Classificação_de_comidas.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Classificação de comidas.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "mount_file_id": "1IARxaBajSK4afcWq9tS5D8m1zHdt0YY3",
      "authorship_tag": "ABX9TyNzwqm20h1KdYdeal079Kis"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvoovXI2lpjl"
      },
      "source": [
        "# Aprendizado Multilabel aplicado na recomendação de comida de food trucks\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O presente notebook apresenta modelos de classificação multilabel visando inferências de pratos de comidas do seguimento de food trucks, baseado no histórico, informações sobre perfil, gostos e preferências de usuários desse tipo de negócio. O objetivo é oferecer uma alternativa competitiva para o segmento."
      ],
      "metadata": {
        "id": "4nmuEsHejSN3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "O dataset *foodtruck* é utlizado como fonte de dados neste trabalho, por ser disponibilizado gratuitamente e já ser mapeado para o aprendizado multilabel. "
      ],
      "metadata": {
        "id": "yvSX_d4nlv5P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Características do *dataset*:\n",
        "* 407 instâncias de dados\n",
        "* 33 atributos: \n",
        "  * 21 atributos de entrada (informações de perfil e preferências de usários)\n",
        "  * 12 atributos de classe (representado pelas classes de comidas)\n",
        "\n",
        "O *dataset* pode ser baixado no [site](https://www.uco.es/kdis/mllresources/) do grupo de pesquisa *Knowledge Discovery and Intelligent Systems (KDIS)*  da Universidade de Córdoba, Espanha."
      ],
      "metadata": {
        "id": "X-264xZ6mfrT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[Acesse o relatório completo aqui](https://docs.google.com/document/d/1x3_CbZHFwOk9psTIJ1uJiBLOiDhlPe88/edit?usp=sharing&ouid=100274702842793876190&rtpof=true&sd=true)\n"
      ],
      "metadata": {
        "id": "7WHDXrYgi7Dm"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqBDz33knuFT"
      },
      "source": [
        "# Importações de pacotes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hVwDA-hon-zt"
      },
      "source": [
        "# Data Preprocessing Packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from pandas_profiling import ProfileReport\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "import re\n",
        "from scipy.io.arff import loadarff\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "# Data Visualization Packages\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-multilearn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wLzK48zd6_4g",
        "outputId": "7bc74d5b-f615-4c59-dae9-248be3d383e8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-multilearn in /usr/local/lib/python3.7/dist-packages (0.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xz1zLA7Kn0nd"
      },
      "source": [
        "# Carregando Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFuqj1kCl2J7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5d704af1-9aa7-4081-d805-4b1eb7b64a8f"
      },
      "source": [
        "#raw_data = loadarff('foodtruck.arff')\n",
        "raw_data = loadarff('/content/drive/MyDrive/Colab Notebooks/datasets/foodtruck/foodtruck.arff')\n",
        "df = pd.DataFrame(raw_data[0])\n",
        "df.head(30)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-23f3289f-fd5c-48e9-96b3-afe8d75ddbdb\">\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>frequency</th>\n",
              "      <th>time</th>\n",
              "      <th>expenses</th>\n",
              "      <th>motivation</th>\n",
              "      <th>taste</th>\n",
              "      <th>hygiene</th>\n",
              "      <th>menu</th>\n",
              "      <th>presentation</th>\n",
              "      <th>attendance</th>\n",
              "      <th>ingredients</th>\n",
              "      <th>place.to.sit</th>\n",
              "      <th>takeaway</th>\n",
              "      <th>variation</th>\n",
              "      <th>stop.strucks</th>\n",
              "      <th>schedule</th>\n",
              "      <th>gender</th>\n",
              "      <th>age.group</th>\n",
              "      <th>scholarity</th>\n",
              "      <th>average.income</th>\n",
              "      <th>has.work</th>\n",
              "      <th>marital.status</th>\n",
              "      <th>street_food</th>\n",
              "      <th>gourmet</th>\n",
              "      <th>italian_food</th>\n",
              "      <th>brazilian_food</th>\n",
              "      <th>mexican_food</th>\n",
              "      <th>chinese_food</th>\n",
              "      <th>japanese_food</th>\n",
              "      <th>arabic_food</th>\n",
              "      <th>snacks</th>\n",
              "      <th>healthy_food</th>\n",
              "      <th>fitness_food</th>\n",
              "      <th>sweets_desserts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>30.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'by_chance'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'married'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'afternoon'</td>\n",
              "      <td>15.0</td>\n",
              "      <td>b'by_chance'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'lunch'</td>\n",
              "      <td>40.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>15.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>15.0</td>\n",
              "      <td>b'by_chance'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>40.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>15.0</td>\n",
              "      <td>b'by_chance'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'married'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>40.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>15.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>30.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>15.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>30.0</td>\n",
              "      <td>b'social_network'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'happy_hour'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'married'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>4.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2.0</td>\n",
              "      <td>b'happy_hour'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'friend'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>30.0</td>\n",
              "      <td>b'by_chance'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'happy_hour'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'by_chance'</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>b'F'</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'1'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>0.0</td>\n",
              "      <td>b'dinner'</td>\n",
              "      <td>20.0</td>\n",
              "      <td>b'web'</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>b'M'</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>b'single'</td>\n",
              "      <td>b'1'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "      <td>b'0'</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23f3289f-fd5c-48e9-96b3-afe8d75ddbdb')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-23f3289f-fd5c-48e9-96b3-afe8d75ddbdb 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-23f3289f-fd5c-48e9-96b3-afe8d75ddbdb');\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",
              "  "
            ],
            "text/plain": [
              "    frequency           time  ...  fitness_food sweets_desserts\n",
              "0         2.0      b'dinner'  ...          b'0'            b'1'\n",
              "1         0.0      b'dinner'  ...          b'0'            b'1'\n",
              "2         1.0   b'afternoon'  ...          b'0'            b'0'\n",
              "3         0.0       b'lunch'  ...          b'0'            b'0'\n",
              "4         0.0      b'dinner'  ...          b'0'            b'0'\n",
              "5         1.0      b'dinner'  ...          b'0'            b'1'\n",
              "6         1.0      b'dinner'  ...          b'0'            b'1'\n",
              "7         0.0      b'dinner'  ...          b'0'            b'1'\n",
              "8         1.0      b'dinner'  ...          b'0'            b'1'\n",
              "9         0.0      b'dinner'  ...          b'0'            b'0'\n",
              "10        0.0      b'dinner'  ...          b'0'            b'1'\n",
              "11        1.0      b'dinner'  ...          b'0'            b'0'\n",
              "12        1.0      b'dinner'  ...          b'0'            b'1'\n",
              "13        1.0      b'dinner'  ...          b'0'            b'0'\n",
              "14        1.0      b'dinner'  ...          b'0'            b'0'\n",
              "15        1.0      b'dinner'  ...          b'0'            b'0'\n",
              "16        1.0      b'dinner'  ...          b'0'            b'1'\n",
              "17        1.0      b'dinner'  ...          b'0'            b'1'\n",
              "18        1.0      b'dinner'  ...          b'0'            b'0'\n",
              "19        0.0      b'dinner'  ...          b'0'            b'1'\n",
              "20        1.0      b'dinner'  ...          b'1'            b'1'\n",
              "21        1.0      b'dinner'  ...          b'0'            b'1'\n",
              "22        0.0      b'dinner'  ...          b'0'            b'1'\n",
              "23        1.0      b'dinner'  ...          b'0'            b'0'\n",
              "24        0.0  b'happy_hour'  ...          b'0'            b'1'\n",
              "25        4.0      b'dinner'  ...          b'0'            b'1'\n",
              "26        2.0  b'happy_hour'  ...          b'0'            b'1'\n",
              "27        0.0      b'dinner'  ...          b'0'            b'1'\n",
              "28        0.0  b'happy_hour'  ...          b'0'            b'1'\n",
              "29        0.0      b'dinner'  ...          b'0'            b'0'\n",
              "\n",
              "[30 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rQ4hAHTu0_x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81eb3d05-1a81-41f5-cd13-37e8bbfdc175"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "frequency          float64\n",
              "time                object\n",
              "expenses           float64\n",
              "motivation          object\n",
              "taste              float64\n",
              "hygiene            float64\n",
              "menu               float64\n",
              "presentation       float64\n",
              "attendance         float64\n",
              "ingredients        float64\n",
              "place.to.sit       float64\n",
              "takeaway           float64\n",
              "variation          float64\n",
              "stop.strucks       float64\n",
              "schedule           float64\n",
              "gender              object\n",
              "age.group          float64\n",
              "scholarity         float64\n",
              "average.income     float64\n",
              "has.work           float64\n",
              "marital.status      object\n",
              "street_food         object\n",
              "gourmet             object\n",
              "italian_food        object\n",
              "brazilian_food      object\n",
              "mexican_food        object\n",
              "chinese_food        object\n",
              "japanese_food       object\n",
              "arabic_food         object\n",
              "snacks              object\n",
              "healthy_food        object\n",
              "fitness_food        object\n",
              "sweets_desserts     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "t-rp_iCtu_P0",
        "outputId": "4363c343-a2bd-4212-bea0-84eeb0ccfb6a"
      },
      "source": [
        "#Ajustando data types\n",
        "df[\"street_food\"] = pd.to_numeric(df[\"street_food\"])\n",
        "df[\"gourmet\"] = pd.to_numeric(df[\"gourmet\"])\n",
        "df[\"italian_food\"] = pd.to_numeric(df[\"italian_food\"])\n",
        "df[\"brazilian_food\"] = pd.to_numeric(df[\"brazilian_food\"])\n",
        "df[\"mexican_food\"] = pd.to_numeric(df[\"mexican_food\"])\n",
        "df[\"chinese_food\"] = pd.to_numeric(df[\"chinese_food\"])\n",
        "df[\"japanese_food\"] = pd.to_numeric(df[\"japanese_food\"])\n",
        "df[\"arabic_food\"] = pd.to_numeric(df[\"arabic_food\"])\n",
        "df[\"snacks\"] = pd.to_numeric(df[\"snacks\"])\n",
        "df[\"healthy_food\"] = pd.to_numeric(df[\"healthy_food\"])\n",
        "df[\"fitness_food\"] = pd.to_numeric(df[\"fitness_food\"])\n",
        "df[\"sweets_desserts\"] = pd.to_numeric(df[\"sweets_desserts\"])\n",
        "\n",
        "df[\"time\"] =df.iloc[:,1].str.decode('utf8')\n",
        "df[\"motivation\"] =df.iloc[:,3].str.decode('utf8')\n",
        "df[\"gender\"] =df.iloc[:,15].str.decode('utf8')\n",
        "df[\"marital.status\"] =df.iloc[:,20].str.decode('utf8')\n",
        "\n",
        "df"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-99a90967-a814-4512-a9fd-67444da15576\">\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>frequency</th>\n",
              "      <th>time</th>\n",
              "      <th>expenses</th>\n",
              "      <th>motivation</th>\n",
              "      <th>taste</th>\n",
              "      <th>hygiene</th>\n",
              "      <th>menu</th>\n",
              "      <th>presentation</th>\n",
              "      <th>attendance</th>\n",
              "      <th>ingredients</th>\n",
              "      <th>place.to.sit</th>\n",
              "      <th>takeaway</th>\n",
              "      <th>variation</th>\n",
              "      <th>stop.strucks</th>\n",
              "      <th>schedule</th>\n",
              "      <th>gender</th>\n",
              "      <th>age.group</th>\n",
              "      <th>scholarity</th>\n",
              "      <th>average.income</th>\n",
              "      <th>has.work</th>\n",
              "      <th>marital.status</th>\n",
              "      <th>street_food</th>\n",
              "      <th>gourmet</th>\n",
              "      <th>italian_food</th>\n",
              "      <th>brazilian_food</th>\n",
              "      <th>mexican_food</th>\n",
              "      <th>chinese_food</th>\n",
              "      <th>japanese_food</th>\n",
              "      <th>arabic_food</th>\n",
              "      <th>snacks</th>\n",
              "      <th>healthy_food</th>\n",
              "      <th>fitness_food</th>\n",
              "      <th>sweets_desserts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>friend</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>single</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>20.0</td>\n",
              "      <td>by_chance</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>F</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>married</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>afternoon</td>\n",
              "      <td>15.0</td>\n",
              "      <td>by_chance</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>lunch</td>\n",
              "      <td>40.0</td>\n",
              "      <td>friend</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</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",
              "      <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>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>15.0</td>\n",
              "      <td>social_network</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</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",
              "      <td>0</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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>402</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>friend</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>F</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</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",
              "      <td>0</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>403</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>social_network</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>F</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>single</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>social_network</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>1.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>social_network</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>M</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>married</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>406</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>15.0</td>\n",
              "      <td>friend</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>M</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>single</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>407 rows × 33 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99a90967-a814-4512-a9fd-67444da15576')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-99a90967-a814-4512-a9fd-67444da15576 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-99a90967-a814-4512-a9fd-67444da15576');\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",
              "  "
            ],
            "text/plain": [
              "     frequency       time  ...  fitness_food sweets_desserts\n",
              "0          2.0     dinner  ...             0               1\n",
              "1          0.0     dinner  ...             0               1\n",
