Hashtag_IA.ipynb

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        "<a href=\"https://colab.research.google.com/github/rregorr/Hei-Jav-ic/blob/main/Hashtag_IA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 1,
      "metadata": {
        "id": "dC2x7qSoxfFY"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "tabela = pd.read_csv(\"/content/drive/MyDrive/estudos/python/datascience/Hashtag - IA - clientes.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabela"
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      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id_cliente  mes  idade  profissao  salario_anual  num_contas  \\\n",
              "0            3392    1   23.0  cientista       19114.12         3.0   \n",
              "1            3392    2   23.0  cientista       19114.12         3.0   \n",
              "2            3392    3   23.0  cientista       19114.12         3.0   \n",
              "3            3392    4   23.0  cientista       19114.12         3.0   \n",
              "4            3392    5   23.0  cientista       19114.12         3.0   \n",
              "...           ...  ...    ...        ...            ...         ...   \n",
              "99995       37932    4   25.0   mecanico       39628.99         4.0   \n",
              "99996       37932    5   25.0   mecanico       39628.99         4.0   \n",
              "99997       37932    6   25.0   mecanico       39628.99         4.0   \n",
              "99998       37932    7   25.0   mecanico       39628.99         4.0   \n",
              "99999       37932    8   25.0   mecanico       39628.99         4.0   \n",
              "\n",
              "       num_cartoes  juros_emprestimo  num_emprestimos  dias_atraso  ...  \\\n",
              "0              4.0               3.0              4.0          3.0  ...   \n",
              "1              4.0               3.0              4.0          3.0  ...   \n",
              "2              4.0               3.0              4.0          3.0  ...   \n",
              "3              4.0               3.0              4.0          5.0  ...   \n",
              "4              4.0               3.0              4.0          6.0  ...   \n",
              "...            ...               ...              ...          ...  ...   \n",
              "99995          6.0               7.0              2.0         23.0  ...   \n",
              "99996          6.0               7.0              2.0         18.0  ...   \n",
              "99997          6.0               7.0              2.0         27.0  ...   \n",
              "99998          6.0               7.0              2.0         20.0  ...   \n",
              "99999          6.0               7.0              2.0         18.0  ...   \n",
              "\n",
              "       idade_historico_credito  investimento_mensal  \\\n",
              "0                        265.0            21.465380   \n",
              "1                        266.0            21.465380   \n",
              "2                        267.0            21.465380   \n",
              "3                        268.0            21.465380   \n",
              "4                        269.0            21.465380   \n",
              "...                        ...                  ...   \n",
              "99995                    378.0            24.028477   \n",
              "99996                    379.0            24.028477   \n",
              "99997                    380.0            24.028477   \n",
              "99998                    381.0            24.028477   \n",
              "99999                    382.0            24.028477   \n",
              "\n",
              "           comportamento_pagamento  saldo_final_mes  score_credito  \\\n",
              "0      alto_gasto_pagamento_baixos       312.494089           Good   \n",
              "1       baixo_gasto_pagamento_alto       284.629162           Good   \n",
              "2      baixo_gasto_pagamento_medio       331.209863           Good   \n",
              "3      baixo_gasto_pagamento_baixo       223.451310           Good   \n",
              "4       alto_gasto_pagamento_medio       341.489231           Good   \n",
              "...                            ...              ...            ...   \n",
              "99995    alto_gasto_pagamento_alto       479.866228           Poor   \n",
              "99996   alto_gasto_pagamento_medio       496.651610           Poor   \n",
              "99997    alto_gasto_pagamento_alto       516.809083           Poor   \n",
              "99998   baixo_gasto_pagamento_alto       319.164979       Standard   \n",
              "99999   alto_gasto_pagamento_medio       393.673696           Poor   \n",
              "\n",
              "       emprestimo_carro  emprestimo_casa emprestimo_pessoal  \\\n",
              "0                     1                1                  1   \n",
              "1                     1                1                  1   \n",
              "2                     1                1                  1   \n",
              "3                     1                1                  1   \n",
              "4                     1                1                  1   \n",
              "...                 ...              ...                ...   \n",
              "99995                 1                0                  0   \n",
              "99996                 1                0                  0   \n",
