Python_para_ML_08.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Python para ML - 08.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMKWg1GWlXG+3t6huT76BEr",
      "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_08.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%2008.jpg)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-b5mjxUoSzou"
      },
      "source": [
        "# **Python para Machine Learning** - Parte 8"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5ci8BYxxrLn",
        "colab_type": "text"
      },
      "source": [
        "Nesse notebook vamos ver:\n",
        "- O Passo-a-passo com o Scikit-Learn\n",
        "- Usando o Modelo KNN\n",
        "- Modelos de Regressão\n",
        "- Exercício\n",
        "- Links Úteis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wuN1mXjMTLuZ",
        "colab_type": "text"
      },
      "source": [
        "# **O Passo-a-passo com o Scikit-Learn**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-X6mOIx5tRe",
        "colab_type": "text"
      },
      "source": [
        "## Carregando o Conjunto de Dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCgrsXXFAhMm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "url = 'https://raw.githubusercontent.com/ricardorocha86/Dados-Redes-Sociais/master/Social_Network_Ads.csv'\n",
        "ads = pd.read_csv(url)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21R8spDjJraE",
        "colab_type": "text"
      },
      "source": [
        "## Fazendo a Divisão em Treinamento e Teste, Separados por Variáveis Resposta e Explicativas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jtu4nnceuBhr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "Xtreino, Xteste, Ytreino, Yteste = train_test_split(ads.loc[:, ['Age', 'EstimatedSalary']], \n",
        "                                                    ads['Purchased'], \n",
        "                                                    test_size = 0.25)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1OnCxkmJlZ0",
        "colab_type": "text"
      },
      "source": [
        "## Fazendo a Padronização das Variáveis Contínuas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEkuhGJRzKms",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "Xtreino = sc.fit_transform(Xtreino)\n",
        "Xteste = sc.transform(Xteste)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YuUy-7DouBmG",
        "colab_type": "text"
      },
      "source": [
        "## Ajustando o Modelo de Regressão Logística "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QBL1b7JluB19",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "e5e20951-6219-4eac-896f-f794bb747f18"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "import matplotlib.pyplot as plt  \n",
        "\n",
        "# Ajustando o modelo de Regressão Logística\n",
        "reglog = LogisticRegression()\n",
        "reglog.fit(Xtreino, Ytreino)"
      ],
      "execution_count": 7,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EA76wq1DJd0P",
        "colab_type": "text"
      },
      "source": [
        "## Calculando a Acurácia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYQfoHW_ycBP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "4a0c30a4-fd3b-41c4-f35f-32ac32eec69d"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "preditos_treino = reglog.predict(Xtreino)\n",
        "preditos_teste = reglog.predict(Xteste)\n",
        "\n",
        "acc_treino = accuracy_score(preditos_treino, Ytreino)\n",
        "acc_teste = accuracy_score(preditos_teste, Yteste)\n",
        "\n",
        "print('A Acurácia de TREINO foi: {:.2f}%'.format(100*acc_treino))\n",
        "print('A Acurácia de TESTE foi:  {:.2f}%'.format(100*acc_teste))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A Acurácia de TREINO foi: 85.00%\n",
            "A Acurácia de TESTE foi:  81.00%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6smtI4ODKtcz",
        "colab_type": "text"
      },
      "source": [
        "## Prevendo Novas Amostras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7hM_tJAKxtH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "95034261-6afe-4919-e5f6-759e30c54a0e"
      },
      "source": [
        "eu = {'Age': [45], 'EstimatedSalary': [100000]}\n",
        "\n",
        "eu = pd.DataFrame(eu)\n",
        "\n",
        "eu = sc.transform(eu)\n",
        "\n",
