AW2022_Utilizando_o_Arduino_em_Ciência_de_Dados.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AW2022 - Utilizando o Arduino em Ciência de Dados.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Utilizando o Arduino em Ciência de Dados**\n",
        "\n",
        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)\n",
        "\n",
        "\n",
        "Olá, bem-vindo ao workshop de como utilizar o Arduino em ciência de dados. Neste workshop vamos aprender:\n",
        "\n",
        "1. Manipular dados com as bibliotecas \n",
        "\n",
        "*   Pandas\n",
        "*   Numpy\n",
        "\n",
        "\n",
        "2. Como importar os dados provenientes de sensores do Arduino a partir de arquivos .CSV\n",
        "\n",
        "3. Introdução à Análise Exploratória dos Dados\n",
        "\n",
        "4. Construindo meu primeiro Modelo a partir de dados capturados \n",
        "\n"
      ],
      "metadata": {
        "id": "fU44TMU8lJHL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Bibliotecas para data science"
      ],
      "metadata": {
        "id": "R2ufs4e6lJO6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Numpy\n",
        "\n",
        "Vamos observar na prática o quanto o Numpy torna mais eficiente alguns cálculos matemáticos...."
      ],
      "metadata": {
        "id": "2iVJ9y30nVtz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1Jcyta5lHun",
        "outputId": "fa2db222-601f-4c90-c877-1c4192c58f8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.10993695259094238\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "\n",
        "start_time = time.time()\n",
        "a = range(105000)\n",
        "b = range(105000)\n",
        "c = []\n",
        "\n",
        "for i in range(len(a)):\n",
        "  c.append(a[i] + b[i])\n",
        "elapsed_time = time.time() - start_time\n",
        "# Mostrar o resultado temporal deste cálculo matemático\n",
        "print(elapsed_time)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "start_time = time.time()\n",
        "a = np.arange(105000)\n",
        "b = np.arange(105000)\n",
        "c = []\n",
        "\n",
        "c = a + b\n",
        "\n",
        "elapsed_time = time.time() - start_time\n",
        "# Mostrar o resultado\n",
        "print(elapsed_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UqycheEsnXMC",
        "outputId": "efc42e87-4344-4fcf-8b9b-978ea384ecd5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.006888866424560547\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pandas"
      ],
      "metadata": {
        "id": "XSqrQLlinTIF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "5EXHTXsKlKQx"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Série**"
      ],
      "metadata": {
        "id": "fdi81E6yoQtb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t_ts = pd.Series([23.3, 24.1, 32.1, 25.0, 31.2])"
      ],
      "metadata": {
        "id": "A8MkrrIRoDGP"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t_ts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lkKI8sBhoDJA",
        "outputId": "80dae690-c8d6-49ae-80c7-5b741ecb04db"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    23.3\n",
              "1    24.1\n",
              "2    32.1\n",
              "3    25.0\n",
              "4    31.2\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_ts.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uO2jsowboDLk",
        "outputId": "60b6d4db-ecd7-43f2-bd0e-061904f1048d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([23.3, 24.1, 32.1, 25. , 31.2])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_ts.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_xGEvrrocxN",
        "outputId": "4d5c26f0-0fb1-4d86-fe30-57bf9eaf7cdb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RangeIndex(start=0, stop=5, step=1)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_ts = pd.Series([23.3, 24.1, 32.1, 25.0, 31.2], index=['Seg', 'Ter', 'Qua', 'Qui', 'Sex'])"
      ],
      "metadata": {
        "id": "Sx8CTpy1oibT"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t_ts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6A9iSeOoDOk",
        "outputId": "abf0d327-cbaf-4f6e-db25-e44002e6396d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Seg    23.3\n",
              "Ter    24.1\n",
              "Qua    32.1\n",
              "Qui    25.0\n",
              "Sex    31.2\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Imprime a média da série: {t_ts.mean()}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkH2FCc-oq__",
        "outputId": "2e9f4cfd-dafc-4f1c-c55e-936c549f024e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Imprime a média da série: 27.139999999999997\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DataFrame**"
      ],
      "metadata": {
        "id": "iZpYFYgLo5xH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t_df = pd.DataFrame({'Temp': [23.3, 24.1, 32.1, 25.0, 31.2], \n",