              "2          1.0  afternoon  ...             0               0\n",
              "3          0.0      lunch  ...             0               0\n",
              "4          0.0     dinner  ...             0               0\n",
              "..         ...        ...  ...           ...             ...\n",
              "402        0.0     dinner  ...             0               0\n",
              "403        0.0     dinner  ...             0               1\n",
              "404        0.0     dinner  ...             0               1\n",
              "405        1.0     dinner  ...             0               1\n",
              "406        0.0     dinner  ...             0               0\n",
              "\n",
              "[407 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QG5RLNGqJrig",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "276ad19d-9054-4199-9025-44e8dbd3255b"
      },
      "source": [
        "! pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n",
            "  Using cached https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n",
            "Requirement already satisfied: joblib~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (1.1.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (1.7.3)\n",
            "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (1.1.5)\n",
            "Requirement already satisfied: matplotlib>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (3.2.2)\n",
            "Requirement already satisfied: pydantic>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (1.9.0)\n",
            "Requirement already satisfied: PyYAML>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (6.0)\n",
            "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (2.11.3)\n",
            "Requirement already satisfied: markupsafe~=2.0.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (2.0.1)\n",
            "Requirement already satisfied: visions[type_image_path]==0.7.4 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (0.7.4)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (1.19.5)\n",
            "Requirement already satisfied: htmlmin>=0.1.12 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (0.1.12)\n",
            "Requirement already satisfied: missingno>=0.4.2 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (0.5.0)\n",
            "Requirement already satisfied: phik>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (0.12.0)\n",
            "Requirement already satisfied: tangled-up-in-unicode==0.2.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (0.2.0)\n",
            "Requirement already satisfied: requests>=2.24.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (2.26.0)\n",
            "Requirement already satisfied: tqdm>=4.48.2 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (4.62.3)\n",
            "Requirement already satisfied: seaborn>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (0.11.2)\n",
            "Requirement already satisfied: multimethod>=1.4 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.1.1) (1.6)\n",
            "Requirement already satisfied: networkx>=2.4 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.4->pandas-profiling==3.1.1) (2.6.3)\n",
            "Requirement already satisfied: attrs>=19.3.0 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.4->pandas-profiling==3.1.1) (21.2.0)\n",
            "Requirement already satisfied: imagehash in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.4->pandas-profiling==3.1.1) (4.2.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.4->pandas-profiling==3.1.1) (7.1.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.1.1) (3.0.6)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.1.1) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.1.1) (0.11.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.1.1) (1.3.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas-profiling==3.1.1) (2018.9)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from pydantic>=1.8.1->pandas-profiling==3.1.1) (3.10.0.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.2.0->pandas-profiling==3.1.1) (1.15.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.1.1) (1.24.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.1.1) (2.0.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.1.1) (2021.10.8)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.1.1) (2.10)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash->visions[type_image_path]==0.7.4->pandas-profiling==3.1.1) (1.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M99PYEQ1KlcN"
      },
      "source": [
        "#profile = ProfileReport(df, title='tt', html={'style':{'full_width':True}})"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8Sg-_KoNA0o"
      },
      "source": [
        "#profile"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTYXlPAZsHh5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "dfe770ca-ad34-407e-8e12-04a5967efdd0"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-9bccd8fc-d36b-41d4-91f3-ec4412cd8096\">\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>frequency</th>\n",
              "      <th>expenses</th>\n",
              "      <th>taste</th>\n",
              "      <th>hygiene</th>\n",
              "      <th>menu</th>\n",
              "      <th>presentation</th>\n",
              "      <th>attendance</th>\n",
              "      <th>ingredients</th>\n",
              "      <th>place.to.sit</th>\n",
              "      <th>takeaway</th>\n",
              "      <th>variation</th>\n",
              "      <th>stop.strucks</th>\n",
              "      <th>schedule</th>\n",
              "      <th>age.group</th>\n",
              "      <th>scholarity</th>\n",
              "      <th>average.income</th>\n",
              "      <th>has.work</th>\n",
              "      <th>street_food</th>\n",
              "      <th>gourmet</th>\n",
              "      <th>italian_food</th>\n",
              "      <th>brazilian_food</th>\n",
              "      <th>mexican_food</th>\n",
              "      <th>chinese_food</th>\n",
              "      <th>japanese_food</th>\n",
              "      <th>arabic_food</th>\n",
              "      <th>snacks</th>\n",
              "      <th>healthy_food</th>\n",
              "      <th>fitness_food</th>\n",
              "      <th>sweets_desserts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.000000</td>\n",
              "      <td>407.00000</td>\n",
              "      <td>407.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.761671</td>\n",
              "      <td>21.486486</td>\n",
              "      <td>4.771499</td>\n",
              "      <td>4.651106</td>\n",
              "      <td>3.773956</td>\n",
              "      <td>4.272727</td>\n",
              "      <td>4.535627</td>\n",
              "      <td>4.481572</td>\n",
              "      <td>3.786241</td>\n",
              "      <td>2.732187</td>\n",
              "      <td>3.420147</td>\n",
              "      <td>2.690418</td>\n",
              "      <td>2.663391</td>\n",
              "      <td>2.358722</td>\n",
              "      <td>1.640049</td>\n",
              "      <td>2.958231</td>\n",
              "      <td>0.511057</td>\n",
              "      <td>0.724816</td>\n",
              "      <td>0.294840</td>\n",
              "      <td>0.105651</td>\n",
              "      <td>0.176904</td>\n",
              "      <td>0.100737</td>\n",
              "      <td>0.039312</td>\n",
              "      <td>0.088452</td>\n",
              "      <td>0.061425</td>\n",
              "      <td>0.164619</td>\n",
              "      <td>0.081081</td>\n",
              "      <td>0.07371</td>\n",
              "      <td>0.378378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.987461</td>\n",
              "      <td>7.247503</td>\n",
              "      <td>0.595032</td>\n",
              "      <td>0.699150</td>\n",
              "      <td>1.097696</td>\n",
              "      <td>0.877647</td>\n",
              "      <td>0.724711</td>\n",
              "      <td>0.799649</td>\n",
              "      <td>1.164333</td>\n",
              "      <td>0.982367</td>\n",
              "      <td>1.070374</td>\n",
              "      <td>1.283653</td>\n",
              "      <td>1.356325</td>\n",
              "      <td>1.412145</td>\n",
              "      <td>0.907029</td>\n",
              "      <td>1.569640</td>\n",
              "      <td>0.500493</td>\n",
              "      <td>0.447157</td>\n",
              "      <td>0.456532</td>\n",
              "      <td>0.307769</td>\n",
              "      <td>0.382057</td>\n",
              "      <td>0.301351</td>\n",
              "      <td>0.194575</td>\n",
              "      <td>0.284301</td>\n",
              "      <td>0.240404</td>\n",
              "      <td>0.371293</td>\n",
              "      <td>0.273296</td>\n",
              "      <td>0.26162</td>\n",
              "      <td>0.485580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.000000</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",
              "      <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>0.000000</td>\n",
              "      <td>1.000000</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",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</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",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</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",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</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",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>6.000000</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",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9bccd8fc-d36b-41d4-91f3-ec4412cd8096')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-9bccd8fc-d36b-41d4-91f3-ec4412cd8096 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-9bccd8fc-d36b-41d4-91f3-ec4412cd8096');\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",
              "  "
            ],
            "text/plain": [
              "        frequency    expenses  ...  fitness_food  sweets_desserts\n",
              "count  407.000000  407.000000  ...     407.00000       407.000000\n",
              "mean     0.761671   21.486486  ...       0.07371         0.378378\n",
              "std      0.987461    7.247503  ...       0.26162         0.485580\n",
              "min      0.000000   15.000000  ...       0.00000         0.000000\n",
              "25%      0.000000   15.000000  ...       0.00000         0.000000\n",
              "50%      0.000000   20.000000  ...       0.00000         0.000000\n",
              "75%      1.000000   20.000000  ...       0.00000         1.000000\n",
              "max      4.000000   50.000000  ...       1.00000         1.000000\n",
              "\n",
              "[8 rows x 29 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cuZrqIDEnkpr"
      },
      "source": [
        "# Pré-processamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Omwmig2OtQp2"
      },
      "source": [
        "labels = ['street_food', 'gourmet', 'italian_food', 'brazilian_food', 'mexican_food', 'chinese_food', 'japanese_food', 'arabic_food', 'snacks', 'healthy_food', 'fitness_food', 'sweets_desserts']\n",
        "\n",
        "#dividindo dataset (features e labels)\n",
        "feats_df = df.drop(columns = labels)\n",
        "labels_df = df[labels]"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "feats_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "o9WSXlYQu4k2",
        "outputId": "80b44fe5-44b3-4460-a5b0-16a796879ef4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d69af04a-1986-4e3b-a8c9-bab96b56e3a4\">\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>frequency</th>\n",
              "      <th>time</th>\n",
              "      <th>expenses</th>\n",
              "      <th>motivation</th>\n",
              "      <th>taste</th>\n",
              "      <th>hygiene</th>\n",
              "      <th>menu</th>\n",
              "      <th>presentation</th>\n",
              "      <th>attendance</th>\n",
              "      <th>ingredients</th>\n",
              "      <th>place.to.sit</th>\n",
              "      <th>takeaway</th>\n",
              "      <th>variation</th>\n",
              "      <th>stop.strucks</th>\n",
              "      <th>schedule</th>\n",
              "      <th>gender</th>\n",
              "      <th>age.group</th>\n",
              "      <th>scholarity</th>\n",
              "      <th>average.income</th>\n",
              "      <th>has.work</th>\n",
              "      <th>marital.status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>friend</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>single</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>20.0</td>\n",
              "      <td>by_chance</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>F</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>married</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>afternoon</td>\n",
              "      <td>15.0</td>\n",
              "      <td>by_chance</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>lunch</td>\n",
              "      <td>40.0</td>\n",
              "      <td>friend</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>15.0</td>\n",
              "      <td>social_network</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>402</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>friend</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>F</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>403</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>social_network</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>F</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>single</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>social_network</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>single</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>1.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>30.0</td>\n",
              "      <td>social_network</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>M</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>married</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>406</th>\n",
              "      <td>0.0</td>\n",
              "      <td>dinner</td>\n",
              "      <td>15.0</td>\n",
              "      <td>friend</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>M</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>single</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>407 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d69af04a-1986-4e3b-a8c9-bab96b56e3a4')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-d69af04a-1986-4e3b-a8c9-bab96b56e3a4 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-d69af04a-1986-4e3b-a8c9-bab96b56e3a4');\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",
              "  "
            ],
            "text/plain": [
              "     frequency       time  expenses  ... average.income  has.work  marital.status\n",
              "0          2.0     dinner      30.0  ...            4.0       0.0          single\n",
              "1          0.0     dinner      20.0  ...            5.0       1.0         married\n",
              "2          1.0  afternoon      15.0  ...            4.0       1.0          single\n",
              "3          0.0      lunch      40.0  ...            6.0       1.0          single\n",
              "4          0.0     dinner      15.0  ...            4.0       1.0          single\n",
              "..         ...        ...       ...  ...            ...       ...             ...\n",
              "402        0.0     dinner      30.0  ...            3.0       1.0          single\n",
              "403        0.0     dinner      30.0  ...            4.0       0.0          single\n",
              "404        0.0     dinner      30.0  ...            1.0       1.0          single\n",
              "405        1.0     dinner      30.0  ...            2.0       1.0         married\n",
              "406        0.0     dinner      15.0  ...            1.0       0.0          single\n",
              "\n",
              "[407 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "4SvgIp1CvHUV",
        "outputId": "678d973d-521f-4d68-aef8-2cf7f00a05f0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7ca383ce-50f1-47ae-983b-187a90c1ef66\">\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>street_food</th>\n",
              "      <th>gourmet</th>\n",
              "      <th>italian_food</th>\n",
              "      <th>brazilian_food</th>\n",
              "      <th>mexican_food</th>\n",
              "      <th>chinese_food</th>\n",
              "      <th>japanese_food</th>\n",
              "      <th>arabic_food</th>\n",
              "      <th>snacks</th>\n",
              "      <th>healthy_food</th>\n",
              "      <th>fitness_food</th>\n",
              "      <th>sweets_desserts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>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",
              "      <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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>402</th>\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",
