python_igti/01_trabalho_pratico_modulo1.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "01-trabalho_pratico_modulo1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ferris77/my_notebooks/blob/main/python_igti/01_trabalho_pratico_modulo1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mfd1Q8Zlgpx2"
      },
      "source": [
        "#Este programa é utilizado para o desenvolvimento do trabalho prático da disciplina FAM do bootcamp de MLE"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XkAa6kElhAz9"
      },
      "source": [
        "#importando as bibliotecas\n",
        "import pandas as pd #biblioteca utilizada para o tratamento de dados via dataframes \n",
        "import numpy as np #biblioteca utilizada para o tratamento de valores numéricos (vetores e matrizes)\n",
        "import matplotlib.pyplot as plt #biblioteca utilizada para construir os gráficos\n"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5QoWSBvlFph",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "cf9ac4e4-c98e-4508-971d-f715f9cbc943"
      },
      "source": [
        "from google.colab import files  #biblioteca utilizada para carregar os dados para o google colab\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-23edd7d7-5b5b-4c7f-b105-1eaf5ca085f0\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-23edd7d7-5b5b-4c7f-b105-1eaf5ca085f0\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving data.csv to data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3aot9E-li4L"
      },
      "source": [
        "#realizando a leitura do arquivo (dataset)\n",
        "nome_do_arquivo=\"data.csv\"\n",
        "dataframe_envio_portos= pd.read_csv(nome_do_arquivo)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKF37jCWl5SG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "500eb11a-ce39-4fd3-e395-a90b3515e7a6"
      },
      "source": [
        "#apresentando as 5 primeiras linhas do dataset\n",
        "dataframe_envio_portos.head()"
      ],
      "execution_count": 9,
      "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>valid_import</th>\n",
              "      <th>item</th>\n",
              "      <th>importer_id</th>\n",
              "      <th>exporter_id</th>\n",
              "      <th>country_of_origin</th>\n",
              "      <th>declared_quantity</th>\n",
              "      <th>declared_cost</th>\n",
              "      <th>mode_of_transport</th>\n",
              "      <th>route</th>\n",
              "      <th>date_of_departure</th>\n",
              "      <th>date_of_arrival</th>\n",
              "      <th>declared_weight</th>\n",
              "      <th>actual_weight</th>\n",
              "      <th>days_in_transit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>True</td>\n",
              "      <td>cigar</td>\n",
              "      <td>111</td>\n",
              "      <td>222</td>\n",
              "      <td>India</td>\n",
              "      <td>129</td>\n",
              "      <td>3784.402551</td>\n",
              "      <td>sea</td>\n",
              "      <td>asia</td>\n",
              "      <td>04/25/2019</td>\n",
              "      <td>05/13/2019</td>\n",
              "      <td>1608.605135</td>\n",
              "      <td>1637.661221</td>\n",
              "      <td>18.232857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>True</td>\n",
              "      <td>cigar</td>\n",
              "      <td>111</td>\n",
              "      <td>222</td>\n",
              "      <td>India</td>\n",
              "      <td>104</td>\n",
              "      <td>3081.350806</td>\n",
              "      <td>sea</td>\n",
              "      <td>america</td>\n",
              "      <td>04/22/2019</td>\n",
              "      <td>05/24/2019</td>\n",
              "      <td>831.719301</td>\n",
              "      <td>848.273419</td>\n",
              "      <td>32.436029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>True</td>\n",
              "      <td>cigar</td>\n",
              "      <td>111</td>\n",
              "      <td>222</td>\n",
              "      <td>India</td>\n",
              "      <td>130</td>\n",
              "      <td>4414.125741</td>\n",
              "      <td>sea</td>\n",
              "      <td>europe</td>\n",
              "      <td>04/29/2019</td>\n",
              "      <td>05/16/2019</td>\n",
              "      <td>1527.704165</td>\n",
              "      <td>1582.063911</td>\n",
              "      <td>16.996206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>True</td>\n",
              "      <td>cigar</td>\n",
              "      <td>111</td>\n",
              "      <td>222</td>\n",
              "      <td>India</td>\n",
              "      <td>143</td>\n",
              "      <td>2533.535991</td>\n",
              "      <td>sea</td>\n",
              "      <td>panama</td>\n",
              "      <td>05/05/2019</td>\n",
              "      <td>05/25/2019</td>\n",
              "      <td>1138.680563</td>\n",
