Projeto_Tumores.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neste exercício, você usará seu novo conhecimento para propor uma solução para um cenário do mundo real. Para ter sucesso, você precisará importar dados para Python, responder a perguntas usando os dados e gerar **histogramas** e **gráficos de densidade** para entender os padrões nos dados.\n",
    "\n",
    "#### Cenário\n",
    "\n",
    "Você trabalhará com um conjunto de dados do mundo real contendo informações coletadas de imagens microscópicas de tumores de câncer de mama, semelhantes à imagem abaixo.\n",
    "\n",
    "![ex4_cancer_image](https://i.imgur.com/qUESsJe.png)\n",
    "\n",
    "Cada tumor foi rotulado como [**benigno**](https://en.wikipedia.org/wiki/Benign_tumor) (não cancerígeno) ou **maligno** (cancerígeno).\n",
    "\n",
    "Para saber mais sobre como esse tipo de dados é usado para criar algoritmos inteligentes para classificar tumores em ambientes médicos, **assista ao breve vídeo [nesse link](https://www.youtube.com/watch?v=9Mz84cwVmS0)**!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "#### Configurações\n",
    "\n",
    "Execute a próxima célula para importar e configurar as bibliotecas Python necessárias para concluir o exercício."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-02T00:31:58.160092Z",
     "iopub.status.busy": "2021-06-02T00:31:58.159358Z",
     "iopub.status.idle": "2021-06-02T00:31:59.242837Z",
     "shell.execute_reply": "2021-06-02T00:31:59.241549Z",
     "shell.execute_reply.started": "2021-06-02T00:31:58.159952Z"
    }
   },
   "outputs": [],
   "source": [
    "# Importar bibliotecas necessárias\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Passo 1: Carregar os dados\n",
    "\n",
    "Nesta etapa, você carregará dois arquivos de dados.\n",
    "- Carregue o arquivo de dados correspondente aos tumores **benignos** em um DataFrame chamado `cancer_b_data`. O caminho de arquivo correspondente é `cancer_b`. Use a coluna `'Id'` para rotular as linhas.\n",
    "- Carregue o arquivo de dados correspondente aos tumores **malignos** em um DataFrame chamado `cancer_m_data`. O caminho de arquivo correspondente é `cancer_m`. Use a coluna `'Id'` para rotular as linhas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-02T00:33:21.352701Z",
     "iopub.status.busy": "2021-06-02T00:33:21.352306Z",
     "iopub.status.idle": "2021-06-02T00:33:21.383269Z",
     "shell.execute_reply": "2021-06-02T00:33:21.382037Z",
     "shell.execute_reply.started": "2021-06-02T00:33:21.352661Z"
    }
   },
   "outputs": [],
   "source": [
    "# Arquivos contendo os conjuntos de dados\n",
    "cancer_b = 'cancer_b.csv'\n",
    "cancer_m = 'cancer_m.csv'\n",
    "\n",
    "# Dados referentes a tumores benignos\n",
    "cancer_b_data = pd.read_csv(cancer_b, index_col = 'Id')\n",
    "\n",
    "# Dados referentes a tumores malignos\n",
    "cancer_m_data = pd.read_csv(cancer_m, index_col = 'Id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Passo 2: Revisar os dados\n",
    "\n",
    "Use um comando Python para imprimir as primeiras 5 linhas dos dados para tumores benignos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-02T00:34:10.357332Z",
     "iopub.status.busy": "2021-06-02T00:34:10.356894Z",
     "iopub.status.idle": "2021-06-02T00:34:10.402289Z",
     "shell.execute_reply": "2021-06-02T00:34:10.400749Z",
     "shell.execute_reply.started": "2021-06-02T00:34:10.357299Z"
    }
   },
   "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>Diagnosis</th>\n",
       "      <th>Radius (mean)</th>\n",
       "      <th>Texture (mean)</th>\n",
       "      <th>Perimeter (mean)</th>\n",
       "      <th>Area (mean)</th>\n",
       "      <th>Smoothness (mean)</th>\n",
       "      <th>Compactness (mean)</th>\n",
       "      <th>Concavity (mean)</th>\n",
       "      <th>Concave points (mean)</th>\n",
       "      <th>Symmetry (mean)</th>\n",
       "      <th>...</th>\n",
       "      <th>Radius (worst)</th>\n",
       "      <th>Texture (worst)</th>\n",
       "      <th>Perimeter (worst)</th>\n",
       "      <th>Area (worst)</th>\n",
       "      <th>Smoothness (worst)</th>\n",
       "      <th>Compactness (worst)</th>\n",
       "      <th>Concavity (worst)</th>\n",
       "      <th>Concave points (worst)</th>\n",
       "      <th>Symmetry (worst)</th>\n",
       "      <th>Fractal dimension (worst)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8510426</th>\n",
