Projeto Final Tec Prog Inadimplência.ipynb

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   "source": [
    "# Projeto de Inadimplência de Clientes de Cartão de Crédito"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49547205-0687-4cb6-97c6-a1a7986be325",
   "metadata": {},
   "source": [
    "_Projeto final do módulo Técnicas de Programação I da trilha de Data Science do Santander Coders 2024.2, ministrado por Roberto Pontes. O projeto foi elaborado por Maria Paula Andrade, Gabriel Marques, Filipe Sousa e Gabriel Santos._"
   ]
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    "> 1. [Problema de negócios](#problema)\n",
    "> 2. [Dicionário dos dados](#dicionario) \n",
    "> 3. [Importando as bibliotecas](#importando)\n",
    "> 4. [Carregando os dados](#carregandodados)\n",
    "> 5. [Entendendo a base de dados](#entendendo)\n",
    "> 6. [Tratando os dados](#tratandodados) \n",
    "> 7. [Análises Gerais](#analises-gerais)\n",
    "> 8. [Analisando perfís](#perfil-pagadores)"
   ]
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  {
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   "id": "54afe2dd-2859-4f38-9b94-744d03383032",
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   "source": [
    "## Problema de Negócios <a id='problema'></a>\n",
    "\n",
    "A inadimplência é um problema recorrente em diversas empresas, especialmente aquelas que operam no setor de concessão de crédito. Empresas de crédito enfrentam o desafio contínuo de melhorar a eficiência de suas estratégias de recuperação de crédito, uma vez que a inadimplência representa um risco financeiro significativo. A incapacidade de recuperar valores devidos pode impactar negativamente o fluxo de caixa e a sustentabilidade das operações, afetando diretamente a lucratividade da empresa.\n",
    "\n",
    "Para melhorar a eficiência na recuperação de crédito, é fundamental o mapeamento adequado dos clientes inadimplentes. Este mapeamento é essencial para a adoção de estratégias de cobrança mais eficazes, direcionando esforços para recuperar o crédito de maneira mais assertiva. Empresas de cobrança e concessão de crédito, ao entenderem o perfil dos inadimplentes, conseguem otimizar seus processos, reduzindo custos operacionais e aumentando a taxa de recuperação.\n",
    "\n",
    "Além disso, um mapeamento preciso permite uma avaliação mais criteriosa na concessão de crédito, ajudando a reduzir o risco associado à inadimplência. Ao entender os padrões de comportamento de pagamento dos clientes, as empresas podem ajustar seus critérios de avaliação de crédito, minimizando o risco de novos casos de inadimplência e melhorando a sustentabilidade financeira de suas operações.\n",
    "\n",
    "Contudo, não é apenas o mapeamento de clientes inadimplentes que é crucial para a estratégia de uma empresa de crédito. O mapeamento de clientes adimplentes também é vital, pois permite identificar aqueles que mantêm um histórico de pagamento positivo e consistente. Esse entendimento possibilita a oferta de novas linhas de crédito ou o aumento do limite de crédito já existente para esses clientes, promovendo a fidelização e potencializando as oportunidades de receita.\n",
    "\n",
    "Portanto, o problema de negócios a ser resolvido consiste em desenvolver um modelo que permita não apenas identificar de forma eficaz os clientes inadimplentes, melhorando a eficiência das estratégias de cobrança, mas também otimizar o mapeamento de clientes adimplentes para ampliar oportunidades de negócios, assegurando a saúde financeira da empresa.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f0574c9-9b06-4fe1-ad34-05b1e9088ac5",
   "metadata": {},
   "source": [
    "## Dicionário dos dados <a id='dicionario'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c116eda9-25df-4a3d-b0cd-5b38664afc5a",
   "metadata": {},
   "source": [
    "### Descrição das Variáveis <a id='descricao'></a>"
   ]
  },
  {
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   "id": "9df58c3c-99ec-4a91-9c48-90a5377848da",
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   "source": [
    "_Variáveis contendo informações pessoais dos clientes:_\n",
    "- **ID** Código de identificação do cliente.\n",
    "- **LIMIT_BAL** Limite de crédito concedido aos clientes (em dólares de Taiwan - \\$NT).\n",
    "- **SEX** Sexo (1 = Homem, 2 = Mulher).\n",
    "- **EDUCATION** Nível de Escolaridade (1 = Pós-graduação, 2 = Graduação, 3 = Ensino Médio, 4 = Outros).\n",
    "- **MARRIAGE** Estado Civil (1 = Solteiro, 2 = Casado, 3 = Outros).\n",
    "- **AGE** Idade (em anos).\n",
    "\n",
    "_Variáveis contendo informações do status de pagamento dos clientes em um determinado mês:_\n",
    "- **PAY_1** Status de Pagamento em Setembro de 2005 (-1 = pagamento em dia, 1 = pagamento atrasado há 1 mês, 2 = pagamento atrasado há 2 meses, ..., 8 = pagamento atrasado há 8 meses).\n",
    "- **PAY_2** Status de Pagamento em Agosto de 2005 (mesmos valores descritos na variável anterior).\n",
    "- **PAY_3** Status de Pagamento em Julho de 2005 (mesmos valores descritos na variável anterior).\n",
    "- **PAY_4** Status de Pahamento em Junho de 2005 (mesmos valores descritos na variável anterior).\n",
    "- **PAY_5** Status de Pagamento em Maio de 2005 (mesmos valores descritos na variável anterior).\n",
    "- **PAY_6** Status de Pagamento em Abril de 2005 (mesmos valores descritos na variável anterior).\n",
    "\n",
    "_Variáveis contendo informações do valor da fatura dos clientes em um determinado mês:_\n",
    "- **BILL_AMT1** Valor do Extrato da Fatura do Cartão em Setembro de 2005 (em \\$NT).\n",
    "- **BILL_AMT2** Valor do Extrato da Fatura do Cartão em Agosto de 2005 (em \\$NT).\n",
    "- **BILL_AMT3** Valor do Extrato da Fatura do Cartão em Julho de 2005 (em \\$NT).\n",
    "- **BILL_AMT4** Valor do Extrato da Fatura do Cartão em Junho de 2005 (em \\$NT).\n",
    "- **BILL_AMT5** Valor do Extrato da Fatura do Cartão em Maio de 2005 (em \\$NT).\n",
    "- **BILL_AMT6** Valor do Extrato da Fatura do Cartão em Abril de 2005 (em \\$NT).\n",
    "\n",
    "_Variáveis contendo informações do valor do pagamento da fatura dos clientes no mês anterior:_\n",
    "- **PAY_AMT1** Valor pago na fatura anterior a Setembro de 2005 (em \\$NT).\n",
    "- **PAY_AMT2** Valor pago na fatura anterior a Agosto de 2005 (em \\$NT).\n",
    "- **PAY_AMT3** Valor pago na fatura anterior a Julho de 2005 (em \\$NT).\n",
    "- **PAY_AMT4** Valor pago na fatura anterior a Junho de 2005 (em \\$NT).\n",
    "- **PAY_AMT5** Valor pago na fatura anterior a Maio de 2005 (em \\$NT).\n",
    "- **PAY_AMT6** Valor pago na fatura anterior a Abril de 2005 (em \\$NT).\n",
    "\n",
    "_Informação sobre Inadimplência:_\n",
    "- **default payment next month** - Indica se o cliente está adimplente (0) ou Inadimplente (1) em Outubro de 2005."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bf945cd-5c7f-467e-bac3-69eb3b7de36a",
   "metadata": {},
   "source": [
    "### Classificação das Variáveis <a id='classificacao'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3afbc28-750c-4c2c-9c53-5f5821858fc0",
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   "source": [
    "- **Variáveis Numéricas:** LIMIT_BAL, AGE, BILL_AMT1 a BILL_AMT6, PAY_AMT1 a PAY_AMT6.\n",
    "- **Variáveis Categóricas:** SEX, EDUCATION, MARRIAGE, default payment next month.\n",
    "- **Variáveis Numéricas e Categóricas:** PAY_1 a PAY_6.\n",
    "\n",
    "Observação: As variáveis de PAY_1 a PAY_6 são do tipo híbrida (categórica e numérica). O valor -1 corresponde a uma categoria e os valores de 1 a 8 representam quantidade de meses com pagamento atrasado."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab56d150-632c-478c-9bb3-186df0edf4d7",
   "metadata": {},
   "source": [
    "## Importando as bibliotecas <a id='importando'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a0d0512-a545-4376-a01c-deef0266802c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd # Manipulação de Dados Tabulares\n",
    "\n",
    "import warnings # Controle de Warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86efd88-5024-4ae7-9316-1704e6a9d5fc",
   "metadata": {},
   "source": [
    "## Carregando os dados <a id=\"carregandodados\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d969e6a6-9926-4db1-b01f-57e71fcff739",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
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       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
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       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
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       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0737c11b-be42</td>\n",
       "      <td>50000</td>\n",
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      "text/plain": [
       "              ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE PAY_1  PAY_2  \\\n",
       "0  798fc410-45c1      20000    2          2         1   24     2      2   \n",
       "1  8a8c8f3b-8eb4     120000    2          2         2   26    -1      2   \n",
       "2  85698822-43f5      90000    2          2         2   34     0      0   \n",
       "3  0737c11b-be42      50000    2          2         1   37     0      0   \n",
       "4  3b7f77cc-dbc0      50000    1          2         1   57    -1      0   \n",
       "\n",
       "   PAY_3  PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "0     -1     -1  ...          0          0          0         0       689   \n",
       "1      0      0  ...       3272       3455       3261         0      1000   \n",
       "2      0      0  ...      14331      14948      15549      1518      1500   \n",
       "3      0      0  ...      28314      28959      29547      2000      2019   \n",
       "4     -1      0  ...      20940      19146      19131      2000     36681   \n",
       "\n",
       "   PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "0         0         0         0         0                           1  \n",
       "1      1000      1000         0      2000                           1  \n",
       "2      1000      1000      1000      5000                           0  \n",
       "3      1200      1100      1069      1000                           0  \n",
