Projeto House Price.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9ab1f7a6",
   "metadata": {},
   "source": [
    "# House Prices - Advanced Regression Techniques\n",
    "(Preços de casas - Técnicas Avançadas de Regressão)\n",
    "\n",
    "<img src=\"https://storage.googleapis.com/kaggle-competitions/kaggle/5407/media/housesbanner.png\" width=800>\n",
    "\n",
    "\n",
    "- Vamos utilizar o [dataset disponível no Kaggle](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview)\n",
    "    - É um dataset de **competição**\n",
    "    - Temos uma base com a **descrição de cada uma das colunas (data_description.txt)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0233c934",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o pandas\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3c8d95e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o dataset de treino\n",
    "data = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bfd24d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\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>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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando essa base\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3bbab431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retornando o shape da base\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ba3f0cd6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# E as informações\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db0cfd3",
   "metadata": {},
   "source": [
    "## Começando a explorar os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b6528772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC          1453\n",
       "MiscFeature     1406\n",
       "Alley           1369\n",
       "Fence           1179\n",
       "FireplaceQu      690\n",
       "LotFrontage      259\n",
       "GarageYrBlt       81\n",
       "GarageCond        81\n",
       "GarageType        81\n",
       "GarageFinish      81\n",
       "GarageQual        81\n",
       "BsmtFinType2      38\n",
       "BsmtExposure      38\n",
       "BsmtQual          37\n",
       "BsmtCond          37\n",
       "BsmtFinType1      37\n",
       "MasVnrArea         8\n",
       "MasVnrType         8\n",
       "Electrical         1\n",
       "Id                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando quantidade de valores vazios\n",
    "data.isnull().sum().sort_values(ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bda71526",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LotFrontage', 'Alley', 'FireplaceQu', 'PoolQC', 'Fence',\n",
       "       'MiscFeature'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Podemos eliminar as colunas com mais de 10% de valores vazios\n",
    "eliminar = data.columns[(data.isnull().sum()/data.shape[0]) > 0.1]\n",
    "eliminar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff97453f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminando essas colunas\n",
    "data = data.drop(eliminar,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e83cd85",
   "metadata": {},
   "source": [
    "- Queremos criar um primeiro modelo para verificar o quanto estamos errando e depois planejar como melhorar. Para isso:\n",
    "    - Vamos **eliminar as colunas de texto**\n",
    "    - Precisamos **tratar os valores vazios**\n",
    "    - Vamos **escolher alguns algoritmos para testar e um método de avaliação de erro**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "572df872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
       "       'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
       "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
       "       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
       "       'MoSold', 'YrSold', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando apenas as colunas numéricas\n",
    "colunas = data.columns[data.dtypes != 'object']\n",
    "colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2bd4e288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass  LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  \\\n",
       "0   1          60     8450            7            5       2003          2003   \n",
       "1   2          20     9600            6            8       1976          1976   \n",
       "2   3          60    11250            7            5       2001          2002   \n",
       "3   4          70     9550            7            5       1915          1970   \n",
       "4   5          60    14260            8            5       2000          2000   \n",
       "\n",
       "   MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  WoodDeckSF  OpenPorchSF  \\\n",
       "0       196.0         706           0  ...           0           61   \n",
       "1         0.0         978           0  ...         298            0   \n",
       "2       162.0         486           0  ...           0           42   \n",
       "3         0.0         216           0  ...           0           35   \n",
       "4       350.0         655           0  ...         192           84   \n",
       "\n",
       "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
