Python_para_ML_07.ipynb

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  "metadata": {
    "colab": {
      "name": "Python para ML - 07.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM7Ygybgd/La2mNuebuIsM4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ricardorocha86/Python-para-Machine-Learning/blob/master/Python_para_ML_07.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QvWwixKvVqYe",
        "colab_type": "text"
      },
      "source": [
        "![texto alternativo](https://raw.githubusercontent.com/ricardorocha86/Python-para-Machine-Learning/master/Imagens/capa%20-%20python%20para%20ml%2007.jpg)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BNsCsDcRxq5K",
        "colab_type": "text"
      },
      "source": [
        "# **Python para Machine Learning** - Parte 7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5ci8BYxxrLn",
        "colab_type": "text"
      },
      "source": [
        "Nesse notebook vamos ver:\n",
        "- Análise gráfica\n",
        "- Introdução ao pré-processamento para Machine Learning\n",
        "- A Biblioteca Scikit-learn\n",
        "- Projeto\n",
        "- Exercício\n",
        "- Links Úteis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eoy45bSX5s0l",
        "colab_type": "text"
      },
      "source": [
        "## Importação dos Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-X6mOIx5tRe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCgrsXXFAhMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/ricardorocha86/Dados-Redes-Sociais/master/Social_Network_Ads.csv'\n",
        "ads = pd.read_csv(url)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fk6NoZUxAjoW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "cd7afa20-5bce-4171-df8b-fd6f8dc243e8"
      },
      "source": [
        "ads.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>User ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15624510</td>\n",
              "      <td>Male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15810944</td>\n",
              "      <td>Male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15668575</td>\n",
              "      <td>Female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15603246</td>\n",
              "      <td>Female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>57000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15804002</td>\n",
              "      <td>Male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>76000.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    User ID  Gender   Age  EstimatedSalary  Purchased\n",
              "0  15624510    Male  19.0          19000.0          0\n",
              "1  15810944    Male  35.0          20000.0          0\n",
              "2  15668575  Female  26.0          43000.0          0\n",
              "3  15603246  Female  27.0          57000.0          0\n",
              "4  15804002    Male  19.0          76000.0          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uSBAJilj5tX4",
        "colab_type": "text"
      },
      "source": [
        "## Análise Exploratória dos Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stkf9sFrKEMg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "94dadc05-aa20-4786-d811-fbc06059552a"
      },
      "source": [
        "ads.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>User ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>User ID</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.000721</td>\n",
              "      <td>0.071097</td>\n",
              "      <td>0.007120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>-0.000721</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.155238</td>\n",
              "      <td>0.622454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <td>0.071097</td>\n",
              "      <td>0.155238</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.362083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Purchased</th>\n",
              "      <td>0.007120</td>\n",
              "      <td>0.622454</td>\n",
              "      <td>0.362083</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  User ID       Age  EstimatedSalary  Purchased\n",
              "User ID          1.000000 -0.000721         0.071097   0.007120\n",
              "Age             -0.000721  1.000000         0.155238   0.622454\n",
              "EstimatedSalary  0.071097  0.155238         1.000000   0.362083\n",
              "Purchased        0.007120  0.622454         0.362083   1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjVXoI4a5ted",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upbEZ_liAr7q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "d326ccac-eef9-4af3-d319-a81f96c8103f"
      },
      "source": [
        "plt.scatter(x = ads['Age'], \n",
        "            y = ads['EstimatedSalary'], \n",
        "            c = ads['Purchased'],\n",
        "            marker = 'o', alpha = 0.7, cmap = 'rainbow')\n",
        "plt.title('Gráfico de Dispersão: Idade e Salário Estimado', size = 15)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-ZIBbJWBJjD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "08a9250b-6eea-457e-e9ea-d26ff4c9c292"
      },
      "source": [
        "plt.hist(ads[ ads['Purchased'] == 0 ]['Age'], bins = 20, alpha = .75)\n",