       "4     10000      9000       689       679                           0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel(\"dados/default_of_credit_card_clients__courseware_version_1_21_19.xls\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1ec4ea8-7f1e-4fe3-ba4c-2cfabe5851bf",
   "metadata": {},
   "source": [
    "## Entendendo a base de dados <a id=\"entendendo\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ad1d7e-7eb1-4819-a4d9-1d1b72c88663",
   "metadata": {},
   "source": [
    "Os objetivos desta subseção são:\n",
    "* Verificar a dimensão da base de dados, quantidade de valores duplicados, quantidade de _missing values_ e quantidade de valores nulos nas variáveis.\n",
    "* Verificar o número de ocorrências de cada valor em cada variável e checar se está de acordo com os valores mencionados na descrição das variáveis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d23210a5-1135-478e-aba1-0a491c9534e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de linhas: 30000\n",
      "Número de colunas: 25\n"
     ]
    }
   ],
   "source": [
    "# Verificando a dimensão da base de dados\n",
    "print('Número de linhas:', df.shape[0])\n",
    "print('Número de colunas:', df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1d627e38-075c-4d69-a83e-2b17cc727bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1',\n",
       "       'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2',\n",
       "       'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1',\n",
       "       'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "       'default payment next month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando as colunas da base de dados\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c6c9966-b7cb-4709-acbe-bff00e5f0f0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                      Non-Null Count  Dtype \n",
      "---  ------                      --------------  ----- \n",
      " 0   ID                          30000 non-null  object\n",
      " 1   LIMIT_BAL                   30000 non-null  int64 \n",
      " 2   SEX                         30000 non-null  int64 \n",
      " 3   EDUCATION                   30000 non-null  int64 \n",
      " 4   MARRIAGE                    30000 non-null  int64 \n",
      " 5   AGE                         30000 non-null  int64 \n",
      " 6   PAY_1                       30000 non-null  object\n",
      " 7   PAY_2                       30000 non-null  int64 \n",
      " 8   PAY_3                       30000 non-null  int64 \n",
      " 9   PAY_4                       30000 non-null  int64 \n",
      " 10  PAY_5                       30000 non-null  int64 \n",
      " 11  PAY_6                       30000 non-null  int64 \n",
      " 12  BILL_AMT1                   30000 non-null  int64 \n",
      " 13  BILL_AMT2                   30000 non-null  int64 \n",
      " 14  BILL_AMT3                   30000 non-null  int64 \n",
      " 15  BILL_AMT4                   30000 non-null  int64 \n",
      " 16  BILL_AMT5                   30000 non-null  int64 \n",
      " 17  BILL_AMT6                   30000 non-null  int64 \n",
      " 18  PAY_AMT1                    30000 non-null  int64 \n",
      " 19  PAY_AMT2                    30000 non-null  int64 \n",
      " 20  PAY_AMT3                    30000 non-null  int64 \n",
      " 21  PAY_AMT4                    30000 non-null  int64 \n",
      " 22  PAY_AMT5                    30000 non-null  int64 \n",
      " 23  PAY_AMT6                    30000 non-null  int64 \n",
      " 24  default payment next month  30000 non-null  int64 \n",
      "dtypes: int64(23), object(2)\n",
      "memory usage: 5.7+ MB\n",
      "Há 23 variáveis do tipo numérica (int ou float)!\n",
      "Há 2 variáveis do tipo objeto!\n"
     ]
    }
   ],
   "source": [
    "# Verificando informações sobre as variáveis\n",
    "df.info()\n",
    "print('Há', len(df.select_dtypes(include=['int', 'float']).columns), 'variáveis do tipo numérica (int ou float)!')\n",
    "print('Há', len(df.select_dtypes(include=['object']).columns), 'variáveis do tipo objeto!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b394a93-81c7-4349-9dea-677a1ad0f640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nenhuma variável possui missing values!\n"
     ]
    }
   ],
   "source": [
    "# Criando máscara para identificar variáveis com missing values\n",
    "null_mask = df.isnull().sum().values != 0\n",
    "\n",
    "# Verificando a quantidade de missing values nas variáveis \n",
    "if len(df.isnull().sum()[null_mask].index) == 0:\n",
    "    print('Nenhuma variável possui missing values!')\n",
    "else: \n",
    "    print('As variáveis com missing values são:', list(df.isnull().sum()[df.isnull().sum().values != 0].index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "389754ba-006f-460f-9440-3ecd7eab663b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            29687\n",
       "LIMIT_BAL                        82\n",
       "SEX                               3\n",
       "EDUCATION                         7\n",
       "MARRIAGE                          4\n",
       "AGE                              57\n",
       "PAY_1                            12\n",
       "PAY_2                            11\n",
       "PAY_3                            11\n",
       "PAY_4                            11\n",
       "PAY_5                            10\n",
       "PAY_6                            10\n",
       "BILL_AMT1                     22510\n",
       "BILL_AMT2                     22146\n",
       "BILL_AMT3                     21822\n",
       "BILL_AMT4                     21350\n",
       "BILL_AMT5                     20831\n",
       "BILL_AMT6                     20417\n",
       "PAY_AMT1                       7890\n",
       "PAY_AMT2                       7847\n",
       "PAY_AMT3                       7463\n",
       "PAY_AMT4                       6901\n",
       "PAY_AMT5                       6857\n",
       "PAY_AMT6                       6895\n",
       "default payment next month        2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a quantidade de valores únicos nas variáveis\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a55f726f-d414-435d-a3b2-b8a77ba3c130",
   "metadata": {},
   "source": [
    "* Espera-se que a base tenha 30.000 linhas onde cada qual possui um ID único representando uma conta bancária (um cliente). A quantidade de IDs (29.687) é menor que a quantidade de linhas (30.000) da base de dados sugerindo que alguns IDs estão repetidos na base de dados.\n",
    "* A quantidade de categorias na variável `SEX` é 3, mas a descrição da variável menciona apenas 2 classes (Homem e Mulher).\n",
    "* A quantidade de categorias na variável `EDUCATION` é 7, mas a descrição da variável menciona apenas 4 classes (Pós-graduação, Graduação, Ensino Médio e Outros).\n",
    "* A quantidade de categorias na variável `MARRIAGE` é 4, mas a descrição da variável menciona apenas 3 classes (Solteiro, Casado e Outros).\n",
    "* A descrição das variáveis `PAY_1` a `PAY_6` menciona 9 categorias, mas na base de dados há entre 10 a 12 classes em cada variável."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307f5d9e-38fe-4503-90ab-375f2b92ffba",
   "metadata": {},
   "source": [
    "## Tratando os dados <a id='tratandodados'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e83f8ea-5414-4964-ba01-dd354ec2fa39",
   "metadata": {},
   "source": [
    "Os objetivos desta subseção são:\n",
    "\n",
    "* Igualar a quantidade de classes nas variáveis categóricas à quantidade de classes mencionadas na descrição de variáveis\n",
    "* Limpar e organizar a base de dados de forma que cada linha represente uma observação e uma coluna represente uma variável."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8420db8-575a-48a4-9dbb-616b98119cb0",
   "metadata": {},
   "source": [
    "### Tratando a variável **ID** <a id=\"tratandoID\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50347dbb-adfc-4145-8816-da937cdec9c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quantidade de IDs únicos na base de dados: 29687\n"
     ]
    }
   ],
   "source": [
    "# Verificando quantos IDs existem\n",
    "print('Quantidade de IDs únicos na base de dados:', df['ID'].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c61ea8f2-af16-4a48-8961-b325d564e911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "89f8f447-fca8    2\n",
       "7c9b7473-cc2f    2\n",
       "90330d02-82d9    2\n",
       "75938fec-e5ec    2\n",
       "2a793ecf-05c6    2\n",
       "b44b81b2-7789    2\n",
       "7be61027-a493    2\n",
       "998fa9b2-b341    2\n",
       "a3a5c0fc-fdd6    2\n",
       "4e2380e6-a8cf    2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificando os IDs que se repetem na base de dados\n",
    "id_counts = df['ID'].value_counts()\n",
    "id_counts.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2fe3199-f491-4688-af24-d9183d875e3a",
   "metadata": {},
   "source": [
    "\n",
    "* Os IDs repetidos aparecem no máximo 2 vezes na base de dados. \n",
    "* Para lidar com o problema de IDs duplicados, será usado o _boolean masking_. Essa técnica consiste em usar uma expressão bolena (\"True or False\") baseada em uma ou mais condições para extrair ou modificar parte dos elementos de uma array. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d963baa9-6c45-4858-b31d-219538b59994",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID\n",
       "89f8f447-fca8    True\n",
       "7c9b7473-cc2f    True\n",
       "90330d02-82d9    True\n",
       "75938fec-e5ec    True\n",
       "2a793ecf-05c6    True\n",
       "Name: count, dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando a variável boleana \n",
    "dupli_mask = id_counts == 2\n",
    "dupli_mask.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f378bfe-ce45-47b8-bda6-368adda0c218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['89f8f447-fca8', '7c9b7473-cc2f', '90330d02-82d9', '75938fec-e5ec',\n",
       "       '2a793ecf-05c6', 'b44b81b2-7789', '7be61027-a493', '998fa9b2-b341',\n",
       "       'a3a5c0fc-fdd6', '4e2380e6-a8cf'],\n",
       "      dtype='object', name='ID')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificando os IDs duplicados\n",
    "dupli_ids = id_counts.index[dupli_mask]\n",
    "dupli_ids[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bbf9eb2d-d34d-401e-9169-307697041e16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Existem 313 IDs duplicados.\n"
     ]
    }
   ],
   "source": [