       "0              0          0            0         0        0       2    2008   \n",
       "1              0          0            0         0        0       5    2007   \n",
       "2              0          0            0         0        0       9    2008   \n",
       "3            272          0            0         0        0       2    2006   \n",
       "4              0          0            0         0        0      12    2008   \n",
       "\n",
       "   SalePrice  \n",
       "0     208500  \n",
       "1     181500  \n",
       "2     223500  \n",
       "3     140000  \n",
       "4     250000  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# E criar uma nova base com esses valores\n",
    "base = data.loc[:,colunas]\n",
    "base.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3718a7dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GarageYrBlt    81\n",
       "MasVnrArea      8\n",
       "Id              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando os valores vazios\n",
    "base.isnull().sum().sort_values(ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4a656932",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituindo os valores vazios por -1\n",
    "base = base.fillna(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe0ffdf",
   "metadata": {},
   "source": [
    "- Essa será nossa base inicial para começar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a686ce6",
   "metadata": {},
   "source": [
    "## Criando nosso modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0b769c",
   "metadata": {},
   "source": [
    "- **Vamos separar em treino e teste**\n",
    "    - https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7f7b8193",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando X e y\n",
    "X = base.drop('SalePrice', axis=1)\n",
    "y = base.SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "149956b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o train_test_split\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6ec49ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separando essa base em treino e teste\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72614e5e",
   "metadata": {},
   "source": [
    "- **O próximo passo é selecionar os algoritmos que vamos utilizar. Podemos começar com os algoritmos mais simples como:**\n",
    "    - Regressão Linear\n",
    "        - https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\n",
    "    - Árvore de Regressão\n",
    "        - https://scikit-learn.org/stable/modules/tree.html#regression\n",
    "    - KNeighborsRegressor\n",
    "        - https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html#sklearn.neighbors.KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "50bf2902",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando a regressão linear\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "76235128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando o regressor e fazendo o fit com os dados de treino\n",
    "reg_rl = LinearRegression().fit(X_train, y_train)\n",
    "reg_rl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "581ebb2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fazendo a previsão pros dados de teste\n",
    "pred_rg = reg_rl.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ee06e323",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       237826.70348636, 214708.1831282 ,  97189.19954346,  98839.88996765,\n",
       "       132112.92188741, 182745.77785927,  94465.15081149, 159215.58071261,\n",
       "       122502.73195521, 147057.08913272, 180878.15401654, 106337.77439787,\n",
       "       201145.93025397, 228690.30804468, 118501.65516286, 158621.13910661,\n",
       "       210539.85409131, 242400.86328028, 167599.46499362, 205890.64625416,\n",
       "       227382.60394549, 135726.93888169, 165190.59396191, 222915.78895763,\n",
       "        80161.17660092, 227206.29937969, 121012.64564845, 186588.87035779,\n",
       "       206302.77794794, 162925.81875648, 250473.98220547,  69214.01129559,\n",
       "       210066.58192532, 120008.80654336, 118905.30341545, 109792.2461815 ,\n",
       "       219901.87005196, 176073.23858614, 133397.16761245, 215273.80045524,\n",
       "       192541.88917029,  71956.16273417, 163876.37686224, 149951.80947988,\n",
       "       120380.56957426, 259233.28507594, 166403.68452763, 124122.56282822,\n",
       "       144051.74675043,  67777.8286761 , 113875.11099403, 193678.25461913,\n",
       "       206383.67132347, 140048.94131606, 108670.63860994, 202426.05750099,\n",
       "       238229.94210321, 337732.53383596, 322418.66217739, 121318.11920881,\n",
       "       261067.83118557, 113012.56014617, 294778.40861751, 341547.45812914,\n",
       "       249907.27449868, 188573.06146784, 254355.16895872, 125628.45878489,\n",
       "       108467.17383786, 100887.79783156, 211798.3803235 , 325283.74024984,\n",
       "       193175.10783346, 116196.69043171, 247286.08202744, 226569.27002175,\n",
       "       113715.78698764, 201745.72186549,  84074.79702299, 120332.36817928,\n",
       "       122797.85759484, 146867.77627803, 107603.90717616, 188257.37615932,\n",
