Aula_9_shePY.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Aula 9 - ML Classificação pt2 - shePY.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPUogOZaAA8NPeKTBp/362F",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/victoriapaganotto/shepy/blob/main/Aula_9_shePY.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-SdHORD6diZ"
      },
      "source": [
        "## Aula 10 - shePY\n",
        "## Machine Learning para aprender a classificar vinhos em tinto e branco.\n",
        "![](https://i.giphy.com/media/BO5lR6Kw6giXe/200.gif)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TyKeNd36P84"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "PwMISo-y66Tc",
        "outputId": "9592deef-484e-4cea-a253-301b24c2e44f"
      },
      "source": [
        "wine_df = pd.read_csv('https://raw.githubusercontent.com/mariacmartins/shepy/main/wine_dataset.csv')\n",
        "wine_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\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>fixed_acidity</th>\n",
              "      <th>volatile_acidity</th>\n",
              "      <th>citric_acid</th>\n",
              "      <th>residual_sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>style</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed_acidity  volatile_acidity  citric_acid  ...  alcohol  quality  style\n",
              "0            7.4              0.70         0.00  ...      9.4        5    red\n",
              "1            7.8              0.88         0.00  ...      9.8        5    red\n",
              "2            7.8              0.76         0.04  ...      9.8        5    red\n",
              "3           11.2              0.28         0.56  ...      9.8        6    red\n",
              "4            7.4              0.70         0.00  ...      9.4        5    red\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IiRTVYh48ZOu"
      },
      "source": [
        "## Análise exploratória"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZ0--IaU8cJJ",
        "outputId": "2fc1dc17-0ba5-4d9d-de21-501a5734d22a"
      },
      "source": [
        "wine_df.shape # Quantidade de linhas e colunas"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6497, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "HkPCl8fy8lHJ",
        "outputId": "6d1b10ea-9760-4483-d86c-a4651f509561"
      },
      "source": [
        "wine_df.describe() # Descrição dos dados"
      ],
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed_acidity</th>\n",
              "      <th>volatile_acidity</th>\n",
              "      <th>citric_acid</th>\n",
              "      <th>residual_sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "      <td>6497.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.215307</td>\n",
              "      <td>0.339666</td>\n",
              "      <td>0.318633</td>\n",
              "      <td>5.443235</td>\n",
              "      <td>0.056034</td>\n",
              "      <td>30.525319</td>\n",
              "      <td>115.744574</td>\n",
              "      <td>0.994697</td>\n",
              "      <td>3.218501</td>\n",
              "      <td>0.531268</td>\n",
              "      <td>10.491801</td>\n",
              "      <td>5.818378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.296434</td>\n",
              "      <td>0.164636</td>\n",
              "      <td>0.145318</td>\n",
              "      <td>4.757804</td>\n",
              "      <td>0.035034</td>\n",
              "      <td>17.749400</td>\n",
              "      <td>56.521855</td>\n",
              "      <td>0.002999</td>\n",
              "      <td>0.160787</td>\n",
              "      <td>0.148806</td>\n",
              "      <td>1.192712</td>\n",
              "      <td>0.873255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.800000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.009000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.987110</td>\n",
              "      <td>2.720000</td>\n",
              "      <td>0.220000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>0.230000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>1.800000</td>\n",
              "      <td>0.038000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>0.992340</td>\n",
              "      <td>3.110000</td>\n",
              "      <td>0.430000</td>\n",
              "      <td>9.500000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.290000</td>\n",
              "      <td>0.310000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.047000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>0.994890</td>\n",
              "      <td>3.210000</td>\n",
              "      <td>0.510000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.700000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.390000</td>\n",
              "      <td>8.100000</td>\n",
              "      <td>0.065000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>0.996990</td>\n",
