FoodTruckDataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FoodTruckDataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1n0ocoZiWT0fbnPHaSw05fqpcZrR18yAQ",
      "authorship_tag": "ABX9TyPqcYIwJ262/QnmUp1LUZKM"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvoovXI2lpjl"
      },
      "source": [
        "# Aprendizado Multilabel para recomendação de comidas\n",
        "Dataset food trucck\n",
        "\n"
      ]
    },
    {
      "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",
        "\n",
        "# Data Visualization Packages\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xz1zLA7Kn0nd"
      },
      "source": [
        "# Carregando Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFuqj1kCl2J7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "67ab0508-475b-4b81-9e5d-c1b062930407"
      },
      "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",
        "#convert_dtypes()\n",
        "#df.drop(columns = ['Unnamed: 0'],inplace = True)\n",
        "df.head(30)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>"
            ],
            "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": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rQ4hAHTu0_x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f1ea19a-f610-4414-805e-f05be12aabbf"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "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": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "t-rp_iCtu_P0",
        "outputId": "d4e59c65-1630-4373-9310-1851d35fbafa"
      },
      "source": [
        "#adjusting str for numeric colls\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",
        "#adjusting cols object types\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": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_luhGRCyriv3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "828dc30f-b766-452e-ac7c-cdcfd4f60ac1"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "<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",
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              "      <td>3.0</td>\n",
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              "      <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",
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              "    <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",
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              "      <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",
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              "      <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",
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              "      <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>"
            ],
            "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": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QG5RLNGqJrig"
      },
      "source": [
        "#! pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M99PYEQ1KlcN"
      },
      "source": [
        "#profile = ProfileReport(df, title='tt', html={'style':{'full_width':True}})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r8Sg-_KoNA0o",
        "outputId": "893aedcb-02bb-472a-d4fb-d160a694350b"
      },
      "source": [
        "profile"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "default\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/magics/basic.py:300: UserWarning: %profile is now deprecated. Please use get_ipython().profile instead.\n",
            "  warn(\"%profile is now deprecated. Please use get_ipython().profile instead.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTYXlPAZsHh5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "a791ed5a-223e-4634-9da3-ec412dfd8949"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvDCqAFmndf1"
      },
      "source": [
        "# Características do dataset"
      ]
    },
    {
      "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",
        "feats_df = df.drop(columns = labels)\n",
        "labels_df = df[labels]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "OGazQ8djsXus",
        "outputId": "94adfc6d-9d22-432b-a1dc-72a91736c111"
      },
      "source": [
        "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",
        "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n+vv7K25rbJwQra2tVVwFY11jo6/6LGU8bN/QUF/xJ8eP7cuyfzm2L8v+5di+LPuXY/uy7M+hjId/3/11qoqImcHBwWhpaam4raWlJXbu3Bn/9V//FaOjo+97fGBg4LCfo7u7OzZs2FBxW2dnZ3R1dR35iTPmTJ58SulTGLfG0/bNzSeXPoVxy/Zl2b8c25dl/3JsX5b9y7F9Wfbn3cbTv+8eSs0iYkT82u83PNrvP1y8eHHMnz+/4rbGxgkxMLD3qB6XscXroZzxsH1DQ300N58cQ0P7YmTkQOnTGVdsX5b9y7F9WfYvx/Zl2b8c25dlfw5lrP/77uFE0ppFxMmTJ8fg4GDFbYODgzFlypSYNGlS1NfXv+/xqVOnHvZztLa2vuejy/39b8TwsL+w+RWvh3LG0/YjIwfG1fWeSGxflv3LsX1Z9i/H9mXZvxzbl2V/3s3rocpfrJKZOXNm9Pb2VtzW09MTs2fPjqampjj33HOjr6/v4LGhoaH4+c9/HhdeeGGtTgEAAAAAOAZqFhEXLVoU27dvjyeffDL2798fW7dujVdffTUWLlwYERFLly6NBx54IHbt2hVvvvlmrF27Ni644IKYNWtWrU4BAAAAADgGqvo48/8Gv+Hh4YiI+MEPfhAR//OOwxkzZsTatWtjzZo1sXv37jjnnHNi06ZNcdppp0VExJIlS6K/vz+uv/762Lt3b8ybN+89vyQFAAAAADjxVBURe3p60uMLFiyIBQsWvO+xurq66Orq8puUAQAAAOADpmYfZwYAAAAAxiYREQAAAABIiYgAAAAAQEpEBAAAAABSIiIAAAAAkBIRAQAAAICUiAgAAAAApEREAAAAACAlIgIAAAAAKRERAAAAAEiJiAAAAABASkQEAAAAAFIiIgAAAACQEhEBAAAAgJSICAAAAACkREQAAAAAICUiAgAAAAApEREAAAAASImIAAAAAEBKRAQAAAAAUiIiAAAAAJASEQEAAACAlIgIAAAAAKRERAAAAAAgJSICAAAAACkREQAAAABIiYgAAAAAQEpEBAAAAABSIiIAAAAAkBIRAQAAAICUiAgAAAAApEREAAAAACAlIgIAAAAAKRERAAAAAEiJiAAAALVxafEAACAASURBVABASkQEAAAAAFIiIgAAAACQEhEBAAAAgJSICAAAAACkREQAAAAAICUiAgAAAAApEREAAAAASImIAAAAAEBKRAQAAAAAUiIiAAAAAJASEQEAAACAlIgIAAAAAKRERAAAAAAg1Vj6BAA48cxd93TpU/hAeW7FZaVPAQAA4JjyTkQAAAAAICUiAgAAAAApEREAAAAASImIAAAAAEBKRAQAAAAAUiIiAAAAAJASEQEAAACAlIgIAAAAAKRERAAAAAAg1Vj6BAAAAMazueueLn0KHyjPrbis9CkAjEveiQgAAAAApEREAAAAACAlIgIAAAAAKRERAAAAAEiJiAAAAABASkQEAAAAAFIiIgAAAACQEhEBAAAAgJSICAAAAACkREQAAAAAICUiAgAAAAApEREAAAAASImIAAAAAEBKRAQAAAAAUiIiAAAAAJASEQEAAACAlIgIAAAAAKRERAAAAAAgJSICAAAAACkREQAAAABIiYgAAAAAQEpEBAAAAABSIiIAAAAAkBIRAQAAAICUiAgAAAAApEREAAAAACAlIgIAAAAAKRERAAAAAEiJiAAAAABAqrGWD3beeefFhz70oairqzt426JFi+Luu++OHTt2xLp16+KVV16J3/zN34ybbropFi5cWMunBwAAAACOgZpGxIiIJ554Is4888yK2/bs2RO33HJLfOELX4irr746nn/++Vi2bFn89m//dsyaNavWpwAAAAAA1NBx+Tjzo48+GtOnT49rr702mpqaor29PebPnx9btmw5Hk8PAAAAAByFmr8Tcd26dfFP//RP8eabb8YnPvGJuOuuu6Kvry/a2toq7tfW1haPP/54VY+9Z8+e6O/vr7itsXFCtLa2HvV5M3Y0Nvqqz1LGw/YNDfUVf0KE1z7Hlu3Lsn85tudQxvo/d732y7I/hzLW/95zOGoaES+66KJob2+Pe+65J1577bW4/fbb4y//8i9jcHAwTj/99Ir7Tpo0KQYGBqp6/O7u7tiwYUPFbZ2dndHV1XXU587YMXnyKaVPYdwaT9s3N59c+hQ4gXjtczzYviz7l2N73m28/HPXa78s+/Nu4+XvPZmaRsTu7u6D//3ss8+OlStXxrJly2LOnDk1efzFixfH/PnzK25rbJwQAwN7a/L4jA1eD+WMh+0bGuqjufnkGBraFyMjB0qfDicIr32OJduXZf9ybM+hjPV/7nrtl2V/DmWs/73ncCJpzT/O/H+deeaZMTIyEvX19TE4OFhxbGBgIKZMmVLV47W2tr7no8v9/W/E8LC/sPkVr4dyxtP2IyMHxtX1khtPrwWv/XJsX5b9y7E97zZeXg9e+2XZn3fzeqjhL1b56U9/Gn/zN39TcduuXbvipJNOio6Ojujt7a041tvbG7Nnz67V0wMAAAAAx0jNIuLUqVOju7s7Nm/eHG+//Xb867/+a9x3332xePHi+OQnPxm7d++OLVu2xP79++Opp56Kp556KhYtWlSrpwcAAAAAjpGaRcTTTz89Nm/eHNu2bYt58+bFkiVL4vd+7/fijjvuiKlTp8amTZviwQcfjDlz5sSXv/zluPfee+P888+v1dMDAAAAAMdITb8Tce7cufHtb3/7kMcefvjhWj4dAAAAAHAc1OydiAAAAADA2CQiAgAAAAApEREAAAAASImIAAAAAEBKRAQAAAAAUiIiAAAAAJASEQEAAACAVGPpEwB4P3PXPV36FD5wnltxWelTAAAAYIzyTkQAAAAAICUiAgAAAAApEREAAAAASPlORAA4gfg+0Or5PlAAADj2vBMRAAAAAEiJiAAAAABASkQEAAAAAFIiIgAAAACQEhEBAAAAgJSICAAAAACkREQAAAAAICUiAgAAAAApEREAAAAASDWWPgEAAIiImLvu6dKn8IHy3IrLSp8CADCOeCciAAAAAJASEQEAAACAlIgIAAAAAKRERAAAAAAgJSICAAAAACm/nRkAAIBxyW+Fr57fDA/jl3ciAgAAAAApEREAAAAASImIAAAAAEBKRAQAAAAAUiIiAAAAAJDy25kBAACA485vx66O34xNad6JCAAAAACkREQAAAAAICUiAgAAAAApEREAAAAASImIAAAAAEBKRAQAAAAAUiIiAAAAAJASEQEAAACAlIgIAAAAAKQaS58AAABQ1tx1T5c+hQ+U51ZcVvoUAOC4805EAAAAACAlIgIAAAAAKRERAAAAAEiJiAAAAABASkQEAAAAAFIiIgAAAACQEhEBAAAAgJSICP+vvTuPi6re/wf+wpUUr6Xfr5Z72R2UdYZVBEVEEWQRt8jEBcylFDW1skVvXrX0irkgamaYppleNC3JTE2u5RZqipq7JoiCiIKgwLC8f3/w41xGYQYQGOj7ej4ePh4yZ86Zz3mfOZ/lPeecDxERERERERER6cUkIhEREREREREREenFJCIRERERERERERHpxSQiERERERERERER6cUkIhEREREREREREenFJCIRERERERERERHpxSQiERERERERERER6cUkIhEREREREREREenFJCIRERERERERERHpxSQiERERERERERER6dXA2AUgIiIiqi0cFx80dhHqlLjpPY1dBCIiIiKqIbwSkYiIiIiIiIiIiPRiEpGIiIiIiIiIiIj0YhKRiIiIiIiIiIiI9GISkYiIiIiIiIiIiPRiEpGIiIiIiIiIiIj0YhKRiIiIiIiIiIiI9GISkYiIiIiIiIiIiPRiEpGIiIiIiIiIiIj0YhKRiIiIiIiIiIiI9GISkYiIiIiIiIiIiPRiEpGIiIiIiIiIiIj0YhKRiIiIiIiIiIiI9GISkYiIiIiIiIiIiPRiEpGIiIiIiIiIiIj0YhKRiIiIiIiIiIiI9GISkYiIiIiIiIiIiPRiEpGIiIiIiIiIiIj0YhKRiIiIiIiIiIiI9GISkYiIiIiIiIiIiPRiEpGIiIiIiIiIiIj0YhKRiIiIiIiIiIiI9GISkYiIiIiIiIiIiPRiEpGIiIiIiIiIiIj0YhKRiIiIiIiIiIiI9GISkYiIiIiIiIiIiPRiEpGIiIiIiIiIiIj0qtEkYlJSEsaNGwdnZ2d4eHhg0aJFKCwsrMkiEBERERERERERUQU1qMkPCwsLg6WlJfbt24e0tDSMHz8e//M//4OQkJCaLAYRERERERERERFVQI1diXjmzBlcuHABM2bMQLNmzdCpUyeMHj0aW7ZsqakiEBERERERERERUSXU2JWI586dQ9u2bdG8eXPlNUtLS1y/fh1ZWVkwMzMzuI07d+4gNTVV57UGDZqgVatWVV5eqrsaNOCjPo2FsTcuxt94GHvjYvyNh7E3LsbfeBh742L8jYvxNx7G3rgYf8BERKQmPmj16tXYu3cvtm3bprx248YNeHl5Yd++fWjfvr3BbURERGDFihU6r02aNAlhYWFVXl7S786dO9iyZQuCgoKYxK1hjL1xMf7Gw9gbF+NvPIy9cTH+xsPYGxfjbzyMvXEx/sbD2Nd+NZpGfdp8ZVBQELZv367zLygoqIpKRxWRmpqKFStWPHFlKFU/xt64GH/jYeyNi/E3HsbeuBh/42HsjYvxNx7G3rgYf+Nh7Gu/GruduUWLFkhPT9d5LT09HSYmJmjRokW5ttGqVStmo4mIiIiIiIiIiGpYjV2JaGVlhdu3b+PevXvKa2fOnMHLL7+Mpk2b1lQxiIiIiIiIiIiIqIJqLIloYWEBa2trLF68GFlZWbh69SrWrVuHYcOG1VQRiIiIiIiIiIiIqBLqf/TRRx/V1If16NEDu3btwty5cxETE4MhQ4ZgzJgxMDExqakiUBVq2rQpnJyceCWpETD2xsX4Gw9jb1yMv/Ew9sbF+BsPY29cjL/xMPbGxfgbD2Nfu9XY7MxERERERERERERUN9Xo7MxERERERERERERU9zCJSERERERERERERHoxiUhERERERERERER6MYlIREREREREREREejGJSERERERERERERHoxiUhERERERERERER6MYlIREREREREREREejGJSERERERERERERHoxiUhERERERERERER6MYlIRERERDUmKSkJ1tbWuH79urGLYhQpKSkYNGgQbG1tcfv27Srf/iuvvIKIiIgq3y7R40aMGIHw8HBjF+OpmJub4+DBg9Wy7d69e2Pz5s2lLqvO2K1duxYODg746KOPqnzbBw8ehLm5eZVvl4jqDiYRqdrMnz8fGo0Ga9asqfJtb968Gb17967y7f5fl5iYiB9//NHYxShVZQed27dvh6urKwAgLi4O1tbW0Gq11VFEHXVtkHjz5k2Ym5vj6tWrVbbNsvTr1w///ve/Aeh2oj/88EO888471f75APD999+jW7duGDt2bJVv++rVqzA3N8fNmzerfNtPIzQ0FEuXLjV2Merc4EbfAHPHjh11oi2qbTFv27Ytzpw5gxdffLHKy1MX7N69G2lpaTh27BheeOEFYxen2ulLpFRlQvn/Yr1OdUdN9rFXrVqFKVOmVEudX52OHj2Knj17okuXLjXWX68qrH+qV3h4OEaMGGHsYtD/18DYBaCqEx0djd69e6NFixbVsv2ffvoJ5ubm6Nixo8H3pqenY8OGDVi1alWdGGBRkZ9++glnz56Ft7e3sYvyhOJBJwAcOXIEZmZmsLa2rtA2HB0dlW1Ut5KDRFNT0xr5zLpiz549pb4+b968GivD559/joEDB+Ldd9+tsc80tqioKGMXAUDR4Gbq1Kl/ic5gYGAgAgMDjV0Mg/5KMf8ryMrKQuvWrdk2QLdtf1r/F+t1qjtqso+dlZVVrvFabbN+/Xqo1WosXboU9er991qnioxBjYX1T1Gi/Ny5c7VyHFkdCgoKsGHDBoSEhBi7KDWOVyL+RRQUFGDBggW4f/9+tX3G8uXLcePGjXK99+HDhwBQqyv76hQfH49+/frB1tYWEyZMwMaNG5Vk6vHjx/HKK69Ao9HAzc0NS5YsQWFhIQBg5syZeOutt5Tt5ObmwtzcHMeOHQNQdNXWokWL4O/vj3HjxgEoukomJiYGgwYNgo2NDcaNG4fk5GSMGTMGGo0GgwYN0vnl6ocffsCAAQOgVqvh6emJLVu2AAC++OILhIeH48cff4S1tTUKCgpqJFaV8eWXX+Ls2bPGLoZeHCTWbllZWejQoYOxi/F/Ul0d3NRltS3mJa98TkhIwJgxY+Ds7AxnZ2dMmzYNDx48AAAcO3YMlpaWOHDgADw9PWFjY4NJkyYpfQygqD3o06cPNBoNfHx88NNPPynLZs6ciblz5+KTTz6Bk5MTunXrhs8//1xZnp6ejhkzZsDNzQ0ajQZvvPEGUlJSAACFhYVYsGAB3NzcoFarERAQgF9++UVZ98iRIwgKCoJGo0GPHj0QGRlZrn1funQpVq5cifj4eFhbWyMpKQkZGRl45513lHKMGzdOp92+fPkyRo4cCQcHBzg7O+Mf//gHcnNzleWRkZFwc3ODs7NzucvxV8R6HVizZg08PDxga2uLfv36YefOncr5dujQIQQGBkKtVuPVV1/V+Y7pO48KCgoQHh4OV1dXODo6YsqUKUhPT3/is0UEU6dORUhICPLy8nD69Gmlv+vs7IwPPvgAOTk5NRKHikpNTcWoUaNgY2OD/v3749KlS8oyfee6iCA8PBzu7u7QaDQYOHAg4uLinth+WX3sgoICzJ49G3Z2dnBxccEPP/wAABg1ahQWLFigs43IyEi8+uqrevdDq9UqP7C/+eab+PDDDwEA+/btQ0BAANRqNXr37o0NGzYo6xQWFiIyMhJ9+/aFjY0NBg4ciCNHjijL//zzT7z66qvQaDQYOnRouceClVF8DpdMIAIVG4MaC+ufomRvWRcK/BX98ccfWLt2rbGLYRxCdc5nn30mvXr1EhsbG/Hy8pIdO3aInZ2dqFQqsbS0lIiICDl69Kio1WpZt26daDQaOXnypIiIfPXVV+Lt7S02NjbSv39/2bt3r7Ld7OxsmTNnjri7u4utra0EBwfL5cuXRUTE399f2f7MmTP1lu/atWtiaWmpvD8yMlJERDZv3qx8dr9+/SQmJkZZJycnR+bOnSvu7u5iY2Mjr732mvzxxx/K8lOnTom/v7/Y2trK6NGjJSIiQjw8PKosplUpNzdXXFxc5JNPPpHs7GyJjY2V7t27i4eHh6SmpoqNjY1s3LhRcnNz5dy5c9K9e3fZuHGjiIi8++67MnXqVGVbOTk5olKp5OjRoyIiEhwcLO7u7nL69GkpLCwUERGVSiXBwcGSkpIi169fFysrK/Hz85Nz585JZmamBAQEyD//+U8REYmPjxe1Wi2//PKL5OfnS1xcnKjVajlx4kSpn1+bJCYmikqlkpCQEFGpVGJhYSEjR44UkaL9GjZsmNjb24uLi4vMnj1btFqtiIhs27ZNunfvLiIiR48eFZVKJTk5OQbXO3r0qNjZ2cl//vMf6devn9ja2kpoaKikp6cbLOuSJUuka9euYm5uLlZWVnLz5k1JT0+Xt99+W1xdXUWtVsvYsWMlMTFRWefSpUsyYsQIsbe3FycnJ5k9e7ZSThGRFStWiKurqzg5OcmKFStk6NChsnz58qoJrvw3vt9++634+vqKWq2WkSNHSnJycqn1SWFhoSxatEh69uwparVaAgMD5bfffhMRkZs3b4qVlZXOP5VKJRERESIi4uHhIV9//bWIFH2nFy1aJCJPfv/WrVsnnp6eolarxdvbW/bs2aMse/fdd+Wf//ynfPzxx+Lo6CjOzs6yZs2acu2rh4eH8h0KCQkREZG4uDgZOnSoqNVqcXV1lU8//VQKCgqUdfTVX3fv3pUxY8aIWq2W/v37y/bt20WlUukcX31UKpXs2rVLBg4cKNbW1jJ27Fi5ffu2hIaGilqtloEDB+psKyYmRgICAsTW1lZ69+4t33zzjYiIpKamiqOjoxw4cEB571tvvSUTJkx4ItYiIl988YX06tVLNBqNhIaGKp+h79gWb2fVqlUyY8YM0Wg04ubmJjt27DC4n7m5ucp3wdLSUj744AMREdm7d69Sv3t4eMj69euVdQoKCmTFihXSp08fsba2lsDAQDl8+LCy/Pr16xIUFCRqtVqGDBkiGzZsEJVKVa64Py4hIUFCQkJErVZLr169lHKoVCqJjo6WkSNHirW1tfj4+MjFixdFRLd+KT6Hfv31VxkwYIDY2tpKUFBQuY6dSFE7V/wddHJykvfff1+ys7NFRH/7XBdjXhyrK1euyIgRI2TmzJmSm5sraWlpEhgYKJ988omI/LfOnjx5sqSnp0tycrL4+PjI/PnzRUTkt99+EysrK7l48aIUFhbKtm3bxNraWtLS0kSkqJ5wcnKS7du3i1arlejoaOnataukpKSIiMj48eNl4sSJkp6eLpmZmTJt2jSlTvjuu+/E399f0tLSJD8/X6Kjo8XFxUW0Wq3cvn1b1Gq1fPvtt5Kfny8XLlwQNzc3+e6778q1/8uXL5ehQ4cqf0+aNElGjx4taWlpkpmZKWFhYTJ48GDlGLq5uUl4eLhkZ2fLjRs3xMfHRxYuXCgiIr/88ovY2NjI8ePHJScnRyIiIsTW1vap24ey2seKtgciRXVuZGSkhIaGiq2trfj6+kpcXJyI6H4XRETS0tIkLCxM7OzsxNXVVRYvXqz0d/SpbfW6MZw4cUJcXV0lKSlJCgsL5ZdffhG1Wi2nT58WlUol48aNk+TkZLl//774+/vLP/7xDxExfB6tW7dOvLy8JDExUbKysmTs2LEybdo0EdFtVyIiIsTPz08yMzNFRMTLy0u2bt0qBQUFkpKSIoMHD5Yvv/yy5gNjgEqlkqFDh8q1a9ckMzNTRowYIePHjxcRMXiuf/vtt+Lq6iq3b9+W/Px8iYiIkG7dukl+fr6I6PZ3Hu/jBAcHi5ubmxw8eFByc3Nl8eLF4ujoKHl5ebJ9+3Zxc3PT+b4GBAQo44Xy7NN//vMfERE5f/68WFhYyN69e0Wr1crhw4fFxsZGYmNjRURkw4YN4u7uLhcuXJDc3Fz54osvRK1Wy927d0VEJCgoSKZNmyYPHz6UK1euiK+vb6XbWX2GDx8u5ubm0rVrV/Hy8lL664+PQauzrT1w4ID4+fkpdca//vUvnWNQltpU/zyeJ/j222/F0tJSLly4oLzH3d1dpkyZovy9detWCQwMFJGi78vIkSPF3t5enJ2dZe7cucrYSF9s165dK126dJEuXbqIlZWV5OfnVzqeIiL79+8XLy8vUavVMmXKFJk7d64EBwcbLIdI5Y/x/fv3Zfr06cpYbcKECZKcnCwi/22rNm3aJI6OjhIRESEWFhaiUqnEyspKjhw5IteuXZNRo0aJvb29ODg4yMSJE+XevXvl2t+6hknEOsZQB6G4E3b06FGxtLSUjz76SLKzs6WwsFD27Nkjzs7OcubMGcnLy5M9e/aIpaWlJCUliYjI3LlzJSgoSJKTkyU7O1sWLlwo/fr100lWFTdIhjzeKdy/f7/Y2dlJXFycaLVaiYmJEQsLC6VC++STTyQgIEASExMlOztb5s2bJz169JDc3FzJz8+Xnj17ysKFCyUnJ0dOnTolPXr0qLVJxOPHj4u5ublOsmnatGni4eEh69atE29vb533L1y4UIYPHy4i5UsihoWF6ayvUqlk8+bNyt8BAQHy3nvvKX/PmzdPQkNDRUTko48+eiJJ+N5778ns2bNL/fzapOR3qmSnTETE09NTli1bJvn5+ZKUlCQ9evSQDRs2iIj+JKK+9Y4ePSpdu3aVd955R9LT0+X27dvi6upa7kRVXRgkllQc32HDhklSUpKkp6fL8OHDZezYsaXWJ4Y6ziXFxsaKnZ2dXL9+XUTKl0R82uSAISXLYCi5b6j+mjZtmgQHBytJjuDg4AonEavqh4CtW7eKl5eX5ObmSlxcnNjb2ysdoJKx3rt3r3Tr1k3Onz8vubm58sEHH0hQUJCIGB4UlRz4aLVaWb58udjZ2el0Mg3tb20c3AQEBMi8efPk0aNH8scff4hGo5Fff/1V7wCztCRiWQN1Q8dO34DbUPtc12Jesj7Pzs7W+cFk8eLFSptYXGefPn1aWb5+/Xrp27eviBQlPDMyMpRlubm5olKp5MiRIyJSVE8MGjRIWf7o0SNRqVRy7NgxuXv3rk4/RUTk1q1bolKp5M6dO7Jx40YZOHCgPHr0SFlePND4/PPPdep3kaIfesaMGVOu/S/ZPty/f1/Mzc3l1KlTyvLLly+LSqWShIQE2bt3r9jb20tubq6yfOPGjdKrVy8REZk9e7ZMmjRJWabVasXR0fGp24ey2sfKtAceHh7So0cPiYuLk+zsbFmwYIE4OTlJTk7OE/3FN954Q8LCwiQzM1MSExPF3d1dtmzZUq4y16Z63RhiY2OlR48eSjspUvSdLY5xyR+YFi5cqAzIDZ1H/v7+sm7dOmV5QkKCsq3idmX37t3i5uYmt27dUt7XrVs32bVrl05ZaiOVSqWTnCtOmooYPtfz8/PlwYMHyrJr164p566I4STi5MmTlb/PnTsnKpVKUlJSJCsrS9RqtfIDTkJCglhaWuocW0P7VFznz5s374m6afLkyfLOO++ISFHb99lnn+ksd3FxkejoaLlz546oVCqdBNQXX3xRLUlEkf9+nx7vr5fcn+pqa7VarajVajl06JAUFhbK9evXxdPTU+eCG31qQ/1TVp7Ax8dHGSsmJCSIl5eXuLu7K+u9++67snDhQnn06JG4urrK6tWrJTc3VxISEsTPz09WrVpVrtiW/I4/TTwzMjLExsZGNmzYILm5uRIbGyv29vZKnVVdx1jfD4vF37spU6ZIZmamMjYp7gOKiISEhMiyZcskLy9P0tPTZfz48cqPnn81vJ25jsnMzES9evVgamoKExMTuLm54cSJE6U+BzEvLw+vvfaa8t7o6GgMGTIEVlZWaNCgAby8vGBvb49du3ahsLAQ27dvx5tvvqncgjl16lTcunUL8fHxT13u6Oho+Pn5wcHBAQ0bNkT//v3RtWtX5ZLn6OhojB8/Hu3atVM+OzU1FSdPnsTZs2dx584dvPHGG2jcuDFsbW3Rt2/fpy5TdUlNTYWZmRmaN2+uvFZ8a8HNmzfRuXNnnfd37NgRSUlJ5d5+27Ztn3it5IPZGzdujNatW+v8Xfxg4oSEBOzZswfW1tbKv++++065fauu2rFjByZMmID69eujTZs2cHR0LNftzobWKygowOuvv47mzZvj+eefh729Pa5du1bh8qWnp2Pv3r2YOnUqWrRoATMzM0yePBlnzpxBYmIiDh48iOzsbISFhcHU1BQdOnTA8OHDsXv3bgDA3r170bNnT9jb26Nx48YYP348GjVqVOFylMfw4cPRpk0bNG/eHKNHj8bhw4dRUFDwRH3i7++P3bt34/nnn0f9+vXh6+uLe/fu4datWzrbS0lJwcyZMzFnzhx06tSp3OWwt7fHoUOHoFKpYGJiAj8/P+Tm5urcYtSuXTsMHDhQqVMKCgrw559/Vnifd+3ahTZt2mD48OFo1KgRLCwsMGDAACX+huqvffv2ISQkBM2bN0fr1q0RHBxc4TL4+vqiVatW6NSpE1566SVYW1vDwsICZmZmcHJyUvZr+/bt6NWrF9zc3FC/fn04ODjAx8cHO3fuBAAMGTIErVq1QlRUFObPn4+3335bpz4otm3bNvj6+qJLly5o1KgR3nrrLYwaNQqFhYXlOrbFt3U1bNgQPj4+yMrKwp07dyq839u2bYOLiwv69OmDhg0bwsXFBb169VJu6YqOjsZrr70Gc3NzNGrUCKGhoXjmmWcQGxuL1NRU/P777xg3bhyaNGmCzp07Y9CgQRUuA1B0S8rFixcxceJEPPPMM+jatStWrFiB559/HgAwYMAAvPjiizAzM0Pv3r31TgIxbNgwtG7dGs8++yzc3NyUyYoMHbsHDx6gSZMmqFevHlq1aoWtppAJ2QAAFhZJREFUW7cqx6Qq2+faEvNiZ8+exejRo2FnZwdra2usXbv2iYfpv/TSS8r/27Rpo3zXCgoKEBkZCXd3d1hbW8Pe3h4AdNZv166d8v9nnnkGAJCTk4PExEQARc+1LG4Pvby8UL9+fdy+fRu+vr5o0KABevbsialTp2LHjh3KLYgJCQk4c+aMTlu6atWqSrWlt27dgojo9A2Kb4lLSkrCzZs30b59e506v2PHjrh16xYKCwuRkpKis48NGzbU+buy9LWPlWkPevXqBQcHB5iammLChAnIyMjA6dOndT7z/v37OHDgACZMmAAzMzO0a9cOS5YsQdeuXStc/tpQr9c0FxcXWFhYoHfv3njjjTewefNmnduHHz8Xim+JN3QeJSYm6qzbvn179OrVS/n7/PnzmDlzJiZPnqzTH502bRref/99DBo0CJ9++mmtno295P41btwYeXl5AAyf69nZ2fj444/h6uoKKysr+Pv7A0C5JwR5/HOL123atCn69OmD7777DkDRbaKurq6Veu69obFHacs7dOiApKQkZT9LlrMifbnqVNVtbW5uLnJyctCkSROYmJigU6dO+Omnn9CnT58Kl81Y9U9ZeQIfHx/8/vvvAIoeq+Xo6IgmTZoo7eDx48fh4uKC2NhYiIgyzmjfvj3GjBmjxM5QbEt6mnj++uuvaNKkiRI/d3d3ODg4KMur4xinpaXhwIEDeOutt9C8eXOYmZlhxowZOHToEFJTU5XPDgwMhJmZGUxMTJ4o94MHD2BqaooGDRqgefPmWLlyJd5///1yHbu6hhOr1DElOwguLi7o2bMnBgwYUOb727Rpo/w/ISEBhw4dwvr165XXRAQvv/wy0tLS8PDhQ7z55ps6J0VhYSFu374NW1vbpyr3zZs30a1bN53XihuwjIwMZGZm6gwSmjZtipYtWyIpKQlmZmb429/+hmbNminLa0sDVprCwkI0aKB7ahXHtKxORWkVEYBSn0tYv359g+s//iyRYqamphg2bBhmzZpV6vK66ujRo4iMjMSff/6J/Px85Ofnl+uhvuVZ7/FOd2We51MVg8SSs5hW1SCxNI+XMS8vD2lpaQB065PijvPBgweRkZGhvF7yO15YWIgZM2bA09MTfn5+FSpH8aDmxx9/xL1790rdflnJgYoqq4Nd3NnTV3/dv38fOTk5T93BrsgPAUeOHNGZVEhE4ObmBqCoLpg3bx78/f1hZWWFV155pdTPS0xMhLOzs/J3y5Yt4ePjA6DouT6Gjm3J/S1+7mdVxv7kyZNlLq+OwU1CQgLMzMzw7LPPKq91795d+X9ZA8zSlDVQN3TsigfcX3zxBdzc3DBgwAB07ty5ytvn2hJzoKjDPW7cOAwbNgyff/45zMzMsHTpUhw+fFjnfY+3hcVxiIyMxO7du7F69Wp06dIFIgILCwud9+prD4Gi2aWfe+65Ut+zdetWnDx5EgcOHMDy5cuxefNmbNq0CaampnB3d8fq1asrtd8l6Us2mJiYGOw3aLVa5Ofn6ywrfs7y0zDUPla0PXj55ZeV/zdv3hzNmzdHSkqKznZu3ryJwsJCne+XRqOpVPlrQ71e0xo1aoTVq1fjwoUL2L9/PzZt2oSoqChEREQAKLuvaeg8MjEx0fudOnHiBDw8PLBq1Sr4+PjAzMwMADB06FD06dMHP//8M/bv34/AwEAsWbKkUkmZ6lZWbAyd63PmzMHFixexadMmdOzYEYmJiRW60KGszwWKkhWTJ0/GnDlzsHfv3konsstTh5S1vHhZyTq4KuqXqlDVba2ZmRkmTpyI4OBg2NjYwNXVFYMGDdLpn5WXseqfsvIEzs7O+P777wEAcXFxcHBwQEFBAU6cOIFGjRohOTkZDg4O+Oqrr5CWlvZE7IrHJ4ZiW9LTxDM5ORkvvPCCTvvdqVMnnDt3rlzlqMwxLvnDYknFPywWJ/BLtlmPmzRpEt5++23s2LEDbm5u8PPzg42NjcH9rYt4JWIdU9xB+Oabb2BlZYVNmzZhwIAByMrKKvX9JZNZpqammD59Os6cOaP8O3v2LGbNmqV0pr/55hud5VU1w1J5Gih9yx8fQNSWBqw0LVu2REZGhs4xKZ55sEOHDk9cyXbt2jW0b98eQNHxzc7OVpYlJCRUadk6dOiAixcv6ryWnJxcqydRMeTq1auYMmWK8iDoM2fOlCthVd71yhqAVkRtHSSWpuT+igiA//46XrI+mTNnDs6dO4dNmzbhzJkzylVMJa1cuRLp6enKg70ronhQs2rVKpw+fRqnTp3SW9anUdUd7OK4VURFfwh4vB4vOcBJTk5GgwYNkJycrFOfPP55ZZWzPMe2tsa+sudFvXr19K6rb6BX3vcaOnZDhw5FbGwshg8fjitXriAwMBD79u2r8va5tsS82MOHDzFmzBgl8fDHH3888Z6SbWFSUpKSZD9z5gw8PT1hYWGBevXqKQOM8mjbti3q1aun0ybm5eUpidLc3FxkZ2fDzs4O06dPx65du3Dp0iVcuHABHTp0wKVLl3TOodTU1HJffVRScftfsm9Q/P8OHTqgffv2SExM1Nn2tWvX0K5dO+Uqi+TkZGWZVqtVBkOVVZ72saLtQWl1RnHb8vh7qqJ9qw31ek3Ly8tDVlYWunTpgokTJ2LHjh0wMTF5Iin/OEPnUfv27XWuIrxx4wY2bdqk/D1s2DCEh4fjf//3f/Hxxx8rr9+/fx/PPfccBg8ejJUrV2L8+PGIjo6uor2tGYbO9fj4eAQEBKBTp04wMTGpUB1kiIuLC5o2bYp///vfuHz5Mjw9PSu1HUNjj8eX5+fn48aNG2jfvj1atWoFALh9+7ayvPiKP2Or6rYWKEoC7d+/H76+vjh+/Dj69+9fqSv+jVX/lJUn6Nq1K1JSUnDv3j0cP34c9vb2sLOzw8mTJ3HixAloNBo888wzaNy4Mf7+978/EbviHxnL0wctqbLxNDT2r45jXPKHxZLb/eOPP3QSgaVdzFOsV69eiI2NxaRJk5CWlobg4GBs3LjR4P7WRUwi1jGV7SAApSeQiq+QatasGZ599tknlpecue1p6GvAWrZsiaZNm+osz8jIQFpaGjp06IBWrVohKysLmZmZyvLa0oCVxsrKCs888ww+//xzaLVaHDx4EL/99hsAwMfHB4mJidiyZQvy8/MRHx+Pb7/9FgMHDgRQ9CvL6dOnkZycjMzMTERFRemtrCpqyJAhOHnyJLZt2watVovz589j6NChyuXzjRs3xu3bt/HgwYMnkla11fnz59GoUSOMHDkSpqamEBGcP3++2tarjNo4SCxLyYFCYmIiTE1NdW7NL2ao4/zbb78hKioKS5curdQM1U+THKioinawSy5v0aIFGjZsqNPBvnLlSrWWVd8PAVqtFrNnz8acOXPw97//HUuXLi11O48PCu/du4eoqCjk5eVV66CotP2pDYOb9u3b4+HDhzq3ZO/bt0+pu6uCoWNX1oC7qtvn2hJzoGhQUK9ePfz+++949OgRvvzyS9y9exd3797VaYO+/PJLZGZmIjk5GVu2bIGHhweAokTghQsXkJ2djStXrmDt2rVo1qxZuW4rbtasGfr374/w8HAkJycjJycHn376KUJDQyEimD9/Pt59913cu3cPIoJz586hsLAQbdq0ga+vL9LT07Fy5Url1ujQ0FCdOz3Kq2XLlnBzc8OyZcuQnp6OjIwMLF26FM7OznjhhRfQs2dPNGjQAJGRkdBqtbh27Ro2bNigXC3Rs2dP/Prrr4iPj0dOTg5WrFjx1Em4iraP5akzStY3GRkZyMjIeOJRC8WJ3ZLvPXr0KH7++ecK70NdqterSlRUFMaOHav0F65evYqMjAy4uLjoXc/QeTR48GBs3rwZ165dw8OHD7Fo0SIcP35cWb9+/fqoX78+FixYgJiYGBw4cADJycno3bs3fv31VxQWFiIzMxOXLl2qc7PXGjrX27VrhzNnzkCr1eLUqVOIiYkBgFIf71HRPna9evXg7++PTz/9FJ6ensodFxUVEBCAQ4cO4cCBA8jPz8cvv/yC2NhYpQ4ZMGAAvv76a1y9ehVarRarV69GQUEBevfujXbt2qFz586IiopCdnY2Ll26VOqtq7VJZdtaoOjxQ61bt8bw4cOxbt06eHt7V2p/jVX/6MsTaDQa7NmzB48ePULHjh2h0WiUJGJxHdGhQwckJibi4cOHyjbv37+vXBhT0YtRKhvPVq1aISUlRSd5WrKvUR3H2NAPi+Vx//59NG3aFP3798fixYsxZ84cbNmypdzr1yVMItYxZXUQip9f8ueff5Z5VWJQUBB++OEHxMbGIj8/H0ePHoWfn5/yTJpXX30Vq1atwtWrV5GXl4cvv/wSQ4YMUa5kady4MW7cuFHm9vUZMGAAvv/+e5w6dQp5eXnYvn07Ll++DF9fX9SrVw9+fn5Ys2YNkpOT8ejRI4SHh6N9+/bQaDSwtbVF8+bNleckHT9+HAcOHKhM+GpE06ZNsXTpUuzYsQPOzs7YuXMnRo8eDRMTE7Rt2xYrVqzAli1b4OjoiLfffhtTpkxRGvIhQ4bA0tIS3t7eGDx4MPz8/CqVgClL586dsXjxYqxduxYODg4ICwvDmDFj0L9/fwCAv78/rl+/Dg8Pj0o936ymNG7cGAkJCcjMzETbtm2Rk5OD8+fPIyMjA4sWLUKjRo1w584dvb/cVXa9yqiNg8SybNq0CampqcjMzMT69evLvO1IX8f53r17mDFjBj788MMnbucor6dJDlSUoeS+vvqrYcOG6NatGzZs2IDMzEwkJSXpXKFR1Qz9EBAZGYlWrVrB398fH3zwAbZu3frEc8eAokFhTEwMTp8+Da1Wq9w6XnyrfHkHRU+rtgxuunbtCgsLCyxduhQPHz7EpUuX8MEHH1TqFu2y6Dt2hgbchtrniqgtMQeAZ599VrntyMPDAxkZGQgPD4dWq8Vrr72mvM/T0xOBgYHo27cvXnrpJUyePBkAMH78eBQUFKBbt26YOXMmwsLCMHDgQMybNw/79+83+PmzZs1Cx44d4evrix49euDKlStYuXIlTExMMH36dNSrVw/9+vWDnZ0d5s+fj8WLF6NFixZ47rnnsHLlSuzfvx+Ojo4IDg6Gh4cHQkNDKxWHhQsXokmTJvDx8UH//v1hZmaGZcuWASjqU6xZswZxcXFwcXHB2LFjMWDAAEyYMAFAUf01cuRITJgwAe7u7mjUqBHUanWlylGsou1jeeqMn3/+GWfPnoVWq8WaNWvQunVrWFlZ6Wzn2WefhaenJyIjI5Geno5bt25h1qxZlar361K9XlVCQkKgUqkQGBgItVqNqVOnYsaMGTqPAyqNofNoxIgRCAwMxLBhw+Dh4YH69euX+licF198EdOmTcOsWbPQqFEjzJ8/H/Pnz4dGo4G3tzeaNm2qnLt1haFzffr06bh69SqcnJywZMkSzJo1C3379sWbb775RDK9Mn3swMBAZGVlKc9arAyNRqPUX46OjvjXv/6F8PBwODk5AQBCQ0Ph7e2NsWPHonv37jh27Bg2bNiAv/3tbwCA5cuX49q1a3BxccF7772HMWPGVLoslVWRMWhl29rff/8dPj4+iI+Ph4ggLS0N169fr1Ti21j1T1l5gg4dOqBbt25Yv369kjPo3LkzUlNTcfjwYSWJ6ObmhhYtWmDhwoXIyspCamoqpkyZgvDwcIOxBXQT5cePH690PLt3746srCx888030Gq12Ldvn05ftjqOsaEfFktjamqKzMxMpKSkICcnB/369cPOnTuRn5+PnJwcnDt3rs79cFJu1T51C1Wp3NxcmT17tjg7O4utra14e3vL1q1bRUQkLCxMLC0tZe7cuU/MalXsq6++Eg8PD7G2thYfHx/ZuXOnsiwnJ0c++ugjcXJyEo1GI6+99prEx8cry+fPny9WVlYyYcIEg+V8fLY9kaIp5/v27SsajUYGDx4sx44dU5Y9fPhQZs6cKW5ubuLk5CTjx4+XmzdvKsvj4uLE19dXbGxsZNSoURIVFVVrZ2cWKZqtreQstcuWLZNhw4YZsUR1X8nvVFRUlNjY2EhAQICIFM1camdnJ66urrJx40aJi4sTBwcHmTJlit7ZmfWtV9o5VJHZqx+fnTk1NVUmTZok3bp1k+7du8v06dPl3r17yvLTp0/LsGHDxM7OTnr37i3Lly+XvLw8EREpLCyUTz/9VFxcXMTJyUkiIiJk9OjR1TI7c0xMjPj4+IharZbQ0FC5e/duqbE4d+6c+Pr6iq2trYwcOVKSk5Nl4sSJolarJSIiQlQqlVhZWen8K57hrDyzM9+8eVOGDh0qNjY2MnjwYLl06ZLMmzdPbGxsZN++faUei4rMIP/4DN+xsbEycOBAUavV4uXlpTNTo4j++is5OVlGjRqlzIC3e/fuCs/OXLLcQ4cO1Tm2ixYtUmakExH54YcfxNvbW6ytrcXT01OZNfPixYtia2srly9fVt67bNky8fPzE61WqxNrkaL2wNXVVTQajYSGhiqzSeo7tmfPnn1iO6XV9xXZ3+3bt4uvr6+o1Wrx8/OT3bt3K8vy8vJkwYIF4uHhoczMd/78eWX55cuXZciQIWJrayuDBg2S6OjoSs8amZycLCNHjhQbGxvx8PBQZkZ+vLxff/210v6UNjtzyTg8Xg+UdexERGJiYsTb21tsbGyke/fuMnPmTMnMzBQRw+2zIbUt5sWxunr1qt73ldWXoepXVvvo4uJSofbg7Nmz4uHhIWvXrpVRo0aJra2t+Pn5KbNRP37ePHjwQMLCwkStVoubm5uEh4eXe1bf2lSvE1WFI0eOiIeHR62d2bqqlTU7c8kxaHW2tevXr1fGyT169JCPP/5Y6YsbUhvqH315gpMnT4pKpZL169cr7x87dqzY2dnpjFnPnz8vw4cPV+Iza9YsefToUbliGxcXJ05OTmJnZydJSUlPFc8ff/xR+vTpI7a2thIWFiaLFy8uV19YpPLH+P79+zJt2jSxs7MTBwcHef311+XPP/8UkdL7eGlpaeLj4yNWVlYSExMjhw8flsDAQLG1tRVnZ2eZOHGiJCcnl2t/6xoTkTrwkA+iOkRE4OXlhX79+mHKlCm4desWQkJCEBQUhPHjxxu7eEREREZ15MgRZfb3li1blvm+Y8eOYeTIkYiPj3/i+XlERH9ld+7cweuvv46goCAMHz7c2MUhIlJwdmaiKmZiYoIlS5Zg/vz5cHJyQrNmzdCvXz+EhIQYu2hERERGtXPnTnz44YcYNGiQ3gRiXTVhwgQcOnSozOVz5859YvZHIqKSPvvsM6xZs0a5jbxYfHy83oRimzZtlFtLiYiqC69EpArbvXs33nnnnTKXOzo6IioqqgZLRFSzOEisve7evatMulCW4tnSqWpxcFPzGHP6v4D1OhEZC+ufqsV4/jUwiUhERERERERERER6cXZmIiIiIiIiIiIi0otJRCIiIiIiIiIiItKLSUQiIiIiIiIiIiLSi0lEIiIiIiIiIiIi0otJRCIiIiIiIiIiItKLSUQiIiIiIiIiIiLSi0lEIiIiIiIiIiIi0uv/AYLysEGV2zPcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3isxcWI63CSQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZwDie4psysJm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcc76b0d-b55e-4a78-c05f-b19bce1c03c5"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "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          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": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgIrppdJ9g-P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "43d2a1aa-9b68-48ab-a483-afd2d9df1139"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ydmBzwB4JbMc"