       "       124829.0927653 , 209587.865438  , 187126.49439461, 127313.20006403,\n",
       "       198294.27579761, 212006.00507728,  96026.26527142, 219376.93851133,\n",
       "       150825.60296459, 187578.02984462, 206535.47874164, 154313.22357273,\n",
       "       111690.10095571, 244454.68527567, 146142.26624628, 145414.33273484,\n",
       "       258016.76027486, 194913.96882713, 108471.63865269, 178117.20352517,\n",
       "       133646.17553519, 325012.33970574, 156284.0763447 , 361262.92673687,\n",
       "       134432.88765763, 170726.8507966 , 179457.08910461, 161338.5160676 ,\n",
       "       182098.86398399, 336030.36070085, 107550.43044425, 172255.33585731,\n",
       "       118736.43743457,  90918.86982925, 208330.11885925, 128193.57395773,\n",
       "       228610.0304958 , 213070.51272689, 238179.34490757, 229967.92647703,\n",
       "        80281.75366673, 297464.61945341, 119571.80059089, 324964.13169661,\n",
       "       100720.44465807, 322673.40990692, 267241.55413495, 117942.09939996,\n",
       "       231472.98480222, 236119.20781015, 215476.95150262, 191935.24788013,\n",
       "       246972.10719755, 100440.03415237, 214613.37119808, 200953.3658751 ,\n",
       "       206540.2312547 , 219723.8994172 , 102510.34471819, 147479.33318195,\n",
       "       180615.56738439, 128235.15326058, 100579.32013872, 204829.13224718,\n",
       "       147123.28533181, 367029.04716439, 214421.25305327, 127342.91898317,\n",
       "       202666.88188204, 291036.08799253, 217764.48149556, 166568.97295269,\n",
       "       210518.37808076, 122770.21059882, 183720.51860967, 225605.4714994 ,\n",
       "       127667.68423614, 179097.85915566,  92528.7860709 , 170907.95981941,\n",
       "       263914.91820968, 123899.76975269,  97621.15695308, 215412.90742688,\n",
       "       317032.80161846, 318125.96931415, 249745.31953705, 164444.28658441,\n",
       "       107245.90962856,  85277.12854813, 160829.51113581, 217451.22131353,\n",
       "       174513.27803712, 341332.2745792 , 240864.79528385, 212079.55234123,\n",
       "        94683.82658793, 269869.34593072, 133136.03004147, 124562.82219907,\n",
       "        98713.56012733, 120592.12164688, 224834.89426814, 264311.86299721,\n",
       "       105454.60242932, 109712.14665698, 107093.34280155, 231580.55300259,\n",
       "       118623.5304654 , 206087.4792779 ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fazendo a previsão\n",
    "pred_rg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1bfbbbdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando a árvore de regressão\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b4bacf9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando o regressor e fazendo o fit com os dados de treino\n",
    "reg_tr = tree.DecisionTreeRegressor(random_state=42).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8f44b273",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([154000., 345000., 119200., 167000., 325000.,  80000., 176000.,\n",
       "       148500.,  80000., 177000., 167000., 127500.,  93000., 212900.,\n",
       "       175000., 109000., 202500., 145500., 109900., 213490., 139600.,\n",
       "       212000., 173000., 117000., 179900., 162000., 130000.,  82500.,\n",
       "       163990., 176000., 141000., 233000., 177000., 106500., 275000.,\n",
       "       155000.,  79000., 193000., 354000., 115000., 141000., 233000.,\n",
       "       100000., 372500., 137500., 104900., 125000., 125500., 426000.,\n",
       "       145250., 117000., 180000., 118000., 430000., 140000., 240000.,\n",
       "       198500., 158000., 140000., 119750.,  79900., 150750., 250000.,\n",
       "       555000., 233230., 190000., 108000., 261500.,  80000., 177000.,\n",
       "       118964., 124000.,  88000.,  82000., 582933., 143500., 340000.,\n",
       "       290000., 139950., 145000., 106000., 108000.,  73000., 109900.,\n",
       "       156932., 140200., 244600., 199900., 136500., 179400., 117000.,\n",
       "       165000., 138500., 262280., 115000., 194700., 189950., 188500.,\n",
       "       205000., 200000., 180000., 235000., 180000., 179900., 176000.,\n",
       "       193000., 154000., 278000., 138000., 206900.,  55000., 147000.,\n",
       "       134000., 140200., 226000., 117000., 120000., 120500., 122500.,\n",
       "       325624., 160000., 141000., 175000., 193000., 217000., 149900.,\n",
       "       274725., 116000., 138800., 141000., 186500., 440000., 226700.,\n",
       "       115000., 100000., 325000., 555000., 106000., 224900., 625000.,\n",
       "       430000., 138500., 179000., 137900., 136500., 140000., 222000.,\n",
       "       180500., 142000.,  58500.,  82000., 143250., 237500., 206900.,\n",
       "       135000., 138887., 150000., 163000., 113000., 138500., 212000.,\n",
       "       140000., 306000., 127000., 118000.,  91500., 231500., 345000.,\n",
       "       412500., 214000., 326000.,  82000., 115000., 141500., 430000.,\n",
       "       177000.,  79000., 180000., 116000., 185000., 163000., 110000.,\n",
       "       148000., 155000., 272000., 144000., 306000., 241000., 202665.,\n",
       "        88000., 131000., 110500., 123000., 148000., 194700., 152000.,\n",