        "plt.hist(ads[ ads['Purchased'] == 1 ]['Age'], bins = 20, alpha = .75) \n",
        "plt.title('Histogramas da Idade', size = 15) \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e22jth_1Bk7p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "794df6b8-07d3-4a50-b7c7-a6ed185a30b8"
      },
      "source": [
        "plt.hist(ads[ ads['Purchased'] == 0 ]['EstimatedSalary'], bins = 20, alpha = .75)\n",
        "plt.hist(ads[ ads['Purchased'] == 1 ]['EstimatedSalary'], bins = 20, alpha = .75) \n",
        "plt.title('Histogramas do Salário Estimado', size = 15) \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3oiYYJsBuss",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "fde41ed2-2887-4253-ba9c-a56d6ff053f1"
      },
      "source": [
        "ads['Gender'].groupby(ads['Purchased']).value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Purchased  Gender\n",
              "0          Male      130\n",
              "           Female    127\n",
              "1          Female     77\n",
              "           Male       66\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2TwcsUNEMdc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "93b693bc-dbdd-4c74-f2f6-7399afc0f755"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.barplot(x='Purchased', y = 'Gender', data = ads)\n",
        "plt.title('Comparação dos Sexos em relação à Compras', size = 15)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A-ua_nY-DJsx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "14f831ae-48f0-4b0a-c351-9e5b67eae751"
      },
      "source": [
        "plt.figure(figsize = (16, 9))\n",
        "\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.scatter(x = ads['Age'], y = ads['EstimatedSalary'], c = ads['Purchased'],\n",
        "            marker = 'o', alpha = 0.7, cmap = 'rainbow')\n",
        "plt.title('Gráfico de Dispersão: Idade e Salário Estimado', size = 15) \n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.hist(ads[ ads['Purchased'] == 0 ]['Age'], bins = 20, alpha = .75)\n",
        "plt.hist(ads[ ads['Purchased'] == 1 ]['Age'], bins = 20, alpha = .75) \n",
        "plt.title('Histogramas da Idade', size = 15) \n",
        "\n",
        "plt.subplot(2, 2, 3)\n",
        "plt.hist(ads[ ads['Purchased'] == 0 ]['EstimatedSalary'], bins = 20, alpha = .75)\n",
        "plt.hist(ads[ ads['Purchased'] == 1 ]['EstimatedSalary'], bins = 20, alpha = .75) \n",
        "plt.title('Histogramas do Salário Estimado', size = 15) \n",
        "\n",
        "plt.subplot(2, 2, 4)\n",
        "sns.barplot(x='Purchased', y = 'Gender', data = ads)\n",
        "plt.title('Comparação dos Sexos em relação à Compras', size = 15)\n",
        "\n",
        "plt.tight_layout()  \n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1152x648 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "saemUmaf5tkk",
        "colab_type": "text"
      },
      "source": [
        "## Pré-processamento Básico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sH1Gpb9C5ts9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ads['Sexo'] = 1\n",
        "ads.loc[ads['Gender'] == 'Female', 'Sexo'] = 0 "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NeLGiOPYHtHH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "86572e4c-9b19-4ae9-e525-5c0ac5c8fdb3"
      },
      "source": [
        "med_age = ads['Age'].mean()\n",
        "std_age = ads['Age'].std()\n",
        "\n",
        "med_es = ads['EstimatedSalary'].mean()\n",
        "std_es = ads['EstimatedSalary'].std()\n",
        "\n",
        "print(med_age, std_age, med_es, std_es)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "37.655 10.48287659730792 69742.5 34096.960282424785\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yeAxJkhIVeY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ads['Idade'] = (ads['Age'] - med_age)/std_age\n",
        "ads['Salario'] = (ads['EstimatedSalary'] - med_es)/std_es"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38Vq9tn7IkpC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6be9d877-e7d0-45d3-f133-190f5c5bf083"
      },
      "source": [
        "ads.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>User ID</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Salario</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>4.000000e+02</td>\n",
              "      <td>4.000000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.569154e+07</td>\n",
              "      <td>37.655000</td>\n",
              "      <td>69742.500000</td>\n",
              "      <td>0.357500</td>\n",
              "      <td>0.490000</td>\n",
              "      <td>-2.486900e-16</td>\n",
              "      <td>-1.554312e-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.165832e+04</td>\n",
              "      <td>10.482877</td>\n",
              "      <td>34096.960282</td>\n",
              "      <td>0.479864</td>\n",
              "      <td>0.500526</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.556669e+07</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.874962e+00</td>\n",
              "      <td>-1.605495e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.562676e+07</td>\n",
              "      <td>29.750000</td>\n",
              "      <td>43000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-7.540869e-01</td>\n",