              "      <td>3.320000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>11.300000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.900000</td>\n",
              "      <td>1.580000</td>\n",
              "      <td>1.660000</td>\n",
              "      <td>65.800000</td>\n",
              "      <td>0.611000</td>\n",
              "      <td>289.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>1.038980</td>\n",
              "      <td>4.010000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>14.900000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       fixed_acidity  volatile_acidity  ...      alcohol      quality\n",
              "count    6497.000000       6497.000000  ...  6497.000000  6497.000000\n",
              "mean        7.215307          0.339666  ...    10.491801     5.818378\n",
              "std         1.296434          0.164636  ...     1.192712     0.873255\n",
              "min         3.800000          0.080000  ...     8.000000     3.000000\n",
              "25%         6.400000          0.230000  ...     9.500000     5.000000\n",
              "50%         7.000000          0.290000  ...    10.300000     6.000000\n",
              "75%         7.700000          0.400000  ...    11.300000     6.000000\n",
              "max        15.900000          1.580000  ...    14.900000     9.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "_T-08mP082aN",
        "outputId": "a19a6eb6-1dbe-4f3b-971b-af61d76b5bfb"
      },
      "source": [
        "sns.countplot(\n",
        "    x='style',\n",
        "    palette='rocket',\n",
        "    data=wine_df,\n",
        "    order=wine_df['style'].value_counts().index\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa889353ed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2ldNQkqRFZFhIkroMC91BktvmqL80yQvb8ouSPGyynUnzl+S1SV6x2H3sTnaJC9xafFX17rHVFwFXAd9anG60XCUJo2utv1zsXpYbjyyWmSSvTPKytnxakovb8uFJPtCWT03y5SSXJtmv1V6b5BVJjgWmgA8kuSLJfZI8Lsmnk2xKcmGS/Rfr99PuJ8nqNono2Yz+kfL3SS5LcmWS142Ne02SryX5HPCIRWt4N2VYLD+fBZ7UlqeA+yfZq9U+A9wPuLSqHtPWXzL+5qr6MDANPL+qDga2A+8Ajq2qxwFnAadO4hfRsrIGeCfw14ym/zkUOBh4XJI/TvI4Rs9fHQwcBfzBYjW6u/I01PKzidF/YA8Efgp8iVFoPAl4GfAz4GNjY5/a2d8jgEcDG0dnCNgDuHnh29Yyd0NVXZrkrcBa4PJWvz+jIHkA8NGquh0giQ/tLjDDYpmpqp8n+Qaj6w7/A1wJPBl4OHAt8PP69cM3v6D/NxLg6qp6wjAdSwD8qL0G+Ieq+tfxjUlOmnxLy4unoZanzwKvYHSa6bPAS4HLa/5PaP6Q0b/kAK4DViR5AkCSvZI8aoH7lXa4EHhxkvsDJFmZ5KGM/paPadfQHgA8YzGb3B15ZLE8fRZ4DfD5qvpRkp+02ny9D3h3kh8DTwCOBU5P8iBGf1NvA5yORQuuqj6Z5HeBz7fTnrcBL6iqLyX5EPBlYAuj+eS0gJzuQ5LU5WkoSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRbSAJKclOS+8xg36yy/0lJjWEjDOAnohoW0qzAspLspyf2S/HebqfeqJKcADwMuSXJJkhcnedvY+JckOW2W/bxyttlUpaXAsJDuviOBb1XVY6rq0YyeYP8W8OSqejJwHvCMNrsvwPGMZuf9lSRrGU2Id4fZVCf1C0g9hoV0930FeGqSNyd5UlXdOr6xqm4DLgaenuSRwF5V9ZWd9rGWX8+m+iXgkYzCQ1oSnBtKupuq6mtJDmH0PQpvTHLRLMPeA7wa+Crw3lm2zzqbqrRUeGQh3U3t+8hvr6p/A94CHMIdZ+alqr4AHAA8Dzhnlt3MNZuqtCR4ZCHdfb8HvCXJL4GfA3/BaDbeTyT5VrtuAaNrFwdX1S0772Cu2VQZzaAqLTpnnZUmJMnHgNOqarbTVNKS5mkoaWBJ9k7yNeDHBoV2VR5ZSJK6PLKQJHUZFpKkLsNCktRlWEiSugwLSVLX/wGVsoBeOO541AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "5Jdg3ifZ9jhN",
        "outputId": "eac10244-14e3-4b6a-b2c3-078e8f29d9f3"
      },
      "source": [
        "sns.scatterplot(data=wine_df, x='pH', y='fixed_acidity', hue='style', palette='deep');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VoP-C5pt-5xY"
      },
      "source": [
        "##Analisando a correlação entre os dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "Yp-c7YK6-cUD",
        "outputId": "71c46e2a-5cad-42c8-f4cb-2bab3d9c1d96"
      },
      "source": [
        "corr = wine_df.corr()\n",
        "corr"
      ],
      "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>fixed_acidity</th>\n",
              "      <th>volatile_acidity</th>\n",
              "      <th>citric_acid</th>\n",
              "      <th>residual_sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fixed_acidity</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.219008</td>\n",
              "      <td>0.324436</td>\n",
              "      <td>-0.111981</td>\n",
              "      <td>0.298195</td>\n",
              "      <td>-0.282735</td>\n",
              "      <td>-0.329054</td>\n",
              "      <td>0.458910</td>\n",
              "      <td>-0.252700</td>\n",
              "      <td>0.299568</td>\n",
              "      <td>-0.095452</td>\n",
              "      <td>-0.076743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>volatile_acidity</th>\n",
              "      <td>0.219008</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.377981</td>\n",
              "      <td>-0.196011</td>\n",
              "      <td>0.377124</td>\n",
              "      <td>-0.352557</td>\n",
              "      <td>-0.414476</td>\n",
              "      <td>0.271296</td>\n",
              "      <td>0.261454</td>\n",
              "      <td>0.225984</td>\n",
              "      <td>-0.037640</td>\n",