       "       187500.,  55000., 230000., 113000., 302000., 179540., 120500.,\n",
       "       345000., 200141., 100000., 268000., 153500., 135000., 100000.,\n",
       "       226000., 164500.,  94750., 185000.,  82500., 282922., 172500.,\n",
       "       129000.,  98600., 122500., 147000., 220000., 277000., 113000.,\n",
       "       212000., 143000.,  92900., 111000., 187500.,  90000., 108000.,\n",
       "       179200., 149900., 143000., 324000., 159434., 185500., 140000.,\n",
       "       226000., 118000., 128000., 280000., 237000., 402861., 140000.,\n",
       "       109900., 140000., 155000., 134000.,  81000., 181000., 163900.,\n",
       "       145000., 127000., 156000., 140000., 103600., 118000., 168500.,\n",
       "       261500., 372500., 143500.,  95000., 221000., 216837., 181000.,\n",
       "       162500., 136500.,  90000., 163900., 426000., 202500., 239799.,\n",
       "        83000.,  90000., 137500., 130000., 415298., 130500., 139000.,\n",
       "       210000., 117000., 183000., 119750., 318000., 193000., 235000.,\n",
       "       120000., 213000., 154000., 138887., 110000., 139000., 174000.,\n",
       "       130000., 146500., 143000., 123000., 173000., 128000., 180000.,\n",
       "       212000., 125000., 159000., 152000., 176000., 167000., 191000.,\n",
       "       139600., 115000., 148500., 228950.,  76000., 206900., 140200.,\n",
       "       173900., 179900., 151000., 302000.,  82000., 235000., 134450.,\n",
       "       123000., 104900., 180000., 180000., 137000., 202500., 180500.,\n",
       "       120500., 143500., 176000., 136500., 179000., 155000., 128900.,\n",
       "       134900., 115000.,  76000., 179600., 178000., 129900., 137000.,\n",
       "       187000., 235000., 402861., 315000.,  73000., 200000., 120000.,\n",
       "       426000., 412500., 235000., 179200., 275000., 144000., 114500.,\n",
       "       127000., 140000., 440000., 129000., 108000., 290000., 236000.,\n",
       "       138800., 192500., 143500.,  83000., 135000., 180500., 123000.,\n",
       "       181000., 151000., 227000., 164500., 132000., 160000., 190000.,\n",
       "       125000., 197500., 146800., 224000., 179200., 160000., 160000.,\n",
       "       230500., 117500.,  90000., 325300., 187000., 114504., 139600.,\n",
       "       140000., 555000., 135000., 394432.,  88000., 157000., 162000.,\n",
       "       181900., 174000., 301500., 138887., 154000., 136000.,  94750.,\n",
       "       210000., 136905., 200000., 250580., 194500., 213250.,  81000.,\n",
       "       306000., 150750., 440000., 129900., 555000., 394617., 156000.,\n",
       "       274725., 215000., 250580., 158000., 222500., 139000., 250580.,\n",
       "       154000., 200141., 274725., 125000., 128900., 223000., 129000.,\n",
       "       148000., 179400., 141000., 394432., 167000., 140000., 186500.,\n",
       "       555000., 184000., 164500., 274970.,  97500., 235000., 227875.,\n",
       "       139000., 155000., 119750., 170000., 159500., 165000., 109900.,\n",
       "       208500., 415298., 342643., 261500., 193000., 118500.,  89000.,\n",
       "       223000., 221000., 166000., 160000., 205000., 192500., 163000.,\n",
       "       377500., 127500., 122900., 119750., 122900., 210000., 239686.,\n",
       "       107000.,  78000., 126000., 274300., 139000., 159434.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fazendo a previsão\n",
    "pred_tr = reg_tr.predict(X_test)\n",
    "pred_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a2286fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o KNN\n",
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "11210713",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando o regressor e fazendo o fit com os dados de treino\n",
    "reg_kn = KNeighborsRegressor(n_neighbors=42).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b337d25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       115998.4047619 , 207862.97619048, 125322.97619048, 135611.9047619 ,\n",
       "       213245.5952381 , 192308.19047619, 166108.26190476, 135463.23809524,\n",
       "       177121.78571429, 136287.14285714, 147701.30952381, 194445.38095238,\n",
       "       179121.11904762, 173504.28571429, 173818.97619048, 118254.76190476,\n",
       "       179059.45238095, 155495.        , 172631.52380952, 221777.47619048,\n",
       "       183043.45238095, 165197.95238095, 138292.        , 217671.04761905,\n",
       "       229847.5952381 , 138230.47619048, 229819.4047619 , 149434.66666667,\n",
       "       156226.88095238, 176776.54761905, 264567.0952381 , 157010.71428571,\n",
       "       119543.21428571, 201339.33333333, 134178.57142857, 247056.52380952,\n",
       "       142894.04761905, 186359.57142857, 130110.71428571, 140318.0952381 ,\n",
       "       283180.28571429, 175114.28571429, 137346.66666667, 193039.76190476,\n",
       "       133885.45238095, 281654.45238095, 143870.21428571, 239036.5       ,\n",
       "       175554.28571429, 224423.85714286, 142540.47619048, 123029.14285714,\n",
       "       112605.95238095, 142957.14285714, 219643.38095238, 238755.45238095,\n",
       "       257739.57142857, 216908.97619048, 140556.54761905, 256130.85714286,\n",
