQovFRD3yTFK80sIvL033oN91I4khMUb9FQEmccy+GXxTtyCPeg4pO0NJSt7CBYmOy6TI+8doKKsgj5/GSDFQAgj0Wg1jPlgAk6dnIm5bxP5p2q+KKM1k4JgISrKKtj32h7iP43F3sOBAf83mPbdXNSOJUSrYudix6TPbwVg021fU3DhqsqJWpYUBAtwOTWP9RO/IO6zWHxu6ES/RwZi62KndiwhWiUX/w5M+uJWSvNL2XTbNxTntJ07maUgmDHFoHD846OsG7WaK2fzGfvuzfSYGoRWTh4LYVIeYV5Err6Fq+cvE337NxRlFqodqUVIQTBTl0/ns/HWr9j93A46Dvbh9h330G2Mn9qxhGgzfId25uZPJpOfksv6SV9wuZYbPVsTKQhmRl+kZ//re/lixGdkH89k5NvjmLTuVpw7tVM7mhBtTtfRfkz+ZjplV0pZP+kLMo5cUjuSSUlBMBOKQSFlYyKfD/uUg2/+it/NAczYPZted4Wg0WjUjidEm+U90Iepm2dg5WDNt7es4/jHR1vtzWtyH4LKFEXh9JYUDrz5KznxWbgFezDmnxPwieikdjQhxDUdAlyZ9v1d7Pi/H9j93A7Obj/NqLfH4eDpqHY0o5KCoJLyYj0pG5M49uFhcuKzaN/dhdH/vJketwah1cmOmxDmxsHDgYlrphD3n6PsXfIzXwz/jIFPRhA8KwydTeu40EMKQgvLScgm6asTJKyNoyS3hA6Bbox672Z6TgtCayWFQAhzptFoCJ3TD98bOrN74U/8svAnjq88wpBFw+geGWDx72EpCCamGBRy4rM48+NpUr49SW5CDhqtBr8J/oTO6YvPDZ3lHIEQFsY1yJ3J30zn3PbT7HtpN1vnbsbRx4nge8LoNTMER28ntSM2iUkLQnR0NP/617/Q6/Xce++9zJw5s8r0hIQEFi9eTEFBAQMHDuTFF1/Eysqya1SFvoLchGwyDl3i0oELnN91luKsIgC8B/tw47JR+Ef1aHXHHoVoazQaDV3HdKfzyG6ciTlF/Gex7H9tL/tf34tX/450HdedrqO74dbbw2L2HEz26ZuRkcHy5ctZv349NjY2zJgxgyFDhhAQEFA5z1NPPcUrr7xC3759WbhwIV9++SV33XWXqSIZhaIolOSXkHsym4KLBRReKqAg7Sp5ybnkJeWQfyqPitIKAOzdHeg0vAudb+pK55FdcfSyzG8NQojaaXVauk/sQfeJPbicmkfi1wmc/fE0+5ftYf+yPVjZW+Ee4olHH09c/DvQrpsL7bq2x8HTERtnG7M6QmCygrB3717Cw8NxcXEBYPz48cTExPDoo48CkJ6eTklJCX379gXg1ltv5d13321UQdBqG78hK0orSN6QQGleKQaDgkFfgWJQMOgNGMoNGCoMKOUGDOUK5cV69AVl6IvKr/1fhr5QX/mBX0kDzr7t8OrnTc9bg3Dr7YF7iAeOHZ2M+svWWWuxa2drhHZ0zW5HZ9P8Nprajo2jDXaaqpf9qZmnse3UlF+tLE1pp6H5WypPU/z5NeistU36LKlJhwBXwp+9gfBnb6A4u5hLB9LJScgm50Q2F35J49y201Xm11prsXWxw7a9HbbtbNDZWqGz1WFlZ4XWVoeVrRU6Gx0anQaN7vecWmst/Wf2QWvf+L2O+l6nyQpCZmYmHh4elY89PT2JjY2tdbqHhwcZGRmNWkeHDk077OL5SHiTllOdmxNeAe5Gacqjl0f9M9XDJ9TLCEmkHVO3Ie2oxM2JToHNf5+1JJMd2Krpxo0/f1uub7oQQoiWZbKC4OXlRXZ2duXjzMxMPD09a52elZVVZboQQoiWZbKCMHToUPbt20dubi7FxcVs3bqV4cOHV0739fXF1taWQ4cOAfDtt99WmS6EEKJlaRQTdsoRHR3Nhx9+iF6vZ/r06cybN4958+Yxf/58QkNDOXnyJIsXL6awsJDevXuzbNkybGxkkHghhFCDSQuCEEIIy2EZd0sIIYQwOSkIQgghACkIQgghrpGCIIQQApCC0CjvvPMO7733Xo3TLly4QL9+/bjlllu45ZZbmDNnTgunq19d+cvKynjqqaeYMGECU6dO5dSpUy2crnYXLlxg5syZ3HzzzfzlL3+hsLD6gOfmuP2jo6OJjIxk7NixrFmzptr0hIQEpk2bxvjx41m0aBHl5eUqpKxdffnff/99Ro4cWbnNa5pHbQUFBUyaNIm0tLRq08x9+0Pd+U2y/RVRrytXrijPPfecEhYWprz77rs1zhMTE6M8//zzLZysYRqS/6OPPqrMv3//fmX69OktGbFODzzwgLJ582ZFURTl/fffV15//fVq85jb9r906ZIycuRIJS8vTyksLFSioqKU5OTkKvNMnDhROXLkiKIoivLcc88pa9asUSFpzRqS/8EHH1QOHz6sUsL6HT16VJk0aZISHBysnD9/vtp0c97+ilJ/flNsf9lDaIDt27fTrVs37rvvvlrnOX78OElJSdx6663MmjWLxMTEFkxYt4bk37lzJ5MnTwZg0KBB5OXlceHChZaKWCu9Xs+BAwcYP3488HsniDExMdXmM7ft/+fOHR0cHCo7d7yups4da3pdaqkvP0BcXBwrV64kKiqKl156idLSUpXS1uzLL7/kb3/7W409IJj79oe684Nptr8UhAaYMmUKDzzwADpd7cPk2draMmXKFNavX8+cOXN45JFHKCsra8GUtWtI/po6G7x06VJLxKtTXl4eTk5OleNk1NYJorlt/5o6d/xzbmN07mhK9eUvLCykV69ePPPMM2zYsIErV67wwQcfqBG1VkuXLmXgwIE1TjP37Q915zfV9rfs0WiM7Pvvv2fZsmVVnuvevTuffvppvcs+9thjlT+PGDGCt956i9TUVIKCgowds1bNyV8TrbZlvy/UlL9bt27V5qupE0Rz2P5/plh454715XN0dGTlypWVj++//34WLlzIggULWiRfc5n79q+Pqba/FIQ/mTBhAhMmTGjSsqtWrWLSpEl06NAB+P0PrqVHf2tOfk9PT7KysujatSugTmeDNeXX6/UMGTKEiooKdDpdrbnMYfv/mZeXFwcPHqx8bGmdO9aX/8KFC+zdu5fp06cD6m/vxjL37V8fU21/OWRkJAcOHODrr78GYP/+/RgMBrp3765yqoYbMWIEGzduBODgwYPY2tri4+OjciqwtrZm4MCBbNmyBai9E0Rz2/6W3rljffnt7Ox44403OH/+PIqisGbNGsaOHati4sYx9+1fH5Ntf6Oeom7l3n333SpX6axdu1Z5++23FUX5/aqMe++9V5k4caJy6623KgkJCWrFrFVd+UtKSpSnn35aiYyMVKZMmaLExcWpFbOatLQ05e6771YmTJig3H///Up+fr6iKOa//Tdt2qRMnDhRGTdunLJixQpFURRl7ty5SmxsrKIoipKQkKBMmzZNufnmm5UnnnhCKS0tVTNuNfXlj4mJqZz+7LPPml3+60aOHFl5lY4lbf/rastviu0vndsJIYQA5JCREEKIa6QgCCGEAKQgCCGEuEYKghBCCEAKghBCiGukIAghhACkIAghhLhGCoIQQggA/h/101T5myCkyAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vamos a graficar los residuos\n",
    "x_test_OLS=x_test_OLS.reset_index(drop=True)\n",
    "y_test_OLS= y_test_OLS.reset_index(drop=True)\n",
    "\n",
    "# Hacemos las predicciones con el de prueba\n",
    "prediccion_OLS = linreg_stats.predict(x_test_OLS)\n",
    "\n",
    "sns.distplot(y_train_OLS-prediccion_OLS,color='purple')\n",
    "plt.title('Residuales',size=18);\n",
