machine_learning_programacao_dinamica.ipynb

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  "metadata": {
    "colab": {
      "name": "machine_learning-programacao_dinamica.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyONDtbuw63PxG9jmNQaawEu"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9VbNWvr7wH76",
        "colab_type": "text"
      },
      "source": [
        "# Primeiros passos no Scikit-Learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6s8wFIo8wZgX",
        "colab_type": "text"
      },
      "source": [
        "Exemplo dataset iris"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RzW4B2rDv00I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_iris"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDbne29swz2R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# carregar o dataset para a variável data\n",
        "iris = load_iris()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQMq9J9TxBhY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f7012513-35e2-491f-ecd1-9743d5d76fe2"
      },
      "source": [
        "# listagem dos nomes dos alvos\n",
        "list(iris.target_names)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['setosa', 'versicolor', 'virginica']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cy_2_0cIxHdY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1f409eb9-a081-4b8b-87b6-a35f6cde3c33"
      },
      "source": [
        "iris.target[[10, 25, 50]]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyH6f788yUOw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X, y = load_iris(return_X_y=True)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5BzKRJcyn0f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "923812d2-2742-4629-ab93-c49bd163e98b"
      },
      "source": [
        "X"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.1, 3.5, 1.4, 0.2],\n",
              "       [4.9, 3. , 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.3, 0.2],\n",
              "       [4.6, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.6, 1.4, 0.2],\n",
              "       [5.4, 3.9, 1.7, 0.4],\n",
              "       [4.6, 3.4, 1.4, 0.3],\n",
              "       [5. , 3.4, 1.5, 0.2],\n",
              "       [4.4, 2.9, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.1],\n",
              "       [5.4, 3.7, 1.5, 0.2],\n",
              "       [4.8, 3.4, 1.6, 0.2],\n",
              "       [4.8, 3. , 1.4, 0.1],\n",
              "       [4.3, 3. , 1.1, 0.1],\n",
              "       [5.8, 4. , 1.2, 0.2],\n",
              "       [5.7, 4.4, 1.5, 0.4],\n",
              "       [5.4, 3.9, 1.3, 0.4],\n",
              "       [5.1, 3.5, 1.4, 0.3],\n",
              "       [5.7, 3.8, 1.7, 0.3],\n",
              "       [5.1, 3.8, 1.5, 0.3],\n",
              "       [5.4, 3.4, 1.7, 0.2],\n",
              "       [5.1, 3.7, 1.5, 0.4],\n",
              "       [4.6, 3.6, 1. , 0.2],\n",
              "       [5.1, 3.3, 1.7, 0.5],\n",
              "       [4.8, 3.4, 1.9, 0.2],\n",
              "       [5. , 3. , 1.6, 0.2],\n",
              "       [5. , 3.4, 1.6, 0.4],\n",
              "       [5.2, 3.5, 1.5, 0.2],\n",
              "       [5.2, 3.4, 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.6, 0.2],\n",
              "       [4.8, 3.1, 1.6, 0.2],\n",
              "       [5.4, 3.4, 1.5, 0.4],\n",
              "       [5.2, 4.1, 1.5, 0.1],\n",
              "       [5.5, 4.2, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.2],\n",
              "       [5. , 3.2, 1.2, 0.2],\n",
              "       [5.5, 3.5, 1.3, 0.2],\n",
              "       [4.9, 3.6, 1.4, 0.1],\n",
              "       [4.4, 3. , 1.3, 0.2],\n",
              "       [5.1, 3.4, 1.5, 0.2],\n",
              "       [5. , 3.5, 1.3, 0.3],\n",
              "       [4.5, 2.3, 1.3, 0.3],\n",
              "       [4.4, 3.2, 1.3, 0.2],\n",
              "       [5. , 3.5, 1.6, 0.6],\n",
              "       [5.1, 3.8, 1.9, 0.4],\n",
              "       [4.8, 3. , 1.4, 0.3],\n",
              "       [5.1, 3.8, 1.6, 0.2],\n",
              "       [4.6, 3.2, 1.4, 0.2],\n",
              "       [5.3, 3.7, 1.5, 0.2],\n",
              "       [5. , 3.3, 1.4, 0.2],\n",
              "       [7. , 3.2, 4.7, 1.4],\n",
              "       [6.4, 3.2, 4.5, 1.5],\n",
              "       [6.9, 3.1, 4.9, 1.5],\n",
              "       [5.5, 2.3, 4. , 1.3],\n",
              "       [6.5, 2.8, 4.6, 1.5],\n",
              "       [5.7, 2.8, 4.5, 1.3],\n",
              "       [6.3, 3.3, 4.7, 1.6],\n",
              "       [4.9, 2.4, 3.3, 1. ],\n",