        "                     'Dia': ['Seg', 'Ter', 'Qua', 'Qui', 'Sex'],\n",
        "                     'Agua': [1, 1, 0, 1, 0]\n",
        "                     })"
      ],
      "metadata": {
        "id": "Ml1BskvAosnl"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "2tSny8ZUo6H-",
        "outputId": "0b4d8b5e-fc66-4886-bb41-a150ef0748bd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Temp  Dia  Agua\n",
              "0  23.3  Seg     1\n",
              "1  24.1  Ter     1\n",
              "2  32.1  Qua     0\n",
              "3  25.0  Qui     1\n",
              "4  31.2  Sex     0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff8d7dae-a579-48c2-9841-5e1bb7f1bcd1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Temp</th>\n",
              "      <th>Dia</th>\n",
              "      <th>Agua</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23.3</td>\n",
              "      <td>Seg</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24.1</td>\n",
              "      <td>Ter</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32.1</td>\n",
              "      <td>Qua</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>25.0</td>\n",
              "      <td>Qui</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31.2</td>\n",
              "      <td>Sex</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff8d7dae-a579-48c2-9841-5e1bb7f1bcd1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ff8d7dae-a579-48c2-9841-5e1bb7f1bcd1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ff8d7dae-a579-48c2-9841-5e1bb7f1bcd1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NLZ0cfCfo6RB",
        "outputId": "efa65831-c109-466d-b0a2-8eac45114ff1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Temp    float64\n",
              "Dia      object\n",
              "Agua      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDSYcSVNo6TT",
        "outputId": "7cb98280-f807-4c9d-f50a-37a29d867191"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Temp', 'Dia', 'Agua'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_df['Temp']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dwqmpwcpsu9",
        "outputId": "0e6332c7-79d1-4b5a-ba39-64c0e10c2e95"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    23.3\n",
              "1    24.1\n",
              "2    32.1\n",
              "3    25.0\n",
              "4    31.2\n",
              "Name: Temp, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "XCZrQrk6pwLH",
        "outputId": "db8ca3e9-e168-4f11-b6a7-1bc2f3fdf0dc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Temp      Agua\n",
              "count   5.000000  5.000000\n",
              "mean   27.140000  0.600000\n",
              "std     4.172889  0.547723\n",
              "min    23.300000  0.000000\n",
              "25%    24.100000  0.000000\n",
              "50%    25.000000  1.000000\n",
              "75%    31.200000  1.000000\n",
              "max    32.100000  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5b23d8eb-df2e-4607-9903-5901fb35f6d9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Temp</th>\n",
              "      <th>Agua</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>27.140000</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.172889</td>\n",
              "      <td>0.547723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>23.300000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>31.200000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>32.100000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5b23d8eb-df2e-4607-9903-5901fb35f6d9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5b23d8eb-df2e-4607-9903-5901fb35f6d9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5b23d8eb-df2e-4607-9903-5901fb35f6d9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Matplotlib e Seaborn\n",
        "\n",
        "Para que é utilizada a visualização de dados?\n",
        "\n",
        "- Identificação de relações e padrões\n",
        "\n",
        "- Identificar últimas tendências\n",
        "\n",
        "- Contar uma _história_ "
      ],
      "metadata": {
        "id": "EPpgC-RklJaX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "e3-36E_J2KeQ"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "2MWP3R1I2ami"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Importando dados de arquivos csv\n",
        "\n",
        "A lib pandas nos oferece uma série de métodos para leitura de dados! \n",
        "\n",
        "Alguns dos mais importantes são: \n",
        "\n",
        "- **read_csv**: para ler arquivos .csv\n",
        "- **read_xlsx**: para ler arquivos Excel .xlsx"
      ],
      "metadata": {
        "id": "t15hHnaIlJSQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car = pd.read_csv('car_trip.csv')"
      ],
      "metadata": {
        "id": "3vAOmqU0lKTk"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "tmh0GsjP7uMg",
