PyLadies_Iris.ipynb

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  "metadata": {
    "colab": {
      "name": "PyLadies Iris.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RbNVcr9Lq_eg",
        "colab_type": "text"
      },
      "source": [
        "# **WORKSHOP INTRODUÇÃO À MACHINE LEARNING**\n",
        "\n",
        "**Autoria**: Lilianne M. I. Nakazono | **Organização**: Pyladies SP | **Local**: FIAP Paulista <br>\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iMLKrHGatX3a",
        "colab_type": "text"
      },
      "source": [
        "# 1. Bibliotecas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrkyfqNSuDBY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importando bibliotecas:\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import datasets\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "from sklearn import metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KTzCWgN3xRSu",
        "colab_type": "text"
      },
      "source": [
        "# 2. Funções"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQtLqVxRxQdI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Definindo funções:\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.3f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbBa12Zjxbc_",
        "colab_type": "text"
      },
      "source": [
        "# 3. Leitura de dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzphhyXPxuTw",
        "colab_type": "text"
      },
      "source": [
        "## Iris dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-MKvkurxf5b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#puxando o data set\n",
        "target = pd.DataFrame(datasets.load_iris().target, columns=['target'])\n",
        "iris = pd.DataFrame(datasets.load_iris().data, columns=datasets.load_iris().feature_names) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnbBAXPDzQfG",
        "colab_type": "text"
      },
      "source": [
        "# 4. Pré-análise dos dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgP_7e7lsZ62",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "target\n",
        "iris"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0RUcZpDsjXM",
        "colab_type": "code",
        "outputId": "cc00e77b-0b87-4e1b-d846-4baa76dbab11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#cabeçalho do dataset\n",
        "iris.head()\n",
        "target.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   target\n",
              "0       0\n",
              "1       0\n",
              "2       0\n",
              "3       0\n",
              "4       0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvQ1JTNSszlL",
        "colab_type": "code",
        "outputId": "5fb08c1b-9616-409e-b292-fcb54a951491",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "#nome das colunas\n",
        "list(iris)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sepal length (cm)',\n",
              " 'sepal width (cm)',\n",
              " 'petal length (cm)',\n",
              " 'petal width (cm)']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X1gNt2VytA2w",
        "colab_type": "code",
        "outputId": "2ecafa03-a161-41e6-981c-17f8ad9ac09d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#alterando nome das colunas\n",
        "iris.columns = ['sepal_lenght', 'sepal_width', 'petal_lenght', 'petal_width']\n",
        "iris.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .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_lenght</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_lenght</th>\n",
              "      <th>petal_width</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_lenght  sepal_width  petal_lenght  petal_width\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RREvHNNCtqY3",
        "colab_type": "code",
        "outputId": "caaa5953-f7d6-4ed1-a6a6-9d746222257a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "#tipagem das colunas\n",
        "iris.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sepal_lenght    float64\n",
              "sepal_width     float64\n",
              "petal_lenght    float64\n",
              "petal_width     float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "411cIiIst1TI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#checagem e agrupamento de dados faltantes\n",
        "iris.isna().sum()\n",
        "iris = iris.dropna()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kx5SX8PnulpU",
        "colab_type": "code",
        "outputId": "9bef0fb6-5c29-4dea-bb73-284dfe49065b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "#descrevendo quartis, media, count,desvio padrao, min, max do dataset\n",
        "iris.describe().T"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sepal_lenght</th>\n",
              "      <td>150.0</td>\n",
              "      <td>5.843333</td>\n",
              "      <td>0.828066</td>\n",
              "      <td>4.3</td>\n",
              "      <td>5.1</td>\n",
              "      <td>5.80</td>\n",
              "      <td>6.4</td>\n",
              "      <td>7.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sepal_width</th>\n",
              "      <td>150.0</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>3.00</td>\n",
              "      <td>3.3</td>\n",
              "      <td>4.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal_lenght</th>\n",
              "      <td>150.0</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4.35</td>\n",