       "      <td>B</td>\n",
       "      <td>13.540</td>\n",
       "      <td>14.36</td>\n",
       "      <td>87.46</td>\n",
       "      <td>566.3</td>\n",
       "      <td>0.09779</td>\n",
       "      <td>0.08129</td>\n",
       "      <td>0.06664</td>\n",
       "      <td>0.047810</td>\n",
       "      <td>0.1885</td>\n",
       "      <td>...</td>\n",
       "      <td>15.110</td>\n",
       "      <td>19.26</td>\n",
       "      <td>99.70</td>\n",
       "      <td>711.2</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.17730</td>\n",
       "      <td>0.23900</td>\n",
       "      <td>0.12880</td>\n",
       "      <td>0.2977</td>\n",
       "      <td>0.07259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8510653</th>\n",
       "      <td>B</td>\n",
       "      <td>13.080</td>\n",
       "      <td>15.71</td>\n",
       "      <td>85.63</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.10750</td>\n",
       "      <td>0.12700</td>\n",
       "      <td>0.04568</td>\n",
       "      <td>0.031100</td>\n",
       "      <td>0.1967</td>\n",
       "      <td>...</td>\n",
       "      <td>14.500</td>\n",
       "      <td>20.49</td>\n",
       "      <td>96.09</td>\n",
       "      <td>630.5</td>\n",
       "      <td>0.13120</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.18900</td>\n",
       "      <td>0.07283</td>\n",
       "      <td>0.3184</td>\n",
       "      <td>0.08183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8510824</th>\n",
       "      <td>B</td>\n",
       "      <td>9.504</td>\n",
       "      <td>12.44</td>\n",
       "      <td>60.34</td>\n",
       "      <td>273.9</td>\n",
       "      <td>0.10240</td>\n",
       "      <td>0.06492</td>\n",
       "      <td>0.02956</td>\n",
       "      <td>0.020760</td>\n",
       "      <td>0.1815</td>\n",
       "      <td>...</td>\n",
       "      <td>10.230</td>\n",
       "      <td>15.66</td>\n",
       "      <td>65.13</td>\n",
       "      <td>314.9</td>\n",
       "      <td>0.13240</td>\n",
       "      <td>0.11480</td>\n",
       "      <td>0.08867</td>\n",
       "      <td>0.06227</td>\n",
       "      <td>0.2450</td>\n",
       "      <td>0.07773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854941</th>\n",
       "      <td>B</td>\n",
       "      <td>13.030</td>\n",
       "      <td>18.42</td>\n",
       "      <td>82.61</td>\n",
       "      <td>523.8</td>\n",
       "      <td>0.08983</td>\n",
       "      <td>0.03766</td>\n",
       "      <td>0.02562</td>\n",
       "      <td>0.029230</td>\n",
       "      <td>0.1467</td>\n",
       "      <td>...</td>\n",
       "      <td>13.300</td>\n",
       "      <td>22.81</td>\n",
       "      <td>84.46</td>\n",
       "      <td>545.9</td>\n",
       "      <td>0.09701</td>\n",
       "      <td>0.04619</td>\n",
       "      <td>0.04833</td>\n",
       "      <td>0.05013</td>\n",
       "      <td>0.1987</td>\n",
       "      <td>0.06169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85713702</th>\n",
       "      <td>B</td>\n",
       "      <td>8.196</td>\n",
       "      <td>16.84</td>\n",
       "      <td>51.71</td>\n",
       "      <td>201.9</td>\n",
       "      <td>0.08600</td>\n",
       "      <td>0.05943</td>\n",
       "      <td>0.01588</td>\n",
       "      <td>0.005917</td>\n",
       "      <td>0.1769</td>\n",
       "      <td>...</td>\n",
       "      <td>8.964</td>\n",
       "      <td>21.96</td>\n",
       "      <td>57.26</td>\n",
       "      <td>242.2</td>\n",
       "      <td>0.12970</td>\n",
       "      <td>0.13570</td>\n",
       "      <td>0.06880</td>\n",
       "      <td>0.02564</td>\n",
       "      <td>0.3105</td>\n",
       "      <td>0.07409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Diagnosis  Radius (mean)  Texture (mean)  Perimeter (mean)  \\\n",
       "Id                                                                    \n",
       "8510426          B         13.540           14.36             87.46   \n",
       "8510653          B         13.080           15.71             85.63   \n",
       "8510824          B          9.504           12.44             60.34   \n",
       "854941           B         13.030           18.42             82.61   \n",
       "85713702         B          8.196           16.84             51.71   \n",
       "\n",
       "          Area (mean)  Smoothness (mean)  Compactness (mean)  \\\n",
