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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "eb7af684-1700-41db-b370-14d824cf438f",
+   "metadata": {},
+   "source": [
+    "PROYECTO 5: P. DENNIS RODRÍGUEZ TOVAR\n",
+    "\n",
+    "DAPT - ABRIL 2022"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "b97ca8b0-ded8-4aee-b4e9-71b57603f971",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import scipy.stats as stats\n",
+    "from scipy.stats import ttest_1samp\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "df = pd.read_csv(r'C:\\Users\\denni\\train.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d38a25c-34d0-479d-8a36-47c1f314f0d8",
+   "metadata": {},
+   "source": [
+    "### 1. Exploración de los datos\n",
+    "\n",
+    "    En esta sección, se trabajó la revisión de las columnas, los valores de los datos, así como la eliminación de nulos. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ebd64e3d-763c-4f7e-814a-59e83b3ed62b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "25d8cc25-9858-45e6-897e-c175cff45170",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1460, 81)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9f19fc7c-6813-46cd-9368-7752dcc89b60",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
+       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
+       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
+       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
+       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
+       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
+       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
+       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
+       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
+       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
+       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
+       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
+       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
+       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
+       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
+       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
+       "       'SaleCondition', 'SalePrice'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "53e225e5-2d08-4a12-8239-48d75e82ded4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(['Id'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "842bd3ef-412a-4e63-96ed-0f2f4c854a31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>column_name</th>\n",
+       "      <th>percent_missing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PoolQC</th>\n",
+       "      <td>PoolQC</td>\n",
+       "      <td>99.520548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MiscFeature</th>\n",
+       "      <td>MiscFeature</td>\n",
+       "      <td>96.301370</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Alley</th>\n",
+       "      <td>Alley</td>\n",
+       "      <td>93.767123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fence</th>\n",
+       "      <td>Fence</td>\n",
+       "      <td>80.753425</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FireplaceQu</th>\n",
+       "      <td>FireplaceQu</td>\n",
+       "      <td>47.260274</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Heating</th>\n",
+       "      <td>Heating</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HeatingQC</th>\n",
+       "      <td>HeatingQC</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSZoning</th>\n",
+       "      <td>MSZoning</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1stFlrSF</th>\n",
+       "      <td>1stFlrSF</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SalePrice</th>\n",
+       "      <td>SalePrice</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>80 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             column_name  percent_missing\n",
+       "PoolQC            PoolQC        99.520548\n",
+       "MiscFeature  MiscFeature        96.301370\n",
+       "Alley              Alley        93.767123\n",
+       "Fence              Fence        80.753425\n",
+       "FireplaceQu  FireplaceQu        47.260274\n",
+       "...                  ...              ...\n",
+       "Heating          Heating         0.000000\n",
+       "HeatingQC      HeatingQC         0.000000\n",
+       "MSZoning        MSZoning         0.000000\n",
+       "1stFlrSF        1stFlrSF         0.000000\n",
+       "SalePrice      SalePrice         0.000000\n",
+       "\n",
+       "[80 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "percent_missing = df.isnull().sum() * 100 / len(df)\n",
+    "missing_value_df = pd.DataFrame({'column_name': df.columns,\n",
+    "                                 'percent_missing': percent_missing})\n",
+    "missing_value_df.sort_values('percent_missing', inplace=True, ascending=[False])\n",
+    "missing_value_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fa11f795-cfea-488d-997e-c03e23ffdd5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PoolQC', 'MiscFeature', 'Alley', 'Fence', 'FireplaceQu',\n",
+       "       'LotFrontage'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "col_drop=missing_value_df[missing_value_df.percent_missing >15 ].index\n",
+    "col_drop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "967d9930-259c-4d73-81d7-3975e3b0e7c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop(col_drop, axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9aadb63e-9dce-4ffe-8b61-5ecbbdb26667",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1460, 74)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "e9c9f4e2-7a93-4134-a8f8-79ba5786fb7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>column_name</th>\n",
+       "      <th>suma_nulos</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>GarageType</th>\n",
+       "      <td>GarageType</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageYrBlt</th>\n",
+       "      <td>GarageYrBlt</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageFinish</th>\n",
+       "      <td>GarageFinish</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageQual</th>\n",
+       "      <td>GarageQual</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageCond</th>\n",
+       "      <td>GarageCond</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BsmtUnfSF</th>\n",
+       "      <td>BsmtUnfSF</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalBsmtSF</th>\n",
+       "      <td>TotalBsmtSF</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Heating</th>\n",
+       "      <td>Heating</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MSZoning</th>\n",
+       "      <td>MSZoning</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SalePrice</th>\n",
+       "      <td>SalePrice</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>74 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               column_name  suma_nulos\n",
+       "GarageType      GarageType          81\n",
+       "GarageYrBlt    GarageYrBlt          81\n",
+       "GarageFinish  GarageFinish          81\n",
+       "GarageQual      GarageQual          81\n",
+       "GarageCond      GarageCond          81\n",
+       "...                    ...         ...\n",