    "# Convertendo os IDs duplicados para lista \n",
    "dupli_ids = list(dupli_ids)\n",
    "print(\"Existem\", len(dupli_ids), \"IDs duplicados.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef09be3-5202-409d-9251-e57f49a3fc14",
   "metadata": {},
   "source": [
    "* Os dados dos 313 IDs duplicados serão analisados para serem extraídos ou unificados na base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c51803e8-50f1-4a6a-af6a-259a8a018915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5033</th>\n",
       "      <td>89f8f447-fca8</td>\n",
       "      <td>320000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>169371</td>\n",
       "      <td>172868</td>\n",
       "      <td>150827</td>\n",
       "      <td>8000</td>\n",
       "      <td>8000</td>\n",
       "      <td>5500</td>\n",
       "      <td>6100</td>\n",
       "      <td>6000</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5133</th>\n",
       "      <td>89f8f447-fca8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15879</th>\n",
       "      <td>7c9b7473-cc2f</td>\n",
       "      <td>90000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>27751</td>\n",
       "      <td>20292</td>\n",
       "      <td>14937</td>\n",
       "      <td>2967</td>\n",
       "      <td>2007</td>\n",
       "      <td>1429</td>\n",
       "      <td>1092</td>\n",
       "      <td>412</td>\n",
       "      <td>263</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15979</th>\n",
       "      <td>7c9b7473-cc2f</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29646</th>\n",
       "      <td>90330d02-82d9</td>\n",
       "      <td>70000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10694</td>\n",
       "      <td>27908</td>\n",
       "      <td>11192</td>\n",
       "      <td>2009</td>\n",
       "      <td>1404</td>\n",
       "      <td>3016</td>\n",
       "      <td>20001</td>\n",
       "      <td>2000</td>\n",
       "      <td>5002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29746</th>\n",
       "      <td>90330d02-82d9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE PAY_1  PAY_2  \\\n",
       "5033   89f8f447-fca8     320000    2          2         1   32     0      0   \n",
       "5133   89f8f447-fca8          0    0          0         0    0     0      0   \n",
       "15879  7c9b7473-cc2f      90000    2          1         1   29     0      0   \n",
       "15979  7c9b7473-cc2f          0    0          0         0    0     0      0   \n",
       "29646  90330d02-82d9      70000    1          2         1   29     0      0   \n",
       "29746  90330d02-82d9          0    0          0         0    0     0      0   \n",
       "\n",
       "       PAY_3  PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "5033       0      0  ...     169371     172868     150827      8000      8000   \n",
       "5133       0      0  ...          0          0          0         0         0   \n",
       "15879      0      0  ...      27751      20292      14937      2967      2007   \n",
       "15979      0      0  ...          0          0          0         0         0   \n",
       "29646      0      0  ...      10694      27908      11192      2009      1404   \n",
       "29746      0      0  ...          0          0          0         0         0   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "5033       5500      6100      6000      5000                           0  \n",
       "5133          0         0         0         0                           0  \n",
       "15879      1429      1092       412       263                           0  \n",
       "15979         0         0         0         0                           0  \n",
       "29646      3016     20001      2000      5002                           0  \n",
       "29746         0         0         0         0                           0  \n",
       "\n",
       "[6 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando os dados dos 3 primeiros IDs duplicados na base de dados para analisar\n",
    "df.loc[df['ID'].isin(dupli_ids[0:3]), :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5ef588b-6b0c-4965-86c4-6bc36e510697",
   "metadata": {},
   "source": [
    "* A partir da amostra da tabela acima percebe-se que cada IDs duplicado contém 1 linha com dados aparentmente válidos e 1 linha com valores nulos.\n",
    "* A técnica _boolean masking_ será utilizada novamente para selecionar as linhas com valores zerados.\n",
    "* Os dados zerados são inválidos porque não é provável uma conta bancária cujo proprietário tem 0 de idade, por exemplo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7f0e8264-9b6b-42fa-ad08-2ec2d47dd364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Existem 315 linhas com valores zerados.\n"
     ]
    }
   ],
   "source": [
    "# Identificando as linhas com os valores zerado na base de dados \n",
    "df_zero_mask = df == 0\n",
    "\n",
    "# Criando uma coluna que terá o valor 1 caso todos os valores restantes na linha sejam iguais a zero. \n",
    "feature_zero_mask = df_zero_mask.iloc[:, 1:].all(axis=1)\n",
    "\n",
    "# Contando a quantidade de linhas que possuem valores zerados \n",
    "sum(feature_zero_mask)\n",
    "print(\"Existem\", sum(feature_zero_mask),\"linhas com valores zerados.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea7eecf-3527-4688-bfa2-271bbf83e672",
   "metadata": {},
   "source": [
    " * Existem 315 linhas com valores zerados e 313 IDs duplicados. Portanto, ao eliminar as linhas com valores zerados, o problema de IDs duplicados será resolvido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ec6596c7-2410-4516-8722-e4b6c77b03c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de linhas do novo dataframe: 29685\n",
      "Número de colunas do novo dataframe: 25\n"
     ]
    }
   ],
   "source": [
    "# Extraindo as linhas com os valores zerados da base de dados e criando uma novo DataFrame\n",
    "df_clean_1 = df.loc[~feature_zero_mask, :].copy()\n",
    "\n",
    "# Verificando as novas dimensões do DataFrame \"df_clean_1\"\n",
    "print('Número de linhas do novo dataframe:', df_clean_1.shape[0])\n",
    "print('Número de colunas do novo dataframe:', df_clean_1.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "67d818a0-3c26-4808-9c3a-54485cbf9c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29685"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando quantos IDs únicos existem no novo DataFrame \"df_clean_1\"\n",
    "df_clean_1['ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "afd469b8-915b-459e-8264-8f379513a3bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Salvando o DataFrame sem IDs duplicados no formato CSV \n",
    "df_clean_1.to_csv(\"dados/df_clean_1.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a70b364-d60d-4f9f-b34a-044e2ac3bfbf",
   "metadata": {},
   "source": [
    "##### (Extra) Outro Método de Tratamento para a variável ID\n",
    "* Outra forma de remover os IDs duplicados é utilizando o método _drop_duplicates()_ da biblioteca pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56ab17cd-421b-42de-b000-7ce22b685687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensões do DataFrame com IDs duplicados removidos: (29687, 25)\n",
      "Número de IDs únicos: 29687\n"
     ]
    }
   ],
   "source": [
    "# Usando o método drop_duplicates() para remover linhas com IDs duplicados\n",
    "df_dupli_removed = df.drop_duplicates(['ID'], keep = 'first', ignore_index = True)\n",
    "print('Dimensões do DataFrame com IDs duplicados removidos:', df_dupli_removed.shape)\n",
    "print('Número de IDs únicos:', df_dupli_removed['ID'].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84457179-80d0-49c4-bd3f-ca4ccecc6ec6",
   "metadata": {},
   "source": [
    "* Nota-se que a quantidade de linhas do DataFrame `df_dupli_removed` (29687) é maior que a do `df_clean_1` (29685). Isso ocorre porque no `df_clean_1` foram removidas as linhas com a 2ª ocorrência do ID duplicado *e* todas as linhas com valores zerados.\n",
    "* Para equalizar o número de linhas nos 2 DataFrames, vou selecionar e remover as linhas com valores zerados no DataFrame `df_dupli_removed`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "de11c6dc-6457-4b09-bfdc-eb3948b23c8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
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       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17150</th>\n",
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       "      <th>19659</th>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE PAY_1  PAY_2  \\\n",
       "17150  17fb5354-37ec          0    0          0         0    0     0      0   \n",
       "19659  5d7d605d-f03c          0    0          0         0    0     0      0   \n",
       "\n",
       "       PAY_3  PAY_4  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  \\\n",
       "17150      0      0  ...          0          0          0         0         0   \n",
       "19659      0      0  ...          0          0          0         0         0   \n",
       "\n",
       "       PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "17150         0         0         0         0                           0  \n",
       "19659         0         0         0         0                           0  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionando as linhas com valores zerados \n",
    "df_dupli_removed[df_dupli_removed['SEX'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c9fa56de-84da-41aa-b094-c4f6467e2f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29685, 25)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo as linhas com valores zerados \n",
    "df_dupli_removed_1 = df_dupli_removed.drop([17150, 19659], axis = 0)\n",
    "df_dupli_removed_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2be1d27d-4d14-4c96-96f8-921fbd070454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ID, LIMIT_BAL, SEX, EDUCATION, MARRIAGE, AGE, PAY_1, PAY_2, PAY_3, PAY_4, PAY_5, PAY_6, BILL_AMT1, BILL_AMT2, BILL_AMT3, BILL_AMT4, BILL_AMT5, BILL_AMT6, PAY_AMT1, PAY_AMT2, PAY_AMT3, PAY_AMT4, PAY_AMT5, PAY_AMT6, default payment next month]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 25 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirmando se as linhas com valores zerados foram de fato eliminadas\n",
    "df_dupli_removed_1[df_dupli_removed_1['SEX'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca4ea0e5-8e62-43da-a67a-35d905e8a984",