       "Id                                                             \n",
       "8510426         566.3            0.09779             0.08129   \n",
       "8510653         520.0            0.10750             0.12700   \n",
       "8510824         273.9            0.10240             0.06492   \n",
       "854941          523.8            0.08983             0.03766   \n",
       "85713702        201.9            0.08600             0.05943   \n",
       "\n",
       "          Concavity (mean)  Concave points (mean)  Symmetry (mean)  ...  \\\n",
       "Id                                                                  ...   \n",
       "8510426            0.06664               0.047810           0.1885  ...   \n",
       "8510653            0.04568               0.031100           0.1967  ...   \n",
       "8510824            0.02956               0.020760           0.1815  ...   \n",
       "854941             0.02562               0.029230           0.1467  ...   \n",
       "85713702           0.01588               0.005917           0.1769  ...   \n",
       "\n",
       "          Radius (worst)  Texture (worst)  Perimeter (worst)  Area (worst)  \\\n",
       "Id                                                                           \n",
       "8510426           15.110            19.26              99.70         711.2   \n",
       "8510653           14.500            20.49              96.09         630.5   \n",
       "8510824           10.230            15.66              65.13         314.9   \n",
       "854941            13.300            22.81              84.46         545.9   \n",
       "85713702           8.964            21.96              57.26         242.2   \n",
       "\n",
       "          Smoothness (worst)  Compactness (worst)  Concavity (worst)  \\\n",
       "Id                                                                     \n",
       "8510426              0.14400              0.17730            0.23900   \n",
       "8510653              0.13120              0.27760            0.18900   \n",
       "8510824              0.13240              0.11480            0.08867   \n",
       "854941               0.09701              0.04619            0.04833   \n",
       "85713702             0.12970              0.13570            0.06880   \n",
       "\n",
       "          Concave points (worst)  Symmetry (worst)  Fractal dimension (worst)  \n",
       "Id                                                                             \n",
       "8510426                  0.12880            0.2977                    0.07259  \n",
       "8510653                  0.07283            0.3184                    0.08183  \n",
       "8510824                  0.06227            0.2450                    0.07773  \n",
       "854941                   0.05013            0.1987                    0.06169  \n",
       "85713702                 0.02564            0.3105                    0.07409  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apresenta as 5 primeiras linhas da base de dados (tumores benignos)\n",
    "cancer_b_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use um comando Python para imprimir as primeiras 5 linhas de dados para tumores malignos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-02T00:34:23.946646Z",
     "iopub.status.busy": "2021-06-02T00:34:23.946291Z",
     "iopub.status.idle": "2021-06-02T00:34:23.984774Z",
     "shell.execute_reply": "2021-06-02T00:34:23.983336Z",
     "shell.execute_reply.started": "2021-06-02T00:34:23.946617Z"
    }
   },
   "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>Diagnosis</th>\n",
       "      <th>Radius (mean)</th>\n",
       "      <th>Texture (mean)</th>\n",
       "      <th>Perimeter (mean)</th>\n",
       "      <th>Area (mean)</th>\n",
       "      <th>Smoothness (mean)</th>\n",
       "      <th>Compactness (mean)</th>\n",
       "      <th>Concavity (mean)</th>\n",
       "      <th>Concave points (mean)</th>\n",
       "      <th>Symmetry (mean)</th>\n",
       "      <th>...</th>\n",
       "      <th>Radius (worst)</th>\n",
       "      <th>Texture (worst)</th>\n",
       "      <th>Perimeter (worst)</th>\n",
       "      <th>Area (worst)</th>\n",
       "      <th>Smoothness (worst)</th>\n",
       "      <th>Compactness (worst)</th>\n",
       "      <th>Concavity (worst)</th>\n",
       "      <th>Concave points (worst)</th>\n",
       "      <th>Symmetry (worst)</th>\n",