       "       131741.45238095, 144618.16666667, 126588.5       , 133543.        ,\n",
       "       129901.19047619, 123357.14285714, 299264.38095238, 162909.73809524,\n",
       "       239935.26190476, 240030.57142857, 142666.66666667, 133109.76190476,\n",
       "       131284.02380952, 144738.73809524, 223638.11904762, 110484.52380952,\n",
       "       165851.78571429, 124247.61904762, 206123.21428571, 170693.11904762,\n",
       "       143201.16666667, 178072.02380952, 183379.85714286, 182658.33333333,\n",
       "       132803.57142857, 229420.92857143, 187628.47619048, 175226.04761905,\n",
       "       156761.07142857, 151452.92857143, 186407.47619048, 249841.47619048,\n",
       "       231405.71428571, 172558.73809524, 291688.66666667, 117355.11904762,\n",
       "       226671.47619048, 166335.        , 135760.71428571, 188404.16666667,\n",
       "       136252.38095238, 222656.66666667,  94955.95238095, 117688.0952381 ,\n",
       "       138785.95238095, 124328.07142857, 181206.07142857, 132350.23809524,\n",
       "       114251.54761905, 136582.54761905, 159863.61904762, 249010.61904762,\n",
       "       147050.80952381, 151503.54761905, 158953.71428571, 186831.4047619 ,\n",
       "       186422.19047619, 167910.4047619 , 201794.73809524, 129758.0952381 ,\n",
       "       148743.73809524, 176772.02380952, 182675.5952381 , 249852.52380952,\n",
       "       187312.5       , 115668.66666667, 119404.76190476, 231714.42857143,\n",
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       "       282078.21428571, 162693.73809524, 178197.14285714, 184146.9047619 ,\n",
       "       238255.97619048, 136160.71428571, 203030.95238095, 218382.02380952,\n",
       "       135618.0952381 , 111508.33333333, 147071.21428571, 149684.16666667,\n",
       "       231133.        , 226983.54761905, 138209.92857143, 132801.71428571,\n",
       "       190868.26190476, 194692.95238095, 110944.04761905, 139598.80952381,\n",
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       "       106166.66666667, 130397.35714286, 260124.19047619, 210532.02380952,\n",
       "       161595.95238095, 210899.73809524, 145555.45238095, 131592.5952381 ,\n",
       "       151356.0952381 , 130808.0952381 , 144465.47619048, 157359.47619048,\n",
       "       210197.14285714, 175398.21428571, 243473.61904762, 185503.33333333,\n",
       "       147728.42857143, 131116.4047619 , 132372.02380952, 121489.28571429,\n",
       "       136752.38095238, 144186.9047619 , 171002.38095238, 181949.11904762,\n",
       "       162363.83333333, 159329.28571429, 137892.30952381, 143976.95238095,\n",
       "       245101.16666667, 226304.78571429, 163545.83333333, 243233.61904762,\n",
       "       159838.23809524, 135310.11904762, 186576.54761905, 139965.61904762,\n",
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       "       169544.04761905, 154050.23809524, 225224.80952381, 114078.57142857,\n",
       "       186215.26190476, 165720.95238095, 143553.14285714, 140649.78571429,\n",
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       "       130047.85714286, 215917.5952381 , 202157.5       , 156329.28571429,\n",
       "       142200.5952381 , 140010.85714286, 236590.80952381, 126641.66666667,\n",
       "       126366.42857143, 246030.9047619 , 184798.92857143, 271912.64285714,\n",
       "       183446.19047619, 114744.28571429, 154969.64285714, 205289.33333333,\n",
       "       158494.02380952, 114967.85714286, 227209.54761905, 167356.54761905,\n",
       "       163613.80952381, 111184.52380952, 155897.61904762, 160529.42857143,\n",
       "       149439.71428571, 131044.88095238, 154928.21428571, 217501.42857143,\n",
       "       267368.5       , 154229.76190476, 138140.47619048, 227820.19047619,\n",
       "       202833.19047619, 213247.42857143, 176262.97619048, 144119.07142857,\n",
       "       143231.92857143, 156276.78571429, 284290.80952381, 162904.76190476,\n",
       "       186657.5       , 132837.83333333, 136250.        , 140063.23809524,\n",
       "       149714.38095238, 183884.16666667, 223376.21428571, 167666.66666667,\n",
       "       198932.26190476, 118581.73809524, 165429.69047619, 130717.61904762,\n",
       "       215939.23809524, 187665.0952381 , 188787.61904762, 120452.73809524,\n",
       "       238264.33333333, 175182.02380952, 159284.80952381, 128041.66666667,\n",
       "       159943.        , 162393.26190476, 111976.19047619, 163134.23809524,\n",
       "       137977.        , 144039.28571429, 193641.66666667, 131241.28571429,\n",
       "       183649.52380952, 188477.16666667, 124635.71428571, 175353.57142857,\n",
       "       194031.5952381 , 196080.64285714, 160479.45238095, 182505.47619048,\n",
       "       222152.4047619 , 129675.        , 162294.45238095, 239870.9047619 ,\n",
       "       135917.85714286, 244787.88095238, 126805.80952381, 164107.73809524,\n",
       "       151669.04761905, 144845.78571429, 236027.78571429, 125602.14285714,\n",
       "       188655.5952381 , 160948.69047619, 154366.66666667,  96330.95238095,\n",
       "       182694.45238095, 187446.47619048, 137509.76190476, 153374.85714286,\n",
