tech-challange-01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\hsouz\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.2.2)\n",
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      "Collecting statsmodels\n",
      "  Downloading statsmodels-0.14.4-cp312-cp312-win_amd64.whl.metadata (9.5 kB)\n",
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      "Collecting patsy>=0.5.6 (from statsmodels)\n",
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      "Downloading patsy-0.5.6-py2.py3-none-any.whl (233 kB)\n",
      "Installing collected packages: patsy, statsmodels\n",
      "Successfully installed patsy-0.5.6 statsmodels-0.14.4\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.2 -> 24.3.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install pandas numpy matplotlib seaborn scikit-learn statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:2: SyntaxWarning: invalid escape sequence '\\g'\n",
      "<>:2: SyntaxWarning: invalid escape sequence '\\g'\n",
      "C:\\Users\\hsouz\\AppData\\Local\\Temp\\ipykernel_26784\\3862841578.py:2: SyntaxWarning: invalid escape sequence '\\g'\n",
      "  df = pd.read_csv('data\\generated_dataset.csv')\n"
     ]
    }
   ],
   "source": [
    "# Carregar o dataset gerado\n",
    "df = pd.read_csv('data\\generated_dataset.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definir variáveis independentes e dependente\n",
    "X = df.drop('encargos', axis=1)\n",
    "y = df['encargos']\n",
    "\n",
    "# Identificar colunas numéricas e categóricas\n",
    "num_features = ['idade', 'imc', 'filhos']\n",
    "cat_features = ['gênero', 'fumante', 'região']\n",
    "\n",
    "# Pré-processamento para variáveis numéricas e categóricas\n",
    "num_transformer = Pipeline(steps=[\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "cat_transformer = Pipeline(steps=[\n",
    "    ('onehot', OneHotEncoder(drop='first', handle_unknown='ignore'))\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', num_transformer, num_features),\n",
    "        ('cat', cat_transformer, cat_features)\n",
    "    ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pipeline completo com o pré-processador e o modelo de Regressão Linear\n",
    "model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', LinearRegression())\n",
    "])\n",
    "\n",
    "# Dividir os dados em conjunto de treino e teste\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;idade&#x27;, &#x27;imc&#x27;, &#x27;filhos&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;gênero&#x27;, &#x27;fumante&#x27;,\n",
       "                                                   &#x27;região&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, 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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;idade&#x27;, &#x27;imc&#x27;, &#x27;filhos&#x27;]),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                                 handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                                  [&#x27;gênero&#x27;, &#x27;fumante&#x27;,\n",
       "                                                   &#x27;região&#x27;])])),\n",
       "                (&#x27;regressor&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
       "                                 [&#x27;idade&#x27;, &#x27;imc&#x27;, &#x27;filhos&#x27;]),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
       "                                                                handle_unknown=&#x27;ignore&#x27;))]),\n",
       "                                 [&#x27;gênero&#x27;, &#x27;fumante&#x27;, &#x27;região&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;idade&#x27;, &#x27;imc&#x27;, &#x27;filhos&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;gênero&#x27;, &#x27;fumante&#x27;, &#x27;região&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['idade', 'imc', 'filhos']),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('onehot',\n",
       "                                                                   OneHotEncoder(drop='first',\n",
       "                                                                                 handle_unknown='ignore'))]),\n",
       "                                                  ['gênero', 'fumante',\n",
       "                                                   'região'])])),\n",
       "                ('regressor', LinearRegression())])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Treinar o modelo\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 14468.850279251386\n",
      "R²: -0.24702823832817056\n"
     ]
    }
   ],
   "source": [
    "# Fazer previsões e avaliar o modelo\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Avaliar o modelo\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f'RMSE: {rmse}')\n",
    "print(f'R²: {r2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensão de X_train_sm: (80, 9)\n",
      "Dimensão de y_train: (80,)\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.066\n",
      "Model:                            OLS   Adj. R-squared:                 -0.039\n",
      "Method:                 Least Squares   F-statistic:                    0.6271\n",
      "Date:                Tue, 05 Nov 2024   Prob (F-statistic):              0.752\n",
      "Time:                        19:16:37   Log-Likelihood:                -876.90\n",
      "No. Observations:                  80   AIC:                             1772.\n",
      "Df Residuals:                      71   BIC:                             1793.\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "const             1.931e+04   1.13e+04      1.705      0.093   -3277.477    4.19e+04\n",
      "idade              111.3026    118.895      0.936      0.352    -125.768     348.373\n",
      "imc                156.6566    282.844      0.554      0.581    -407.319     720.632\n",
      "filhos            1455.0429   1229.141      1.184      0.240    -995.794    3905.880\n",
      "gênero_masculino   -40.6851   3545.708     -0.011      0.991   -7110.626    7029.255\n",
      "fumante_sim      -2681.9793   3437.048     -0.780      0.438   -9535.258    4171.299\n",
      "região_noroeste  -4354.4460   4878.966     -0.892      0.375   -1.41e+04    5373.936\n",
      "região_sudeste   -4310.4816   4630.774     -0.931      0.355   -1.35e+04    4923.019\n",
      "região_sudoeste    343.5479   4428.849      0.078      0.938   -8487.327    9174.423\n",
      "==============================================================================\n",
      "Omnibus:                       31.424   Durbin-Watson:                   1.839\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                5.683\n",
      "Skew:                          -0.177   Prob(JB):                       0.0583\n",
      "Kurtosis:                       1.743   Cond. No.                         362.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Adicionar coluna de constante e criar dummies para `statsmodels`\n",
    "X_train_sm = sm.add_constant(pd.get_dummies(X_train, drop_first=True))\n",
    "\n",
    "# Verificar se todas as colunas estão no tipo float e se há NaNs\n",
    "X_train_sm = X_train_sm.apply(pd.to_numeric, errors='coerce').fillna(0).astype(float)\n",
    "\n",
    "# Converter `y_train` para numpy array e verificar se está no formato adequado\n",
    "y_train = np.asarray(y_train).astype(float)\n",
    "\n",
    "# Verificar se as dimensões coincidem\n",
    "print(\"Dimensão de X_train_sm:\", X_train_sm.shape)\n",
    "print(\"Dimensão de y_train:\", y_train.shape)\n",
    "\n",
    "# Treinar o modelo OLS\n",
    "model_sm = sm.OLS(y_train, X_train_sm).fit()\n",
    "\n",
    "# Exibir o resumo estatístico\n",
    "print(model_sm.summary())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de dispersão para valores reais vs. previstos\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5, color='b')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')\n",
    "plt.xlabel('Valores Reais')\n",
    "plt.ylabel('Valores Previstos')\n",
    "plt.title('Valores Reais vs. Previstos')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}