Classificação_de_Animais_cachorro_porco.ipynb

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  "metadata": {
    "colab": {
      "name": "Classificação_de_Animais_cachorro_porco.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/OseiasBeu/Data-Science/blob/master/Classifica%C3%A7%C3%A3o_de_Animais_cachorro_porco.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "iN35zFmNyYIc"
      },
      "cell_type": "code",
      "source": [
        "# features (1 sim, 0 não)\n",
        "# pelo longo?\n",
        "# perna curta?\n",
        "# faz auau?\n",
        "porco1 = [0, 1, 0]\n",
        "porco2 = [0, 1, 1]\n",
        "porco3 = [1, 1, 0]\n",
        "\n",
        "cachorro1 = [0, 1, 1]\n",
        "cachorro2 = [1, 0, 1]\n",
        "cachorro3 = [1, 1, 1]\n",
        "\n",
        "# 1 => porco, 0 => cachorro\n",
        "treino_x = [porco1, porco2, porco3, cachorro1, cachorro2, cachorro3]\n",
        "treino_y = [1,1,1,0,0,0] # labels / etiqueta"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tcWrSPHkzaby",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "0675eaa5-68bd-4df2-cafe-a3d94a9fcec9"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "model = LinearSVC()\n",
        "model.fit(treino_x, treino_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
              "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
              "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
              "     verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "YaUuFWOx0YZF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "defc66aa-ce1b-460c-895c-e422ffe287c5"
      },
      "cell_type": "code",
      "source": [
        "animal_misterioso = [1,1,1]\n",
        "model.predict([animal_misterioso])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "yJEFM8mx0jtR"
      },
      "cell_type": "code",
      "source": [
        "misterio1 = [1,1,1]\n",
        "misterio2 = [1,1,0]\n",
        "misterio3 = [0,1,1]\n",
        "\n",
        "teste_x = [misterio1, misterio2, misterio3]\n",
        "teste_y = [0, 1, 1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3xOA4L4e03GO"
      },
      "cell_type": "code",
      "source": [
        "previsoes = model.predict(teste_x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cUaaDQol1b_D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4cd12076-c2e0-433c-b023-b0c3936b5b36"
      },
      "cell_type": "code",
      "source": [
        "corretos = (previsoes == teste_y).sum()\n",
        "total = len(teste_x)\n",
        "taxa_de_acerto = corretos/total\n",
        "print(\"Taxa de acerto %.2f\" % (taxa_de_acerto * 100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Taxa de acerto 66.67\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "T6zhFhQa1c28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "29ed71f9-5a9d-4de3-84f5-825121a92a0b"
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "taxa_de_acerto = accuracy_score(teste_y, previsoes)\n",
        "print(\"Taxa de acerto %.2f\" % (taxa_de_acerto * 100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Taxa de acerto 66.67\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Z9wOaemi2fzE"
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}