              "      <td>5.1</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal_width</th>\n",
              "      <td>150.0</td>\n",
              "      <td>1.199333</td>\n",
              "      <td>0.762238</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.3</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              count      mean       std  min  25%   50%  75%  max\n",
              "sepal_lenght  150.0  5.843333  0.828066  4.3  5.1  5.80  6.4  7.9\n",
              "sepal_width   150.0  3.057333  0.435866  2.0  2.8  3.00  3.3  4.4\n",
              "petal_lenght  150.0  3.758000  1.765298  1.0  1.6  4.35  5.1  6.9\n",
              "petal_width   150.0  1.199333  0.762238  0.1  0.3  1.30  1.8  2.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r378rbuiu8Am",
        "colab_type": "code",
        "outputId": "79fb3094-09a0-4d03-c18e-a200daf38c53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#encontrando a media de uma coluna especifica\n",
        "iris.sepal_lenght.mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.843333333333335"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONIBvAKAvGWl",
        "colab_type": "code",
        "outputId": "d470bf6a-4e39-493f-9c5d-f9a36d866ae4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#similar ao iris.head(), selecionando as colunas por index\n",
        "iris.iloc[0:5, :]"
      ],
      "execution_count": 0,
      "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_lenght</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_lenght</th>\n",
              "      <th>petal_width</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_lenght  sepal_width  petal_lenght  petal_width\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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GalK-CBAvqki",
        "colab_type": "code",
        "outputId": "03564d32-0f32-429f-e397-1ef43dbf13b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "#histograma\n",
        "iris.hist(bins = 5, figsize=(10,6))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZmZlZQVxYmZmZmRXEhZWZmZlZQVxYmZmZmRXEhZWZmZlZQVxYmZmZmRXEhZWZ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\nhZWZmZlZQf4/FvQ3n0jlvKIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4b9by-m3w3Pw",
        "colab_type": "code",
        "outputId": "d4847f3b-cfe0-4e9f-b3af-3cfa7782cb5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 975
        }
      },
      "source": [
        "#começando a utilizar o seaborn, separando o data set por classe de atributo\n",
        "data = pd.concat([iris, target], axis=1)\n",
        "sns.pairplot(data, hue = 'target')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/nonparametric/kde.py:487: RuntimeWarning: invalid value encountered in true_divide\n",
            "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6h2qIIpeDqoYWlFJoWsa95BCyncg37ZoWnf7AW4v/skfxfGUqAWVg13RKP1yD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fC2jHnpNkysPBJlZ+wwg8nDJvOr53/Fx1s+LnVzSkJhSjKQDKiBaneDUjKFhaRuQ2FIIh2C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\nh0HR8eQ7AAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 942.375x900 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYz6ZPs03BGK",
        "colab_type": "code",
        "outputId": "811e32c9-e1a1-4485-a984-29da1ea2b8d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        }
      },
      "source": [
        "#plot de correlação\n",
        "sns.heatmap(iris.corr(), cmap='bwr', vmax=1, vmin=-1,center=0, square=True, annot=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb3ec1bdba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TphTpXJZo0CAS+UdSnpr0u8BZkt4BtiOqRwPXSHoLWAD0idqOA8ZIWkDotd4F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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WqrzadwuzCah",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#retirando dados (erros) da tabela\n",
        "iris_2 = iris.query('sepal_lenght>1 & sepal_width>1 & petal_lenght>1 & petal_width>1')\n",
        "print(iris_2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bohfpDIg0R22",
        "colab_type": "code",
        "outputId": "fb303e7d-8a94-4eea-f145-86bef8e01eb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137
        }
      },
      "source": [
        "#acessando dados por posição ou por linha\n",
        "iris_2.loc[50:52, :] #ou\n",
        "iris_2.iloc[0:3,] #acessando a mesma porção de dados"
      ],
      "execution_count": 0,
      "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_lenght</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_lenght</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.7</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>6.4</td>\n",
              "      <td>3.2</td>\n",
              "      <td>4.5</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>6.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.9</td>\n",
              "      <td>1.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sepal_lenght  sepal_width  petal_lenght  petal_width\n",
              "50           7.0          3.2           4.7          1.4\n",
              "51           6.4          3.2           4.5          1.5\n",
              "52           6.9          3.1           4.9          1.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKcBhFRz1ofW",
        "colab_type": "code",