      },
      "source": [
        "##label encoding"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eyu7_8YiJap0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "a0dfe76d-9228-4ac2-9a0e-7476fd727608"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "leTime = LabelEncoder()\n",
        "leMotivation = LabelEncoder()\n",
        "leGender = LabelEncoder()\n",
        "leMaritalstatus = LabelEncoder()\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": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYWXdNvne_1j",
        "outputId": "677e4fde-d260-4f33-afb4-d3ccb2e38615"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "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": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YcQrxSyWJ_E"
      },
      "source": [
        "##OneHotEncoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY4rthyyWJWc"
      },
      "source": [
        "#from sklearn.preprocessing import OneHotEncoder\n",
        "#from sklearn.compose import ColumnTransformer\n",
        "\n",
        "#ohe = ColumnTransformer(transformers=[('OneHot', OneHotEncoder(), [1,3,15,20])], remainder='passthrough')\n",
        "#df  = ohe.fit_transform(df)\n",
        "#df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsNM5lx9bJ9Z",
        "outputId": "49fa5de4-f39b-4a2c-979f-3d881d4ffb0a"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(407, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJueAPmQcxO_"
      },
      "source": [
        "#Split dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKGBwNOJc0AF"
      },
      "source": [
        ""
      ]
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "xsW9Dew3ipbj",
        "outputId": "5060e22c-1594-4e04-9099-6452aa5ebd42"
      },
      "source": [
        "df_X"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FdyW5UcdlF5M",
        "outputId": "7fa57a02-8b3d-4e00-8158-8ae6c8ea1162"
      },
      "source": [
        "X.shape, y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((407, 21), (407, 12))"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tnm4isLTla7r"
      },
      "source": [
        "#!pip install scikit-multilearn"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kg1Eao103X4Q"
      },
      "source": [
        "Removendo desalamceamento - Oversampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZASfAaAjKi9"
      },
      "source": [
        "#feats_df\n",
        "#labels_df\n",
        "#labels_df.columns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zbk0P4lxkQdP"
      },
      "source": [
        "def get_tail_label(df):\n",
        "    \"\"\"\n",
        "    Give tail label colums of the given target dataframe\n",
        "    \n",
        "    args\n",
        "    df: pandas.DataFrame, target label df whose tail label has to identified\n",
        "    \n",
        "    return\n",
        "    tail_label: list, a list containing column name of all the tail label\n",
        "    \"\"\"\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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ef7rMMJDl1ei",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34a096eb-8882-46e4-ae17-584ff6fbec62"
      },
      "source": [
        "a = get_tail_label(df_y)\n",
        "a"
      ],
      "execution_count": null,
      "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": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHj9yzntkyai"
      },
      "source": [
        "def get_index(df):\n",
        "  \"\"\"\n",
        "  give the index of all tail_label rows\n",
        "  args\n",
        "  df: pandas.DataFrame, target label df from which index for tail label has to identified\n",
        "    \n",
        "  return\n",
        "  index: list, a list containing index number of all the tail label\n",
        "  \"\"\"\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)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mRsK4NMCnO4H",
        "outputId": "99083a1c-6be1-4b3e-9798-cac1bf523e8e"
      },
      "source": [
        "b = get_index(df_y)\n",
        "print(b)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[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": [
        "def get_minority_instace(X, y):\n",
        "    \"\"\"\n",
        "    Give minority dataframe containing all the tail labels\n",
        "    \n",
        "    args\n",
        "    X: pandas.DataFrame, the feature vector dataframe\n",
        "    y: pandas.DataFrame, the target vector dataframe\n",
        "    \n",
        "    return\n",
        "    X_sub: pandas.DataFrame, the feature vector minority dataframe\n",
        "    y_sub: pandas.DataFrame, the target vector minority dataframe\n",
        "    \"\"\"\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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "a92zqCqjn0w-",
        "outputId": "a97ff8ee-0273-4cc9-9ef2-2a58cb79da03"
      },
      "source": [
        "xsub, ysub = get_minority_instace(df_X,df_y)\n",
        "ysub\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "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": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZYgc8qJlDdv"
      },
      "source": [
        "def nearest_neighbour(X):\n",
        "    \"\"\"\n",
        "    Give index of 5 nearest neighbor of all the instance\n",
        "    \n",
        "    args\n",
        "    X: np.array, array whose nearest neighbor has to find\n",
        "    \n",
        "    return\n",
        "    indices: list of list, index of 5 NN of each element in X\n",
        "    \"\"\"\n",
        "    nbs=NearestNeighbors(n_neighbors=5,metric='euclidean',algorithm='kd_tree').fit(X)\n",
        "    euclidean,indices= nbs.kneighbors(X)\n",
        "    return indices"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "92PwUHUglLZU"
      },
      "source": [
        "def MLSMOTE(X,y, n_sample):\n",
        "    \"\"\"\n",
        "    Give the augmented data using MLSMOTE algorithm\n",
        "    \n",
        "    args\n",
        "    X: pandas.DataFrame, input vector DataFrame\n",
        "    y: pandas.DataFrame, feature vector dataframe\n",
        "    n_sample: int, number of newly generated sample\n",
        "    \n",
        "    return\n",
        "    new_X: pandas.DataFrame, augmented feature vector data\n",
        "    target: pandas.DataFrame, augmented target vector data\n",
        "    \"\"\"\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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ucAgYChtlQ30"
      },
      "source": [
        "\n",
        "X_sub, y_sub = get_minority_instace(df_X, df_y)   #Getting minority instance of that datframe\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zr-R370ZqglB"
      },
      "source": [
        "from sklearn.neighbors import NearestNeighbors\n",
        "import random\n",
        "X_res,y_res =MLSMOTE(X_sub, y_sub, 500)     #Applying MLSMOTE to augment the dataframe"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFPXoByVq3e0",
        "outputId": "5546c54a-ddf6-4b1c-ba0c-93de97f1c891"
      },
      "source": [
        "y_res.dtypes"
      ],
      "execution_count": null,
      "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": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "1qlIsz66rIul",
        "outputId": "bb4502ca-957e-4e9d-8731-80bbe9c4a0d3"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgZq7skHFFAl"
      },
      "source": [
        "Padronização"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A57HZmG9FI4y"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "#X_over = scaler.fit_transform(X_over)\n",
        "\n",
        "#utilzando os dados com oversampling\n",
        "##X = scaler.fit_transform(X_res) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rj0BGrIQB4KJ"
      },
      "source": [
        "#utlizando os dados sem oversampling\n",
        "X = scaler.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKNkoa3ntiXQ"
      },
      "source": [
        "#y utilizando oversampling\n",
        "#y = y_res\n",
        "#y= y.values\n",
        "#y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awwDI7LmCXW5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shSiighJqlUb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7180fad4-d001-4903-d63c-6508b4df40cc"
      },
      "source": [
        "X[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.25559743,  0.18196657,  1.17612812, -0.23762347,  0.38448796,\n",
              "       -0.93242832,  0.20617958, -1.45194336, -0.73999989, -0.60297132,\n",
              "        0.18381531,  0.27295588, -0.39300688, -0.5385158 , -0.48971097,\n",
              "        1.03755993, -0.25433919, -0.15459421,  0.6645164 , -1.02236301,\n",
              "        0.41120099])"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E3PwWkxsqfmm"
      },
      "source": [
        "#X_over"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlKvBOoKzoCE"
      },
      "source": [
        "from skmultilearn.model_selection import iterative_train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7QWvbt1nQU6w",
        "outputId": "6a57cc5d-ec79-4353-bd92-f8c9a7ee86a3"
      },
      "source": [
        "!pip install iterative-stratification\n",
        "from iterstrat.ml_stratifiers import MultilabelStratifiedKFold"
      ],
      "execution_count": null,
      "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: numpy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from iterative-stratification) (1.0.1)\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": "1586b317-f143-476e-f36a-d2508fd954f4"
      },
      "source": [
        "mskf = MultilabelStratifiedKFold(n_splits=10, shuffle=True, random_state=0)\n",
        "\n",
        "for train_index, test_index in mskf.split(X, y):\n",
        "   print(\"TRAIN:\", train_index, \"TEST:\", test_index)\n",
        "   X_train, X_test = X[train_index], X[test_index]\n",
        "   y_train, y_test = y[train_index], y[test_index]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  19  20  21  22  23  24  25  26  27  29  30  31  32  33  34  35  36  37\n",
            "  38  39  40  41  42  43  44  45  46  47  48  49  51  52  53  54  55  56\n",
            "  57  58  59  61  63  64  65  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  97\n",
            "  98  99 100 101 102 103 105 106 107 108 109 110 111 112 113 114 115 117\n",
            " 118 119 120 121 122 123 124 125 126 127 128 129 132 133 135 137 138 139\n",
            " 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157\n",