              "      <td>-0.265699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>citric_acid</th>\n",
              "      <td>0.324436</td>\n",
              "      <td>-0.377981</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.142451</td>\n",
              "      <td>0.038998</td>\n",
              "      <td>0.133126</td>\n",
              "      <td>0.195242</td>\n",
              "      <td>0.096154</td>\n",
              "      <td>-0.329808</td>\n",
              "      <td>0.056197</td>\n",
              "      <td>-0.010493</td>\n",
              "      <td>0.085532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>residual_sugar</th>\n",
              "      <td>-0.111981</td>\n",
              "      <td>-0.196011</td>\n",
              "      <td>0.142451</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.128940</td>\n",
              "      <td>0.402871</td>\n",
              "      <td>0.495482</td>\n",
              "      <td>0.552517</td>\n",
              "      <td>-0.267320</td>\n",
              "      <td>-0.185927</td>\n",
              "      <td>-0.359415</td>\n",
              "      <td>-0.036980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chlorides</th>\n",
              "      <td>0.298195</td>\n",
              "      <td>0.377124</td>\n",
              "      <td>0.038998</td>\n",
              "      <td>-0.128940</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.195045</td>\n",
              "      <td>-0.279630</td>\n",
              "      <td>0.362615</td>\n",
              "      <td>0.044708</td>\n",
              "      <td>0.395593</td>\n",
              "      <td>-0.256916</td>\n",
              "      <td>-0.200666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <td>-0.282735</td>\n",
              "      <td>-0.352557</td>\n",
              "      <td>0.133126</td>\n",
              "      <td>0.402871</td>\n",
              "      <td>-0.195045</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.720934</td>\n",
              "      <td>0.025717</td>\n",
              "      <td>-0.145854</td>\n",
              "      <td>-0.188457</td>\n",
              "      <td>-0.179838</td>\n",
              "      <td>0.055463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <td>-0.329054</td>\n",
              "      <td>-0.414476</td>\n",
              "      <td>0.195242</td>\n",
              "      <td>0.495482</td>\n",
              "      <td>-0.279630</td>\n",
              "      <td>0.720934</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.032395</td>\n",
              "      <td>-0.238413</td>\n",
              "      <td>-0.275727</td>\n",
              "      <td>-0.265740</td>\n",
              "      <td>-0.041385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>density</th>\n",
              "      <td>0.458910</td>\n",
              "      <td>0.271296</td>\n",
              "      <td>0.096154</td>\n",
              "      <td>0.552517</td>\n",
              "      <td>0.362615</td>\n",
              "      <td>0.025717</td>\n",
              "      <td>0.032395</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011686</td>\n",
              "      <td>0.259478</td>\n",
              "      <td>-0.686745</td>\n",
              "      <td>-0.305858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>-0.252700</td>\n",
              "      <td>0.261454</td>\n",
              "      <td>-0.329808</td>\n",
              "      <td>-0.267320</td>\n",
              "      <td>0.044708</td>\n",
              "      <td>-0.145854</td>\n",
              "      <td>-0.238413</td>\n",
              "      <td>0.011686</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.192123</td>\n",
              "      <td>0.121248</td>\n",
              "      <td>0.019506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sulphates</th>\n",
              "      <td>0.299568</td>\n",
              "      <td>0.225984</td>\n",
              "      <td>0.056197</td>\n",
              "      <td>-0.185927</td>\n",
              "      <td>0.395593</td>\n",
              "      <td>-0.188457</td>\n",
              "      <td>-0.275727</td>\n",
              "      <td>0.259478</td>\n",
              "      <td>0.192123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003029</td>\n",
              "      <td>0.038485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alcohol</th>\n",
              "      <td>-0.095452</td>\n",
              "      <td>-0.037640</td>\n",
              "      <td>-0.010493</td>\n",
              "      <td>-0.359415</td>\n",
              "      <td>-0.256916</td>\n",
              "      <td>-0.179838</td>\n",
              "      <td>-0.265740</td>\n",
              "      <td>-0.686745</td>\n",
              "      <td>0.121248</td>\n",
              "      <td>-0.003029</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.444319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quality</th>\n",
              "      <td>-0.076743</td>\n",