              "      <td>-7.843075e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.569434e+07</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>70000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6.248285e-02</td>\n",
              "      <td>7.551993e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.575036e+07</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>88000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.960601e-01</td>\n",
              "      <td>5.354583e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.581524e+07</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>150000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.131571e+00</td>\n",
              "      <td>2.353802e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            User ID         Age  ...         Idade       Salario\n",
              "count  4.000000e+02  400.000000  ...  4.000000e+02  4.000000e+02\n",
              "mean   1.569154e+07   37.655000  ... -2.486900e-16 -1.554312e-17\n",
              "std    7.165832e+04   10.482877  ...  1.000000e+00  1.000000e+00\n",
              "min    1.556669e+07   18.000000  ... -1.874962e+00 -1.605495e+00\n",
              "25%    1.562676e+07   29.750000  ... -7.540869e-01 -7.843075e-01\n",
              "50%    1.569434e+07   37.000000  ... -6.248285e-02  7.551993e-03\n",
              "75%    1.575036e+07   46.000000  ...  7.960601e-01  5.354583e-01\n",
              "max    1.581524e+07   60.000000  ...  2.131571e+00  2.353802e+00\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Xetrf12I4xQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "28f094a3-9d16-4ace-f3b6-11bc2501a4d3"
      },
      "source": [
        "ads.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>User ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Purchased</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Salario</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15624510</td>\n",
              "      <td>Male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.779569</td>\n",
              "      <td>-1.488183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15810944</td>\n",
              "      <td>Male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.253270</td>\n",
              "      <td>-1.458854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15668575</td>\n",
              "      <td>Female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>43000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.111813</td>\n",
              "      <td>-0.784307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15603246</td>\n",
              "      <td>Female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>57000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.016419</td>\n",
              "      <td>-0.373714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15804002</td>\n",
              "      <td>Male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>76000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.779569</td>\n",
              "      <td>0.183521</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    User ID  Gender   Age  EstimatedSalary  Purchased  Sexo     Idade   Salario\n",
              "0  15624510    Male  19.0          19000.0          0     1 -1.779569 -1.488183\n",
              "1  15810944    Male  35.0          20000.0          0     1 -0.253270 -1.458854\n",
              "2  15668575  Female  26.0          43000.0          0     0 -1.111813 -0.784307\n",
              "3  15603246  Female  27.0          57000.0          0     0 -1.016419 -0.373714\n",
              "4  15804002    Male  19.0          76000.0          0     1 -1.779569  0.183521"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfO4M5iLKc9-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ads.drop(['User ID', 'Gender', 'EstimatedSalary', 'Age'], axis = 1, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TH1ymS-f5tkN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "outputId": "54955b35-b99e-4664-c35f-70014782d61c"
      },
      "source": [
        "ads.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Purchased</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Salario</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Purchased</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.042469</td>\n",
              "      <td>0.622454</td>\n",
              "      <td>0.362083</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sexo</th>\n",
              "      <td>-0.042469</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.073741</td>\n",
              "      <td>-0.060435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Idade</th>\n",
              "      <td>0.622454</td>\n",
              "      <td>-0.073741</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.155238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Salario</th>\n",
              "      <td>0.362083</td>\n",
              "      <td>-0.060435</td>\n",
              "      <td>0.155238</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Purchased      Sexo     Idade   Salario\n",
              "Purchased   1.000000 -0.042469  0.622454  0.362083\n",
              "Sexo       -0.042469  1.000000 -0.073741 -0.060435\n",
              "Idade       0.622454 -0.073741  1.000000  0.155238\n",