            " 158 159 160 161 162 164 165 166 167 168 169 170 171 172 175 176 177 178\n",
            " 179 180 181 182 183 184 185 186 188 189 190 191 192 194 195 196 197 198\n",
            " 199 200 201 202 203 205 206 208 209 210 211 212 213 214 216 217 218 219\n",
            " 220 221 222 223 224 225 227 228 229 230 231 232 233 234 235 236 238 239\n",
            " 240 241 242 243 245 246 248 249 250 251 252 254 255 256 257 258 259 260\n",
            " 261 262 263 264 265 266 267 268 269 270 271 272 274 275 276 277 278 279\n",
            " 280 282 283 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299\n",
            " 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317\n",
            " 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335\n",
            " 336 337 338 339 340 341 342 343 344 345 346 347 348 349 351 352 353 354\n",
            " 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 372 373\n",
            " 374 375 376 377 378 379 380 382 383 384 385 386 387 388 389 390 391 392\n",
            " 393 394 395 396 397 398 399 400 401 402 404 405 406] TEST: [ 18  28  50  60  62  66  67  96 104 116 130 131 134 136 163 173 174 187\n",
            " 193 204 207 215 226 237 244 247 253 273 281 284 350 371 381 403]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  16  17  18\n",
            "  19  20  21  22  23  24  25  26  27  28  29  31  32  33  35  36  37  38\n",
            "  39  40  41  42  43  44  45  46  47  49  50  51  52  53  54  55  56  57\n",
            "  58  59  60  61  62  64  65  66  67  68  69  71  72  73  74  75  76  77\n",
            "  78  79  80  81  82  83  84  85  88  89  90  91  92  93  95  96  97  98\n",
            "  99 101 102 103 104 105 106 107 108 109 111 112 114 115 116 117 118 119\n",
            " 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 138\n",
            " 139 140 141 142 143 144 145 148 149 150 151 152 153 154 155 157 158 159\n",
            " 160 161 162 163 164 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
            " 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 197 198 200\n",
            " 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218\n",
            " 220 221 222 223 224 225 226 227 228 229 230 231 233 234 235 237 238 239\n",
            " 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257\n",
            " 258 259 260 261 264 265 266 267 268 269 270 271 272 273 274 275 276 277\n",
            " 278 279 280 281 283 284 285 286 287 288 289 290 292 293 294 295 296 297\n",
            " 298 300 301 302 303 304 305 306 307 308 310 312 313 314 315 316 317 318\n",
            " 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 335 336 337\n",
            " 340 341 342 343 344 345 346 347 348 350 351 352 354 355 356 357 358 359\n",
            " 360 361 362 363 364 366 367 368 369 370 371 372 373 374 375 376 377 378\n",
            " 380 381 383 384 385 386 387 388 390 391 392 393 394 395 397 398 399 400\n",
            " 401 402 403 405 406] TEST: [ 15  30  34  48  63  70  86  87  94 100 110 113 137 146 147 156 165 166\n",
            " 180 196 199 219 232 236 262 263 282 291 299 309 311 334 338 339 349 353\n",
            " 365 379 382 389 396 404]\n",
            "TRAIN: [  0   1   3   4   6   8   9  10  11  12  13  14  15  16  17  18  19  20\n",
            "  22  23  24  25  27  28  29  30  31  32  33  34  36  37  41  42  44  45\n",
            "  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  62  63  64\n",
            "  66  67  68  69  70  71  72  73  75  76  77  78  79  80  81  82  83  84\n",
            "  85  86  87  88  89  91  92  93  94  95  96  98  99 100 101 102 104 105\n",
            " 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123\n",
            " 124 125 128 129 130 131 132 133 134 135 136 137 138 139 140 142 143 144\n",
            " 145 146 147 148 150 152 153 156 157 158 159 160 161 162 163 164 165 166\n",
            " 167 168 170 171 172 173 174 175 176 178 179 180 181 182 183 184 185 186\n",
            " 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 204 205\n",
            " 206 207 208 209 210 211 212 213 214 215 216 217 218 219 221 222 224 225\n",
            " 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 242 243 244\n",
            " 245 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263\n",
            " 264 265 266 267 268 269 270 272 273 275 276 278 279 280 281 282 283 284\n",
            " 285 286 287 288 289 291 292 294 295 296 297 298 299 302 303 304 305 306\n",
            " 307 308 309 310 311 312 313 314 315 316 318 319 320 321 322 323 324 325\n",
            " 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344\n",
            " 345 346 347 348 349 350 352 353 354 355 356 357 358 359 361 362 363 364\n",
            " 365 366 368 369 371 372 373 374 376 377 378 379 380 381 382 383 384 385\n",
            " 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 403 404 405\n",
            " 406] TEST: [  2   5   7  21  26  35  38  39  40  43  61  65  74  90  97 103 126 127\n",
            " 141 149 151 154 155 169 177 203 220 223 241 246 271 274 277 290 293 300\n",
            " 301 317 326 351 360 367 370 375 401 402]\n",
            "TRAIN: [  0   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  18  20\n",
            "  21  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39\n",
            "  40  42  43  44  45  47  48  49  50  51  52  53  54  55  56  57  58  59\n",
            "  60  61  62  63  65  66  67  68  69  70  71  72  73  74  75  76  77  78\n",
            "  79  81  82  83  84  85  86  87  88  89  90  91  93  94  95  96  97  99\n",
            " 100 101 102 103 104 105 106 107 109 110 111 112 113 114 116 117 118 119\n",
            " 120 121 124 125 126 127 128 129 130 131 132 133 134 135 136 137 139 140\n",
            " 141 142 143 144 145 146 147 149 150 151 152 153 154 155 156 157 158 159\n",
            " 160 162 163 165 166 168 169 170 171 173 174 175 176 177 178 180 181 182\n",
            " 184 185 186 187 188 189 190 191 192 193 194 195 196 197 199 200 201 202\n",
            " 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220\n",
            " 221 223 224 225 226 228 229 232 233 235 236 237 238 240 241 243 244 245\n",
            " 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263\n",
            " 264 265 266 267 268 269 270 271 273 274 275 276 277 280 281 282 283 284\n",
            " 285 286 288 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304\n",
            " 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 321 322 323\n",
            " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
            " 342 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360\n",
            " 361 362 363 365 366 367 368 369 370 371 372 373 375 376 377 378 379 381\n",
            " 382 383 384 385 386 387 388 389 391 392 393 394 395 396 397 398 399 400\n",
            " 401 402 403 404 405 406] TEST: [  1  17  19  22  41  46  64  80  92  98 108 115 122 123 138 148 161 164\n",
            " 167 172 179 183 198 222 227 230 231 234 239 242 272 278 279 287 289 320\n",
            " 343 364 374 380 390]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7  10  11  13  14  15  16  17  18  19  20\n",
            "  21  22  23  24  25  26  28  29  30  31  32  33  34  35  36  37  38  39\n",
            "  40  41  42  43  44  46  47  48  50  51  53  54  55  56  58  59  60  61\n",
            "  62  63  64  65  66  67  69  70  71  72  73  74  75  76  77  78  79  80\n",
            "  81  82  83  84  85  86  87  89  90  91  92  93  94  95  96  97  98  99\n",
            " 100 101 102 103 104 105 107 108 110 112 113 114 115 116 117 118 120 121\n",
            " 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139\n",
            " 140 141 142 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158\n",
            " 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176\n",
            " 177 179 180 181 182 183 186 187 188 189 191 192 193 194 195 196 197 198\n",
            " 199 200 201 203 204 206 207 208 210 211 213 214 215 216 217 218 219 220\n",
            " 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238\n",
            " 239 240 241 242 243 244 245 246 247 248 249 250 252 253 255 256 257 258\n",
            " 259 260 261 262 263 264 265 266 268 269 270 271 272 273 274 275 276 277\n",
            " 278 279 280 281 282 283 284 285 286 287 288 289 290 291 293 294 295 296\n",
            " 297 298 299 300 301 303 304 307 308 309 310 311 312 314 315 316 317 318\n",
            " 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337\n",
            " 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355\n",
            " 357 358 360 362 363 364 365 366 367 368 369 370 371 372 374 375 376 377\n",
            " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 393 394 396 397\n",
            " 399 400 401 402 403 404 405 406] TEST: [  8   9  12  27  45  49  52  57  68  88 106 109 111 119 143 178 184 185\n",
            " 190 202 205 209 212 251 254 267 292 302 305 306 313 319 356 359 361 373\n",
            " 392 395 398]\n",
            "TRAIN: [  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  33  34  35  37  38\n",
            "  39  40  41  42  43  44  45  46  47  48  49  50  52  54  55  56  57  58\n",
            "  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  76  77\n",
            "  78  79  80  82  83  84  86  87  88  90  91  92  93  94  95  96  97  98\n",
            "  99 100 101 102 103 104 105 106 108 109 110 111 112 113 115 116 117 118\n",
            " 119 120 121 122 123 124 126 127 128 129 130 131 132 133 134 135 136 137\n",
            " 138 140 141 142 143 146 147 148 149 150 151 153 154 155 156 157 158 159\n",
            " 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 177 178\n",
            " 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196\n",
            " 197 198 199 202 203 204 205 206 207 208 209 210 211 212 213 215 216 217\n",
            " 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235\n",
            " 236 237 238 239 240 241 242 243 244 245 246 247 248 251 253 254 255 256\n",
            " 258 260 261 262 263 264 265 266 267 269 271 272 273 274 275 276 277 278\n",
            " 279 280 281 282 283 284 285 287 288 289 290 291 292 293 294 295 296 297\n",
            " 298 299 300 301 302 303 304 305 306 307 308 309 311 312 313 314 315 316\n",
            " 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 334 335\n",
            " 338 339 340 342 343 345 346 347 348 349 350 351 352 353 355 356 357 358\n",
            " 359 360 361 364 365 366 367 369 370 371 372 373 374 375 376 377 378 379\n",
            " 380 381 382 384 385 389 390 391 392 393 394 395 396 397 398 400 401 402\n",
            " 403 404 405] TEST: [ 31  32  36  51  53  75  81  85  89 107 114 125 139 144 145 152 176 200\n",
            " 201 214 249 250 252 257 259 268 270 286 310 333 336 337 341 344 354 362\n",
            " 363 368 383 386 387 388 399 406]\n",
            "TRAIN: [  0   1   2   3   5   6   7   8   9  10  11  12  15  17  18  19  20  21\n",
            "  22  23  24  26  27  28  30  31  32  34  35  36  37  38  39  40  41  42\n",
            "  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60\n",
            "  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  80  81  82\n",
            "  85  86  87  88  89  90  92  93  94  95  96  97  98  99 100 102 103 104\n",
            " 105 106 107 108 109 110 111 112 113 114 115 116 118 119 120 121 122 123\n",
            " 125 126 127 128 129 130 131 133 134 135 136 137 138 139 140 141 142 143\n",