              "      <td>0</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>403</th>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404</th>\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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>406</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>407 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7ca383ce-50f1-47ae-983b-187a90c1ef66')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-7ca383ce-50f1-47ae-983b-187a90c1ef66 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-7ca383ce-50f1-47ae-983b-187a90c1ef66');\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",
              "  "
            ],
            "text/plain": [
              "     street_food  gourmet  ...  fitness_food  sweets_desserts\n",
              "0              1        0  ...             0                1\n",
              "1              1        0  ...             0                1\n",
              "2              1        0  ...             0                0\n",
              "3              0        1  ...             0                0\n",
              "4              1        0  ...             0                0\n",
              "..           ...      ...  ...           ...              ...\n",
              "402            1        0  ...             0                0\n",
              "403            1        1  ...             0                1\n",
              "404            1        0  ...             0                1\n",
              "405            1        1  ...             0                1\n",
              "406            0        0  ...             0                0\n",
              "\n",
              "[407 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "OGazQ8djsXus",
        "outputId": "3cbe1d1d-35d1-4c5d-d48d-976c5dadbef4"
      },
      "source": [
        "#Calculando as instâncias para cada classe\n",
        "label_d = {'street_food' : 0, 'gourmet': 0, 'italian_food' : 0, 'brazilian_food' : 0, 'mexican_food': 0, 'chinese_food' : 0, 'japanese_food': 0, 'arabic_food':0, 'snacks': 0, 'healthy_food':0, 'fitness_food':0, 'sweets_desserts':0}\n",
        "for col in labels_df.columns:\n",
        "    label_d[col] += len(labels_df[labels_df[col] == 1])\n",
        "    \n",
        "#visualizando o (des)balanceamento do dataset\n",
        "plt.figure(figsize = (16,8))\n",
        "plt.bar(range(len(label_d)), list(label_d.values()), align='center')\n",
        "plt.xticks(range(len(label_d)), list(label_d.keys()))\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAHTCAYAAADfxb6zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de7xuVV0v/s9XIDEvILIjQnT7M9RjpahEZmqonfJyDDx5PR5Fj0WW5i0r6phRR0+Ut5Nd7OAlsEzFO4mpiBpoeQGEDYgUKSqEgoqX8miB4/fHHMv97HVfe621B3vzfr9e67XmM575PHPM25jzM29PtdYCAAAAI9xkdAUAAAC48RJKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhm79EVSJIDDzywbd26dXQ1AAAA2ATnnnvul1prWxZ77wYRSrdu3ZpzzjlndDUAAADYBFX12aXec/kuAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwK4bSqtq3qj5WVRdU1cVV9bu9/A5V9dGquqyq3lhV39PLb9pfX9bf37q5owAAAMDuajVnSr+d5IGttbsnOTzJg6vq3kn+IMnLWms/mOTaJE/p/T8lybW9/GW9PwAAAFhgxVDaJv/aX+7T/1qSByZ5cy8/Jckxvfvo/jr9/QdVVW1YjQEAANhjrOqe0qraq6rOT3J1kjOS/HOSr7bWruu9XJHkkN59SJLPJ0l//2tJbrORlQYAAGDPsKpQ2lq7vrV2eJLbJjkyyV3WO+CqOq6qzqmqc6655pr1fh0AAAC7oTU9fbe19tUkH0jy40n2r6q9+1u3TXJl774yyaFJ0t/fL8mXF/muk1prR7TWjtiyZctOVh8AAIDd2Wqevrulqvbv3TdL8p+TXJIpnD6y93Zsknf07tP66/T3399aaxtZaQAAAPYMe6/cSw5OckpV7ZUpxJ7aWntnVX0yyRuq6gVJPpHk1b3/Vyf5y6q6LMlXkjx2E+oNAADAHmDFUNpa25bkHouUfzrT/aXzy7+V5FEbUjsAAAD2aGu6pxQAAAA2klAKAADAMKu5p5QkW48/fXQVNs3lJz5sdBUAAIAbKWdKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgmBVDaVUdWlUfqKpPVtXFVfXMXn5CVV1ZVef3v4fOfOY3q+qyqrq0qn5mM0cAAACA3dfeq+jnuiS/2lo7r6pumeTcqjqjv/ey1tqLZ3uuqrsmeWySH0ryA0neV1V3aq1dv5EVBwAAYPe34pnS1tpVrbXzevc3klyS5JBlPnJ0kje01r7dWvtMksuSHLkRlQUAAGDPsqZ7Sqtqa5J7JPloL3p6VW2rqtdU1a172SFJPj/zsSuyfIgFAADgRmrVobSqbpHkLUme1Vr7epJXJLljksOTXJXkJWsZcFUdV1XnVNU511xzzVo+CgAAwB5iVaG0qvbJFEhf11p7a5K01r7YWru+tfadJK/M9kt0r0xy6MzHb9vLdtBaO6m1dkRr7YgtW7asZxwAAADYTa3m6buV5NVJLmmtvXSm/OCZ3h6R5KLefVqSx1bVTavqDkkOS/KxjasyAAAAe4rVPH33J5I8IcmFVXV+L/utJI+rqsOTtCSXJ/nFJGmtXVxVpyb5ZKYn9z7Nk3cBAABYzIqhtLX2oSS1yFvvWuYzL0zywnXUCwAAgBuBNT19FwAAADaSUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADDMiqG0qg6tqg9U1Ser6uKqemYvP6Cqzqiqf+r/b93Lq6peXlWXVdW2qrrnZo8EAAAAu6fVnCm9LsmvttbumuTeSZ5WVXdNcnySM1trhyU5s79OkockOaz/HZfkFRteawAAAPYIK4bS1tpVrbXzevc3klyS5JAkRyc5pfd2SpJjevfRSV7bJh9Jsn9VHbzhNQcAAGC3t6Z7Sqtqa5J7JPlokoNaa1f1t76Q5KDefUiSz8987IpeBgAAADtYdSitqlskeUuSZ7XWvj77XmutJWlrGXBVHVdV51TVOddcc81aPgoAAMAeYlWhtKr2yRRIX9dae2sv/uLcZbn9/9W9/Mokh858/La9bAettZNaa0e01o7YsmXLztYfAACA3dhqnr5bSV6d5JLW2ktn3jotybG9+9gk75gpf2J/Cu+9k3xt5jJfAAAA+K69V9HPTyR5QpILq+r8XvZbSU5McmpVPSXJZ5M8ur/3riQPTXJZkm8mefKG1hgAAIA9xoqhtLX2oSS1xNsPWqT/luRp66wXAAAANwJrevouAAAAbCShFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGGbv0RUAAADYbFuPP310FTbN5Sc+bHQV1mXFM6VV9ZqqurqqLpopO6Gqrqyq8/vfQ2fe+82quqyqLq2qn9msigMAALD7W83luycnefAi5S9rrR3e/96VJFV11ySPTfJD/TN/VlV7bVRlAQAA2LOsGEpba2cl+coqv+/oJG9orX27tfaZJJclOXId9QMAAGAPtp4HHT29qrb1y3tv3csOSfL5mX6u6GUAAACwwM6G0lckuWOSw5NcleQla/2Cqjquqs6pqnOuueaanawGAAAAu7OdCqWttS+21q5vrX0nySuz/RLdK5McOtPrbXvZYt9xUmvtiNbaEVu2bNmZagAAALCb26lQWlUHz7x8RJK5J/OeluSxVXXTqrpDksOSfGx9VQQAAGBPteLvlFbV65McleTAqroiye8kOaqqDk/Sklye5BeTpLV2cVWdmuSTSa5L8rTW2vWbU3UAAAB2dyuG0tba4xYpfvUy/b8wyQvXUykAAABuHNbz9F0AAABYF6EUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgmL1HV4Dd19bjTx9dhU1z+YkPG10FAAC4UXCmFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYJgVQ2lVvaaqrq6qi2bKDqiqM6rqn/r/W/fyqqqXV9VlVbWtqu65mZUHAABg97aaM6UnJ3nwvLLjk5zZWjssyZn9dZI8JMlh/e+4JK/YmGoCAACwJ1oxlLbWzkrylXnFRyc5pXefkuSYmfLXtslHkuxfVQdvVGUBAADYs+zsPaUHtdau6t1fSHJQ7z4kyedn+ruily1QVcdV1TlVdc4111yzk9UAAABgd7buBx211lqSthOfO6m1dkRr7YgtW7astxoAAADshnY2lH5x7rLc/v/qXn5lkkNn+rttLwMAAIAFdjaUnpbk2N59bJJ3zJQ/sT+F995JvjZzmS8AAADsYO+Veqiq1yc5KsmBVXVFkt9JcmKSU6vqKUk+m+TRvfd3JXloksuSfDPJkzehzgAAAOwhVgylrbXHLfHWgxbptyV52norBQAAwI3Duh90BAAAADtLKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYYRSAAAAhhFKAQAAGEYoBQAAYJi9R1cA2HNtPf700VXYNJef+LDRVQAA2CM4UwoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAw/hJGACA3Yyf3AL2JM6UAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADD7L2eD1fV5Um+keT6JNe11o6oqgOSvDHJ1iSXJ3l0a+3a9VUTAACAPdFGnCl9QGvt8NbaEf318UnObK0dluTM/hoAAAAW2IzLd49OckrvPiXJMZswDAAAAPYA6w2lLcl7q+rcqjqulx3UWruqd38hyUHrHAYAAAB7qHXdU5rkvq21K6vq+5KcUVWfmn2ztdaqqi32wR5ij0uS293uduusBgAAALujdZ0pba1d2f9fneRtSY5M8sWqOjhJ+v+rl/jsSa21I1prR2zZsmU91QAAAGA3tdOhtKpuXlW3nOtO8tNJLkpyWpJje2/HJnnHeisJAADAnmk9l+8elORtVTX3PX/dWnt3VX08yalV9ZQkn03y6PVXEwAAgD3RTofS1tqnk9x9kfIvJ3nQeioFAADAjcNm/CQMAAAArIpQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADDMen4SBpix9fjTR1dh01x+4sNGVwEAgD2UM6UAAAAM40wpwC7kjDoAwI6cKQUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgGKEUAACAYfwkDADcgOzJPxuU+OkgABZyphQAAIBhhFIAAACGEUoBAAAYRigFAABgGA86AgBgt7cnPyTMA8LY0zlTCgAAwDBCKQAAAMMIpQAAAAzjnlIAANgDuc+W3YUzpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjJ+EAQBu0PysBcCezZlSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGGEUgAAAIYRSgEAABhGKAUAAGAYoRQAAIBhhFIAAACGEUoBAAAYRigFAABgmL1HVwCAG7etx58+ugqb5vITHza6CgBwg+dMKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMI5QCAAAwjFAKAADAMEIpAAAAwwilAAAADCOUAgAAMIxQCgAAwDBCKQAAAMMIpQAAAAwjlAIAADCMUAoAAMAwQikAAADDCKUAAAAMs2mhtKoeXFWXVtVlVXX8Zg0HAACA3demhNKq2ivJnyZ5SJK7JnlcVd11M4YFAADA7muzzpQemeSy1tqnW2v/nuQNSY7epGEBAACwm9qsUHpIks/PvL6ilwEAAMB3VWtt47+06pFJHtxa+/n++glJfqy19vSZfo5Lclx/eeckl254RXZfByb50uhK3MCYJguZJguZJjsyPRYyTRYyTRYyTRYyTXZkeixkmixkmuzo9q21LYu9sfcmDfDKJIfOvL5tL/uu1tpJSU7apOHv1qrqnNbaEaPrcUNimixkmixkmuzI9FjINFnINFnINFnINNmR6bGQabKQabJ6m3X57seTHFZVd6iq70ny2CSnbdKwAAAA2E1typnS1tp1VfX0JO9JsleS17TWLt6MYQEAALD72qzLd9Nae1eSd23W9+/hXNa8kGmykGmykGmyI9NjIdNkIdNkIdNkIdNkR6bHQqbJQqbJKm3Kg44AAABgNTbrnlIAAABYkVDKjUZVHVNVdx1dDwDYU1TV5VV14Oh6ALs3oXQdqupZVfW9u/L7qupRVXVJVX1gA4b3waraLR5TXVUbcf/zMUk2LZRW1d/3/1ur6r+tov+tVXVR7z6iql6+wfXZZcvK7LhslKr62ao6vnefUFXP7d2/V1U/tcHDen1VbauqZ6/zezZ8Osz7/qdW1RM36/tnhnOXqjq/qj5RVXdc53d9d94t08/J/fet55f/QFW9eT3DX4+qumlVva9Pi8es87uOqqp3ziv7+/XVcLyqul9VXdyn0c3W+V2LLgfrtVT7NdvGrPH7ntHb1tdtQN0EunXYqDa3qp5UVX/Su3c4gL0R+0qb3ZYs0d/ccnrtzLZ06MH5Xb1t2Qh9et9nA75nl+9zV9X+VfXLu3KY6yGUrs+zkiwaIqtqr438vhlPSfILrbUH7MT37zJV9dtVdWlVfajv8D+3qg6vqo/0nf+3VdWte7/fXVGr6sCqurx3P6mqTquq9yc5s79+e1Wd0TfkT6+q5/TG7SNVdUD/3B2r6t1VdW5Vnd0bwfsk+dkkL+oN4roaw8W01uYara1JVgyl8z57TmvtGRtcpRvUsrLWdaK1dlpr7cRFyp/fWnvfBtbr+5P8aGvtbq21l23U926G1tqft9ZeuwsGdUySN7fW7tFa++ddMLxFtdb+pbW24SFlDe7R63F4a+2NG/3lM23G7uzxSX6/T6P/N6oSO7PNXaqNWYVfTvKfW2uP34nPDlFVN6+q06vqgqq6qKoe07ejv1tV51XVhVV1l97vkVX1D33b+vdVdedevldVvbh/fltV/cq8Ydysqv62qn5hseGNGO+dtBkHsDe1LVnC3HJ665nlfFMPzq/CDWLbskZHJdnt2uqaTubsn2k52D201vyt4i/JzZOcnuSCJBcl+Z0k/57kwiQf6P38a5KX9H7um+S/J/lYkvOT/N8ke/X+fjrJPyQ5L8mbktwiyTPmf98idXh+H8alSV6UZN8kf9E/84kkD+j9LVV+syRvSHJJkrcl+WiSIzZhWv1oH+d9k9wyyT8leW6SbUl+svfze0n+T+/+4Fw9khyY5PLe/aQkVyQ5YOb1Zf07tyT5WpKn9vdeluRZvfvMJIf17h9L8v7efXKSR27iMvKv/f9Het3OT/LsTCH17D6/z0tyn97f1iQX9e6jkryzdx/Zl49PJPn7JHeeGf+3Jnl3n6Z/uExddumy0sflU0le1z/z5kwHWC5P8gd9vB+b5Bcy/Y7xBUnekuR7++fPn/n7f0l+so/vn/T3T0jy3PnzsY/nxzOtkydl+8PbPtiH+7Ek/5jkfsvUfVsf5vlJ7pfk8D4Pt/Vxv3Xvb6nye/XxuaBP64vmTZOTex1el+Snkny4z78jM7Urr+n1/ESSo/tn/yjJ83v3zyQ5K9NBxNnp8INJ3teHe16SO2ZqS87sry+c+b6tfb68MsnFSd6b5GZLTI+HJvlCkiuzvW17Tp/GF6WvZyuU/88+zh9K8vq5Os+8/8Q+HS9I8pd9Gr080/L+6Zn5u3Vmej4pSyz/WaRN7eUnJvlkH9aLe9mWTMvex/vfTywxHb4vU3szty7fMcmD+ny6sM+3m/Z+lyp/cF8Gzuvj9875bcYK82zBOrUzy32mn2Z7Uf/MtiS/2MsPzrRsnd+/637LTc9FptHPJ/lKks/0elYfzkV9XB7T+1uu/E8ytVPvy/TE/kXb6CRvT3JupuX3uJnpN7vNXW66/NHMeB45s0zNtTEHZVqv59bl+yxRjz/P9m31s5Mc0Ou2LVP7cLfe31Llt8m0/l2c5FVJPpvkwM3aLs3U++eSvHLm9X6Z2udf6a9/Ocmrevetkuzdu38qyVt69y9lWg7n3pvbNl+eaXl9X5InLjW8TRqvrVmkbcu0vr67LzNnJ7lL7//hmbZnn+j1PWh2WcgUPuaW6bn1/oNZfL06K8nhM3X5UJK7j2hLVrGcrnXcVt1m9H5Pzvb1+9lL1Gkzti2/lR33zX8jyVt7v0dn2rZ/T6Z9nU/38qWWjQXbhkzL11yd5/YRHtWHdUGSs5aZB0vuR2Vt26wFw1tm/hzVx+m0Pp3ekO37Ny9abP5tdtuzpvV5dAV2l78s3aAfOFPWkjy6d/+nJH+TZJ/++s8y7Ygd2BeIm/fy38j2nc8dvm+JenxwZqH+1Uy/AZskd0nyub7iLVX+nJnyuyW5LpsTSp+V5HdnXr80U4j/3EzZHZOct8g4zQ+lfzHzmSfNmwefS3JI7/4fSf5Pph28uRVw7u+S3s/J2TWh9KjMbDAyhbN9e/dhSc7p3VuzeChdaofgSZl22Pfr8/OzSQ69ISwrfVxa+g5+po3pczMt078+099tZrpfkL5DNFP28EwN6j5ZXSg9YOazf5nk4TPj/pLe/dAk71uh7hfNvF7q4Mly5ffv3fND6XVJfiRToDy3T5fKtLF8e5L/neS/9/73z7QRuXmmZebiJA/ItMN+x0Wmw0eTPKJ379s/s3eSW82sS5f14c3V5fD+3qlzw11imswO516ZdjRunmn9ujjTUf+Vyr8307J8WWZCaZIf6uN54Nw87PP0TX063TXJZYusI0/KIst/lmhTM+38X5rtwWT//v+vk9y3d98uvX1YYjocle3r5b5JPp/kTv31azO1dSuVH9bnwalZPJQuN88WrFM7s9wnOS7J83r3TZOck+QOmdb//9nL98p0wG/JbdQS0+jkbF8ffy7JGf27DsrUnhy8TPl/nSn/gSRfzdKhdC4A3SzTztRtMrPNXcV0eWXvvn92XKbm2pg3ZvuBzb2yTIjKzLY6yR8n+Z3e/cAk569Q/vJs3+Y/rI/Drgild8r2g4RzwePybN+G/tjM8nJopp3ouZDxqV7+lkxn3habHhckefxyw9uk8dqaRdq2LH1w+tbZ3ib8fLavL7PLwsmzy2GWXq+OzfbtwJ3St+0j2pLlltOdHLe1tBn3SnLGzHfuv0ydTsjGbltOycJ987nw+eJsD5c/meT1vXypZWPRbcNsnfvrC7N9vVluXBfdj8rat1kLhrfM/Dkqyb8lucPM+jG7f7Ng/m1227OWv037ndI90IVJXlJVf5CpMTi7qub3c32mRjuZjnbdK8nHe383S3J1kntn2un6cC//nkxHS3bGfTNt+NJa+1RVfTZTw7hU+f0zbRDTWttWVdt2crgb7bpsv5R833nv/du819+e6f7OzOvvZNq5u0mSr7bWDt/oSq7DPkn+pKoOz7SM3GmF/vdLckpVHZZph2WfmffObK19LUmq6pNJbp9pY7WSXbGsfL619uHe/VeZzv4n087enB+uqhdkCmC3SPKeuTf6+L4o09na/1hk/VrMA6rq1zNtpA7ItPH6m/7eW/v/czM1zCuqqv0yNfp/14tOSfKmZcr37+Vn9fK/TPKQma/8TGvtwv7dF2eaf62qLux1um2Sn525L2bfJLdrrV1SVb+QacP17DbvMqequmWmjdTbkqS19q1evk+S/11V98+0ThySKQTM1eX8tU6TTMvI21pr/9aH8dZMR4trifKb9PJv9vLT5n3fA5O8qbX2pV73r/R5/fbW2neSfLKqDsriFlv+98/iberXknwryav7/Vdz92D9VJK7zixft6qqW7TW/nWF6XDnTNPwH/vrU5I8LckHlij/YC//p17fv8q0IzFfZel5ttg69eKsfbn/6SR3q+33a+6XaQf340le05ebt7fWzq+qn8zOb6Pum2nH7/okX6yqv8t05cxS5fefKf+XfqvGUp5RVY/o3Yf2+s9uc7PCdHl9krTWzqqqW/V1d9YDMx04Tq/P19Ywzj/XP/f+qrpNVd1qmfL7Zwrjaa2dXlXXrnI469Ja+8equmem4Ptqd2IAAAlJSURBVPGCqjqzvzW3Db0+23+7/n9lOpP1iKrammlZXsmHkzy4qv66TRYMr7X2exs0OvMt1rbdJ1MbPdfPTfv/2yZ5Y1UdnGnZ/swqh7HYevWmJL9dVb+W6cD4yav8rs1qS3bWetuMTyf5/6rqjzOdtXzvKoe7EduWf0/y2Hn75v9cVf8p09VIL820zu2V5OyqukWWXjYW3TYsUu8PJzm5qk6dmXaLWWo/aqkcsNQ2a7HhLTV//j3Jx1prSy3XC+bfMvXf5YTSVVqmQZ/1rb4xS6aV6pTW2m/O9lBVD890ROlxm1vjoT6c5P9W1e9nWsb+S6ZLqa6tqvu11s5O8oQkczv4l2cK8B9Lsq77x1prX6+qz1TVo1prb6ppjb9ba+2CJN/IdFRvV3t2ki8muXumRvVbK/S/3A7BbCif3Ym4IWhLvJ49sHBykmNaaxdU1ZMyHdVLb/hPzXQP7FWrGVhV7ZvpCoQjWmufr6oTsuNBjcV2tna1lQ6iXJ/k51prly7y2R9J8uVMZ5BW6/GZLkG6Vw/2l2f7NJm/7KzroTSbYLZ+Sx2RWGz5ryzRplbVkZkOED4yydMzBY+bJLn3XJC/AVhuni1Yp3Zyua9MVyW8J/P0MPywTDs9L01ybW5g26iqOirTDuOPt9a+WVUfzDTO393mrmK6LNU+3ShU1Q8k+Upr7a+q6quZzhIuZb9Mlysm01m2OWck+cWq+kBr7bqqOqC19pX+3vP7358m+eU1Dm+95rcLB2Xpg9N/nOSlrbXT+nJ1whqH8d31qi+LZ2S68uXRmfZjdkfrajNaa6+tqrtnutXkqZmmxf/Y/GonSb6UZP6++VmZDg7/R6ZLtE/OFEp/LcufuFh02zD/AHlr7alV9WOZpsG5VXWv1tqX11DnNW2zFhtelpg/fZmefzJntu5nLTb/1lD3TeVBR6vUG9hvttb+KtPZnHtm+ZBzZpJHVtX39c8fUFW3z3RvyU9U1Q/28ptX1dyZs7WGprMz7dCkf8ftMp36X6r8rPQH8FTVD2e6nGDDtdY+nul69m1J/jbTWeavZbrU5UX9aNHhmS6BTKYj/79UVZ/IdFnDej0+yVOq6oJMR8qP7uVvSPJrtQFPfVvB/Pm4X5Kr+lmgJ2RqHJez1A7BeuyKZeV2VfXjvfu/ZbrnY75bJrmqH6WbfUjIazJdqn32GsZpbofzSz3UrvuBOP0s3LVVdb9e9IQkf7dM+VeTfLWq7tvL1/rgk/ck+ZV+8CRVdY/+//aZLrO5R5KH9A3SbD2/keSKqjqm93/Tmp7cvV+Sq3u4eUCmM4nrdXaSY6rqe6vq5kke0cuWKj+rl9+spjO6D5/3fe9P8qiquk2v+wHrrN+ibWpfJvZrrb0r04Ghu/f+35vkuw9oqekKhtW4NMnWueFk+4G1pco/1cvn2pqlQt5y82yxdWpnlvv3ZGpj90mmdb1Pp9sn+WJr7ZWZ7m+8Z5bfRq3k7CSPqemBOFsynSn42DLlZ82UH5zpcvXF7Jfk2h4C7pLpTMN8K02Xx/TxuW+Sr82dcZ9xZqZ7Juce6LPfGsZ5rg09KsmXWmtfX6Z8tm19SKbLSXeFH0nysao6P9PtNC9Ypt8/TPL7fZs8e0DvVZkuvd7Wt6/zH+j3zCQ3q6o/XOPwNtrXk3ymqh6VJDWZW/9nt6/HLvH5teyLvSrT2bCPt9ZWe9Z7s9qS1VjtuK26zajp6dE3aa29JcnzMrUjq7ER25ZbZeG++dmZLnv+h9baNZkui71zpstYl1s2lto27DDNquqOrbWPttaen+SaTFduLGap/ag1bbOWGN6i82eROsyv+2Jt/g3GDeksyw3dj2QKVN/JdPTll5L8eJJ3V9W/tHlPOG2tfbKqnpfkvVV1k/6Zp7XWPlLTGaLXV9XcJQPPy3SP1UlLfd8S/izJK2q6FPC6JE9qrX27qpYqf0WSv6iqSzLdeH3ueibICl7cWjuh7yifleTcfpnAgp2J1tqnsmPoeV4vPzkzl8Ms8nrrYu/1yxYevMhwPpxd89S5bUmu7xvtkzPNp7fU9FMe784yR7G6P8x0+e7zMl0KsxF2xbJyaZKnVdVrMt2o/4rMNPDdb2e6F/Ka/v+WvZF8ZJI7VdXc0dUVj6q31r5aVa/MdN/TFzJdlrIRjk3y533Z/XSSJ69Q/uRMl8O0rP6ypTn/K9O90Nt6O/GZmq6meHWme1j+paqekumI5o/O++wTMl2R8HuZ2pdHZXrYzN/0+XlOpp2ZdWmtnVdVJ2cKEcn0MJRPJNNPeCxR/sZM95hdnXnzpbV2cVW9MMnfVdX1mR7qsZ76XbNEm/qNJO+o6QxaZbq/J5kugf3Tmg6O7Z2pfXrqKobzrap6cqbLvvbu4/XnfX1Zqvy4JKdX1Tcz7SjN3xlsWX6eLVinejBb63L/qkyX5Z1XVZVp/Tsm05UKv1ZV/5Hp/tYnLjM9/3H+ly7ibZm2ixf0cfv11toXqmq58gf2cftclr5M+N1Jntrbo0sz7dTtYBXtwbd6yNoni5/FeWaSk/r6dn2mbfxqLls+IdP6vy3JN7M96CxV/ruZpu3FmR7s9blVDGPd+hmV+We9ts68f076lSuttX/IjreZzG2Tr8u0Hj1n5r0dtsXZ3i5mkeHtSo/PtG17XqZ5/oZMy98JmdbVazMdILvDIp99Q5JXVtUzssJBn9bauVX19UwPDFyVTWpLVmu147bqNiPTLQd/0bdhSfKbC75tERu0bfn+TAc/ZvfNL850tnzutpptSb6/tTZ3dcRSy8ZS24a/SfLmqjo60z7Ns2u63agyHcy6YIlRXHQ/aie2WS9aZHjbsvj8mT+Nv1xVH67pZ5P+NlP7OH/+3WDU9nkEG6eq/jpTANw302XMvz+4SgA3GDWdKT6vtbbo2eyaLt1/Z2vth3dlvYDVq+kqug9meoLrdwZXB3ZrzpSyKVpra/qdToAbi5kd2RcPrgqwk/rVTy9M8hyBFNbPmdIbqKr6aLY/EWzOE1p/kifMsaysTlX9TKafJ5j1mdbaIxbr/8agqv400+PyZ/1Ra23Vl6LtCfolc8+cV/zh1trTRtTnhqhfajv/UsffWOxBKHuCfiZ7sQcaPmiNDzXhRkRbMrkxbVvsW2wcoRQAAIBhPH0XAACAYYRSAAAAhhFKAQAAGEYoBQAAYBihFAAAgGH+f3Ju9L5VbHWyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VoA0l7qa1cYK",
        "outputId": "b1880f36-32e7-45a3-9e0f-31dd28ec8289"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'arabic_food': 25,\n",
              " 'brazilian_food': 72,\n",
              " 'chinese_food': 16,\n",
              " 'fitness_food': 30,\n",
              " 'gourmet': 120,\n",
              " 'healthy_food': 33,\n",
              " 'italian_food': 43,\n",
              " 'japanese_food': 36,\n",
              " 'mexican_food': 41,\n",
              " 'snacks': 67,\n",
              " 'street_food': 295,\n",
              " 'sweets_desserts': 154}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ydmBzwB4JbMc"
      },
      "source": [
        "##Encoding nos atributos não-numéricos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyu7_8YiJap0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "a421f2cf-0c15-4069-9bc2-51697d50f38c"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "leTime = LabelEncoder()\n",
        "leMotivation = LabelEncoder()\n",
        "leGender = LabelEncoder()\n",
        "leMaritalstatus = LabelEncoder()\n",
        "\n",
        "df[\"time\"]= leTime.fit_transform(df[\"time\"].values)\n",
        "df[\"motivation\"]= leMotivation.fit_transform(df[\"motivation\"].values)\n",
        "df[\"gender\"]= leGender.fit_transform(df[\"gender\"].values)\n",
        "df[\"marital.status\"]= leMaritalstatus.fit_transform(df[\"marital.status\"].values)\n",
        "df"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-199d13e0-7642-4fd4-b0e4-3923a96cbf56\">\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>frequency</th>\n",
              "      <th>time</th>\n",
              "      <th>expenses</th>\n",
              "      <th>motivation</th>\n",
              "      <th>taste</th>\n",
              "      <th>hygiene</th>\n",
              "      <th>menu</th>\n",
              "      <th>presentation</th>\n",
              "      <th>attendance</th>\n",
              "      <th>ingredients</th>\n",
              "      <th>place.to.sit</th>\n",
              "      <th>takeaway</th>\n",
              "      <th>variation</th>\n",
              "      <th>stop.strucks</th>\n",
              "      <th>schedule</th>\n",
              "      <th>gender</th>\n",
              "      <th>age.group</th>\n",
              "      <th>scholarity</th>\n",
              "      <th>average.income</th>\n",
              "      <th>has.work</th>\n",
              "      <th>marital.status</th>\n",
              "      <th>street_food</th>\n",
              "      <th>gourmet</th>\n",
              "      <th>italian_food</th>\n",
              "      <th>brazilian_food</th>\n",
              "      <th>mexican_food</th>\n",
              "      <th>chinese_food</th>\n",
              "      <th>japanese_food</th>\n",
              "      <th>arabic_food</th>\n",
              "      <th>snacks</th>\n",
              "      <th>healthy_food</th>\n",
              "      <th>fitness_food</th>\n",
              "      <th>sweets_desserts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</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",