        "outputId": "08e63bed-ab2b-4978-87d1-08c89ce2de2c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ENGINE_RUN_TINE ()  ENGINE_RPM ()  VEHICLE_SPEED ()  THROTTLE ()  \\\n",
              "0                 0.0            0.0               0.0    17.647058   \n",
              "1                 0.0            0.0               0.0    17.647058   \n",
              "2                 0.0            0.0               0.0    17.647058   \n",
              "3                 0.0            0.0               0.0    18.039215   \n",
              "4                 0.0            0.0               0.0    18.039215   \n",
              "\n",
              "   ENGINE_LOAD ()  COOLANT_TEMPERATURE ()  LONG_TERM_FUEL_TRIM_BANK_1 ()  \\\n",
              "0             0.0                    56.0                       -3.90625   \n",
              "1             0.0                    56.0                       -3.90625   \n",
              "2             0.0                    56.0                       -3.90625   \n",
              "3             0.0                    56.0                       -3.90625   \n",
              "4             0.0                    56.0                       -3.90625   \n",
              "\n",
              "   SHORT_TERM_FUEL_TRIM_BANK_1 ()  INTAKE_MANIFOLD_PRESSURE ()  FUEL_TANK ()  \\\n",
              "0                             0.0                        101.0     67.058823   \n",
              "1                             0.0                        101.0     67.058823   \n",
              "2                             0.0                         92.0     67.058823   \n",
              "3                             0.0                         92.0     67.058823   \n",
              "4                             0.0                         92.0     67.058823   \n",
              "\n",
              "   ...  INTAKE_AIR_TEMP ()  TIMING_ADVANCE ()  \\\n",
              "0  ...                37.0                5.0   \n",
              "1  ...                37.0                5.0   \n",
              "2  ...                37.0                5.0   \n",
              "3  ...                37.0                5.0   \n",
              "4  ...                37.0                5.0   \n",
              "\n",
              "   CATALYST_TEMPERATURE_BANK1_SENSOR1 ()  \\\n",
              "0                                  506.5   \n",
              "1                                  506.5   \n",
              "2                                  506.5   \n",
              "3                                  506.5   \n",
              "4                                  506.5   \n",
              "\n",
              "   CATALYST_TEMPERATURE_BANK1_SENSOR2 ()  CONTROL_MODULE_VOLTAGE ()  \\\n",
              "0                             352.100006                     12.480   \n",
              "1                             352.100006                     12.480   \n",
              "2                             352.100006                     12.480   \n",
              "3                             352.100006                     12.480   \n",
              "4                             352.100006                     12.011   \n",
              "\n",
              "   COMMANDED_EVAPORATIVE_PURGE ()  TIME_RUN_WITH_MIL_ON ()  \\\n",
              "0                             0.0                      0.0   \n",
              "1                             0.0                      0.0   \n",
              "2                             0.0                      0.0   \n",
              "3                             0.0                      0.0   \n",
              "4                             0.0                      0.0   \n",
              "\n",
              "   TIME_SINCE_TROUBLE_CODES_CLEARED ()  DISTANCE_TRAVELED_WITH_MIL_ON ()  \\\n",
              "0                               1383.0                               0.0   \n",
              "1                               1383.0                               0.0   \n",
              "2                               1383.0                               0.0   \n",
              "3                               1383.0                               0.0   \n",
              "4                               1383.0                               0.0   \n",
              "\n",
              "   Unnamed: 26  \n",
              "0         47.0  \n",
              "1         47.0  \n",
              "2         47.0  \n",
              "3         47.0  \n",
              "4         47.0  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ENGINE_RUN_TINE ()</th>\n",
              "      <th>ENGINE_RPM ()</th>\n",
              "      <th>VEHICLE_SPEED ()</th>\n",
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              "      <th>CATALYST_TEMPERATURE_BANK1_SENSOR1 ()</th>\n",
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              "      <th>Unnamed: 26</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>17.647058</td>\n",