            " 144 145 146 147 148 149 150 151 152 153 154 155 156 158 159 160 161 163\n",
            " 164 165 166 167 168 169 171 172 173 174 175 176 177 178 179 180 181 182\n",
            " 183 184 185 186 187 188 189 190 191 192 193 194 195 196 198 199 200 201\n",
            " 202 203 204 205 206 207 208 209 210 212 213 214 215 216 218 219 220 222\n",
            " 223 224 225 226 227 228 229 230 231 232 233 234 236 237 238 239 240 241\n",
            " 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259\n",
            " 260 261 262 263 264 266 267 268 269 270 271 272 273 274 276 277 278 279\n",
            " 280 281 282 283 284 286 287 288 289 290 291 292 293 295 296 297 298 299\n",
            " 300 301 302 303 304 305 306 307 308 309 310 311 312 313 315 317 319 320\n",
            " 321 323 324 325 326 327 328 330 331 332 333 334 335 336 337 338 339 340\n",
            " 341 343 344 346 347 348 349 350 351 352 353 354 355 356 357 359 360 361\n",
            " 362 363 364 365 366 367 368 369 370 371 372 373 374 375 377 378 379 380\n",
            " 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398\n",
            " 399 400 401 402 403 404 406] TEST: [  4  13  14  16  25  29  33  76  77  78  79  83  84  91 101 117 124 132\n",
            " 157 162 170 197 211 217 221 235 265 275 285 294 314 316 318 322 329 342\n",
            " 345 358 376 405]\n",
            "TRAIN: [  0   1   2   4   5   7   8   9  11  12  13  14  15  16  17  18  19  20\n",
            "  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38\n",
            "  39  40  41  43  44  45  46  47  48  49  50  51  52  53  56  57  58  60\n",
            "  61  62  63  64  65  66  67  68  69  70  71  74  75  76  77  78  79  80\n",
            "  81  82  83  84  85  86  87  88  89  90  91  92  94  95  96  97  98  99\n",
            " 100 101 102 103 104 106 107 108 109 110 111 112 113 114 115 116 117 118\n",
            " 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 136 137\n",
            " 138 139 141 142 143 144 145 146 147 148 149 150 151 152 154 155 156 157\n",
            " 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176\n",
            " 177 178 179 180 183 184 185 186 187 188 189 190 192 193 194 195 196 197\n",
            " 198 199 200 201 202 203 204 205 206 207 209 211 212 214 215 217 218 219\n",
            " 220 221 222 223 225 226 227 228 229 230 231 232 233 234 235 236 237 238\n",
            " 239 241 242 244 245 246 247 248 249 250 251 252 253 254 255 256 257 259\n",
            " 260 261 262 263 264 265 267 268 270 271 272 273 274 275 276 277 278 279\n",
            " 281 282 283 284 285 286 287 288 289 290 291 292 293 294 297 298 299 300\n",
            " 301 302 303 305 306 307 309 310 311 312 313 314 316 317 318 319 320 321\n",
            " 322 323 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340\n",
            " 341 342 343 344 345 346 347 349 350 351 352 353 354 355 356 357 358 359\n",
            " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
            " 378 379 380 381 382 383 384 386 387 388 389 390 391 392 395 396 398 399\n",
            " 401 402 403 404 405 406] TEST: [  3   6  10  42  54  55  59  72  73  93 105 135 140 153 158 181 182 191\n",
            " 208 210 213 216 224 240 243 258 266 269 280 295 296 304 308 315 324 348\n",
            " 385 393 394 397 400]\n",
            "TRAIN: [  1   2   3   4   5   6   7   8   9  10  12  13  14  15  16  17  18  19\n",
            "  21  22  23  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39\n",
            "  40  41  42  43  44  45  46  48  49  50  51  52  53  54  55  56  57  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  83  84  85  86  87  88  89  90  91  92  93  94  95\n",
            "  96  97  98 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114\n",
            " 115 116 117 119 122 123 124 125 126 127 128 130 131 132 134 135 136 137\n",
            " 138 139 140 141 143 144 145 146 147 148 149 151 152 153 154 155 156 157\n",
            " 158 159 160 161 162 163 164 165 166 167 168 169 170 172 173 174 175 176\n",
            " 177 178 179 180 181 182 183 184 185 187 190 191 192 193 195 196 197 198\n",
            " 199 200 201 202 203 204 205 207 208 209 210 211 212 213 214 215 216 217\n",
            " 218 219 220 221 222 223 224 226 227 228 229 230 231 232 233 234 235 236\n",
            " 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254\n",
            " 257 258 259 260 262 263 264 265 266 267 268 269 270 271 272 273 274 275\n",
            " 276 277 278 279 280 281 282 284 285 286 287 288 289 290 291 292 293 294\n",
            " 295 296 299 300 301 302 304 305 306 308 309 310 311 312 313 314 315 316\n",
            " 317 318 319 320 321 322 324 326 328 329 330 332 333 334 336 337 338 339\n",
            " 341 342 343 344 345 346 347 348 349 350 351 353 354 355 356 358 359 360\n",
            " 361 362 363 364 365 367 368 370 371 372 373 374 375 376 377 378 379 380\n",
            " 381 382 383 385 386 387 388 389 390 392 393 394 395 396 397 398 399 400\n",
            " 401 402 403 404 405 406] TEST: [  0  11  20  24  47  58  99 118 120 121 129 133 142 150 171 186 188 189\n",
            " 194 206 225 255 256 261 283 297 298 303 307 323 325 327 331 335 340 352\n",
            " 357 366 369 384 391]\n",
            "TRAIN: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  21  22  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
            "  38  39  40  41  42  43  45  46  47  48  49  50  51  52  53  54  55  57\n",
            "  58  59  60  61  62  63  64  65  66  67  68  70  72  73  74  75  76  77\n",
            "  78  79  80  81  83  84  85  86  87  88  89  90  91  92  93  94  96  97\n",
            "  98  99 100 101 103 104 105 106 107 108 109 110 111 113 114 115 116 117\n",
            " 118 119 120 121 122 123 124 125 126 127 129 130 131 132 133 134 135 136\n",
            " 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154\n",
            " 155 156 157 158 161 162 163 164 165 166 167 169 170 171 172 173 174 176\n",
            " 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 193 194 196\n",
            " 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214\n",
            " 215 216 217 219 220 221 222 223 224 225 226 227 230 231 232 234 235 236\n",
            " 237 239 240 241 242 243 244 246 247 249 250 251 252 253 254 255 256 257\n",
            " 258 259 261 262 263 265 266 267 268 269 270 271 272 273 274 275 277 278\n",
            " 279 280 281 282 283 284 285 286 287 289 290 291 292 293 294 295 296 297\n",
            " 298 299 300 301 302 303 304 305 306 307 308 309 310 311 313 314 315 316\n",
            " 317 318 319 320 322 323 324 325 326 327 329 331 333 334 335 336 337 338\n",
            " 339 340 341 342 343 344 345 348 349 350 351 352 353 354 356 357 358 359\n",
            " 360 361 362 363 364 365 366 367 368 369 370 371 373 374 375 376 379 380\n",
            " 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398\n",
            " 399 400 401 402 403 404 405 406] TEST: [ 23  37  44  56  69  71  82  95 102 112 128 159 160 168 175 192 195 218\n",
            " 228 229 233 238 245 248 260 264 276 288 312 321 328 330 332 346 347 355\n",
            " 372 377 378]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmlgAEYR4cr2",
        "outputId": "b126fa28-8775-4be0-b41f-ed0b70611ecf"
      },
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((368, 21), (368, 12))"
            ]
          },
          "metadata": {},
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "qrvOdiZ2nGWk",
        "outputId": "eec2ab59-0720-455a-d62d-f04ddc12d67c"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>(0,)</th>\n",
              "      <th>(11,)</th>\n",
              "      <th>(2,)</th>\n",
              "      <th>(4,)</th>\n",
              "      <th>(3,)</th>\n",
              "      <th>(1,)</th>\n",
              "      <th>(7,)</th>\n",
              "      <th>(9,)</th>\n",
              "      <th>(6,)</th>\n",
              "      <th>(10,)</th>\n",
              "      <th>(5,)</th>\n",
              "      <th>(8,)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>train</th>\n",
              "      <td>265</td>\n",
              "      <td>138</td>\n",
              "      <td>39</td>\n",
              "      <td>37</td>\n",
              "      <td>65</td>\n",
              "      <td>108</td>\n",
              "      <td>23</td>\n",
              "      <td>30</td>\n",
              "      <td>32</td>\n",
              "      <td>27</td>\n",
              "      <td>14</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>test</th>\n",
              "      <td>30</td>\n",
              "      <td>16</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       (0,)  (11,)  (2,)  (4,)  (3,)  (1,)  (7,)  (9,)  (6,)  (10,)  (5,)  (8,)\n",
              "train   265    138    39    37    65   108    23    30    32     27    14    61\n",
              "test     30     16     4     4     7    12     2     3     4      3     2     6"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGJ6K0cXoOY7"
      },
      "source": [
        "#ML Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7qrc5gKoZAi"
      },
      "source": [
        "from skmultilearn.problem_transform import BinaryRelevance\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yTAHbfdiom8R",
        "outputId": "a88ddc1f-e294-4ef7-b0f0-a41b70e81a36"
      },
      "source": [
        "br_classifier = BinaryRelevance(classifier=SVC(),require_dense=[False, True])\n",
        "br_classifier.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BinaryRelevance(classifier=SVC(), require_dense=[False, True])"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xMmQT00o5Er",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9c4abfc9-8113-4bb0-858e-069ccee77682"
      },
      "source": [
        "prediction = br_classifier.predict(X_test)\n",
        "predicoes_br = pd.DataFrame(prediction.toarray())\n",
        "predicoes_br"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <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",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <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",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <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",
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              "    <tr>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>30</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",
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              "    <tr>\n",
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              "      <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",
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              "    <tr>\n",
              "      <th>32</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>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <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",
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              "    <tr>\n",
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              "      <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",
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              "      <td>0</td>\n",
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              "      <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",
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              "      <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>38</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    0   1   2   3   4   5   6   7   8   9   10  11\n",