        "outputId": "fc93353d-4e10-459a-cd0a-dec0230e05c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#filtrando colunas específicas e armazenando em uma nova tabela\n",
        "petal = iris[['petal_lenght', 'petal_width']]\n",
        "petal.head()"
      ],
      "execution_count": 0,
      "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>petal_lenght</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   petal_lenght  petal_width\n",
              "0           1.4          0.2\n",
              "1           1.4          0.2\n",
              "2           1.3          0.2\n",
              "3           1.5          0.2\n",
              "4           1.4          0.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRs5DCSD13rd",
        "colab_type": "code",
        "outputId": "fb2dc148-fb6c-46d3-fc56-3f924393caaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        }
      },
      "source": [
        "#correlação: variação entre -1 e +1, sendo -1 negativamente correlacionada e +1 positivamente correlacionadas\n",
        "iris.corr()"
      ],
      "execution_count": 0,
      "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_lenght</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_lenght</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>sepal_lenght</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.117570</td>\n",
              "      <td>0.871754</td>\n",
              "      <td>0.817941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sepal_width</th>\n",
              "      <td>-0.117570</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>-0.366126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal_lenght</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.428440</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>petal_width</th>\n",
              "      <td>0.817941</td>\n",
              "      <td>-0.366126</td>\n",
              "      <td>0.962865</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              sepal_lenght  sepal_width  petal_lenght  petal_width\n",
              "sepal_lenght      1.000000    -0.117570      0.871754     0.817941\n",
              "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
              "petal_lenght      0.871754    -0.428440      1.000000     0.962865\n",
              "petal_width       0.817941    -0.366126      0.962865     1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsAekj6cFXua",
        "colab_type": "code",
        "outputId": "af9bfd41-9252-48ba-f613-8f8ab297bd17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#holdout\n",
        "x_train, x_test, y_train, y_test = train_test_split(iris, target, test_size = 0.3, random_state = 2)\n",
        "#para a etapa de validação, basta repetir com as amostras de treino (x_train e y_train)\n"
      ],
      "execution_count": 0,
      "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_lenght</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_lenght</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\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>27</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_lenght  sepal_width  petal_lenght  petal_width\n",
              "134           6.1          2.6           5.6          1.4\n",
              "28            5.2          3.4           1.4          0.2\n",
              "65            6.7          3.1           4.4          1.4\n",
              "0             5.1          3.5           1.4          0.2\n",
              "27            5.2          3.5           1.5          0.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMpgps4WGYDd",
        "colab_type": "code",
        "outputId": "cd3a8685-4002-43bd-fdc8-7dc0d129c7a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "x_train.head()"
      ],
      "execution_count": 0,
      "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_lenght</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_lenght</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>6.1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.1</td>\n",
              "      <td>4.4</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\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>27</th>\n",
              "      <td>5.2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal_lenght  sepal_width  petal_lenght  petal_width\n",
              "134           6.1          2.6           5.6          1.4\n",
              "28            5.2          3.4           1.4          0.2\n",
              "65            6.7          3.1           4.4          1.4\n",
              "0             5.1          3.5           1.4          0.2\n",
              "27            5.2          3.5           1.5          0.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTFdM0FTHMmo",
        "colab_type": "code",
        "outputId": "176bc5be-1035-43cf-f89b-8fb252b1f346",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "#treinando o modelo\n",
        "clf = SVC(kernel='linear')\n",
        "#clf = RandomForestClassifier()\n",
        "clf.fit(x_train, y_train.values.ravel())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
              "    kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
              "    shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usgEE-hNHxgk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#predicao do modelo\n",
        "y_pred = clf.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQ_ET9LSH5Hu",
