MachineLearning2.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "uri = 'https://gist.githubusercontent.com/guilhermesilveira/4d1d4a16ccbf6ea4e0a64a38a24ec884/raw/afd05cb0c796d18f3f5a6537053ded308ba94bf7/car-prices.csv'\n",
        "dados = pd.read_csv(uri)"
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        "dados.head()"
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              "   Unnamed: 0  mileage_per_year  model_year      price sold\n",
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        "nome_traduzido = {\n",
        "    \"mileage_per_year\" : \"milhas_por_ano\",\n",
        "    \"model_year\" : \"ano_do_modelo\",\n",
        "    \"price\" : \"preco\",\n",
        "    \"sold\" : \"vendido\"\n",
        "}\n",
        "dados = dados.rename(columns= nome_traduzido)"
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        "a_trocar = {\n",
        "    'no': 0,\n",
        "    'yes': 1\n",
        "}\n",
        "dados.vendido = dados.vendido.map(a_trocar)\n",
        "dados.head()"
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              "   Unnamed: 0  milhas_por_ano  ano_do_modelo      preco  vendido\n",
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        "from datetime import datetime\n",
        "\n",
        "ano_atual = datetime.today().year\n",
        "dados['idade_do_modelo'] = ano_atual - dados.ano_do_modelo\n",
        "dados ['km_por_ano'] = dados.milhas_por_ano * 1.60934\n",
        "dados.head()\n"
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              "   Unnamed: 0  milhas_por_ano  ano_do_modelo      preco  vendido  \\\n",
              "0           0           21801           2000   30941.02        1   \n",
              "1           1            7843           1998   40557.96        1   \n",
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              "4           4            7935           2014  117384.68        1   \n",
              "\n",
              "   idade_do_modelo   km_por_ano  \n",
              "0               23  35085.22134  \n",
              "1               25  12622.05362  \n",
              "2               17  11440.79806  \n",
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              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>7935</td>\n",
              "      <td>2014</td>\n",
              "      <td>117384.68</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>12770.11290</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          const element = document.querySelector('#df-e7c08ce9-ba83-41aa-af62-ac40f421352b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados ['km_por_ano'] = dados.km_por_ano * 1.60934\n",
        "dados = dados.drop(columns = [\"Unnamed: 0\", \"milhas_por_ano\", \"ano_do_modelo\"], axis=1)\n",
        "\n",
        "dados.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "by9sIwj5qkXX",
        "outputId": "10e35938-5d17-454f-f025-6e16474b3943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       preco  vendido  idade_do_modelo    km_por_ano\n",
              "0   30941.02        1               23  56464.050111\n",
              "1   40557.96        1               25  20313.175773\n",
              "2   89627.50        0               17  18412.133950\n",
              "3   95276.14        0                8  69470.905744\n",
              "4  117384.68        1                9  20551.453494"
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              "      <th>preco</th>\n",
              "      <th>vendido</th>\n",
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              "          const element = document.querySelector('#df-885771cc-05b3-4c04-b179-47989ff5ea49');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = dados[[\"preco\", \"idade_do_modelo\", \"km_por_ano\"]]\n",
        "y = dados[\"vendido\"]"
      ],
      "metadata": {
        "id": "aIPMP7b1r10Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 5\n",
        "np.random.seed(SEED)\n",
        "treino_x, teste_x, treino_y, teste_y = train_test_split(x, y, test_size = 0.25,\n",
        "                                                         stratify = y)\n",
        "print(\"Treinaremos com %d elementos e testaremos com %d elementos\" % (len(treino_x), len(teste_x)))\n",
        "\n",
        "modelo = LinearSVC()\n",
        "modelo.fit(treino_x, treino_y)\n",
        "previsoes = modelo.predict(teste_x)\n",
        "\n",
        "acuracia = accuracy_score(teste_y, previsoes) * 100\n",
        "print(\"A acurácia foi %.2f%%\" % acuracia)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y14TbVlk80xC",
        "outputId": "7b679129-b8cf-4342-9bdd-9bcb2ace1474"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Treinaremos com 7500 elementos e testaremos com 2500 elementos\n",
            "A acurácia foi 57.96%\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.dummy import DummyClassifier"
      ],
      "metadata": {
        "id": "b61InU4R9z6Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dummy = DummyClassifier(strategy='stratified')\n",
        "dummy.fit(treino_x, treino_y)\n",
        "previsoes = dummy.predict(teste_x)\n",
        "\n",
        "acuracia = accuracy_score(teste_y, previsoes) * 100\n",
        "print(\"A acurácia do dummy foi %.2f%%\" % acuracia)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFeZZYSY90TJ",
        "outputId": "92ae1fdc-7135-4358-f3b4-ac1664f8a12e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A acurácia do dummy foi 52.44%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy = DummyClassifier(strategy=\"most_frequent\")\n",
        "dummy.fit(treino_x, treino_y)\n",
        "previsoes = dummy.predict(teste_x)\n",