        "reglog.predict(eu)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrKx3kFdIpsT",
        "colab_type": "text"
      },
      "source": [
        "## Gráfico de Região de Classificação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VcAFlrRyXrY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "34f7d529-e51f-42ea-e460-8d8bcc81b940"
      },
      "source": [
        "from mlxtend.plotting import plot_decision_regions\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize = (15, 7))\n",
        "\n",
        "plt.subplot(1, 2, 1) \n",
        "plot_decision_regions(Xtreino, Ytreino.values, clf = reglog, legend = 1)\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Salário Estimado')\n",
        "plt.title('Regressão Logística no Treino') \n",
        "\n",
        "plt.subplot(1, 2, 2) \n",
        "plot_decision_regions(Xteste, Yteste.values, clf = reglog, legend = 1)\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Salário Estimado')\n",
        "plt.title('Regressão Logística no Teste')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elUEhp4iTbNC",
        "colab_type": "text"
      },
      "source": [
        "# **Usando o Modelo KNN**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9yzgWU1Sk0E5",
        "colab_type": "text"
      },
      "source": [
        "## Testando outros modelos: K-Vizinhos Mais Próximos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XWVm-D27oy5l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "8a4dc9a3-9fbf-4c12-c052-2572f1e3962e"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors = 1)\n",
        "knn.fit(Xtreino, Ytreino)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nCanz0P5oy6I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "bf3a3e46-d635-4b03-c697-ad8e8496de09"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "preditos_treino = knn.predict(Xtreino)\n",
        "preditos_teste = knn.predict(Xteste)\n",
        "\n",
        "acc_treino = accuracy_score(preditos_treino, Ytreino)\n",
        "acc_teste = accuracy_score(preditos_teste, Yteste)\n",
        "\n",
        "print('A Acurácia de TREINO foi: {:.2f}%'.format(100*acc_treino))\n",
        "print('A Acurácia de TESTE foi:  {:.2f}%'.format(100*acc_teste))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A Acurácia de TREINO foi: 99.33%\n",
            "A Acurácia de TESTE foi:  81.00%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7tGzbq2Joy6h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "a94a50f6-ba75-4aa2-a820-6c909dadc49e"
      },
      "source": [
        "from mlxtend.plotting import plot_decision_regions\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Plot para o 1-NN\n",
        "knn1 = KNeighborsClassifier(n_neighbors = 1)\n",
        "knn1.fit(Xtreino, Ytreino)\n",
        "\n",
        "plt.figure(figsize = (15, 7))\n",
        "\n",
        "plt.subplot(1, 2, 1) \n",
        "plot_decision_regions(Xtreino, Ytreino.values, clf = knn1, legend = 1)\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Salário Estimado')\n",
        "plt.title('1-NN no Treino') \n",
        "\n",
        "plt.subplot(1, 2, 2) \n",
        "plot_decision_regions(Xteste, Yteste.values, clf = knn1, legend = 1)\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Salário Estimado')\n",
        "plt.title('1-NN no Teste')\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlQPQijJuzJP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "8f11ac05-8163-42bd-82b8-d6491813211d"
      },
      "source": [
        "#Plot para o 51-NN\n",
        "knn51 = KNeighborsClassifier(n_neighbors = 51)\n",
        "knn51.fit(Xtreino, Ytreino)\n",
        "\n",
        "plt.figure(figsize = (15, 7))\n",
        "\n",
        "plt.subplot(1, 2, 1) \n",
        "plot_decision_regions(Xtreino, Ytreino.values, clf = knn51, legend = 1)\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Salário Estimado')\n",
        "plt.title('51-NN no Treino') \n",
        "\n",
        "plt.subplot(1, 2, 2) \n",
        "plot_decision_regions(Xteste, Yteste.values, clf = knn51, legend = 1)\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Salário Estimado')\n",
        "plt.title('51-NN no Teste')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZ6HdCVPu0Gd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "5263e315-0412-4ca9-b254-5d92fffc6df6"
      },
      "source": [
        "#Plot para o 101-NN\n",
        "knn101 = KNeighborsClassifier(n_neighbors = 101)\n",