+       "BsmtUnfSF        BsmtUnfSF           0\n",
+       "TotalBsmtSF    TotalBsmtSF           0\n",
+       "Heating            Heating           0\n",
+       "MSZoning          MSZoning           0\n",
+       "SalePrice        SalePrice           0\n",
+       "\n",
+       "[74 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nulos = df.isnull().sum() \n",
+    "nulos_df= pd.DataFrame({'column_name': df.columns,\n",
+    "                                 'suma_nulos': nulos})\n",
+    "nulos_df.sort_values('suma_nulos', inplace=True, ascending=[False])\n",
+    "nulos_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4f763a63-fb1d-45e9-beef-8d9483fbd4c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1d9caebb-7635-4a14-a963-1402610f043b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1338, 74)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3dd1238-9f2e-4578-b997-26930e86e7ce",
+   "metadata": {},
+   "source": [
+    "### 2. Evaluación de variables no numéricas\n",
+    "\n",
+    "    Revisión de las variables no numéricas, para conocer su influencia en los precios promedio de las casas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9b194490-cbac-4458-84bd-78d408108d8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities',\n",
+       "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
+       "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
+       "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
+       "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
+       "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
+       "       'Functional', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond',\n",
+       "       'PavedDrive', 'SaleType', 'SaleCondition'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_numericas = df.select_dtypes(include = [\"object\"])\n",
+    "no_numericas.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b7d344b8-7f54-483c-ab09-7f565350b8f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>MSZoning</th>\n",
+       "      <td>RL</td>\n",
+       "      <td>RL</td>\n",
+       "      <td>RL</td>\n",
+       "      <td>RL</td>\n",
+       "      <td>RL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Street</th>\n",
+       "      <td>Pave</td>\n",
+       "      <td>Pave</td>\n",
+       "      <td>Pave</td>\n",
+       "      <td>Pave</td>\n",
+       "      <td>Pave</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LotShape</th>\n",
+       "      <td>Reg</td>\n",
+       "      <td>Reg</td>\n",
+       "      <td>IR1</td>\n",
+       "      <td>IR1</td>\n",
+       "      <td>IR1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LandContour</th>\n",
+       "      <td>Lvl</td>\n",
+       "      <td>Lvl</td>\n",
+       "      <td>Lvl</td>\n",
+       "      <td>Lvl</td>\n",
+       "      <td>Lvl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Utilities</th>\n",
+       "      <td>AllPub</td>\n",
+       "      <td>AllPub</td>\n",
+       "      <td>AllPub</td>\n",
+       "      <td>AllPub</td>\n",
+       "      <td>AllPub</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LotConfig</th>\n",
+       "      <td>Inside</td>\n",
+       "      <td>FR2</td>\n",
+       "      <td>Inside</td>\n",
+       "      <td>Corner</td>\n",
+       "      <td>FR2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LandSlope</th>\n",
+       "      <td>Gtl</td>\n",
+       "      <td>Gtl</td>\n",
+       "      <td>Gtl</td>\n",
+       "      <td>Gtl</td>\n",
+       "      <td>Gtl</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Neighborhood</th>\n",
+       "      <td>CollgCr</td>\n",
+       "      <td>Veenker</td>\n",
+       "      <td>CollgCr</td>\n",
+       "      <td>Crawfor</td>\n",
+       "      <td>NoRidge</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Condition1</th>\n",
+       "      <td>Norm</td>\n",
+       "      <td>Feedr</td>\n",
+       "      <td>Norm</td>\n",
+       "      <td>Norm</td>\n",
+       "      <td>Norm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Condition2</th>\n",
+       "      <td>Norm</td>\n",
+       "      <td>Norm</td>\n",
+       "      <td>Norm</td>\n",
+       "      <td>Norm</td>\n",
+       "      <td>Norm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BldgType</th>\n",
+       "      <td>1Fam</td>\n",
+       "      <td>1Fam</td>\n",
+       "      <td>1Fam</td>\n",
+       "      <td>1Fam</td>\n",
+       "      <td>1Fam</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HouseStyle</th>\n",
+       "      <td>2Story</td>\n",
+       "      <td>1Story</td>\n",
+       "      <td>2Story</td>\n",
+       "      <td>2Story</td>\n",
+       "      <td>2Story</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RoofStyle</th>\n",
+       "      <td>Gable</td>\n",
+       "      <td>Gable</td>\n",
+       "      <td>Gable</td>\n",
+       "      <td>Gable</td>\n",
+       "      <td>Gable</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RoofMatl</th>\n",
+       "      <td>CompShg</td>\n",
+       "      <td>CompShg</td>\n",
+       "      <td>CompShg</td>\n",
+       "      <td>CompShg</td>\n",
+       "      <td>CompShg</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exterior1st</th>\n",
+       "      <td>VinylSd</td>\n",
+       "      <td>MetalSd</td>\n",
+       "      <td>VinylSd</td>\n",
+       "      <td>Wd Sdng</td>\n",
+       "      <td>VinylSd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Exterior2nd</th>\n",
+       "      <td>VinylSd</td>\n",
+       "      <td>MetalSd</td>\n",
+       "      <td>VinylSd</td>\n",
+       "      <td>Wd Shng</td>\n",
+       "      <td>VinylSd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MasVnrType</th>\n",
+       "      <td>BrkFace</td>\n",
+       "      <td>None</td>\n",
+       "      <td>BrkFace</td>\n",
+       "      <td>None</td>\n",
+       "      <td>BrkFace</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ExterQual</th>\n",
+       "      <td>Gd</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>Gd</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>Gd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ExterCond</th>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Foundation</th>\n",
+       "      <td>PConc</td>\n",
+       "      <td>CBlock</td>\n",
+       "      <td>PConc</td>\n",
+       "      <td>BrkTil</td>\n",
+       "      <td>PConc</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BsmtQual</th>\n",
+       "      <td>Gd</td>\n",
+       "      <td>Gd</td>\n",
+       "      <td>Gd</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>Gd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BsmtCond</th>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>Gd</td>\n",
+       "      <td>TA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BsmtExposure</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Gd</td>\n",
+       "      <td>Mn</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Av</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BsmtFinType1</th>\n",
+       "      <td>GLQ</td>\n",
+       "      <td>ALQ</td>\n",
+       "      <td>GLQ</td>\n",
+       "      <td>ALQ</td>\n",
+       "      <td>GLQ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BsmtFinType2</th>\n",