       "       218090.23809524, 133267.07142857, 145707.38095238, 203853.9047619 ,\n",
       "       124569.28571429, 157683.57142857, 149649.95238095, 157452.78571429,\n",
       "       118605.57142857, 111298.52380952, 117265.47619048, 195431.04761905,\n",
       "       170562.30952381, 134989.52380952, 123289.28571429, 170615.78571429,\n",
       "       201925.42857143, 208877.85714286, 254921.42857143, 146130.        ,\n",
       "       222526.4047619 , 136760.71428571, 250073.78571429, 280018.73809524,\n",
       "       185703.47619048, 176302.38095238, 206506.88095238, 159578.57142857,\n",
       "       140796.42857143, 103488.0952381 , 164114.69047619, 245325.88095238,\n",
       "       206318.61904762, 152223.21428571, 196840.14285714, 229696.45238095,\n",
       "       133988.69047619, 164364.26190476, 145078.57142857, 140477.9047619 ,\n",
       "       168113.07142857, 165594.28571429, 156607.14285714, 171617.02380952,\n",
       "       135075.57142857, 155339.57142857, 152957.14285714, 138935.71428571,\n",
       "       218631.26190476, 228187.04761905, 129230.69047619, 205095.19047619,\n",
       "       137155.71428571, 231101.21428571, 177192.85714286, 218335.0952381 ,\n",
       "       184617.54761905, 181371.54761905, 149370.38095238, 175059.14285714,\n",
       "       237300.80952381, 177443.80952381, 120530.45238095, 226304.78571429,\n",
       "       134762.71428571, 217266.5952381 , 185180.95238095, 282696.38095238,\n",
       "       124340.47619048, 138925.45238095, 142941.66666667, 164904.76190476,\n",
       "       159607.66666667, 241910.69047619, 137944.80952381, 148160.71428571,\n",
       "       141289.28571429, 126192.85714286, 158380.02380952, 223685.73809524,\n",
       "       242565.0952381 , 177012.97619048, 184227.14285714, 184286.97619048,\n",
       "       101208.33333333, 197284.38095238, 152133.33333333, 232854.78571429,\n",
       "       130207.14285714, 218909.04761905, 231368.47619048, 148726.19047619,\n",
       "       188271.78571429, 159701.78571429, 170758.21428571, 203397.61904762,\n",
       "       206907.5952381 , 108485.71428571, 147944.14285714, 161284.88095238,\n",
       "       141695.92857143, 192551.        , 130670.23809524, 144876.19047619,\n",
       "       239391.04761905, 146079.76190476, 122358.69047619, 169572.54761905,\n",
       "       169513.0952381 , 282476.42857143, 194153.4047619 , 154850.69047619,\n",
       "       173249.0952381 , 223014.21428571, 187007.73809524, 153660.85714286,\n",
       "       197719.42857143, 182242.54761905, 177058.02380952, 226894.19047619,\n",
       "       141980.        , 140012.5       , 115822.61904762, 175002.11904762,\n",
       "       259252.69047619, 189396.21428571, 133557.14285714, 157091.80952381,\n",
       "       231844.5952381 , 237631.80952381, 195875.92857143, 185690.47619048,\n",
       "       148266.45238095, 137600.5       , 227698.23809524, 200070.0952381 ,\n",
       "       183226.95238095, 313333.5       , 204537.5952381 , 164364.26190476,\n",
       "       164388.97619048, 213216.76190476, 137185.71428571, 125932.71428571,\n",
       "       134150.        , 133619.04761905, 199513.69047619, 289942.83333333,\n",
       "       149948.80952381, 223757.16666667, 151683.0952381 , 234364.14285714,\n",
       "       206231.        , 196973.5952381 ])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fazendo a previsão\n",
    "pred_kn = reg_kn.predict(X_test)\n",
    "pred_kn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdd63bff",
   "metadata": {},
   "source": [
    "- **E avaliar esses dados, utilizando tanto o erro absoluto quanto o quadrático:**\n",
    "    - Erro médio absoluto\n",
    "        - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html\n",
    "    - Erro quadrático médio\n",
    "        - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "269a3bcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o erro médio absoluto\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "12eb2132",
   "metadata": {},
   "outputs": [],
   "source": [
    "# E o erro quadrático médio\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "14769a7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23763.187393064058\n",
      "1533982883.444886\n"
     ]
    }
   ],
   "source": [
    "# Avaliando o erro da regressão\n",
    "print(mean_absolute_error(y_test, pred_rg))\n",
    "print(mean_squared_error(y_test, pred_rg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2417d97e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27580.78838174274\n",
      "2530245114.701245\n"
     ]
    }
   ],
   "source": [
    "# da árvore de decisão\n",
    "print(mean_absolute_error(y_test, pred_tr))\n",
    "print(mean_squared_error(y_test, pred_tr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b3159911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36955.93682078641\n",
      "3609191329.2788186\n"
     ]
    }
   ],
   "source": [
    "# e do knn\n",
    "print(mean_absolute_error(y_test, pred_kn))\n",
    "print(mean_squared_error(y_test, pred_kn))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "162028e9",
   "metadata": {},
   "source": [
    "- **Podemos plotar visualmente a relação do y_test com as previsões feitas**\n",