        "knn101.fit(Xtreino, Ytreino)\n",
        "\n",
        "plt.figure(figsize = (15, 7))\n",
        "\n",
        "plt.subplot(1, 2, 1) \n",
        "plot_decision_regions(Xtreino, Ytreino.values, clf = knn101, legend = 1)\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Salário Estimado')\n",
        "plt.title('101-NN no Treino') \n",
        "\n",
        "plt.subplot(1, 2, 2) \n",
        "plot_decision_regions(Xteste, Yteste.values, clf = knn101, legend = 1)\n",
        "plt.xlabel('Idade')\n",
        "plt.ylabel('Salário Estimado')\n",
        "plt.title('101-NN no Teste')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AyhhFAban-Ch",
        "colab_type": "text"
      },
      "source": [
        "# **Modelos de Regressão**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5d-TcAeSsMfk",
        "colab_type": "text"
      },
      "source": [
        "## Importando o conjunto de dados **mtcars**, tradicional do R, através da biblioteca **statmodels**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tsvOln7qn-bq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c3b5d262-78fd-4914-e320-643ce80ee932"
      },
      "source": [
        "import statsmodels.api as sm \n",
        "\n",
        "mtcars = sm.datasets.get_rdataset('mtcars', 'datasets').data\n",
        "mtcars"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "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>mpg</th>\n",
              "      <th>cyl</th>\n",
              "      <th>disp</th>\n",
              "      <th>hp</th>\n",
              "      <th>drat</th>\n",
              "      <th>wt</th>\n",
              "      <th>qsec</th>\n",
              "      <th>vs</th>\n",
              "      <th>am</th>\n",
              "      <th>gear</th>\n",
              "      <th>carb</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Mazda RX4</th>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.620</td>\n",
              "      <td>16.46</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mazda RX4 Wag</th>\n",
              "      <td>21.0</td>\n",
              "      <td>6</td>\n",
              "      <td>160.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.90</td>\n",
              "      <td>2.875</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Datsun 710</th>\n",
              "      <td>22.8</td>\n",
              "      <td>4</td>\n",
              "      <td>108.0</td>\n",
              "      <td>93</td>\n",
              "      <td>3.85</td>\n",
              "      <td>2.320</td>\n",
              "      <td>18.61</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hornet 4 Drive</th>\n",
              "      <td>21.4</td>\n",
              "      <td>6</td>\n",
              "      <td>258.0</td>\n",
              "      <td>110</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.215</td>\n",
              "      <td>19.44</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hornet Sportabout</th>\n",
              "      <td>18.7</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.440</td>\n",
              "      <td>17.02</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Valiant</th>\n",
              "      <td>18.1</td>\n",
              "      <td>6</td>\n",
              "      <td>225.0</td>\n",
              "      <td>105</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.460</td>\n",
              "      <td>20.22</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Duster 360</th>\n",
              "      <td>14.3</td>\n",
              "      <td>8</td>\n",
              "      <td>360.0</td>\n",
              "      <td>245</td>\n",
              "      <td>3.21</td>\n",
              "      <td>3.570</td>\n",
              "      <td>15.84</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 240D</th>\n",
              "      <td>24.4</td>\n",
              "      <td>4</td>\n",
              "      <td>146.7</td>\n",
              "      <td>62</td>\n",
              "      <td>3.69</td>\n",
              "      <td>3.190</td>\n",
              "      <td>20.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 230</th>\n",
              "      <td>22.8</td>\n",
              "      <td>4</td>\n",
              "      <td>140.8</td>\n",
              "      <td>95</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.150</td>\n",
              "      <td>22.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 280</th>\n",