+       "      <td>Unf</td>\n",
+       "      <td>Unf</td>\n",
+       "      <td>Unf</td>\n",
+       "      <td>Unf</td>\n",
+       "      <td>Unf</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Heating</th>\n",
+       "      <td>GasA</td>\n",
+       "      <td>GasA</td>\n",
+       "      <td>GasA</td>\n",
+       "      <td>GasA</td>\n",
+       "      <td>GasA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HeatingQC</th>\n",
+       "      <td>Ex</td>\n",
+       "      <td>Ex</td>\n",
+       "      <td>Ex</td>\n",
+       "      <td>Gd</td>\n",
+       "      <td>Ex</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CentralAir</th>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Electrical</th>\n",
+       "      <td>SBrkr</td>\n",
+       "      <td>SBrkr</td>\n",
+       "      <td>SBrkr</td>\n",
+       "      <td>SBrkr</td>\n",
+       "      <td>SBrkr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KitchenQual</th>\n",
+       "      <td>Gd</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>Gd</td>\n",
+       "      <td>Gd</td>\n",
+       "      <td>Gd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Functional</th>\n",
+       "      <td>Typ</td>\n",
+       "      <td>Typ</td>\n",
+       "      <td>Typ</td>\n",
+       "      <td>Typ</td>\n",
+       "      <td>Typ</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageType</th>\n",
+       "      <td>Attchd</td>\n",
+       "      <td>Attchd</td>\n",
+       "      <td>Attchd</td>\n",
+       "      <td>Detchd</td>\n",
+       "      <td>Attchd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageFinish</th>\n",
+       "      <td>RFn</td>\n",
+       "      <td>RFn</td>\n",
+       "      <td>RFn</td>\n",
+       "      <td>Unf</td>\n",
+       "      <td>RFn</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageQual</th>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageCond</th>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "      <td>TA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PavedDrive</th>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>Y</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SaleType</th>\n",
+       "      <td>WD</td>\n",
+       "      <td>WD</td>\n",
+       "      <td>WD</td>\n",
+       "      <td>WD</td>\n",
+       "      <td>WD</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SaleCondition</th>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>Abnorml</td>\n",
+       "      <td>Normal</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     0        1        2        3        4\n",
+       "MSZoning            RL       RL       RL       RL       RL\n",
+       "Street            Pave     Pave     Pave     Pave     Pave\n",
+       "LotShape           Reg      Reg      IR1      IR1      IR1\n",
+       "LandContour        Lvl      Lvl      Lvl      Lvl      Lvl\n",
+       "Utilities       AllPub   AllPub   AllPub   AllPub   AllPub\n",
+       "LotConfig       Inside      FR2   Inside   Corner      FR2\n",
+       "LandSlope          Gtl      Gtl      Gtl      Gtl      Gtl\n",
+       "Neighborhood   CollgCr  Veenker  CollgCr  Crawfor  NoRidge\n",
+       "Condition1        Norm    Feedr     Norm     Norm     Norm\n",
+       "Condition2        Norm     Norm     Norm     Norm     Norm\n",
+       "BldgType          1Fam     1Fam     1Fam     1Fam     1Fam\n",
+       "HouseStyle      2Story   1Story   2Story   2Story   2Story\n",
+       "RoofStyle        Gable    Gable    Gable    Gable    Gable\n",
+       "RoofMatl       CompShg  CompShg  CompShg  CompShg  CompShg\n",
+       "Exterior1st    VinylSd  MetalSd  VinylSd  Wd Sdng  VinylSd\n",
+       "Exterior2nd    VinylSd  MetalSd  VinylSd  Wd Shng  VinylSd\n",
+       "MasVnrType     BrkFace     None  BrkFace     None  BrkFace\n",
+       "ExterQual           Gd       TA       Gd       TA       Gd\n",
+       "ExterCond           TA       TA       TA       TA       TA\n",
+       "Foundation       PConc   CBlock    PConc   BrkTil    PConc\n",
+       "BsmtQual            Gd       Gd       Gd       TA       Gd\n",
+       "BsmtCond            TA       TA       TA       Gd       TA\n",
+       "BsmtExposure        No       Gd       Mn       No       Av\n",
+       "BsmtFinType1       GLQ      ALQ      GLQ      ALQ      GLQ\n",
+       "BsmtFinType2       Unf      Unf      Unf      Unf      Unf\n",
+       "Heating           GasA     GasA     GasA     GasA     GasA\n",
+       "HeatingQC           Ex       Ex       Ex       Gd       Ex\n",
+       "CentralAir           Y        Y        Y        Y        Y\n",
+       "Electrical       SBrkr    SBrkr    SBrkr    SBrkr    SBrkr\n",
+       "KitchenQual         Gd       TA       Gd       Gd       Gd\n",
+       "Functional         Typ      Typ      Typ      Typ      Typ\n",
+       "GarageType      Attchd   Attchd   Attchd   Detchd   Attchd\n",
+       "GarageFinish       RFn      RFn      RFn      Unf      RFn\n",
+       "GarageQual          TA       TA       TA       TA       TA\n",
+       "GarageCond          TA       TA       TA       TA       TA\n",
+       "PavedDrive           Y        Y        Y        Y        Y\n",
+       "SaleType            WD       WD       WD       WD       WD\n",
+       "SaleCondition   Normal   Normal   Normal  Abnorml   Normal"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_numericas.head(5).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1773511e-4b7b-4bda-a7cd-8615d978f320",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_categories= df[['SalePrice','LotShape', 'Neighborhood', 'Condition1', 'BldgType', 'Foundation','Heating','Electrical','GarageType','SaleType', 'SaleCondition']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4c1d352e-a21e-4d6e-8e7e-a152ca59d08c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "from statsmodels.formula.api import ols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6aa4be93-1fd3-441c-b257-2a11d1dbcd5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>df</th>\n",
+       "      <th>sum_sq</th>\n",
+       "      <th>mean_sq</th>\n",
+       "      <th>F</th>\n",
+       "      <th>PR(&gt;F)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Neighborhood</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>3.851126e+12</td>\n",
+       "      <td>1.604636e+11</td>\n",
+       "      <td>67.213816</td>\n",
+       "      <td>2.897000e-206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LotShape</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.176728e+11</td>\n",
+       "      <td>2.058909e+11</td>\n",
+       "      <td>86.242104</td>\n",
+       "      <td>8.699290e-51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BldgType</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.146794e+11</td>\n",
+       "      <td>7.866985e+10</td>\n",
+       "      <td>32.952654</td>\n",
+       "      <td>3.563455e-26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Foundation</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.693349e+11</td>\n",
+       "      <td>4.233372e+10</td>\n",
+       "      <td>17.732442</td>\n",
+       "      <td>3.562604e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageType</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.378166e+11</td>\n",
