Oficina_1_ML.ipynb

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  "metadata": {
    "colab": {
      "name": "Oficina_1_ML.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM/0rga6Hi9FLa5Hs45Un9U",
      "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/gabifc/machine_learning_dell/blob/main/Oficina_1_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZG2MXpfQO2n"
      },
      "source": [
        "# Oficina 1 \n",
        "## Modelo de previsão de preço de carros\n",
        "**Aluna:** Gabrielle F Campos\n",
        "\n",
        "Estudando um pouco sobre Machine Learning, você aprendeu que utilizar dados históricos para realizar previsões pode ser interessante, quando se quer agregar valor em pesquisas ou nos negócios de uma empresa, não é mesmo?\n",
        "\n",
        "Os modelos de regressão em Machine Learning são, amplamente, utilizados para realizar essas previsões. Então, para que você possa exercitar seus conhecimentos nessa área, baixe o arquivo através do link “Regressao.ipynb”; carregue-o no ambiente do Jupyter notebook; siga as instruções contidas no arquivo; e execute como utilizar a biblioteca scikit-learn para construir um **modelo de previsão de preço de carros**, usando regressão linear múltipla. Para isso, use também um conjunto de dados com várias informações sobre carros usados em outro arquivo, que você pode baixar, acessando o link “car_data.csv”.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GyYlswPMSBDB"
      },
      "source": [
        "# Resposta"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TvX3YRTOH1Q"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2q_zIMX4XIBw"
      },
      "source": [
        "Método utilizado para carregar os dados do drive. Esta atividade foi realizada no google colab."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NxlyK2mlQdmo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b501901-e2b9-4bf6-c53e-d7e6788406f2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T0bliSo5XT0T"
      },
      "source": [
        "Lendo os dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_wSNdn9RgrJ"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/car_data.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "phuOJ6LoSwqk",
        "outputId": "a41d0c3b-556e-4bb1-8f97-bd47019fc706"
      },
      "source": [
        "display(df)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "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>nome_carro</th>\n",
              "      <th>ano</th>\n",
              "      <th>preco_venda</th>\n",
              "      <th>preco_atual</th>\n",
              "      <th>kms_rodados</th>\n",
              "      <th>tipo_combustivel</th>\n",
              "      <th>tipo_vendedor</th>\n",
              "      <th>tipo_transmissao</th>\n",
              "      <th>n_donos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>Gasolina</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>Gasolina</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>Gasolina</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>city</td>\n",
              "      <td>2016</td>\n",
              "      <td>9.50</td>\n",
              "      <td>11.60</td>\n",
              "      <td>33988</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>297</th>\n",
              "      <td>brio</td>\n",
              "      <td>2015</td>\n",
              "      <td>4.00</td>\n",
              "      <td>5.90</td>\n",
              "      <td>60000</td>\n",
              "      <td>Gasolina</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>298</th>\n",
              "      <td>city</td>\n",
              "      <td>2009</td>\n",
              "      <td>3.35</td>\n",
              "      <td>11.00</td>\n",
              "      <td>87934</td>\n",
              "      <td>Gasolina</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>city</td>\n",
              "      <td>2017</td>\n",
              "      <td>11.50</td>\n",
              "      <td>12.50</td>\n",
              "      <td>9000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>brio</td>\n",
              "      <td>2016</td>\n",
              "      <td>5.30</td>\n",
              "      <td>5.90</td>\n",
              "      <td>5464</td>\n",
              "      <td>Gasolina</td>\n",
              "      <td>Revendedor</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>301 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    nome_carro   ano  preco_venda  ...  tipo_vendedor  tipo_transmissao n_donos\n",
              "0         ritz  2014         3.35  ...     Revendedor            Manual       0\n",
              "1          sx4  2013         4.75  ...     Revendedor            Manual       0\n",
              "2         ciaz  2017         7.25  ...     Revendedor            Manual       0\n",
              "3      wagon r  2011         2.85  ...     Revendedor            Manual       0\n",
              "4        swift  2014         4.60  ...     Revendedor            Manual       0\n",
              "..         ...   ...          ...  ...            ...               ...     ...\n",
