Atividade_5_IA.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "### Exercício 1 - Dataset \"car_crashes\" do seaborn"
      ],
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        "id": "-TT2oycPuHXR"
      }
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      "source": [
        "# 1) Carregar o dataset \"car_crashes\"do seaborn\n",
        "# 2) Dropar a coluna \"abbrev\"\n",
        "# 3) Separar o dataframe, deixando a coluna 'total' para o target (coluna objetivo - y) e o restante para o X\n",
        "# 4) Normalizar todo o X\n",
        "\n",
        "# df = X.values\n",
        "# min_max_scaler = preprocessing.MinMaxScaler()\n",
        "# x_scaled = min_max_scaler.fit_transform(df)\n",
        "# X = pd.DataFrame(x_scaled)\n",
        "\n",
        "# 5) Separar os dados em treinamento e teste\n",
        "# 6) Treinar um modelo linear\n",
        "# 7) Treinar uma árvores de regressão\n",
        "# 8) Treinar um KNN para regressão\n",
        "# 9) Apresentar os resultados dos erros usando: MSE, MAE e RMSE para os 3 modelos"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Resposta de 1), 2), 3) e 4)\n",
        "\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "dataset=sns.load_dataset('car_crashes')\n",
        "\n",
        "dataset=dataset.drop('abbrev', axis=1)\n",
        "\n",
        "y=dataset['total']\n",
        "X=dataset.drop('total', axis=1)\n",
        "\n",
        "X['speeding']=MinMaxScaler().fit_transform(np.array(X['speeding']).reshape(-1,1))\n",
        "X['alcohol']=MinMaxScaler().fit_transform(np.array(X['alcohol']).reshape(-1,1))\n",
        "X['not_distracted']=MinMaxScaler().fit_transform(np.array(X['not_distracted']).reshape(-1,1))\n",
        "X['no_previous']=MinMaxScaler().fit_transform(np.array(X['no_previous']).reshape(-1,1))\n",
        "X['ins_premium']=MinMaxScaler().fit_transform(np.array(X['ins_premium']).reshape(-1,1))\n",
        "X['ins_losses']=MinMaxScaler().fit_transform(np.array(X['ins_losses']).reshape(-1,1))"
      ],
      "metadata": {
        "id": "-cN6JaL6uUBg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Resposta de 5)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_treino, X_teste, y_treino, y_teste = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=None)"
      ],
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      "outputs": []
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      "cell_type": "code",
      "source": [
        "#Resposta de 6), 7) e 8)\n",
        "\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "\n",
        "regressor1=LinearRegression()\n",
        "regressor1.fit(X_treino,y_treino)\n",
        "\n",
        "regressor2=DecisionTreeRegressor(criterion='squared_error', max_depth=2)\n",
        "regressor2.fit(X_treino,y_treino)\n",
        "\n",
        "regressor3=KNeighborsRegressor(n_neighbors=5, metric='euclidean', algorithm='brute')\n",
        "regressor3.fit(X_treino, y_treino)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
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        "id": "PTP6YGbp1aOl",
        "outputId": "bab16a5c-0f39-451b-a39b-f45e6a0b1bb7"
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              "KNeighborsRegressor(algorithm='brute', metric='euclidean', n_neighbors=10)"
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            ]
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      ]
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      "cell_type": "code",
      "source": [
        "#Resposta de 9)\n",
        "\n",
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "\n",
        "print(\"Predições do Regressor Linear:\")\n",
        "resultados1=regressor1.predict(X_teste)\n",
        "print(\"MSE = %.2f\" %metrics.mean_squared_error(y_teste,resultados1))\n",
        "print(\"MAE = %.2f\" %metrics.mean_absolute_error(y_teste,resultados1))\n",
        "print(\"RMSE = %.2f\" %np.sqrt(metrics.mean_squared_error(y_teste,resultados1)))\n",
        "\n",
        "print('\\n')\n",
