Hands_on_Regressao.ipynb

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      "name": "Hands_on_Regressão.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/luiz-star/Hands_on_Regressao/blob/main/Hands_on_Regressao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wIl-k3lr8Qiv"
      },
      "source": [
        "# **Hands-on Regressão**\n",
        "\n",
        "---\n",
        "\n",
        "Regressão com sklearn "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SN6weYKF8N8v"
      },
      "source": [
        "Vamos começar a analisar algoritmos de regressão de acordo com as métricas.\n",
        "\n",
        "Para isso, usaremos 3 regressores diferentes. Todas as implementações são provenivientes do sklearn. São eles: \n",
        "\n",
        "\n",
        "*   <a href = http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html> Regressão Linear </a>\n",
        "*   <a href = http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html>Support Vector Regressor (SVR)</a>\n",
        "*   <a href = http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html> Random Forest Regressor </a>\n",
        "\n",
        "Esses regressores são amplamente utilizados no dia-a-dia quando se trata de uma tarefa de regressão. Mais informações sobre esses modelos podem ser encontradas nos links para a documentação do sklearn. \n",
        "\n",
        "Vamos utilizar a base de dados **data-temp.csv** </a> que pode ser encontrada no Kaggle em: https://www.kaggle.com/ilayaraja97/temperature-data-seattle. Essa base descreve o problema de predizer a temperatura máxima dos próximos dias a partir das informações dos dias anteriores em Seatlle. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5wwHwTQ8N8v"
      },
      "source": [
        "### *Importando* bibliotecas\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_5G2bRaHU8i"
      },
      "source": [
        "import pandas as pd #biblioteca para manipulação de dados\n",
        "import numpy as np #biblioteca para utilizacao de vetores e matrizes\n",
        "import matplotlib.pyplot as plt #bibloteca para plotar graficos"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufc7rjN0Hbb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "796ba9ca-26e5-4795-e8cf-9a06b9bfd782"
      },
      "source": [
        "#liberando acesso do colab aos arquivos no drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9aBPfZwHjwK"
      },
      "source": [
        "### Carregamento dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FTLPdcSaHsIo"
      },
      "source": [
        "#lendo o csv que contem a base de dados e armazanando em um df\n",
        "df = pd.read_csv('/content/gdrive/My Drive/IGTI/Aulas Práticas/data-temps.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yw9wSFNd8N8w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "0e81fd03-85c9-476e-fc7b-774b9daaaeb1"
      },
      "source": [
        "#imprimindo as 5 primeiras linhas do df para confirmação\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
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              "        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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>week</th>\n",
              "      <th>temp_2</th>\n",
              "      <th>temp_1</th>\n",
              "      <th>average</th>\n",
              "      <th>actual</th>\n",
              "      <th>friend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Fri</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45.6</td>\n",
              "      <td>45</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Sat</td>\n",
              "      <td>44</td>\n",
              "      <td>45</td>\n",
              "      <td>45.7</td>\n",
              "      <td>44</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sun</td>\n",
              "      <td>45</td>\n",
              "      <td>44</td>\n",
              "      <td>45.8</td>\n",
              "      <td>41</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Mon</td>\n",
              "      <td>44</td>\n",
              "      <td>41</td>\n",
              "      <td>45.9</td>\n",
              "      <td>40</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Tues</td>\n",
              "      <td>41</td>\n",
              "      <td>40</td>\n",
              "      <td>46.0</td>\n",
              "      <td>44</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  month  day  week  temp_2  temp_1  average  actual  friend\n",
              "0  2016      1    1   Fri      45      45     45.6      45      29\n",
              "1  2016      1    2   Sat      44      45     45.7      44      61\n",
              "2  2016      1    3   Sun      45      44     45.8      41      56\n",
              "3  2016      1    4   Mon      44      41     45.9      40      53\n",
              "4  2016      1    5  Tues      41      40     46.0      44      41"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFaZnXnAIonA"
      },
      "source": [
        "*   year: ano de registro da temperatura\n",
        "*   month: mês de registro da temperatura\n",
        "*   day: dia de registro da temperatura\n",
        "*   week: dia da semana em que a temperatura foi registrada\n",
        "*   temp2: temperatura máxima obtida 2 dias antes do dia atual\n",
        "*   temp1: temperatura máxima obtida 1 dia antes do dia atual\n",
        "*   average: máxima média histórica\n",
        "*   actual: temperatura máxima real\n",
        "*   friend: previsão de um oráculo sobre a temperatura máxima\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_lxVAD-8N86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bb64f602-e6eb-4e9f-9f21-48ba81c91fb0"
      },
      "source": [
        "# Verificando o numero de amostras (linhas) e features (colunas) do dataset. \n",