              "99997                 1                0                  0   \n",
              "99998                 1                0                  0   \n",
              "99999                 1                0                  0   \n",
              "\n",
              "       emprestimo_credito emprestimo_estudantil  \n",
              "0                       1                     0  \n",
              "1                       1                     0  \n",
              "2                       1                     0  \n",
              "3                       1                     0  \n",
              "4                       1                     0  \n",
              "...                   ...                   ...  \n",
              "99995                   0                     1  \n",
              "99996                   0                     1  \n",
              "99997                   0                     1  \n",
              "99998                   0                     1  \n",
              "99999                   0                     1  \n",
              "\n",
              "[100000 rows x 25 columns]"
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              "        const element = document.querySelector('#df-157934e6-d5df-4e0d-bb61-3e136b0e481f');\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",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "\n",
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              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('tabela')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('tabela');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tabela"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabela.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z2_qe2N2zSvX",
        "outputId": "571e0afe-2a46-4aee-ff73-79d7bef9b16f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 25 columns):\n",
            " #   Column                    Non-Null Count   Dtype  \n",
            "---  ------                    --------------   -----  \n",
            " 0   id_cliente                100000 non-null  int64  \n",
            " 1   mes                       100000 non-null  int64  \n",
            " 2   idade                     100000 non-null  float64\n",
            " 3   profissao                 100000 non-null  object \n",
            " 4   salario_anual             100000 non-null  float64\n",
            " 5   num_contas                100000 non-null  float64\n",
            " 6   num_cartoes               100000 non-null  float64\n",
            " 7   juros_emprestimo          100000 non-null  float64\n",
            " 8   num_emprestimos           100000 non-null  float64\n",
            " 9   dias_atraso               100000 non-null  float64\n",
            " 10  num_pagamentos_atrasados  100000 non-null  float64\n",
            " 11  num_verificacoes_credito  100000 non-null  float64\n",
            " 12  mix_credito               100000 non-null  object \n",
            " 13  divida_total              100000 non-null  float64\n",
            " 14  taxa_uso_credito          100000 non-null  float64\n",
            " 15  idade_historico_credito   100000 non-null  float64\n",
            " 16  investimento_mensal       100000 non-null  float64\n",
            " 17  comportamento_pagamento   100000 non-null  object \n",
            " 18  saldo_final_mes           100000 non-null  float64\n",
            " 19  score_credito             100000 non-null  object \n",
            " 20  emprestimo_carro          100000 non-null  int64  \n",
            " 21  emprestimo_casa           100000 non-null  int64  \n",
            " 22  emprestimo_pessoal        100000 non-null  int64  \n",
            " 23  emprestimo_credito        100000 non-null  int64  \n",
            " 24  emprestimo_estudantil     100000 non-null  int64  \n",
            "dtypes: float64(14), int64(7), object(4)\n",
            "memory usage: 19.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preparar a base de dados"
      ],
      "metadata": {
        "id": "7hB7fQnJ0vHv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# converter dados das colunas em números: LabelEncoder. Não precisa converter a target, neste caso, já que ele deve prever em formato texto.\n",
        "# Cliente bom, mau etc.\n",
        "\n",
        "#importar label encoder\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "#criar label encoder\n",
        "codificador = LabelEncoder()\n",
        "\n",
        "#aplicar o label encoder na coluna desejada\n",
        "tabela[\"profissao\"] = codificador.fit_transform(tabela[\"profissao\"])\n",
        "\n",
        "tabela[\"mix_credito\"] = codificador.fit_transform(tabela[\"mix_credito\"])\n",
        "\n",
        "tabela[\"comportamento_pagamento\"] = codificador.fit_transform(tabela[\"comportamento_pagamento\"])\n"
      ],
      "metadata": {
        "id": "mURuM0b_0uey"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Separo as colunas que quero usar para features(X) e para target(y)\n",
        "y = tabela[\"score_credito\"]\n",
        "X = tabela.drop(columns=[\"score_credito\", \"id_cliente\"])#posso passar outras colunas que não quero usar\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "#Informo qual coluna da feature ira pra treino e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) #automaticamente divide minha base em 4. Deve seguir essa ordem."