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              "28   1   0   0   0   0   0   0   0   0   0   0   0\n",
              "29   1   0   0   0   0   0   0   0   0   0   0   0\n",
              "30   1   0   0   0   0   0   0   0   0   0   0   0\n",
              "31   1   0   0   0   0   0   0   0   0   0   0   0\n",
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              "34   1   0   0   0   0   0   0   0   0   0   0   0\n",
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              "38   1   0   0   0   0   0   0   0   0   0   0   0"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e7fcwv1S1od4",
        "outputId": "eba00380-fa84-4242-c5cd-f56419580611"
      },
      "source": [
        "y_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0, 0, 0, 0, 0, 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, 1, 0, 1, 0, 1],\n",
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              "       [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              "       [1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              "       [1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1],\n",
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              "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
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              "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1],\n",
              "       [0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              "       [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1],\n",
              "       [1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
              "       [1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1],\n",
              "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
              "       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4JevwhIpBnI",
        "outputId": "77b95846-cb2f-4813-e794-464bded7a092"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.28205128205128205 0.14316239316239315 0.5179856115107914\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R5JkXOvn5Guf",
        "outputId": "fb2fbcb5-52ff-460b-8d4b-b461fe3d39c5"
      },
      "source": [
        "print(metrics.classification_report(y_test,predicoes_br))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.97      0.89        30\n",
            "           1       0.75      0.25      0.38        12\n",
            "           2       0.00      0.00      0.00         4\n",
            "           3       0.00      0.00      0.00         7\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00         2\n",
            "           6       0.00      0.00      0.00         4\n",
            "           7       0.00      0.00      0.00         2\n",
            "           8       0.00      0.00      0.00         6\n",
            "           9       0.00      0.00      0.00         3\n",
            "          10       0.00      0.00      0.00         3\n",
            "          11       0.67      0.25      0.36        16\n",
            "\n",
            "   micro avg       0.78      0.39      0.52        93\n",
            "   macro avg       0.19      0.12      0.14        93\n",
            "weighted avg       0.48      0.39      0.40        93\n",
            " samples avg       0.77      0.51      0.55        93\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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jsmjxw96pE8X"
      },
      "source": [
        "MLP "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSryCn5QpD-2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "039aed7f-850f-4711-c9e9-e280b9e02963"
      },
      "source": [
        "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration 1, loss = 8.08133367\n",
            "Iteration 2, loss = 7.83629372\n",
            "Iteration 3, loss = 7.61124797\n",
            "Iteration 4, loss = 7.39484743\n",
            "Iteration 5, loss = 7.19000062\n",
            "Iteration 6, loss = 6.99808665\n",
            "Iteration 7, loss = 6.81063764\n",
            "Iteration 8, loss = 6.63809594\n",
            "Iteration 9, loss = 6.47320042\n",
            "Iteration 10, loss = 6.31729696\n",
            "Iteration 11, loss = 6.17131204\n",
            "Iteration 12, loss = 6.03404551\n",
            "Iteration 13, loss = 5.90679916\n",
            "Iteration 14, loss = 5.78600373\n",
            "Iteration 15, loss = 5.67292617\n",
            "Iteration 16, loss = 5.56781878\n",
            "Iteration 17, loss = 5.46897403\n",
            "Iteration 18, loss = 5.37749383\n",
            "Iteration 19, loss = 5.28950757\n",
            "Iteration 20, loss = 5.20935791\n",
            "Iteration 21, loss = 5.13617491\n",
            "Iteration 22, loss = 5.06687258\n",
            "Iteration 23, loss = 5.00164385\n",
            "Iteration 24, loss = 4.94173951\n",
            "Iteration 25, loss = 4.88457495\n",
            "Iteration 26, loss = 4.83304176\n",
            "Iteration 27, loss = 4.78498741\n",
            "Iteration 28, loss = 4.73881497\n",
            "Iteration 29, loss = 4.69738825\n",
            "Iteration 30, loss = 4.65802560\n",
            "Iteration 31, loss = 4.62203710\n",
            "Iteration 32, loss = 4.58798758\n",
            "Iteration 33, loss = 4.55564626\n",
            "Iteration 34, loss = 4.52543812\n",
            "Iteration 35, loss = 4.49754779\n",
            "Iteration 36, loss = 4.47079420\n",
            "Iteration 37, loss = 4.44641408\n",
            "Iteration 38, loss = 4.42244385\n",
            "Iteration 39, loss = 4.40009044\n",
            "Iteration 40, loss = 4.37941371\n",
            "Iteration 41, loss = 4.35926350\n",
            "Iteration 42, loss = 4.34054557\n",
            "Iteration 43, loss = 4.32212446\n",
            "Iteration 44, loss = 4.30468823\n",
            "Iteration 45, loss = 4.28794751\n",
            "Iteration 46, loss = 4.27202945\n",
            "Iteration 47, loss = 4.25655234\n",
            "Iteration 48, loss = 4.24219896\n",
            "Iteration 49, loss = 4.22720976\n",
            "Iteration 50, loss = 4.21381700\n",
            "Iteration 51, loss = 4.20026692\n",
            "Iteration 52, loss = 4.18748734\n",
            "Iteration 53, loss = 4.17476691\n",
            "Iteration 54, loss = 4.16238156\n",
            "Iteration 55, loss = 4.15071691\n",
            "Iteration 56, loss = 4.13922230\n",
            "Iteration 57, loss = 4.12790290\n",
            "Iteration 58, loss = 4.11666542\n",
            "Iteration 59, loss = 4.10589898\n",
            "Iteration 60, loss = 4.09529191\n",
            "Iteration 61, loss = 4.08459794\n",
            "Iteration 62, loss = 4.07446630\n",
            "Iteration 63, loss = 4.06440152\n",
            "Iteration 64, loss = 4.05456291\n",
            "Iteration 65, loss = 4.04449206\n",
            "Iteration 66, loss = 4.03495598\n",
            "Iteration 67, loss = 4.02534027\n",
            "Iteration 68, loss = 4.01594172\n",
            "Iteration 69, loss = 4.00667027\n",
            "Iteration 70, loss = 3.99760656\n",
            "Iteration 71, loss = 3.98845817\n",
            "Iteration 72, loss = 3.97988189\n",
            "Iteration 73, loss = 3.97107330\n",
            "Iteration 74, loss = 3.96217199\n",
            "Iteration 75, loss = 3.95350328\n",
            "Iteration 76, loss = 3.94511312\n",
            "Iteration 77, loss = 3.93677658\n",
            "Iteration 78, loss = 3.92848595\n",
            "Iteration 79, loss = 3.92012319\n",
            "Iteration 80, loss = 3.91196599\n",
            "Iteration 81, loss = 3.90384180\n",
            "Iteration 82, loss = 3.89579165\n",
            "Iteration 83, loss = 3.88769631\n",
            "Iteration 84, loss = 3.87957053\n",
            "Iteration 85, loss = 3.87149139\n",
            "Iteration 86, loss = 3.86383123\n",
            "Iteration 87, loss = 3.85596631\n",
            "Iteration 88, loss = 3.84820740\n",
            "Iteration 89, loss = 3.84051877\n",
            "Iteration 90, loss = 3.83300783\n",
            "Iteration 91, loss = 3.82516909\n",
            "Iteration 92, loss = 3.81787937\n",
            "Iteration 93, loss = 3.81019059\n",
            "Iteration 94, loss = 3.80255780\n",
            "Iteration 95, loss = 3.79508933\n",
            "Iteration 96, loss = 3.78778839\n",
            "Iteration 97, loss = 3.78025844\n",
            "Iteration 98, loss = 3.77315653\n",
            "Iteration 99, loss = 3.76576279\n",
            "Iteration 100, loss = 3.75841154\n",
            "Iteration 101, loss = 3.75103825\n",
            "Iteration 102, loss = 3.74398810\n",
            "Iteration 103, loss = 3.73667499\n",
            "Iteration 104, loss = 3.72968259\n",
            "Iteration 105, loss = 3.72226531\n",
            "Iteration 106, loss = 3.71498548\n",
            "Iteration 107, loss = 3.70814050\n",
            "Iteration 108, loss = 3.70097330\n",
            "Iteration 109, loss = 3.69394754\n",
            "Iteration 110, loss = 3.68672590\n",
            "Iteration 111, loss = 3.67983322\n",
            "Iteration 112, loss = 3.67309397\n",
            "Iteration 113, loss = 3.66619809\n",
            "Iteration 114, loss = 3.65927145\n",
            "Iteration 115, loss = 3.65236331\n",
            "Iteration 116, loss = 3.64537437\n",
            "Iteration 117, loss = 3.63869057\n",
            "Iteration 118, loss = 3.63183897\n",
            "Iteration 119, loss = 3.62519922\n",
            "Iteration 120, loss = 3.61842997\n",
            "Iteration 121, loss = 3.61169371\n",
            "Iteration 122, loss = 3.60485902\n",
            "Iteration 123, loss = 3.59829877\n",
            "Iteration 124, loss = 3.59156492\n",
            "Iteration 125, loss = 3.58513245\n",
            "Iteration 126, loss = 3.57871936\n",
            "Iteration 127, loss = 3.57180498\n",
            "Iteration 128, loss = 3.56522637\n",
            "Iteration 129, loss = 3.55882095\n",
            "Iteration 130, loss = 3.55206237\n",
            "Iteration 131, loss = 3.54564955\n",
            "Iteration 132, loss = 3.53881758\n",
            "Iteration 133, loss = 3.53222822\n",
            "Iteration 134, loss = 3.52587095\n",
            "Iteration 135, loss = 3.51919353\n",
            "Iteration 136, loss = 3.51276902\n",
            "Iteration 137, loss = 3.50601676\n",
            "Iteration 138, loss = 3.49956681\n",
            "Iteration 139, loss = 3.49299830\n",
            "Iteration 140, loss = 3.48649194\n",
            "Iteration 141, loss = 3.47979689\n",
            "Iteration 142, loss = 3.47319825\n",
            "Iteration 143, loss = 3.46643478\n",
            "Iteration 144, loss = 3.46026975\n",
            "Iteration 145, loss = 3.45355987\n",
            "Iteration 146, loss = 3.44695528\n",
            "Iteration 147, loss = 3.44042787\n",
            "Iteration 148, loss = 3.43400519\n",
            "Iteration 149, loss = 3.42716255\n",
            "Iteration 150, loss = 3.42084331\n",
            "Iteration 151, loss = 3.41420776\n",
            "Iteration 152, loss = 3.40773247\n",
            "Iteration 153, loss = 3.40149234\n",
            "Iteration 154, loss = 3.39488643\n",
            "Iteration 155, loss = 3.38843446\n",
            "Iteration 156, loss = 3.38194712\n",
            "Iteration 157, loss = 3.37531570\n",
            "Iteration 158, loss = 3.36877922\n",
            "Iteration 159, loss = 3.36237184\n",
            "Iteration 160, loss = 3.35577808\n",
            "Iteration 161, loss = 3.34977297\n",