              "      <td>-0.265699</td>\n",
              "      <td>0.085532</td>\n",
              "      <td>-0.036980</td>\n",
              "      <td>-0.200666</td>\n",
              "      <td>0.055463</td>\n",
              "      <td>-0.041385</td>\n",
              "      <td>-0.305858</td>\n",
              "      <td>0.019506</td>\n",
              "      <td>0.038485</td>\n",
              "      <td>0.444319</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      fixed_acidity  volatile_acidity  ...   alcohol   quality\n",
              "fixed_acidity              1.000000          0.219008  ... -0.095452 -0.076743\n",
              "volatile_acidity           0.219008          1.000000  ... -0.037640 -0.265699\n",
              "citric_acid                0.324436         -0.377981  ... -0.010493  0.085532\n",
              "residual_sugar            -0.111981         -0.196011  ... -0.359415 -0.036980\n",
              "chlorides                  0.298195          0.377124  ... -0.256916 -0.200666\n",
              "free_sulfur_dioxide       -0.282735         -0.352557  ... -0.179838  0.055463\n",
              "total_sulfur_dioxide      -0.329054         -0.414476  ... -0.265740 -0.041385\n",
              "density                    0.458910          0.271296  ... -0.686745 -0.305858\n",
              "pH                        -0.252700          0.261454  ...  0.121248  0.019506\n",
              "sulphates                  0.299568          0.225984  ... -0.003029  0.038485\n",
              "alcohol                   -0.095452         -0.037640  ...  1.000000  0.444319\n",
              "quality                   -0.076743         -0.265699  ...  0.444319  1.000000\n",
              "\n",
              "[12 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "GMLXhU9v_2m8",
        "outputId": "d5b31055-feb3-4fba-b0c4-7f54ffff420d"
      },
      "source": [
        "cmap = sns.diverging_palette(230,20, as_cmap=True)\n",
        "\n",
        "sns.heatmap(corr, cmap=cmap, center=0, vmin=-1, vmax=1,\n",
        "            square=True, linewidths=5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa86f43c650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "RS4L0zTGAWMA",
        "outputId": "a7c1431d-61da-4624-d146-6d505afc16a3"
      },
      "source": [
        "sns.lmplot(x='alcohol', y='density', line_kws={'color': 'black'}, data=wine_df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PMLz-MRdCPh_"
      },
      "source": [
        "## Machine Learning:\n",
        "\n",
        "### Transformar vinhos red e white que são o nosso alvo (target) em valores numéricos.\n",
        "\n",
        "Red: 0\n",
        "\n",
        "White: 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "W2ACweLvB8yg",
        "outputId": "36844562-cd9c-48a6-b861-9745312ed50f"
      },
      "source": [
        "wine_df.head()"
      ],
      "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>fixed_acidity</th>\n",
              "      <th>volatile_acidity</th>\n",
              "      <th>citric_acid</th>\n",
              "      <th>residual_sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>style</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "      <td>red</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed_acidity  volatile_acidity  citric_acid  ...  alcohol  quality  style\n",
              "0            7.4              0.70         0.00  ...      9.4        5    red\n",
              "1            7.8              0.88         0.00  ...      9.8        5    red\n",
              "2            7.8              0.76         0.04  ...      9.8        5    red\n",
              "3           11.2              0.28         0.56  ...      9.8        6    red\n",
              "4            7.4              0.70         0.00  ...      9.4        5    red\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "g0WTM8zvCwzI",
        "outputId": "6a9c70c5-697b-4705-d26c-56c2c72c60de"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "wine_df['style'] = encoder.fit_transform(wine_df['style'])\n",
        "wine_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>fixed_acidity</th>\n",
              "      <th>volatile_acidity</th>\n",
              "      <th>citric_acid</th>\n",
              "      <th>residual_sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "      <th>style</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.99680</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.99700</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.99800</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6492</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.039</td>\n",
              "      <td>24.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.99114</td>\n",
              "      <td>3.27</td>\n",
              "      <td>0.50</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6493</th>\n",