        "print(\"Predições da Arvore de Regreção:\")\n",
        "resultados2=regressor2.predict(X_teste)\n",
        "print(\"MSE = %.2f\" %metrics.mean_squared_error(y_teste,resultados2))\n",
        "print(\"MAE = %.2f\" %metrics.mean_absolute_error(y_teste,resultados2))\n",
        "print(\"RMSE = %.2f\" %np.sqrt(metrics.mean_squared_error(y_teste,resultados2)))\n",
        "\n",
        "print('\\n')\n",
        "print(\"Predições do 5-NN Regressor:\")\n",
        "resultados3=regressor3.predict(X_teste)\n",
        "print(\"MSE = %.2f\" %metrics.mean_squared_error(y_teste,resultados3))\n",
        "print(\"MAE = %.2f\" %metrics.mean_absolute_error(y_teste,resultados3))\n",
        "print(\"RMSE = %.2f\" %np.sqrt(metrics.mean_squared_error(y_teste,resultados3)))"
      ],
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        "colab": {
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      },
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          "text": [
            "Predições do Regressor Linear:\n",
            "MSE = 0.62\n",
            "MAE = 0.61\n",
            "RMSE = 0.78\n",
            "\n",
            "\n",
            "Predições da Arvore de Regreção:\n",
            "MSE = 2.27\n",
            "MAE = 1.36\n",
            "RMSE = 1.51\n",
            "\n",
            "\n",
            "Predições do 5-NN Regressor:\n",
            "MSE = 3.90\n",
            "MAE = 1.60\n",
            "RMSE = 1.97\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exercício 2 - Dataset Parkinsons (dataset com 2 classes)"
      ],
      "metadata": {
        "id": "CePRuTlSNTfL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) Carregar o dataset \"parkinsons\" pela URL abaixo:\n",
        "\n",
        "# url = \"https://raw.githubusercontent.com/tmoura/machinelearning/master/parkinsons.data\"\n",
        "\n",
        "# 2) Separar os dados y e X. A coluna y é a coluna 0 (zero)\n",
        "# 3) Normalizar todas as colunas de X (usando o mesmo código da questão anterior)\n",
        "# 4) Separar oa dados em treinamento e teste\n",
        "# 5) Treinar um modelo de regressão logística\n",
        "# 6) Treinar uma árvore de decisão\n",
        "# 7) Treinar um KNN para classificação\n",
        "# 8) Apresentar os resultados de acurácia em percentual"
      ],
      "metadata": {
        "id": "FTjCyrDNNHmd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resposta de 1), 2), 3)\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "url = \"https://raw.githubusercontent.com/tmoura/machinelearning/master/parkinsons.data\"\n",
        "dataset=pd.read_csv(url, header=None)\n",
        "\n",
        "colunas=len(dataset.columns)\n",
        "y=dataset[0]\n",
        "X=dataset.loc[:,1:colunas-1]\n",
        "\n",
        "#codigo mágico que normaliza todas as colunas bem rápido\n",
        "df=X.values\n",
        "min_max_scaler=MinMaxScaler()\n",
        "x_scale=min_max_scaler.fit_transform(df)\n",
        "X=pd.DataFrame(data=x_scale)"
      ],
      "metadata": {
        "id": "Jgxgi_44NaNT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resposta de 4)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_treino, X_teste, y_treino, y_teste = train_test_split(X, y, train_size=0.8, test_size=0.2, stratify=y)"
      ],
      "metadata": {
        "id": "POK6PmM8TpRU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resposta de 5), 6) e 7)\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "modelo1=LogisticRegression()\n",
        "modelo1.fit(X_treino,y_treino)\n",
        "\n",
        "modelo2=DecisionTreeClassifier()\n",
        "modelo2.fit(X_treino,y_treino)\n",
        "\n",
        "modelo3=KNeighborsClassifier()\n",
        "modelo3.fit(X_treino,y_treino)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "tKCxjzmcUe0_",
        "outputId": "c7d1d300-dfd7-4994-d33c-3efbead2e9ba"
      },
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              "KNeighborsClassifier()"
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            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Resposta de 8)\n",