   "metadata": {},
   "source": [
    "* Agora, os DataFrames `df_clean_1` e `df_dupli_removed_1` (renomeado) possuem as mesmas dimensões e valores. O primeiro pela técinca de _boolean masking_ e o segundo pelo método _drop_duplicates()_."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e993aa57-5062-4587-84d7-83b77c03bc35",
   "metadata": {},
   "source": [
    "### Tratando a variável **SEX** <a id='tratandoSEX'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cbc0d936-1dad-4386-89a1-e81ad25a910d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A variável SEX possui 2 classes: [2, 1]\n"
     ]
    }
   ],
   "source": [
    "# Verificando quantidade de classes em SEX\n",
    "print('A variável SEX possui', df_clean_1['SEX'].nunique(), 'classes:', df_clean_1['SEX'].value_counts().index.to_list())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01030415-bf56-4a69-ad6c-f5ea99705d64",
   "metadata": {},
   "source": [
    "* Após o tratamento da variável `ID`, a variável `SEX` apresentou 2 classes, que corresponde as categorias mencionadas na sua descrição (homem e mulher)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e7d87cbf-38af-43b4-a911-821b019485f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEX\n",
       "Mulher    17910\n",
       "Homem     11775\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Substituindo os valores da variável 'SEX' pelo nome das classes correspondentes\n",
    "df_clean_1['SEX'] = df_clean_1['SEX'].replace({1:'Homem', 2:'Mulher'})\n",
    "df_clean_1['SEX'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "288bd4ef-c490-4f2c-a036-4522ab5f37df",
   "metadata": {},
   "source": [
    "### Tratando a variável **EDUCATION** <a id='tratandoEDUCATION'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "78905526-12f0-4074-a962-29b553fad816",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A variável EDUCATION possui 7 classes.\n"
     ]
    }
   ],
   "source": [
    "# Verificando quantidade de classes em EDUCATION\n",
    "print('A variável EDUCATION possui', df_clean_1['EDUCATION'].nunique(), 'classes.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "352f5dca-71cc-4466-bf15-6eae7e220b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EDUCATION\n",
       "2    13884\n",
       "1    10474\n",
       "3     4867\n",
       "5      275\n",
       "4      122\n",
       "6       49\n",
       "0       14\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1['EDUCATION'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9641290-6444-4603-a7ff-ccf10645bde6",
   "metadata": {},
   "source": [
    "* Além das classes mencionadas na descrição da variável - 1 (Pós-graduação), 2 (Graduação), 3 (Ensino Médio) e 4 (Outros) - a variável `EDUCATION` possui outras 3 classes: 0, 5 e 6. As novas classes serão substituídas por 4 que indica 'Outros'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7a297f17-2289-46d6-95e6-ededb8bbdf52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EDUCATION\n",
       "2    13884\n",
       "1    10474\n",
       "3     4867\n",
       "4      460\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Substituindo os valores não descritos por 4 = outros no DataFrame df_clean_1\n",
    "df_clean_1['EDUCATION'].replace(to_replace=[0, 5, 6], value = 4, inplace=True)\n",
    "\n",
    "# Verificando o número de ocorrências para cada valor da variável EDUCATION após as substituições\n",
    "df_clean_1['EDUCATION'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "68a34e2c-bd06-4db3-b04a-c9d358f097bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituindo os valores da variável 'EDUCATION' pelo nome das classes correspondentes\n",
    "df_clean_1['EDUCATION'] = df_clean_1['EDUCATION'].replace({1: 'Pós-graduação', 2: 'Graduação', 3: 'Ensino Médio', 4: 'Outros'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7599ab-a722-4151-9e2e-117e7f71af5d",
   "metadata": {},
   "source": [
    "### Tratando a variável **MARRIAGE** <a id='tratandoMARRIAGE'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7f8ef806-e0ae-4bf1-86f7-134b7d233bc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A variável MARRIAGE possui 4 classes.\n"
     ]
    }
   ],
   "source": [
    "# Verificando quantidade de classes em MARRIAGE\n",
    "print('A variável MARRIAGE possui', df_clean_1['MARRIAGE'].nunique(), 'classes.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c00ffaea-3573-491c-846c-d7fab594629f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MARRIAGE\n",
       "2    15810\n",
       "1    13503\n",
       "3      318\n",
       "0       54\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1['MARRIAGE'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3745bc5e-74a3-4f30-a5de-bf31867657a0",
   "metadata": {},
   "source": [
    "* Além das classes mencionadas na descrição da variável - 1 (casado), 2 (solteiro) e 3 (outros) - a variável `MARRIAGE` possui mais uma classe: 0. A nova classe será substituída por 3 que indica 'Outros'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "76b20bee-5da3-4d12-a692-c5d5be492f2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituindo os valores não descritos por 3 = outros no DataFrame df_clean_1\n",
    "df_clean_1['MARRIAGE'].replace(to_replace=0, value = 3, inplace=True)\n",
    "\n",
    "# Substituindo os valores da variável 'MARRIAGE' pelo nome das classes correspondentes\n",
    "df_clean_1['MARRIAGE'] = df_clean_1['MARRIAGE'].replace({1: 'Casado', 2: 'Solteiro', 3: 'Outros'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d8eddd-2c3e-4dd5-a1d0-63136073a86f",
   "metadata": {},
   "source": [
    "### Tratando as variáveis **PAY_1** a **PAY_6** <a id='tratandoPAY1aPAY6'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c65b7b67-9d36-4655-b38d-4722ecadf829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAY_1\n",
       "0                13087\n",
       "-1                5047\n",
       "1                 3261\n",
       "Not available     3021\n",
       "-2                2476\n",
       "2                 2378\n",
       "3                  292\n",
       "4                   63\n",
       "5                   23\n",
       "8                   17\n",
       "6                   11\n",
       "7                    9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analisando os valores da coluna PAY_1\n",
    "df_clean_1['PAY_1'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8314bc1c-3add-4ed7-86b0-e515b2c3e8da",
   "metadata": {},
   "source": [
    "* Existem dois valores inteiros (0 e -2) que não foram listados na descrição das variáveis para a variável `PAY_1`;\n",
    "* Além dos valores inteiros não listados, a string *\"Not available\"* tem 3021 ocorrências indicando que existem *missing values*;\n",
    "* A técnica de *logical masking* será usada novamente para excluir as linhas com as strings *\"Not available\"* e gerar um novo DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "75bed822-d92f-41da-b954-ba2bdb5ef2ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Existem 26664 linhas sem missing values na coluna PAY_1!\n"
     ]
    }
   ],
   "source": [
    "# Identificando os valores da coluna PAY_1 com os valores \"Not available\" através do operador lógico !=\n",
    "valid_pay_1_mask = df_clean_1['PAY_1'] != \"Not available\"\n",
    "print(\"Existem\", sum(valid_pay_1_mask), \"linhas sem missing values na coluna PAY_1!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "686c7a71-06ac-4945-b3f7-3706f885900e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de linhas: 26664\n",
      "Número de colunas: 25\n"
     ]
    }
   ],
   "source": [
    "# Criando um novo DataFrame sem as linhas com missing values na coluna PAY_1\n",
    "df_clean_1 = df_clean_1.loc[valid_pay_1_mask, :]\n",
    "print('Número de linhas:', df_clean_1.shape[0])\n",
    "print('Número de colunas:', df_clean_1.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "38a3cef5-07a3-4f6b-9a07-96d1ecf1f0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAY_1\n",
       "0     13087\n",
       "-1     5047\n",
       "1      3261\n",
       "-2     2476\n",
       "2      2378\n",
       "3       292\n",
       "4        63\n",
       "5        23\n",
       "8        17\n",
       "6        11\n",
       "7         9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando se as linhas com missing values de fato foram removida do DataFrame df_clean_2\n",
    "df_clean_1['PAY_1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d49accdc-2402-4318-a118-a370cb0b44f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Index: 26664 entries, 0 to 29999\n",
      "Series name: PAY_1\n",
      "Non-Null Count  Dtype \n",
      "--------------  ----- \n",
      "26664 non-null  object\n",
      "dtypes: object(1)\n",
      "memory usage: 416.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Verificando o tipo de variável da coluna PAY_1\n",
    "df_clean_1['PAY_1'].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d75ccbb-882e-4afe-b0a4-ed539247c602",
   "metadata": {},
   "source": [
    "* A variável `PAY_1` é do tipo _object_, mas esperava-se que fosse do tipo _int_ por causa da descrição da variável, que tem natureza híbrida pois é numérica de 1 a 8 representando a quantidade de meses em atraso e o número -1 representa uma categoria (se o pagamento está em dia)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "981e7938-d359-47f6-b032-c6d1079e8565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Index: 26664 entries, 0 to 29999\n",
      "Series name: PAY_1\n",
      "Non-Null Count  Dtype\n",
      "--------------  -----\n",
      "26664 non-null  int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 416.6 KB\n"
     ]
    }
   ],
   "source": [
    "# Convertendo o tipo de variável PAY_1 de object para int64\n",
    "df_clean_1['PAY_1'] = df_clean_1['PAY_1'].astype('int64')\n",
    "df_clean_1['PAY_1'].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c490f5b-fbc3-4a1c-a56d-ec97be99d4cc",
   "metadata": {},
   "source": [
    "Os valores -2 e 0 não foram documentados anteriormente na descrição das variáveis. Mas o estudo de caso menciona que a empresa informou posteriormente os outros valores da variável `PAY_1`. A descrição dos valores -2, -1 e 0 são:\n",
    "* __-2__ A pessoa abriu a conta naquele mês com saldo 0 e nunca usou nenhum crédito anteriormente.\n",
    "* __-1__ A conta tinha um saldo que foi pago integralmente.\n",