              "      <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>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</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",
              "      <td>0</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>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>402</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</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",
              "      <td>0</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>403</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>406</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>407 rows × 33 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-199d13e0-7642-4fd4-b0e4-3923a96cbf56')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-199d13e0-7642-4fd4-b0e4-3923a96cbf56 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-199d13e0-7642-4fd4-b0e4-3923a96cbf56');\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",
              "  "
            ],
            "text/plain": [
              "     frequency  time  expenses  ...  healthy_food  fitness_food  sweets_desserts\n",
              "0          2.0     2      30.0  ...             0             0                1\n",
              "1          0.0     2      20.0  ...             0             0                1\n",
              "2          1.0     0      15.0  ...             0             0                0\n",
              "3          0.0     4      40.0  ...             0             0                0\n",
              "4          0.0     2      15.0  ...             0             0                0\n",
              "..         ...   ...       ...  ...           ...           ...              ...\n",
              "402        0.0     2      30.0  ...             0             0                0\n",
              "403        0.0     2      30.0  ...             0             0                1\n",
              "404        0.0     2      30.0  ...             0             0                1\n",
              "405        1.0     2      30.0  ...             0             0                1\n",
              "406        0.0     2      15.0  ...             0             0                0\n",
              "\n",
              "[407 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYWXdNvne_1j",
        "outputId": "0549092f-d2db-4c66-f955-c02865a11b46"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "frequency          float64\n",
              "time                 int64\n",
              "expenses           float64\n",
              "motivation           int64\n",
              "taste              float64\n",
              "hygiene            float64\n",
              "menu               float64\n",
              "presentation       float64\n",
              "attendance         float64\n",
              "ingredients        float64\n",
              "place.to.sit       float64\n",
              "takeaway           float64\n",
              "variation          float64\n",
              "stop.strucks       float64\n",
              "schedule           float64\n",
              "gender               int64\n",
              "age.group          float64\n",
              "scholarity         float64\n",
              "average.income     float64\n",
              "has.work           float64\n",
              "marital.status       int64\n",
              "street_food          int64\n",
              "gourmet              int64\n",
              "italian_food         int64\n",
              "brazilian_food       int64\n",
              "mexican_food         int64\n",
              "chinese_food         int64\n",
              "japanese_food        int64\n",
              "arabic_food          int64\n",
              "snacks               int64\n",
              "healthy_food         int64\n",
              "fitness_food         int64\n",
              "sweets_desserts      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsNM5lx9bJ9Z",
        "outputId": "b86871b8-8495-442c-ec73-5185c7a42530"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(407, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJueAPmQcxO_"
      },
      "source": [
        "## Split dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yr8aaK1qc2He"
      },
      "source": [
        "dataset = pd.DataFrame(df)\n",
        "X  = dataset.iloc[:,0:-12].values\n",
        "y = dataset.iloc[:,21:].values\n",
        "X\n",
        "df_X = dataset.iloc[:,0:-12]\n",
        "df_y = dataset.iloc[:,21:]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "xsW9Dew3ipbj",
        "outputId": "b3e93941-954c-4b03-b01d-438ce115cbed"
      },
      "source": [
        "df_X"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-2b754b70-a3d1-47a9-9d87-7a9b339794d8\">\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>frequency</th>\n",
              "      <th>time</th>\n",
              "      <th>expenses</th>\n",
              "      <th>motivation</th>\n",
              "      <th>taste</th>\n",
              "      <th>hygiene</th>\n",
              "      <th>menu</th>\n",
              "      <th>presentation</th>\n",
              "      <th>attendance</th>\n",
              "      <th>ingredients</th>\n",
              "      <th>place.to.sit</th>\n",
              "      <th>takeaway</th>\n",
              "      <th>variation</th>\n",
              "      <th>stop.strucks</th>\n",
              "      <th>schedule</th>\n",
              "      <th>gender</th>\n",
              "      <th>age.group</th>\n",
              "      <th>scholarity</th>\n",
              "      <th>average.income</th>\n",
              "      <th>has.work</th>\n",
              "      <th>marital.status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>402</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>403</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>404</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>406</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>407 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b754b70-a3d1-47a9-9d87-7a9b339794d8')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-2b754b70-a3d1-47a9-9d87-7a9b339794d8 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-2b754b70-a3d1-47a9-9d87-7a9b339794d8');\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",
              "  "
            ],
            "text/plain": [
              "     frequency  time  expenses  ...  average.income  has.work  marital.status\n",
              "0          2.0     2      30.0  ...             4.0       0.0               2\n",
              "1          0.0     2      20.0  ...             5.0       1.0               1\n",
              "2          1.0     0      15.0  ...             4.0       1.0               2\n",
              "3          0.0     4      40.0  ...             6.0       1.0               2\n",
              "4          0.0     2      15.0  ...             4.0       1.0               2\n",
              "..         ...   ...       ...  ...             ...       ...             ...\n",
              "402        0.0     2      30.0  ...             3.0       1.0               2\n",
              "403        0.0     2      30.0  ...             4.0       0.0               2\n",
              "404        0.0     2      30.0  ...             1.0       1.0               2\n",
              "405        1.0     2      30.0  ...             2.0       1.0               1\n",
              "406        0.0     2      15.0  ...             1.0       0.0               2\n",
              "\n",
              "[407 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdyW5UcdlF5M",
        "outputId": "b2258595-59a8-4a21-c346-c1c7921a9784"
      },
      "source": [
        "X.shape, y.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((407, 21), (407, 12))"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kg1Eao103X4Q"
      },
      "source": [
        "## Trabalhando no balanceamento do dataset - Oversampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZASfAaAjKi9"
      },
      "source": [
        "#feats_df\n",
        "#labels_df\n",
        "#labels_df.columns"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zbk0P4lxkQdP"
      },
      "source": [
        "#Give tail label colums of the given target dataframe\n",
        "def get_tail_label(df):\n",
        "    columns = labels_df.columns\n",
        "    n = len(columns)\n",
        "    irpl = np.zeros(n)\n",
        "    for column in range(n):\n",
        "        irpl[column] = df[columns[column]].value_counts()[1]\n",
        "    irpl = max(irpl)/irpl\n",
        "    mir = np.average(irpl)\n",
        "    tail_label = []\n",
        "    for i in range(n):\n",
        "        if irpl[i] > mir:\n",
        "            tail_label.append(columns[i])\n",
        "    return tail_label #list containing column name of all the tail label"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ef7rMMJDl1ei",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d939c0a9-2837-4db5-aecc-e4ef7c5038d8"
      },
      "source": [
        "a = get_tail_label(df_y)\n",
        "a"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['mexican_food',\n",
              " 'chinese_food',\n",
              " 'japanese_food',\n",
              " 'arabic_food',\n",
              " 'healthy_food',\n",
              " 'fitness_food']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHj9yzntkyai"
      },
      "source": [
        "#give the index of all tail_label rows\n",
        "def get_index(df):\n",
        "  tail_labels = get_tail_label(df)\n",
        "  index = set()\n",
        "  for tail_label in tail_labels:\n",
        "    sub_index = set(df[df[tail_label]==1].index)\n",
        "    index = index.union(sub_index)\n",
        "  return list(index)#list containing index number of all the tail label"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRsK4NMCnO4H",
        "outputId": "79e9eadf-8a4a-4e68-9a0c-bf868713b72d"
      },
      "source": [
        "b = get_index(df_y)\n",
        "print(len(b))\n",
        "print(b)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "112\n",
            "[1, 5, 6, 261, 8, 9, 266, 11, 268, 269, 272, 16, 20, 277, 25, 27, 284, 285, 31, 38, 40, 297, 41, 44, 302, 303, 48, 50, 51, 53, 312, 57, 316, 320, 65, 69, 325, 71, 326, 76, 77, 82, 83, 86, 87, 347, 93, 94, 351, 352, 97, 98, 100, 102, 104, 107, 108, 110, 367, 112, 116, 117, 373, 121, 122, 377, 124, 125, 127, 128, 129, 130, 384, 131, 135, 136, 394, 395, 140, 143, 144, 148, 405, 155, 160, 163, 174, 179, 180, 184, 188, 189, 190, 198, 199, 200, 201, 203, 207, 209, 210, 211, 213, 214, 215, 221, 222, 224, 233, 242, 243, 251]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_F2ataHk8Ge"
      },
      "source": [
        "#Give minority dataframe containing all the tail labels\n",
        "def get_minority_instance(X, y):\n",
        "    index = get_index(y) #y\n",
        "    X_sub = X[X.index.isin(index)].reset_index(drop = True)\n",
        "    y_sub = y[y.index.isin(index)].reset_index(drop = True)\n",
        "    return X_sub, y_sub #the feature vector minority dataframe and the target vector minority dataframe"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "a92zqCqjn0w-",
        "outputId": "78c79849-4adc-46e1-df9f-95d411f52253"
      },
      "source": [
        "xsub, ysub = get_minority_instance(df_X,df_y)\n",
        "xsub\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-02192ba3-63ee-49fa-b1b3-cacd257d3eb7\">\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>frequency</th>\n",
              "      <th>time</th>\n",
              "      <th>expenses</th>\n",
              "      <th>motivation</th>\n",
              "      <th>taste</th>\n",
              "      <th>hygiene</th>\n",
              "      <th>menu</th>\n",
              "      <th>presentation</th>\n",
              "      <th>attendance</th>\n",
              "      <th>ingredients</th>\n",
              "      <th>place.to.sit</th>\n",
              "      <th>takeaway</th>\n",
              "      <th>variation</th>\n",
              "      <th>stop.strucks</th>\n",
              "      <th>schedule</th>\n",
              "      <th>gender</th>\n",
              "      <th>age.group</th>\n",
              "      <th>scholarity</th>\n",
              "      <th>average.income</th>\n",
              "      <th>has.work</th>\n",
              "      <th>marital.status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>40.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>40.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "      <td>20.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>112 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02192ba3-63ee-49fa-b1b3-cacd257d3eb7')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-02192ba3-63ee-49fa-b1b3-cacd257d3eb7 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-02192ba3-63ee-49fa-b1b3-cacd257d3eb7');\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",
              "  "
            ],
            "text/plain": [
              "     frequency  time  expenses  ...  average.income  has.work  marital.status\n",
              "0          0.0     2      20.0  ...             5.0       1.0               1\n",
              "1          1.0     2      20.0  ...             1.0       0.0               2\n",
              "2          1.0     2      20.0  ...             1.0       1.0               2\n",
              "3          1.0     2      20.0  ...             6.0       0.0               2\n",
              "4          0.0     2      40.0  ...             3.0       1.0               2\n",
              "..         ...   ...       ...  ...             ...       ...             ...\n",
              "107        0.0     0      15.0  ...             1.0       1.0               2\n",
              "108        1.0     2      40.0  ...             4.0       0.0               2\n",
              "109        2.0     2      20.0  ...             1.0       1.0               1\n",
              "110        1.0     2      30.0  ...             5.0       0.0               2\n",
              "111        1.0     2      30.0  ...             2.0       1.0               1\n",
              "\n",
              "[112 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ysub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "NrCSkBXJ6P8A",
        "outputId": "e597f4ef-ff23-4ed2-a2aa-293d59754af9"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4f9df733-05a8-434a-9dee-053d28b1c56b\">\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>street_food</th>\n",
              "      <th>gourmet</th>\n",
              "      <th>italian_food</th>\n",
              "      <th>brazilian_food</th>\n",
              "      <th>mexican_food</th>\n",
              "      <th>chinese_food</th>\n",
              "      <th>japanese_food</th>\n",
              "      <th>arabic_food</th>\n",
              "      <th>snacks</th>\n",
              "      <th>healthy_food</th>\n",
              "      <th>fitness_food</th>\n",
              "      <th>sweets_desserts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    <tr>\n",
              "      <th>108</th>\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",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>112 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f9df733-05a8-434a-9dee-053d28b1c56b')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-4f9df733-05a8-434a-9dee-053d28b1c56b 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-4f9df733-05a8-434a-9dee-053d28b1c56b');\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",
              "  "
            ],
            "text/plain": [
              "     street_food  gourmet  ...  fitness_food  sweets_desserts\n",
              "0              1        0  ...             0                1\n",
              "1              1        0  ...             0                1\n",
              "2              1        0  ...             0                1\n",
              "3              1        1  ...             0                1\n",
              "4              0        0  ...             0                0\n",
              "..           ...      ...  ...           ...              ...\n",
              "107            0        0  ...             1                0\n",
              "108            1        0  ...             0                1\n",
              "109            1        1  ...             0                1\n",
              "110            1        1  ...             0                1\n",
              "111            1        1  ...             0                1\n",
              "\n",
              "[112 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xsub.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gJY7CjVi58ES",
        "outputId": "c82656fa-e7d8-4bd5-b9a9-2d9a3a3ea1b8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(112, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZYgc8qJlDdv"
      },
      "source": [
        "#Give index of 5 nearest neighbor of all the instance\n",
        "from sklearn.neighbors import NearestNeighbors\n",
        "def nearest_neighbour(X):\n",
        "  nbs= NearestNeighbors(n_neighbors=5,metric='euclidean',algorithm='kd_tree').fit(X)\n",
        "  euclidean, indices= nbs.kneighbors(X)\n",
        "  return indices"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nearest_neighbour(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYSei2fA6lbr",
        "outputId": "b6bb784c-d0f0-4ce2-f070-7f80c5678bbd"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,  75, 143,  86, 249],\n",
              "       [  1, 114, 155,  99, 396],\n",