       "      <th>Fractal dimension (worst)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>842302</th>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842517</th>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84300903</th>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84348301</th>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84358402</th>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Diagnosis  Radius (mean)  Texture (mean)  Perimeter (mean)  \\\n",
       "Id                                                                    \n",
       "842302           M          17.99           10.38            122.80   \n",
       "842517           M          20.57           17.77            132.90   \n",
       "84300903         M          19.69           21.25            130.00   \n",
       "84348301         M          11.42           20.38             77.58   \n",
       "84358402         M          20.29           14.34            135.10   \n",
       "\n",
       "          Area (mean)  Smoothness (mean)  Compactness (mean)  \\\n",
       "Id                                                             \n",
       "842302         1001.0            0.11840             0.27760   \n",
       "842517         1326.0            0.08474             0.07864   \n",
       "84300903       1203.0            0.10960             0.15990   \n",
       "84348301        386.1            0.14250             0.28390   \n",
       "84358402       1297.0            0.10030             0.13280   \n",
       "\n",
       "          Concavity (mean)  Concave points (mean)  Symmetry (mean)  ...  \\\n",
       "Id                                                                  ...   \n",
       "842302              0.3001                0.14710           0.2419  ...   \n",
       "842517              0.0869                0.07017           0.1812  ...   \n",
       "84300903            0.1974                0.12790           0.2069  ...   \n",
       "84348301            0.2414                0.10520           0.2597  ...   \n",
       "84358402            0.1980                0.10430           0.1809  ...   \n",
       "\n",
       "          Radius (worst)  Texture (worst)  Perimeter (worst)  Area (worst)  \\\n",
       "Id                                                                           \n",
       "842302             25.38            17.33             184.60        2019.0   \n",
       "842517             24.99            23.41             158.80        1956.0   \n",
       "84300903           23.57            25.53             152.50        1709.0   \n",
       "84348301           14.91            26.50              98.87         567.7   \n",
       "84358402           22.54            16.67             152.20        1575.0   \n",
       "\n",
       "          Smoothness (worst)  Compactness (worst)  Concavity (worst)  \\\n",
       "Id                                                                     \n",
       "842302                0.1622               0.6656             0.7119   \n",
       "842517                0.1238               0.1866             0.2416   \n",
       "84300903              0.1444               0.4245             0.4504   \n",
       "84348301              0.2098               0.8663             0.6869   \n",
       "84358402              0.1374               0.2050             0.4000   \n",
       "\n",
       "          Concave points (worst)  Symmetry (worst)  Fractal dimension (worst)  \n",
       "Id                                                                             \n",
       "842302                    0.2654            0.4601                    0.11890  \n",
       "842517                    0.1860            0.2750                    0.08902  \n",
       "84300903                  0.2430            0.3613                    0.08758  \n",
       "84348301                  0.2575            0.6638                    0.17300  \n",
       "84358402                  0.1625            0.2364                    0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apresenta as 5 primeiras linhas da base de dados (tumores malignos)\n",
    "cancer_m_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos conjuntos de dados, cada linha corresponde a uma imagem diferente. Cada conjunto de dados possui 31 colunas diferentes, correspondendo a:\n",
    "- 1 coluna (`'Diagnóstico'`) que classifica os tumores como benignos (que aparece no conjunto de dados como **`B`**) ou malignos (__`M`__), e\n",