        "print('Amostras e Features:', df.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Amostras e Features: (348, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MILEATwd8N9A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "6ebc15c9-fc32-402f-e967-835d6bfd11cb"
      },
      "source": [
        "# Verificando quais são os tipos das features\n",
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['year', 'month', 'day', 'week', 'temp_2', 'temp_1', 'average', 'actual',\n",
              "       'friend'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b2aq9YGyJ_YN"
      },
      "source": [
        "### Pré processamento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WD1KwfP08N9E"
      },
      "source": [
        "Muitos modelos de regressão não são capazes de lidar com dados categóricos. Nesse caso, é preciso pré-processar alguns dados para que eles sejam entendidos pelo modelo. \n",
        "\n",
        "No nosso problema, a feature week é categórica. Dois dos modelos que vamos usar não lidam com esse tipo de dado. Nesse caso, precisamos transformar o atributo categórico **week** em um atributo numérico que não forneça pesos distintos para dias das semana distintos.\n",
        "\n",
        "O processo que vamos utilizar se chama binarização, ou ainda One-Hot Encoding. \n",
        "O pandas possui a função <a href = https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html > get_dummies()</a> que faz essa transformação diretamente. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DGwAHyx78N9H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fc921ae0-92ad-4150-daf8-887910b78cc3"
      },
      "source": [
        "# binarizando a feature week\n",
        "df = pd.get_dummies(df)\n",
        "\n",
        "# visualizando o resultado\n",
        "df.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>temp_2</th>\n",
              "      <th>temp_1</th>\n",
              "      <th>average</th>\n",
              "      <th>actual</th>\n",
              "      <th>friend</th>\n",
              "      <th>week_Fri</th>\n",
              "      <th>week_Mon</th>\n",
              "      <th>week_Sat</th>\n",
              "      <th>week_Sun</th>\n",
              "      <th>week_Thurs</th>\n",
              "      <th>week_Tues</th>\n",
              "      <th>week_Wed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>45</td>\n",
              "      <td>45</td>\n",
              "      <td>45.6</td>\n",
              "      <td>45</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>44</td>\n",
              "      <td>45</td>\n",
              "      <td>45.7</td>\n",
              "      <td>44</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>45</td>\n",
              "      <td>44</td>\n",
              "      <td>45.8</td>\n",
              "      <td>41</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>44</td>\n",
              "      <td>41</td>\n",
              "      <td>45.9</td>\n",
              "      <td>40</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>2016</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>41</td>\n",
              "      <td>40</td>\n",
              "      <td>46.0</td>\n",
              "      <td>44</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  month  day  temp_2  ...  week_Sun  week_Thurs  week_Tues  week_Wed\n",
              "0  2016      1    1      45  ...         0           0          0         0\n",
              "1  2016      1    2      44  ...         0           0          0         0\n",
              "2  2016      1    3      45  ...         1           0          0         0\n",
              "3  2016      1    4      44  ...         0           0          0         0\n",
              "4  2016      1    5      41  ...         0           0          1         0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_J94oUYW8N9Q"
      },
      "source": [
        "Uma outra etapa importante do pré-processamento consiste na avaliação de dados faltantes. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fOQWr4kR8N9R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "020fda12-3081-4d2d-a2ee-31794dfa1b7d"
      },
      "source": [
        "# Analisando o resumo da base\n",
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>temp_2</th>\n",
              "      <th>temp_1</th>\n",
              "      <th>average</th>\n",
              "      <th>actual</th>\n",
              "      <th>friend</th>\n",
              "      <th>week_Fri</th>\n",
              "      <th>week_Mon</th>\n",
              "      <th>week_Sat</th>\n",
              "      <th>week_Sun</th>\n",
              "      <th>week_Thurs</th>\n",
              "      <th>week_Tues</th>\n",
              "      <th>week_Wed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>348.0</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>348.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2016.0</td>\n",
              "      <td>6.477011</td>\n",
              "      <td>15.514368</td>\n",
              "      <td>62.652299</td>\n",
              "      <td>62.701149</td>\n",
              "      <td>59.760632</td>\n",
              "      <td>62.543103</td>\n",
              "      <td>60.034483</td>\n",
              "      <td>0.143678</td>\n",
              "      <td>0.140805</td>\n",
              "      <td>0.143678</td>\n",
              "      <td>0.140805</td>\n",
              "      <td>0.140805</td>\n",
              "      <td>0.149425</td>\n",
              "      <td>0.140805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.498380</td>\n",
              "      <td>8.772982</td>\n",
              "      <td>12.165398</td>\n",
              "      <td>12.120542</td>\n",
              "      <td>10.527306</td>\n",
              "      <td>11.794146</td>\n",
              "      <td>15.626179</td>\n",
              "      <td>0.351268</td>\n",
              "      <td>0.348321</td>\n",
              "      <td>0.351268</td>\n",
              "      <td>0.348321</td>\n",
              "      <td>0.348321</td>\n",