              "      <td>0.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>-3.90625</td>\n",
              "      <td>0.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>67.058823</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd2c915e-4ffb-4558-9850-600d6c63985a')\"\n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car = car.drop_duplicates()"
      ],
      "metadata": {
        "id": "0HiuD5oO3FnM"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10,6))\n",
        "plt.plot(car['ENGINE_RUN_TINE ()'], car['COOLANT_TEMPERATURE ()'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "P2pdHmMV6vJQ",
        "outputId": "adcbea25-4f06-49c7-8c88-16b21208e780"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f182a332b50>]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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T6jsryc/WsxsP6Nle+gzFH09JfkARJ91870r5s0zffs9x+rfY9vXshZP0h0+ckdL9tbaHdc0dryd2F7/xjQs1uSgaJG99ZL2e2XC4jle2VidC4cSCgJZ/82L9v7+t00uxthTJ2geMj9X7+NpKnThzvK7+zWuJHnmdm/R2vU5A5btqu2x3PnPeAv3rO49JOv9vX9mu+8r39lgHezM+P6CG1o4u25wHP/N2nTK7pMu2pLPCXH+iv1n8+fzj67v1z+ct6Pf+0oUANciaY83kNlb03Qgxri22oj/++bMV8B/+lP7yW9WJN6CPvn2uPnzmbEnSB379mlo7ku96aIsdEPvwZ89SQU7ylby1PaLc7Kwu99HaHkk675GqjXU7fuqL56ggx6eZJfn6yNvm6juPbVAoHFFbR1gTCgK671PRN63vPbFJmw/0vtxCscaVX7xood514jQVxkJIfKOSH/Br6S0X6N8f3aBXU/xk2hZ77B0Rp3iD57aOSKIHSXzZtLVHtKC0QA9+5qzEda89bZbeNn9il47hd3zkVH3viU2657VdievGn4+CHL+mFedJkqYW52rZ1y/UtXe8rsrYqMCt716ssxdOUl2wXe//9WuH77vT890ejiRqvuujZQr4stTWEU50Jo+vT9+4/Bj9z/PbEutKfPofPnG65kwoUF7Ap0/eU65IxKmtI6IZ4/N098dPkyRNLMhRTXObPvTbZWptP3x9M+nlfz1fM0u6fvL+6iVH6wOnztLEgoBqgyHNm1SgvbUtenVbtb7217U91tf48/i584/SsxsPpO04JOeib4S//vCp+vBdy474ddDaHta4HL9++cGT9dHfv9nr63Qgt3v8jCLd/bHTu7x5feGiRZpRkqev/XWt2jrCam0P67jpRfrCRYt007kLdPytT6mtPaI2f/RxPfsv50iScvy+xDr77hOn6cQZxeqIRFRamKvivGy98rXz9e1H1id9LdYG23XFCdMSfc8k6b0nz5Bz0pfvX626YKhr7R3hxPqbWBc7Lee6YEht7RGdPHu87rrhNJ1y2zPy+7LU1hHR4mlF+vWHT9UVv3i5y3Xi2jrCOmX2eP30miWa3UfX/u6e/uK5qmpKPhInSXMnRkPHx8+ap/OPmaw/L9utu17ZkdjlO29SQeJ1kYqOiFM44jRvUoF2VDcnXotS9PGfNGu8fvyBEzUuJ1tNbdFt5u0vbNeTsUaUre1hnTSzWD+/9mTNSTLqFe+o7o+Fj1BHRNefOUc3vH1OIqh198dPnqHK+sOjju/55dI+1/+2johy/Fl65WsXpNQK5auXHh3r7O+0ek+9vnz/6sQya22PKMukp790TpfrTCzISQSon16zRE+se9LTck4HAlSGWDC5oMtw/9aDh4dfJxQEdNTk6FC6L6v/w9bmlxakNOzZ+T6GQrC9QwFflo6eeng3QPdPL/4sSzy20sJcrdrTfxfxyYW5iet0N2lcjorzjmy17u0U/By/T8V5h5ermWlut094+QF/j92rvT0fk4tyVZyfrd010V0V04rzdNTkwsRumL5ML85NNMVMtjt3SlFuYoPa2dyJBYk3zwn52aqJ3VeOP6vLMi0pCPRoeWBSj/AkSX5flo6K7U6Mh9lZE/I1o6bvN7WZJXmaPSG/z67XQykryzSzJF8FOX4VBPxqGYSNdZYdfgMeTHnZPk0cl9Nlmi/LEoE8et+m+aXj5Msyjcvxd1lXJSV9zSRbh2eW5Kuol+1HSyisKUW5XV7HZpZ4Y/faviI+f37ApwkFAQX8h9e5vIBPsyfm99j921lBjl9zPC7v4vxsFef3v33MyjItKB2nieO6vr5y/AM7dDjZ9YKx5Xn4uYkGnpJu9eUFfD2ep866f4VO5/eMZMbl+Ltc7kvhEI4sM03qtg72VU98m1Dd1HN7ZmZ91pfKKFcm4CByDJmWUFj5vYyEJVMQ8GVE/6DhPHusIODPiMcM9Mc5p2CoI3GSQGd5iTMvva3LY/1A/pZQOOnyxMhAgMKQCYbCyvfwSSI/4FNLezhtZ2XFDWegyYs9ZiDTxXdtJ/tQVBAbDW1p9/bhY6x/eGjuJZBiZCBAYci0hMKevjokL+CXcxqyY7JSNZwbdTaeGCkSu9uSfCiKr8fNbQPbhTdWBUPhfr+fEpmLAIUh0xzqSKmVQ1z8wPfmNDdgHM5deGw8MVLEXxfJ1tk8j72/ut/mWOScYxfeCEeAwpAJhsLK87ALLz5vuo+LCHr8FH0k2HhipIiPFiUbVY6HqoEeRD4WhcIRdUQc24ARjI+/g+zL90V7sbyytVqHmkOaUBDQzfeu1Mrd0X4bX7hwoT5QNkuS9MOnNukPr+3yfB8Pr9yX6AvSWWPrwD7NffT3byjgj/ZQmlyYqz21h8+K+ufzFuhDZ8yRJP3iubd0X/kejcvx6+6Pn64pvZwi29oe1nV3vq71+xp01lETk85z/V3LtOVAkyZ2OoMsPlr197UV+vS5XXt/rNxdqy/8ZVXKj+lQMKR3fP8fif/nTSrQPR8/PdFTJd44MNDpTLMfPrVZ33r3Yv3lzd2Jaf/x+EadvWiSnt14QMdM7f2skWR++fzWfucp6GPjeesj69URcV16C5V991lJ0pSirmfDRJw095a/95j+0Ip9+uPru3uc1RO3saJBbx1o0rTins+lc9KDK/bp3EWl+u9/9P9YevOZP63ocgZS57YFERdtpOmc69HMtbqpTdff9YYaW9t7NHo0kyYX5vT55cgBX5Z+du0Sle+s1e+W7khMH5+frWOnFqmqsa1THU7Ld9Vq68HGxNlBv315e48+ZXFZZvrmuxbr4sVTJEkvbD6o/311Z5fn87bHNurHSfrddHb2wlL91/tOSHrZ8l21+sr9q7WvrkUndeqjlcw/3f6aJOm4GV3ne2D5Xg20R+7+ula94/v/UNmcEv3s2pP1s2ejjyXZG74vy5Tjz1Iw1KGvxvpRSdLPn30rcWbtw6v262fXLNGrnRqy/n1NhcIRp9PmlUiKnoIfX99Pnj2+y+N4duMBFedl6/MXLtR/Pb5R++tadcb8w81vh1rn1/PqPfVdti+9aWztUH1Le5dp1935un5y9RJ9/8lNkqKHLiTTHArrHd//hw42tumUTsuiN3e/ulN/W7Wv3/mSaY9E9LulO3TOokmJtgid/e+rOxU5wnYjH7xzmT5w6kw9ua4y5ev8adkufeniRZKkr96/OtH3K+47Vx6nC46ZckR1HQkC1CA72GmjvOdQUBMKAnph80FNLszR/rpWvbHjUCJAPb+pSuNy/Lrx7Pk9OhafPm+CPnjGbLV3RHT5CVMT079w0cJEGEtmZkl+orFkf+L30doeVlNrh57ecEDbqpo1Y3yezpg/QU+tq9QbOw4lAtTSrdWJN7Kd1c29Bqia5pBW7q7TKbPH66Nnzety2blHl0qSthxokiRduWRG4rIz50fDVrJuyev3N2j3oaCuWjI9cRu9+adTZqolFJGTi91Xo15+q1ptHRG9tKVKWZ3eUS49fqpOnFGs/3h8Y2LDHu/TcvyMIq3b16BX3opOP3VOSZ/3G3f5CdO0r7ZFYef6fT7efdJ0VdS3KtufpdPnRd8MSvKz9d6TZ+ihlfv0p9d3qaqxTRcvnqKWUDjRZO+a02YnbuOKE6clAs6C0nG64JgpevuCSfrCRQv1k2e2SKGwxuX69YGyWZox/vBp7x95+9xE24G3LzjcHTnu5guO0i0PrtXTsUZ/80u9nS5+4qxiXX/mnKS7ZAO+LJ2zqLRLE8HudtUEtbEiGsK7ByjnpAMNbSqbU5K0I3RLKKwn1lVqw/4Gvb69Rg0t7bpo8RTtrW3RGzsOJTp7f6BspqRokHl1W402VzYlAtTr2w+psbVDFx7b8w3loZX7tHJ3bSJAxUPCly85WrMm5OvGs+clWkT0pnxnbdIPQnHr99drR3Wzrjhxmt59YvKGnEtmj9fkwpzEdueDpx9eL7500UKtiXX9X9jHKePJXHPaLMmktXvr9ezGg5KkfXXR12XZ3OShJduXpY6I0/ObDz+m7uGhpT2s/bHb+do7j9FbB6O9pi4/Pvr4jplaqE2xbxk4Y97