              "      <td>1179.993817</td>\n",
              "      <td>19.965886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>True</td>\n",
              "      <td>cigar</td>\n",
              "      <td>111</td>\n",
              "      <td>222</td>\n",
              "      <td>China</td>\n",
              "      <td>141</td>\n",
              "      <td>4396.397887</td>\n",
              "      <td>sea</td>\n",
              "      <td>asia</td>\n",
              "      <td>05/14/2019</td>\n",
              "      <td>06/05/2019</td>\n",
              "      <td>761.744581</td>\n",
              "      <td>781.735080</td>\n",
              "      <td>22.160034</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   valid_import   item  ...  actual_weight  days_in_transit\n",
              "0          True  cigar  ...    1637.661221        18.232857\n",
              "1          True  cigar  ...     848.273419        32.436029\n",
              "2          True  cigar  ...    1582.063911        16.996206\n",
              "3          True  cigar  ...    1179.993817        19.965886\n",
              "4          True  cigar  ...     781.735080        22.160034\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OL2cLcK_mPBO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a413081-b179-4da7-c766-87a7f49d9e14"
      },
      "source": [
        "dataframe_envio_portos.info() #verificando os tipos de variáveis e se existem ou não valores nulos"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 120 entries, 0 to 119\n",
            "Data columns (total 14 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   valid_import       120 non-null    bool   \n",
            " 1   item               120 non-null    object \n",
            " 2   importer_id        120 non-null    int64  \n",
            " 3   exporter_id        120 non-null    int64  \n",
            " 4   country_of_origin  120 non-null    object \n",
            " 5   declared_quantity  120 non-null    int64  \n",
            " 6   declared_cost      120 non-null    float64\n",
            " 7   mode_of_transport  120 non-null    object \n",
            " 8   route              120 non-null    object \n",
            " 9   date_of_departure  120 non-null    object \n",
            " 10  date_of_arrival    120 non-null    object \n",
            " 11  declared_weight    120 non-null    float64\n",
            " 12  actual_weight      120 non-null    float64\n",
            " 13  days_in_transit    120 non-null    float64\n",
            "dtypes: bool(1), float64(4), int64(3), object(6)\n",
            "memory usage: 12.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6H6KdP5EomAM"
      },
      "source": [
        "**Existem Colunas Com Valores Nulos?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6E-2Hd7tyBXA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e593de0a-5545-429f-b9ea-b2bda4716ff3"
      },
      "source": [
        "dataframe_envio_portos.shape"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(120, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mKtkghQso5X3"
      },
      "source": [
        "**Quantas Instâncias e Características Existem no Dataset?**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EK9PIlANo1NA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "52ae398a-9200-420d-96f5-bef3e5b9738d"
      },
      "source": [
        "#analisando a \"estatística\" do dataset\n",
        "dataframe_envio_portos.describe()"
      ],
      "execution_count": 12,
      "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>importer_id</th>\n",
              "      <th>exporter_id</th>\n",
              "      <th>declared_quantity</th>\n",
              "      <th>declared_cost</th>\n",
              "      <th>declared_weight</th>\n",
              "      <th>actual_weight</th>\n",
              "      <th>days_in_transit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>120.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>111.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>127.458333</td>\n",
              "      <td>6743.649881</td>\n",
              "      <td>1264.702934</td>\n",
              "      <td>1306.429806</td>\n",
              "      <td>35.424705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>14.641311</td>\n",
              "      <td>2991.797050</td>\n",
              "      <td>633.149971</td>\n",
              "      <td>656.911704</td>\n",
              "      <td>26.571591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>111.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1441.012419</td>\n",
              "      <td>18.459509</td>\n",
              "      <td>19.275241</td>\n",
              "      <td>12.410325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>111.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>115.750000</td>\n",
              "      <td>4442.903914</td>\n",
              "      <td>820.314400</td>\n",
              "      <td>841.763738</td>\n",
