ml_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------------------------------------------------------------\n",
    "| Passo 1: Importar bibliotecas e carregar o dataset |\n",
    "---------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importando bibliotecas necessárias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy>=1.26.0 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from pandas) (2.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
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      "Using cached pyparsing-3.1.4-py3-none-any.whl (104 kB)\n",
      "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
      "Successfully installed contourpy-1.3.0 cycler-0.12.1 fonttools-4.54.1 kiwisolver-1.4.7 matplotlib-3.9.2 pillow-10.4.0 pyparsing-3.1.4\n"
     ]
    }
   ],
   "source": [
    "! pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Using cached seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
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      "Using cached seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.13.2\n"
     ]
    }
   ],
   "source": [
    "! pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (2.2.3)\n",
      "Requirement already satisfied: numpy in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (2.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from pandas) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from pandas) (2024.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install pandas numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (1.5.2)\n",
      "Requirement already satisfied: numpy>=1.19.5 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from scikit-learn) (2.1.1)\n",
      "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from scikit-learn) (1.14.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from scikit-learn) (1.4.2)\n",
      "Requirement already satisfied: threadpoolctl>=3.1.0 in c:\\users\\ead\\desktop\\aulas pyhon leandro\\aulas_ml\\semana_machine_learning\\.venv\\lib\\site-packages (from scikit-learn) (3.5.0)\n"
     ]
    }
   ],
   "source": [
    "! pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carregar o dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"datasets/Housing.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------------------------------------\n",
    "| Passo 2: Análise Exploratória de Dados (EDA) |\n",
    "-----------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['preço', 'área', 'quartos', 'banheiros', 'andares', 'via_principal',\n",
       "       'quarto_de_hóspedes', 'porão', 'aquecimento_agua', 'ar_condicionado',\n",
       "       'estacionamento', 'área_preferencial', 'status_mobiliado'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# data = pd.DataFrame(columns=['price', 'area', 'bedrooms', 'bathrooms', 'stories',\n",
    "#                           'mainroad', 'guestroom', 'basement', 'hotwaterheating',\n",
    "#                          'airconditioning', 'parking', 'prefarea', 'furnishingstatus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#novos_nomes = {\n",
    "#    'price': 'preço',\n",
    "#    'area': 'área',\n",
    "#    'bedrooms': 'quartos',\n",
    "#    'bathrooms': 'banheiros',\n",
    "#    'stories': 'andares',\n",
    "#    'mainroad': 'via_principal',\n",
    "#    'guestroom': 'quarto_de_hóspedes',\n",
    "#    'basement': 'porão',\n",
    "#    'hotwaterheating': 'aquecimento_agua',\n",
    "#    'airconditioning': 'ar_condicionado',\n",
    "#    'parking': 'estacionamento',\n",
    "#    'prefarea': 'área_preferencial',\n",
    "#    'furnishingstatus': 'status_mobiliado'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns=novos_nomes, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['preço', 'área', 'quartos', 'banheiros', 'andares', 'via_principal',\n",
       "       'quarto_de_hóspedes', 'porão', 'aquecimento_agua', 'ar_condicionado',\n",
       "       'estacionamento', 'área_preferencial', 'status_mobiliado'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizando as primeiras linhas do dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\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>preço</th>\n",
       "      <th>área</th>\n",
       "      <th>quartos</th>\n",
       "      <th>banheiros</th>\n",
       "      <th>andares</th>\n",
       "      <th>via_principal</th>\n",
       "      <th>quarto_de_hóspedes</th>\n",
       "      <th>porão</th>\n",
       "      <th>aquecimento_agua</th>\n",
       "      <th>ar_condicionado</th>\n",
       "      <th>estacionamento</th>\n",
       "      <th>área_preferencial</th>\n",
       "      <th>status_mobiliado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      preço  área  quartos  banheiros  andares via_principal  \\\n",
       "0  13300000  7420        4          2        3           yes   \n",
       "1  12250000  8960        4          4        4           yes   \n",