    "    - Para isso vamos utilizar o matplotlib\n",
    "        - https://matplotlib.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7e5f78f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando o matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a6bf59eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criando esse gráfico\n",
    "fig, ax = plt.subplots(ncols=3,figsize=(15,5))\n",
    "\n",
    "ax[0].scatter(y_test/100000,pred_rg/100000)\n",
    "ax[0].plot([0,700000],[0,700000],'--r')\n",
    "ax[1].scatter(y_test/100000,pred_tr/100000)\n",
    "ax[1].plot([0,700000],[0,700000],'--r')\n",
    "ax[2].scatter(y_test/100000,pred_kn/100000)\n",
    "ax[2].plot([0,700000],[0,700000],'--r')\n",
    "\n",
    "ax[0].set(xlim=(0, 7),ylim=(0, 7))\n",
    "ax[0].set_xlabel('Real')\n",
    "ax[0].set_ylabel('Previsão')\n",
    "ax[1].set(xlim=(0, 7),ylim=(0, 7))\n",
    "ax[1].set_xlabel('Real')\n",
    "ax[1].set_ylabel('Previsão')\n",
    "ax[2].set(xlim=(0, 7),ylim=(0, 7))\n",
    "ax[2].set_xlabel('Real')\n",
    "ax[2].set_ylabel('Previsão')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e262938a",
   "metadata": {},
   "source": [
    "- **Vamos utilizar a Regressão Linear por ter sido o algoritmo com menor erro quadrático médio, a mesma métrica avaliada pelo Kaggle na hora de classificar os modelos**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d855508c",
   "metadata": {},
   "source": [
    "## Fazendo a previsão para a base de teste da competição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8d98c739",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando a base de teste\n",
    "teste = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "83a9ca6f",
   "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>ScreenPorch</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</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>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</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>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
       "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
       "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
       "3         Lvl    AllPub  ...           0        0    NaN    NaN         NaN   \n",
       "4         HLS    AllPub  ...         144        0    NaN    NaN         NaN   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0       0      6    2010        WD         Normal  \n",
       "1   12500      6    2010        WD         Normal  \n",
       "2       0      3    2010        WD         Normal  \n",
       "3       0      6    2010        WD         Normal  \n",
       "4       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando a base\n",
    "teste.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78b0e24",
   "metadata": {},
   "source": [
    "- **Agora vamos repetir os mesmos tratamentos que fizemos na base de treino**\n",
    "    - Obs: **não podemos excluir linhas**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ffe3cade",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eliminando as mesmas colunas da base de treino\n",
    "teste = teste.drop(eliminar, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "cfce9334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',\n",
       "       'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF',\n",
       "       'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath',\n",
       "       'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',\n",
       "       'MoSold', 'YrSold'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando as colunas numéricas\n",
    "colunas = teste.columns[teste.dtypes != 'object']\n",
    "colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f1cab80b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mantendo também apenas as colunas numéricas\n",
    "base_teste = teste.loc[:,colunas]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "eb322627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>11622</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>...</td>\n",
       "      <td>730.0</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>14267</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>108.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>312.0</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>13830</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>482.0</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>9978</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1998</td>\n",
       "      <td>1998</td>\n",
       "      <td>20.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>470.0</td>\n",
       "      <td>360</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>5005</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1992</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>506.0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0  1461          20    11622            5            6       1961   \n",
       "1  1462          20    14267            6            6       1958   \n",
       "2  1463          60    13830            5            5       1997   \n",