+       "      <td>2.756332e+10</td>\n",
+       "      <td>11.545521</td>\n",
+       "      <td>6.351807e-11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SaleType</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.849654e+10</td>\n",
+       "      <td>1.106207e+10</td>\n",
+       "      <td>4.633599</td>\n",
+       "      <td>1.349472e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Condition1</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5.034906e+10</td>\n",
+       "      <td>6.293633e+09</td>\n",
+       "      <td>2.636231</td>\n",
+       "      <td>7.238660e-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Heating</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.308967e+10</td>\n",
+       "      <td>7.696558e+09</td>\n",
+       "      <td>3.223878</td>\n",
+       "      <td>2.190012e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Electrical</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.533431e+10</td>\n",
+       "      <td>6.333578e+09</td>\n",
+       "      <td>2.652963</td>\n",
+       "      <td>3.176542e-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SaleCondition</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.856873e+10</td>\n",
+       "      <td>3.713746e+09</td>\n",
+       "      <td>1.555587</td>\n",
+       "      <td>1.697691e-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Residual</th>\n",
+       "      <td>1269.0</td>\n",
+       "      <td>3.029560e+12</td>\n",
+       "      <td>2.387360e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   df        sum_sq       mean_sq          F         PR(>F)\n",
+       "Neighborhood     24.0  3.851126e+12  1.604636e+11  67.213816  2.897000e-206\n",
+       "LotShape          3.0  6.176728e+11  2.058909e+11  86.242104   8.699290e-51\n",
+       "BldgType          4.0  3.146794e+11  7.866985e+10  32.952654   3.563455e-26\n",
+       "Foundation        4.0  1.693349e+11  4.233372e+10  17.732442   3.562604e-14\n",
+       "GarageType        5.0  1.378166e+11  2.756332e+10  11.545521   6.351807e-11\n",
+       "SaleType          8.0  8.849654e+10  1.106207e+10   4.633599   1.349472e-05\n",
+       "Condition1        8.0  5.034906e+10  6.293633e+09   2.636231   7.238660e-03\n",
+       "Heating           3.0  2.308967e+10  7.696558e+09   3.223878   2.190012e-02\n",
+       "Electrical        4.0  2.533431e+10  6.333578e+09   2.652963   3.176542e-02\n",
+       "SaleCondition     5.0  1.856873e+10  3.713746e+09   1.555587   1.697691e-01\n",
+       "Residual       1269.0  3.029560e+12  2.387360e+09        NaN            NaN"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = ols('SalePrice ~ LotShape + Neighborhood + Condition1+ BldgType+Foundation+Heating+Electrical+GarageType+SaleType+SaleCondition', data=df_categories).fit()\n",
+    "anova_table = sm.stats.anova_lm(model)\n",
+    "anova_table.sort_values(by=['PR(>F)']) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6ad9fff-a23c-4f76-9a54-f926de6ca539",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "    Las variables que se eligieron para realizar el análisis por ANOVA, se seleccionaron por ser variables que podían no tener alguna relación con las variables numéricas y parecían generalizar el estado de las casas, por lo tanto a partir de los resultados obtenidos en el ANOVA, podemos concluir que dichas variables tienen influencia en el costo promedio de las casas, principalmente en el caso del vecindario donde se ubican.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8fa2771-a3b4-483d-b5de-3b1d944dca81",
+   "metadata": {},
+   "source": [
+    "### 3. Distribución de los datos \n",
+    "     En esta sección se trabajo en la visualización de los datos, así como en la normalización de la distribución."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c8d4babc-2eb8-4b62-8ae2-372fbd7f849e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (6, 3))\n",
+    "plt.hist(df['SalePrice'], alpha=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d22c6863-6d43-4dda-9628-94898a6d1568",
+   "metadata": {},
+   "source": [
+    "    Se le realizó un log a la columna del precio de venta, el resultado se muestra a continuación:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7d59449a-6634-439d-825a-2b916c315a37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import math\n",
+    "df['SalePriceAdjusted'] = df['SalePrice'].apply(math.log)\n",
+    "df['SalePriceAdjusted'].plot.hist(alpha=0.5, grid=True, legend=None)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "812d575a-8ac0-4f7a-8e4c-a556c25a4a21",
+   "metadata": {},
+   "source": [
+    "#### 4. Visualización y agrupación de datos\n",
+    "    Para la siguiente sección, se observan alguna de las primeras visualizaciones a través de diagramas de dispersión para mostrar los valores de dos variables que tuvieron algunos cambios para poder utilizarse posteriormente en la correlación."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a47a38c-380e-4fb2-b45b-5d5d13489810",
+   "metadata": {},
+   "source": [
+    "    Se agruparon las habitaciones totales de la casa, con el fin de reducir el número de columnas del DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ac0ee317-9f4f-4a64-859a-81d2070c1b0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['MSSubClass', 'MSZoning', 'LotArea', 'Street', 'LotShape',\n",
+       "       'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood',\n",
+       "       'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'OverallQual',\n",
+       "       'OverallCond', 'YearBuilt', 'YearRemodAdd', 'RoofStyle', 'RoofMatl',\n",
+       "       'Exterior1st', 'Exterior2nd', 'MasVnrType', 'MasVnrArea', 'ExterQual',\n",
+       "       'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure',\n",
+       "       'BsmtFinType1', 'BsmtFinSF1', 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF',\n",
+       "       'TotalBsmtSF', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical',\n",
+       "       '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath',\n",
+       "       'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr',\n",
+       "       'KitchenQual', 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'GarageType',\n",
+       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
+       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
+       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
+       "       'MoSold', 'YrSold', 'SaleType', 'SaleCondition', 'SalePrice',\n",
+       "       'SalePriceAdjusted', 'Total_Rooms'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Total_Rooms\"]=df[['BedroomAbvGr', 'KitchenAbvGr', 'FullBath', 'HalfBath', 'BsmtFullBath', 'BsmtHalfBath']].sum(axis=1)\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "672ddb8d-9326-442e-b55f-65d7879fe09d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Total_Rooms', ylabel='SalePrice'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(x = 'Total_Rooms', y = 'SalePrice', data = df, palette=\"Blues_d\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d7369b0-1cd6-4103-bfe7-90c2f890d56e",
+   "metadata": {},
+   "source": [