              "       [  2, 141,  56,  55, 129],\n",
              "       ...,\n",
              "       [404,  35, 370, 241, 206],\n",
              "       [405, 399,  23, 310,  67],\n",
              "       [406, 308, 236, 139, 294]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92PwUHUglLZU"
      },
      "source": [
        "#Give the augmented data using MLSMOTE algorithm\n",
        "def MLSMOTE(X,y, n_sample):\n",
        "    indices2 = nearest_neighbour(X)\n",
        "    n = len(indices2)\n",
        "    new_X = np.zeros((n_sample, X.shape[1]))\n",
        "    target = np.zeros((n_sample, y.shape[1]))\n",
        "    for i in range(n_sample):\n",
        "        reference = random.randint(0,n-1)\n",
        "        neighbour = random.choice(indices2[reference,1:])\n",
        "        all_point = indices2[reference]\n",
        "        nn_df = y[y.index.isin(all_point)]\n",
        "        ser = nn_df.sum(axis = 0, skipna = True)\n",
        "        target[i] = np.array([1 if val>2 else 0 for val in ser])\n",
        "        ratio = random.random()\n",
        "        gap = X.loc[reference,:] - X.loc[neighbour,:]\n",
        "        new_X[i] = np.array(X.loc[reference,:] + ratio * gap)\n",
        "    new_X = pd.DataFrame(new_X, columns=X.columns)\n",
        "    target = pd.DataFrame(target, columns=y.columns)\n",
        "    new_X = pd.concat([X, new_X], axis=0)\n",
        "    target = pd.concat([y, target], axis=0)\n",
        "    return new_X, target #augmented feature vector data and augmented target vector data"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zr-R370ZqglB"
      },
      "source": [
        "#from sklearn.neighbors import NearestNeighbors\n",
        "import random\n",
        "X_res,y_res = MLSMOTE(xsub, ysub, 500)     #Applying MLSMOTE to augment the dataframe"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFPXoByVq3e0",
        "outputId": "0443f282-705d-4197-babb-abbcd5080856"
      },
      "source": [
        "y_res.dtypes"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "street_food        float64\n",
              "gourmet            float64\n",
              "italian_food       float64\n",
              "brazilian_food     float64\n",
              "mexican_food       float64\n",
              "chinese_food       float64\n",
              "japanese_food      float64\n",
              "arabic_food        float64\n",
              "snacks             float64\n",
              "healthy_food       float64\n",
              "fitness_food       float64\n",
              "sweets_desserts    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "1qlIsz66rIul",
        "outputId": "876c2622-f1c2-4ae7-f6bf-36593c3837ac"
      },
      "source": [
        "label_dsub = {'street_food' : 0, 'gourmet': 0, 'italian_food' : 0, 'brazilian_food' : 0, 'mexican_food': 0, 'chinese_food' : 0, 'japanese_food': 0, 'arabic_food':0, 'snacks': 0, 'healthy_food':0, 'fitness_food':0, 'sweets_desserts':0}\n",
        "for col in y_res.columns:\n",
        "    label_dsub[col] += len(y_res[y_res[col] == 1])\n",
        "    \n",
        "plt.figure(figsize = (16,8))\n",
        "plt.bar(range(len(label_dsub)), list(label_dsub.values()), align='center')\n",
        "plt.xticks(range(len(label_dsub)), list(label_dsub.keys()))\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_dsub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9tqO88O_sAH",
        "outputId": "8d6c0a97-e0cd-4f69-e7fa-a0a2dc11e3c3"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'arabic_food': 55,\n",
              " 'brazilian_food': 86,\n",
              " 'chinese_food': 53,\n",
              " 'fitness_food': 101,\n",
              " 'gourmet': 269,\n",
              " 'healthy_food': 84,\n",
              " 'italian_food': 40,\n",
              " 'japanese_food': 200,\n",
              " 'mexican_food': 165,\n",
              " 'snacks': 14,\n",
              " 'street_food': 474,\n",
              " 'sweets_desserts': 310}"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_res.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8iBo6o3_7hJ",
        "outputId": "c932d677-b276-45c4-fc16-b3325201d880"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(612, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgZq7skHFFAl"
      },
      "source": [
        "## Padronizando dados "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A57HZmG9FI4y"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X_res) "
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKNkoa3ntiXQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc4c998a-2157-4925-8bb8-a17e6f60a82e"
      },
      "source": [
        "y = y_res\n",
        "y= y.values\n",
        "y"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 1., ..., 0., 0., 1.],\n",
              "       [1., 0., 0., ..., 0., 0., 1.],\n",
              "       [1., 0., 0., ..., 0., 0., 1.],\n",
              "       ...,\n",
              "       [1., 1., 0., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 1.],\n",
              "       [1., 0., 0., ..., 0., 1., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shSiighJqlUb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "346e261c-58e1-4238-c782-3e7d23626d6e"
      },
      "source": [
        "X"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.53052709,  0.10142858, -0.30202053, ...,  0.91149897,\n",
              "         0.65795003, -1.38297668],\n",
              "       [ 0.25779603,  0.10142858, -0.30202053, ..., -1.1817511 ,\n",
              "        -0.75433021,  0.38277688],\n",
              "       [ 0.25779603,  0.10142858, -0.30202053, ..., -1.1817511 ,\n",
              "         0.65795003,  0.38277688],\n",
              "       ...,\n",
              "       [-0.53052709, -0.8724231 , -0.30202053, ..., -0.13512606,\n",
              "         0.65795003,  0.38277688],\n",
              "       [ 0.25779603,  1.4987006 ,  1.13575547, ...,  0.10284364,\n",
              "        -0.75433021,  0.38277688],\n",
              "       [-0.53052709,  1.38663342, -1.02090853, ..., -1.4022363 ,\n",
              "        -0.75433021,  0.38277688]])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Estratificação iterativa\n",
        "\n"
      ],
      "metadata": {
        "id": "K-20UiFSBM5-"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3Mu7MqCmpwa"
      },
      "source": [
        "#\n",
        "#X_train, y_train, X_test, y_test = iterative_train_test_split(X, y, test_size = 0.40)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlKvBOoKzoCE"
      },
      "source": [
        "from skmultilearn.model_selection import iterative_train_test_split"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7QWvbt1nQU6w",
        "outputId": "b9c71410-cc36-4f50-efca-de48adb623be"
      },
      "source": [
        "!pip install iterative-stratification\n",
        "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: iterative-stratification in /usr/local/lib/python3.7/dist-packages (0.1.7)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.0.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.19.5)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->iterative-stratification) (3.0.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->iterative-stratification) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SlFXeoL6R7H7",
        "outputId": "c26e356c-cbb5-4add-8778-4a574b594255"
      },
      "source": [
        "mskf = MultilabelStratifiedKFold(n_splits=10, shuffle=True, random_state=0)\n",
        "for train_index, test_index in mskf.split(X, y):\n",
        "   print(\"\\nInstâncias de treino:\\n\", train_index, \"\\nInstâncias de teste:\", test_index)\n",
        "   print('==================================================================================')\n",
        "   X_train, X_test = X[train_index], X[test_index]\n",
        "   y_train, y_test = y[train_index], y[test_index]"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Instâncias de treino:\n",
            " [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  34  35  37\n",
            "  38  39  40  41  43  44  45  46  47  48  49  51  52  53  54  57  58  59\n",
            "  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  76  77  78\n",
            "  79  80  81  82  83  84  86  87  88  89  90  91  92  93  94  95  96  97\n",
            "  98 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117\n",
            " 118 119 120 121 122 123 124 125 126 128 129 130 131 132 133 134 135 136\n",
            " 137 138 139 140 141 142 143 144 145 146 147 148 150 151 152 153 154 155\n",
            " 156 157 158 159 160 161 162 163 164 165 166 167 168 169 171 172 173 174\n",
            " 175 176 177 178 179 180 181 184 185 186 187 188 189 190 192 193 194 195\n",
            " 196 197 198 199 200 201 202 204 205 206 207 208 209 210 211 213 214 216\n",
            " 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234\n",
            " 235 236 237 238 239 240 241 242 243 244 245 246 248 249 250 251 252 253\n",
            " 254 255 256 257 258 259 260 261 262 263 264 265 266 268 269 270 271 272\n",
            " 273 274 275 276 277 279 280 281 282 283 285 286 287 288 289 290 291 292\n",
            " 293 294 295 296 297 298 299 300 302 304 305 306 307 308 310 312 313 315\n",
            " 317 318 319 321 322 323 324 325 326 328 329 330 331 332 333 334 335 336\n",
            " 337 338 339 340 341 342 343 344 345 346 347 348 350 351 352 353 354 355\n",
            " 356 357 359 360 361 363 364 365 367 368 369 370 371 372 373 374 375 376\n",
            " 377 378 379 381 382 383 384 386 387 388 389 390 391 393 394 395 396 397\n",
            " 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 415 416\n",
            " 417 418 419 420 421 422 423 424 425 426 427 428 431 432 433 434 435 436\n",
            " 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455\n",
            " 456 457 458 459 460 461 462 464 465 466 467 468 469 471 473 474 475 476\n",
            " 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494\n",
            " 495 496 497 498 499 500 501 502 503 504 505 506 507 508 510 511 514 516\n",
            " 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 533 534 535\n",
            " 536 537 538 539 540 543 544 545 546 547 548 549 551 552 553 554 555 556\n",
            " 557 558 559 560 561 562 563 564 565 566 567 568 570 572 574 575 577 578\n",
            " 579 580 582 583 584 585 586 587 588 590 591 592 593 594 595 597 598 599\n",
            " 600 601 602 604 605 606 607 608 609 610 611] \n",
            "Instâncias de teste: [ 33  36  42  50  55  56  75  85  99 100 127 149 170 182 183 191 203 212\n",
            " 215 247 267 278 284 301 303 309 311 314 316 320 327 349 358 362 366 380\n",
            " 385 392 414 429 430 437 463 470 472 509 512 513 515 532 541 542 550 569\n",
            " 571 573 576 581 589 596 603]\n",
            "==================================================================================\n",
            "\n",
            "Instâncias de treino:\n",
            " [  0   1   2   3   4   5   6   7   8   9  10  12  13  14  15  17  18  19\n",
            "  20  21  22  23  24  25  26  28  29  30  31  32  33  34  35  36  37  38\n",
            "  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56\n",
            "  57  58  59  60  61  63  64  65  66  67  68  69  70  71  72  73  74  75\n",
            "  76  77  79  80  82  83  84  85  86  87  88  89  90  91  92  93  94  95\n",
            "  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112 113\n",
            " 114 117 118 119 121 122 123 124 125 126 127 128 130 132 133 134 135 136\n",
            " 138 139 140 142 143 144 145 147 148 149 150 151 152 153 154 155 156 157\n",
            " 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175\n",
            " 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 193 194\n",
            " 195 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213\n",
            " 214 215 216 217 218 219 220 221 222 223 225 226 227 228 229 230 231 232\n",
            " 233 234 235 236 237 238 239 240 241 242 243 245 246 247 248 249 250 251\n",
            " 252 253 254 255 256 257 258 259 260 261 262 263 264 267 268 269 271 272\n",
            " 273 274 275 276 277 278 279 280 281 282 284 285 286 287 288 289 290 291\n",
            " 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309\n",
            " 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327\n",
            " 328 329 330 331 332 333 334 335 336 337 340 341 342 343 344 345 346 347\n",
            " 348 349 350 352 353 354 355 356 357 358 360 361 362 364 365 366 367 368\n",
            " 369 370 371 372 374 375 377 378 379 380 381 382 383 385 386 387 388 389\n",
            " 390 391 392 393 395 396 398 399 400 401 404 405 406 407 408 409 412 414\n",
            " 415 416 417 418 419 420 422 423 424 427 429 430 431 432 433 434 435 436\n",
            " 437 438 439 440 441 442 444 446 447 448 449 450 451 452 453 454 455 456\n",
            " 457 458 459 460 461 462 463 464 466 467 468 469 470 471 472 473 474 475\n",
            " 476 477 478 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494\n",
            " 495 496 498 500 501 503 505 506 509 510 511 512 513 514 515 516 517 518\n",
            " 519 520 521 522 523 524 525 526 527 528 529 530 531 532 534 535 536 537\n",
            " 538 539 540 541 542 543 544 545 547 548 549 550 551 552 553 554 556 557\n",
            " 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575\n",
            " 576 577 578 580 581 583 585 586 589 590 591 592 593 594 595 596 597 598\n",
            " 599 600 601 602 603 604 605 606 607 608 610] \n",
            "Instâncias de teste: [ 11  16  27  62  78  81 115 116 120 129 131 137 141 146 192 196 224 244\n",
            " 265 266 270 283 338 339 351 359 363 373 376 384 394 397 402 403 410 411\n",
            " 413 421 425 426 428 443 445 465 479 497 499 502 504 507 508 533 546 555\n",
            " 579 582 584 587 588 609 611]\n",
            "==================================================================================\n",
            "\n",
            "Instâncias de treino:\n",
            " [  1   3   4   5   6   7   8   9  10  11  12  13  15  16  17  18  19  20\n",
            "  21  22  23  24  26  27  28  29  30  31  32  33  35  36  37  38  40  41\n",
            "  42  43  44  45  47  48  49  50  51  53  54  55  56  57  58  59  60  61\n",
            "  62  64  65  66  68  69  70  71  72  73  74  75  76  78  79  80  81  82\n",
            "  83  84  85  86  87  88  89  91  92  93  94  95  96  97  98  99 100 101\n",
            " 102 103 104 105 106 107 109 110 111 112 113 114 115 116 117 118 119 120\n",
            " 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138\n",
            " 139 140 141 142 143 144 145 146 148 149 150 151 152 153 154 155 156 157\n",
            " 158 159 160 162 163 164 165 166 168 169 170 171 172 173 174 177 178 179\n",
            " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 196 197 198\n",
            " 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217\n",
            " 218 219 220 222 224 225 226 227 228 229 230 231 232 233 234 235 236 237\n",
            " 238 240 241 243 244 245 247 248 249 250 251 252 253 254 255 256 257 258\n",
            " 259 260 262 264 265 266 267 268 269 270 271 272 273 275 276 277 278 279\n",
            " 281 282 283 284 285 286 287 288 289 290 291 293 294 295 296 297 298 299\n",
            " 300 301 302 303 304 306 307 308 309 310 311 312 313 314 315 316 317 318\n",
            " 319 320 321 322 323 324 327 330 331 332 333 334 335 336 337 338 339 341\n",
            " 342 343 345 346 347 349 350 351 352 353 355 356 357 358 359 360 361 362\n",
            " 363 364 365 366 369 370 371 373 374 375 376 377 378 379 380 382 383 384\n",
            " 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402\n",
            " 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420\n",
            " 421 422 423 424 425 426 427 428 429 430 432 433 434 435 436 437 438 439\n",
            " 441 442 443 444 445 446 447 449 450 451 452 453 456 457 458 459 460 461\n",
            " 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480\n",
            " 482 483 484 485 486 487 488 489 490 491 493 494 495 496 497 498 499 500\n",
            " 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518\n",
            " 519 521 522 523 524 525 526 527 528 529 530 532 533 536 537 538 539 541\n",
            " 542 544 545 546 547 548 549 550 551 552 554 555 556 557 558 559 560 561\n",
            " 563 564 565 566 567 568 569 571 572 573 574 575 576 577 578 579 580 581\n",
            " 582 583 584 585 586 587 588 589 591 592 593 594 595 596 597 598 599 600\n",
            " 601 603 604 605 606 607 608 609 610 611] \n",
            "Instâncias de teste: [  0   2  14  25  34  39  46  52  63  67  77  90 108 147 161 167 175 176\n",
            " 195 199 221 223 239 242 246 261 263 274 280 292 305 325 326 328 329 340\n",
            " 344 348 354 367 368 372 381 431 440 448 454 455 462 481 492 520 531 534\n",
            " 535 540 543 553 562 570 590 602]\n",
            "==================================================================================\n",
            "\n",
            "Instâncias de treino:\n",
            " [  0   1   2   3   4   5   7   8   9  10  11  12  13  14  16  17  18  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n",
            "  38  39  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56\n",
            "  57  58  59  60  61  62  63  64  65  66  67  70  71  72  73  74  75  76\n",
            "  77  78  79  80  81  82  83  84  85  87  88  89  90  91  92  93  94  96\n",