    "# A diferencia del modelo anterior, tenemos una distribucion ligeramente sesgada a la derecha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:23.530072Z",
     "start_time": "2021-01-28T05:39:22.837896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OOy4nJ4fFixczefJkfvrTn7Js2TIefPBBnE4nqqqm96mqqurSoM9UV/v0Mbi6dO7BxlCXD4SM3WF87lgaIg14bd70toZIA+Nzx/bKta7OuYxMr4PVe1dzrOkYFxVcyNIZ32Ny3mR2VO5g9d7VvLj//2OkZyTFOcX8pfIdpuRfxIngCWpCNYT9Qb5/1fe5+oLLznqtO3K+xiVjpqSvNdIzkvkT56dLV3SWr01dwE8++Alx2YzH6qEp2kRMCvG1qXf1yj0ZzN/TLjWmueaaa3jxxRcpLExp0KqqKhYtWsQNN9zAPffcc8Zjm5qauPbaa/nDH/7ATTfdxPvvvw9AMpnk4osvZufOnZ0etGhMk2KoywdCxu7SUZOY3o6AaeuQnph5IRuOrm913S3Vf2OsazxjM8alj2t2SPdm6Gd3xttb+Q4D/XvaZ41pDh48yJ133sktt9zC7bffftr7gUCAN954g9tuuw0AwzBQVZXMzEyCwSCapqEoCjU1NWmTk0Ag6B16mmDWGdqLwvnptpXpAnCQMhUltAQVofJWiuBc2eaHY0RQZ+iWIggGg9xxxx3cd999zJs3r9197HY7zz33HBdffDFTpkzht7/9Lddddx0mk4mSkhLWr1/PnDlzWLNmDVdddVWPhBAIBKfT15NeeyGhSf30ST/HnkNFoLX5V9jmBxbdSih7/fXXqa2t5YUXXkiHhj711FMAPPLII7z77rsoisKTTz7Jo48+ysyZM9m9ezf3338/AEuXLmXVqlXMmjWLbdu28a1vfavXBBIIBP1Dew7pTGsWddG6VtsK3YWYZLVXawIJehfRvH4QM9TlAyFjf9Bdu/nKLStOSz471HiQvQ17uHzElWkfQUwKMT3vOvbW7zln3dP6mnP9GZ6Ns/kIRIkJgWAY02znP9R4kMONh1h34Pfc86fFrDuw5qzHtpd8pigK3576YDoPIa7FcZgcvHvs/wBYPPnuDkNSBecOoQgEggFIf3XJeuvQOjRNY2/DHmJ6jExbFkjw048eP+s1O0o+mzvuRh649Lssnnw3ES2MWTGfs/aZgs4hag0JBAOM/uySdSJYRnnwBBbFilVJ1e1xm9xUhMr57qb7Ge0Zc0ZTzpkc0s3OZK/NSygUG9BNXvqyjPZgQKwIBIIBRk9q4nR1JVHgLKQ+WodFsaS3NcYaCCWC1EZre/QkP9BLOjTfq1vX38w9797JwcYDw3blIhSBQDDA6O4E2p1Cc6VFc1FlE/64H8MwiCaj1EZrcZk9ZFuze1ScbSCXdOhqGe2eXKc/THw9RSgCgWCA0d0JtDsriUnZxXy75AHAoC5Wh0W1YFfsWFUL53snpPfrzpN8szO5IdIw4MJG2yujbVEsfNqwD+idlUt3K8CeC4SPQCAYYHS3S9aJYBn5jhGttnVmQps77kbGZoxL28glJPIc+fjspzL+u/ok32xzD8T8VFdVYJMcFGdP7vXs5u7S8l65zG6iWvSMZbS7Q3d7MJwLhCIQCAYY3S0P0ZN+uy2dvi2fZLvTrrGls/vCrGKSSpSKpuoB5YBtea/GeyewtepDYloMt9mdXrn0tD1ldxXzuUAoAoFgANKd8hC91W+3p3WK2j4Je21ewpH4gHoSbnmvmsto763/JF1GuzdWLj1RzP2NUAQCwQClO60me6vQXFtF1Oz07MxYBsOTcNt7VZQxlns/e1+vKqreUsz9gSgxMYgZ6vLB8JWxv8pId4aOxnL9qFntlo1oW3rC4bBwor7qnJSd7i86+p4OlPyEPitDLRAI+o6B4Ghcd2ANz+/8Jbtqd6AbBl5LBue5RzHeO4GkluSn21ZyecHnT0t6a/sk3BBp6JUn4YEyqXaFwVL2WigCgWAAciJYhiqpbK7dRCDux2V2My7jfE4k+se8su7AGv7r7z8gqWmEEiEMDCLJMJqu0xRvRJVUgokAu2t3Uh2uIqbFkCWZMv9xVlz541Zml/G5Y/ni6Jt7NCH2Z7b1cEQoAoFgAGKWzWwu34TL7MZpdhHVonxQsZkrRlzZ5XO110XsbJVAn9/5SxQUqmPVyJKMhIRu6NREq/FYPRwNHUGVTTREG2iKNYIkYeg6J4Jl6Qm62QzUU/Pe7tpdfHfT/dRGa8m2ZnO+d0I6tHUgOaAHM0IRCAQDEKPlH8apDQZdM5G0fZI+1HiQ/93/GmPd5xNOhviocivrD/2Bb5c8wNxxN6aPqwxXENNiGIaBWTET1+InlYFBQ6yBaDJKgTObYCKAKptQZZWoEUU39FZJbG8dWkddsoosNbdbppzm8ddFasm0ZhJNRtlWuYWSvEvJtmWf5oAejOajgYDILBYIBiAJPc5l+VdgVa0EEgGsqpXL8q+gOlzZpWzVttnGFaFyFFR21H18xmqjefZ8QokQJllFQsZ8shaRLEkk9AQ51hwsqoVwIowsyST1JGBgli24zW521e5Ij7PQXcjBxgPc8+6d3Lr+5i6VWmgef5Ytm6ZYIzWRaipDFbx37B0ONx1qFYo5mDJ5BxpiRSAQDECaY9CvKDhlCmqMNdIUa6LQNbJdJ3Lzv7tqd9AUa8Jj8XA8cJzP+qZSo1Wzv2Efn9TvJqklkWWlVbXRulhdKzPLHRd9g2/9+W7iyTgJI4mMhCwp+Gw+PDYPJbnTCMT9BOIBwskwdtWOx5xBpi0zFVXUYpzVoWr21e8FSaIx2tgl+35zKGq2LYc9dbswKWasSko5/qN6W6tyFQPBwT5Y6bQiCAaD3HzzzfziF7+gsLCQ3/3ud7z00ktIkkRxcTGPPfYYZrO51TEfffQRK1asIJlMkpGRwYoVKygoKGDr1q38+7//O3l5eQBceOGF/OhHP+pdyQSCQUR7dvwNR9cDrWPQPRZPuwXpdtXu4GjgCJqmcdR/BEmSaYw1YBgGG8vewySb8FgycKhOauLVKCiE4kEcZicxLUaWNauVmWVsxjjGuIr4tHE/CjKaoaEbOiEtxKLzv8nVI6/hme1Pc2ne5/ikfjeSJGMYOnmO/NPGubd2LxbFgkW1EIwHujRBNyvE2kgN+Y4CgokA4UQYl8nFVN8l7K3fw1xuBAZH/sJApVOmoe3bt7NgwQKOHDkCwOHDh3n++ed57bXXWLduHbqu88orr5x23P3338/y5ctZu3Ytc+bM4Yc//CEAO3fu5Pbbb2ft2rWsXbtWKAHBsGZH5Y7TTBobjq7n+lGzTmv6Upw9OV0PpyZczeYTm/jDwbXsqt2JpmlUhMqxqjY8Zg9WxYZZNhOMB/HH/FhkCy6zC0mSUDFRF60jmowS02LkO0a0MrO8dWgd0wou57pR15Nh9eK1ZuK1ZCIj8//2PM9vdj3H9aNmUZQxllHu0XjMHka5RzM2Y9xp42yKNmFRLMS0GK6TyqGzE3Rz4bq6SC0ei4ccm488Rz7XjLyO0Z4xrc4xkKudDnQ6tSJYtWoVS5cu5YEHHgDAbDbz6KOP4nSmEhTGjx9PeXl5q2Pi8ThLlixh4sSJAEyYMIHf/va3QEoR1NXV8cc//pG8vDyWLl1Kfn5+rwklEAwmVu9d3a5JY2/9nnYTsJ7Z/jT1kbr0k7giKyS0BO+X/TlVL8fiwS7bCGsRQskQGAYxPcHBpgMnz++lIVZPdSQCwAhHIfsaPiGSDLNyywpKi+amn653R3dynmskmpakPHQC3TDIsHjZUbOdsBZuFR3UzO7aXdSEq/lL2Z/xWjORZRl/wg8YTM6eAnStBtLdU/6Db713N5827EeRFfLt+UjtnGMwZfIONDq1Ili+fDklJSXp1wUFBVx++eUA1NfX8/LLLzNjxoxWx5jNZubNmweAruv87Gc/49prrwXA5XJx6623smbNGqZPn859993XK8IIBIORY03HTjP3xJJR3j781ml17JsnxopQOUlDw2P2MNZzPhoamqFjGBCMBTkWPEYoEcKhOgCJWDJl/sl35BPX45hkc+oJX1bY07CLSDzMcf8x3j36Dj/6cBkm2Yw/7icQ92NRLNTH6gEJu8mOVbWS0OPtlrhudtiaFDNXFkwHA6pD1USTESZ6LyTLlt2tctQ+Ry6ZtkwKnIXIssLm8k0c8x9pdY6OWmcK/8DZ6ZGzuKqqikWLFvGlL32JadOmtbtPPB7noYceIplM8o1vfAOAZcuWpd9fsGABTzzxBIFAAJfL1anrnilVui05OZ0752BlqMsHQ1/GkZ6RNEQa8Nq8AFQGK9la/XfcFjfjfGNoijbxwt5n+c5l32Fy3mSuzrmMifvHU+i+BlmS2XhkI3nOXKpD1RjoxLU4AHE9RqG9gHAghMVkwZ9sQtVUTIqKLEuc5ykECQLxJmJGjPOc5xFNRjkWPILH7sKQrNgtNjQjQVSLIMkSuS4fupQky5lJvjeHMn9Zq89n4+4N5Ht8aVmKfKNoiDQQ02LkOfM41nSMkZkjuXfiXUzOm9yp+7Nx9wYuyJ1AUfZo9tbupSnaRKbdy5jsUVx9wWWt9r0657LTtvUXg/l72m1FcPDgQe68805uueUWbr/99nb3CYVC3HXXXWRkZPDss89iMpnQdZ1f/vKXLF68GEVRTg1E7fxQRK2hFENdPhj6Mu6u3UVVsIq3P92A15pJcdZF7KrbSVLTuCCjmEg4QVM4yK6aPSx8/RZuGFNKadFcstRcKhpqiGtx9lR/gmEYGCfDNzVdQ5ZlJElCNlQwQDZkGiKNqJKC2+Ih15xJNBGnOlyFWbaQ0JIkEhoKJqyynT3Ve/nZjF/ym13P8ZeyP6NIJrwWD7oGES3EBRnFVDTUkGXJbfX57K86SL5jBKFQLL3NY/dwoPow/zXtP1vJ3tnPtfmcZsnOJTmpSV43dCpC5QPmuzHQv6dnqzXUrTyCYDDIHXfcwZIlSzpUApByFo8aNYqnnnoqHVEkyzLvvPMOGzZsAGDNmjVMmTIFm83WnaEIBIOWZjOKuYUZ5d1j73Ck6RC6rrO/YR/76veytepDjJP/NYdeTsy8kF0123nz4BoCMT+hRJC4FsdpdlLkGUuOPRe7audo4AhxLQGA2+TBolpxmlyoiiltjoolo0STUQ40fsrxwDHCiTASKVPLj69+kl/f8DLXj55JVIuBAVN9l2BSzO2ad9pz2DZFm3rksBVO4L6nW4rg9ddfp7a2lhdeeIF58+Yxb948nnrqKQAeeeQR3n33Xfbs2cO7777LP/7xD2688UbmzZvHnXfeCcDjjz/Oiy++SGlpKW+88UY6mkggGE40x717bV5yHXlMzpmC3WTHrFhxWzypshLlm9F1HQkJt9mTbj/5ftlGqiPVGIZBUk8S0SIEEwFqw7VUh6qojlQS0+JYZAsm2UQ4GcaqWsi2ZlEdriKQ8DMu43zsqp1QMoQiKZgkE9FklIrQCUa7i9LjnJRdzE9OKoQZo64jaSQ7tL83R/k0xhrT7Skbog09ak/Z3jkHSsvLoYIoQz2IGerywdCWccl7d6NKKsfCh6kL1tMYa8ShOknoCayqFYti4UjTYcyqmSxrNiV5l+Kz+9ANnf878sdUVEy0kZgeB8MgoScx0AHwWjKJaGEsigWHyYlJUgklw+iGnrLX2/PQ0amL1pPQYlhVG3EtRjSZKhORbcvmm1PuJZQMdrlcQ9uciK9NXUC+MqZH92qgl44Y6N9TUYZaIBigNBeWy7R7cZpdlIfKCcWDjHSPYqRrFP+s+QdhLUQkGeHS3GnpQmv+uB8D0HSNuBFHkRRkWUaWZKJaFEmSsKgWTIqJmBbDrtqpCdekqobKJpyqg6gew6KYybJmokomykNlRJIRTLIZp8lBKBnmJx/9iGm5l/OZ3Iu7lA3ctvRyb0ySg6Wc82BFKAKBoIv01tOp0eaFWTYT0cOEE2EO+g+QY/fhNDmoClfzScMeMqxeLKqVpngjJbmX8Ofj76IbOhIySAZxPVUYTpVUoskoha7zOOI/zBH/4ZO1gECSJALxAAmSFDgKCSYDuG0edMPAJJvJsmWR1JPoSR3N0PigcjMRLcz53gnpcFExIQ89RNE5gaAL9GZhs+bCcjaTjUAigM+ei9eaSV20DrNsSUX7SCpXjLgSl9nNP6o/StvmbytehNPkwqbYkCSIJWMkjeTJktEyOjqKpGCRzCR1DQOwKBY8Zg+qoqJpWipHQE5l/Ca0OIahk9STRLVo6nxaklgyyuGmQ/z1xF+IJaOiXMMQRSgCgaALtK3m2ey8bZtYdTZ21+7iSNNh/l75AQBTcy/hX0bfwMW+qeiGzongccqCx1FllUxbFl84bwaTc6aks3h/ves5/LFGgvEgUS0KEpglCxISZtWEiokTwTIaE43IkoTH5MFjycCsmFFlFd3QiCQj+By5lORdikkxY0gGqqwiGxJIBgY6qpwyGjREG9hauUVE6gxRhGlIIOgCvVHYrHlVkVpRNNAYaaQ68HcmZl5IMBEg05aJVbWlGsOEq1h7YHVK4VgyuHX9zexr2EfiZKioO+mhPlaHbhjYVRsmxYRVtqUKzkkGKiqKoqKTmvhtqg2TZCJOHMMwGJdxfroQXP3J2kOhZAgFhSRJrIot5YM4WcJaROoMTYQiEAi6QHM1zOZ6QND1mPaWqwqX2cWR0EEqmiqpDFUw3jsBl8nNhxUfUBetBVIlWkKJIDo6hmEQiPmJ6zECsQA2kw2rYgMDbCY7F2YWs63yQ3QjZRrKc+RRE60haehYZROGYZAgSbYth8/mllATqeZY4CiX5n+OeDLOtuqtxPU4VtlKriMfg1SUkSqrZFgyhH9giCIUgUDQBXqjsFnLVUWO3cfonPMIBCNUhMqJ63EcJgehZCjlBAZ0IwmyhILCwaZPUyWhdR1FUnFZXJhklbiWQDd0DjUdwHpyZWCSTeQ5R2BRrdRH64nrcbLtOZTkXsJtxYuYlF3Myi0rWvU3uCD7Qt48uJbqcBXZtux01dBAws/l3WiTKRgcCEUgEHSB5sJmLaOGFl5wa5eelM+2qnj36Dvoho4qK6lJHx10OB44BhKYZDNJI4F+sq6QZEgkjSThRDjVY0B1ENfi5NlT/T4yLF40dOaN/eJplULbM3VdmjeNPx39v1QdooQfs2JhjKeIrxcv6uZdEwx0hCIQ9AoDPeGnN2kZ094s9692PNOh3GdrOtMQaWi1qvjNrucJxgNIkgRIKUVwEhWVhBYHDAwDGiINaCSRkOBkvaEkSVyqG1lWMDDwJ/yYZLVd+357SsmiWrlu9A347L5h8XkKhCIQ9AJtG6R3JfloMNMsd1JLUhEqZ1vVVtYffpNvT30w3Qi+vXvT3HRmb/0eTgTLGJ87li+OvplJ2cXsrt1FUk+gSipJI4mO1uqaqqSioSMZEjoGCVJlJBwmJwYGdtWOLMs4VCcW2UJ9pA5VNvHtkgfb/Sw6MnUN9c9O0BqhCAQ9Zrj2in3r0DqSWpJ99alWjFbJQkW4gv/c+B9sKtvIbcWLOrw3LZvOtMy8/c2u51JJYpKERbYQ0aI0p55JSMiyjIKCSTaT58jjiP8ITpMTq2ol05qFIisktSQ6OmMyivi886ozPs33hqlLMPgRikDQY4Zrr9gTwTIqQuVYFAuanqQiUoEiKWho7KjZzjPbnyYQ83Nh1qlJtTpczf6GvVSFK4HUE/nVJ0sr767dxV/K/oxNtSMhEU6GaVYCqqRiUsy4zR70Folf473jiWtx6qP1HGjcn9pXVvniuH/lx1c/2Sk5RPkGgVAEgh7TGyGVA4ndtbv49a7n+EfVVgxIR9kArZ6cTbKZumgdWZYsyiLVqFLq56TKKmXB4wSTQSKJMDbVztiMcVSHq9lWuQUkyLXlpU1omV4H+coY3jq0DqtipSHaQCARQJZk5JNZwkkjidkwE4gHMDA