              "      <td>19.2</td>\n",
              "      <td>6</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 280C</th>\n",
              "      <td>17.8</td>\n",
              "      <td>6</td>\n",
              "      <td>167.6</td>\n",
              "      <td>123</td>\n",
              "      <td>3.92</td>\n",
              "      <td>3.440</td>\n",
              "      <td>18.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 450SE</th>\n",
              "      <td>16.4</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>4.070</td>\n",
              "      <td>17.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 450SL</th>\n",
              "      <td>17.3</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.730</td>\n",
              "      <td>17.60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Merc 450SLC</th>\n",
              "      <td>15.2</td>\n",
              "      <td>8</td>\n",
              "      <td>275.8</td>\n",
              "      <td>180</td>\n",
              "      <td>3.07</td>\n",
              "      <td>3.780</td>\n",
              "      <td>18.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cadillac Fleetwood</th>\n",
              "      <td>10.4</td>\n",
              "      <td>8</td>\n",
              "      <td>472.0</td>\n",
              "      <td>205</td>\n",
              "      <td>2.93</td>\n",
              "      <td>5.250</td>\n",
              "      <td>17.98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lincoln Continental</th>\n",
              "      <td>10.4</td>\n",
              "      <td>8</td>\n",
              "      <td>460.0</td>\n",
              "      <td>215</td>\n",
              "      <td>3.00</td>\n",
              "      <td>5.424</td>\n",
              "      <td>17.82</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chrysler Imperial</th>\n",
              "      <td>14.7</td>\n",
              "      <td>8</td>\n",
              "      <td>440.0</td>\n",
              "      <td>230</td>\n",
              "      <td>3.23</td>\n",
              "      <td>5.345</td>\n",
              "      <td>17.42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fiat 128</th>\n",
              "      <td>32.4</td>\n",
              "      <td>4</td>\n",
              "      <td>78.7</td>\n",
              "      <td>66</td>\n",
              "      <td>4.08</td>\n",
              "      <td>2.200</td>\n",
              "      <td>19.47</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Honda Civic</th>\n",
              "      <td>30.4</td>\n",
              "      <td>4</td>\n",
              "      <td>75.7</td>\n",
              "      <td>52</td>\n",
              "      <td>4.93</td>\n",
              "      <td>1.615</td>\n",
              "      <td>18.52</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Toyota Corolla</th>\n",
              "      <td>33.9</td>\n",
              "      <td>4</td>\n",
              "      <td>71.1</td>\n",
              "      <td>65</td>\n",
              "      <td>4.22</td>\n",
              "      <td>1.835</td>\n",
              "      <td>19.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Toyota Corona</th>\n",
              "      <td>21.5</td>\n",
              "      <td>4</td>\n",
              "      <td>120.1</td>\n",
              "      <td>97</td>\n",
              "      <td>3.70</td>\n",
              "      <td>2.465</td>\n",
              "      <td>20.01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dodge Challenger</th>\n",
              "      <td>15.5</td>\n",
              "      <td>8</td>\n",
              "      <td>318.0</td>\n",
              "      <td>150</td>\n",
              "      <td>2.76</td>\n",
              "      <td>3.520</td>\n",
              "      <td>16.87</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMC Javelin</th>\n",
              "      <td>15.2</td>\n",
              "      <td>8</td>\n",
              "      <td>304.0</td>\n",
              "      <td>150</td>\n",
              "      <td>3.15</td>\n",
              "      <td>3.435</td>\n",
              "      <td>17.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Camaro Z28</th>\n",
              "      <td>13.3</td>\n",
              "      <td>8</td>\n",
              "      <td>350.0</td>\n",
              "      <td>245</td>\n",