            "  99 100 101 102 103 104 105 108 109 110 111 112 114 115 116 117 118 119\n",
            " 120 121 122 123 124 125 127 128 129 130 131 132 133 134 135 137 138 139\n",
            " 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 157 158\n",
            " 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176\n",
            " 177 178 179 180 181 182 183 184 185 187 188 189 191 192 193 194 195 196\n",
            " 198 199 201 202 203 205 206 208 209 210 211 212 214 215 216 217 219 220\n",
            " 221 222 223 224 225 227 228 229 230 231 232 234 235 236 237 238 239 240\n",
            " 241 242 243 244 245 246 247 248 249 250 251 252 253 254 256 258 259 260\n",
            " 261 263 264 265 266 267 268 269 270 271 272 274 275 277 278 279 280 281\n",
            " 282 283 284 285 286 287 288 289 290 291 292 294 295 296 297 298 299 301\n",
            " 302 303 304 305 307 308 309 310 311 312 313 314 315 316 318 319 320 321\n",
            " 323 324 325 326 327 328 329 331 332 334 335 336 337 338 339 340 341 342\n",
            " 343 344 345 346 347 348 349 350 351 353 354 356 358 359 360 361 362 363\n",
            " 364 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382\n",
            " 383 384 385 386 387 388 389 390 391 392 393 394 395 397 398 399 400 401\n",
            " 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419\n",
            " 420 421 423 424 425 426 427 428 429 430 431 432 433 435 436 437 438 439\n",
            " 440 441 442 443 444 445 447 448 450 451 452 454 455 456 457 458 459 460\n",
            " 461 462 463 464 465 466 467 468 470 471 472 473 474 476 477 478 479 480\n",
            " 481 482 484 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500\n",
            " 501 502 503 504 505 506 507 508 509 510 512 513 515 516 517 518 520 521\n",
            " 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 539 540\n",
            " 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558\n",
            " 559 560 562 563 564 565 566 567 568 569 570 571 573 575 576 577 578 579\n",
            " 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597\n",
            " 598 599 600 602 603 604 605 606 607 608 609 611] \n",
            "Instâncias de teste: [  6  15  40  68  69  86  95  97  98 106 107 113 126 136 156 186 190 197\n",
            " 200 204 207 213 218 226 233 255 257 262 273 276 293 300 306 317 322 330\n",
            " 333 352 355 357 365 396 422 434 446 449 453 469 475 483 485 511 514 519\n",
            " 538 561 572 574 601 610]\n",
            "==================================================================================\n",
            "\n",
            "Instâncias de treino:\n",
            " [  0   2   3   4   5   6   7   9  10  11  12  14  15  16  17  18  19  20\n",
            "  21  22  23  24  25  26  27  29  30  31  32  33  34  35  36  37  38  39\n",
            "  40  41  42  43  44  45  46  48  50  51  52  53  54  55  56  57  58  59\n",
            "  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77\n",
            "  78  79  80  81  82  84  85  86  88  89  90  92  93  94  95  97  98  99\n",
            " 100 101 102 104 106 107 108 109 110 111 112 113 115 116 117 118 119 120\n",
            " 121 122 123 124 125 126 127 128 129 130 131 133 135 136 137 138 139 140\n",
            " 141 143 145 146 147 148 149 150 151 153 154 155 156 157 159 161 162 163\n",
            " 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 182 183\n",
            " 185 186 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203\n",
            " 204 205 207 208 209 210 211 212 213 215 216 217 218 219 221 222 223 224\n",
            " 226 228 229 230 231 233 234 235 236 237 238 239 240 241 242 243 244 246\n",
            " 247 248 249 251 252 254 255 256 257 258 259 260 261 262 263 265 266 267\n",
            " 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285\n",
            " 286 287 288 289 290 291 292 293 294 296 297 298 299 300 301 303 304 305\n",
            " 306 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 325 326\n",
            " 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 343 344 345\n",
            " 346 348 349 350 351 352 354 355 356 357 358 359 360 361 362 363 364 365\n",
            " 366 367 368 369 370 371 372 373 375 376 378 379 380 381 382 383 384 385\n",
            " 386 387 388 389 390 391 392 394 395 396 397 398 399 400 401 402 403 405\n",
            " 406 407 408 409 410 411 412 413 414 416 417 418 420 421 422 423 424 425\n",
            " 426 427 428 429 430 431 432 433 434 436 437 439 440 441 443 444 445 446\n",
            " 447 448 449 450 451 452 453 454 455 456 457 458 459 460 462 463 464 465\n",
            " 466 468 469 470 471 472 473 474 475 476 477 479 480 481 482 483 484 485\n",
            " 487 488 489 490 492 493 494 495 496 497 498 499 500 501 502 503 504 505\n",
            " 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523\n",
            " 525 526 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543\n",
            " 544 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562\n",
            " 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580\n",
            " 581 582 583 584 585 586 587 588 589 590 591 592 595 596 598 599 600 601\n",
            " 602 603 604 605 606 607 608 609 610 611] \n",
            "Instâncias de teste: [  1   8  13  28  47  49  83  87  91  96 103 105 114 132 134 142 144 152\n",
            " 158 160 164 181 184 187 206 214 220 225 227 232 245 250 253 264 295 302\n",
            " 307 308 324 342 347 353 374 377 393 404 415 419 435 438 442 461 467 478\n",
            " 486 491 524 527 545 593 594 597]\n",
            "==================================================================================\n",
            "\n",
            "Instâncias de treino:\n",
            " [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  22  23  24  25  26  27  28  33  34  35  36  37  38  39  40  42  45\n",
            "  46  47  48  49  50  51  52  54  55  56  57  58  59  60  61  62  63  64\n",
            "  65  67  68  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83\n",
            "  84  85  86  87  88  90  91  92  93  94  95  96  97  98  99 100 101 102\n",
            " 103 104 105 106 107 108 109 110 112 113 114 115 116 118 119 120 121 122\n",
            " 123 124 126 127 128 129 130 131 132 134 136 137 138 140 141 142 143 144\n",
            " 145 146 147 148 149 152 153 154 156 157 158 159 160 161 162 163 164 165\n",
            " 166 167 168 169 170 171 172 173 174 175 176 177 179 180 181 182 183 184\n",
            " 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202\n",
            " 203 204 205 206 207 208 209 210 211 212 213 214 215 216 218 219 220 221\n",
            " 223 224 225 226 227 228 229 230 232 233 234 235 236 237 238 239 240 241\n",
            " 242 243 244 245 246 247 248 249 250 252 253 254 255 256 257 258 259 260\n",
            " 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 278 279\n",
            " 280 281 282 283 284 285 286 287 289 290 291 292 293 294 295 296 297 298\n",
            " 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316\n",
            " 317 319 320 321 322 323 324 325 326 327 328 329 330 331 333 335 338 339\n",
            " 340 342 343 344 345 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
            " 361 362 363 364 365 366 367 368 369 370 372 373 374 376 377 378 379 380\n",
            " 381 382 383 384 385 386 387 389 390 391 392 393 394 395 396 397 398 399\n",
            " 402 403 404 405 407 408 409 410 411 412 413 414 415 416 418 419 421 422\n",
            " 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440\n",
            " 442 443 444 445 446 448 449 450 451 452 453 454 455 456 457 458 459 460\n",
            " 461 462 463 464 465 466 467 468 469 470 471 472 473 475 477 478 479 480\n",
            " 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 499\n",
            " 500 501 502 504 505 506 507 508 509 510 511 512 513 514 515 518 519 520\n",
            " 521 522 523 524 525 526 527 528 530 531 532 533 534 535 537 538 539 540\n",
            " 541 542 543 545 546 547 548 549 550 551 552 553 555 556 557 558 559 560\n",
            " 561 562 563 564 565 566 567 568 569 570 571 572 573 574 576 577 579 580\n",
            " 581 582 583 584 585 586 587 588 589 590 593 594 595 596 597 598 599 600\n",
            " 601 602 603 604 605 606 607 608 609 610 611] \n",
            "Instâncias de teste: [ 19  20  21  29  30  31  32  41  43  44  53  66  89 111 117 125 133 135\n",
            " 139 150 151 155 178 217 222 231 251 277 288 318 332 334 336 337 341 346\n",
            " 360 371 375 388 400 401 406 417 420 441 447 474 476 498 503 516 517 529\n",
            " 536 544 554 575 578 591 592]\n",
            "==================================================================================\n",
            "\n",
            "Instâncias de treino:\n",
            " [  0   1   2   3   4   5   6   7   8  10  11  12  13  14  15  16  19  20\n",
            "  21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  39  40\n",
            "  41  42  43  44  45  46  47  48  49  50  51  52  53  55  56  57  59  60\n",
            "  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78\n",
            "  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96\n",
            "  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 113 114 115\n",
            " 116 117 118 119 120 122 123 124 125 126 127 129 130 131 132 133 134 135\n",
            " 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153\n",
            " 154 155 156 157 158 159 160 161 162 163 164 165 166 167 169 170 174 175\n",
            " 176 177 178 179 180 181 182 183 184 186 187 188 189 190 191 192 195 196\n",
            " 197 198 199 200 201 202 203 204 205 206 207 210 211 212 213 214 215 216\n",
            " 217 218 220 221 222 223 224 225 226 227 229 230 231 232 233 234 235 236\n",
            " 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 253 255 257\n",
            " 259 261 262 263 264 265 266 267 268 269 270 271 272 273 274 276 277 278\n",
            " 280 283 284 285 286 287 288 289 290 292 293 295 296 298 299 300 301 302\n",
            " 303 305 306 307 308 309 310 311 312 314 315 316 317 318 319 320 321 322\n",
            " 323 324 325 326 327 328 329 330 332 333 334 336 337 338 339 340 341 342\n",
            " 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361\n",
            " 362 363 364 365 366 367 368 370 371 372 373 374 375 376 377 378 380 381\n",
            " 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400\n",
            " 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 417 418 419\n",
            " 420 421 422 423 425 426 427 428 429 430 431 432 433 434 435 436 437 438\n",
            " 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456\n",
            " 457 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476\n",
            " 477 478 479 480 481 483 485 486 487 488 489 490 491 492 493 494 495 496\n",
            " 497 498 499 500 501 502 503 504 506 507 508 509 510 511 512 513 514 515\n",
            " 516 517 519 520 523 524 525 527 528 529 530 531 532 533 534 535 536 537\n",
            " 538 539 540 541 542 543 544 545 546 548 549 550 551 552 553 554 555 557\n",
            " 558 560 561 562 567 568 569 570 571 572 573 574 575 576 577 578 579 580\n",
            " 581 582 583 584 585 586 587 588 589 590 591 592 593 594 596 597 598 599\n",
            " 600 601 602 603 604 605 606 607 609 610 611] \n",
            "Instâncias de teste: [  9  17  18  22  38  54  58 112 121 128 168 171 172 173 185 193 194 208\n",
            " 209 219 228 252 254 256 258 260 275 279 281 282 291 294 297 304 313 331\n",
            " 335 343 369 379 382 416 424 458 459 482 484 505 518 521 522 526 547 556\n",
            " 559 563 564 565 566 595 608]\n",
            "==================================================================================\n",
            "\n",
            "Instâncias de treino:\n",
            " [  0   1   2   3   4   5   6   8   9  10  11  12  13  14  15  16  17  18\n",
            "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  36  37\n",
            "  38  39  40  41  42  43  44  45  46  47  49  50  51  52  53  54  55  56\n",
            "  57  58  60  61  62  63  66  67  68  69  72  73  74  75  77  78  81  82\n",
            "  83  85  86  87  89  90  91  92  95  96  97  98  99 100 101 102 103 104\n",
            " 105 106 107 108 110 111 112 113 114 115 116 117 118 119 120 121 122 123\n",
            " 125 126 127 128 129 130 131 132 133 134 135 136 137 139 141 142 143 144\n",
            " 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163\n",
            " 164 165 166 167 168 170 171 172 173 174 175 176 177 178 180 181 182 183\n",
            " 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201\n",
            " 202 203 204 205 206 207 208 209 210 212 213 214 215 216 217 218 219 220\n",
            " 221 222 223 224 225 226 227 228 229 231 232 233 234 235 236 237 238 239\n",
            " 240 241 242 243 244 245 246 247 249 250 251 252 253 254 255 256 257 258\n",
            " 260 261 262 263 264 265 266 267 268 270 273 274 275 276 277 278 279 280\n",
            " 281 282 283 284 285 286 287 288 291 292 293 294 295 297 298 299 300 301\n",
            " 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319\n",
            " 320 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338\n",
            " 339 340 341 342 343 344 345 346 347 348 349 351 352 353 354 355 356 357\n",
            " 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375\n",
            " 376 377 379 380 381 382 383 384 385 386 387 388 389 391 392 393 394 395\n",
            " 396 397 400 401 402 403 404 406 407 410 411 413 414 415 416 417 419 420\n",
            " 421 422 424 425 426 427 428 429 430 431 432 433 434 435 437 438 439 440\n",
            " 441 442 443 445 446 447 448 449 450 452 453 454 455 456 458 459 461 462\n",
            " 463 465 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482\n",
            " 483 484 485 486 487 489 490 491 492 494 496 497 498 499 501 502 503 504\n",
            " 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522\n",
            " 523 524 525 526 527 528 529 531 532 533 534 535 536 537 538 539 540 541\n",
            " 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559\n",
            " 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578\n",
            " 579 581 582 584 586 587 588 589 590 591 592 593 594 595 596 597 598 599\n",
            " 601 602 603 604 605 607 608 609 610 611] \n",
            "Instâncias de teste: [  7  35  48  59  64  65  70  71  76  79  80  84  88  93  94 109 124 138\n",
            " 140 145 169 179 211 230 248 259 269 271 272 289 290 296 321 350 378 390\n",
            " 398 399 405 408 409 412 418 423 436 444 451 457 460 464 466 488 493 495\n",
            " 500 530 560 580 583 585 600 606]\n",
            "==================================================================================\n",
            "\n",
            "Instâncias de treino:\n",
            " [  0   1   2   4   5   6   7   8   9  11  12  13  14  15  16  17  18  19\n",
            "  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n",
            "  38  39  40  41  42  43  44  46  47  48  49  50  51  52  53  54  55  56\n",
            "  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74\n",
            "  75  76  77  78  79  80  81  83  84  85  86  87  88  89  90  91  92  93\n",
            "  94  95  96  97  98  99 100 103 105 106 107 108 109 110 111 112 113 114\n",
            " 115 116 117 119 120 121 124 125 126 127 128 129 130 131 132 133 134 135\n",
            " 136 137 138 139 140 141 142 144 145 146 147 149 150 151 152 154 155 156\n",
            " 158 160 161 162 163 164 167 168 169 170 171 172 173 174 175 176 178 179\n",
            " 181 182 183 184 185 186 187 188 190 191 192 193 194 195 196 197 199 200\n",
            " 201 202 203 204 205 206 207 208 209 211 212 213 214 215 217 218 219 220\n",
            " 221 222 223 224 225 226 227 228 230 231 232 233 235 239 240 242 244 245\n",
            " 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263\n",
            " 264 265 266 267 269 270 271 272 273 274 275 276 277 278 279 280 281 282\n",
            " 283 284 286 287 288 289 290 291 292 293 294 295 296 297 298 300 301 302\n",
            " 303 304 305 306 307 308 309 310 311 312 313 314 316 317 318 319 320 321\n",
            " 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339\n",
            " 340 341 342 343 344 346 347 348 349 350 351 352 353 354 355 357 358 359\n",
            " 360 361 362 363 364 365 366 367 368 369 371 372 373 374 375 376 377 378\n",
            " 379 380 381 382 383 384 385 386 388 390 391 392 393 394 395 396 397 398\n",
            " 399 400 401 402 403 404 405 406 408 409 410 411 412 413 414 415 416 417\n",
            " 418 419 420 421 422 423 424 425 426 427 428 429 430 431 434 435 436 437\n",
            " 438 440 441 442 443 444 445 446 447 448 449 451 453 454 455 457 458 459\n",
            " 460 461 462 463 464 465 466 467 468 469 470 472 473 474 475 476 478 479\n",
            " 481 482 483 484 485 486 488 491 492 493 495 496 497 498 499 500 502 503\n",
            " 504 505 507 508 509 511 512 513 514 515 516 517 518 519 520 521 522 523\n",
            " 524 525 526 527 529 530 531 532 533 534 535 536 537 538 539 540 541 542\n",
            " 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 559 560 561\n",
            " 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579\n",
            " 580 581 582 583 584 585 587 588 589 590 591 592 593 594 595 596 597 598\n",