+    "    Para las varibales con el año, se realizó un cálculo para en su lugar, tomar el tiempo de diferencia entre las fechas y el año actual con el mismo fin inicial."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "0b23b6fb-ab7a-4b0c-927f-21dd5f508553",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     19\n",
+       "1     46\n",
+       "2     21\n",
+       "3    107\n",
+       "4     22\n",
+       "Name: TimeBuilt, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['TimeBuilt']= 2022- df['YearBuilt']\n",
+    "df['TimeBuilt'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b6712615-89d3-4d54-ab6f-a9f5cef9a0a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='TimeBuilt', ylabel='SalePrice'>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(x = 'TimeBuilt', y = 'SalePrice', data = df, palette=\"Blues_d\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7f7f8cd2-f660-4abc-a291-be0bd72937b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    19\n",
+       "1    46\n",
+       "2    20\n",
+       "3    52\n",
+       "4    22\n",
+       "Name: TimeRemo, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['TimeRemo']= 2022- df['YearRemodAdd']\n",
+    "df['TimeRemo'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "413d24b8-c635-4b27-b571-22b348b01137",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='TimeRemo', ylabel='SalePrice'>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.scatterplot(x = 'TimeRemo', y = 'SalePrice', data = df, palette=\"Blues_d\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "408b101a-694b-494a-bf4f-6f3631d9e185",
+   "metadata": {},
+   "source": [
+    "#### 5. Tabla de correlación\n",
+    "    A continuación se puede observar la tabla de correlación y la selección de las variables con mayor correlación (positiva y negativa) respecto al precio de venta."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "8340ad31-77db-4333-92c5-41bbe884a072",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numerical= df.select_dtypes(include=['number'])\n",
+    "corr= numerical.corr()\n",
+    "mask = np.triu(np.ones_like(corr, dtype = bool))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f3c6e0e1-aae6-484e-b5a8-8c013e85ee81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cmap = sns.diverging_palette(2020, 10, as_cmap = True) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "25cebdf7-cfc0-4bb5-8913-d524c668c5e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (10,10))\n",
+    "sns.heatmap(corr, mask=mask,cmap = cmap, vmax = 1, vmin = -1, annot = False, square = True, center = 0, linewidths = 0.3, )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "6e717f87-35de-45c7-9334-ee240181981c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_corr= df.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "25946fd2-c29d-42ea-b0ee-c539c1cc295b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales= df_corr['SalePrice'].sort_values(ascending=False)\n",
+    "df_sales=pd.DataFrame(sales)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f3513a8c-3438-4e04-a5cb-978f619ab543",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SalePrice</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>SalePriceAdjusted</th>\n",
+       "      <td>0.954241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OverallQual</th>\n",
+       "      <td>0.783546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GrLivArea</th>\n",
+       "      <td>0.711706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageCars</th>\n",
+       "      <td>0.640154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GarageArea</th>\n",
+       "      <td>0.607535</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1stFlrSF</th>\n",
+       "      <td>0.604714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotalBsmtSF</th>\n",
+       "      <td>0.602042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FullBath</th>\n",
+       "      <td>0.569313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TotRmsAbvGrd</th>\n",
+       "      <td>0.551821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YearBuilt</th>\n",
+       "      <td>0.504297</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>YearRemodAdd</th>\n",
+       "      <td>0.501435</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   SalePrice\n",
+       "SalePriceAdjusted   0.954241\n",
+       "OverallQual         0.783546\n",
+       "GrLivArea           0.711706\n",
+       "GarageCars          0.640154\n",
+       "GarageArea          0.607535\n",
+       "1stFlrSF            0.604714\n",
+       "TotalBsmtSF         0.602042\n",
+       "FullBath            0.569313\n",
+       "TotRmsAbvGrd        0.551821\n",
+       "YearBuilt           0.504297\n",
+       "YearRemodAdd        0.501435"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "positive_corr=df_sales[(df_sales['SalePrice'] > 0.50) & (df_sales['SalePrice']<1)]\n",
+    "positive_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "769bd19b-1964-4feb-8491-5b2d16d29a53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SalePrice</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TimeRemo</th>\n",
+       "      <td>-0.501435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TimeBuilt</th>\n",
+       "      <td>-0.504297</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           SalePrice\n",
+       "TimeRemo   -0.501435\n",
+       "TimeBuilt  -0.504297"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negative_corr=df_sales[(df_sales['SalePrice'] < (-0.50)) & (df_sales['SalePrice'] > (-1))]\n",
+    "negative_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "290d059b-2c1a-4a25-973a-fa4bfb60b74d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>SalePrice</th>\n",
+       "      <th>SalePriceAdjusted</th>\n",
+       "      <th>OverallQual</th>\n",
+       "      <th>GrLivArea</th>\n",
+       "      <th>GarageCars</th>\n",
+       "      <th>GarageArea</th>\n",
+       "      <th>1stFlrSF</th>\n",
+       "      <th>TotalBsmtSF</th>\n",
+       "      <th>FullBath</th>\n",
+       "      <th>TotRmsAbvGrd</th>\n",
+       "      <th>TimeRemo</th>\n",
+       "      <th>TimeBuilt</th>\n",
+       "      <th>LotShape</th>\n",
+       "      <th>Neighborhood</th>\n",
+       "      <th>BldgType</th>\n",
+       "      <th>Foundation</th>\n",
+       "      <th>GarageType</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>208500</td>\n",
+       "      <td>12.247694</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1710</td>\n",
+       "      <td>2</td>\n",
+       "      <td>548</td>\n",
+       "      <td>856</td>\n",
+       "      <td>856</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>19</td>\n",
+       "      <td>19</td>\n",
+       "      <td>Reg</td>\n",
+       "      <td>CollgCr</td>\n",
+       "      <td>1Fam</td>\n",
+       "      <td>PConc</td>\n",
+       "      <td>Attchd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>181500</td>\n",
+       "      <td>12.109011</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1262</td>\n",
+       "      <td>2</td>\n",
+       "      <td>460</td>\n",
+       "      <td>1262</td>\n",
+       "      <td>1262</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>46</td>\n",
+       "      <td>46</td>\n",