              "      <td>3.73</td>\n",
              "      <td>3.840</td>\n",
              "      <td>15.41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pontiac Firebird</th>\n",
              "      <td>19.2</td>\n",
              "      <td>8</td>\n",
              "      <td>400.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.08</td>\n",
              "      <td>3.845</td>\n",
              "      <td>17.05</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fiat X1-9</th>\n",
              "      <td>27.3</td>\n",
              "      <td>4</td>\n",
              "      <td>79.0</td>\n",
              "      <td>66</td>\n",
              "      <td>4.08</td>\n",
              "      <td>1.935</td>\n",
              "      <td>18.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Porsche 914-2</th>\n",
              "      <td>26.0</td>\n",
              "      <td>4</td>\n",
              "      <td>120.3</td>\n",
              "      <td>91</td>\n",
              "      <td>4.43</td>\n",
              "      <td>2.140</td>\n",
              "      <td>16.70</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lotus Europa</th>\n",
              "      <td>30.4</td>\n",
              "      <td>4</td>\n",
              "      <td>95.1</td>\n",
              "      <td>113</td>\n",
              "      <td>3.77</td>\n",
              "      <td>1.513</td>\n",
              "      <td>16.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ford Pantera L</th>\n",
              "      <td>15.8</td>\n",
              "      <td>8</td>\n",
              "      <td>351.0</td>\n",
              "      <td>264</td>\n",
              "      <td>4.22</td>\n",
              "      <td>3.170</td>\n",
              "      <td>14.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ferrari Dino</th>\n",
              "      <td>19.7</td>\n",
              "      <td>6</td>\n",
              "      <td>145.0</td>\n",
              "      <td>175</td>\n",
              "      <td>3.62</td>\n",
              "      <td>2.770</td>\n",
              "      <td>15.50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maserati Bora</th>\n",
              "      <td>15.0</td>\n",
              "      <td>8</td>\n",
              "      <td>301.0</td>\n",
              "      <td>335</td>\n",
              "      <td>3.54</td>\n",
              "      <td>3.570</td>\n",
              "      <td>14.60</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Volvo 142E</th>\n",
              "      <td>21.4</td>\n",
              "      <td>4</td>\n",
              "      <td>121.0</td>\n",
              "      <td>109</td>\n",
              "      <td>4.11</td>\n",
              "      <td>2.780</td>\n",
              "      <td>18.60</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      mpg  cyl   disp   hp  drat  ...   qsec  vs  am  gear  carb\n",
              "Mazda RX4            21.0    6  160.0  110  3.90  ...  16.46   0   1     4     4\n",
              "Mazda RX4 Wag        21.0    6  160.0  110  3.90  ...  17.02   0   1     4     4\n",
              "Datsun 710           22.8    4  108.0   93  3.85  ...  18.61   1   1     4     1\n",
              "Hornet 4 Drive       21.4    6  258.0  110  3.08  ...  19.44   1   0     3     1\n",
              "Hornet Sportabout    18.7    8  360.0  175  3.15  ...  17.02   0   0     3     2\n",
              "Valiant              18.1    6  225.0  105  2.76  ...  20.22   1   0     3     1\n",
              "Duster 360           14.3    8  360.0  245  3.21  ...  15.84   0   0     3     4\n",
              "Merc 240D            24.4    4  146.7   62  3.69  ...  20.00   1   0     4     2\n",
              "Merc 230             22.8    4  140.8   95  3.92  ...  22.90   1   0     4     2\n",
              "Merc 280             19.2    6  167.6  123  3.92  ...  18.30   1   0     4     4\n",
              "Merc 280C            17.8    6  167.6  123  3.92  ...  18.90   1   0     4     4\n",
              "Merc 450SE           16.4    8  275.8  180  3.07  ...  17.40   0   0     3     3\n",
              "Merc 450SL           17.3    8  275.8  180  3.07  ...  17.60   0   0     3     3\n",
              "Merc 450SLC          15.2    8  275.8  180  3.07  ...  18.00   0   0     3     3\n",
              "Cadillac Fleetwood   10.4    8  472.0  205  2.93  ...  17.98   0   0     3     4\n",