            " 600 601 602 603 604 605 606 608 609 610 611] \n",
            "Instâncias de teste: [  3  10  45  82 101 102 104 118 122 123 143 148 153 157 159 165 166 177\n",
            " 180 189 198 210 216 229 234 236 237 238 241 243 268 285 299 315 345 356\n",
            " 370 387 389 407 432 433 439 450 452 456 471 477 480 487 489 490 494 501\n",
            " 506 510 528 558 586 599 607]\n",
            "==================================================================================\n",
            "\n",
            "Instâncias de treino:\n",
            " [  0   1   2   3   6   7   8   9  10  11  13  14  15  16  17  18  19  20\n",
            "  21  22  25  27  28  29  30  31  32  33  34  35  36  38  39  40  41  42\n",
            "  43  44  45  46  47  48  49  50  52  53  54  55  56  58  59  62  63  64\n",
            "  65  66  67  68  69  70  71  75  76  77  78  79  80  81  82  83  84  85\n",
            "  86  87  88  89  90  91  93  94  95  96  97  98  99 100 101 102 103 104\n",
            " 105 106 107 108 109 111 112 113 114 115 116 117 118 120 121 122 123 124\n",
            " 125 126 127 128 129 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
            " 144 145 146 147 148 149 150 151 152 153 155 156 157 158 159 160 161 164\n",
            " 165 166 167 168 169 170 171 172 173 175 176 177 178 179 180 181 182 183\n",
            " 184 185 186 187 189 190 191 192 193 194 195 196 197 198 199 200 203 204\n",
            " 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223\n",
            " 224 225 226 227 228 229 230 231 232 233 234 236 237 238 239 241 242 243\n",
            " 244 245 246 247 248 250 251 252 253 254 255 256 257 258 259 260 261 262\n",
            " 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280\n",
            " 281 282 283 284 285 288 289 290 291 292 293 294 295 296 297 299 300 301\n",
            " 302 303 304 305 306 307 308 309 311 313 314 315 316 317 318 320 321 322\n",
            " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
            " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
            " 360 362 363 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379\n",
            " 380 381 382 384 385 387 388 389 390 392 393 394 396 397 398 399 400 401\n",
            " 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419\n",
            " 420 421 422 423 424 425 426 428 429 430 431 432 433 434 435 436 437 438\n",
            " 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456\n",
            " 457 458 459 460 461 462 463 464 465 466 467 469 470 471 472 474 475 476\n",
            " 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494\n",
            " 495 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513\n",
            " 514 515 516 517 518 519 520 521 522 524 526 527 528 529 530 531 532 533\n",
            " 534 535 536 538 540 541 542 543 544 545 546 547 550 553 554 555 556 558\n",
            " 559 560 561 562 563 564 565 566 569 570 571 572 573 574 575 576 578 579\n",
            " 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597\n",
            " 599 600 601 602 603 606 607 608 609 610 611] \n",
            "Instâncias de teste: [  4   5  12  23  24  26  37  51  57  60  61  72  73  74  92 110 119 130\n",
            " 154 162 163 174 188 201 202 205 235 240 249 286 287 298 310 312 319 323\n",
            " 361 364 383 386 391 395 427 468 473 496 523 525 537 539 548 549 551 552\n",
            " 557 567 568 577 598 604 605]\n",
            "==================================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmlgAEYR4cr2",
        "outputId": "87695061-827c-4a88-9232-bce592c67eeb"
      },
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((551, 21), (551, 12))"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QeVT1KGDs0J",
        "outputId": "6545962a-d588-4887-a992-ab090b627337"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((61, 21), (61, 12))"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "qrvOdiZ2nGWk",
        "outputId": "a3ec0f68-3979-46e0-ea67-5600d704e6d6"
      },
      "source": [
        "from skmultilearn.model_selection.measures import get_combination_wise_output_matrix\n",
        "from collections import Counter\n",
        "pd.DataFrame({\n",
        "    'train': Counter(str(combination) for row in get_combination_wise_output_matrix(y_train, order=1) for combination in row),\n",
        "    'test' : Counter(str(combination) for row in get_combination_wise_output_matrix(y_test, order=1) for combination in row)\n",
        "}).T.fillna(0.0)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-43c17d9c-1d71-4951-bca1-c10f7939054d\">\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>(2,)</th>\n",
              "      <th>(0,)</th>\n",
              "      <th>(11,)</th>\n",
              "      <th>(4,)</th>\n",
              "      <th>(7,)</th>\n",
              "      <th>(1,)</th>\n",
              "      <th>(9,)</th>\n",
              "      <th>(6,)</th>\n",
              "      <th>(10,)</th>\n",
              "      <th>(3,)</th>\n",
              "      <th>(5,)</th>\n",
              "      <th>(8,)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>36</td>\n",
              "      <td>427</td>\n",
              "      <td>279</td>\n",
              "      <td>149</td>\n",
              "      <td>49</td>\n",
              "      <td>242</td>\n",
              "      <td>76</td>\n",
              "      <td>180</td>\n",
              "      <td>91</td>\n",
              "      <td>77</td>\n",
              "      <td>47</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test</th>\n",
              "      <td>4</td>\n",
              "      <td>47</td>\n",
              "      <td>31</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>27</td>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43c17d9c-1d71-4951-bca1-c10f7939054d')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-43c17d9c-1d71-4951-bca1-c10f7939054d 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-43c17d9c-1d71-4951-bca1-c10f7939054d');\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",
              "  "
            ],
            "text/plain": [
              "       (2,)  (0,)  (11,)  (4,)  (7,)  (1,)  (9,)  (6,)  (10,)  (3,)  (5,)  (8,)\n",
              "train    36   427    279   149    49   242    76   180     91    77    47    13\n",
              "test      4    47     31    16     6    27     8    20     10     9     6     1"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGJ6K0cXoOY7"
      },
      "source": [
        "# Aprendizado Multilabel\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Binary Relevance/SVM "
      ],
      "metadata": {
        "id": "TInESLWqHSGA"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7qrc5gKoZAi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "493fc1b2-5fdb-45c9-8805-55b07eff313f"
      },
      "source": [
        "from skmultilearn.problem_transform import BinaryRelevance\n",
        "from sklearn.svm import SVC\n",
        "br_classifier = BinaryRelevance(classifier=SVC(),require_dense=[False, True])\n",
        "br_classifier.fit(X_train, y_train)\n",
        "prediction = br_classifier.predict(X_test)\n",
        "predicoes_br = pd.DataFrame(prediction.toarray())\n",
        "predicoes_br"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-319054b5-d5c7-4b7a-a505-a5cd7b8ea5de\">\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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-319054b5-d5c7-4b7a-a505-a5cd7b8ea5de')\"\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",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-319054b5-d5c7-4b7a-a505-a5cd7b8ea5de 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-319054b5-d5c7-4b7a-a505-a5cd7b8ea5de');\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",
              "  "
            ],
            "text/plain": [
              "     0    1    2    3    4    5    6    7    8    9    10   11\n",
              "0   0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0\n",
              "1   1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0\n",
              "2   1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0\n",
              "3   1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0\n",
              "4   1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0  1.0\n",
              "..  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
              "56  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0\n",
              "57  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  1.0\n",
              "58  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0\n",
              "59  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0\n",
              "60  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0\n",
              "\n",
              "[61 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEMbeq9OH5FH",
        "outputId": "6079ea77-d6b3-44e3-da3d-8b6318fdabe2"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1.],\n",
              "       [1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 1., 0., 0., 1., 0., 1., 0., 0., 1., 0., 1.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1.],\n",
              "       [0., 1., 0., 0., 1., 1., 1., 0., 0., 1., 0., 0.],\n",
              "       [1., 1., 1., 0., 0., 1., 1., 1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1.],\n",
              "       [1., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 0.],\n",
              "       [1., 1., 0., 1., 1., 0., 1., 0., 0., 0., 0., 1.],\n",
              "       [1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 1., 1., 1., 1., 0., 0., 0., 1., 0., 0., 1.],\n",
              "       [1., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 1., 0., 0., 1., 0., 1., 0., 0., 0., 0., 1.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 1., 1., 0., 1., 1., 0., 0., 0., 0., 0.],\n",
              "       [1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1.],\n",
              "       [1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 0., 0., 1., 0., 0., 1., 0., 0., 0., 1., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1.],\n",
              "       [1., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.],\n",
              "       [1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 1., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0.],\n",
              "       [1., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
              "       [1., 1., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1.],\n",
              "       [1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
              "       [1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4JevwhIpBnI",
        "outputId": "89234ee3-48aa-4814-b43b-dac71a591c11"
      },
      "source": [
        "import sklearn.metrics as metrics\n",
        "hl = metrics.hamming_loss(y_test, predicoes_br)\n",
        "acc = metrics.accuracy_score(y_test, predicoes_br)\n",
        "F1 = metrics.f1_score(y_test, predicoes_br, average='micro')\n",
        "print(acc, hl, F1)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6721311475409836 0.06967213114754098 0.8547008547008548\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CKcaJ0SxTuJP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5JkXOvn5Guf",
        "outputId": "91f26c40-c6ce-4232-83e6-f047897c303d"
      },
      "source": [
        "print(metrics.classification_report(y_test,predicoes_br))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.98      0.93        47\n",
            "           1       0.91      0.78      0.84        27\n",
            "           2       1.00      0.25      0.40         4\n",
            "           3       1.00      0.56      0.71         9\n",
            "           4       0.92      0.69      0.79        16\n",
            "           5       1.00      0.83      0.91         6\n",
            "           6       0.79      0.75      0.77        20\n",
            "           7       1.00      0.33      0.50         6\n",
            "           8       0.00      0.00      0.00         1\n",
            "           9       1.00      0.62      0.77         8\n",
            "          10       0.91      1.00      0.95        10\n",
            "          11       0.94      0.94      0.94        31\n",
            "\n",
            "   micro avg       0.90      0.81      0.85       185\n",
            "   macro avg       0.86      0.64      0.71       185\n",
            "weighted avg       0.91      0.81      0.84       185\n",
            " samples avg       0.88      0.85      0.85       185\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSryCn5QpD-2"
      },
      "source": [
        "#MLP -teste\n",
        "#from sklearn.neural_network import MLPClassifier\n",
        "#mlp = MLPClassifier(random_state=1, max_iter=1000, verbose=True, tol=0.000001).fit(X_train, y_train)\n",
        "#mlp_pred = mlp.predict_proba(X_test)"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9AOCSYmqG0E"
      },
      "source": [
        "### Matriz de confusão"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XA8Sq_T0KP0",
        "outputId": "d2ddd975-b04f-4ed1-8b47-3e90b6fbc263"
      },
      "source": [
        "labels"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['street_food',\n",
              " 'gourmet',\n",
              " 'italian_food',\n",
              " 'brazilian_food',\n",
              " 'mexican_food',\n",
              " 'chinese_food',\n",
              " 'japanese_food',\n",
              " 'arabic_food',\n",
              " 'snacks',\n",
              " 'healthy_food',\n",
              " 'fitness_food',\n",
              " 'sweets_desserts']"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1cX7J9Wzh4a-"
      },
      "source": [
        "multilabel_confusion_matrix\n",
        "* (0,0)verdadeiro negativo - (0,1)falsos positivos\n",
        "* (1,0)falsos negativos - (1,1)verdadeiros positivos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdTB8Nt0qKac",
        "outputId": "e6cc2d39-039c-4f04-e5ac-930f44c9c973"
      },
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "cm = multilabel_confusion_matrix(y_test, prediction)\n",
        "cm"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 8,  6],\n",
              "        [ 1, 46]],\n",
              "\n",
              "       [[32,  2],\n",
              "        [ 6, 21]],\n",
              "\n",
              "       [[57,  0],\n",
              "        [ 3,  1]],\n",
              "\n",
              "       [[52,  0],\n",
              "        [ 4,  5]],\n",
              "\n",
              "       [[44,  1],\n",
              "        [ 5, 11]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 1,  5]],\n",
              "\n",
              "       [[37,  4],\n",
              "        [ 5, 15]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 4,  2]],\n",
              "\n",
              "       [[60,  0],\n",
              "        [ 1,  0]],\n",
              "\n",
              "       [[53,  0],\n",
              "        [ 3,  5]],\n",
              "\n",
              "       [[50,  1],\n",
              "        [ 0, 10]],\n",
              "\n",
              "       [[28,  2],\n",
              "        [ 2, 29]]])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3F73CO0Ggi_l"
      },
      "source": [
        "## Bunary Relevance/Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZPtGqXIhJkD"
      },
      "source": [
        "avaliacoes = {}"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "wWPGxXPNMGeR"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xejD1QFVgq_b",
        "outputId": "da4e1450-ff13-4527-cdbb-adc5ccb30afe"
      },
      "source": [
        "classifier = BinaryRelevance(classifier = RandomForestClassifier(),require_dense = [False, True])\n",
        "classifier.fit(X_train, y_train)\n",
        "prediction_br_RFC=classifier.predict(X_test)\n",
        "\n",
        "ACC_BR_RFC = metrics.accuracy_score(y_test, prediction_br_RFC)\n",
        "HL_BR_RFC = metrics.hamming_loss(y_test, prediction_br_RFC)\n",
        "FS_BR_RFC = metrics.f1_score(y_test, prediction_br_RFC, average='micro')\n",
        "print(ACC_BR_RFC, HL_BR_RFC, FS_BR_RFC)\n",
        "avaliacoes[\"ACC_BR_RFC\"] = ACC_BR_RFC\n",
        "avaliacoes[\"HL_BR_RFC\"] = HL_BR_RFC\n",
        "avaliacoes[\"FS_BR_RFC\"] = FS_BR_RFC\n"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5901639344262295 0.08333333333333333 0.8242074927953891\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GUFij5v8ToA",
        "outputId": "686b7348-d53f-4742-ccfa-7bd878a50208"
      },
      "source": [
        "print(metrics.classification_report(y_test,prediction_br_RFC))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.96      0.92        47\n",
            "           1       0.95      0.70      0.81        27\n",
            "           2       0.50      0.25      0.33         4\n",
            "           3       0.71      0.56      0.63         9\n",
            "           4       0.91      0.62      0.74        16\n",
            "           5       1.00      0.83      0.91         6\n",
            "           6       0.78      0.70      0.74        20\n",
            "           7       1.00      0.17      0.29         6\n",
            "           8       0.00      0.00      0.00         1\n",
            "           9       1.00      0.62      0.77         8\n",
            "          10       0.91      1.00      0.95        10\n",
            "          11       0.90      0.90      0.90        31\n",
            "\n",