        "\n",
        "acuracia = accuracy_score(teste_y, previsoes) * 100\n",
        "print(\"A acurácia do dummy foi %.2f%%\" % acuracia)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GYnPwCmw99F6",
        "outputId": "9d4bd9b7-dc02-433c-8e34-e506823cff51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A acurácia do dummy foi 58.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummy = DummyClassifier(strategy=\"most_frequent\")\n",
        "dummy.fit(treino_x, treino_y)\n",
        "acuracia = dummy.score(teste_x, teste_y) * 100\n",
        "\n",
        "print(\"A acurácia do dummy stratified foi %.2f%%\" % acuracia)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulJzNA-3-Jth",
        "outputId": "916e35de-caa6-49e6-e79d-29876683bc84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A acurácia do dummy stratified foi 58.00%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "SEED = 5\n",
        "np.random.seed(SEED)\n",
        "raw_treino_x, raw_teste_x, treino_y, teste_y = train_test_split(x, y, test_size = 0.25,\n",
        "                                                         stratify = y)\n",
        "print(\"Treinaremos com %d elementos e testaremos com %d elementos\" % (len(treino_x), len(teste_x)))\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(raw_treino_x)\n",
        "treino_x = scaler.transform(raw_treino_x)\n",
        "teste_x = scaler.transform(raw_teste_x)\n",
        "\n",
        "modelo = SVC()\n",
        "modelo.fit(treino_x, treino_y)\n",
        "previsoes = modelo.predict(teste_x)\n",
        "\n",
        "acuracia = accuracy_score(teste_y, previsoes) * 100\n",
        "print(\"A acurácia foi %.2f%%\" % acuracia)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVDL_IZB-Qtq",
        "outputId": "03ad8a7b-2d24-4a4d-e71e-c749bc51b01a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Treinaremos com 7500 elementos e testaremos com 2500 elementos\n",
            "A acurácia foi 77.48%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install graphviz==0.1\n",
        "!apt-get install graphviz"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6bDiZFWA3Ez",
        "outputId": "4b81b93a-5438-4082-e3d5-a60315dc1772"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting graphviz==0.1\n",
            "  Downloading graphviz-0.1.zip (8.9 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: graphviz\n",
            "  Building wheel for graphviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for graphviz: filename=graphviz-0.1-py3-none-any.whl size=5590 sha256=0c16e8bb545c25ffe0f94f49920c2e7685335953981e8de595e8add1682da8f4\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/c4/c6/fdc5db3eec5a7e3df14c01add3764fa3578bd0ad12a7735214\n",
            "Successfully built graphviz\n",
            "Installing collected packages: graphviz\n",
            "  Attempting uninstall: graphviz\n",
            "    Found existing installation: graphviz 0.2\n",
            "    Uninstalling graphviz-0.2:\n",
            "      Successfully uninstalled graphviz-0.2\n",
            "Successfully installed graphviz-0.1\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "graphviz is already the newest version (2.42.2-3build2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 23 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "SEED = 5\n",
        "np.random.seed(SEED)\n",
        "raw_treino_x, raw_teste_x, treino_y, teste_y = train_test_split(x, y, test_size = 0.25,\n",
        "                                                         stratify = y)\n",
        "print(\"Treinaremos com %d elementos e testaremos com %d elementos\" % (len(treino_x), len(teste_x)))\n",
        "\n",
        "modelo = DecisionTreeClassifier()\n",
        "modelo.fit(raw_treino_x, treino_y)\n",
        "previsoes = modelo.predict(raw_teste_x)\n",
        "\n",
        "acuracia = accuracy_score(teste_y, previsoes) * 100\n",
        "print(\"A acurácia foi %.2f%%\" % acuracia)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0biYByKE_3j",
        "outputId": "9542448a-7313-4874-e54f-5bc2c459aede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Treinaremos com 7500 elementos e testaremos com 2500 elementos\n",
            "A acurácia foi 73.36%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 5\n",
        "np.random.seed(SEED)\n",
        "raw_treino_x, raw_teste_x, treino_y, teste_y = train_test_split(x, y, test_size = 0.25,\n",
        "                                                         stratify = y)\n",
        "print(\"Treinaremos com %d elementos e testaremos com %d elementos\" % (len(treino_x), len(teste_x)))\n",
        "\n",
        "modelo = DecisionTreeClassifier(max_depth=3)\n",
        "modelo.fit(raw_treino_x, treino_y)\n",
        "previsoes = modelo.predict(raw_teste_x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YM0RdVCFZ60",
        "outputId": "0b253cd8-add4-407a-cba4-dbda101f4632"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Treinaremos com 7500 elementos e testaremos com 2500 elementos\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo = DecisionTreeClassifier(max_depth=3)\n",
        "modelo.fit(raw_treino_x, treino_y)\n",
        "previsoes = modelo.predict(raw_teste_x)\n",
        "\n",
        "acuracia = accuracy_score(teste_y, previsoes) * 100\n",
        "print(\"A acurácia foi %.2f%%\" % acuracia)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYsI6FMnFtN0",
        "outputId": "b58ad598-be37-467f-d25f-1d9c55fa903d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A acurácia foi 79.28%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "en0P0vYCM0qd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}