              "296       city  2016         9.50  ...     Revendedor            Manual       0\n",
              "297       brio  2015         4.00  ...     Revendedor            Manual       0\n",
              "298       city  2009         3.35  ...     Revendedor            Manual       0\n",
              "299       city  2017        11.50  ...     Revendedor            Manual       0\n",
              "300       brio  2016         5.30  ...     Revendedor            Manual       0\n",
              "\n",
              "[301 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ySA-vPYDVynE"
      },
      "source": [
        "# substituindo os dados que possuem textos por números."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "aagZBQHtSzpv",
        "outputId": "f6bc6c92-af6b-43c7-8d82-51833dba00bd"
      },
      "source": [
        "df['tipo_transmissao'].replace({'Manual': 0, 'Automatico': 1}, inplace=True)\n",
        "df['tipo_vendedor'].replace({'Revendedor': 0, 'Individual': 1}, inplace=True)\n",
        "df['tipo_combustivel'].replace({'Gasolina': 0, 'Diesel': 1, 'GasNatural': 2}, inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 6,
      "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>nome_carro</th>\n",
              "      <th>ano</th>\n",
              "      <th>preco_venda</th>\n",
              "      <th>preco_atual</th>\n",
              "      <th>kms_rodados</th>\n",
              "      <th>tipo_combustivel</th>\n",
              "      <th>tipo_vendedor</th>\n",
              "      <th>tipo_transmissao</th>\n",
              "      <th>n_donos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2013</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2011</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>2014</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  nome_carro   ano  preco_venda  ...  tipo_vendedor  tipo_transmissao  n_donos\n",
              "0       ritz  2014         3.35  ...              0                 0        0\n",
              "1        sx4  2013         4.75  ...              0                 0        0\n",
              "2       ciaz  2017         7.25  ...              0                 0        0\n",
              "3    wagon r  2011         2.85  ...              0                 0        0\n",
              "4      swift  2014         4.60  ...              0                 0        0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WuVlcL8hWvS0"
      },
      "source": [
        "## Separando a variável dependente e as variáveis independentes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZcg_Mf8WWS3"
      },
      "source": [
        "# escolhendo as variáveis independentes\n",
        "# nome do carro não entra, vamos trabalhar apenas com campos numéricos\n",
        "\n",
        "x = df[['ano', 'preco_atual', 'kms_rodados', 'tipo_combustivel', 'tipo_vendedor', 'tipo_transmissao', 'n_donos']].to_numpy()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cu7vu6f-YgbG"
      },
      "source": [
        "# variável dependente - depende de todas as outras para definir o preço de venda.\n",
        "\n",
        "y = df[['preco_venda']].to_numpy()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1q4Em8fBYrQ6"
      },
      "source": [
        "## Separar dados em treino e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GBDOcVFiYpYq"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gnnYHFsTZQsi",
        "outputId": "acfe7455-0637-4897-c900-e05460e6cf6a"
      },
      "source": [
        "# Vendo o número de registros que serão usados para o treino e teste\n",
        "print('Treino')\n",
        "print(\"x_train: \",x_train.shape)\n",
        "print(\"y_train: \",y_train.shape)\n",
        "\n",
        "print('\\nTeste')\n",
        "print(\"x_test: \",x_test.shape)\n",
        "print(\"y_test: \",y_test.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Treino\n",
            "x_train:  (240, 7)\n",
            "y_train:  (240, 1)\n",
            "\n",
            "Teste\n",
            "x_test:  (61, 7)\n",
            "y_test:  (61, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FSdgQjlWZ8e-"
      },
      "source": [
        "## Treinando o modelo\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3LsnPIbZVPh",
        "outputId": "a177f7d9-36f1-4b6e-8292-9b49542819c7"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# define um modelo de regressão\n",
        "model = LinearRegression()\n",
        "\n",
        "# treina o modelo com os dados de treino\n",
        "model.fit(x_train, y_train)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPwt_8jNaZI0"
      },
      "source": [
        "# Previsão dos Preços - usando os 20% de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uST4Fi-4aImt"
      },
      "source": [
        "# y_pred contém a previsão dos preços do modelo\n",
        "y_pred = model.predict(x_test)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYR4mTDjaj-i",
        "outputId": "21dd167d-d1b0-44e9-87e5-c396e5ebee92"
      },
      "source": [
        "# comparando a quantidade de dados usado para previsão com os dados teste com o que havia antes no y_test \n",
        "print(y_pred.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(61, 1)\n",