            "   micro avg       0.88      0.77      0.82       185\n",
            "   macro avg       0.80      0.61      0.67       185\n",
            "weighted avg       0.88      0.77      0.81       185\n",
            " samples avg       0.85      0.79      0.80       185\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Matriz de confusão"
      ],
      "metadata": {
        "id": "VEs3tmHXN-G5"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pFUSJ-ekiQr",
        "outputId": "c4c855dc-b529-47b7-8a81-a688de7f434b"
      },
      "source": [
        "cmm = multilabel_confusion_matrix(y_test, prediction_br_RFC)\n",
        "cmm"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 8,  6],\n",
              "        [ 2, 45]],\n",
              "\n",
              "       [[33,  1],\n",
              "        [ 8, 19]],\n",
              "\n",
              "       [[56,  1],\n",
              "        [ 3,  1]],\n",
              "\n",
              "       [[50,  2],\n",
              "        [ 4,  5]],\n",
              "\n",
              "       [[44,  1],\n",
              "        [ 6, 10]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 1,  5]],\n",
              "\n",
              "       [[37,  4],\n",
              "        [ 6, 14]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 5,  1]],\n",
              "\n",
              "       [[60,  0],\n",
              "        [ 1,  0]],\n",
              "\n",
              "       [[53,  0],\n",
              "        [ 3,  5]],\n",
              "\n",
              "       [[50,  1],\n",
              "        [ 0, 10]],\n",
              "\n",
              "       [[27,  3],\n",
              "        [ 3, 28]]])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwE5YFs3hydl"
      },
      "source": [
        "## Label Power Set/RandomForest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXU6DJHYhw6R",
        "outputId": "5b6cea94-2cac-4f6d-c426-8cf8ca7bc6c1"
      },
      "source": [
        "from skmultilearn.problem_transform import LabelPowerset\n",
        "classifier = LabelPowerset(classifier = RandomForestClassifier(), require_dense = [False, True])\n",
        "classifier.fit(X_train, y_train)\n",
        "prediction_LP_RFC=classifier.predict(X_test)\n",
        "\n",
        "ACC_LP_RFC = metrics.accuracy_score(y_test, prediction_LP_RFC)\n",
        "HL_LP_RFC = metrics.hamming_loss(y_test, prediction_LP_RFC)\n",
        "FS_LP_RFC = metrics.f1_score(y_test, prediction_LP_RFC, average='micro')\n",
        "print(ACC_LP_RFC, HL_LP_RFC, FS_LP_RFC)\n",
        "avaliacoes[\"ACC_LP_RFC\"] = ACC_LP_RFC\n",
        "avaliacoes[\"HL_LP_RFC\"] = HL_LP_RFC\n",
        "avaliacoes[\"FS_LP_RFC\"] = FS_LP_RFC"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6229508196721312 0.08469945355191257 0.8268156424581007\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ZDnvDvh6C9L",
        "outputId": "bfff2791-1b6d-47ba-abcb-40be0954de7f"
      },
      "source": [
        "print(metrics.classification_report(y_test,prediction_LP_RFC))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.91      0.91        47\n",
            "           1       0.96      0.85      0.90        27\n",
            "           2       0.50      0.25      0.33         4\n",
            "           3       0.62      0.56      0.59         9\n",
            "           4       0.80      0.75      0.77        16\n",
            "           5       1.00      0.83      0.91         6\n",
            "           6       0.75      0.75      0.75        20\n",
            "           7       0.67      0.33      0.44         6\n",
            "           8       0.00      0.00      0.00         1\n",
            "           9       0.83      0.62      0.71         8\n",
            "          10       0.77      1.00      0.87        10\n",
            "          11       0.90      0.87      0.89        31\n",
            "\n",
            "   micro avg       0.86      0.80      0.83       185\n",
            "   macro avg       0.73      0.64      0.67       185\n",
            "weighted avg       0.85      0.80      0.82       185\n",
            " samples avg       0.85      0.82      0.81       185\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Matriz de confusão"
      ],
      "metadata": {
        "id": "1G8MKjo-OLkw"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1oN7Yrtk7m5",
        "outputId": "1e7b6af0-a01f-4966-d3e9-ec52066fa1a9"
      },
      "source": [
        "cmlp = multilabel_confusion_matrix(y_test, prediction_LP_RFC)\n",
        "cmlp"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[10,  4],\n",
              "        [ 4, 43]],\n",
              "\n",
              "       [[33,  1],\n",
              "        [ 4, 23]],\n",
              "\n",
              "       [[56,  1],\n",
              "        [ 3,  1]],\n",
              "\n",
              "       [[49,  3],\n",
              "        [ 4,  5]],\n",
              "\n",
              "       [[42,  3],\n",
              "        [ 4, 12]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 1,  5]],\n",
              "\n",
              "       [[36,  5],\n",
              "        [ 5, 15]],\n",
              "\n",
              "       [[54,  1],\n",
              "        [ 4,  2]],\n",
              "\n",
              "       [[60,  0],\n",
              "        [ 1,  0]],\n",
              "\n",
              "       [[52,  1],\n",
              "        [ 3,  5]],\n",
              "\n",
              "       [[48,  3],\n",
              "        [ 0, 10]],\n",
              "\n",
              "       [[27,  3],\n",
              "        [ 4, 27]]])"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14fcpfupCmEV"
      },
      "source": [
        "\n",
        "## Label Power Set/SVM\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QAMnyxonCIe0",
        "outputId": "ecea33c9-a2b5-4e3c-b36d-7c6879a2b648"
      },
      "source": [
        "classifier = LabelPowerset(classifier = SVC(), require_dense = [False, True])\n",
        "classifier.fit(X_train, y_train)\n",
        "prediction_LP_SVM=classifier.predict(X_test)\n",
        "\n",
        "ACC_LP_SVM = metrics.accuracy_score(y_test, prediction_LP_SVM)\n",
        "HL_LP_SVM = metrics.hamming_loss(y_test, prediction_LP_SVM)\n",
        "FS_LP_SVM = metrics.f1_score(y_test, prediction_LP_SVM, average='micro')\n",
        "print(ACC_LP_SVM, HL_LP_SVM, FS_LP_SVM)\n",
        "avaliacoes[\"ACC_LP_SVM\"] = ACC_LP_SVM\n",
        "avaliacoes[\"HL_LP_SVM\"] = HL_LP_SVM\n",
        "avaliacoes[\"FS_LP_SVM\"] = FS_LP_SVM"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7213114754098361 0.06967213114754098 0.8579387186629527\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_16mYBO877Yf",
        "outputId": "a9a7a86f-dadc-4081-db89-aa2ea3af14bf"
      },
      "source": [
        "print(metrics.classification_report(y_test,prediction_LP_SVM))"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.98      0.94        47\n",
            "           1       0.92      0.81      0.86        27\n",
            "           2       1.00      0.50      0.67         4\n",
            "           3       1.00      0.56      0.71         9\n",
            "           4       0.80      0.75      0.77        16\n",
            "           5       0.83      0.83      0.83         6\n",
            "           6       0.78      0.90      0.84        20\n",
            "           7       1.00      0.33      0.50         6\n",
            "           8       0.00      0.00      0.00         1\n",
            "           9       1.00      0.62      0.77         8\n",
            "          10       0.90      0.90      0.90        10\n",
            "          11       0.90      0.90      0.90        31\n",
            "\n",
            "   micro avg       0.89      0.83      0.86       185\n",
            "   macro avg       0.84      0.67      0.72       185\n",
            "weighted avg       0.89      0.83      0.85       185\n",
            " samples avg       0.88      0.86      0.86       185\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Matriz de Confusão"
      ],
      "metadata": {
        "id": "_0tggwL2Ohua"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXMqGXyGlF5S",
        "outputId": "08c24e62-f433-44d8-8460-8c99061d56b5"
      },
      "source": [
        "cmlpsvm = multilabel_confusion_matrix(y_test, prediction_LP_SVM)\n",
        "cmlpsvm"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 9,  5],\n",
              "        [ 1, 46]],\n",
              "\n",
              "       [[32,  2],\n",
              "        [ 5, 22]],\n",
              "\n",
              "       [[57,  0],\n",
              "        [ 2,  2]],\n",
              "\n",
              "       [[52,  0],\n",
              "        [ 4,  5]],\n",
              "\n",
              "       [[42,  3],\n",
              "        [ 4, 12]],\n",
              "\n",
              "       [[54,  1],\n",
              "        [ 1,  5]],\n",
              "\n",
              "       [[36,  5],\n",
              "        [ 2, 18]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 4,  2]],\n",
              "\n",
              "       [[60,  0],\n",
              "        [ 1,  0]],\n",
              "\n",
              "       [[53,  0],\n",
              "        [ 3,  5]],\n",
              "\n",
              "       [[50,  1],\n",
              "        [ 1,  9]],\n",
              "\n",
              "       [[27,  3],\n",
              "        [ 3, 28]]])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czS2TuneC2DK"
      },
      "source": [
        "## Classifier Chain/RandonForest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYldO-a1_v_k",
        "outputId": "6bc53547-1b55-4a31-913e-d7ab0afec430"
      },
      "source": [
        "from skmultilearn.problem_transform import ClassifierChain\n",
        "\n",
        "classifier = ClassifierChain(classifier = RandomForestClassifier(),require_dense = [False, True])\n",
        "classifier.fit(X_train,y_train)\n",
        "predict_CC_RFC = classifier.predict(X_test)\n",
        "\n",
        "ACC_CC_RFC = metrics.accuracy_score(y_test, predict_CC_RFC)\n",
        "HL_CC_RFC = metrics.hamming_loss(y_test, predict_CC_RFC) \n",
        "FS_CC_RFC = metrics.f1_score(y_test, predict_CC_RFC, average='micro')\n",
        "print(ACC_CC_RFC, HL_CC_RFC, FS_CC_RFC)\n",
        "avaliacoes[\"ACC_CC_RFC\"] = ACC_CC_RFC\n",
        "avaliacoes[\"HL_CC_RFC\"] = HL_CC_RFC\n",
        "avaliacoes[\"FS_CC_RFC\"] = FS_CC_RFC"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6229508196721312 0.07650273224043716 0.8409090909090909\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYYrMEpn-Q-a",
        "outputId": "96d42945-f240-492f-b229-61f3cfd1adef"
      },
      "source": [
        "print(metrics.classification_report(y_test,predict_CC_RFC))"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.96      0.92        47\n",
            "           1       0.91      0.74      0.82        27\n",
            "           2       0.50      0.25      0.33         4\n",
            "           3       1.00      0.56      0.71         9\n",
            "           4       0.92      0.69      0.79        16\n",
            "           5       1.00      0.83      0.91         6\n",
            "           6       0.77      0.85      0.81        20\n",
            "           7       1.00      0.33      0.50         6\n",
            "           8       0.00      0.00      0.00         1\n",
            "           9       0.83      0.62      0.71         8\n",
            "          10       0.91      1.00      0.95        10\n",
            "          11       0.93      0.87      0.90        31\n",
            "\n",
            "   micro avg       0.89      0.80      0.84       185\n",
            "   macro avg       0.80      0.64      0.70       185\n",
            "weighted avg       0.89      0.80      0.83       185\n",
            " samples avg       0.87      0.84      0.83       185\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Matriz de confusão"
      ],
      "metadata": {
        "id": "V0GKROAPQlWM"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYIxXkftl8u1",
        "outputId": "93d7102c-01cc-4b36-ae30-de8ae198da98"
      },
      "source": [
        "cmccrf = multilabel_confusion_matrix(y_test, predict_CC_RFC)\n",
        "cmccrf"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 8,  6],\n",
              "        [ 2, 45]],\n",
              "\n",
              "       [[32,  2],\n",
              "        [ 7, 20]],\n",
              "\n",
              "       [[56,  1],\n",
              "        [ 3,  1]],\n",
              "\n",
              "       [[52,  0],\n",
              "        [ 4,  5]],\n",
              "\n",
              "       [[44,  1],\n",
              "        [ 5, 11]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 1,  5]],\n",
              "\n",
              "       [[36,  5],\n",
              "        [ 3, 17]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 4,  2]],\n",
              "\n",
              "       [[60,  0],\n",
              "        [ 1,  0]],\n",
              "\n",
              "       [[52,  1],\n",
              "        [ 3,  5]],\n",
              "\n",
              "       [[50,  1],\n",
              "        [ 0, 10]],\n",
              "\n",
              "       [[28,  2],\n",
              "        [ 4, 27]]])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hj9VBY53F1RW"
      },
      "source": [
        "#classifierChain =>SVM"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Classifier Chain/SVM"
      ],
      "metadata": {
        "id": "MEPeazZLOyn6"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvRt3wq0FGqK",
        "outputId": "3461cfeb-c32e-4528-8ec7-bfebf7f118ed"
      },
      "source": [
        "from skmultilearn.problem_transform import ClassifierChain\n",
        "\n",
        "classifier = ClassifierChain(classifier = SVC(),require_dense = [False, True])\n",
        "classifier.fit(X_train,y_train)\n",
        "predict_CC_SVM = classifier.predict(X_test)\n",
        "\n",
        "ACC_CC_SVM = metrics.accuracy_score(y_test, predict_CC_SVM)\n",
        "HL_CC_SVM = metrics.hamming_loss(y_test, predict_CC_SVM) \n",
        "FS_CC_SVM = metrics.f1_score(y_test, predict_CC_SVM, average='micro')\n",
        "print(ACC_CC_SVM, HL_CC_SVM, FS_CC_SVM)\n",
        "avaliacoes[\"ACC_CC_SVM\"] = ACC_CC_SVM\n",
        "avaliacoes[\"HL_CC_SVM\"] = HL_CC_SVM\n",
        "avaliacoes[\"FS_CC_SVM\"] = FS_CC_SVM"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6721311475409836 0.06967213114754098 0.8547008547008548\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08dNS9Rc_4b1",
        "outputId": "6032f4de-b691-4e99-b246-d781bbe99090"
      },
      "source": [
        "print(metrics.classification_report(y_test,predict_CC_SVM))"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.98      0.93        47\n",
            "           1       0.91      0.78      0.84        27\n",
            "           2       1.00      0.25      0.40         4\n",
            "           3       1.00      0.56      0.71         9\n",
            "           4       0.92      0.69      0.79        16\n",
            "           5       1.00      0.83      0.91         6\n",
            "           6       0.79      0.75      0.77        20\n",
            "           7       1.00      0.33      0.50         6\n",
            "           8       0.00      0.00      0.00         1\n",
            "           9       1.00      0.62      0.77         8\n",
            "          10       0.91      1.00      0.95        10\n",
            "          11       0.94      0.94      0.94        31\n",
            "\n",
            "   micro avg       0.90      0.81      0.85       185\n",
            "   macro avg       0.86      0.64      0.71       185\n",
            "weighted avg       0.91      0.81      0.84       185\n",
            " samples avg       0.88      0.85      0.85       185\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in samples with no true labels. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Matriz de Confusão"
      ],
      "metadata": {
        "id": "LHgkRcKiO5JG"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Juq1aahxmNbY",
        "outputId": "ea5fd21b-f1be-4bb4-c5c5-ca0fd9579fce"
      },
      "source": [
        "cmccsvm = multilabel_confusion_matrix(y_test, predict_CC_SVM)\n",
        "cmccsvm"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 8,  6],\n",
              "        [ 1, 46]],\n",
              "\n",
              "       [[32,  2],\n",
              "        [ 6, 21]],\n",
              "\n",
              "       [[57,  0],\n",
              "        [ 3,  1]],\n",
              "\n",
              "       [[52,  0],\n",
              "        [ 4,  5]],\n",
              "\n",
              "       [[44,  1],\n",
              "        [ 5, 11]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 1,  5]],\n",
              "\n",
              "       [[37,  4],\n",
              "        [ 5, 15]],\n",
              "\n",
              "       [[55,  0],\n",
              "        [ 4,  2]],\n",
              "\n",
              "       [[60,  0],\n",
              "        [ 1,  0]],\n",
              "\n",
              "       [[53,  0],\n",
              "        [ 3,  5]],\n",
              "\n",
              "       [[50,  1],\n",
              "        [ 0, 10]],\n",
              "\n",
              "       [[28,  2],\n",
              "        [ 2, 29]]])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    }
  ]
}