              "       [6.6, 2.9, 4.6, 1.3],\n",
              "       [5.2, 2.7, 3.9, 1.4],\n",
              "       [5. , 2. , 3.5, 1. ],\n",
              "       [5.9, 3. , 4.2, 1.5],\n",
              "       [6. , 2.2, 4. , 1. ],\n",
              "       [6.1, 2.9, 4.7, 1.4],\n",
              "       [5.6, 2.9, 3.6, 1.3],\n",
              "       [6.7, 3.1, 4.4, 1.4],\n",
              "       [5.6, 3. , 4.5, 1.5],\n",
              "       [5.8, 2.7, 4.1, 1. ],\n",
              "       [6.2, 2.2, 4.5, 1.5],\n",
              "       [5.6, 2.5, 3.9, 1.1],\n",
              "       [5.9, 3.2, 4.8, 1.8],\n",
              "       [6.1, 2.8, 4. , 1.3],\n",
              "       [6.3, 2.5, 4.9, 1.5],\n",
              "       [6.1, 2.8, 4.7, 1.2],\n",
              "       [6.4, 2.9, 4.3, 1.3],\n",
              "       [6.6, 3. , 4.4, 1.4],\n",
              "       [6.8, 2.8, 4.8, 1.4],\n",
              "       [6.7, 3. , 5. , 1.7],\n",
              "       [6. , 2.9, 4.5, 1.5],\n",
              "       [5.7, 2.6, 3.5, 1. ],\n",
              "       [5.5, 2.4, 3.8, 1.1],\n",
              "       [5.5, 2.4, 3.7, 1. ],\n",
              "       [5.8, 2.7, 3.9, 1.2],\n",
              "       [6. , 2.7, 5.1, 1.6],\n",
              "       [5.4, 3. , 4.5, 1.5],\n",
              "       [6. , 3.4, 4.5, 1.6],\n",
              "       [6.7, 3.1, 4.7, 1.5],\n",
              "       [6.3, 2.3, 4.4, 1.3],\n",
              "       [5.6, 3. , 4.1, 1.3],\n",
              "       [5.5, 2.5, 4. , 1.3],\n",
              "       [5.5, 2.6, 4.4, 1.2],\n",
              "       [6.1, 3. , 4.6, 1.4],\n",
              "       [5.8, 2.6, 4. , 1.2],\n",
              "       [5. , 2.3, 3.3, 1. ],\n",
              "       [5.6, 2.7, 4.2, 1.3],\n",
              "       [5.7, 3. , 4.2, 1.2],\n",
              "       [5.7, 2.9, 4.2, 1.3],\n",
              "       [6.2, 2.9, 4.3, 1.3],\n",
              "       [5.1, 2.5, 3. , 1.1],\n",
              "       [5.7, 2.8, 4.1, 1.3],\n",
              "       [6.3, 3.3, 6. , 2.5],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [7.1, 3. , 5.9, 2.1],\n",
              "       [6.3, 2.9, 5.6, 1.8],\n",
              "       [6.5, 3. , 5.8, 2.2],\n",
              "       [7.6, 3. , 6.6, 2.1],\n",
              "       [4.9, 2.5, 4.5, 1.7],\n",
              "       [7.3, 2.9, 6.3, 1.8],\n",
              "       [6.7, 2.5, 5.8, 1.8],\n",
              "       [7.2, 3.6, 6.1, 2.5],\n",
              "       [6.5, 3.2, 5.1, 2. ],\n",
              "       [6.4, 2.7, 5.3, 1.9],\n",
              "       [6.8, 3. , 5.5, 2.1],\n",
              "       [5.7, 2.5, 5. , 2. ],\n",
              "       [5.8, 2.8, 5.1, 2.4],\n",
              "       [6.4, 3.2, 5.3, 2.3],\n",
              "       [6.5, 3. , 5.5, 1.8],\n",
              "       [7.7, 3.8, 6.7, 2.2],\n",
              "       [7.7, 2.6, 6.9, 2.3],\n",
              "       [6. , 2.2, 5. , 1.5],\n",
              "       [6.9, 3.2, 5.7, 2.3],\n",
              "       [5.6, 2.8, 4.9, 2. ],\n",
              "       [7.7, 2.8, 6.7, 2. ],\n",
              "       [6.3, 2.7, 4.9, 1.8],\n",
              "       [6.7, 3.3, 5.7, 2.1],\n",
              "       [7.2, 3.2, 6. , 1.8],\n",
              "       [6.2, 2.8, 4.8, 1.8],\n",
              "       [6.1, 3. , 4.9, 1.8],\n",
              "       [6.4, 2.8, 5.6, 2.1],\n",
              "       [7.2, 3. , 5.8, 1.6],\n",
              "       [7.4, 2.8, 6.1, 1.9],\n",
              "       [7.9, 3.8, 6.4, 2. ],\n",
              "       [6.4, 2.8, 5.6, 2.2],\n",
              "       [6.3, 2.8, 5.1, 1.5],\n",
              "       [6.1, 2.6, 5.6, 1.4],\n",
              "       [7.7, 3. , 6.1, 2.3],\n",
              "       [6.3, 3.4, 5.6, 2.4],\n",
              "       [6.4, 3.1, 5.5, 1.8],\n",
              "       [6. , 3. , 4.8, 1.8],\n",
              "       [6.9, 3.1, 5.4, 2.1],\n",
              "       [6.7, 3.1, 5.6, 2.4],\n",
              "       [6.9, 3.1, 5.1, 2.3],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [6.8, 3.2, 5.9, 2.3],\n",
              "       [6.7, 3.3, 5.7, 2.5],\n",
              "       [6.7, 3. , 5.2, 2.3],\n",
              "       [6.3, 2.5, 5. , 1.9],\n",
              "       [6.5, 3. , 5.2, 2. ],\n",
              "       [6.2, 3.4, 5.4, 2.3],\n",
              "       [5.9, 3. , 5.1, 1.8]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_m7bqHYy6Lg",
        "colab_type": "text"
      },
      "source": [