4P+N8pvg+y3H/MbZU/Z2EFk+ZjQwtvUrZXburTyb44eQPGi6IzGJBjxlKZYJ31+5ixYeP8bfyTahyKgRzc/kmvrvpflZ8+Fir0hKVoXKSWgJ/wk9Mi2IYBpFkhJgeQ5EUMi2ZyJLMP6q3cbDxAPsb9oIEkUSYSDLMB+WbOdDwKc9sewaAXbU7UuUdtBiqbAKDtKNYxYSBgU214bZ4uPez3+brxYuoilSkVgty6n1ZlhnhLCCmxbqc7dzZ+9NbJTYEAwehCAQ9Zij1in3r0DpqI7W4TG5sqg2basNldnO46RC1kdpWpSVGukcz0jWK6nAVTbEmgokAmqERS8aIaVGO+o/gtXiZ6ruEylAFxwPHqAlVUxOpoSpchYKMYRj86dCf2F27i6ZYE3aTA6tixaKYMavm1CSPmVxHLhbVyvne8VyWfzl76/cwKbuYka5RmBUTNtVOli2b8RkTyHPmE9difWKa660SG4KBhTANCXqFoWJnPhEsI67FcJlONZO3KBYiyTBx7VT7xZpwNf+o+ojD/oMUOM7Da86gPFROOBHCqToxn0zEqo3UEk6EqQxXEtdiGIaBy+wmrsX5tGk/ZtmMw+zg17uew2Px0BhrwKpaMTCQkKiP1OGwOPDZc7GqVq4ouPJkLaLUJF+cPZmqcBUYYFWtADREU70NdtZsZ+WWFZ0y3XTW3DNc/UFDHaEIBAJOTYRbKj6kMlSBIsm4LR6yrFnIsoJNtWNWLEBKCWyt+pDqUDUO1YndbCemRRnhKKAsdJy4Eccte3CbPVSFK6mJVGOSTaiyiVAySF2sFkVSUFCI6XEK3YW8X/Znrir8AnbVwcHGTzniP5JajZhcSLJMTItxUc4UoLX/pbRoLttr/snhpkMYGEQSYcpDJ8iwZnKxb2radNMyXLXtRN+V8N+h5g8SpOiUaSgYDDJ79mzKylJa/3e/+x2zZ89mzpw5PPzww8Tj8dOOKS8vZ+HChdxwww3cddddhEIhAPx+P4sXL2bmzJksXLiQmpqaXhRHIOg6zRPhocaD6IaGgUFUixKIBzjqP0ptpIYxniKybdk0xhrZV78XkEjoCXw230lTjpXGeCNj3edjUazk2HyEEyFiyWiqlWQySjQZSV9TMzQ0Q8OkqDjMDryWTCRAURQuyf8cc8feSL5jBBaTFZfJxYTMienrH/UfpiZczZL37uatQ+v4yoSFXD7iSpJ6gvpYPQXO8/jCeTPIdeSRYclA0zR++tHjHdr1u2LuGUr+IMEpzqoItm/fzoIFCzhy5AgAhw8f5vnnn+e1115j3bp16LrOK6+8ctpxjz32GF/96ld5++23KS4u5plnUg6xJ598kpKSEv74xz9y0003sXz58t6VSCDoIs0TYUWonAyrl3EZ5+M0uYjrcWRZxmvJZMWVP+YrExayu3Ynu+p20BRrxGfNQVVMAGh6klAiyPHgsVQ4qaHhj/tJ6AkUFAx0tDbJYRoakUSEhmgDxdkXEdfjaV9L0kgyY9R1vFz6v7xww28ZmzEuVYtISzWfMSnmVglqXy9exHtf2cy1o/6FWUWz093MAMqDJ0joyQ4n+hPBsnQj+2Y6MvcMJX+Q4BRnNQ2tWrWKpUuX8sADDwBgNpt59NFHcTpT/S/Hjx9PeXl5q2MSiQRbt27l5z//OQDz58/nlltu4f7772fjxo28/PLLAMyePZtly5aRSCQwmUy9KphA0Fma7d6BuB+n2YWkSIz3TiCQCHD96JlUhFLf7w1H1zMp+yIMI1W2IZqM0BhrxJI0UxmqxCxb0NFxmJwk9Dhm2UwUBUmWaFElohWGlMoTCCVCFGWM7dDX0rxt5ZYVmBVzh8l77Zlu6qN1ZNmyWp2v5UTfVXPPUPEHCU5x1hXB8uXLKSkpSb8uKCjg8ssvB6C+vp6XX36ZGTNmtDqmoaEBp9OJqqb0TE5ODlVVVQBUV1eTk5MDgKqqOJ1O6uvre0cagaAbmGQzfz7+LlXhKg43HSYUDxLTYumSCwXOwlbmkwmZEwEDq2pDlWSOB44TSPhJ6HFsipWEHicUD6EZGlZTyoFrtHNdGRlFUrGrdvbWf0Jp0Vx21+5i5ZYVLHnvblZuWXFaWOaJYBmxZJTNJzbx9uG32HxiU6vOYe2ZblTZdJqDt62fQZh7hjfddhZXVVWxaNEivvSlLzFt2rRW7xnG6V/7VAGt9pHlrkWxZmU5O71vTo6rS+cebAx1+aBvZdxRuYP6RDURLYTPkUNlsJJjwaNk27OZ6BtPTArxtal38eSHT1LoLUSWZByO87DZzGwp28LxwFFUWcGtuDEpqVh+j9WNLMtIMjRGG5GRaasKZGSQAMlAkRVGZ48m0+vgJx/8BK/VyzjfGA7UHWDJxm8yJmMMn8n/DPMnzsfjcLLxyEbcFjeZDi/RZJSt1X/n6tFXk5Pj4uqcy8j0Onh227O8V/Z/GBhcmDuRaDJKXA7jsXpoijal5Wp5zOq9qznWdIyRmSO5d+JdTM6b3Gv3WXxPBzbdUgQHDx7kzjvv5JZbbuH2228/7f3MzEyCwSCapqEoCjU1Nfh8KZulz+ejtraWvLw8kskkwWCQjIyMLl2/ri6Irrf3jNWaljVchiJDXT7oexlf+uhV8qyFZOX62N+wj6glRigRwtChwDaK0qK55CtjyFJzqWioIaHF2d+wj0DcT024NtUAPpFqFykZEsFEiED8MBbZgmYkUWUTTrMTu+GgPlYHNK8EFOwmB3n2fFRZ5Xz3Bbz00asEwxEO1ByiOlxNU6wRl9lNlb+GE5YqfrjxR0TjEZKaRiKhIetJEppGUtMIR+Lp+1TfEKIu2EiJ73O4T+ZA7K75hE+q92FTrXw29xJuL76LfGVM+ph8ZQz3TPrPVvemt+67+J6ee2RZOuMDdJcTyoLBIHfccQdLlixpVwkAmEwmSkpKWL8+VWp3zZo1XHXVVQBMnz6dNWvWALB+/XpKSkqEf0Bwzmh2lObYfVxRcCVfPP9f+eoFX+PS/M/xwKXfTdvCS4vmcsx/hM3lm4gkI6iyibAWIqbFkJBIagma4k0Yhn4ywziMZmgUZ02Gk6thk2TCLJlRZAWX2c1I10hsJht1kTpKi+ayq3YHe+v3EG0RYRRINFETriauxTnQ8Cmbyt/HrtrR9CSBRACrauWy/CtI6Kci91qasWojteyr34tNtZFjy+FzI64gooX7/0YLBjRdXhG8/vrr1NbW8sILL/DCCy8AcM0117BkyRIeeeQRrrnmGmbMmMHSpUt56KGHePbZZ8nPz+enP/0pAEuWLOGhhx6itLQUl8vFT37yk96VSCA4A20Tp8yyGX/cf1ZH6aTsYvIcI6iJ1BDX4rjNbsa4i0gaSfyxJhRZRdFVNCOJSTYjATbVTtJIMGvMHD5t2MeBxv1ous4VIz5PbbSWQNyPhMR1RdcxKbuYplgTkiRjVa3E9XiL7mABtlVuSTWel1SShgYGlORdis/uozHWSG6L8bdM+vq0YR8WxYJFtaR7E8PQrwwr6BqdVgTvvfceALfddhu33XZbu/u0DAUtKCjgpZdeOm2fjIwMfvGLX3RxmAJBz2kvcao8dCLV1MU9usPWk83KY9OJjeTa8picPYUcu4/qcDVbK/+OLMlEk9FUDoJhIBkQ0+MgSRxo/JTzvRO4ouBK8hz57KvfS64zn/MzJ6SvdVfJXQDpzOKoFsWsmIlpMQwM4noCi2IBCUY4C0gaCZAk9jfsxayYTxtvyyggf9yPy+QipsVwnQwRFZnAgraIWkOCYUN7iVOj3GPIc4zoMC6+ZZG1XHse/oSfrVUfUhOuxmf3MTHzQrwWLyebiWGSTcSMBKqspM1EWyv/zsHGA6iKyrdLHjjtWs1O2eLsyVyQOQmrYsWqWMEAjzkD08lOYzEtysW+z3JJ7jTcJjdV4cp24/hbRgG5zK50Ubzx3gmAyAQWnI4oMSEYNnRUJ6ciVH5aL99mWiqP8d6JJ8tIS+yr34tJMaMqKlN8n6UoYxz76vdSF6kDDDQ0knqSEc4CoskIlaEKVlz5YyZlFzOXG9u9VnO3sEnZFzEueT5bKz+kIlSBhEQ0GWFa/mXknEwUMylmPm+5qt1xt2w2k2HJwB/zMzHzQrJOZia3XUEIBEIRCIYN3amT06w8qsPVfNqwj5gWI6pFKA+coCx4PGW60eJcmvs5SvIu5Y+H/4BhGFhUCx6zh4t9n2Vf/V4qwxXpTN6zdQv7za7n2HTiL3itmcwdeyPBRJB/VG8jEA+QZctu13zV3rna9lUWHcgEHSEUgWDY0FF/3jNNqAXOQg42Hki3o8yx5XA8cIxQIojXmonP7uNg4wHeP/Fnri68hnEZ5xNNRkECTdfYWvUhIJFrzzutmFvzBF2XrCJLzU0Xgsux+7h21PVphZV7ciwVoXIsqqXLk7nIBBacDaEIBMOG7vTnLS2ayz3v3gmShEVNRfHUx+qxqjYSehxZSjWCOdR0gPeOv0uuPZeGWANOsxOv2QtIYMB478RWETtA2nFd6C1kd/le7vnTYka6R1IWOM7FvqlgOTWOMZ4irKqVp655pg/vkGC4IhSBYFhxpqfjdQfW8PzOX1IZriDPns8dF32DueNuZKRrFI3RRoLxAC6zG1VS0pE44USIylAF0UT0pJkolVfQEK2nMdrAGE8Rn8mdmi4C1xyx09L3UBOqYW/DHiLJKDtrdxBJRDgRPMEVI65kYtYFgHDwCvoWoQgEw4KzNV5Zd2AN//X3H+AwOfHZcmmKN/Fff/8Bx/3HaIo1URerI9uazfneCSdNQyHMsokToRNEEhEUWUFCJpAIkO8YgVW1URmqONmA/hT+uB+TbObtw29hYOA2e4gbUTRdJxBvQjcMCp2FHAmkkte8Vi8W1SocvII+RSgCQa8yEBubn6nxCqRMNf9v9wsYhoHHnAot9Zg9RBMRnvrnT8iz51MVrKQhUk9lqPJkyeoKDENHkiQ0Q0NFxW1xoBsGJ4InsKoWkloSk2JiZ+12vnDeDPxxP0f9h1NlpGVzqu9BMsoR/6FUj2Ik7CYbDrOT0e7RlAXK+Gf1R9wwplQ4eAV9ilAEgl6jK52u+pOWZhhIlW6uj9TxrffupinehNeaSSQZwa7aKQ+eYISzAIfJQSAeIBgPYvPYGe0ZTXW4mspQOapsIsOcQUOsHt1I1Zc2MAjEA+joSIaEbmhoho4eNcBIOXoLnIUkHAXpXgJbqz7EolhTZSbCdThNLjKtqXLRiqwy3juBMRlFHYa2CgS9hVAEgl6jvQm3eXtfKYL2ViDN12zetqt2Bxdmnbp+TbiaT+p3UxWqYqR7FBigGUniWhyraqU+WofD5KAp3pRqMqMlqY/VE9NixLUEES2CRbYgSwpgoBs6BgZJIwmklIKkSWkFUxupoSFaT4GzkKpwJRdmFSNLMpfkTmN/w76TZSOSxPQYddFaYloMRZYZ4y4SfgFBvyAUgaDX6O/G5u2tQH704TIMDEa5x6S3HfMfw6baGZsxDoD9DfuQJBlFVrCqViQkcm25VIQqUGWFpH6qgJzb5KY8XA66QSgeRCM12cf02GnjkZAwMJCRMckmdF0jpsUwySYaY40cbDzAtqqt7KnbTYGzkPHeCYz3TqAhXsdI10j8sdQKJJaMMjn7YhRFET0BBP2CUASCXqO/G5u3twLZ1HSUxngj5cFy3GY353snMDHzAvY27CHLlo3b7KYuUosiK+Tb84lpMayKlTzHCJK6RkJPEtOjeMweLsu/gp11O1AkhZgeQ2/TZqx54m/GwEBCQpZk4noCRY9jU2w4zU5qwjX4Y35cJjeBeBNN8SbeL9tIIOYnokdwqE7sJntakUS0MHdPeUz4BQT9gqg1JOg1+rvTVdteu9XhairD5SS0GC6Ti2gyyrbKLThMDka6RqVr/GTZspmYeSEX504lpkWJnvw/x+Fjim8Kc8d+kdGeMYzOKEKVFRRJIa7FMQwDlVMl01sqAUgpBomU81iRZCQDTKqKKqlUh6uoDFUQ12NYZSuqpFIbrcUf92OWzeiGTjgZxmly4jA78Fg8QgkI+g2hCAS9Rn83Ni9wFuKP+9OvP23Yh0k2Y1YsSJKEVbViUSzsqttJcfZkHrj0uzx1zTOsuPLHqIqKSTEz1XcJGNAYa6DQUYiBgfmkM9esmHGa3XitXtwWD1bVhsfqSVUrbQdZknGZ3UhI6LqOIRmYJSuN8QYArIqVWDJG1IiBYTDWMw5FUTEpJqyqFZNsIpgIIkkyTbGmPrlnAkF7CNOQoFfpz3IGEzMv5KfbVpLUE2Ras6gIleMyu5AkiagWxaJYMDBojDak+wE3O5Ftip2EFidpJJkx6jpKi+by1qF1rUxbGZYMPpNzMdtr/kmWNYtwPIQ/1nTaSiCNARbFgmLJIJgIARBKBsmyZhPTY8S0GEiQbc2mIlxBgbMQ68kxJvUkiqQQToRxmV14LJ5+uYcCAQhFIBik7K7dxYaj65mQOZGKUDl10TpCyRBTsj9Doeu8dDtJk2xmeuEXAFo5lpvrDLXMJXjj01U4VSeSJKMZGm6zm0xLFpFEBI8zgzxnPicCJ0gYidPGIyNjNznQDR1ZVvBavIxwFVAVqkRGJpwIEU6EcZpdmGQTmqFRG6nBLJtP9jKIIUkSbrObCzInUZQxtl/vp2B4IxSBYFDS0lHcHA3UXBxuTMZYLhtxRXqyv614UYehrb/e9RwRLYzHnIFTcXIscBQZhULXeUSTUf5W/1d89lxmjLwOgN/vf50TwTKSehIkKdUkBtDRSegJzIoZXddxW91clD2FSCJMVbiK0Z4iND1JdbiaI/7DeC2ZJIwEdpMTQzJIaEk0PckE70QRLSTodzqtCILBIDfffDO/+MUvKCxMRYE8+OCDTJs2jfnz55+2//z589E0DYBoNMrx48d5//33icfjlJaWMnLkSACys7N5/vnne0MWwTCiOVS1Jlydfvp3mlxkWFKTfduicr/a8Uy7+9dEqvnCyGtTzWpkGVlSUGSFqnAlsiSfXFWYqD7ZiCamx8CAJElSFiKJk38Q12LYVCsjXaOY4vssPrsPk2xO5RcYYFcd5DtGEEj4sSt2xnnHp1YzMYVwPJIyDSXDfE9ECwn6mU4pgu3bt/O9732PI0eOAFBVVcXSpUv54IMPmDZtWrvHrF69Ov33Aw88wBe/+EWys7PZsGEDc+bMYdmyZT0fvWDA0JPSEt05tsBZyKHGg+xt2INFseI0u046jo12j+9o/9poLbFkFCyQNDTOc55HZbiShlg9WdZsvJZMYnqMbZVbKMm7FAyIG6lG8c1OYwOwyFYyrF5GOEYwY9R16evGtChj3GOwqlb8cT9us5vi7Iv4a/n7jPaMoShjLA6HhVAohm7oVITKhRIQ9DudihpatWoVS5cuxedLVVB88803mTFjBjNnzjzrsR988AF79+7lzjvvBGDnzp3s37+f+fPnc+utt7Jv374eDF9wLthdu4uVW1aw5L27WbllBesOrEm3c2xZWmJ37a5Onas7x5YWzWVv/SeAhEW2EEumntQneC9Il3lueY2acDUbjvyRw02HONJ4iMONh4kkIuTb89lVuxNIJb8psoosyWRZsxnjKSLLloVJNoEE+xv2kjCSSMjILX46MjKakSQUD3Co6SCHGg+mw2dV2cTYjPO5ouBKZo4p5YqCK7GoVvLs+a0inkBUGBWcOzqlCJYvX05JSUn69aJFi7jppps6dYGnn36a++67D0VRALBYLNx4442sXr2aO+64g3vuuYd4PN6NoQvOBe1N3D/dtpKklmzVC9hjzjhtQm6PZtt9XIvzQflmPijfzIGGT/n1rufOeNyk7GJGukfiNrsJJAJoehJVVtnfsJe3D7+VViTN4/XH/am+wgaEEiGSegJJgrGe86kKV/Lu0XeoClVyxH8Yf9xPji2HaDKKLClclnd5ukew0+TEptoAUKRUjoGOjmZoWFQLeY589jbsYU/dLjIsGXy75AFURT0tt+KOi77RrzkXAsGZ6FNn8aeffkpDQwNf+MIX0tvuvffe9N/Tp0/niSee4NChQ0ycOLHT583KcnZ635wcV6f3HYz0t3wbd28g3+PDa/MC4MKGUaFRl6hmsmNSej+bPYcyf9lZx1eXrMJkMvFx9TasipUsh5dIMsLmir9QoR0mh8kdnmPaqEtoiDQQ02J8UPZBKhTTMCFJEi/sfZbvXPYdNlalxru9ajtuq7vZnI9JMZHtyOJo6BCKIlMVqSCSTJWTVg2FoBZgpHMkE7NLkJCoqqjAbXWRYc0gpkdIhOMktEQ6EzjVf8bg6qKrsCgWvDYvj179KACXjJnC6r2r+bji43R46vHYQb580ZfYVbOLY03HGJk5knsn3pVuZD/UGOq/QxjcMvapIvjTn/7ErFmzWm176aWXmD17Nl5vaiIxDANV7dow6uqC6HoHsdwtyMlxUVMT6NK5BxPnQr79VQfJd4wgFDpVa8ejeqnwV7Xa1hhrJMuSe9bxZam5vHvsHRTDhCKbSCQ0kpqOapi59Y1/Q1IgmdT4bO4lfL14USv7+dW51/PM9qc50PAp0XicqngNUS3CaNdoguEIL330atqpXBusJ9ucQ3m4HEVSSGhh4uYkRxqP4LVmkWvzYVEsNMYaKA+doDpYQ4Y5k7K6CvY1fgLAZflXEEwE2RbdRr59BKFEiLpoLUjgVj1kmDNxSV50TWd/1cG07PnKGK7OvZ49lfs4352fqr9UX8Weyn3cPeU/uPrqy9L7DsXv61D/HcLAl1GWpTM+QPdpZvHHH3/cyqQEsHXrVl5//XUAtmzZgq7rFBUV9eUwBL1I22xegBHOAkzy6eaPzpg5Sovm0hCtxzj5X1SL0hhtIJhI2dtNsglVNvG38k2s+PCxVr6D5kzmpmgTVeEKJGCUcxSypLC3fg9/O/FXjjQd5g8H19IUaySuxylwFJDUk4QSIY75j5LQkjhUG1bVSiQZpjZai1k2Y1JMYMBfy99HkVQuH3EluY48xmaMo9A5kkgyis+RywhnAednpMpF5zpS3YXbs/W3DF/tqvlMIOhrelURPPXUU7z66qvp18ePHyc3N7fVPo888gh/+9vfmD17No8//jhPPPEEsiwqXQwW2qsnpCgK3576YLdKS0zKLmZ64ReQkAjGA1gVK26zm5gWw2FyYjfbsak2XCY3tZHa0ybOSdnF5DpyKXCdxxhPEQ6zE6tqJZaMs6d+F/XReqrClQRiAY4Hj1MXrSOhxcmz55PryMWimKkMVRFOhKiL1qFKKoqkYpJNzBh1Hbn2XLxWb7rVJMCledOwmaxcNuIKvnDeDDQjSU2kmlAiyNqDv+dv5ZuYmHlhq3G2rYsEfVuZVSDoCl2yybz33nutXv/3f/93q9dLlixp9Xr9+vWnnSM3N5df//rXXbmsYABxpgbwc7mxW+e8rXhROus3lozy1uE3CcT8eK2ZBGNBzFKqZlAg4W934vRYPDTGGtJlJWJaqq5/Qo9jV+2MdI6iOlKDP9FEfbSebFs2uY48zvdOwCT/gyP+w1SFq9D0JLKcKjB3njuV55JpzaIuWtfqehbVylWFX0jlKyTKuMB7IbvrdxFNRsmyZjHCWcCGo+sZmzEurQz7uzKrQNAVRGaxoMv0dj2hZuXy613PsenEXzDLZlxmN7qhUxYoI9eahyKrmBVLuxNncfZk7KqDilA5gbgfl9mNYei4TG6sqhVUK06Li0gywhH/If51/FeQpdQq9DO+zxKIB2iKNSJLMpqu4bV5+Yzvs0DK7OVPNNEYa8RtdrcqTdF8D1ZuWUGuM7/VJN8Ya2zVkKe0aC7PbH8aoNV5RB9iwUBA2GQEA4JJ2cX47D6uHXU914+eSYY1A83QMAyDqnAVgYSfbFt2u36H0qK5KIrCpOyL+JfRM5mUfRGSJONpMTE3Y5LNrXwcPruPz/guZkLmREryLsHn8DEl52KybdmdNnudCJYRS0bZfGITfzz8FptPbCKWjLZavfR3ZVaBoCuIFYFgwHAiWIYqqRxo/BQJCbNsIq7HMQy4fsQsriq8mrcOreNXO55plYHcnrnqqoLpfNKwp5W5KJgI8Ln8y2mKNwKnnsxVRWXFlT9mUnbxaVnOnTF7mWQzfyvfhMvkTvdB+KBiM5ePuLLVfv1ZmVUg6ApCEZwDelKOYShjls1sLt+Ey+wmx+7DbfEQ0UJMy728lR+hZQZy81N120l2d+0ufvThMmrCNQRifsyKhSLPWL5d8gDQuqfx5/Ivb1fBdBap5R/SqQ3tdy0QCAYeQhH0M+312W05oQ1njJZ/GKc2GLTflpKT29u7b5Oyi3l42g86VLjN//bG5xHX41yWfwUHGj9N+yiK8y8irouMecHgQCiCfqarE9pwItFiQm0u0HZJ/lQC4XA6MawlLcMvd9fu4te7nuMfVVsxgJLcS7iteBEPXPrdM16zM5/H2VZwzRFBVxScMgU1xhrJbcdHIRAMRIQi6GfONqENZ9qbUONyGLczE6DD8MvdtbtY8eFjHG46hNOUSvPfXL6JilA5D0/7QbsKtnlyf+WTl1JtLRUbPruP870TyLZlt1IwZ1sxiIggwWBHKIJ+RsSTd0x7E2pMCnH7xJsBOpxs3zq0jtpI7alwUUCSJGrCNe2utJon96SWJK7F0AydaCKKSVb564m/YJLMmBSVlVtWUB2uPuuK4Uy5FQLBYEAogn5mKD49tjWdTMy8kL31ezp83ZEztr0J9WtT7yJfGQPQ4WT7qx3PENdiuEynMnctioVArP0EtGZz0O7anWTbcqiN1gJQH60nqkVRJIXZRXNpjDXyftmfubJgOlhOHd/eCk5EBAkGM0IR9DOD8emxPRs5pCbUXbU7OBY4ygTvBYzxFHGw8QD/u/81pvouYbRnDIcaD/K/+1/js74SxniKzuqMbTuhtizm1fK95jH9asczHGk6TFLXiGmx9IogpsU6TEBrNs/5434yLF4sioXaSC210VoyzBk4zS5yHXkAeC2Z7KrbmX4NYgUnGHoIRXAOGExPj+3ZyFd8+BgSEiPdo2mMNgIS++r34jK7qQxV4DA5qQiVU5QxlopQOQ6Tk8pQBWMzxvWKc7ztmGLJGIf9hwklgmTbcgAIJgIUeca2m4Bmls38+di7VIerqJNqybXn4nPkEtfj5DtHpPsNABRnX8SmE385LbN4MK/gBIK2CEUgOCPtRdV8FKkFAybnfIZgIpAqEpeM8WnDPvxxPy6Ti8DJ7N3Aydcts3l76hxvO6aijLEAHGj8lKSewACuGHElt7UpWw0pJVIeOkEg4cdryaQmUs1h/2GyrFlkWbMIJgJMzvlMen+LamV6c12hQbKCEwi6ilAEw4CeJLC1F+UU12LpGH+X2Z3O3m0O+WyKN+Exe9Lvt3wNPTettDemWDJGRagcp9lJnj2fKwuvblfGtw6tY5R7DPmOAj5t2JcqSZ0MIUsK0/Ivozx0ArNiRjf0dusKtTRJiWRAwVBB1Boa4nS3J3Az7fUfMCsWzErKezreO4GYFsWf8OMyu8hz5BNKBMl3jEA39FQTm0SQPEd+r7VkbDumfXWf8P6JP2MYBj5bLk3xJv7r7z9g3YE1px3bXA7aZ/dxRcGVzB//ryy84Gtcmj+NH1/9JN+dtrTDekA9vZcCwUBFrAiGOD1NYGsvyinblo2ElOpCZstmovdC9tZ/QoYlg7EZ4042lk9FCRVljGVW0ZxWUUM9Na20HdOWyi1IkswIZwGyJKdXH0//46enRSudLXz3TP4bkQwoGKoIRTDE6WkCW7tRTtOWAqfq9RRljOXez97XajJsW6TtTEXbumq6ajummB5llGsUDpMjvY+CzKeN+1GOKsS1GHvrPmFHzcd8ecJX2XB0ffo++ON+jvoPk3AUsOS9u894fZEMKBiqCEUwxOmNBLaOnpJ74ym4u7V+Wo7pyO9n0hRvavX+8cBxDF0HCVyWVMezQ00H2VS2sZUSMclmJCRMipmsk6WnO7q+SAYUDFWEj2CI015ryZ7a6HuT3ujle8dF3yCUCNIUb0I3dJriTYSSQXLteVgVKxISVsWK0+RiW9VWJmUX88Cl3+Wpa57BZ/cx0j26U9cf6PdSIOgunVYEwWCQ2bNnU1Z2ahn84IMPsnr16nb337p1K9OmTWPevHnMmzePhx9+GAC/38/ixYuZOXMmCxcupKampociCM7EQG+I0hu9fOeOu5Hvf24ZHrOH6kgVHrOHXFsuXlvmafu2LQ3dlesP9HvZFXbX7mLllhUsee9uVm5ZIRzew5xOmYa2b9/O9773PY4cOQJAVVUVS5cu5YMPPmDatGntHrNz505uv/12vvGNb7Ta/uSTT1JSUsKvfvUr1qxZw/Lly3nyySd7JITgzLRXq3/llhUDoh9CZ80tZ/MjzB13I3PH3Zh+/Z2N3+Jv5ZuQkFo1pmnbLKar5p7BlAzYEaIUuqAtnVoRrFq1iqVLl+Lz+QB48803mTFjBjNnzuzwmJ07d7J582ZuvPFGvvnNb1JRUQHAxo0bmTNnDgCzZ8/m/fffJ5FI9FQOQScZaCGQnTG3dGfMXy9exBhPEUgQSPhBgjGeIr5evKjL1x9q9IY5TjC06NSKYPny5a1eL1qU+jF99NFHHR7jcrkoLS3l2muv5dVXX+W+++7jtddeo7q6mpycVBkAVVVxOp3U19eTm5vb6UFnZTk7vW9OjqvT+w5Guirfxt0byPf48Nq8ALiwYY+Y2Vi1gasvuKwvhnhGrs65jEyvg9V7V3Os6RgjM0dy78S7mJw3+dSYq7o+5tR5V546r2ck8yfOb3Xezl6/P+jP72ldsopCbyGydOo50GbPocxf1mfjGOq/QxjcMvZZ1NCyZcvSfy9YsIAnnniCQCDQ7r6y3DWfdV1dEF03zrpfy4JlQ5HuyLe/6mAqySsUS29TDSv7qw6es3uVr4zhnkn/2Wpb81hyclzdHvOZztud/fqK/v6eZqm5VDTUtDKHNcYaybLk9sk4hvrvEAa+jLIsnfEBuk+ihnRd59lnn0XTtFbbVVXF5/NRW5sq+5tMJgkGg2RkZPTFMATt0F6m8EAPgRyMYx7IDEdzmODM9IkikGWZd955hw0bNgCwZs0apkyZgs1mY/r06axZswaA9evXU1JSgslk6othCNphME4Cg3HMA5mhFP0k6B161TT01FNP4fP5WLBgAY8//jjf//73+fnPf05mZiYrV64EYMmSJTz00EOUlpbicrn4yU9+0ptDEJyFgdQPobMZxV0Zc08K7A0nhkL0k6D3kAzDOLuxfYAhfAQpBqp8nZmMW4Ywtqzz3/bJtCsydvacA42B+jn2FkNdPhj4Mp4TH4Fg+NLZUM++CGEUYZECQfcQtYaGCf1lMulshc6+KOAmisIJBN1DrAiGAf2ZRNbZkg19EQkkoosEgu4hFMEwoD9NJp2djPsiEkhEFwkE3UMogmFAbxR26yydnYz7IoRRhEUKBN1D+AiGAf1ZR78roZ59EcIowiIFgq4jFMEwoL12k03xRhZecGufXE9MxgLB4EKYhoYBwmQiEAjOhFgRDBPEU7pAIOgIsSIQCASCYY5QBAKBQDDMEYpAIBAIhjlCEQgEAsEwRygCgUAgGOYM26ghUbdeIBAIUgzLFUF/FmETCASCgc6wVASibr1AIBCcotOKIBgMMnv2bMrKThUqe/DBB1m9enW7+x88eJCvfvWrzJs3j6985St88sknAJSXl3PxxRczb9485s2bxx133NFDEbpOfxZhEwgEgoFOp3wE27dv53vf+x5HjhwBoKqqiqVLl/LBBx8wbdq0do/53ve+x+LFi/nCF77ABx98wIMPPsi6devYuXMnc+bMYdmyZb0mRFfpzyJsAoFAMNDp1Ipg1apVLF26FJ/PB8Cbb77JjBkzmDlzZofH3HTTTVx11VUATJgwgYqKCgB27tzJ/v37mT9/Prfeeiv79u3rqQxdRtStFwgEglN0akWwfPnyVq8XLVoEwEcffdThMfPnz0///fTTT3PttdcCYLFYuPHGG7n55pv5y1/+wj333MP69esxm81dHnxXaBsldP2oWeyt33PWUskCgUAw1OnT8FHDMFi5ciXbt2/nxRdfBODee+9Nvz99+nSeeOIJDh06xMSJEzt93qwsZ6f3zclxsaNyBy/sfRav1cs43xiaok38pfIdvnPZd5icN7nzAg1AcnJc53oIfY6QcfAz1OWDwS1jnymCZDLJgw8+SFVVFS+++CIuV+omvfTSS8yePRuv1wuklIWqdm0YdXVBdN046345OS5qagK89NGrWAwHZt1OJJzAjB2LEeelj17lgUvHdF24AUKzfEMZIePgZ6jLBwNfRlmWzvgA3Wfho48//jjBYJAXXnghrQQAtm7dyuuvvw7Ali1b0HWdoqKivhoGIKKEBAKB4Ez06orgqaeewufzcf311/Pyyy9TWFjITTfdlH5/7dq1PPLIIzz00EOsXbsWi8XCE088gSz3bTqDiBIaGohscIGgb5AMwzi7jWWA0VXTUHMmscec0apV42Dv0jXQl6O9wVD/DGHof45DXT4Y+DKeM9PQQEK0ahz8iGxwgaDvGDZF5zpq1SjMDYODE8Ey8h0jWm0Tfh6BoHcYFiuCjhDF5wYPBc5C/HF/q23CzyMQ9A7DWhEIc8PgQWSDCwR9x7BWBCKsdPAg/DwCQd8xbHwE7SHCSgcXHfl5BAJBzxjWKwJhbhAIBIJhrgiEuUEgEAiGuWkIhLlBIBAIhvWKQCAQCARCEQgEAsGwRygCgUAgGOYIRSAQCATDnEHpLJZlqU/2HYwMdflAyDgUGOrywcCW8WxjG5RlqAUCgUDQewjTkEAgEAxzhCIQCASCYY5QBAKBQDDMEYpAIBAIhjlCEQgEAsEwRygCgUAgGOYIRSAQCATDHKEIBAKBYJgjFIFAIBAMcwatIggGg8yePZuyslR/4b/97W/MmTOHf/mXf+F//ud/2j1mzZo1fP7zn2fevHnMmzevw/0GCm1lBHjwwQdZvXp1u/v7/X4WL17MzJkzWbhwITU1Nf011G7TVRm3bt3KtGnT0p/hww8/3F9D7RZt5fvd737H7NmzmTNnDg8//DDxePy0Y8rLy1m4cCE33HADd911F6FQqL+H3SW6I+Ng+i22le+VV16htLSUWbNm8fjjj9NecYbB9hliDEI+/vhjY/bs2cakSZOM48ePG5FIxJg+fbpx7NgxI5FIGLfffruxcePG045btmyZ8eabb56DEXedtjJWVlYa3/jGN4zJkycbb7zxRrvHPPbYY8Yvf/lLwzAM4/e//72xZMmSfhxx1+mOjM8//7zxi1/8op9H2j3aynfo0CHjuuuuMwKBgKHruvHAAw8Yv/71r087bvHixcYf/vAHwzAM42c/+5mxcuXKfh555+mujIPlt9hWvmPHjhnXXXedEQqFjGQyaXzlK18xNm3adNpxg+kzNAzDGJQrglWrVrF06VJ8Ph8AO3bsYNSoUZx33nmoqsqcOXN4++23Tztu586drFmzhrlz5/Kd73yHpqam/h56p2kr45tvvsmMGTOYOXNmh8ds3LiROXPmADB79mzef/99EolEv4y3O3RHxp07d7J582ZuvPFGvvnNb1JRUdFfw+0ybeUzm808+uijOJ1OJEli/PjxlJeXtzomkUiwdetWrr/+egDmz5/f7nd5oNAdGWHw/Bbbynfeeefx1ltvYbfb8fv9BINB3G53q2MG22cIg9Q0tHz5ckpKStKvq6urycnJSb/2+XxUVVWddlxOTg733nsva9euJT8/n2XLlvXLeLtDWxkXLVrETTfddMZjWt4HVVVxOp3U19f36Th7QndkdLlc3HrrraxZs4bp06dz33339fUwu01b+QoKCrj88ssBqK+v5+WXX2bGjBmtjmloaMDpdKKqqcLAOTk57X6XBwrdkREGz2+xrXwAJpOJVatWce2115KTk8PEiRNbvT/YPkMYpIqgLUY7NjpJOr3s6s9//nOmTJmCJEksWrSI999/vz+Gd06R5SHxEadZtmwZ1157LQALFizgwIEDBAKBczyqrlFVVcW//du/8aUvfYlp06a1eq+z3+WBzplkhMH/W/zyl7/Mhx9+SHZ2Nj/72c9avTcYP8MhMUvk5uZSW1ubfl1dXZ1eyjUTCAT4zW9+k35tGEZaYw8VfD5f+j4kk0mCwSAZGRnndlC9iK7rPPvss2ia1mr7YPocDx48yIIFC/jiF7/IPffcc9r7mZmZBIPBtIw1NTWnfZcHOmeTcTD/FisqKvjoo4+A1PeutLSUffv2tdpnMH6GQ0IRTJkyhcOHD3P06FE0TeMPf/gDV111Vat97HY7zz33HNu3bwfgt7/9Ldddd925GG6fMX36dNasWQPA+vXrKSkpwWQyndtB9SKyLPPOO++wYcMGIBV5MmXKFGw22zkeWecIBoPccccdLFmyhNtvv73dfUwmEyUlJaxfvx5Iydj2uzyQ6YyMg/m3GAgEuP/++/H7/RiGwYYNG5g6dWqrfQbjZzgkFIHFYuG///u/uffee5k1axZFRUXccMMNADzyyCO8++67KIrCk08+yaOPPsrMmTPZvXs3999//zkeec956qmnePXVVwFYsmQJH3/8MaWlpbzyyiv84Ac/OMej6x1ayvj444/z4osvUlpayhtvvMEPf/jDczy6zvP6669TW1vLCy+8kA6bfOqpp4BT31OApUuXsmrVKmbNmsW2bdv41re+dQ5H3TU6I+Ng/i2OHz+exYsXc/PNNzN37lysVitf//rXgcH9GYoOZQKBQDDMGRIrAoFAIBB0H6EIBAKBYJgjFIFAIBAMc4QiEAgEgmGOUAQCgUAwzBGKQCAQCIY5QhEIBALBMEcoAoFAIBjm/P8OMquyAPm/3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test_OLS,prediccion_OLS,alpha=0.5,color='green');\n",
    "# Por la concentracion de los datos, podemos inferir que nuestro modelo es bueno para predecir aquellos precios medios\n",
    "# mientras que los mas altos y los mas bajos no se tendrá tanta certeza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:23.600111Z",
     "start_time": "2021-01-28T05:39:23.537073Z"
    }
   },
   "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>Prediccion</th>\n",
       "      <th>Objetivo</th>\n",
       "      <th>Residual</th>\n",
       "      <th>Dif %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>207648.543518</td>\n",
       "      <td>213000.0</td>\n",
       "      <td>5351.456482</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>118094.497407</td>\n",
       "      <td>109000.0</td>\n",
       "      <td>-9094.497407</td>\n",
       "      <td>-8.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114707.520323</td>\n",
       "      <td>179000.0</td>\n",
       "      <td>64292.479677</td>\n",
       "      <td>35.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160961.591895</td>\n",
       "      <td>169500.0</td>\n",
       "      <td>8538.408105</td>\n",
       "      <td>5.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>152193.517740</td>\n",
       "      <td>151500.0</td>\n",
       "      <td>-693.517740</td>\n",
       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Prediccion  Objetivo      Residual  Dif %\n",
       "0  207648.543518  213000.0   5351.456482   2.51\n",
       "1  118094.497407  109000.0  -9094.497407  -8.34\n",
       "2  114707.520323  179000.0  64292.479677  35.92\n",
       "3  160961.591895  169500.0   8538.408105   5.04\n",
       "4  152193.517740  151500.0   -693.517740  -0.46"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hacemos nuestra tabla para tener los datos concentrados\n",
    "predicciones_OLS = pd.DataFrame({'Objetivo':y_test_OLS,'Prediccion':round(prediccion_OLS,2)}).reset_index(drop=True)\n",
    "predicciones_OLS['Residual'] = predicciones_OLS.Objetivo - predicciones_OLS.Prediccion\n",
    "predicciones_OLS['Dif %'] = round((predicciones_OLS['Residual']/predicciones_OLS['Objetivo'])*100,2)\n",
    "df_predicciones.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-28T05:39:23.642102Z",
     "start_time": "2021-01-28T05:39:23.618096Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prediccion    178789.313365\n",
       "Objetivo      179465.729452\n",
       "Residual         676.416087\n",
       "Dif %             -2.848390\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predicciones.mean()\n",
    "# Con esto, podemos observar que en promedio tenemos un precio de venta de: 179,465.72 (Objetivo)\n",
    "# un promedio de prediccion de: 178,789.31\n",
    "# una diferencia promedio de: -2.84\n",
    "\n",
    "# Podemos inferir que nuestro modelo es suficientemente acertado"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Las diferencias entre los modelos es ya que se utiliza un random diferente a la hora de separar los datos para entrenamiento y prueba, pero de usar el mismo random debiera salir el mismo resultado. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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