              "Salario     0.362083 -0.060435  0.155238  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1fx6IocVL_o4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "724542e3-ff53-4f62-999f-317780eb4b14"
      },
      "source": [
        "ads.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FlE9NRnK5txj",
        "colab_type": "text"
      },
      "source": [
        "## Preparando o Conjunto para Validação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IwBOjSTy5t39",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "1c98e46d-f68d-48d8-99aa-c1782dc0d1c9"
      },
      "source": [
        "import numpy as np\n",
        "n = ads.shape[0] \n",
        "indices = np.random.choice(n, n, replace = False)\n",
        "\n",
        "ads.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Purchased</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Salario</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.779569</td>\n",
              "      <td>-1.488183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.253270</td>\n",
              "      <td>-1.458854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.111813</td>\n",
              "      <td>-0.784307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.016419</td>\n",
              "      <td>-0.373714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.779569</td>\n",
              "      <td>0.183521</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Purchased  Sexo     Idade   Salario\n",
              "0          0     1 -1.779569 -1.488183\n",
              "1          0     1 -0.253270 -1.458854\n",
              "2          0     0 -1.111813 -0.784307\n",
              "3          0     0 -1.016419 -0.373714\n",
              "4          0     1 -1.779569  0.183521"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qCY_5lqnPmy2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "811c5407-6c2e-4232-96a1-ba9ef4018280"
      },
      "source": [
        "ads.iloc[indices].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Purchased</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Salario</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>259</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.700666</td>\n",
              "      <td>1.796568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>257</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.223698</td>\n",
              "      <td>-0.373714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.874962</td>\n",
              "      <td>-0.051104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.062483</td>\n",
              "      <td>1.972537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.062483</td>\n",
              "      <td>0.682099</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Purchased  Sexo     Idade   Salario\n",
              "259          1     0  0.700666  1.796568\n",
              "257          0     1  0.223698 -0.373714\n",
              "141          0     0 -1.874962 -0.051104\n",
              "302          1     0 -0.062483  1.972537\n",
              "285          1     0 -0.062483  0.682099"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 240
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-M41lBYP0Ya",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "c745c5f6-ee73-4677-888c-49972719d80c"
      },
      "source": [
        "ads.iloc[indices].reset_index(drop = True).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Purchased</th>\n",
              "      <th>Sexo</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Salario</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.700666</td>\n",
              "      <td>1.796568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.223698</td>\n",
              "      <td>-0.373714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1.874962</td>\n",
              "      <td>-0.051104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.062483</td>\n",
              "      <td>1.972537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.062483</td>\n",
              "      <td>0.682099</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Purchased  Sexo     Idade   Salario\n",
              "0          1     0  0.700666  1.796568\n",
              "1          0     1  0.223698 -0.373714\n",
              "2          0     0 -1.874962 -0.051104\n",
              "3          1     0 -0.062483  1.972537\n",
              "4          1     0 -0.062483  0.682099"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqTYakMvQN6u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ads0 = ads.iloc[indices].reset_index(drop = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2TNYYboQTeI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8ac3f4cf-39bc-421a-cec7-efa1ab3b130a"
      },
      "source": [
        "prop = 0.75\n",
        "n_treino = int(prop*n)\n",
        "treino = ads0[: n_treino]\n",
        "teste  = ads0[n_treino: ]\n",
        "\n",
        "print(treino.shape)\n",
        "print(teste.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(300, 4)\n",
            "(100, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "af7rbrAqjP6a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Xtreino = treino.loc[:, treino.columns != 'Purchased']\n",
        "Ytreino = treino.loc[:, 'Purchased']\n",
        "Xteste = teste.loc[:, teste.columns != 'Purchased']\n",