        "colab_type": "code",
        "outputId": "ed8bee1c-e4b1-4d6e-ffd8-0963e27b45bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "#plot matriz de confusão\n",
        "matrix = confusion_matrix(y_test, y_pred)\n",
        "fig = plot_confusion_matrix(matrix, classes = ['0', '1', '2'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5kVpSR0cHx3/pi1xz7Z+46965XDn1Mh6cO7fosAp38V8eZt/TbnjP/p/98T62+crVbPOV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\nzFqOE5uZtRwnNjNrOU5sZtZy/j8gMyXXax/EXgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ry7Ap5bGJXLQ",
        "colab_type": "code",
        "outputId": "ac444f83-7377-4e1b-abb3-b5af5ffa87c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "#precisão de cada classe do modelo\n",
        "print(metrics.classification_report(y_test, y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        17\n",
            "           1       1.00      1.00      1.00        15\n",
            "           2       1.00      1.00      1.00        13\n",
            "\n",
            "    accuracy                           1.00        45\n",
            "   macro avg       1.00      1.00      1.00        45\n",
            "weighted avg       1.00      1.00      1.00        45\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MA9ThZxKpuq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6z3E0BVF4MZ_",
        "colab_type": "text"
      },
      "source": [
        "Imprimir tabela: \n",
        "\n",
        "> `print(iris)`\n",
        "\n",
        "Contagem de linhas para um certo atributo:\n",
        "\n",
        "> `iris.atributo.value_counts()`\n",
        "\n",
        "\n",
        "Tipo dos atributos:\n",
        "\n",
        "> `iris.dtypes`\n",
        "\n",
        "Nome dos atributos (colunas):\n",
        "\n",
        "> `list(iris)`\n",
        "\n",
        "Estatísticas:\n",
        "\n",
        "> `iris.describe().T`\n",
        "\n",
        "Média de um atributo:\n",
        "\n",
        "> `iris.atributo.mean()`\n",
        "\n",
        "Desvio padrão de um atributo:\n",
        "\n",
        "> `iris.atributo.std()`\n",
        "\n",
        "Concatenar linhas/colunas:\n",
        "\n",
        "> `iris.concat()`\n",
        "\n",
        "Checar se tem NaNs:\n",
        "\n",
        "> `iris.isna().sum()`\n",
        "\n",
        "Remover NaNs:\n",
        "\n",
        "> `iris.dropna()`\n",
        "\n",
        "Acessar todas as colunas da linha em posição i: \n",
        "\n",
        "> `iris.iloc[i,:]`\n",
        "\n",
        "Acessar todas as colunas da linha de índice i:\n",
        "\n",
        "> `iris.loc[i,:]`\n",
        "\n",
        "Filtrando tabela:\n",
        "\n",
        "> `iris.query('condição1 & condição2')`\n",
        "\n",
        "\n",
        "> `iris[(condição1) & (condição2)]`\n",
        "\n",
        "Histograma:\n",
        "\n",
        "> `iris.hist()`\n",
        "\n",
        "Pairplot: \n",
        "\n",
        "> `sns.pairplot(data, hue=\"target\")` <br> # data contém uma coluna chamada target com as classes\n",
        "\n",
        "Matriz de correlação:\n",
        "\n",
        "> `iris.corr()`\n",
        "\n",
        "Visualização matriz de correlação:\n",
        "> `sns.heatmap(iris.corr(), cmap='bwr', vmax=1, vmin=-1,center=0, square=True, annot=True)`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DE3Vmm4dzi3Q",
        "colab_type": "text"
      },
      "source": [
        "# 5. Amostragem para validação cruzada"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vSP94CDU8q55",
        "colab_type": "text"
      },
      "source": [
        "Criando amostra de treinamento (70%) e de teste (30%):\n",
        "> `X_train, X_test, y_train, y_test = train_test_split(iris, target,  test_size=0.3, random_state=42)`\n",
        "\n",
        "Para fazer uma amostragem estratificada, adicionar o parâmetro:\n",
        "\n",
        "> `stratify = target`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V1iXrDUi0D0_",
        "colab_type": "text"
      },
      "source": [
        "# 6. Validação de modelo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3A5pG0m69yT_",
        "colab_type": "text"
      },
      "source": [
        "## Algoritmos:\n",
        "\n",
        "Eu recomendo fortemente que vocês procurem a documentação de cada algoritmo e deem uma lida nos parâmetros pedidos de cada um! \n",
        "\n",
        "k-Nearest Neighbors: \n",
        "\n",
        "> `clf = KNeighborsClassifier()`\n",
        "\n",
        "Support Vector Machine:\n",
        "\n",
        "> `clf = SVC()`\n",
        "\n",
        "Decision Tree:\n",
        "\n",
        "> `clf = DecisionTreeClassifier()`\n",
        "\n",
        "Random Forest: \n",
        "\n",
        "> `clf = RandomForestClassifier()`\n",
        "\n",
        "## Treinamento do modelo:\n",
        "\n",
        "> `clf.fit(X_train, y_train.values.ravel())`\n",
        "\n",
        "## Teste:\n",
        "\n",
        "> `y_pred = clf.predict(X_test)`\n",
        "\n",
        "## Matriz de confusão: \n",
        "> `matrix = confusion_matrix(y_test, y_pred)`\n",
        "\n",
        "> `fig = plot_confusion_matrix(matrix, classes=['0','1', '2'])`\n",
        "\n",
        "## Métricas de performance:\n",
        "\n",
        "> `metrics.classification_report(y_test, y_pred)`\n"
      ]
    }
  ]
}