+       "      <td>Reg</td>\n",
+       "      <td>Veenker</td>\n",
+       "      <td>1Fam</td>\n",
+       "      <td>CBlock</td>\n",
+       "      <td>Attchd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>223500</td>\n",
+       "      <td>12.317167</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1786</td>\n",
+       "      <td>2</td>\n",
+       "      <td>608</td>\n",
+       "      <td>920</td>\n",
+       "      <td>920</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>20</td>\n",
+       "      <td>21</td>\n",
+       "      <td>IR1</td>\n",
+       "      <td>CollgCr</td>\n",
+       "      <td>1Fam</td>\n",
+       "      <td>PConc</td>\n",
+       "      <td>Attchd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>140000</td>\n",
+       "      <td>11.849398</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1717</td>\n",
+       "      <td>3</td>\n",
+       "      <td>642</td>\n",
+       "      <td>961</td>\n",
+       "      <td>756</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>52</td>\n",
+       "      <td>107</td>\n",
+       "      <td>IR1</td>\n",
+       "      <td>Crawfor</td>\n",
+       "      <td>1Fam</td>\n",
+       "      <td>BrkTil</td>\n",
+       "      <td>Detchd</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>250000</td>\n",
+       "      <td>12.429216</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2198</td>\n",
+       "      <td>3</td>\n",
+       "      <td>836</td>\n",
+       "      <td>1145</td>\n",
+       "      <td>1145</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>22</td>\n",
+       "      <td>22</td>\n",
+       "      <td>IR1</td>\n",
+       "      <td>NoRidge</td>\n",
+       "      <td>1Fam</td>\n",
+       "      <td>PConc</td>\n",
+       "      <td>Attchd</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   SalePrice  SalePriceAdjusted  OverallQual  GrLivArea  GarageCars  \\\n",
+       "0     208500          12.247694            7       1710           2   \n",
+       "1     181500          12.109011            6       1262           2   \n",
+       "2     223500          12.317167            7       1786           2   \n",
+       "3     140000          11.849398            7       1717           3   \n",
+       "4     250000          12.429216            8       2198           3   \n",
+       "\n",
+       "   GarageArea  1stFlrSF  TotalBsmtSF  FullBath  TotRmsAbvGrd  TimeRemo  \\\n",
+       "0         548       856          856         2             8        19   \n",
+       "1         460      1262         1262         2             6        46   \n",
+       "2         608       920          920         2             6        20   \n",
+       "3         642       961          756         1             7        52   \n",
+       "4         836      1145         1145         2             9        22   \n",
+       "\n",
+       "   TimeBuilt LotShape Neighborhood BldgType Foundation GarageType  \n",
+       "0         19      Reg      CollgCr     1Fam      PConc     Attchd  \n",
+       "1         46      Reg      Veenker     1Fam     CBlock     Attchd  \n",
+       "2         21      IR1      CollgCr     1Fam      PConc     Attchd  \n",
+       "3        107      IR1      Crawfor     1Fam     BrkTil     Detchd  \n",
+       "4         22      IR1      NoRidge     1Fam      PConc     Attchd  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_select= df[['SalePrice','SalePriceAdjusted','OverallQual','GrLivArea', 'GarageCars', 'GarageArea', '1stFlrSF','TotalBsmtSF', 'FullBath', 'TotRmsAbvGrd','TimeRemo', 'TimeBuilt',\n",
+    "             'LotShape', 'Neighborhood','BldgType','Foundation','GarageType']]\n",
+    "df_select.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6441b12-ee53-40fa-b860-b1267b393b21",
+   "metadata": {},
+   "source": [
+    "#### 6. Prueba de Hipótesis\n",
+    "    Se llevo a cabo una prueba de hipótesis con los datos, de manera que se pudieran evaluar las hipótesos nula y confirmar el comportamiento de nuestros datos. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf2ceac6-6c77-40a5-95ed-ac3fec74aa9a",
+   "metadata": {},
+   "source": [
+    "$$H_0 = \\text{El tipo de viviendas unifamiliares tiene en promedio un tiempo de remodelación de 40 años}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "70802d49-e481-4d07-8426-15ba7a591559",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1Fam      1138\n",
+       "TwnhsE     112\n",
+       "Twnhs       38\n",
+       "Duplex      28\n",
+       "2fmCon      22\n",
+       "Name: BldgType, dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_select.BldgType.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "df2e89af-744f-445a-85a9-b50ed30e40f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_type= df_select[df_select['BldgType']== \"1Fam\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "5998a628-df07-4600-9ec3-6bddd6041a94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_1sampResult(statistic=-5.353675982213851, pvalue=1.0421165706258885e-07)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ttest_1samp(df_type['TimeRemo'],40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2b9ef45-5a90-44c7-9a09-4796fabfd981",
+   "metadata": {},
+   "source": [
+    "#### Resultado: \n",
+    "A partir de los resultados, podemos rechazar la hipótesis nula con una confianza del 99%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e100f54-9f9b-4a5f-aaab-b67483cb540f",
+   "metadata": {},
+   "source": [
+    "$$H_0 = \\text{El sistema de electricidad de Interruptores automáticos estándar y RomexLos (SBrkr) tienen un precio promedio de 250000 USD }$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "0928bca7-dce8-49eb-869a-188f2ea27be9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_electrical= df[df['Electrical']== \"SBrkr\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "187acc63-6d05-4667-8202-e9079936bcb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_1sampResult(statistic=-25.96372858104104, pvalue=4.618584973873539e-119)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ttest_1samp(df_electrical['SalePrice'],250_000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2be539fc-5dde-45a5-8bac-167a194a9b26",
+   "metadata": {},
+   "source": [
+    "#### Resultado: \n",
+    "A partir de los resultados, podemos rechazar la hipótesis nula con una confianza del 99%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01160d5d-63f6-4ee4-98fa-82ff155e3120",
+   "metadata": {},
+   "source": [
+    "#### 7. Visualización de las variables correlacionadas\n",
+    "    Se realizó la visualización de las variables que mostraron alta correlación con el precio de venta, utilizando el precio ajustado y de esta manera considerarlas en el objetivo final, el modelo de regresión."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "7d401471-0d04-4b3c-af4e-9b5396858ae1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_style(\"darkgrid\")\n",
+    "sns.scatterplot(x = 'OverallQual', y = 'SalePriceAdjusted', data = df_select, color=\"#9b59b0\", alpha = 0.7)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "0942e6f5-52dd-40dc-bad7-c4483236ed93",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_style(\"darkgrid\")\n",