       "2  12250000  9960        3          2        2           yes   \n",
       "3  12215000  7500        4          2        2           yes   \n",
       "4  11410000  7420        4          1        2           yes   \n",
       "\n",
       "  quarto_de_hóspedes porão aquecimento_agua ar_condicionado  estacionamento  \\\n",
       "0                 no    no               no             yes               2   \n",
       "1                 no    no               no             yes               3   \n",
       "2                 no   yes               no              no               2   \n",
       "3                 no   yes               no             yes               3   \n",
       "4                yes   yes               no             yes               2   \n",
       "\n",
       "  área_preferencial status_mobiliado  \n",
       "0               yes        furnished  \n",
       "1                no        furnished  \n",
       "2               yes   semi-furnished  \n",
       "3               yes        furnished  \n",
       "4                no        furnished  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizando as últimas linhas do DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>preço</th>\n",
       "      <th>área</th>\n",
       "      <th>quartos</th>\n",
       "      <th>banheiros</th>\n",
       "      <th>andares</th>\n",
       "      <th>via_principal</th>\n",
       "      <th>quarto_de_hóspedes</th>\n",
       "      <th>porão</th>\n",
       "      <th>aquecimento_agua</th>\n",
       "      <th>ar_condicionado</th>\n",
       "      <th>estacionamento</th>\n",
       "      <th>área_preferencial</th>\n",
       "      <th>status_mobiliado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>1820000</td>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>1767150</td>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>1750000</td>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>1750000</td>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       preço  área  quartos  banheiros  andares via_principal  \\\n",
       "540  1820000  3000        2          1        1           yes   \n",
       "541  1767150  2400        3          1        1            no   \n",
       "542  1750000  3620        2          1        1           yes   \n",
       "543  1750000  2910        3          1        1            no   \n",
       "544  1750000  3850        3          1        2           yes   \n",
       "\n",
       "    quarto_de_hóspedes porão aquecimento_agua ar_condicionado  estacionamento  \\\n",
       "540                 no   yes               no              no               2   \n",
       "541                 no    no               no              no               0   \n",
       "542                 no    no               no              no               0   \n",
       "543                 no    no               no              no               0   \n",
       "544                 no    no               no              no               0   \n",
       "\n",
       "    área_preferencial status_mobiliado  \n",
       "540                no      unfurnished  \n",
       "541                no   semi-furnished  \n",
       "542                no      unfurnished  \n",
       "543                no        furnished  \n",
       "544                no      unfurnished  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Informações gerais do dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 13 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   preço               545 non-null    int64 \n",
      " 1   área                545 non-null    int64 \n",
      " 2   quartos             545 non-null    int64 \n",
      " 3   banheiros           545 non-null    int64 \n",
      " 4   andares             545 non-null    int64 \n",
      " 5   via_principal       545 non-null    object\n",
      " 6   quarto_de_hóspedes  545 non-null    object\n",
      " 7   porão               545 non-null    object\n",
      " 8   aquecimento_agua    545 non-null    object\n",
      " 9   ar_condicionado     545 non-null    object\n",
      " 10  estacionamento      545 non-null    int64 \n",
      " 11  área_preferencial   545 non-null    object\n",
      " 12  status_mobiliado    545 non-null    object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 55.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estatísticas descritivas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>preço</th>\n",
       "      <th>área</th>\n",
       "      <th>quartos</th>\n",
       "      <th>banheiros</th>\n",
       "      <th>andares</th>\n",
       "      <th>estacionamento</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.450000e+02</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.766729e+06</td>\n",
       "      <td>5150.541284</td>\n",
       "      <td>2.965138</td>\n",
       "      <td>1.286239</td>\n",