              "      <td>0.357021</td>\n",
              "      <td>0.348321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2016.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>45.100000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2016.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>49.975000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>47.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2016.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>58.200000</td>\n",
              "      <td>62.500000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>69.025000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2016.0</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>77.400000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         year       month         day  ...  week_Thurs   week_Tues    week_Wed\n",
              "count   348.0  348.000000  348.000000  ...  348.000000  348.000000  348.000000\n",
              "mean   2016.0    6.477011   15.514368  ...    0.140805    0.149425    0.140805\n",
              "std       0.0    3.498380    8.772982  ...    0.348321    0.357021    0.348321\n",
              "min    2016.0    1.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%    2016.0    3.000000    8.000000  ...    0.000000    0.000000    0.000000\n",
              "50%    2016.0    6.000000   15.000000  ...    0.000000    0.000000    0.000000\n",
              "75%    2016.0   10.000000   23.000000  ...    0.000000    0.000000    0.000000\n",
              "max    2016.0   12.000000   31.000000  ...    1.000000    1.000000    1.000000\n",
              "\n",
              "[8 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AxL9q7KJMSZO"
      },
      "source": [
        "Particularmente, quando estamos falando de Regressão, é interessante avaliar a correlação entre os atributos da base de dados. Em geral, atributos com alta correlação com a variável que queremos prever são atributos que influenciam bastante no comportamento do modelo. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3WK7AXTi8N9V",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e1d855d6-885e-498f-a351-cf005de1f245"
      },
      "source": [
        "#plotando gráficos de correlação das features\n",
        "for i in df.columns[1:]:\n",
        "    if(i != 'actual'):\n",
        "        df.plot(x = 'actual', y = i, style='o') \n",
        "        plt.xlabel('Actual Temperature')  \n",
        "        plt.ylabel(i) \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": [],
            "needs_background": "light"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LaWxs5KCDDkISVVVV3H///dTV1TFs2DDGjRvHPffcAyTfFH7yySeBJETuuusugN0uOndXKc8svrrNdIWk1pZExPQea5WZWYG5U0ax9PLj2bDgZJZefnyP3C00c+ZMXn31VWprazn44IMZMmQIM2fOpKqqittuu4358+czceJEamtree655xg8eDD33nsvX/3qV5k0aRKTJ09m2bJlu9V56aWXMmXKFD7zmc+wc+fOzPV+6EMfoqqqiiOPPLK1rLa2ls2bNzNp0iQgOcjfeuutTJo0iSOPPJL7778fSG4/vemmm6iurmbTpp67hVbJkyKLmFH6IbAuIq6VtB9wN7AqIr7RY60pUk1NTRRzz66Z9U3PPvssH//4x3u7Gfu0rNdYUkNE1LSdt5RTQ58FqiVdAfxvYElvhICZmfWsYkYfPbpg8gbge8BS4L8kHR0Rj5ercWZm/dW0adNa7/Zpcfvtt1Nd3fcGZCjmrqF/aDP9O5LnEfwDyS2jx/d0o8zM+rsVK1b0dhOKVszoo3+0NxpiZvkSER6BtEyKvfbbopS7hg6WdKuk/0ynj5B0XontMzNjyJAhvP766yUfsKxzLQ+m2dN3Gdoq5QtltwE/BFq+NrcO+Alwawl1mJkxevRoNm7cSGNjY283ZZ/U8qjKYpUSBAdGxN3pXUNExA5JnQ1EZ2bWTss3e61vKOX20XcljSQdU0jSdGBLWVplZmZ7TSmfCC4FFgHjJS0FqoDTytIqMzPba4oOgoh4XNIngMNJhpxeGxFNZWuZmZntFaXcNfQB4HLgkoh4Ghgr6dNla5mZme0VpVwj+CHJCKO16fQmIPvpC2Zm1m+UEgTjI+LvgSaAiHiPIp5KJmm2pLWS1ku6fA/z/amkkNRuQCQzMyufUoJgu6RKdt01NB54f08LSKoAbgI+RTIsxXxJR2TMNxS4GOg/38k2M9tHlBIEXwd+DoxJn0PwMPDXnSxzDLA+Il6IiO3AXcApGfP9DfB3wLYS2mNmZj2glCA4B3gAuBr4MVATEb/qZJlRwMsF0xvTslbp6KZjIuKBPVUk6QJJ9ZLq/W1EM7OeU0oQ3AoMAeYANwLfk3Rxd1YuaQBwPfDlzuaNiFsioiYiaqqqqrqzWjMzK1DK9wiWSPov4A+APwL+EjiS5BkFHdkEjCmYHp2WtRgKHAX8Kh2F8PeARZLmRIQfQWZmthcUHQSSHgb2Bx4DHgX+ICI2d7LYSmCCpHEkAXAm8Gctv4yILcCBBev4FfAVh4CZ2d5Tyqmhp0i+R3AUMBE4Kr2LqEMRsQP4PLAYeBa4OyKekXS1pDldbLOZmfWgoh9e37pAcqvnucBXgN+LiP3K0K498sPrzcxK19HD60s5NfR5YCYwFXgR+BeSU0RmZtaPlTL66BCSO3wa0lM+Zma2DyjlrqHrytkQMzPrHaVcLDYzs32Qg8DMLOccBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc45CMzMcs5BYGaWcw4CM7OccxCYmeWcg8DMLOccBGZmOVf2IJA0W9JaSeslXZ7x+0slrZH0lKSHJX243G0yM7NdyhoEkiqAm4BPAUcA8yUd0Wa2VUBNREwE7gX+vpxtMjOz3ZX7E8ExwPqIeCEitgN3AacUzhARSyLivXRyOTC6zG0yM7MC5Q6CUcDLBdMb07KOnAf8Z9YvJF0gqV5SfWNjYw820cws3/rMxWJJZwM1wMKs30fELRFRExE1VVVVe7dxZmb7sIFlrn8TMKZgenRathtJJwBXAp+IiPfL3CYzMytQ7k8EK4EJksZJGgycCSwqnEHSFOB7wJyI2Fzm9piZWRtlDYKI2AF8HlgMPAvcHRHPSLpa0px0toXAAcA9kp6QtKiD6szMrAzKfWqIiHgQeLBN2dcKfj6h3G0wM7OO9ZmLxWZm1jscBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHARmZjnnIDAzyzkHgZlZzjkIzMxyzkFgZpZzDgIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8s5B4GZWc45CMzMcs5BYGaWcw4CM7OccxCYmeWcg8DMLOccBGZmOecgMDPLOQeBmVnOOQjMzHLOQWBmlnMOAjOznBtY7hVImg3cAFQAP4iIBW1+vx/wI2Aq8DpwRkS82NPtuOq+1dy54mWaI6iQmD9tDNfMrea+VZtYuHgtr7y5lUOHV3LZiYczd8qobtX70DP/zWtvb2+d5+Chg/noQQew9Pk3Wsvqxo/gjvNrOev7j7Urn1dzWLs23bTk1/x687ut8004aH8euvQ4Zl3/q3blQLuyaR8Zmdn/j135INuao3XeIRVi8MABvPV+c2vZsP0qqB79wcz2j7v8AXYtDUr/b1s2dL+KdnU+9c3ZfPSKB9hRMPNAQXMUv/zYyx+grYOHDm73+m9+e3u7OjcsODlz+SEVKuo1eeqbszP7Xzi9Jy8uOJmJX/95u3rf297c7jXZkVHpiwtOZtrfPtSur8MqB7Xb/oXTnS3/+jvb263/A4OzX/+s5be819Tu9SucLlx/sfsP0K5sxZWzMt//Hb3Xs/Z1oOh9LWv5+t+80W5dNR8ekXlMKXb9HR1/unus2hNFFPu27ULlUgWwDpgFbARWAvMjYk3BPJ8DJkbEX0o6E/iTiDhjT/XW1NREfX190e246r7V/Nvyl9qV140fweMvbWFr0643eOWgCq49tbqoF7ijeos1rM3BrSMdHVw6OkAUq5SDlllfU8r7N2tfHzRAIGgqCKmO6jx46GDe2ta82/IDBDszZm5bXjmogj+dOoqfNmzqdP0dHX/uW7WJK362usvHqtb+SQ0RUdOuzUXX0DXHAOsj4oWI2A7cBZzSZp5TgH9Nf74X+KQk0YPuXPFyZvnS59/Y7YUF2NrUzMLFa7tVb7GKCQHo+M3enRDYU71m/UEp79+sfb1pZ+x2EN5Tna+9vb3d8lkhkFW+tamZO1e8XNT6Ozr+LFy8tlvHqs6UOwhGAYVHy41pWeY8EbED2AKMbFuRpAsk1Uuqb2xsLKkRzSV+6nnlza1lqdfM8qmUY0XW8aejY1Kxx6rO9JuLxRFxS0TURERNVVVVSctWlPgB49DhlWWp18zyqZRjRdbxp6NjUrHHqs6UOwg2AWMKpkenZZnzSBoIfJDkonGPmT9tTGZ53fgRVA6q2K2sclBF60WcrtZbrGH7VXQ+E7suwLY1sJs55Biz/qyU92/Wvj5ogBhUsXstHdV58NDB7ZYf0MHMbcsrB1Uwf9qYotbf0fHnshMP79axqjPlDoKVwARJ4yQNBs4EFrWZZxFwTvrzacAj0cNXsK+ZW83Z0w9rTeUKibOnH8Yd59dy7anVjBpeiYBRwytLuvjSUb0tdzm0OHjoYOrGj9itrG78CJ765uzM8m+fMXm3Nv3jGZNb7wZqMeGg/Vl/7cmZ5VllWe3csOBkhrR5Iw6pULuAGrZfRWY7X1xwcrsdR7TfmUT70Bu2XwUvLji5XZgNVGnLZ8l6/bPq7Gj5Yl+TjvpfrBcXnJxZb9Zr0tHyWX3N2v6lLJ+1/o76n7V81uvX0fqL3X+yyjYsyH7/F7uvL5w3iYWnTSpqX1tx5ax2y19/+uTMdV1/+uR2x5Rr5lYXtf6Ojj9zp4zq1rGqM2W9awhA0knAt0luH/2XiPhbSVcD9RGxSNIQ4HZgCvAGcGZEvLCnOku9a8jMzDq+a6js3yOIiAeBB9uUfa3g523AvHK3w8zMsvWbi8VmZlYeDgIzs5xzEJiZ5ZyDwMws58p+11A5SGoEftPb7SjCgcBve7sRPWxf7BPsm/1yn/qPvdWvD0dEu2/k9ssg6C8k1WfdqtWf7Yt9gn2zX+5T/9Hb/fKpITOznHMQmJnlnIOgvG7p7QaUwb7YJ9g3++U+9R+92i9fIzAzyzl/IjAzyzkHgZlZzjkIepCkCkmrJP1HOj1O0gpJ6yX9JB2Ku1+R9KKk1ZKekFSflo2Q9JCkX6f/f6i321kKScMl3SvpOUnPSqrdB/p0eLqNWv69JemSfaBfX5L0jKSnJd0paUh/368kXZz25xlJl6RlvbqdHAQ962Lg2YLpvwP+MSI+CvwOOK9XWtV9fxQRkwvuc74ceDgiJgAPp9P9yQ3AzyPiY8Akkm3Wr/sUEWvTbTQZmAq8B/w7/bhfkkYBXwRqIuIokqHsz6Qf71eSjgLOJ3me+yTg05I+Si9vJwdBD5E0GjgZ+EE6LeB44N50ln8F5vZO63rcKST9gX7WL0kfBI4FbgWIiO0R8Sb9uE8ZPgk8HxG/of/3ayBQmT698APAq/Tv/erjwIqIeC99Rvv/AU6ll7eTg6DnfBv4a2BnOj0SeDPd2AAbgZ55nNDeFcAvJDVIuiAtOzgiXk1//m/g4N5pWpeMAxqBH6an8X4gaX/6d5/aOhO4M/253/YrIjYB1wEvkQTAFqCB/r1fPQ3MlDRS0geAk0ge1dur28lB0AMkfRrYHBENvd2WMvjDiDga+BRwkaRjC3+ZPla0P92DPBA4GvjniJgCvEubj+H9sE+t0vPlc4B72v6uv/UrPU9+Ckl4HwrsD8zu1UZ1U0Q8S3Jq6xfAz4EngOY28+z17eQg6Bl1wBxJLwJ3kXx0vQEYnn6kBRgNbOqd5nVd+lcZEbGZ5JzzMcBrkg4BSP/f3HstLNlGYGNErEin7yUJhv7cp0KfAh6PiNfS6f7crxOADRHRGBFNwM9I9rV+vV9FxK0RMTUijiW5xrGOXt5ODoIeEBFXRMToiBhL8rH8kYg4C1gCnJbOdg5wfy81sUsk7S9paMvPwB+TfLRdRNIf6Gf9ioj/Bl6WdHha9ElgDf24T23MZ9dpIejf/XoJmC7pA+k1t5Zt1d/3q4PS/w8juT7wY3p5O/mbxT1M0nHAVyLi05I+QvIJYQSwCjg7It7vzfaVIm3/v6eTA4EfR8TfShoJ3A0cRjIc+OkR8UYvNbNkkiaTXNQfDLwA/DnJH0X9tk/QGtYvAR+JiC1pWX/fVt8EzgB2kOxDf0FyTaA/71ePklxDbAIujYiHe3s7OQjMzHLOp4bMzHLOQWBmlnMOAjOznHMQmJnlnIPAzCznHATWJ0maKykkfayIeS9Jv67f1XWdK+m7bcr+vGAkz+0FI7Au6Op6yqm7r4Hlm28ftT5J0k9IhhV4JCK+3sm8L5KMUPnbLq7r3HT5z5ej/p6QfqFKEbGzg9+/SIltlDSwYMweyzF/IrA+R9IBwB+SDC98ZkF5haTr0rHcn5L0BUlfJAmMJZKWpPO9U7DMaZJuS3/+H+k49qsk/VJSyQN7SbpM0sp0/d9My8YqebbBbZLWSbpD0gmSlqbjyx+TzvcNSbdLeiwtP7+IetdK+hHJN7rHSPpnSfXpWPYt85XyGtwm6WZJK4C/lzRe0s/TQQUfLeYTmO17BnY+i9ledwrJ8wLWSXpd0tR0QL8LgLHA5IjYIWlERLwh6VKSZyZ09tfw/wWmR0RI+guS0WK/XGyjJP0xMIFkvCUBi9JB+F4CPgrMAz4LrAT+jCTM5gD/i13DCk8EppMMoLZK0gPAUXuodwJwTkQsT9twZdrnCuBhSRMj4jslvAaQjM8zIyKaJT0M/GVE/FrSNOCfSMbKshxxEFhfNJ9k0D5IhhKYTzL88AnAzS2nM7rwFfzRwE/SQb0GAxtKXP6P03+r0ukDSA7UL5EMjrYaQNIzJA8ZCUmrScKrxf0RsRXYmv71fgxJYHRU729aQiB1upLhwAcChwBHAE+V2I970hA4AJgB3JOceQJgvxLrsn2Ag8D6FEkjSP4irZYUJE+lCkmXlVBN4YWvIQU/3whcHxGL0jGhvlFq84BrI+J7bdo8Figc62ZnwfROdt/P2l6Ui07qfbdgehzwFeAPIuJ36emewv61rbdF23la6hxAMrb/5A7qsJzwNQLra04Dbo+ID0fE2IgYQ/KX+0zgIeBCpUMQp6EB8DYwtKCO1yR9XNIA4E8Kyj/IriGLz6F0i4HPpn9JI2mU0pEkS3CKkufujryYZbIAAADXSURBVASOIzmNVGy9w0gO4lvS6xufKvhdsa9Bq4h4C9ggaV66XkmaVGJ/bB/gILC+Zj67Rjxt8dO0/Ackp0uekvQkyXl4gFuAn7dcKCV50Mx/AMtInmzV4hskp0EagJLvAIqIX5AMGfxYesrnXnY/+BbjKZJhlJcDfxMRrxRbb0Q8SXL66Ll0/qUFvy72NWjrLOC89PV8huT6jOWMbx8120skfQN4JyKu6+22mBXyJwIzs5zzJwIzs5zzJwIzs5xzEJiZ5ZyDwMws5xwEZmY55yAwM8u5/w+y6VXu1fZ2TgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mH3u8WT8N9f"
      },
      "source": [
        "Os modelos implementados no sklearn recebem como entrada para a modelagam um ou mais arrays. Dessa forma, precisamos modificar o df original para que seja possível a modelagem correta. \n",
        "\n",
        "Para isso, vamos separar o label das amotras, armazenar o nome das featues já que os arrays não fazem isso e depois retirar a coluna de labels do df original. Em seguida, vamos converter o df para array usando o numpy!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZ3jLCuEOfbJ"
      },
      "source": [
        "# armazenando os labels em um array\n",
        "labels = np.array(df['actual'])\n",
        "\n",
        "# salvando a ordem das features\n",
        "feature_list = list(df.columns)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q5wIxGj68N9g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "32feb1a4-d96a-4a43-d346-a18adf58bc03"
      },
      "source": [
        "# removendo a coluna de labels do df original\n",
        "df = df.drop('actual', axis = 1)\n",
        "\n",
        "# check\n",
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['year', 'month', 'day', 'temp_2', 'temp_1', 'average', 'friend',\n",
              "       'week_Fri', 'week_Mon', 'week_Sat', 'week_Sun', 'week_Thurs',\n",
              "       'week_Tues', 'week_Wed'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H24By9Vf8N9k"
      },
      "source": [
        "# convertendo df para array\n",
        "data = np.array(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WY6_Mcme8N9o"
      },
      "source": [
        "Agora estamos quase prontos para a modelagem em si!\n",
        "\n",
        "Precisamos apenas separar uma parte dos nossos dados para que seja possível avaliar os modelos que vamos treinar. O sklearn tem uma função para isso: <a href = http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html> train_test_split<a>."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ews_1IMu8N9p"
      },
      "source": [
        "# importar train_test_split do scikitlearn \n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# aplicando a funcao train_test_split para separar os conjuntos de treino e \n",
        "# teste segundo uma porcentagem de separação definida. \n",
        "train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size = 0.25, random_state = 42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYsqiOG18N9v"
      },
      "source": [
        "### Baseline: Comparando com a média!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JpkQtbyW8N9w"
      },
      "source": [
        "Como falamos durante as aulas teóricas, precisamos criar uma base de comparação para os modelos que queremos avaliar. \n",
        "\n",
        "Várias medidas de erro apresentadas usam como baseline, isto é, uma linha de base, o modelo médio. Para o nosso problema, o baseline utilizado é a média máxima histórica. Podemos pensar que o modelo baseline é equivalente a prever a média máxima histórica para todos os dias do nosso conjunto de teste. \n",