        "Yteste = teste.loc[:, 'Purchased']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3j_GLavVj6T6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "outputId": "94cddf14-88e4-4404-9926-7f9fb41fecd2"
      },
      "source": [
        "Ytreino.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    1\n",
              "3    1\n",
              "4    0\n",
              "Name: Purchased, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 233
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9lVtZvE6Io9",
        "colab_type": "text"
      },
      "source": [
        "## Aplicação do Modelo de Machine Learning\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgawxpshlMY5",
        "colab_type": "text"
      },
      "source": [
        "Agora vem a parte \"difícil\": criamos um objeto que representa o modelo de regressão logística e o alteramos utilizando a função **fit** para que se faça o ajuste dos dados. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GaXRVsT6I9k",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        },
        "outputId": "1e21c07b-7d4c-4fc7-f03f-5fef6e100069"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "modelo = LogisticRegression()\n",
        "modelo.fit(Xtreino, Ytreino)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKDW9uGknDbJ",
        "colab_type": "text"
      },
      "source": [
        "Utilizamos o método **predict** para utilizar o modelo ajustado nas variáveis explicativas de teste, para depois, enfim, fazermos a sua comparação com a variável resposta de teste."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0knIvzhAk_PQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 103
        },
        "outputId": "ec7be90a-ae8e-4947-8a00-2805332644eb"
      },
      "source": [
        "preditos = modelo.predict(Xteste)\n",
        "preditos"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdlHBDw0k-N6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d1e5b81a-0ac2-48af-c93e-13b0b6f30223"
      },
      "source": [
        "modelo.intercept_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1.33952011])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6ssp_tI3csP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8d40ca77-ded7-498b-8ba9-9887e48b5bf2"
      },
      "source": [
        "modelo.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.44430662, 2.22714106, 1.2296927 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dTbrn7eN4eL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "6e8e7422-6cfc-44b1-a420-c111fc204537"
      },
      "source": [
        "Xtreino.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Sexo</th>\n",
              "      <th>Idade</th>\n",
              "      <th>Salario</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.700666</td>\n",
              "      <td>1.796568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.223698</td>\n",
              "      <td>-0.373714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.874962</td>\n",
              "      <td>-0.051104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.062483</td>\n",
              "      <td>1.972537</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>-0.062483</td>\n",
              "      <td>0.682099</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sexo     Idade   Salario\n",
              "0     0  0.700666  1.796568\n",
              "1     1  0.223698 -0.373714\n",
              "2     0 -1.874962 -0.051104\n",
              "3     0 -0.062483  1.972537\n",
              "4     0 -0.062483  0.682099"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hn-my1OP6OFp",
        "colab_type": "text"
      },
      "source": [
        "## Verificação da performance da métrica, nesse caso, a **acurácia**:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28gUmOSwl2OJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "19eeae28-737a-4c41-da46-a3dc6b6e38d8"
      },
      "source": [
        "(preditos == Yteste).mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.79"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKzEuksI3Msa",
        "colab_type": "text"
      },
      "source": [
        "## **Exercício 9**\n",
        "\n",
        "Escreva um programa que faça a divisão em teste e treinamento de um conjunto de dados respeite a proporção da variável resposta, isto é, que a divisão seja estratificada na variável resposta. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q6qRG5W85PTY",
        "colab_type": "text"
      },
      "source": [
        "## **Exercício 10**\n",
        "\n",
        "Compare o modelo de regressão logística utilizando a variável sexo e sem utilizar a variável sexo. O que você pode concluir?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hPLspSG3RnG",
        "colab_type": "text"
      },
      "source": [
        "## **Exercício 11**\n",
        "\n",
        "Reaplique o modelo de regressão logística fazendo modificações nas variáveis Age e EstimatedSalary. Transforme-as em variáveis categóricas, com 5 categorias cada e reveja o resultado do modelo de regressão logística.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hVudiYjRml9M",
        "colab_type": "text"
      },
      "source": [
        "## **Links Úteis**\n",
        "\n",
        "- [Documentação Scikit-learn](https://scikit-learn.org/stable/)"
      ]
    }
  ]
}