            "(61, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6z9MprrHcWkS"
      },
      "source": [
        "# Plotar o preço dos carros, previsto pelo modelo versus os dados de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Rsqmk8XabMZg",
        "outputId": "1c2f4654-376d-436e-c278-2548a0006d76"
      },
      "source": [
        "plt.plot(range(y_pred.shape[0]), y_pred,'r--')\n",
        "plt.plot(range(y_test.shape[0]), y_test,'g--')\n",
        "plt.legend(['preço previsto', 'preço real'])\n",
        "plt.ylabel('Preço')\n",
        "plt.xlabel('Índice')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "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": "0omMdCNTcoNv"
      },
      "source": [
        "**Análise dos dados:** em alguns casos o modelo tende a prever preços mais baixos do que o preço real de venda.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Verificar o coeficiente de determinação do modelo com medida r2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3993SVcxcb_G",
        "outputId": "9c168c3c-3896-4c5a-8849-1dcb9cf7f972"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "print('R2-score: ', r2_score(y_test, y_pred))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2-score:  0.7884320399281436\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6EoypI2sdTO-"
      },
      "source": [
        "**Análise dos dados:** Este modelo está prevendo corretamente 78% dos preços."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDqvuC6kdwTE"
      },
      "source": [
        "# **Para melhorar o R2 vou criar outro modelo com novas variáveis.**\n",
        "\n",
        "Separando variáveis dependente e indepentende"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYy6Lz9ldIeT"
      },
      "source": [
        "# variáveis independentes\n",
        "new_x = df[['ano', 'preco_atual', 'kms_rodados', 'tipo_combustivel']]\n",
        "\n",
        "# variável dependente\n",
        "new_y = df[['preco_venda']]"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSZwCokremPQ"
      },
      "source": [
        "Separando os dados de treino e de teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Gp1AuYVeKgm"
      },
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(new_x, new_y, test_size=0.2)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2m5s--RewkD",
        "outputId": "c3dd0991-3c45-4e0d-8dde-56582feb90a7"
      },
      "source": [
        "# Vendo o número de registros que serão usados para o treino e teste\n",
        "print('Treino')\n",
        "print(\"x_train: \",x_train.shape)\n",
        "print(\"y_train: \",y_train.shape)\n",
        "\n",
        "print('\\nTeste')\n",
        "print(\"x_test: \",x_test.shape)\n",
        "print(\"y_test: \",y_test.shape)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Treino\n",
            "x_train:  (240, 4)\n",
            "y_train:  (240, 1)\n",
            "\n",
            "Teste\n",
            "x_test:  (61, 4)\n",
            "y_test:  (61, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X3R0D32DfbPu"
      },
      "source": [
        "Treinando o modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RP1yjuOje26S",
        "outputId": "da8e181d-2b28-403f-b5b2-4ea33c927c62"
      },
      "source": [
        "# define um modelo de regressão\n",
        "new_model = LinearRegression()\n",
        "\n",
        "# treina o modelo com os dados de treino\n",
        "new_model.fit(x_train, y_train)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKZwjBC4fe4z"
      },
      "source": [
        "Realizando a previsão"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rPPduVA_fUYQ"
      },
      "source": [
        "# y_pred contém a previsão dos preços do nosso modelo\n",
        "y_pred = new_model.predict(x_test)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QW91o_Q1fjq0",
        "outputId": "88a40f31-0f42-4f7e-99d3-c124f2dd3ec7"
      },
      "source": [
        "print(y_pred.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(61, 1)\n",
            "(61, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K78FO3vSf3k8"
      },
      "source": [
        "Exibindo o gráfico com as previsões e os valores reais"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "4dWPwxopfqx7",
        "outputId": "e1d57ad7-bc37-4072-b3f4-a8c90ab67e4a"
      },
      "source": [
        "plt.plot(range(y_pred.shape[0]), y_pred,'r--')\n",
        "plt.plot(range(y_test.shape[0]), y_test,'g--')\n",
        "plt.legend(['preço previsto', 'preço real'])\n",
        "plt.ylabel('Preço')\n",
        "plt.xlabel('Índice')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "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": "BKfTtYRGf9yk"
      },
      "source": [
        "R2 Score de 85% de acertividade"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPLSvAY2fu7l",
        "outputId": "0f82bde5-18ed-4280-beaf-436380b6a251"
      },
      "source": [
        "print('R2-score: ', r2_score(y_test, y_pred))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2-score:  0.8537534916636657\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}