              "Lincoln Continental  10.4    8  460.0  215  3.00  ...  17.82   0   0     3     4\n",
              "Chrysler Imperial    14.7    8  440.0  230  3.23  ...  17.42   0   0     3     4\n",
              "Fiat 128             32.4    4   78.7   66  4.08  ...  19.47   1   1     4     1\n",
              "Honda Civic          30.4    4   75.7   52  4.93  ...  18.52   1   1     4     2\n",
              "Toyota Corolla       33.9    4   71.1   65  4.22  ...  19.90   1   1     4     1\n",
              "Toyota Corona        21.5    4  120.1   97  3.70  ...  20.01   1   0     3     1\n",
              "Dodge Challenger     15.5    8  318.0  150  2.76  ...  16.87   0   0     3     2\n",
              "AMC Javelin          15.2    8  304.0  150  3.15  ...  17.30   0   0     3     2\n",
              "Camaro Z28           13.3    8  350.0  245  3.73  ...  15.41   0   0     3     4\n",
              "Pontiac Firebird     19.2    8  400.0  175  3.08  ...  17.05   0   0     3     2\n",
              "Fiat X1-9            27.3    4   79.0   66  4.08  ...  18.90   1   1     4     1\n",
              "Porsche 914-2        26.0    4  120.3   91  4.43  ...  16.70   0   1     5     2\n",
              "Lotus Europa         30.4    4   95.1  113  3.77  ...  16.90   1   1     5     2\n",
              "Ford Pantera L       15.8    8  351.0  264  4.22  ...  14.50   0   1     5     4\n",
              "Ferrari Dino         19.7    6  145.0  175  3.62  ...  15.50   0   1     5     6\n",
              "Maserati Bora        15.0    8  301.0  335  3.54  ...  14.60   0   1     5     8\n",
              "Volvo 142E           21.4    4  121.0  109  4.11  ...  18.60   1   1     4     2\n",
              "\n",
              "[32 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTiVlH1asZZs",
        "colab_type": "text"
      },
      "source": [
        "## Vamos utilizar a variável **hp**, horse power, para tentar prever a variável **mpg**, miles per galon."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lcZ5Js7ORJSO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "d6b89279-c859-4b9c-aadf-d0052ecb6972"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(mtcars['hp'], mtcars['mpg'], marker = 's', alpha = 0.6, color = 'darkblue')\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSVEWazAso6X",
        "colab_type": "text"
      },
      "source": [
        "## Fazendo as devidas divisões para validação"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8BVH9pAbByl6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "Xtreino, Xteste, Ytreino, Yteste = train_test_split(mtcars['hp'], mtcars['mpg'], test_size = 0.25)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIrgGaV7R9es",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Xtreino = Xtreino.values.reshape(-1, 1)\n",
        "Xteste = Xteste.values.reshape(-1, 1)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJ2Jb9ExsuDb",
        "colab_type": "text"
      },
      "source": [
        "## Utilizando o modelo de Regressão Linear"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0iIc9AO9RB3n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9ab3d2fa-b642-4554-8e0d-c1bfa2cd088c"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "reglin = LinearRegression()\n",
        "reglin.fit(Xtreino, Ytreino)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pP6SX0nvSiSy",
        "colab_type": "text"
      },
      "source": [
        "## Verificando o Erro Quadrático Médio nos dados de treinamento e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynVcrgycSfYK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "8e89b0a2-0c5f-4cad-a12b-16e288d758df"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "preditos_treino = reglin.predict(Xtreino)\n",
        "preditos_teste = reglin.predict(Xteste)\n",
        "\n",
        "eqm_treino = mean_squared_error(preditos_treino, Ytreino)\n",
        "eqm_teste = mean_squared_error(preditos_teste, Yteste)\n",
        "\n",
        "print('O Erro Quadrático Médio de TREINO foi: {:.2f}'.format(eqm_treino))\n",
        "print('O Erro Quadrático Médio de TESTE foi:  {:.2f}'.format(eqm_teste))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O Erro Quadrático Médio de TREINO foi: 15.79\n",
            "O Erro Quadrático Médio de TESTE foi:  10.15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXcSOaMys-WL",
        "colab_type": "text"
      },
      "source": [