+    "sns.scatterplot(x = 'TotalBsmtSF', y = 'SalePriceAdjusted', data = df_select, color=\"grey\", alpha = 0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "e1b381f5-3659-4035-92de-7ff763abf81b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cols=['GarageArea','GarageCars', 'SalePriceAdjusted']\n",
+    "df_plot =df_select[cols]\n",
+    "fig,axes = plt.subplots(figsize = (12,6), nrows = 1, ncols = 2)\n",
+    "for i in range(2):\n",
+    "    plt.sca(axes[i])\n",
+    "    plt.scatter(df_plot.iloc[:,i],df['SalePriceAdjusted'], color=\"#cf580f\", alpha = 0.6)\n",
+    "    plt.xlabel(df_plot.iloc[:,i].name)\n",
+    "    plt.ylabel('SalesPrice')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "fb5b7726-5f2e-49ab-8948-e953970a294b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x='GrLivArea',y='SalePriceAdjusted', data=df_select ,fit_reg=True) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "057a1ccc-b687-498d-9a2a-fc63e1d0d70b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cols=['TimeBuilt', \"TimeRemo\", 'SalePriceAdjusted']\n",
+    "df_time=df_select[cols]\n",
+    "fig,axes = plt.subplots(figsize = (12,6), nrows = 1, ncols = 2)\n",
+    "for i in range(2):\n",
+    "    plt.sca(axes[i])\n",
+    "    plt.scatter(df_time.iloc[:,i],df['SalePriceAdjusted'], color='#237c15', alpha = 0.3)\n",
+    "    plt.xlabel(df_time.iloc[:,i].name)\n",
+    "    plt.ylabel('SalesPrice')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1a81a67-a7e1-4d70-96e2-50d73826bc07",
+   "metadata": {},
+   "source": [
+    "#### 8. Modelo de regresión:\n",
+    "    Para este caso se utilizó el modelo de regresión de la librería statsmodel, a partir de las columnas con mejores resultados en la tabla de correlación, así como de la visualización"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "f020be32-352f-49aa-9ec7-9407bca8075e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y= df_select['SalePrice']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "2dbc74eb-a669-4498-b577-10d86e4b605d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:              SalePrice   R-squared:                       0.614\n",
+      "Model:                            OLS   Adj. R-squared:                  0.614\n",
+      "Method:                 Least Squares   F-statistic:                     2125.\n",
+      "Date:                Sun, 31 Jul 2022   Prob (F-statistic):          2.12e-278\n",
+      "Time:                        21:14:41   Log-Likelihood:                -16349.\n",
+      "No. Observations:                1338   AIC:                         3.270e+04\n",
+      "Df Residuals:                    1336   BIC:                         3.271e+04\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "===============================================================================\n",
+      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "-------------------------------------------------------------------------------\n",
+      "const       -1.036e+05   6440.614    -16.086      0.000   -1.16e+05    -9.1e+04\n",
+      "OverallQual  4.668e+04   1012.821     46.094      0.000    4.47e+04    4.87e+04\n",
+      "==============================================================================\n",
+      "Omnibus:                      543.399   Durbin-Watson:                   1.994\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5291.081\n",
+      "Skew:                           1.612   Prob(JB):                         0.00\n",
+      "Kurtosis:                      12.193   Cond. No.                         31.3\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "X=sm.add_constant(df_select['OverallQual'])\n",
+    "modelo_ventas = sm.OLS(Y, X).fit()\n",
+    "predictions = modelo_ventas.predict(X)\n",
+    "print_model= modelo_ventas.summary()\n",
+    "print(print_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc78e18f-9c04-42d3-a48d-055a2ebd4a7f",
+   "metadata": {},
+   "source": [
+    "##### Prueba del modelo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "831a13fd-f451-4db1-81b3-2bd6dc0b5ae8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       223188.430852\n",
+       "1       176503.703381\n",
+       "2       223188.430852\n",
+       "3       223188.430852\n",
+       "4       269873.158324\n",
+       "            ...      \n",
+       "1455    176503.703381\n",
+       "1456    176503.703381\n",
+       "1457    223188.430852\n",
+       "1458    129818.975909\n",
+       "1459    129818.975909\n",
+       "Length: 1338, dtype: float64"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "419aa867-af8b-40f0-9e47-858ae7201279",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\denni\\AppData\\Local\\Temp/ipykernel_10324/3288408767.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_p[\"prediccion\"]= round(predictions)\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>OverallQual</th>\n",
+       "      <th>SalePrice</th>\n",
+       "      <th>prediccion</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7</td>\n",
+       "      <td>208500</td>\n",
+       "      <td>223188.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>181500</td>\n",
+       "      <td>176504.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>223500</td>\n",
+       "      <td>223188.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>140000</td>\n",
+       "      <td>223188.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8</td>\n",
+       "      <td>250000</td>\n",
+       "      <td>269873.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1455</th>\n",
+       "      <td>6</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>176504.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1456</th>\n",
+       "      <td>6</td>\n",
+       "      <td>210000</td>\n",
+       "      <td>176504.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1457</th>\n",
+       "      <td>7</td>\n",
+       "      <td>266500</td>\n",
+       "      <td>223188.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1458</th>\n",
+       "      <td>5</td>\n",
+       "      <td>142125</td>\n",
+       "      <td>129819.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1459</th>\n",
+       "      <td>5</td>\n",
+       "      <td>147500</td>\n",
+       "      <td>129819.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1338 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      OverallQual  SalePrice  prediccion\n",
+       "0               7     208500    223188.0\n",
+       "1               6     181500    176504.0\n",
+       "2               7     223500    223188.0\n",
+       "3               7     140000    223188.0\n",
+       "4               8     250000    269873.0\n",
+       "...           ...        ...         ...\n",
+       "1455            6     175000    176504.0\n",
+       "1456            6     210000    176504.0\n",
+       "1457            7     266500    223188.0\n",
+       "1458            5     142125    129819.0\n",
+       "1459            5     147500    129819.0\n",
+       "\n",
+       "[1338 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_p=df_select[['OverallQual', 'SalePrice']]\n",
+    "df_p[\"prediccion\"]= round(predictions)\n",