    "*   __0__ O pagamento mínimo foi feito. Ou seja, o saldo todo não foi pago, logo um saldo positivo foi transferido para o mês seguinte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "237fab91-0f16-4002-94c4-1dc9a0d72045",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores únicos em PAY_2 : [-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
      "Valores únicos em PAY_3 : [-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
      "Valores únicos em PAY_4 : [-2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
      "Valores únicos em PAY_5 : [-2, -1, 0, 2, 3, 4, 5, 6, 7, 8]\n",
      "Valores únicos em PAY_6 : [-2, -1, 0, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "# Analisando os valores das outras colunas de status de pagamento (PAY_2 a PAY_6)\n",
    "lista_PAY2_PAY6 = ['PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n",
    "\n",
    "# Valores únicos das colunas de status de pagamento (PAY_2 a PAY_6)\n",
    "for status in lista_PAY2_PAY6:\n",
    "    print(f'Valores únicos em {status} : {df_clean_1[status].value_counts().index.sort_values().unique().to_list()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae912134-2b07-4c8d-9671-538e273f3150",
   "metadata": {},
   "source": [
    "* Todos os valores únicos das variáveis `PAY_2` a `PAY_6` correspondem aos valores que foram documentados no dicionário de dados (-1, 1 a 8) e aqueles que foram introduzidos no tratamento da `PAY_1` (-2 e 0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a9ca3608-ea1e-4292-8eef-fd95ef1f152e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13087</td>\n",
       "      <td>13961</td>\n",
       "      <td>13968</td>\n",
       "      <td>14596</td>\n",
       "      <td>15032</td>\n",
       "      <td>14416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3261</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2378</td>\n",
       "      <td>3509</td>\n",
       "      <td>3400</td>\n",
       "      <td>2798</td>\n",
       "      <td>2335</td>\n",
       "      <td>2476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>292</td>\n",
       "      <td>289</td>\n",
       "      <td>214</td>\n",
       "      <td>161</td>\n",
       "      <td>151</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63</td>\n",
       "      <td>85</td>\n",
       "      <td>69</td>\n",
       "      <td>57</td>\n",
       "      <td>71</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>24</td>\n",
       "      <td>51</td>\n",
       "      <td>53</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2</th>\n",
       "      <td>2476</td>\n",
       "      <td>3375</td>\n",
       "      <td>3654</td>\n",
       "      <td>3893</td>\n",
       "      <td>4056</td>\n",
       "      <td>4366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1</th>\n",
       "      <td>5047</td>\n",
       "      <td>5368</td>\n",
       "      <td>5290</td>\n",
       "      <td>5070</td>\n",
       "      <td>4949</td>\n",
       "      <td>5138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PAY_1  PAY_2  PAY_3  PAY_4  PAY_5  PAY_6\n",
       " 0  13087  13961  13968  14596  15032  14416\n",
       " 1   3261     24      3      2      0      0\n",
       " 2   2378   3509   3400   2798   2335   2476\n",
       " 3    292    289    214    161    151    156\n",
       " 4     63     85     69     57     71     43\n",
       " 5     23     24     20     30     14     10\n",
       " 6     11     11     19      4      2     15\n",
       " 7      9     17     24     51     53     42\n",
       " 8     17      1      3      2      1      2\n",
       "-2   2476   3375   3654   3893   4056   4366\n",
       "-1   5047   5368   5290   5070   4949   5138"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtendo todos os índices únicos que aparecem em qualquer uma das colunas PAY\n",
    "indices_status_pagamento = set(df_clean_1[['PAY_1', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']].stack().unique())\n",
    "\n",
    "# Criando dicionário para armazenar a quantidade de ocorrências associada a cada índice em cada variável\n",
    "dicionario_value_counts = {}\n",
    "for coluna in ['PAY_1', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']:\n",
    "    dicionario_values = df_clean_1[coluna].value_counts().reindex(indices_status_pagamento, fill_value=0)\n",
    "    dicionario_value_counts[coluna] = dicionario_values\n",
    "    \n",
    "# Criando o DataFrame a partir do dicionário\n",
    "occorencias_PAY1_PAY6 = pd.DataFrame(dicionario_value_counts)\n",
    "occorencias_PAY1_PAY6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bd220368-18ad-4611-b3ba-abc27c39e681",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando as variáveis que serão removidas do DataFrame\n",
    "lista_remover_variaveis = ['PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']\n",
    "\n",
    "# Eliminando as variáveis selecionadas do conjunto de dados\n",
    "df_clean_1 = df_clean_1.loc[:, ~df_clean_1.columns.isin(lista_remover_variaveis)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0f63461b-7d28-4b2f-a92e-00ff0f2ecfa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QTE_MESES_ATRASADO\n",
       " 0    13087\n",
       "-1     5047\n",
       " 1     3261\n",
       "-2     2476\n",
       " 2     2378\n",
       " 3      292\n",
       " 4       63\n",
       " 5       23\n",
       " 8       17\n",
       " 6       11\n",
       " 7        9\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando uma variável para quantidade de meses em atraso \n",
    "df_clean_1['QTE_MESES_ATRASADO'] = df_clean_1['PAY_1']\n",
    "df_clean_1['QTE_MESES_ATRASADO'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4b558493-3c57-414e-9e32-d825839d9c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Atribuindo os nomes que correspondem aos respectivos status de pagamento mensal\n",
    "df_clean_1['PAY_1'] = df_clean_1['PAY_1'].replace({-2: 'Abriu conta no mês', \n",
    "                                                   -1: 'Pagou totalmente', \n",
    "                                                    0: 'Pagou o mínimo',\n",
    "                                                    1: 'Em atraso', \n",
    "                                                    2: 'Em atraso', \n",
    "                                                    3: 'Em atraso', \n",
    "                                                    4: 'Em atraso', \n",
    "                                                    5: 'Em atraso',\n",
    "                                                    6: 'Em atraso', \n",
    "                                                    7: 'Em atraso', \n",
    "                                                    8: 'Em atraso'})\n",
    "\n",
    "# Unificando aqueles que não usaram o crédito e estão 100% adimplenetes em Setembro na categoria de que devem 0 meses\n",
    "df_clean_1['QTE_MESES_ATRASADO'] = df_clean_1['QTE_MESES_ATRASADO'].replace({-2: 0, -1: 0})\n",
    "\n",
    "# Formatando os valores default payment pelo nomes das classes correspondentes\n",
    "df_clean_1['STATUS_DEFAULT_PAYMENT'] = df_clean_1['default payment next month'].replace({0: 'Adimplente', 1: 'Inadimplente'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3987b3d0-98bc-4460-a198-ff50309575c0",
   "metadata": {},
   "source": [
    "## Convertendo os valores em NT\\$ para U\\$ <a id='convertendovalores'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "be287e02-3b6a-4c4f-969a-7c3df13f5529",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando as variáveis que estão em dólares de Taiwan \n",
    "colunas_valor_fatura_mes = ['BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6']\n",
    "colunas_valor_fatura_anterior = ['PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n",
    "colunas_valor_fatura = colunas_valor_fatura_mes + colunas_valor_fatura_anterior\n",
    "\n",
    "# Convertendo para dólares americanos (cotação 07/09 - NT$ 32 = U$1)\n",
    "## Limite de Crédito\n",
    "df_clean_1['LIMIT_BAL'] = df_clean_1['LIMIT_BAL'].apply(lambda valor: round(valor/32, 2))\n",
    "\n",
    "## Outras variáveis financeiras \n",
    "for coluna in colunas_valor_fatura:\n",
    "    df_clean_1[coluna] = df_clean_1[coluna].apply(lambda valor: round(valor/32, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b6adec79-2704-4be6-b4ee-6265287d3aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PAY_1</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>...</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>default payment next month</th>\n",
       "      <th>QTE_MESES_ATRASADO</th>\n",
       "      <th>STATUS_DEFAULT_PAYMENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>798fc410-45c1</td>\n",
       "      <td>625.0</td>\n",
       "      <td>Mulher</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Casado</td>\n",
       "      <td>24</td>\n",
       "      <td>Em atraso</td>\n",
       "      <td>122.28</td>\n",
       "      <td>96.94</td>\n",
       "      <td>21.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Inadimplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8a8c8f3b-8eb4</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Mulher</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Solteiro</td>\n",
       "      <td>26</td>\n",
       "      <td>Pagou totalmente</td>\n",
       "      <td>83.81</td>\n",
       "      <td>53.91</td>\n",
       "      <td>83.81</td>\n",
       "      <td>...</td>\n",
       "      <td>101.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>62.50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Inadimplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85698822-43f5</td>\n",
       "      <td>2812.5</td>\n",
       "      <td>Mulher</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Solteiro</td>\n",
       "      <td>34</td>\n",
       "      <td>Pagou o mínimo</td>\n",
       "      <td>913.72</td>\n",
       "      <td>438.34</td>\n",
       "      <td>423.72</td>\n",
       "      <td>...</td>\n",
       "      <td>485.91</td>\n",
       "      <td>47.44</td>\n",
       "      <td>46.88</td>\n",
       "      <td>31.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>31.25</td>\n",
       "      <td>156.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Adimplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0737c11b-be42</td>\n",
       "      <td>1562.5</td>\n",