        "\n",
        "Como essa média máxima é um atributo do nosso dataset, podemos apenas copiar a resposta desse atributo como resultado do modelo aplicado ao nosso conjunto de teste. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTl5nyp9SHff"
      },
      "source": [
        "# criando baseline\n",
        "baseline_preds = test_data[:, feature_list.index('average')]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WeZ5topCSLxn"
      },
      "source": [
        "O sklearn tem várias métricas implementadas! :D\n",
        "\n",
        "Vamos testar essas metricas? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Aobv8rd8N9x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "321483de-abf8-4384-be50-4e21b8e5075c"
      },
      "source": [
        "# importar biblioteca para calculo de métricas\n",
        "from sklearn import metrics  \n",
        "\n",
        "# calculando medidas de erro\n",
        "# essas medidas são calculadas a partir da comparação com o valor real do nosso conjunto de teste\n",
        "print('R2:', metrics.r2_score(test_labels, baseline_preds)) \n",
        "print('MSE:', metrics.mean_squared_error(test_labels, baseline_preds))  \n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(test_labels, baseline_preds))) \n",
        "print('RMSLE:', np.sqrt(metrics.mean_squared_log_error(test_labels, baseline_preds))) \n",
        "print('MAE:', metrics.mean_absolute_error(test_labels, baseline_preds))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2: 0.6081589797570233\n",
            "MSE: 54.4928735632184\n",
            "RMSE: 7.381928851134939\n",
            "RMSLE: 0.11221618980253842\n",
            "MAE: 5.05977011494253\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AAwsSBcdVoku"
      },
      "source": [
        "Algumas das medidas faladas em aula não estão implementadas no metrics do sklearn. \n",
        "\n",
        "Podemos criar a função que calula essas metricas! :)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKXqYbwLUkP_"
      },
      "source": [
        "#criando a funcao para calculo do MAPE\n",
        "def mean_absolute_percentage_error(y_true, y_pred): \n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HuYS-dnV2Fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9f3cf614-21bf-4aa8-a59f-98c2797c3c1d"
      },
      "source": [
        "print('MAPE:', mean_absolute_percentage_error(test_labels, baseline_preds))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "MAPE: 7.704879665543076\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NH3QMlf8N91"
      },
      "source": [
        "Agora que avaliamos nosso baseline e identificamos o erro desse modelo, podemos criar outros modelos de regressão e comparar os resultados encontrados!\n",
        "\n",
        "Dica: Se não conseguirmos um erro menor do que o baseline, talvez precisemos repensar nossa abordagem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nstvyr-hdG1S"
      },
      "source": [
        "Vamos dar uma olhada como ficou o nosso baseline em relação as amostras de teste?\n",
        "\n",
        "*Não se preocupe em compreender o que foi realizado na célula a seguir, todas as manipulações foram realizadas no único intuito de gerar o gráfico.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqGMncqndAaJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "d18d0bbf-5eb8-456d-a409-2cd0d198ff70"
      },
      "source": [
        "# importar datetime para criação de objetos do tipo data para plotagem\n",
        "import datetime\n",
        "\n",
        "# datas do conjunto de treino\n",
        "months = data[:, feature_list.index('month')]\n",
        "days = data[:, feature_list.index('day')]\n",
        "years = data[:, feature_list.index('year')]\n",
        "\n",
        "# criar lista contendo os valores de dia, mês e ano unificados em formato de data\n",
        "dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
        "dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]\n",
        "\n",
        "# criar dataframe com valores de temperatura máxima real e datas de registro\n",
        "true_data = pd.DataFrame(data = {'date': dates, 'actual': labels})\n",
        "\n",
        "# datas do conjunto de teste\n",
        "months = test_data[:, feature_list.index('month')]\n",
        "days = test_data[:, feature_list.index('day')]\n",
        "years = test_data[:, feature_list.index('year')]\n",
        "\n",
        "# criar lista contendo os valores de dia, mês e ano unificados em formato de data\n",
        "test_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n",
        "\n",
        "# converter lista gerada anteriormente em objetos datetime\n",
        "test_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]\n",
        "\n",
        "\n",
        "# criar dataframe com valores previstos e datas de registro\n",
        "predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': baseline_preds})\n",
        "\n",
        "# plotar os valores de temperatura reais\n",
        "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'real')\n",
        "\n",
        "# plotar os valores de temperatura previstos\n",
        "plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'previsto')\n",
        "\n",
        "plt.xticks(rotation = '60'); \n",
        "plt.legend()\n",
        "\n",
        "# definição dos nomes dos eixos\n",
        "plt.xlabel('Data');\n",
        "plt.ylabel('Temperatura Máxima (F)'); \n",
        "plt.title('Valores Reais e Previstos');"
      ],
      "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": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_6LHRU0WoaJ"
      },
      "source": [
        "# Regressão Linear"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uem0TvVMWw-X"
      },
      "source": [
        "Vamos aplicar o primeiro modelo de regressão: a regressão linear!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECIYz7rg8N92"