            "Iteration 162, loss = 3.34329457\n",
            "Iteration 163, loss = 3.33669470\n",
            "Iteration 164, loss = 3.33043693\n",
            "Iteration 165, loss = 3.32413058\n",
            "Iteration 166, loss = 3.31770864\n",
            "Iteration 167, loss = 3.31147820\n",
            "Iteration 168, loss = 3.30510352\n",
            "Iteration 169, loss = 3.29873925\n",
            "Iteration 170, loss = 3.29256108\n",
            "Iteration 171, loss = 3.28622507\n",
            "Iteration 172, loss = 3.27974881\n",
            "Iteration 173, loss = 3.27346680\n",
            "Iteration 174, loss = 3.26725911\n",
            "Iteration 175, loss = 3.26102749\n",
            "Iteration 176, loss = 3.25466714\n",
            "Iteration 177, loss = 3.24807383\n",
            "Iteration 178, loss = 3.24197153\n",
            "Iteration 179, loss = 3.23564721\n",
            "Iteration 180, loss = 3.22929657\n",
            "Iteration 181, loss = 3.22347260\n",
            "Iteration 182, loss = 3.21713673\n",
            "Iteration 183, loss = 3.21088539\n",
            "Iteration 184, loss = 3.20499497\n",
            "Iteration 185, loss = 3.19892736\n",
            "Iteration 186, loss = 3.19247539\n",
            "Iteration 187, loss = 3.18632397\n",
            "Iteration 188, loss = 3.18019727\n",
            "Iteration 189, loss = 3.17411812\n",
            "Iteration 190, loss = 3.16791216\n",
            "Iteration 191, loss = 3.16183163\n",
            "Iteration 192, loss = 3.15576539\n",
            "Iteration 193, loss = 3.14955185\n",
            "Iteration 194, loss = 3.14347838\n",
            "Iteration 195, loss = 3.13707552\n",
            "Iteration 196, loss = 3.13119349\n",
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            "Iteration 963, loss = 0.52570314\n",
            "Iteration 964, loss = 0.52431917\n",
            "Iteration 965, loss = 0.52310526\n",
            "Iteration 966, loss = 0.52189988\n",
            "Iteration 967, loss = 0.52059958\n",
            "Iteration 968, loss = 0.51949172\n",
            "Iteration 969, loss = 0.51823324\n",
            "Iteration 970, loss = 0.51686367\n",
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            "Iteration 972, loss = 0.51456336\n",
            "Iteration 973, loss = 0.51330535\n",
            "Iteration 974, loss = 0.51208863\n",
            "Iteration 975, loss = 0.51082775\n",
            "Iteration 976, loss = 0.50955098\n",
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            "Iteration 978, loss = 0.50723533\n",
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            "Iteration 980, loss = 0.50477408\n",
            "Iteration 981, loss = 0.50368663\n",
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            "Iteration 983, loss = 0.50128577\n",
            "Iteration 984, loss = 0.50007368\n",
            "Iteration 985, loss = 0.49894444\n",
            "Iteration 986, loss = 0.49773083\n",
            "Iteration 987, loss = 0.49643218\n",
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            "Iteration 991, loss = 0.49188241\n",
            "Iteration 992, loss = 0.49066081\n",
            "Iteration 993, loss = 0.48942090\n",
            "Iteration 994, loss = 0.48834318\n",
            "Iteration 995, loss = 0.48712262\n",
            "Iteration 996, loss = 0.48603582\n",
            "Iteration 997, loss = 0.48488482\n",
            "Iteration 998, loss = 0.48365570\n",
            "Iteration 999, loss = 0.48250101\n",
            "Iteration 1000, loss = 0.48145561\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9AOCSYmqG0E"
      },
      "source": [
        "Matriz de confusão"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XA8Sq_T0KP0",
        "outputId": "0591b6a2-1f39-4b48-e842-5ed19d34fbdd"
      },
      "source": [
        "labels"
      ],
      "execution_count": null,
      "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": 178
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1cX7J9Wzh4a-"
      },
      "source": [
        "multilabel_confusion_matrix\n",
        "verdadeiro negativo, falsos positivos\n",
        "falsos negativos, verdadeiros positivos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdTB8Nt0qKac",
        "outputId": "a475c5dc-a7f6-4ed0-d40d-a3b4fef46775"
      },
      "source": [
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "cm = multilabel_confusion_matrix(y_test, prediction)\n",
        "cm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 3,  6],\n",
              "        [ 1, 29]],\n",
              "\n",
              "       [[26,  1],\n",
              "        [ 9,  3]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[31,  1],\n",
              "        [ 7,  0]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[33,  0],\n",
              "        [ 6,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[21,  2],\n",
              "        [12,  4]]])"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQ3rkewcKDsR",
        "outputId": "dc5579fa-ef95-4454-9984-c2a2571c113b"
      },
      "source": [
        "print(\"micro: {:.2f}\".format(metrics.precision_score(y_test, predicoes_br, average='micro')))\n",
        "print(\"macro: {:.2f} \".format( metrics.precision_score(y_test, predicoes_br, average='macro')))\n",
        "print(\"weighted: {:.2f} \".format( metrics.precision_score(y_test, predicoes_br, average='weighted')))\n",
        "print(\"samples: {:.2f} \".format( metrics.precision_score(y_test, predicoes_br, average='samples')))  \n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "micro: 0.78\n",
            "macro: 0.19 \n",
            "weighted: 0.48 \n",
            "samples: 0.77 \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1308: UndefinedMetricWarning: Precision is 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 is 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 is 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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3F73CO0Ggi_l"
      },
      "source": [
        "#Testes com divervos ML Clissificadores"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZPtGqXIhJkD"
      },
      "source": [
        "avaliacoes = {}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irjxF9shgsia"
      },
      "source": [
        "#Binary Relevance => Random Forest"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xejD1QFVgq_b",
        "outputId": "0422d438-ebe4-4293-b8a5-83ce38c2d527"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3076923076923077 0.1452991452991453 0.5211267605633803\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GUFij5v8ToA",
        "outputId": "164cb0c1-30b8-4727-cf6b-30a3326f73d8"
      },
      "source": [
        "print(metrics.classification_report(y_test,prediction_br_RFC))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.93      0.89        30\n",
            "           1       0.57      0.33      0.42        12\n",
            "           2       0.00      0.00      0.00         4\n",
            "           3       0.50      0.14      0.22         7\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00         2\n",
            "           6       0.00      0.00      0.00         4\n",
            "           7       0.00      0.00      0.00         2\n",
            "           8       0.50      0.17      0.25         6\n",
            "           9       0.00      0.00      0.00         3\n",
            "          10       0.00      0.00      0.00         3\n",
            "          11       0.60      0.19      0.29        16\n",
            "\n",
            "   micro avg       0.76      0.40      0.52        93\n",
            "   macro avg       0.25      0.15      0.17        93\n",
            "weighted avg       0.52      0.40      0.42        93\n",
            " samples avg       0.76      0.54      0.57        93\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pFUSJ-ekiQr",
        "outputId": "61a7d67c-f7d4-4a5a-f94d-af95544386e9"
      },
      "source": [
        "cmm = multilabel_confusion_matrix(y_test, prediction_br_RFC)\n",
        "cmm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 4,  5],\n",
              "        [ 2, 28]],\n",
              "\n",
              "       [[24,  3],\n",
              "        [ 8,  4]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[31,  1],\n",
              "        [ 6,  1]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[32,  1],\n",
              "        [ 5,  1]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[21,  2],\n",
              "        [13,  3]]])"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwE5YFs3hydl"
      },
      "source": [
        "Label Power Set => RandomForest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXU6DJHYhw6R",
        "outputId": "1a5734b9-9af4-4e55-8c14-0ff62b38cc8e"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.358974358974359 0.14743589743589744 0.48888888888888893\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ZDnvDvh6C9L",
        "outputId": "bf94ced5-17a9-4bc9-be8f-88281a0f1e56"
      },
      "source": [
        "print(metrics.classification_report(y_test,prediction_LP_RFC))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      1.00      0.91        30\n",
            "           1       0.50      0.08      0.14        12\n",
            "           2       0.00      0.00      0.00         4\n",
            "           3       1.00      0.14      0.25         7\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00         2\n",
            "           6       0.00      0.00      0.00         4\n",
            "           7       0.00      0.00      0.00         2\n",
            "           8       0.00      0.00      0.00         6\n",
            "           9       0.00      0.00      0.00         3\n",
            "          10       0.00      0.00      0.00         3\n",
            "          11       0.50      0.06      0.11        16\n",
            "\n",
            "   micro avg       0.79      0.35      0.49        93\n",
            "   macro avg       0.24      0.11      0.12        93\n",
            "weighted avg       0.49      0.35      0.35        93\n",
            " samples avg       0.82      0.51      0.57        93\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1oN7Yrtk7m5",
        "outputId": "22b9e3e5-0d8e-4800-c157-a66927044bb6"
      },
      "source": [
        "cmlp = multilabel_confusion_matrix(y_test, prediction_LP_RFC)\n",
        "cmlp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 3,  6],\n",
              "        [ 0, 30]],\n",
              "\n",
              "       [[26,  1],\n",
              "        [11,  1]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[32,  0],\n",
              "        [ 6,  1]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[32,  1],\n",
              "        [ 6,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[22,  1],\n",
              "        [15,  1]]])"
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ]
    },
    {
      "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": "0d6c834b-fe8f-4efe-942e-3f1284f1ff65"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.358974358974359 0.14957264957264957 0.4696969696969696\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_16mYBO877Yf",
        "outputId": "1dcebf9d-8208-42d9-ff45-0fc3d610e113"
      },
      "source": [
        "print(metrics.classification_report(y_test,prediction_LP_SVM))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.79      1.00      0.88        30\n",
