ClassificaçãoDeCompradoresPorPaginasAcessadas.ipynb

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      "name": "ClassificaçãoDeCompradoresPorPaginasAcessadas.ipynb",
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        "<a href=\"https://colab.research.google.com/github/OseiasBeu/Data-Science/blob/master/Classifica%C3%A7%C3%A3oDeCompradoresPorPaginasAcessadas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "AYDa_kQxinXH"
      },
      "execution_count": 35,
      "outputs": []
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              "    home  how_it_works  contact  bought\n",
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          },
          "metadata": {},
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      ],
      "source": [
        "uri='https://raw.githubusercontent.com/OseiasBeu/Data-Science/master/DataSets/tracking.csv'\n",
        "\n",
        "dados = pd.read_csv(uri)\n",
        "dados"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.shape"
      ],
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(99, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0hzkWsagjRJ",
        "outputId": "adf3831c-2500-4fbd-b9e2-08012080c651"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['home', 'how_it_works', 'contact', 'bought'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.rename(columns = {\n",
        "    \"home\":\"pagina_inicial\",\n",
        "    \"how_it_works\":\"como_funciona\",\n",
        "    \"contact\":\"contato\",\n",
        "    \"bought\":\"compra\",\n",
        "},inplace = True)\n",
        "dados.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jeSAlx_8gdNM",
        "outputId": "bb8f9a75-edbc-4c61-89db-8049f12329d0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   pagina_inicial  como_funciona  contato  compra\n",
              "0               1              1        0       0\n",
              "1               1              1        0       0\n",
              "2               1              1        0       0\n",
              "3               1              1        0       0\n",
              "4               1              1        0       0"
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              "      <th>pagina_inicial</th>\n",
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = dados[['pagina_inicial','como_funciona','contato']]\n",
        "y = dados['compra']"
      ],
      "metadata": {
        "id": "FxFdn0F2gRKV"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_treino = x[:75]\n",
        "x_teste = x[75:]\n",
        "y_treino = y[:75]\n",
        "y_teste = y[75:]\n",
        "print(f'Quantidade de registros para treino: {x_treino.shape[0]} e para teste: {x_teste.shape[0]}')\n",
        "# print(f'Quantidade de registro.shape[0]}')\n",
        "modelo = LinearSVC()\n",
        "modelo.fit(x_treino, y_treino)\n",
        "previsoes = modelo.predict(x_teste)\n",
        "taxa_de_acerto = accuracy_score(y_teste, previsoes)\n",
        "print(\"Taxa de acerto %.2f\" % (taxa_de_acerto * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-mrjUFDhCyK",
        "outputId": "699070e7-73f3-4549-bb15-f83b364938ae"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quantidade de registros para treino: 75 e para teste: 24\n",
            "Taxa de acerto 95.83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 20\n",
        "treino_x, teste_x, treino_y, teste_y =train_test_split(\n",
        "    x, y, \n",
        "    random_state=SEED,\n",
        "    test_size =0.25,\n",
        "    stratify = y\n",
        "    )\n",
        "print(treino_x.shape)\n",
        "print(teste_x.shape)\n",
        "\n",
        "modelo = LinearSVC()\n",
        "modelo.fit(treino_x, treino_y)\n",
        "previsoes = modelo.predict(teste_x)\n",
        "taxa_de_acerto = accuracy_score(teste_y, previsoes)\n",
        "print(\"Taxa de acerto %.2f\" % (taxa_de_acerto * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWckdBCGlU4m",
        "outputId": "1dd89566-f53e-4bfa-b640-1bc9667ae2b7"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(74, 3)\n",
            "(25, 3)\n",
            "Taxa de acerto 96.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "treino_y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mZrwRAGhUfD",
        "outputId": "f3531497-0075-4b3d-df9b-4bc792093d12"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    49\n",
              "1    25\n",
              "Name: compra, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "teste_y.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bcBalNrAmW6p",
        "outputId": "e8eadd94-d594-4f4f-b065-205c534a24b6"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    17\n",
              "1     8\n",
              "Name: compra, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "49/25"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JajLSGWmd3M",
        "outputId": "d0131dc6-a282-4964-8fc3-708f5b1f5c5a"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.96"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "17/8"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uu9NbHQmm5Vr",
        "outputId": "8f9cad3b-b37c-4716-9e1d-9c02cc6cbfc2"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.125"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    }
  ]
}