       "3  1464          60     9978            6            6       1998   \n",
       "4  1465         120     5005            8            5       1992   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  ...  GarageArea  \\\n",
       "0          1961         0.0       468.0       144.0  ...       730.0   \n",
       "1          1958       108.0       923.0         0.0  ...       312.0   \n",
       "2          1998         0.0       791.0         0.0  ...       482.0   \n",
       "3          1998        20.0       602.0         0.0  ...       470.0   \n",
       "4          1992         0.0       263.0         0.0  ...       506.0   \n",
       "\n",
       "   WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
       "0         140            0              0          0          120         0   \n",
       "1         393           36              0          0            0         0   \n",
       "2         212           34              0          0            0         0   \n",
       "3         360           36              0          0            0         0   \n",
       "4           0           82              0          0          144         0   \n",
       "\n",
       "   MiscVal  MoSold  YrSold  \n",
       "0        0       6    2010  \n",
       "1    12500       6    2010  \n",
       "2        0       3    2010  \n",
       "3        0       6    2010  \n",
       "4        0       1    2010  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a base restante\n",
    "base_teste.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "194b4c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GarageYrBlt     78\n",
       "MasVnrArea      15\n",
       "BsmtHalfBath     2\n",
       "BsmtFullBath     2\n",
       "BsmtUnfSF        1\n",
       "GarageCars       1\n",
       "GarageArea       1\n",
       "BsmtFinSF1       1\n",
       "BsmtFinSF2       1\n",
       "TotalBsmtSF      1\n",
       "OpenPorchSF      0\n",
       "WoodDeckSF       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando quantidade de valores vazios\n",
    "base_teste.isnull().sum().sort_values(ascending=False).head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "604cfd7e",
   "metadata": {},
   "source": [
    "- **Vamos precisar retirar os valores vazios pois a regressão linear não vai conseguir trabalhar com valores vazio**\n",
    "    - Se tentarmos usar essa base, ele vai retornar um **erro** dizendo que o **input possui valores NaN**\n",
    "- Podemos apenas substituir por -1 como fizemos acima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "77fdbe03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituindo os valores vazios por -1\n",
    "base_teste = base_teste.fillna(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db767eed",
   "metadata": {},
   "source": [
    "- **Agora podemos usar nosso modelo e ajustar os dados para usarmos no Kaggle**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9b09d278",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vamos usar a regressão para fazer a previsão\n",
    "y_pred = reg_rl.predict(base_teste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "511117c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Podemos adicionar essa coluna de previsão na nossa base\n",
    "base_teste['SalePrice'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bc1fd647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>122234.995960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>139178.263684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>169872.054251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>199138.801656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>196257.109229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  122234.995960\n",
       "1  1462  139178.263684\n",
       "2  1463  169872.054251\n",
       "3  1464  199138.801656\n",
       "4  1465  196257.109229"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# E extrair somente o Id e o SalePrice\n",
    "resultado = base_teste[['Id', 'SalePrice']]\n",
    "resultado.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9513b665",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Podemos então exportar essa base\n",
    "resultado.to_csv('resultado.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06034ac0",
   "metadata": {},
   "source": [
    "- **Próximos passos**\n",
    "    - Você pode tentar melhorar a limpeza dos seus dados \n",
    "        - [Limpeza de Dados em um Dataset Real - Dados do Titanic](https://www.youtube.com/watch?v=RuDxj894BXQ&t=1218s)\n",
    "    - Depois é possível fazer a engenharia de recursos\n",
    "        - [Feature Engineering em Python para Seus Projetos de Ciência de Dados](https://www.youtube.com/watch?v=rigZ7HdfWaM&t=61s)\n",
    "    - A padronização / normalização dos dados\n",
    "        - [Padronização e Normalização de Dados para Ciência de Dados](https://www.youtube.com/watch?v=L6rLSYVBfyM&t=12s)\n",
    "    - E até a seleção de variáveis\n",
    "        - [Seleção de Recursos (Feature Selection) em Projetos de Ciência de Dados](https://www.youtube.com/watch?v=0yM07AURFRY&t=615s)"
   ]
  }
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