            "           1       1.00      0.08      0.15        12\n",
            "           2       0.00      0.00      0.00         4\n",
            "           3       0.00      0.00      0.00         7\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00         2\n",
            "           6       0.00      0.00      0.00         4\n",
            "           7       0.00      0.00      0.00         2\n",
            "           8       0.00      0.00      0.00         6\n",
            "           9       0.00      0.00      0.00         3\n",
            "          10       0.00      0.00      0.00         3\n",
            "          11       0.00      0.00      0.00        16\n",
            "\n",
            "   micro avg       0.79      0.33      0.47        93\n",
            "   macro avg       0.15      0.09      0.09        93\n",
            "weighted avg       0.38      0.33      0.30        93\n",
            " samples avg       0.79      0.48      0.55        93\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uXMqGXyGlF5S",
        "outputId": "1fb3570b-da71-406b-c99b-9546f443fb63"
      },
      "source": [
        "cmlpsvm = multilabel_confusion_matrix(y_test, prediction_LP_SVM)\n",
        "cmlpsvm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 1,  8],\n",
              "        [ 0, 30]],\n",
              "\n",
              "       [[27,  0],\n",
              "        [11,  1]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[32,  0],\n",
              "        [ 7,  0]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[33,  0],\n",
              "        [ 6,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[23,  0],\n",
              "        [16,  0]]])"
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "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": "5544296e-5ba5-4017-910d-7144d5dca5de"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3333333333333333 0.1452991452991453 0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYYrMEpn-Q-a",
        "outputId": "ee532014-81d1-4787-ebec-34a9fa6eeaba"
      },
      "source": [
        "print(metrics.classification_report(y_test,predict_CC_RFC))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.97      0.89        30\n",
            "           1       1.00      0.25      0.40        12\n",
            "           2       0.00      0.00      0.00         4\n",
            "           3       0.50      0.14      0.22         7\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00         2\n",
            "           6       0.00      0.00      0.00         4\n",
            "           7       0.00      0.00      0.00         2\n",
            "           8       0.00      0.00      0.00         6\n",
            "           9       0.00      0.00      0.00         3\n",
            "          10       0.00      0.00      0.00         3\n",
            "          11       0.50      0.06      0.11        16\n",
            "\n",
            "   micro avg       0.79      0.37      0.50        93\n",
            "   macro avg       0.24      0.12      0.14        93\n",
            "weighted avg       0.52      0.37      0.38        93\n",
            " samples avg       0.80      0.50      0.56        93\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYIxXkftl8u1",
        "outputId": "0ab82177-c07c-44eb-f487-2f34cc76e634"
      },
      "source": [
        "cmccrf = multilabel_confusion_matrix(y_test, predict_CC_RFC)\n",
        "cmccrf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 3,  6],\n",
              "        [ 1, 29]],\n",
              "\n",
              "       [[27,  0],\n",
              "        [ 9,  3]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[31,  1],\n",
              "        [ 6,  1]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[32,  1],\n",
              "        [ 6,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[22,  1],\n",
              "        [15,  1]]])"
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hj9VBY53F1RW"
      },
      "source": [
        "#classifierChain =>SVM"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CvRt3wq0FGqK",
        "outputId": "bcc446df-899a-4c08-f815-125de07c70af"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.3076923076923077 0.1452991452991453 0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08dNS9Rc_4b1",
        "outputId": "33edb983-c3c3-4835-f2ef-d61be4219f52"
      },
      "source": [
        "print(metrics.classification_report(y_test,predict_CC_SVM))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.97      0.89        30\n",
            "           1       0.75      0.25      0.38        12\n",
            "           2       0.00      0.00      0.00         4\n",
            "           3       0.00      0.00      0.00         7\n",
            "           4       0.00      0.00      0.00         4\n",
            "           5       0.00      0.00      0.00         2\n",
            "           6       0.00      0.00      0.00         4\n",
            "           7       0.00      0.00      0.00         2\n",
            "           8       0.00      0.00      0.00         6\n",
            "           9       0.00      0.00      0.00         3\n",
            "          10       0.00      0.00      0.00         3\n",
            "          11       0.67      0.12      0.21        16\n",
            "\n",
            "   micro avg       0.79      0.37      0.50        93\n",
            "   macro avg       0.19      0.11      0.12        93\n",
            "weighted avg       0.48      0.37      0.37        93\n",
            " samples avg       0.78      0.50      0.55        93\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Juq1aahxmNbY",
        "outputId": "60c859e3-9a71-4f8c-b147-7d3baa332376"
      },
      "source": [
        "cmccsvm = multilabel_confusion_matrix(y_test, predict_CC_SVM)\n",
        "cmccsvm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 3,  6],\n",
              "        [ 1, 29]],\n",
              "\n",
              "       [[26,  1],\n",
              "        [ 9,  3]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[31,  1],\n",
              "        [ 7,  0]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[35,  0],\n",
              "        [ 4,  0]],\n",
              "\n",
              "       [[37,  0],\n",
              "        [ 2,  0]],\n",
              "\n",
              "       [[33,  0],\n",
              "        [ 6,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[36,  0],\n",
              "        [ 3,  0]],\n",
              "\n",
              "       [[22,  1],\n",
              "        [14,  2]]])"
            ]
          },
          "metadata": {},
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2E-gHr1YmIOG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3RXxxZW_v00"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95ciE_hq_vvn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ztbVa92bjM-Z"
      },
      "source": [
        "BrKNN Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "uUMrDPZ7jPn8",
        "outputId": "09a4fb30-ad91-473b-a7bc-cd9ebb37ed17"
      },
      "source": [
        "from skmultilearn.adapt import BRkNNaClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "parameters = {'k': range(3,5)}\n",
        "score = 'f1_micro'\n",
        "classifier = GridSearchCV(BRkNNaClassifier(), parameters, scoring=score)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "print('best parameters :', classifier.best_params_,'best score: ',classifier.best_score_)\n",
        "\n",
        "prediction_BRkNNaClassifier = classifier.predict(X_test)\n",
        "\n",
        "ACC_BRKNN = metrics.accuracy_score(y_test, prediction_BRkNNaClassifier)\n",
        "HL_BRKNN = metrics.hamming_loss(y_test, prediction_BRkNNaClassifier) \n",
        "FS_BRKNN = metrics.f1_score(y_test, prediction_BRkNNaClassifier, average='micro')\n",
        "print(ACC_BRKNN, HL_BRKNN, FS_BRKNN)\n",
        "avaliacoes[\"ACC_BRKNN\"] = ACC_BRKNN\n",
        "avaliacoes[\"HL_BRKNN\"] = HL_BRKNN\n",
        "avaliacoes[\"FS_BRKNN\"] = FS_BRKNN"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "10 fits failed out of a total of 10.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "10 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 681, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/skmultilearn/adapt/brknn.py\", line 39, in fit\n",
            "    self.knn_ = NearestNeighbors(self.k).fit(X)\n",
            "TypeError: __init__() takes 1 positional argument but 2 were given\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [nan nan]\n",
            "  category=UserWarning,\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-199-cbb227e87ccc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'f1_micro'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mclassifier\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBRkNNaClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'best parameters :'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'best score: '\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    924\u001b[0m             \u001b[0mrefit_start_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    925\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    927\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/skmultilearn/adapt/brknn.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_labelspace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_labelspace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mknn_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNearestNeighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: __init__() takes 1 positional argument but 2 were given"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ld_fCKPuj6kl"
      },
      "source": [
        "MLKNN Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajZwiawhj9ch"
      },
      "source": [
        "from skmultilearn.adapt import MLkNN\n",
        "classifier_mlknn = MLkNN(k=5, s=0.5)\n",
        "prediction_mlknn = classifier_mlknn.fit(X_train, y_train).predict(X_test)\n",
        "ACC_MLKNN = metrics.accuracy_score(y_test, prediction_mlknn)\n",
        "HL_MLKNN = metrics.hamming_loss(y_test, prediction_mlknn) \n",
        "FS_MLKNN = metrics.f1_score(y_test, prediction_mlknn, average='micro')\n",
        "print(ACC_MLKNN, HL_MLKNN, FS_MLKNN)\n",
        "avaliacoes[\"ACC_MLKNN\"] = ACC_MLKNN\n",
        "avaliacoes[\"HL_MLKNN\"] = HL_MLKNN\n",
        "avaliacoes[\"FS_MLKNN\"] = FS_MLKNN\n",
        "\n",
        "pd.DataFrame(prediction_mlknn.toarray())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tC-QpS_wmAza"
      },
      "source": [
        "Resultados: F1, ACC, HL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcLTwPPUmF1u"
      },
      "source": [
        "print(avaliacoes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_ybzd43iYLr"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNcOAhJWiZEJ"
      },
      "source": [
        "from skmultilearn.problem_transform import ClassifierChain\n",
        "\n",
        "classifier = ClassifierChain(classifier = RandomForestClassifier(),require_dense = [False, True],order=[i for i in range(6)])\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",
        "\n",
        "#avaliacoes[\"ACC_CC_RFC\"] = ACC_CC_RFC\n",
        "#avaliacoes[\"HL_CC_RFC\"] = HL_CC_RFC\n",
        "#avaliacoes[\"FS_CC_RFC\"] = FS_CC_RFC\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrgHPpzGoUH0"
      },
      "source": [
        "pd.DataFrame(predict_CC_RFC.toarray())"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}