      },
      "source": [
        "# importar o modelo de regressão linear\n",
        "from sklearn.linear_model import LinearRegression \n",
        "\n",
        "# treinando o modelo no conjunto de dados de treino\n",
        "regression = LinearRegression().fit(train_data, train_labels);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-2XWEDe8N98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "ff14bf44-392c-4daf-fb91-ceed2421e2be"
      },
      "source": [
        "# aplicando o modelo treinado para a previsão da temperatura \n",
        "#em todo o conjunto de teste\n",
        "predictions1_labels = regression.predict(test_data)\n",
        "\n",
        "# Exibindo dataframe com valores 10 reais e suas respectivas previsões\n",
        "p = pd.DataFrame({'Real': test_labels, 'Previsto': predictions1_labels})  \n",
        "p.head(10)"
      ],
      "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>Real</th>\n",
              "      <th>Previsto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66</td>\n",
              "      <td>68.053603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>61</td>\n",
              "      <td>62.714241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>52</td>\n",
              "      <td>48.931776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>66</td>\n",
              "      <td>61.809117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70</td>\n",
              "      <td>67.878007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>82</td>\n",
              "      <td>68.909162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>85</td>\n",
              "      <td>79.026256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>84</td>\n",
              "      <td>80.498872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>65</td>\n",
              "      <td>60.387689</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>92</td>\n",
              "      <td>73.925827</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Real   Previsto\n",
              "0    66  68.053603\n",
              "1    61  62.714241\n",
              "2    52  48.931776\n",
              "3    66  61.809117\n",
              "4    70  67.878007\n",
              "5    82  68.909162\n",
              "6    85  79.026256\n",
              "7    84  80.498872\n",
              "8    65  60.387689\n",
              "9    92  73.925827"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_rj1T77X6Fy"
      },
      "source": [
        "Agora que criamos o modelo de regressão linear e aplicamos o modelo criado ao conjunto de teste, podemos então avaliar o modelo gerado. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UavyKXJ88N-A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "d063c14b-a7b0-4592-d077-b5c3c82ecabb"
      },
      "source": [
        "#avaliando o modelo\n",
        "print('R2:', metrics.r2_score(test_labels, predictions1_labels)) \n",
        "print('MSE:', metrics.mean_squared_error(test_labels, predictions1_labels))  \n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(test_labels, predictions1_labels))) \n",
        "print('RMSLE:', np.sqrt(metrics.mean_squared_log_error(test_labels, predictions1_labels))) \n",
        "print('MAE:', metrics.mean_absolute_error(test_labels, predictions1_labels))\n",
        "print('MAPE:', mean_absolute_percentage_error(test_labels, predictions1_labels))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2: 0.8069774388842328\n",
            "MSE: 26.84342239413264\n",
            "RMSE: 5.181063828417156\n",
            "RMSLE: 0.07811529163036064\n",
            "MAE: 3.8275014537919656\n",
            "MAPE: 6.011806129263038\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7A0leRBV8N-E"
      },
      "source": [
        "**Uaaaaaaaaaaaaaaaaau!**\n",
        "\n",
        "Uma simples regressão linear ja encontrou um erro bem menor do que o encontrado pelo nosso baseline! \n",
        "\n",
        "Vamos dar uma olhada como ficou a regressão linear em relação as amostras de teste?\n",
        "\n",
        "*Novamente, não é preciso se preocupar com o código da célula abaixo. *"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8C1PMnjA8N-F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "91444fc3-36e6-44d1-d722-f76e69045c04"
      },
      "source": [
        "# criar dataframe com valores previstos e datas de registro\n",
        "predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predictions1_labels})\n",
        "\n",
        "# plotar os valores de temperatura reais\n",
        "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'real')\n",
        "\n",
        "# plotar os valores de temperatura previstos\n",
        "plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'previsto')\n",
        "\n",
        "plt.xticks(rotation = '60'); \n",
        "plt.legend()\n",
        "\n",
        "# definição dos nomes dos eixos\n",
        "plt.xlabel('Data');\n",
        "plt.ylabel('Temperatura Máxima (F)'); \n",
        "plt.title('Valores Reais e Previstos');"
      ],
      "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": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJXB1tN4ZSOI"
      },
      "source": [
        "# Support Vector Regressor SVR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mjfmk5HkZgjj"
      },
      "source": [
        "Vamos dar uma olhada agora no comportamento do SVR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vE-1qSaUZsA9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d1045b0e-8943-4ed7-c422-f83b9b3f22da"
      },
      "source": [
        "#importar o modelo SVR\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "# instanciacao e determinacao dos hiperparâmetros do SVR: tipo de kernel e epsilon\n",
        "regression = SVR(kernel='rbf', epsilon=1.0)\n",
        "\n",
        "# treinando o SVR\n",