       "      <td>Mulher</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Casado</td>\n",
       "      <td>37</td>\n",
       "      <td>Pagou o mínimo</td>\n",
       "      <td>1468.44</td>\n",
       "      <td>1507.28</td>\n",
       "      <td>1540.34</td>\n",
       "      <td>...</td>\n",
       "      <td>923.34</td>\n",
       "      <td>62.50</td>\n",
       "      <td>63.09</td>\n",
       "      <td>37.50</td>\n",
       "      <td>34.38</td>\n",
       "      <td>33.41</td>\n",
       "      <td>31.25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Adimplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3b7f77cc-dbc0</td>\n",
       "      <td>1562.5</td>\n",
       "      <td>Homem</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Casado</td>\n",
       "      <td>57</td>\n",
       "      <td>Pagou totalmente</td>\n",
       "      <td>269.28</td>\n",
       "      <td>177.19</td>\n",
       "      <td>1119.84</td>\n",
       "      <td>...</td>\n",
       "      <td>597.84</td>\n",
       "      <td>62.50</td>\n",
       "      <td>1146.28</td>\n",
       "      <td>312.50</td>\n",
       "      <td>281.25</td>\n",
       "      <td>21.53</td>\n",
       "      <td>21.22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Adimplente</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID  LIMIT_BAL     SEX  EDUCATION  MARRIAGE  AGE  \\\n",
       "0  798fc410-45c1      625.0  Mulher  Graduação    Casado   24   \n",
       "1  8a8c8f3b-8eb4     3750.0  Mulher  Graduação  Solteiro   26   \n",
       "2  85698822-43f5     2812.5  Mulher  Graduação  Solteiro   34   \n",
       "3  0737c11b-be42     1562.5  Mulher  Graduação    Casado   37   \n",
       "4  3b7f77cc-dbc0     1562.5   Homem  Graduação    Casado   57   \n",
       "\n",
       "              PAY_1  BILL_AMT1  BILL_AMT2  BILL_AMT3  ...  BILL_AMT6  \\\n",
       "0         Em atraso     122.28      96.94      21.53  ...       0.00   \n",
       "1  Pagou totalmente      83.81      53.91      83.81  ...     101.91   \n",
       "2    Pagou o mínimo     913.72     438.34     423.72  ...     485.91   \n",
       "3    Pagou o mínimo    1468.44    1507.28    1540.34  ...     923.34   \n",
       "4  Pagou totalmente     269.28     177.19    1119.84  ...     597.84   \n",
       "\n",
       "   PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
       "0      0.00     21.53      0.00      0.00      0.00      0.00   \n",
       "1      0.00     31.25     31.25     31.25      0.00     62.50   \n",
       "2     47.44     46.88     31.25     31.25     31.25    156.25   \n",
       "3     62.50     63.09     37.50     34.38     33.41     31.25   \n",
       "4     62.50   1146.28    312.50    281.25     21.53     21.22   \n",
       "\n",
       "   default payment next month  QTE_MESES_ATRASADO  STATUS_DEFAULT_PAYMENT  \n",
       "0                           1                   2            Inadimplente  \n",
       "1                           1                   0            Inadimplente  \n",
       "2                           0                   0              Adimplente  \n",
       "3                           0                   0              Adimplente  \n",
       "4                           0                   0              Adimplente  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8833c3-110f-4e6e-b566-917f705be6bf",
   "metadata": {},
   "source": [
    "## Análises Gerais <a id='analises-gerais'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b39862db-15b9-4e00-aa8a-c34e98b8ff50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>AGE</th>\n",
       "      <th>BILL_AMT_SEPT</th>\n",
       "      <th>BILL_AMT_AUG</th>\n",
       "      <th>BILL_AMT_JULY</th>\n",
       "      <th>BILL_AMT_JUNE</th>\n",
       "      <th>BILL_AMT_MAY</th>\n",
       "      <th>BILL_AMT_APRIL</th>\n",
       "      <th>PAY_AMT_SEPT</th>\n",
       "      <th>PAY_AMT_AUG</th>\n",
       "      <th>PAY_AMT_JULY</th>\n",
       "      <th>PAY_AMT_JUNE</th>\n",
       "      <th>PAY_AMT_MAY</th>\n",
       "      <th>PAY_AMT_APRIL</th>\n",
       "      <th>DEFAULT_OCTOBER</th>\n",
       "      <th>QTE_MESES_ATRASADO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "      <td>26664.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5247.470466</td>\n",
       "      <td>35.505213</td>\n",
       "      <td>1606.429137</td>\n",
       "      <td>1540.625106</td>\n",
       "      <td>1469.573193</td>\n",
       "      <td>1354.340515</td>\n",
       "      <td>1260.566843</td>\n",
       "      <td>1215.308562</td>\n",
       "      <td>178.252747</td>\n",
       "      <td>183.784727</td>\n",
       "      <td>164.359928</td>\n",
       "      <td>152.720355</td>\n",
       "      <td>151.366647</td>\n",
       "      <td>164.307686</td>\n",
       "      <td>0.221797</td>\n",
       "      <td>0.357223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4057.482909</td>\n",
       "      <td>9.227442</td>\n",
       "      <td>2301.052683</td>\n",
       "      <td>2216.704654</td>\n",
       "      <td>2147.042463</td>\n",
       "      <td>2008.601526</td>\n",
       "      <td>1897.060691</td>\n",
       "      <td>1857.266876</td>\n",
       "      <td>521.856194</td>\n",
       "      <td>662.947145</td>\n",
       "      <td>539.544965</td>\n",
       "      <td>498.635909</td>\n",
       "      <td>478.491300</td>\n",
       "      <td>551.108380</td>\n",
       "      <td>0.415463</td>\n",
       "      <td>0.762609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>312.500000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>-5174.380000</td>\n",
       "      <td>-2180.530000</td>\n",
       "      <td>-4914.500000</td>\n",
       "      <td>-5312.500000</td>\n",
       "      <td>-2541.690000</td>\n",
       "      <td>-10612.590000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1562.500000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>111.880000</td>\n",
       "      <td>93.742500</td>\n",
       "      <td>82.097500</td>\n",
       "      <td>73.182500</td>\n",
       "      <td>54.530000</td>\n",
       "      <td>39.250000</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>25.060000</td>\n",
       "      <td>12.190000</td>\n",
       "      <td>9.212500</td>\n",
       "      <td>7.582500</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4375.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>698.785000</td>\n",
       "      <td>660.940000</td>\n",
       "      <td>627.485000</td>\n",
       "      <td>594.910000</td>\n",
       "      <td>564.560000</td>\n",
       "      <td>531.410000</td>\n",
       "      <td>66.075000</td>\n",
       "      <td>62.720000</td>\n",
       "      <td>56.940000</td>\n",
       "      <td>46.880000</td>\n",
       "      <td>46.880000</td>\n",
       "      <td>46.880000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7500.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>2114.055000</td>\n",
       "      <td>2012.357500</td>\n",
       "      <td>1886.250000</td>\n",
       "      <td>1710.237500</td>\n",
       "      <td>1571.575000</td>\n",
       "      <td>1539.175000</td>\n",
       "      <td>157.090000</td>\n",
       "      <td>156.250000</td>\n",
       "      <td>142.387500</td>\n",
       "      <td>126.575000</td>\n",
       "      <td>127.585000</td>\n",
       "      <td>125.470000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25000.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>23337.940000</td>\n",
       "      <td>20986.340000</td>\n",
       "      <td>26721.440000</td>\n",
       "      <td>22089.500000</td>\n",
       "      <td>25735.620000</td>\n",
       "      <td>21873.250000</td>\n",
       "      <td>27298.500000</td>\n",
       "      <td>38346.310000</td>\n",
       "      <td>27782.590000</td>\n",
       "      <td>19406.250000</td>\n",
       "      <td>13329.030000</td>\n",
       "      <td>16520.810000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          LIMIT_BAL           AGE  BILL_AMT_SEPT  BILL_AMT_AUG  BILL_AMT_JULY  \\\n",
       "count  26664.000000  26664.000000   26664.000000  26664.000000   26664.000000   \n",
       "mean    5247.470466     35.505213    1606.429137   1540.625106    1469.573193   \n",
       "std     4057.482909      9.227442    2301.052683   2216.704654    2147.042463   \n",
       "min      312.500000     21.000000   -5174.380000  -2180.530000   -4914.500000   \n",
       "25%     1562.500000     28.000000     111.880000     93.742500      82.097500   \n",
       "50%     4375.000000     34.000000     698.785000    660.940000     627.485000   \n",
       "75%     7500.000000     41.000000    2114.055000   2012.357500    1886.250000   \n",
       "max    25000.000000     79.000000   23337.940000  20986.340000   26721.440000   \n",
       "\n",
       "       BILL_AMT_JUNE  BILL_AMT_MAY  BILL_AMT_APRIL  PAY_AMT_SEPT  \\\n",
       "count   26664.000000  26664.000000    26664.000000  26664.000000   \n",
       "mean     1354.340515   1260.566843     1215.308562    178.252747   \n",
       "std      2008.601526   1897.060691     1857.266876    521.856194   \n",
       "min     -5312.500000  -2541.690000   -10612.590000      0.000000   \n",
       "25%        73.182500     54.530000       39.250000     31.250000   \n",
       "50%       594.910000    564.560000      531.410000     66.075000   \n",
       "75%      1710.237500   1571.575000     1539.175000    157.090000   \n",
       "max     22089.500000  25735.620000    21873.250000  27298.500000   \n",
       "\n",
       "        PAY_AMT_AUG  PAY_AMT_JULY  PAY_AMT_JUNE   PAY_AMT_MAY  PAY_AMT_APRIL  \\\n",
       "count  26664.000000  26664.000000  26664.000000  26664.000000   26664.000000   \n",
       "mean     183.784727    164.359928    152.720355    151.366647     164.307686   \n",
       "std      662.947145    539.544965    498.635909    478.491300     551.108380   \n",
       "min        0.000000      0.000000      0.000000      0.000000       0.000000   \n",
       "25%       25.060000     12.190000      9.212500      7.582500       3.470000   \n",
       "50%       62.720000     56.940000     46.880000     46.880000      46.880000   \n",
       "75%      156.250000    142.387500    126.575000    127.585000     125.470000   \n",
       "max    38346.310000  27782.590000  19406.250000  13329.030000   16520.810000   \n",
       "\n",
       "       DEFAULT_OCTOBER  QTE_MESES_ATRASADO  \n",