              "      <td>6.6</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.36</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.047</td>\n",
              "      <td>57.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.15</td>\n",
              "      <td>0.46</td>\n",
              "      <td>9.6</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6494</th>\n",
              "      <td>6.5</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.041</td>\n",
              "      <td>30.0</td>\n",
              "      <td>111.0</td>\n",
              "      <td>0.99254</td>\n",
              "      <td>2.99</td>\n",
              "      <td>0.46</td>\n",
              "      <td>9.4</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6495</th>\n",
              "      <td>5.5</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.1</td>\n",
              "      <td>0.022</td>\n",
              "      <td>20.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>0.98869</td>\n",
              "      <td>3.34</td>\n",
              "      <td>0.38</td>\n",
              "      <td>12.8</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6496</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.020</td>\n",
              "      <td>22.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.98941</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.32</td>\n",
              "      <td>11.8</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6497 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed_acidity  volatile_acidity  citric_acid  ...  alcohol  quality  style\n",
              "0               7.4              0.70         0.00  ...      9.4        5      0\n",
              "1               7.8              0.88         0.00  ...      9.8        5      0\n",
              "2               7.8              0.76         0.04  ...      9.8        5      0\n",
              "3              11.2              0.28         0.56  ...      9.8        6      0\n",
              "4               7.4              0.70         0.00  ...      9.4        5      0\n",
              "...             ...               ...          ...  ...      ...      ...    ...\n",
              "6492            6.2              0.21         0.29  ...     11.2        6      1\n",
              "6493            6.6              0.32         0.36  ...      9.6        5      1\n",
              "6494            6.5              0.24         0.19  ...      9.4        6      1\n",
              "6495            5.5              0.29         0.30  ...     12.8        7      1\n",
              "6496            6.0              0.21         0.38  ...     11.8        6      1\n",
              "\n",
              "[6497 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NaF6bf-ADkyN"
      },
      "source": [
        "# Outro modo de transformar os valores\n",
        "# wine_df['style'] = wine.df['style'].replace('red', 0)\n",
        "# wine_df['style'] = wine.df['style'].replace('white', 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "KWPCwMpPEGgF",
        "outputId": "adaca379-b855-4c83-f68a-7aa65cd87674"
      },
      "source": [
        "# Separando variáveis preditoras da variável alvo\n",
        "X = wine_df.drop('style', axis=1)\n",
        "X.head()"
      ],
      "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>fixed_acidity</th>\n",
              "      <th>volatile_acidity</th>\n",
              "      <th>citric_acid</th>\n",
              "      <th>residual_sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.9968</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.9970</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.9978</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   fixed_acidity  volatile_acidity  citric_acid  ...  sulphates  alcohol  quality\n",
              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hm_pZ8H3EV3h",
        "outputId": "89f54614-faef-4275-8eb0-7c20c0bd3db4"
      },
      "source": [
        "y = wine_df['style']\n",
        "y.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: style, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2_vfXuIEmM2"
      },
      "source": [
        "## Separando os dados de treino e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I3j5LziBEpJz"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_treino, X_teste, y_treino, y_teste = train_test_split(X, y, test_size=0.3, random_state=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8e8-VrCF8ZA"
      },
      "source": [
        "# Árvore de Decisão (modelo que toma decisões a partir de uma variável)\n",
        "# Escolhas adequadas!"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q8P1c7CNGVYV",
        "outputId": "e5346088-4148-42cb-9fec-6c5833ee870e"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "model = DecisionTreeClassifier()\n",
        "\n",
        "# Treinando o modelo (parâmetros treinados podem ser modificados)\n",
        "model.fit(X_treino, y_treino)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