        "regression.fit(train_data,train_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=1.0, gamma='scale',\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sEqrz0n9Zr5K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "203528c2-5e6a-4ed7-8ae9-2c7e76a2e57f"
      },
      "source": [
        "# aplicando o modelo treinado para a previsão da temperatura \n",
        "#em todo o conjunto de teste\n",
        "predictions2_labels = regression.predict(test_data)\n",
        "\n",
        "# Exibindo dataframe com valores 10 reais e suas respectivas previsões\n",
        "p = pd.DataFrame({'Real': test_labels, 'Previsto': predictions2_labels})  \n",
        "p.head(10)"
      ],
      "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>Real</th>\n",
              "      <th>Previsto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66</td>\n",
              "      <td>62.025961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>61</td>\n",
              "      <td>61.989091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>52</td>\n",
              "      <td>61.945076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>66</td>\n",
              "      <td>61.995669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70</td>\n",
              "      <td>62.026022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>82</td>\n",
              "      <td>62.064812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>85</td>\n",
              "      <td>62.090959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>84</td>\n",
              "      <td>62.097218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>65</td>\n",
              "      <td>61.984746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>92</td>\n",
              "      <td>62.052646</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Real   Previsto\n",
              "0    66  62.025961\n",
              "1    61  61.989091\n",
              "2    52  61.945076\n",
              "3    66  61.995669\n",
              "4    70  62.026022\n",
              "5    82  62.064812\n",
              "6    85  62.090959\n",
              "7    84  62.097218\n",
              "8    65  61.984746\n",
              "9    92  62.052646"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAkezhEKZrwC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "9aa0d4a4-861b-4b0d-b145-7ad2147bdb37"
      },
      "source": [
        "#avaliando o modelo \n",
        "print('R2:', metrics.r2_score(test_labels, predictions2_labels)) \n",
        "print('MSE:', metrics.mean_squared_error(test_labels, predictions2_labels))  \n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(test_labels, predictions2_labels))) \n",
        "print('RMSLE:', np.sqrt(metrics.mean_squared_log_error(test_labels, predictions2_labels))) \n",
        "print('MAE:', metrics.mean_absolute_error(test_labels, predictions2_labels))\n",
        "print('MAPE:', mean_absolute_percentage_error(test_labels, predictions2_labels))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2: 0.00012649703504530319\n",
            "MSE: 139.0512416043\n",
            "RMSE: 11.791999050385817\n",
            "RMSLE: 0.18671091932638212\n",
            "MAE: 9.547606515768958\n",
            "MAPE: 15.685787500492534\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GlmZZD4EbOmY"
      },
      "source": [
        "Vamos olhar como ficou o modelo? :)\n",
        "\n",
        "*Novamente, não precisa se preocupar com o código abaixo.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzlc5wcha-a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "8e9d562d-f131-498f-831a-0060bc7ede7d"
      },
      "source": [
        "# criar dataframe com valores previstos e datas de registro\n",
        "predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predictions2_labels})\n",
        "\n",
        "# plotar os valores de temperatura reais\n",
        "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'real')\n",
        "\n",
        "# plotar os valores de temperatura previstos\n",
        "plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'previsto')\n",
        "\n",
        "plt.xticks(rotation = '60'); \n",
        "plt.legend()\n",
        "\n",
        "# definição dos nomes dos eixos\n",
        "plt.xlabel('Data');\n",
        "plt.ylabel('Temperatura Máxima (F)'); \n",
        "plt.title('Valores Reais e Previstos');"
      ],
      "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": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MglFMlLLbggA"
      },
      "source": [
        "# Random Forest Regressor\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0qjPEqNfbmtk"
      },
      "source": [
        "Vamos agora modelar o nosso problema utilizando o Random Forest Regressor!\n",
        "\n",
        "Esse modelo é um dos mais utilizados tanto na sua versão de regressor quanto para sua versão de classificador e, em geral, apresenta otimos resultados!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7LrKgJhb8N-K"
      },
      "source": [
        "# importar o modelo Random Forest Regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# treinando o modelo \n",
        "regression = RandomForestRegressor(n_estimators= 10, random_state=42).fit(train_data, train_labels);"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o86GOgTY8N-P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "a9eb2fdf-3047-47dd-8cb0-fd62c74a35fb"
      },
      "source": [
        "# aplicando o modelo treinado para a previsão da temperatura \n",
        "#em todo o conjunto de teste\n",
        "predictions3_labels = regression.predict(test_data)\n",
        "\n",
        "# Exibindo dataframe com valores 10 reais e suas respectivas previsões\n",
        "p = pd.DataFrame({'Real': test_labels, 'Previsto': predictions3_labels})  \n",
        "p.head(10)"
      ],
      "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>Real</th>\n",
              "      <th>Previsto</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66</td>\n",