+    "df_p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "272743c9-6952-441a-8003-08c74aedfd5b",
+   "metadata": {},
+   "source": [
+    "##### Modelo con más de una variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "27031bb7-31f4-45da-afbf-c4a2a14dfa72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:              SalePrice   R-squared:                       0.743\n",
+      "Model:                            OLS   Adj. R-squared:                  0.741\n",
+      "Method:                 Least Squares   F-statistic:                     479.0\n",
+      "Date:                Sun, 31 Jul 2022   Prob (F-statistic):               0.00\n",
+      "Time:                        21:48:19   Log-Likelihood:                -16078.\n",
+      "No. Observations:                1338   AIC:                         3.217e+04\n",
+      "Df Residuals:                    1329   BIC:                         3.222e+04\n",
+      "Df Model:                           8                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "================================================================================\n",
+      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "--------------------------------------------------------------------------------\n",
+      "const        -1.062e+05   8948.846    -11.868      0.000   -1.24e+05   -8.86e+04\n",
+      "OverallQual   2.514e+04   1272.652     19.752      0.000    2.26e+04    2.76e+04\n",
+      "GarageCars     1.09e+04   3426.025      3.182      0.001    4179.248    1.76e+04\n",
+      "GarageArea      29.3941     11.049      2.660      0.008       7.718      51.070\n",
+      "1stFlrSF        24.9987      6.836      3.657      0.000      11.589      38.409\n",
+      "TotalBsmtSF     20.2943      6.576      3.086      0.002       7.394      33.195\n",
+      "TotRmsAbvGrd  1.025e+04    845.300     12.125      0.000    8591.107    1.19e+04\n",
+      "TimeRemo      -323.1045     73.276     -4.409      0.000    -466.853    -179.356\n",
+      "TimeBuilt     -113.5426     55.537     -2.044      0.041    -222.492      -4.594\n",
+      "==============================================================================\n",
+      "Omnibus:                      443.549   Durbin-Watson:                   1.963\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            23037.071\n",
+      "Skew:                           0.736   Prob(JB):                         0.00\n",
+      "Kurtosis:                      23.275   Cond. No.                     1.45e+04\n",
+      "==============================================================================\n",
+      "\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+      "[2] The condition number is large, 1.45e+04. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\denni\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
+      "  x = pd.concat(x[::order], 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X2=sm.add_constant(df_select[['OverallQual', 'GarageCars', 'GarageArea', '1stFlrSF','TotalBsmtSF', 'TotRmsAbvGrd','TimeRemo', 'TimeBuilt']])\n",
+    "modelo_ventas2 = sm.OLS(Y, X2).fit()\n",
+    "predictions2 = modelo_ventas2.predict(X2)\n",
+    "print_model2= modelo_ventas2.summary()\n",
+    "\n",
+    "print(print_model2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "840d1acf-de3c-4e97-aac1-048bfc94c4da",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\denni\\AppData\\Local\\Temp/ipykernel_10324/3785986669.py:2: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_pd[\"prediccion\"]= round(predictions)\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>OverallQual</th>\n",
+       "      <th>SalePrice</th>\n",
+       "      <th>prediccion</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7</td>\n",
+       "      <td>208500</td>\n",
+       "      <td>223188.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "      <td>181500</td>\n",
+       "      <td>176504.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>223500</td>\n",
+       "      <td>223188.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>140000</td>\n",
+       "      <td>223188.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8</td>\n",
+       "      <td>250000</td>\n",
+       "      <td>269873.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1455</th>\n",
+       "      <td>6</td>\n",
+       "      <td>175000</td>\n",
+       "      <td>176504.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1456</th>\n",
+       "      <td>6</td>\n",
+       "      <td>210000</td>\n",
+       "      <td>176504.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1457</th>\n",
+       "      <td>7</td>\n",
+       "      <td>266500</td>\n",
+       "      <td>223188.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1458</th>\n",
+       "      <td>5</td>\n",
+       "      <td>142125</td>\n",
+       "      <td>129819.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1459</th>\n",
+       "      <td>5</td>\n",
+       "      <td>147500</td>\n",
+       "      <td>129819.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1338 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      OverallQual  SalePrice  prediccion\n",
+       "0               7     208500    223188.0\n",
+       "1               6     181500    176504.0\n",
+       "2               7     223500    223188.0\n",
+       "3               7     140000    223188.0\n",
+       "4               8     250000    269873.0\n",
+       "...           ...        ...         ...\n",
+       "1455            6     175000    176504.0\n",
+       "1456            6     210000    176504.0\n",
+       "1457            7     266500    223188.0\n",
+       "1458            5     142125    129819.0\n",
+       "1459            5     147500    129819.0\n",
+       "\n",
+       "[1338 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_pd=df_select[['OverallQual', 'SalePrice']]\n",
+    "df_pd[\"prediccion\"]= round(predictions)\n",
+    "df_pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69730751-449c-4786-b42d-c0028e730ec8",
+   "metadata": {},
+   "source": [
+    "#### CONCLUSIONES: \n",
+    "\n",
+    "    DURANTE EL DESARROLLO DEL PROYECTO, PUDIMOS OBSERVAR QUE PUEDEN EXISTIR DIFERENTES VARIABLES QUE NOS AYUDEN A PREDECIR UN VALOR, A PARTIR DE EVALUAR SU CORRELACIÓN CON LOS MISMOS, PERSONALMENTE ME PARECIÓ UN TEMA COMPLEJO, PRINCIPALMENTE EN LA APLCACIÓN DE LAS PREDICCIONES, COMO SE OBSERVA EN LAS ÚLTIMAS PARTES DEL PROYECTO, HUBO ALGUNAS FALLAS EN EL TRATAMIENTO DE LOS DATOS, SIN EMBARGO, FUE POSIBLE OBSERVAR LA DIFERENCIA ENTREN LOS VALORES ORIGINALES Y LOS PREDECIDOS EN EL MODELO.\n",
+    "     \n",
+    "    POR LO QUE LOS VALORES NUMÉRICOS QUE MÁS INFLUYEN EN EL PRECIO DE LAS CASAS, SON AQUELLOS RELACIONADOS CON LA CALIDAD, EL ÁREA, EL NÚMERO DE HABITACIONES Y LA ANTIGUEDAD DE CONSTRUCCIÓN Y REMODELACIÓN.\n",
+    "     \n",
+    "    COMO SIGUIENTES PASOS, PODRÍAMOS REALIZAR LA REVISIÓN CON LAS VARIABLES CUALITATIVAS PARA REFORZAR EL MODELO Y PODER OBTENER VALORES DE PREDICCIÓN MÁS CERCANOS.\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "faae3429-aef6-4d90-a771-03a42e53b6a9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
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