       "count     26664.000000        26664.000000  \n",
       "mean          0.221797            0.357223  \n",
       "std           0.415463            0.762609  \n",
       "min           0.000000            0.000000  \n",
       "25%           0.000000            0.000000  \n",
       "50%           0.000000            0.000000  \n",
       "75%           0.000000            0.000000  \n",
       "max           1.000000            8.000000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renomeando o conjunto de dados para df \n",
    "df = df_clean_1.copy()\n",
    "\n",
    "# Renomeando colunas para melhorar a identificação \n",
    "df.rename(columns = {'PAY_1': 'STATUS_PGTO_SEPTEMBER', 'default payment next month': 'DEFAULT_OCTOBER'}, inplace=True)\n",
    "\n",
    "df.rename(columns = {'BILL_AMT1': 'BILL_AMT_SEPT', 'BILL_AMT2': 'BILL_AMT_AUG', \n",
    "                     'BILL_AMT3': 'BILL_AMT_JULY', 'BILL_AMT4': 'BILL_AMT_JUNE',\n",
    "                     'BILL_AMT5': 'BILL_AMT_MAY', 'BILL_AMT6': 'BILL_AMT_APRIL'}, inplace=True)\n",
    "\n",
    "df.rename(columns = {'PAY_AMT1': 'PAY_AMT_SEPT', 'PAY_AMT2': 'PAY_AMT_AUG', \n",
    "                     'PAY_AMT3': 'PAY_AMT_JULY', 'PAY_AMT4': 'PAY_AMT_JUNE',\n",
    "                     'PAY_AMT5': 'PAY_AMT_MAY', 'PAY_AMT6': 'PAY_AMT_APRIL'}, inplace=True)\n",
    "\n",
    "# Estatísticas descritivas das variáveis numéricas \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "93f74ef6-4348-4aba-8fd0-7b7dee254477",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>STATUS_PGTO_SEPTEMBER</th>\n",
       "      <th>STATUS_DEFAULT_PAYMENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26664</td>\n",
       "      <td>26664</td>\n",
       "      <td>26664</td>\n",
       "      <td>26664</td>\n",
       "      <td>26664</td>\n",
       "      <td>26664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>26664</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>15d69f9f-5ad3</td>\n",
       "      <td>Mulher</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Solteiro</td>\n",
       "      <td>Pagou o mínimo</td>\n",
       "      <td>Adimplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>16080</td>\n",
       "      <td>12458</td>\n",
       "      <td>14158</td>\n",
       "      <td>13087</td>\n",
       "      <td>20750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ID     SEX  EDUCATION  MARRIAGE STATUS_PGTO_SEPTEMBER  \\\n",
       "count           26664   26664      26664     26664                 26664   \n",
       "unique          26664       2          4         3                     4   \n",
       "top     15d69f9f-5ad3  Mulher  Graduação  Solteiro        Pagou o mínimo   \n",
       "freq                1   16080      12458     14158                 13087   \n",
       "\n",
       "       STATUS_DEFAULT_PAYMENT  \n",
       "count                   26664  \n",
       "unique                      2  \n",
       "top                Adimplente  \n",
       "freq                    20750  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estatísticas descritivas das variáveis categóricas\n",
    "df.select_dtypes(include='object').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47136fe1-c113-4340-86cc-97ea5b2a5b24",
   "metadata": {},
   "source": [
    "* O conjunto de dados é majoritariamente composto por clientes que são do sexo feminino, têm graduação, são solteiras e que pagaram o mínimo na fatura mensal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a7f49e0-094e-4833-8e6d-7f913336673a",
   "metadata": {},
   "source": [
    "### Analisando taxa de inadimplência nas variáveis categóricas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b915dfdc-4779-46fa-a4a8-631a49c1a83d",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Inadimplência</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Frequência absoluta</th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Homem</th>\n",
       "      <td>10584</td>\n",
       "      <td>0.242252</td>\n",
       "      <td>0.428467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mulher</th>\n",
       "      <td>16080</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.406129</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "             Inadimplência                         \n",
       "       Frequência absoluta     Média Desvio Padrão \n",
       "SEX                                                \n",
       "Homem                10584  0.242252       0.428467\n",
       "Mulher               16080  0.208333       0.406129"
      ]
     },
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Inadimplência</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Frequência absoluta</th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ensino Médio</th>\n",
       "      <td>4380</td>\n",
       "      <td>0.255479</td>\n",
       "      <td>0.436180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduação</th>\n",
       "      <td>12458</td>\n",
       "      <td>0.235913</td>\n",
       "      <td>0.424585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pós-graduação</th>\n",
       "      <td>9412</td>\n",
       "      <td>0.193901</td>\n",
       "      <td>0.395374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outros</th>\n",
       "      <td>414</td>\n",
       "      <td>0.074879</td>\n",
       "      <td>0.263515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Inadimplência                         \n",
       "              Frequência absoluta     Média Desvio Padrão \n",
       "EDUCATION                                                 \n",
       "Ensino Médio                 4380  0.255479       0.436180\n",
       "Graduação                   12458  0.235913       0.424585\n",
       "Pós-graduação                9412  0.193901       0.395374\n",
       "Outros                        414  0.074879       0.263515"
      ]
     },
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    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Inadimplência</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Frequência absoluta</th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Outros</th>\n",
       "      <td>334</td>\n",
       "      <td>0.239521</td>\n",
       "      <td>0.427431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casado</th>\n",
       "      <td>12172</td>\n",
       "      <td>0.234637</td>\n",
       "      <td>0.423789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solteiro</th>\n",
       "      <td>14158</td>\n",
       "      <td>0.210340</td>\n",
       "      <td>0.407565</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "               Inadimplência                         \n",
       "         Frequência absoluta     Média Desvio Padrão \n",
       "MARRIAGE                                             \n",
       "Outros                   334  0.239521       0.427431\n",
       "Casado                 12172  0.234637       0.423789\n",
       "Solteiro               14158  0.210340       0.407565"
      ]
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    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "        text-align: left;\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Inadimplência</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Frequência absoluta</th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATUS_PGTO_SEPTEMBER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Em atraso</th>\n",
       "      <td>6054</td>\n",
       "      <td>0.503964</td>\n",
       "      <td>0.500026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pagou totalmente</th>\n",
       "      <td>5047</td>\n",
       "      <td>0.170002</td>\n",
       "      <td>0.375672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abriu conta no mês</th>\n",
       "      <td>2476</td>\n",
       "      <td>0.131664</td>\n",
       "      <td>0.338193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pagou o mínimo</th>\n",
       "      <td>13087</td>\n",
       "      <td>0.128295</td>\n",
       "      <td>0.334431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Inadimplência                         \n",
       "                      Frequência absoluta     Média Desvio Padrão \n",
       "STATUS_PGTO_SEPTEMBER                                             \n",
       "Em atraso                            6054  0.503964       0.500026\n",
       "Pagou totalmente                     5047  0.170002       0.375672\n",
       "Abriu conta no mês                   2476  0.131664       0.338193\n",
       "Pagou o mínimo                      13087  0.128295       0.334431"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# % de inadimplenetes por classes das variáveis categóricas \n",
    "for variavel in ['SEX', 'EDUCATION', 'MARRIAGE', 'STATUS_PGTO_SEPTEMBER']:\n",
    "    dados = df.groupby(variavel)['DEFAULT_OCTOBER'].agg(['count', 'mean', 'std']) #.sort_values(by='mean', ascending=False))\n",
    "    dados.columns = (('Inadimplência', 'Frequência absoluta'), ('Inadimplência', 'Média'), ('Inadimplência', 'Desvio Padrão '))\n",
    "    display(dados.sort_values(by=('Inadimplência', 'Média'), ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4bf6830-114b-44f8-9b8e-dc61908fdbf0",
   "metadata": {},
   "source": [
    "- Curiosamente o \\% de inadimplentes é maior para quem 'Pagou totalmente' do que para quem 'Pagou o mínimo'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2abe651b-f025-425f-8276-4fb27ed74424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">BILL_AMT_SEPT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATUS_PGTO_SEPTEMBER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pagou o mínimo</th>\n",
       "      <td>2461.911067</td>\n",
       "      <td>2561.361449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Em atraso</th>\n",
       "      <td>1360.890101</td>\n",
       "      <td>2124.069390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pagou totalmente</th>\n",
       "      <td>331.483878</td>\n",
       "      <td>828.981814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abriu conta no mês</th>\n",
       "      <td>283.913001</td>\n",
       "      <td>914.700604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      BILL_AMT_SEPT              \n",
       "                              Média Desvio Padrão\n",
       "STATUS_PGTO_SEPTEMBER                            \n",
       "Pagou o mínimo          2461.911067   2561.361449\n",