ELo9jX22Llu04pKfWVWpnTVDvOWm6rlwy3dPjGohLj5uqndXN6og4TSvO1ZJZJXoi1qOpPw+uiAaas46aqJsvWKj7y/fqryv2avWeOi2PNbG8ZHHPAHDVyTNU19Ke6Nb9zuOm9pins69eerS2xPp2vW2+6Ype1pfe/Oulx+g7j23QmzsPJQ1QpugHloE4YUaxZpbkaW9ti+5fvlfTi3P12QuO6vd6AX9Wlw+4L71Vpbxsn07ptB320kB1KBCgBlnAl6X5pQWJjUDcOYtK9VS35B0Mdahs7gTdfOHCHrczoSCg/3xvz0+m1585R9efOWdQau18HzuqmxNvlEtmj9dPrl6i83Y9f0S3f+1ps3Xuoq5hZ8b4PL37pOmJFv8fOvPwBn9CQUCFSb4iorNvXHFs4usgenPy7BKdPPvwi+zOl7Zr3b4GtccaOJbNLUn0HvrP9x6vwtxsrdtf36UH1Smzx+ufzztKN95Tnpj2rhNT21gvmlKoH37gpP5nlLQwybwWG914aOW+RPj47+tO1tJOXYo/0SmYHjP18NegfPLseYlPZB9521zdX75XdcF6lc2ZoG9c3rWT8/lHT9b5STaWcdeePlu/fH5rYpeq1/WuKDdbt111fJ/znDCzWM9tOtjnPDeds6BLp+rOrjt9tv7p1Jk9plfUt+iJTq+36ePz9JOrl+jxtRV6I/a1NZefMFWnxNaTC4+dnBgR6GxG7HrdJfsaEEn62FlzZWYpfQvBl+9brde3J39cnX3nPcf16AEVV5SbrW++a7FuvnelzKS3LTg84tv9w4sXZ8yfqDPmT9SPn96sXz6/NTpCqOh2rHt/qe58WdEms01tHdpQ0aBsnynX71NjW0eXXXbJOkx/6Mw5iW9TeOfxU7s8jifXVSS+ckiS/ut9J3g6xnKgFpSO0w/e3/U1enGS0JNMTVNIL26p0ofOmKMz50/UsVOL9NcVh7v+f/niRV0a8cYdP6NYP0pxGyKpx4i9Vx9/xzz99JktSU8CcM4pFI4kfZ2loiDHr2++a7E+9YfotymcMLM4pXrffeL0Hq+PM+dP1Pf+6cQB1TEUOAYqjYIcQDgssn3REaeOcO9D0PkZ0oMqLr+fvjpezm480jri3fWR2by0DElVXsAn59Sle/ZAb0ca3uML4U1ewJf0+NPW9oicG75tzkhCgEojr6f5Y2D8sWHg+AhUMnnZ/rQfvN5Zjj9LZr0fUB8YYDdkr/IHqUM3hl5vx9IciXgoO9IPF/GRoqDHPlEYPgU5fgWTvNYTZ1+OkO7gw4kAlSbOuehp/pzGPuTi+9Hb+zgIsiDHp+ZQR9qbeMaZmQoCfnWk6Xvi4jJtZA696+37L49Efiz4HOkoZN4gBTEMnbxsn4JJnudE/69h2F060hCg0iTe1ZcRqKHnj+3Ca+9jN8Rg7aoYTJmwbuT3MqyPzOOlZUiq4ruSj3QUMn+AfaIwfHr7sBR/7jncpCcCVJrENySslEMvO4VdeIO1q2IwZcK6kR/wp72xKVIzFOtLf8fipXw7gzSShaGT38suvPhzlgnbo0zDmNwgenbDAYU77QJ6an2l1u9vUKjTqMa2qib9ednuxOm9mb5S7qxu1p+XRfsidW7R8Mjq/frbqn06YUbP/iS1wb5P3+7P5srGxH3GvbnzUC9z9y9+EPmjq/erqZcNeLwR4F/e3K2K+lYVdTobcEUfbSOG0mCMKMRbOQzUcB1E/uc3dsvUtWHRrkPNR3y7y3Yc0p5uLRD68srWqsRrc29tsEvLi+7W729IrKdr99YPqL7mUEePdT0/4NPU4ly9udPbejcUXe3zsqO3+djq/apualNRP2fgbaps6BK2Glo6FI64xAeUh1fv19/XpNYCoDfbq498vUi3dG1T+pKf7dPmyoYe6+OO6mjLmfi6MJw6vz4y6cNtHAFqkNQ2h/TJ2Cnvi6cXafOBRv3qhcMNECcX5qq0KFcrdtdpxe7Dp8vPLOl5Cms6FOdlK+DPUqgjkuj3MbkwV2/sPKTVe9cm5jtmaqG2HGjUn2Ir9b3a0+ttlvbSNyR++4O2FlIAABxWSURBVAFflsZ32yBPLszRq9tq9Oq2nqd3FwR8AzpmbGpxnsykX8R6JU0uzNWSWeO1ak9dov/WzAnR/kg/eHKzJOmy46dq0riAzKTH11Yqy6RJ44a358jsCfnaVNmokvxs+bNMk2Knsp+zqGcfrEnjclTd1JaYJ25KYa7WqWHAPVxmTchXe+zsxf7aRwxE/Db/30Prkl5useU+LsevprYOBfxZWjytKNFyord1bFyOX3nZPj20MtqHJ77MOs/f+fGMz89Wts907xt7uqzT5/XSc2xyYa5e3FKlFzv1ceqtlt5MLspRXbBd33hoba/zjMvx9xuM4vd7xrzBbyo5Y3yefFmm374SbUQa79WWzOTCnES7icmFOSrI8au6Kfqh67jpRXphy8FEeOqtN1p8PfVlWZcmu5IS6/aqPXWJ7VWmO2V2iV7cUpV4jnKys1SY60+02JhcNLDX5VCYPTFfT66vTLo++rKsSw85rzpvl1LdjiR7fQx0OzZUbDgPmi0rK3Pl5eX9zzgCHWxs1en/8Zy+ftkxuumc+apvaU8cT2OKbuRC4YjqgocbywV8WYlGhpmgsbVdwVBYpeNylJVlCnVEeowmTSwIqLktrCW3PS3npEc+d1bShprZvixN6OWxRSJOVU1tyg/4VJjbNUC1tod7NN+LK8jxp9wktLu6YCjxfEwuzFFHxCnUEenSR6a2OaRQbDffxIKA/L6sxPVy/T4V99LNe6h0hCOqaQ6pMPfwm2hdMKRxOf7EmYVxre1htbaHNb5bY7n2cESHmkOaXJjTo9N3Kpxzqmpsi76h9dKL6EhVNbYlGgZ2F1/uLaGwOiIR+bOy5PeZmlo75KRe1zFJamrrSIyeTSgIJHblxp/n+Hoe19Da3uMYnc7X66wlFFZDa9f1tCg329Nxa865LqO6krRuX70+cXd0G3lN2Sx9692LU+p1VB9sV1Gef0DPcb+33dKu1tiunUnjcuTLSn4f8ddufHvnnFTV1CYzqXRcjppDYR1/61OSpNe+foGmFSd/Q65papPfl5W031T8uev8mshkzjk1tHZ0eSzNbR1qautQlpnn0D2Ukq2PcbnZvn77f/Unvi3t/rpLtZ74ejUU63hfzGy5c64s2WWZvwaOMAU50Y1Y9zcyScrx+zSlKHN32RXmZncJNAF/VtJwVJwf3bjVBdtVWpjTa0fy3mRlWa/Xyc32KXcIDobt/nxk+6zHG2OyMJvseRwufl/P5d9bPb0tt+wkt+GFmWnyEVw/Fam8iUSDyeHHl8oHj3G9BO7erluUm62i3NTeJPICviM+yN+s5+tgf93h3Y3F+dkpN4ocynBfnJed0ptn93XQTF0eX+fnoq/d030F9Uz6wJkKM+ux7Apy/MPSANSrZOvjYPK6LR3qegZD5o+BIiP19R1kAI5c/Ng9AJmJd0EMSLy3UjjNfYqA0cqfxeYZyGQpvULN7Etmtt7M1pnZvWaWa2b/a2Y7zGxV7KfnF0Zh1Er0Vurj61EADNxIOEgaGMv63RFrZjMkfV7SYudci5ndJ+na2MVfdc49MJQFIjOl0lsJwMD5UzjQFkD6pPoRxy8pz8z8kvIlJf8qcowZBChgaHGcIZDZ+n2FOuf2SfqRpN2SKiTVO+eejl38H2a2xsx+amaZcz5mGtzz6i5J0TNPxoJ4P47uzQ8BDFznU7SzR/EuPLYbGA1S2YVXIulKSfMk1Um638w+LOnrkiolBSTdIelrkr6T5Po3SbpJkmbPnj1ohWeaXYeCkqSLjp2S5kqGx8+uWaLH1uzXsdMK010KMGocO61Qnzv/KLW2h3XJ4tG3Lbnj+lO1o7p52HuqAUMhlWYUF0na4ZyrkiQze1DS251zf4xd3mZmv5f0lWRXds7doWjAUllZ2ag+4nj+pIKM71sxWEoKArr+bXPTXQYwquT4ffrKpUenu4whc8lxU9NdAjBoUhkj3i3pTDPLt+j48oWSNprZNEmKTbtKUvLvYgAAABhl+h2Bcs4tM7MHJK2Q1CFppaIjSk+YWamiHdZXSfr0UBYKAACQKVLqJ++cu1XSrd0mXzD45QAAAGS+0XuaBwAAwBAhQAEAAHhEgBoE9S3tenT1fkXcqD7JEAAAxBCgBsFT6yolSePzA2muBAAADAcC1CDoiERHnm7/8ClprgQAAAwHAtQgyhor3+MCAMAYR4ACAADwiAAFAADgEQEKAADAIwIUAACARwQoAAAAj1L6Ljwk9+yGA/rMn1eoPRyRJHESHgAAYwMB6ghsr25SqCOiT50zXzNK8lQ6LifdJQEAgGFAgBoEn79woQpyWJQAAIwVHAMFAADgEQEKAADAIwIUAACARwQoAAAAjwhQAAAAHhGgBqi2OaRfv7g93WUAAIA0IEAN0FPrK3WoOaSS/Gzl+FmMAACMJbzzD1BTW4ck6cV/PV9+H4sRAICxhHf+AQqGwpKk/GxfmisBAADDjQA1QMFQWAFfFqNPAACMQbz7D1Aw1KH8HEafAAAYiwhQAxQMhdl9BwDAGEWAGqCWUFh5AQIUAABjkT/dBYw0kYhTW0dEjW0dyg+w+AAAGItIAB595k8r9OT6SknS2+ZPTHM1AAAgHQhQHu0+FNRRk8fp/afO1FkLJqW7HAAAkAYEqAGYO7FAnz53QbrLAAAAacJB5AAAAB4RoAAAADwiQAEAAHiUUoAysy+Z2XozW2dm95pZrpnNM7NlZrbVzP7PzAJDXSwAAEAm6DdAmdkMSZ+XVOacO16ST9K1kr4v6afOuaMk1Ur6xFAWCgAAkClS3YXnl5RnZn5J+ZIqJF0g6YHY5XdLumrwy8scj63Zr28/sl6VDa3pLgUAAKRZv20MnHP7zOxHknZLapH0tKTlkuqccx2x2fZKmpHs+mZ2k6SbJGn27NmDUXNafO+JTTrQ0Kq8bJ+WzCpOdzkAACCN+g1QZlYi6UpJ8yTVSbpf0jtTvQPn3B2S7pCksrIyN7Ay06+5rUPXnjZbt111fLpLAQAAaZbKLryLJO1wzlU559olPSjpLEnjY7v0JGmmpH1DVGNGCIbCys/hy4MBAEBqAWq3pDPNLN/MTNKFkjZIel7S+2Pz3CDp4aEpMf3CsS8Qzs+mcTsAAEghQDnnlil6sPgKSWtj17lD0tck/YuZbZU0UdJdQ1hnWgVD0UO98gOMQAEAgBS/C885d6ukW7tN3i7p9EGvKAO1hMKSpDwCFAAAEJ3IU9IcC1AFHAMFAABEgEpJVWObJCmPY6AAAIAIUP062Niqq3/zmiSpKI8ABQAACFD9qgu2S5IuXjxFp8+dkOZqAABAJiBApeiqJTPk97G4AAAAAQoAAMAzAhQAAIBHBCgAAACPCFAAAAAeEaAAAAA8orFRHx5YvldPrqtMdxkAACDDEKD68Kvnt6qivlULSgu0aMq4dJcDAAAyBAGqHxctnqL/vu7kdJcBAAAyCMdAAQAAeESAAgAA8IgABQAA4BEBCgAAwCMCFAAAgEcEqD7UBkPpLgEAAGQgAlQvth5sUm2wXW3t4XSXAgAAMgwBqhcHGlolSe9ZMj3NlQAAgExDgOpFc1uHJGnuxII0VwIAADINAaoXLbFdd3kBX5orAQAAmYYA1YtgKBqgCgJ82w0AAOiKANWL+C48RqAAAEB3BKhetMRGoPIJUAAAoBsCVC+C7WFl+0zZPhYRAADoinSQRKgjottf2KYcP6NPAACgJwJUEturmyRJM0vy0lwJAADIRASoJOJn4H3tsmPSXAkAAMhEBKgkgm2xA8iz2YUHAAB6IkAlEQxFWxgU5NADCgAA9ESASoIu5AAAoC/9DrGY2dGS/q/TpPmSviVpvKQbJVXFpn/DOff4oFeYBkF6QAEAgD70G6Ccc5slLZEkM/NJ2ifpIUkfk/RT59yPhrTCNDgcoNiFBwAAevK6C+9CSducc7uGophM0Noe1o5YGwNGoAAAQDJeA9S1ku7t9P/nzGyNmf3OzEoGsa60+e7fN+iPr+9WYY6fLuQAACCplBOCmQUkvUfS/bFJt0taoOjuvQpJP+7lejeZWbmZlVdVVSWbJaM0tnZocmGOHvzM29NdCgAAyFBehlguk7TCOXdAkpxzB5xzYedcRNKdkk5PdiXn3B3OuTLnXFlpaemRVzwM8gM+LZxSmO4yAABAhvISoK5Tp913Zjat02XvlbRusIoCAADIZCmdZmZmBZIulvSpTpN/YGZLJDlJO7tdBgAAMGqlFKCcc82SJnabdv2QVAQAAJDhOM0MAADAIwIUAACARwSoTpraOvTwqv2KuHRXAgAAMhkBqpNtB6MdyGdNyEtzJQAAIJMRoJL4xDvmpbsEAACQwQhQAAAAHhGgAAAAPCJAAQAAeESAAgAA8IgABQAA4BEBKqa2OaTVe+vSXQYAABgBUvouvLHgW4+s16Or90uSxuVkp7kaAACQyQhQMcG2Ds2fVKBfXHeyjptelO5yAABABiNAdZKf49PxM4rTXQYAAMhwHAMFAADgEQEKAADAIwIUAACARwQoAAAAjwhQAAAAHhGgJB1sbNVzmw7KuXRXAgAARgIClKStB5okScdMpf8TAADoHwGqk6vLZqa7BAAAMAIQoAAAADwiQAEAAHhEgAIAAPCIAAUAAOARAQoAAMCjMR+gQh0RrdxTl+4yAADACDLmA9RT6yv1w6c2S5KK87PTXA0AABgJxnyAam0PS5Ie+PTbaKQJAABSMuYDVNyUotx0lwAAAEYIAhQAAIBHBCgAAACP+g1QZna0ma3q9NNgZl80swlm9oyZvRX7XTIcBQMAAKRbvwHKObfZObfEObdE0qmSgpIeknSLpOeccwslPRf7HwAAYNTzugvvQknbnHO7JF0p6e7Y9LslXTWYhQEAAGQqrwHqWkn3xv6e4pyriP1dKWnKoFU1THZWN+urD6xJdxkAAGCESTlAmVlA0nsk3d/9Mueck+R6ud5NZlZuZuVVVVUDLnQobDnQKEk6c/4ETR+fl+ZqAADASOFlBOoySSuccwdi/x8ws2mSFPt9MNmVnHN3OOfKnHNlpaWlR1btEPm3KxbLl2XpLgMAAIwQXgLUdTq8+06SHpF0Q+zvGyQ9PFhFAQAAZLKUApSZFUi6WNKDnSZ/T9LFZvaWpIti/wMAAIx6/lRmcs41S5rYbVqNomflAQAAjCl0IgcAAPCIAAUAAODRmA5QS7dWp7sEAAAwAo3ZANUSCuvu13ZJkkoKAmmuBgAAjCRjNkB1RCKSpM+ev0AzaKIJAAA8GLMBKq4kn9EnAADgzZgPUAAAAF4RoAAAADwiQAEAAHhEgAIAAPCIAAUAAODRmA1Q//fmnnSXAAAARqgxG6A2VzZKki49bmqaKwEAACPNmA1QkjS9OFezJuSnuwwAADDCjOkABQAAMBAEKAAAAI8IUAAAAB4RoAAAADwiQAEAAHg0ZgPUsh2HFHYu3WUAAIARyJ/uAtIhEnHafSio3Owxmx8BAMARGJMJoqU9LEm6+YKFaa4EAACMRGMyQAVD0QBVlDsmB+AAAMARGpMBqiUWoPICBCgAAODdmAxQwfYOSVJ+wJfmSgAAwEg0NgNUbASKAAUAAAZibAaotniAYhceAADwbmwGqBC78AAAwMCNuQD15LpK3XzvSklSQQ4jUAAAwLsxF6C2HmxUW0dEX3vnMZo7MT/d5QAAgBFozAWouE+ePU9mlu4yAADACDRmAxQAAMBAEaAAAAA8IkABAAB4lFKAMrPxZvaAmW0ys41m9jYz+7aZ7TOzVbGfy4e6WAAAgEyQ6nn8P5f0pHPu/WYWkJQv6VJJP3XO/WjIqgMAAMhA/QYoMyuWdI6kj0qScy4kKTQSz2A71BzSb1/Zke4yAADACJfKLrx5kqok/d7MVprZb82sIHbZ58xsjZn9zsxKkl3ZzG4ys3IzK6+qqhqsugfkiXUVqgu2a0JBQL4RGAABAEBmSCVA+SWdIul259zJkpol3SLpdkkLJC2RVCHpx8mu7Jy7wzlX5pwrKy0tHZyqBygScZKkp754jrKyCFAAAGBgUglQeyXtdc4ti/3/gKRTnHMHnHNh51xE0p2STh+qIgcbg08AAOBI9BugnHOVkvaY2dGxSRdK2mBm0zrN9l5J64agPgAAgIyT6ll4N0v6U+wMvO2SPibpF2a2RJKTtFPSp4akQgAAgAyTUoByzq2SVNZt8vWDXw4AAEDmoxM5AACAR2MqQLl0FwAAAEaFMRWgfhdrokkPKAAAcCTGVIAqystWlkklBYF0lwIAAEawMRWgTNI5i9LbzBMAAIx8YypAAQAADAYCFAAAgEcEKAAAAI8IUAAAAB4RoAAAADwaMwGqLhjS6r316S4DAACMAmMmQD2/+aAkaWpRbporAQAAI92YCVBNbWFJ0r9csijNlQAAgJFuzASollCHJCk/4E9zJQAAYKQbMwEqGIqOQOVl+9JcCQAAGOnGTIBqCYWV48+SL4svEgYAAEdmzASo5lCHCnLYfQcAAI7cmAlQwVCY3XcAAGBQjJkAdaChVfkBAhQAADhyYyJA/W3lPi3dWsMuPAAAMCjGRICqqG+VJH3zXYvTXAkAABgNxkSAijtuelG6SwAAAKPAmApQAAAAg4EABQAA4BEBCgAAwCMCFAAAgEcEKAAAAI9GfYBqbQ/r589tSXcZAABgFBn1AWpzZaNa2yPKzc5SwDfqHy4AABgGoz5RuNjv2z90qrKyLK21AACA0WHUBygAAIDBRoACAADwiAAFAADgUUoByszGm9kDZrbJzDaa2dvMbIKZPWNmb8V+lwx1sQAAAJkg1RGon0t60jl3jKSTJG2UdIuk55xzCyU9F/sfAABg1Os3QJlZsaRzJN0lSc65kHOuTtKVku6OzXa3pKuGqsgjURsMpbsEAAAwyqQyAjVPUpWk35vZSjP7rZkVSJrinKuIzVMpacpQFTlQNU1t+tjv35Qk5fg53AsAAAyOVFKFX9Ipkm53zp0sqVnddtc555wOt1zqwsxuMrNyMyuvqqo60no9aWjtkCRdetwUnT5vwrDeNwAAGL1SCVB7Je11zi2L/f+AooHqgJlNk6TY74PJruycu8M5V+acKystLR2Mmj277Php8tOFHAAADJJ+U4VzrlLSHjM7OjbpQkkbJD0i6YbYtBskPTwkFQIAAGQYf4rz3SzpT2YWkLRd0scUDV/3mdknJO2SdPXQlAgAAJBZUgpQzrlVksqSXHTh4JYDAACQ+TgwCAAAwCMCFAAAgEejOkCt31+f7hIAAMAoNKoD1Ib9DZKkxdOL0lwJAAAYTUZ1gDKT/FmmRVMK010KAAAYRUZ1gAIAABgKBCgAAACPCFAAAAAeEaAAAAA8IkABAAB4NGoDVCTitLmyUR0Rl+5SAADAKDNqA9R95Xv07MaDys0etQ8RAACkyahNFw2t7ZKkP33yjDRXAgAARptRG6DijplKF3IAADC4Rn2AAgAAGGwEKAAAAI8IUAAAAB4RoAAAADwiQAEAAHg0agPUY2sq0l0CAAAYpUZtgGptD0uS8rJ9aa4EAACMNqM2QJlM7zxuqrKyLN2lAACAUWbUBigAAIChQoACAADwiAAFAADgEQEKAADAIwIUAACAR/50FzCYKupbtKsmqAkFATWHOtJdDgAAGKVGVYB6fG2lbntsQ+L/M+ZNTGM1AABgtBpVu/AuO35q4u/LT5iqW9+zOI3VAACA0WpUBajp4/M0Y3yeJGlacZ6KcrPTXBEAABiNRlWAAgAAGA4EKAAAAI8IUAAAAB6lFKDMbKeZrTWzVWZWHpv2bTPbF5u2yswuH9pSAQAAMoOXNgbnO+equ037qXPuR4NZEAAAQKYbdbvwnHPpLgEAAIxyqQYoJ+lpM1tuZjd1mv45M1tjZr8zs5JkVzSzm8ys3MzKq6qqjrjg/nzozDk6d1GpLl48ZcjvCwAAjE