       "      <td>1.805505</td>\n",
       "      <td>0.693578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.870440e+06</td>\n",
       "      <td>2170.141023</td>\n",
       "      <td>0.738064</td>\n",
       "      <td>0.502470</td>\n",
       "      <td>0.867492</td>\n",
       "      <td>0.861586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.430000e+06</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.340000e+06</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.740000e+06</td>\n",
       "      <td>6360.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              preço          área     quartos   banheiros     andares  \\\n",
       "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
       "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
       "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
       "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
       "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
       "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
       "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
       "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
       "\n",
       "       estacionamento  \n",
       "count      545.000000  \n",
       "mean         0.693578  \n",
       "std          0.861586  \n",
       "min          0.000000  \n",
       "25%          0.000000  \n",
       "50%          0.000000  \n",
       "75%          1.000000  \n",
       "max          3.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verificar valores nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "preço                 0\n",
       "área                  0\n",
       "quartos               0\n",
       "banheiros             0\n",
       "andares               0\n",
       "via_principal         0\n",
       "quarto_de_hóspedes    0\n",
       "porão                 0\n",
       "aquecimento_agua      0\n",
       "ar_condicionado       0\n",
       "estacionamento        0\n",
       "área_preferencial     0\n",
       "status_mobiliado      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------------------------------------------------------\n",
    "| Passo 3: Identificar variáveis quantitativas e qualitativas|\n",
    "----------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identificar variáveis quantitativas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variáveis Quantitativas: ['preço', 'área', 'quartos', 'banheiros', 'andares', 'estacionamento']\n"
     ]
    }
   ],
   "source": [
    "quantitative_vars = data.select_dtypes(include=['int64', 'float64']).columns.tolist()\n",
    "print(\"Variáveis Quantitativas:\", quantitative_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Identificar variáveis qualitativas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variáveis Qualitativas: ['via_principal', 'quarto_de_hóspedes', 'porão', 'aquecimento_agua', 'ar_condicionado', 'área_preferencial', 'status_mobiliado']\n"
     ]
    }
   ],
   "source": [
    "qualitative_vars = data.select_dtypes(include=['object']).columns.tolist()\n",
    "print(\"Variáveis Qualitativas:\", qualitative_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criar uma tabela markdown explicando os tipos das variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Variável           | Tipo         |\n",
      "| ------------------ | ------------ |\n",
      "| preço              | Quantitativa |\n",
      "| área               | Quantitativa |\n",
      "| quartos            | Quantitativa |\n",
      "| banheiros          | Quantitativa |\n",
      "| andares            | Quantitativa |\n",
      "| estacionamento     | Quantitativa |\n",
      "| via_principal      | Qualitativa  |\n",
      "| quarto_de_hóspedes | Qualitativa  |\n",
      "| porão              | Qualitativa  |\n",
      "| aquecimento_agua   | Qualitativa  |\n",
      "| ar_condicionado    | Qualitativa  |\n",
      "| área_preferencial  | Qualitativa  |\n",
      "| status_mobiliado   | Qualitativa  |\n",
      "\n"
     ]
    }
   ],
   "source": [
    "variaveis = quantitative_vars + qualitative_vars\n",
    "tipos = ['Quantitativa'] * len(quantitative_vars) + ['Qualitativa'] * len(qualitative_vars)\n",
    "\n",
    "# Encontrar o comprimento máximo para alinhamento\n",
    "max_var_length = max([len(var) for var in variaveis])\n",
    "max_type_length = max([len(tipo) for tipo in tipos])\n",
    "\n",
    "# Criar a tabela formatada\n",
    "tabela_markdown = f\"| {'Variável'.ljust(max_var_length)} | {'Tipo'.ljust(max_type_length)} |\\n\"\n",
    "tabela_markdown += f\"| {'-' * max_var_length} | {'-' * max_type_length} |\\n\"\n",
    "\n",
    "for var, tipo in zip(variaveis, tipos):\n",
    "    tabela_markdown += f\"| {var.ljust(max_var_length)} | {tipo.ljust(max_type_length)} |\\n\"\n",
    "\n",
    "# Exibir a tabela\n",
    "print(tabela_markdown)"
   ]
  }
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