              "      <td>71.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>61</td>\n",
              "      <td>61.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>52</td>\n",
              "      <td>50.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>66</td>\n",
              "      <td>61.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>70</td>\n",
              "      <td>64.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>82</td>\n",
              "      <td>68.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>85</td>\n",
              "      <td>78.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>84</td>\n",
              "      <td>80.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>65</td>\n",
              "      <td>64.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>92</td>\n",
              "      <td>73.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Real  Previsto\n",
              "0    66      71.5\n",
              "1    61      61.7\n",
              "2    52      50.7\n",
              "3    66      61.0\n",
              "4    70      64.2\n",
              "5    82      68.1\n",
              "6    85      78.1\n",
              "7    84      80.1\n",
              "8    65      64.3\n",
              "9    92      73.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j3XYU8C_clmb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "01b433e9-3f24-41fc-ed93-dc83216d4892"
      },
      "source": [
        "#avaliando o modelo\n",
        "print('R2:', metrics.r2_score(test_labels, predictions3_labels)) \n",
        "print('MSE:', metrics.mean_squared_error(test_labels, predictions3_labels))  \n",
        "print('RMSE:', np.sqrt(metrics.mean_squared_error(test_labels, predictions3_labels))) \n",
        "print('RMSLE:', np.sqrt(metrics.mean_squared_log_error(test_labels, predictions3_labels))) \n",
        "print('MAE:', metrics.mean_absolute_error(test_labels, predictions3_labels))\n",
        "print('MAPE:', mean_absolute_percentage_error(test_labels, predictions3_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2: 0.7826272833674706\n",
            "MSE: 30.229770114942525\n",
            "RMSE: 5.498160611963107\n",
            "RMSLE: 0.08430660941594086\n",
            "MAE: 4.30919540229885\n",
            "MAPE: 6.75604632349625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2q6M07mc1Sm"
      },
      "source": [
        "Vamos olhar como ficou o modelo? :)\n",
        "\n",
        "*Novamente, não precisa se preocupar com o código abaixo.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVZgXwW18N-W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "7b6bb434-6df8-4519-cc82-da3032c08863"
      },
      "source": [
        "# criar dataframe com valores previstos e datas de registro\n",
        "predictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predictions3_labels})\n",
        "\n",
        "# plotar os valores de temperatura reais\n",
        "plt.plot(true_data['date'], true_data['actual'], 'b-', label = 'real')\n",
        "\n",
        "# plotar os valores de temperatura previstos\n",
        "plt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'previsto')\n",
        "\n",
        "plt.xticks(rotation = '60'); \n",
        "plt.legend()\n",
        "\n",
        "# definição dos nomes dos eixos\n",
        "plt.xlabel('Data');\n",
        "plt.ylabel('Temperatura Máxima (F)'); \n",
        "plt.title('Valores Reais e Previstos');"
      ],
      "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": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ur8gD8oI8N-Y"
      },
      "source": [
        "Vamos agora comparar os resultados do 3 modelos de regressão e o nosso baseline?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AviWSy_sehpN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "bc61594b-c4fe-42ab-8d09-d89bc9e4bdc5"
      },
      "source": [
        "print('Erros do Baseline')\n",
        "print('MSE:', metrics.mean_squared_error(test_labels, baseline_preds))  \n",
        "print('MAPE:', mean_absolute_percentage_error(test_labels, baseline_preds))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Erros do Baseline\n",
            "MSE: 54.4928735632184\n",
            "MAPE: 7.704879665543076\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6riPr0lehgA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "2efeaeb3-6e9e-4354-f12c-7e42a2e70867"
      },
      "source": [
        "print('Erros da Regressão Linear')\n",
        "print('MSE:', metrics.mean_squared_error(test_labels, predictions1_labels))  \n",
        "print('MAPE:', mean_absolute_percentage_error(test_labels, predictions1_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Erros da Regressão Linear\n",
            "MSE: 26.84342239413264\n",
            "MAPE: 6.011806129263038\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "faWz7JGvehT_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "c75c4ce1-5f59-4d87-e457-5c153098f4f4"
      },
      "source": [
        "print('Erros do SVR')\n",
        "print('MSE:', metrics.mean_squared_error(test_labels, predictions2_labels))  \n",
        "print('MAPE:', mean_absolute_percentage_error(test_labels, predictions2_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Erros do SVR\n",
            "MSE: 139.0512416043\n",
            "MAPE: 15.685787500492534\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2hKjSOSd4sh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "0bbccc78-220d-407f-b49f-63ae046def51"
      },
      "source": [
        "print('Erros do Random Forest Regressor')\n",
        "print('MSE:', metrics.mean_squared_error(test_labels, predictions3_labels))  \n",
        "print('MAPE:', mean_absolute_percentage_error(test_labels, predictions3_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Erros do Random Forest Regressor\n",
            "MSE: 30.229770114942525\n",
            "MAPE: 6.75604632349625\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}