      ],
      "metadata": {
        "id": "bK4W_6xO0F1R"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Criar a Inteligência Artificial(Àrvore de Dicisao e KNN)"
      ],
      "metadata": {
        "id": "qtArvVKU-cw_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#importa a IA\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "#Cria a IA\n",
        "modelo_arvoredecisao = RandomForestClassifier()\n",
        "modelo_knn = KNeighborsClassifier()\n",
        "\n",
        "#Treina a IA\n",
        "modelo_arvoredecisao.fit(X_train, y_train)\n",
        "modelo_knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ROnLQlVx69tO",
        "outputId": "5f991af2-98af-4b9a-80cd-3c9484d8e7fd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo_logistic = LogisticRegression()\n",
        "\n",
        "modelo_logistic.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "_w1589mTKuCO",
        "outputId": "76eada8d-94c1-499e-daac-973f1a21f960"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "modelo_svm = SVC()\n",
        "modelo_svm.fit(X_train, y_train)\n",
        "previsao_svm = modelo_svm.predict(X_test)\n",
        "# previsao_gradiente = modelo_gradiente.predict(X_test)\n",
        "#previsao_kmeans = modelo_kmeans.predict(X_test)\n"
      ],
      "metadata": {
        "id": "kYM_bQlnRfXW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Testar os modelos"
      ],
      "metadata": {
        "id": "Us1R_dhmAy81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#pedir ao modelo para fazer uma previsao\n",
        "previsao_arvoredecisao = modelo_arvoredecisao.predict(X_test)\n",
        "previsao_knn = modelo_knn.predict(X_test)\n",
        "previsao_logistic = modelo_logistic.predict(X_test)\n",
        "#previsao_svm = modelo_svm.predict(X_test)\n",
        "# previsao_gradiente = modelo_gradiente.predict(X_test)\n",
        "#previsao_kmeans = modelo_kmeans.predict(X_test)\n",
        "\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "ac_arvore = accuracy_score(y_test, previsao_arvoredecisao)\n",
        "ac_knn = accuracy_score(y_test, previsao_knn)\n",
        "ac_logistic = accuracy_score(y_test, previsao_logistic)\n",
        "#ac_svm = accuracy_score(y_test, previsao_svm)\n",
        "# ac_gradiente = accuracy_score(y_test, previsao_gradiente)\n",
        "#ac_kmeans = accuracy_score(y_test, previsao_kmeans)\n",
        "\n",
        "\n",
        "print(\"Previsão da árvore de decisão:\", ac_arvore)\n",
        "print(\"Previsão KNN:\", ac_knn)\n",
        "print(\"Previsão Regressão Logistica:\", ac_logistic)\n",
        "#print(\"Previsão SVM:\", previsao_svm)\n",
        "# print(\"Previsão Gradiente:\", previsao_gradiente)\n",
        "#print(\"Previsão KMEANS:\", ac_kmeans)\n"
      ],
      "metadata": {
        "id": "g1IsIobT7AHy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Dados Novos: Produção"
      ],
      "metadata": {
        "id": "ghlmKu9FFUfE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tabela_nova = pd.read_csv(\"/content/drive/MyDrive/estudos/python/datascience/Hashtag - IA - novos_clientes.csv\")\n",
        "tabela_nova"
      ],
      "metadata": {
        "id": "ucGWjLAbCNbc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Sempre que eu submeto dados novos, devo realizar o mesmo tratamento feito nas bases treinadas/testadas anteriormente\n",
        "tabela_nova[\"profissao\"] = codificador.fit_transform(tabela_nova[\"profissao\"])\n",
        "\n",
        "tabela_nova[\"mix_credito\"] = codificador.fit_transform(tabela_nova[\"mix_credito\"])\n",
        "\n",
        "tabela_nova[\"comportamento_pagamento\"] = codificador.fit_transform(tabela_nova[\"comportamento_pagamento\"])\n",
        "\n",
        "previsoes = modelo_arvoredecisao.predict(tabela_nova)\n",
        "\n",
        "print(previsoes)"
      ],
      "metadata": {
        "id": "ArM0njnDFmzS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LBzVdGPBGZsG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}