        "## Passando a notação do scikit learn para um dataframe do pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byUiWBvzypZX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3_jtPmdzGqY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rWNTrgXzPrY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "ca4b2337-7d7d-42d4-aed1-1a67aa7d99c4"
      },
      "source": [
        "iris_df"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                  5.1               3.5                1.4               0.2\n",
              "1                  4.9               3.0                1.4               0.2\n",
              "2                  4.7               3.2                1.3               0.2\n",
              "3                  4.6               3.1                1.5               0.2\n",
              "4                  5.0               3.6                1.4               0.2\n",
              "..                 ...               ...                ...               ...\n",
              "145                6.7               3.0                5.2               2.3\n",
              "146                6.3               2.5                5.0               1.9\n",
              "147                6.5               3.0                5.2               2.0\n",
              "148                6.2               3.4                5.4               2.3\n",
              "149                5.9               3.0                5.1               1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2K6O007zQ2X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# adicionando uma coluna para os targets\n",
        "iris_df['target'] = iris.target"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "623UO0JYzli3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "862568f6-ef67-4b4d-ca00-1805c313cb38"
      },
      "source": [
        "iris_df.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  ...  petal width (cm)  target\n",
              "0                5.1               3.5  ...               0.2       0\n",
              "1                4.9               3.0  ...               0.2       0\n",
              "2                4.7               3.2  ...               0.2       0\n",
              "3                4.6               3.1  ...               0.2       0\n",
              "4                5.0               3.6  ...               0.2       0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Kz4P3AiznLw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# adicionando o nome dos targets - utilizando a classe Categorical do pacote pandas\n",
        "iris_df['target names'] = pd.Categorical.from_codes(iris.target, iris.target_names)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlJMmZwP0L7g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "255e7b61-9af6-4d97-a923-0eff7218be7a"
      },
      "source": [
        "iris_df.head()"
      ],
      "execution_count": 18,
      "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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>target</th>\n",
              "      <th>target names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  ...  target  target names\n",
              "0                5.1               3.5  ...       0        setosa\n",
              "1                4.9               3.0  ...       0        setosa\n",
              "2                4.7               3.2  ...       0        setosa\n",
              "3                4.6               3.1  ...       0        setosa\n",
              "4                5.0               3.6  ...       0        setosa\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSGFfIA40Ndv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "8fb00182-fb70-4e93-8423-822c80ba8713"
      },
      "source": [
        "# visualizar o dataframe\n",
        "%matplotlib inline\n",
        "iris_df.plot.scatter('sepal length (cm)', 'sepal width (cm)', c='target')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f68a5b8de48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "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": {
        "id": "zIDBJeTB0UVY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# divisão de todo conjunto entre teste e treinamento\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VswoLCqn1-UP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7bab559b-0add-4925-ee76-b020acb0b3f7"
      },
      "source": [
        "# forma dos dados de treinamento e de teste\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(105, 4)\n",
            "(45, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRFa9ed52IHg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}