2WyoiNmc1wzu0zs8mSnpF0s6TNkqoVDVe3SZrmnPt4X7dTVlbmysvLj7xqAACAIWZmy51zZckuS2kEyjm3L/b7oKSHJJ3unDvgnAs75yKS7pR0+mAVDAAAkMn6DVBmVmBmhfG/JV0iaZ2ZTes023slrRuaEgEAADJLKmfhTZH0kJnF5/+zc+5JM/uDmS1RdBfeTkmfGrIqAQAAMki/Aco5t13SSUmmXz8kFQEAAGS4UdfGAAAAYKgRoAAAADwiQAEAAHhEgAIAAPCIAAUAAOARAQoAAMAjAhQAAIBHBCgAAACPCFAAAAAemXNu+O7MrErSriG+m0mSqof4PtATy334scyHH8t8+LHM04PlHjXHOVea7IJhDVDDwczKnXNl6a5jrGG5Dz+W+fBjmQ8/lnl6sNz7xy48AAAAjwhQAAAAHo3GAHVHugsYo1juw49lPvxY5sOPZZ4eLPd+jLpjoAAAAIbaaByBAgAAGFKjKkCZ2TvNbLOZbTWzW9Jdz2hiZjvNbK2ZrTKz8ti0CWb2jJm9FftdEptuZvaL2POwxsxOSW/1I4OZ/c7MDprZuk7TPC9jM7shNv9bZnZDOh7LSNLLcv+2me2Lre+rzOzyTpd9PbbcN5vZpZ2ms/1JkZnNMrPnzWyDma03sy/EprO+D5E+ljnr+kA550bFjySfpG2S5ksKSFotaXG66xotP5J2SprUbdoPJN0S+/sWSd+P/X25pCckmaQzJS1Ld/0j4UfSOZJOkbRuoMtY0gRJ22O/S2J/l6T7sWXyTy/L/duSvpJk3sWxbUuOpHmxbY6P7Y/nZT5N0imxvwslbYktW9b34V/mrOsD/BlNI1CnS9rqnNvunAtJ+oukK9Nc02h3paS7Y3/fLemqTtPvcVGvSxpvZtPSUeBI4px7SdKhbpO9LuNLJT3jnDvknKuV9Iykdw599SNXL8u9N1dK+otzrs05t0PSVkW3PWx/PHDOVTjnVsT+bpS0UdIMsb4PmT6WeW9Y1/sxmgLUDEl7Ov2/V32vHPDGSXrazJab2U2xaVOccxWxvyslTYn9zXMxeLwuY5b94PlcbHfR7+K7ksRyH3RmNlfSyZKWifV9WHRb5hLr+oCMpgCFofUO59wpki6T9FkzO6fzhS465sspnUOIZTysbpe0QNISSRWSfpzeckYnMxsn6a+Svuica+h8Gev70EiyzFnXB2g0Bah9kmZ1+n9mbBoGgXNuX+z3QUkPKTqMeyC+ay72+2Bsdp6LweN1GbPsB4Fz7oBzLuyci0i6U9H1XWK5Dxozy1b0jfxPzrkHY5NZ34dQsmXOuj5woylAvSlpoZnNM7OApGslPZLmmkYFMysws8L435IukbRO0eUbP+vlBkkPx/5+RNJHYmfOnCmpvtOwPLzxuoyfknSJmZXEhuIviU2DB92O2Xuvouu7FF3u15pZjpnNk7RQ0hti++OJmZmkuyRtdM79pNNFrO9DpLdlzro+cP50FzBYnHMdZvY5RV88Pkm/c86tT3NZo8UUSQ9FX3/yS/qzc+5JM3tT0n1m9glJuyRdHZv/cUXPmtkqKSjpY8Nf8shjZvdKOk/SJDPbK+lWSd+Th2XsnDtkZrcpupGTpO8451I9QHpM6mW5n2dmSxTdhbRT0qckyTm33szuk7RBUoekzzrnwrHbYfuTurMkXS9prZmtik37hljfh1Jvy/w61vWBoRM5AACAR6NpFx4AAMCwIEABAAB4RIACAADwiAAFAADgEQEKAADAIwIUAACARwQoAAAAjwhQAAAAHv1/uGDzcWyt9WoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eXgqCbtL1G3P",
        "outputId": "e450503e-3239-4b7a-e9a5-20053c9dcbf8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ENGINE_RUN_TINE ()                       float64\n",
              "ENGINE_RPM ()                            float64\n",
              "VEHICLE_SPEED ()                         float64\n",
              "THROTTLE ()                              float64\n",
              "ENGINE_LOAD ()                           float64\n",
              "COOLANT_TEMPERATURE ()                   float64\n",
              "LONG_TERM_FUEL_TRIM_BANK_1 ()            float64\n",
              "SHORT_TERM_FUEL_TRIM_BANK_1 ()           float64\n",
              "INTAKE_MANIFOLD_PRESSURE ()              float64\n",
              "FUEL_TANK ()                             float64\n",
              "ABSOLUTE_THROTTLE_B ()                   float64\n",
              "PEDAL_D ()                               float64\n",
              "PEDAL_E ()                               float64\n",
              "COMMANDED_THROTTLE_ACTUATOR ()           float64\n",
              "FUEL_AIR_COMMANDED_EQUIV_RATIO ()        float64\n",
              "ABSOLUTE_BAROMETRIC_PRESSURE ()          float64\n",
              "RELATIVE_THROTTLE_POSITION ()            float64\n",
              "INTAKE_AIR_TEMP ()                       float64\n",
              "TIMING_ADVANCE ()                        float64\n",
              "CATALYST_TEMPERATURE_BANK1_SENSOR1 ()    float64\n",
              "CATALYST_TEMPERATURE_BANK1_SENSOR2 ()    float64\n",
              "CONTROL_MODULE_VOLTAGE ()                float64\n",
              "COMMANDED_EVAPORATIVE_PURGE ()           float64\n",
              "TIME_RUN_WITH_MIL_ON ()                  float64\n",
              "TIME_SINCE_TROUBLE_CODES_CLEARED ()      float64\n",
              "DISTANCE_TRAVELED_WITH_MIL_ON ()         float64\n",
              "Unnamed: 26                              float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10,6))\n",
        "plt.plot(car['ENGINE_RUN_TINE ()'], car['VEHICLE_SPEED ()'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "X_1A38nnlKbz",
        "outputId": "3dfd0292-57e0-4145-8ee3-ad432ed1e5e9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1828be1b10>]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(10,6))\n",
        "plt.plot(car['ENGINE_RUN_TINE ()'], car['FUEL_TANK ()'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "y9WW7Oeo0FST",
        "outputId": "dcfa1d6b-6a58-4926-fd45-91796302fa41"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1828732050>]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "qvIVsOL32NQa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Introdução à Análise Exploratória dos Dados"
      ],
      "metadata": {
        "id": "9FTCT_FolJVB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Será que a velocidade de condução do veículo é uma distribuição normal?*"
      ],
      "metadata": {
        "id": "AaTHawAe-EH3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 7))\n",
        "sns.histplot(data=car, x=\"VEHICLE_SPEED ()\", kde=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "TfgvtchA9l41",