        "## Plotando a reta de solução junto aos dados de treinamento e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZ8GE2aiSQPp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "x = np.arange(30, 350, 0.01)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGwooZo0Z1zw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "outputId": "f913702a-b3ab-4206-9ade-c4094bf70063"
      },
      "source": [
        "y = reglin.predict(x.reshape(-1, 1))\n",
        "plt.figure(figsize = (15, 7))\n",
        "\n",
        "plt.subplot(1, 2, 1) \n",
        "plt.plot(x, y, color = 'green', linewidth = 2, label = 'Regressão Linear')\n",
        "plt.scatter(Xtreino, Ytreino, marker = 's', alpha = 0.6, color = 'darkblue')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 2, 2) \n",
        "plt.plot(x, y, color = 'green', linewidth = 2, label = 'Regressão Linear')\n",
        "plt.scatter(Xteste, Yteste, marker = 's', alpha = 0.6, color = 'red')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iZU4qwA0tJFD",
        "colab_type": "text"
      },
      "source": [
        "## Testando um segundo modelo: **K Vizinhos Mais Próximos**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AA0JkepGaBCI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "b3e8b70e-29f5-41bf-e0d9-ae724298156c"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "knnreg = KNeighborsRegressor(n_neighbors = 9)\n",
        "knnreg.fit(Xtreino, Ytreino)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                    metric_params=None, n_jobs=None, n_neighbors=9, p=2,\n",
              "                    weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UyPh6i_oQZNu",
        "colab_type": "text"
      },
      "source": [
        "## Calculando o EQM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "707gkyXQbg7j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "63c111c1-6dad-40a1-e2d1-d332207cceb1"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "preditos_treino = knnreg.predict(Xtreino)\n",
        "preditos_teste = knnreg.predict(Xteste)\n",
        "\n",
        "eqm_treino = mean_squared_error(preditos_treino, Ytreino)\n",
        "eqm_teste = mean_squared_error(preditos_teste, Yteste)\n",
        "\n",
        "print('O Erro Quadrático Médio de TREINO foi: {:.2f}'.format(eqm_treino))\n",
        "print('O Erro Quadrático Médio de TESTE foi:  {:.2f}'.format(eqm_teste))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "O Erro Quadrático Médio de TREINO foi: 12.61\n",
            "O Erro Quadrático Médio de TESTE foi:  5.73\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Gpn8x-uQdNQ",
        "colab_type": "text"
      },
      "source": [
        "## Plotando as Soluções"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7gPAgwgEbkLo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "5443bdad-8d4f-49e5-a6f6-58915fe4725d"
      },
      "source": [
        "y = knnreg.predict(x.reshape(-1, 1))\n",
        "plt.figure(figsize = (15, 7))\n",
        "\n",
        "plt.subplot(1, 2, 1) \n",
        "plt.plot(x, y, color = 'green', linewidth = 2, label = 'KNN')\n",
        "plt.scatter(Xtreino, Ytreino, marker = 's', alpha = 0.6, color = 'darkblue')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 2, 2) \n",
        "plt.plot(x, y, color = 'green', linewidth = 2, label = 'KNN')\n",
        "plt.scatter(Xteste, Yteste, marker = 's', alpha = 0.6, color = 'red')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-hKYVH-dRnY",
        "colab_type": "text"
      },
      "source": [
        "## **Exercício 12**\n",
        "\n",
        "Dada uma divisão de teste e treinamento, encontre qual é o valor de 'K' que obtém a melhor predição nos dois casos explorados acima (classificação e regressão)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rpr5XqPidt91",
        "colab_type": "text"
      },
      "source": [
        "## **Exercício 13**\n",
        "\n",
        "Varie a divisão de treinamento e teste e encontre qual é o valor de 'K' que obtém a melhor predição nos dois casos explorados acima (classificação e regressão), nesse novo cenário. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6lqft7XQsYt",
        "colab_type": "text"
      },
      "source": [
        "## **Links Úteis**\n",
        "\n",
        "-[Documentação a respeito do plot de região](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/#example-9-customizing-the-plotting-style)"
      ]
    }
  ]
}