              "      <td>18.225625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>111.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>131.500000</td>\n",
              "      <td>6010.218745</td>\n",
              "      <td>1255.597743</td>\n",
              "      <td>1305.716419</td>\n",
              "      <td>27.044293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>111.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>8887.095370</td>\n",
              "      <td>1711.314045</td>\n",
              "      <td>1763.681083</td>\n",
              "      <td>44.356374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>111.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>14281.325362</td>\n",
              "      <td>2806.338955</td>\n",
              "      <td>2918.681683</td>\n",
              "      <td>147.787560</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       importer_id  exporter_id  ...  actual_weight  days_in_transit\n",
              "count        120.0        120.0  ...     120.000000       120.000000\n",
              "mean         111.0        222.0  ...    1306.429806        35.424705\n",
              "std            0.0          0.0  ...     656.911704        26.571591\n",
              "min          111.0        222.0  ...      19.275241        12.410325\n",
              "25%          111.0        222.0  ...     841.763738        18.225625\n",
              "50%          111.0        222.0  ...    1305.716419        27.044293\n",
              "75%          111.0        222.0  ...    1763.681083        44.356374\n",
              "max          111.0        222.0  ...    2918.681683       147.787560\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMDw3h6RwmEh"
      },
      "source": [
        "**Qual é o Valor Médio Para os Pesos Declarados?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcWZdA5EwlCu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "c94e8f72-6bc0-441f-c6ca-cbaa047e406c"
      },
      "source": [
        "#identificando possíveis outliers\n",
        "dataframe_envio_portos[['declared_quantity','days_in_transit']].boxplot()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f170b079a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "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": "ZqxbwHTtxM6A"
      },
      "source": [
        "**Existem Possíveis Outliers?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IECULZZuxMC_"
      },
      "source": [
        "#realizando a análise de regressão\n",
        "x=dataframe_envio_portos['declared_weight'].values  #variável independente \n",
        "Y=dataframe_envio_portos['actual_weight'].values #variável dependente "
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSoJQBOMy7F8"
      },
      "source": [
        "#importa o modelo de regressão linear univariada\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWHHadB4x9_k"
      },
      "source": [
        "#Realiza a construção do modelo de regressão\n",
        "reg= LinearRegression()\n",
        "x_Reshaped=x.reshape((-1, 1)) #coloca os dados no formato 2D\n",
        "regressao= reg.fit (x_Reshaped,Y) # encontra os coeficientes (realiza a regressão)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvBdinlry6VR"
      },
      "source": [
        "#realiza a previsão\n",
        "previsao=reg.predict(x_Reshaped)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYdLgLmOzhBr"
      },
      "source": [
        "#análise do modelo\n",
        "from sklearn.metrics import r2_score #método para o cálculo do R2 (coeficiente de determinação)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JBYpbs5za47",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "905cfa72-69d1-4dfa-8eb5-9dec3d28aa5c"
      },
      "source": [
        "#parâmetros encontrados\n",
        "print('Y = {}X {}'.format(reg.coef_,reg.intercept_))\n",
        "\n",
        "R_2 = r2_score(Y, previsao)  #realiza o cálculo do R2\n",
        "\n",
        "print(\"Coeficiente de Determinação (R2):\", R_2)\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Y = [1.03718115]X -5.296233030439225\n",
            "Coeficiente de Determinação (R2): 0.9993288165644932\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlguRh0t0oU6"
      },
      "source": [
        "**Pelo Coefiente de Determinação, o Que É Possível Afirmar Sobre a Relação Entre as Variáveis Peso Real x Peso Declarado?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dl1idrd7zuJM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "23d0722d-e2d0-4a1f-e2c7-8e5039b82396"
      },
      "source": [
        "#realiza o plot dos dados\n",
        "plt.figure(figsize=(4, 4), dpi=100)\n",
        "plt.scatter(x, Y,  color='gray') #realiza o plot do gráfico de dispersão\n",
        "plt.plot(x, previsao, color='red', linewidth=2) # realiza o plto da \"linha\"\n",
        "plt.xlabel(\"Peso Declarado\")\n",
        "plt.ylabel(\"Peso Real\")\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 400x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}