        "outputId": "87c21c58-6937-4f36-8219-718526abbe54"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f18286a78d0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 7))\n",
        "sns.boxplot(y='FUEL_TANK ()',data=car, palette='winter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "lZ4TTZFR-Nk0",
        "outputId": "0470d38e-21b0-481c-81a6-b9e2297a59fd"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f182857e250>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "De certa forma, podemos automatizar a análise com o Pandas Profiling"
      ],
      "metadata": {
        "id": "7RNZCy6a_ECi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n",
        "# !jupyter nbextension enable --py widgetsnbextension"
      ],
      "metadata": {
        "id": "pOU8ONxV0Fc2"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from pandas_profiling import ProfileReport\n",
        "# from pandas_profiling.config import Dataset\n",
        "# from pandas_profiling.utils.cache import cache_file"
      ],
      "metadata": {
        "id": "9XCondtZ8_5m"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# profile = ProfileReport(car, title=\"Car Trip\", explorative=True)\n",
        "# profile"
      ],
      "metadata": {
        "id": "reBjXmFK8nGx"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "profile.to_file(\"profile.html\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "AbVrreiLlKZU",
        "outputId": "8a9cc41f-c9fc-48f4-c36c-d0c13284d4d1"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-aa5ed23ef9c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprofile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"profile.html\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'profile' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Construindo Modelos a partir dos dados\n",
        "\n",
        "A inteligência artifical é o campo da ciência da computação que provê inteligência humana similar à humana para sistemas baseados em _software_. \n",
        "\n",
        "**4.1 Machine Learning**\n",
        "\n",
        "Fornece às máquinas técnicas para aprender a partir dos dados.\n",
        "\n",
        "**4.2 Deep Learning**\n",
        "\n",
        "Uso de redes neurais para a solução de problemas complexos utilizando grande base de dados."
      ],
      "metadata": {
        "id": "LuilrLHFlJXw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**Regressão Linear**\n",
        "\n",
        "![img](https://lh4.ggpht.com/_Qmjqb2Gk9no/TDPGMiFaFcI/AAAAAAAAI-A/nHbiCL9VqbI/image_thumb%5B9%5D.png?imgmax=800)"
      ],
      "metadata": {
        "id": "Y_zPViVrmxc7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = car['COOLANT_TEMPERATURE ()']"
      ],
      "metadata": {
        "id": "_7zdSaOJmIu3"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = car.filter(['ENGINE_RUN_TINE ()','ENGINE_LOAD ()'], axis = 1)"
      ],
      "metadata": {
        "id": "i120HQuxqXqs"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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 = 0.25)"
      ],
      "metadata": {
        "id": "iq9UiIK4_5GS"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "\n",
        "model = tree.DecisionTreeRegressor()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "I7ggLm1JrbrS"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import datetime\n",
        "\n",
        "plt.figure(figsize=(20, 7))\n",
        "plt.plot(range(len(y)), y, 'b-', label = 'actual')\n",
        "plt.plot(y_test.index, y_pred, 'ro', label = 'prediction')\n",
        "plt.plot(y_train.index, y_train, 'yo', label = 'training')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "l05SJ199rbwD",
        "outputId": "e2e61e04-d3aa-4d45-e153-9dc15e658746"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f1826893690>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![img2](https://www.researchgate.net/profile/Brian-Mwandau/publication/325870973/figure/fig6/AS:639531594285060@1529487622235/Train-Test-Data-Split.png)"
      ],
      "metadata": {
        "id": "WjhAuvDDnnN9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "T9iUTJJ4rtuk"
      },
      "outputs": [],
      "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 = 0.25, shuffle=False)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import datetime\n",
        "\n",
        "plt.figure(figsize=(20, 7))\n",
        "plt.plot(range(len(y)), y, 'b-', label = 'Valor Verdadeiro')\n",
        "plt.plot(y_test.index, y_test, 'ro', label = 'Predição')\n",
        "plt.plot(y_train.index, y_train, 'yo', label = 'Treinamento')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "h4WE7fyKtX3P",
        "outputId": "1e2aa9eb-c251-4fe3-ddb1-aeff18be3765"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f18267c4b90>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "uGEwtv9ytX6B"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}