              "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pz5lwBvmG3oI"
      },
      "source": [
        "y_pred = model.predict(X_teste)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ek6M0ElQHEKs",
        "outputId": "5a2ea62b-6765-48d0-9e9d-d9083e81759f"
      },
      "source": [
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ykgrHteHI_b",
        "outputId": "e7c73928-46f6-4d45-f39e-e71ef2772ab2"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy_score(y_teste, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9871794871794872"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juTU8tPUHnA9"
      },
      "source": [
        "# ExtraTreeClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3w3crMpHsBr",
        "outputId": "b362ef97-0b4b-42ee-ebaf-0210ff02f1a1"
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "\n",
        "extra_model = ExtraTreesClassifier()\n",
        "# Treinando o modelo\n",
        "extra_model.fit(X_treino, y_treino)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
              "                     criterion='gini', max_depth=None, max_features='auto',\n",
              "                     max_leaf_nodes=None, max_samples=None,\n",
              "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                     min_samples_leaf=1, min_samples_split=2,\n",
              "                     min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                     n_jobs=None, oob_score=False, random_state=None, verbose=0,\n",
              "                     warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUVFWXM7IoIV"
      },
      "source": [
        "y_pred = extra_model.predict(X_teste)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rzQsJ02dIy6c",
        "outputId": "4ad5009d-ef08-41ae-9081-1da338053a3a"
      },
      "source": [
        "accuracy_score(y_teste, y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9953846153846154"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdetIoorJgD0"
      },
      "source": [
        "##Vamos olhar para dados individualmente!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "9Bu-LdygJtrA",
        "outputId": "118426e6-e194-4364-d600-d52189a6eef6"
      },
      "source": [
        "X_teste[400:403]"
      ],
      "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>fixed_acidity</th>\n",
              "      <th>volatile_acidity</th>\n",
              "      <th>citric_acid</th>\n",
              "      <th>residual_sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free_sulfur_dioxide</th>\n",
              "      <th>total_sulfur_dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1492</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.6</td>\n",
              "      <td>0.050</td>\n",
              "      <td>6.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.99348</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.54</td>\n",
              "      <td>11.95</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3593</th>\n",
              "      <td>7.0</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.33</td>\n",
              "      <td>14.6</td>\n",
              "      <td>0.043</td>\n",
              "      <td>47.0</td>\n",
              "      <td>168.0</td>\n",
              "      <td>0.99940</td>\n",
              "      <td>3.34</td>\n",
              "      <td>0.67</td>\n",
              "      <td>8.80</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6397</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.19</td>\n",
              "      <td>14.5</td>\n",
              "      <td>0.042</td>\n",
              "      <td>37.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>0.99684</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.43</td>\n",
              "      <td>10.30</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed_acidity  volatile_acidity  citric_acid  ...  sulphates  alcohol  quality\n",
              "1492            6.2              0.65         0.06  ...       0.54    11.95        5\n",
              "3593            7.0              0.28         0.33  ...       0.67     8.80        6\n",
              "6397            5.9              0.32         0.19  ...       0.43    10.30        5\n",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUfH2T6_J0xv",
        "outputId": "9aef7989-315e-4e55-aad3-16cd97411860"
      },
      "source": [
        "new_y_pred = extra_model.predict(X_teste[400:403])\n",
        "new_y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AToNNonTKCoL",
        "outputId": "bfcfc889-1da7-479c-d848-b907c4eed5a7"
      },
      "source": [
        "y_teste[400:403] # Previsões identicas aos dados reais de y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1492    0\n",
              "3593    1\n",
              "6397    1\n",
              "Name: style, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    }
  ]
}