    "- 30 colunas contendo diferentes medidas coletadas das imagens.\n",
    "\n",
    "Use as primeiras 5 linhas dos dados (para tumores benignos e malignos) para responder às perguntas abaixo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-02T00:38:05.677058Z",
     "iopub.status.busy": "2021-06-02T00:38:05.676690Z",
     "iopub.status.idle": "2021-06-02T00:38:05.684884Z",
     "shell.execute_reply": "2021-06-02T00:38:05.683999Z",
     "shell.execute_reply.started": "2021-06-02T00:38:05.677020Z"
    }
   },
   "outputs": [],
   "source": [
    "# Preencha a linha abaixo: Nas primeiras cinco linhas dos dados para tumores benignos, qual é o\n",
    "# maior valor para 'Perímetro (média)'?\n",
    "max_perim = 87.46\n",
    "\n",
    "# Preencha a linha abaixo: Qual é o valor de 'Raio (média)' para o tumor com Id 842517?\n",
    "mean_radius = 20.57"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Passo 3: Investigar Diferenças"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parte A\n",
    "\n",
    "Use a célula de código abaixo para criar dois histogramas que mostram a distribuição em valores de `'Área (média)'` para tumores benignos e malignos. (__Para permitir uma comparação fácil, crie uma única figura contendo ambos os histogramas na célula de código abaixo.__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-02T00:39:12.716525Z",
     "iopub.status.busy": "2021-06-02T00:39:12.716109Z",
     "iopub.status.idle": "2021-06-02T00:39:12.962313Z",
     "shell.execute_reply": "2021-06-02T00:39:12.960809Z",
     "shell.execute_reply.started": "2021-06-02T00:39:12.716491Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogramas para tumores benignos e malignos\n",
    "sns.distplot(a = cancer_b_data['Area (mean)'], label = 'Benign', kde = False)\n",
    "sns.distplot(a = cancer_m_data['Area (mean)'], label = 'Malignant', kde = False)\n",
    "\n",
    "# Adicionar rótulo ao eixo x\n",
    "plt.xlabel('Area (mean)', fontsize = 'large')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parte B\n",
    "\n",
    "Um pesquisador pede ajuda a você para identificar como a coluna \"Área (média)\" pode ser usada para entender a diferença entre tumores benignos e malignos. Com base nos histogramas acima,\n",
    "- Os tumores malignos têm valores maiores ou menores para `'Área (média)'` (em relação a tumores benignos), em média?\n",
    "- Qual tipo de tumor parece ter uma faixa maior de valores potenciais?\n",
    "\n",
    "Solução: Tumores malignos têm valores mais altos para `'Área (média)'`, em média. Os tumores malignos têm uma gama maior de valores potenciais."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Passo 4: Uma coluna muito útil\n",
    "\n",
    "#### Parte A\n",
    "\n",
    "Use a célula de código abaixo para criar dois gráficos KDE que mostram a distribuição em valores para `'Raio (pior)'` para tumores benignos e malignos. (__Para permitir uma comparação fácil, crie uma única figura contendo ambos os gráficos do KDE na célula de código abaixo.__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-02T00:42:46.269217Z",
     "iopub.status.busy": "2021-06-02T00:42:46.268841Z",
     "iopub.status.idle": "2021-06-02T00:42:46.492361Z",
     "shell.execute_reply": "2021-06-02T00:42:46.491469Z",
     "shell.execute_reply.started": "2021-06-02T00:42:46.269186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráficos KDE para os tumores malignos e benignos\n",
    "sns.kdeplot(data=cancer_b_data['Radius (worst)'], shade = True, label = 'Benign')\n",
    "sns.kdeplot(data=cancer_m_data['Radius (worst)'], shade = True, label = 'Malignant')\n",
    "\n",
    "# Adicionar rótulo ao eixo x\n",
    "plt.xlabel('Radius (worst)', fontsize = 'large')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parte B\n",
    "\n",
    "Um hospital começou recentemente a usar um algoritmo que pode diagnosticar tumores com alta precisão. Dado um tumor com um valor para `'Raio (pior)'` de 25, você acha que o algoritmo tem maior probabilidade de classificar o tumor como benigno ou maligno?\n",
    "\n",
    "Solução: é mais provável que o algoritmo classifique o tumor como maligno. Isso ocorre porque a curva para tumores malignos é muito maior do que a curva para tumores benignos em torno de um valor de 25 - e um algoritmo que obtém alta precisão provavelmente tomará decisões com base neste padrão nos dados."
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}