        "\n",
        "from sklearn import metrics\n",
        "\n",
        "def acuracia(modelo):\n",
        "  resultados=modelo.predict(X_teste)\n",
        "  acertos=metrics.accuracy_score(resultados,y_teste)\n",
        "  return round(acertos*100)\n",
        "\n",
        "print(\"{}% de acertos da Regreção Logistica\".format(acuracia(modelo1)))\n",
        "print(\"{}% de acertos da Arvore de Decisão\".format(acuracia(modelo2)))\n",
        "print(\"{}% de acertos do 5-NN\".format(acuracia(modelo3)))"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "6d370c75-dfa9-480d-9461-6b92b7ac816e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "87% de acertos da Regreção Logistica\n",
            "92% de acertos da Arvore de Decisão\n",
            "87% de acertos do 5-NN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exercício 3 - Dataset attention do seaborn"
      ],
      "metadata": {
        "id": "Z4GfajjPaWLL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1) Carregar o dataset \"attention\" do seaborn\n",
        "# 2) Fazer um LabelEncoder na coluna \"attention\"\n",
        "# 3) Separar o dataframe, deixando a coluna 'attention' para o target (coluna objetivo - y) e as colunas \"solutions\" e \"score\" para o X\n",
        "# 4) Normalizar todo o X usando o mesmo código da questão 1\n",
        "# 5) Separar os dados em treinamento e teste\n",
        "# 6) Treinar um modelo de regressão logística\n",
        "# 7) Treinar uma árvore de decisão\n",
        "# 8) Treinar um KNN para classificação\n",
        "# 9) Apresentar os resultados de acurárica em percentual"
      ],
      "metadata": {
        "id": "wy_vneJzaVT0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resposta de 1), 2), 3) e 4)\n",
        "\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "dataset=sns.load_dataset(\"attention\")\n",
        "\n",
        "dataset['attention']=LabelEncoder().fit_transform(dataset['attention'])\n",
        "\n",
        "y=dataset['attention']\n",
        "X=dataset[['solutions','score']]\n",
        "\n",
        "#Codigo mágico que normaliza tudo\n",
        "df=X.values\n",
        "x_scale=MinMaxScaler().fit_transform(df)\n",
        "X=pd.DataFrame(data=x_scale)"
      ],
      "metadata": {
        "id": "tFep-743cMdH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resposta de 5)\n",
        "\n",
        "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=0.2, stratify=y)"
      ],
      "metadata": {
        "id": "kHMnDOkHgFLF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resposta de 6), 7) e 8)\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "modelo1=LogisticRegression()\n",
        "modelo1.fit(X_treino,y_treino)\n",
        "\n",
        "modelo2=DecisionTreeClassifier()\n",
        "modelo2.fit(X_treino, y_treino)\n",
        "\n",
        "modelo3=KNeighborsClassifier()\n",
        "modelo3.fit(X_treino,y_treino)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "vZGDkeM_gf1K",
        "outputId": "36eae153-7971-4a5f-b479-af2b6dfe468e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Resposta de 9)\n",
        "from sklearn import metrics\n",
        "\n",
        "def percentual_acuracia(modelo):\n",
        "  resultados=modelo.predict(X_teste)\n",
        "  acertos=metrics.accuracy_score(resultados,y_teste)\n",
        "  return round(acertos*100)\n",
        "\n",
        "print(\"{}% de acertos da Regreção Logistica\".format(percentual_acuracia(modelo1)))\n",
        "print(\"{}% de acertos da Arvore de Classificação\".format(percentual_acuracia(modelo2)))\n",
        "print(\"{}% de acertos do Classificador 5-NN\".format(percentual_acuracia(modelo3)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "87nHUfcshu7w",
        "outputId": "a3300088-fda1-443a-df9e-9198053494c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75% de acertos da Regreção Logistica\n",
            "67% de acertos da Arvore de Classificação\n",
            "75% de acertos do Classificador 5-NN\n"
          ]
        }
      ]
    }
  ]
}