       "Em atraso               1360.890101   2124.069390\n",
       "Pagou totalmente         331.483878    828.981814\n",
       "Abriu conta no mês       283.913001    914.700604"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dados_fatura_setembro = df.groupby(['STATUS_PGTO_SEPTEMBER'])['BILL_AMT_SEPT'].agg(['mean', 'std'])\n",
    "dados_fatura_setembro.columns = ('BILL_AMT_SEPT', 'Média'), ('BILL_AMT_SEPT', 'Desvio Padrão')\n",
    "display(dados_fatura_setembro.sort_values(by=('BILL_AMT_SEPT', 'Média'), ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5f2b3c0-941f-4730-88b6-6d29add49f4f",
   "metadata": {},
   "source": [
    "- O valor médio da fatura de setembro para quem 'Pagou totalmente' é menor do que para quem 'Pagou o mínimo'. O que torna ainda mais curioso ter maior inadimplência para quem pagou totalmente, já que eles deviam uma quantia menor do que quem pagou o mínimo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc72bc35-6ca4-4298-889e-802789845a6f",
   "metadata": {},
   "source": [
    "### Analisando inadimplência nas variáveis numéricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0e561191-3780-4e44-9493-d8d6b1db3ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Idade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Não</th>\n",
       "      <td>35.434602</td>\n",
       "      <td>9.081116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>35.752959</td>\n",
       "      <td>9.720185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Idade              \n",
       "                  Média Desvio Padrão\n",
       "Inadimplente                         \n",
       "Não           35.434602      9.081116\n",
       "Sim           35.752959      9.720185"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando média e desvio padrão da idade por status de inadimplência em outubro\n",
    "dados_idade = df.groupby(['DEFAULT_OCTOBER'])['AGE'].agg(['mean', 'std'])\n",
    "dados_idade.columns = ('Idade', 'Média'), ('Idade', 'Desvio Padrão')\n",
    "dados_idade.index = ['Não', 'Sim']\n",
    "dados_idade.index.name = 'Inadimplente'\n",
    "dados_idade"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10260300-29dd-4474-8077-21ffac6862d9",
   "metadata": {},
   "source": [
    "- A média de idade para o grupo de adimplentes e inadimplentes é praticamente a mesma. Isso significa que não podemos desenhar nenhum perfil de 'bom pagador' ou 'mau pagador' baseado em idade.\n",
    "- As idades serão categorizadas em faixas para verificar se as estatísticas de inadimplência mudam entre elas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "eabcfc6b-5324-477e-943d-3d35ee332c0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definindo função para categorizar os intervalos de idade\n",
    "def categorize_age(age):\n",
    "    if 20 <= age < 26:\n",
    "        return 'Jovem Adulto'\n",
    "    elif 26 <= age < 40:\n",
    "        return 'Adulto'\n",
    "    elif 40 <= age < 65:\n",
    "        return 'Meia-idade'\n",
    "    else:\n",
    "        return 'Terceira idade'\n",
    "\n",
    "# Adicionando variável de faixas de idade\n",
    "df['AGE_RANGES'] = df['AGE'].apply(categorize_age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4915d95e-3f7e-4c1e-ab8f-e7a7bdb57060",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Inadimplência</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "      <th>Frequência absoluta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE_RANGES</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adulto</th>\n",
       "      <td>0.202901</td>\n",
       "      <td>0.402173</td>\n",
       "      <td>15096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jovem Adulto</th>\n",
       "      <td>0.266219</td>\n",
       "      <td>0.442045</td>\n",
       "      <td>3422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meia-idade</th>\n",
       "      <td>0.237640</td>\n",
       "      <td>0.425664</td>\n",
       "      <td>8050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terceira idade</th>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.451969</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Inadimplência                                  \n",
       "                       Média Desvio Padrão Frequência absoluta\n",
       "AGE_RANGES                                                    \n",
       "Adulto              0.202901      0.402173               15096\n",
       "Jovem Adulto        0.266219      0.442045                3422\n",
       "Meia-idade          0.237640      0.425664                8050\n",
       "Terceira idade      0.281250      0.451969                  96"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando média e desvio padrão da idade por status de inadimplência em outubro\n",
    "dados_idade_faixas = df.groupby(['AGE_RANGES'])['DEFAULT_OCTOBER'].agg(['mean', 'std', 'count'])\n",
    "dados_idade_faixas.columns = ('Inadimplência', 'Média'), ('Inadimplência', 'Desvio Padrão'), ('Inadimplência', 'Frequência absoluta') \n",
    "dados_idade_faixas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4441ec76-18ae-4227-98cf-57a54a241eaa",
   "metadata": {},
   "source": [
    "- Em média, o maior \\% de inadimplentes está na categoria 'Jovem adulto' e 'Terceira idade'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "72d981e7-c719-4626-abb6-0d6599bd29cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">LIMIT_BAL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Não</th>\n",
       "      <td>5581.966867</td>\n",
       "      <td>4112.409853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>4073.848495</td>\n",
       "      <td>3622.294306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                LIMIT_BAL              \n",
       "                    Média Desvio Padrão\n",
       "Inadimplente                           \n",
       "Não           5581.966867   4112.409853\n",
       "Sim           4073.848495   3622.294306"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando média e devio padrão do limite de crédito\n",
    "dados_limite_credito = df.groupby(['DEFAULT_OCTOBER'])['LIMIT_BAL'].agg(['mean', 'std'])\n",
    "dados_limite_credito.columns = (('LIMIT_BAL', 'Média'), ('LIMIT_BAL', 'Desvio Padrão'))\n",
    "dados_limite_credito.index = ['Não', 'Sim']\n",
    "dados_limite_credito.index.name = 'Inadimplente'\n",
    "dados_limite_credito"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b96bb35f-c54a-4f3d-942f-75b67610cf60",
   "metadata": {},
   "source": [
    "* A média e o desvio padrão do limite de crédito é maior para adimplentes do que para inadimplentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "06a7958a-d61f-4663-899f-2c2a71a39b64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">BILL_AMT_SEPT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Não</th>\n",
       "      <td>1631.622021</td>\n",
       "      <td>2292.673432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>1518.036791</td>\n",
       "      <td>2328.254146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             BILL_AMT_SEPT              \n",
       "                     Média Desvio Padrão\n",
       "Inadimplente                            \n",
       "Não            1631.622021   2292.673432\n",
       "Sim            1518.036791   2328.254146"
      ]
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    {
     "data": {
      "text/html": [
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       "    <tr>\n",
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       "      <th>Média</th>\n",
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       "      <td>1557.878357</td>\n",
       "      <td>2204.667693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>1480.089941</td>\n",
       "      <td>2257.576562</td>\n",
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       "             BILL_AMT_AUG              \n",
       "                    Média Desvio Padrão\n",
       "Inadimplente                           \n",
       "Não           1557.878357   2204.667693\n",
       "Sim           1480.089941   2257.576562"
      ]
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    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">BILL_AMT_JULY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
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       "      <td>1486.364350</td>\n",
       "      <td>2144.089287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>1410.659342</td>\n",
       "      <td>2156.519975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "             BILL_AMT_JULY              \n",
       "                     Média Desvio Padrão\n",
       "Inadimplente                            \n",
       "Não            1486.364350   2144.089287\n",
       "Sim            1410.659342   2156.519975"
      ]
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    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">BILL_AMT_JUNE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
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       "      <th></th>\n",
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       "      <th>Não</th>\n",
       "      <td>1366.510490</td>\n",
       "      <td>2002.328801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>1311.640654</td>\n",
       "      <td>2030.050447</td>\n",
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       "             BILL_AMT_JUNE              \n",
       "                     Média Desvio Padrão\n",
       "Inadimplente                            \n",
       "Não            1366.510490   2002.328801\n",
       "Sim            1311.640654   2030.050447"
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       "      <th colspan=\"2\" halign=\"left\">BILL_AMT_MAY</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
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       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Não</th>\n",
       "      <td>1267.406447</td>\n",
       "      <td>1884.457631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>1236.569248</td>\n",
       "      <td>1940.604140</td>\n",
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       "             BILL_AMT_MAY              \n",
       "                    Média Desvio Padrão\n",
       "Inadimplente                           \n",
       "Não           1267.406447   1884.457631\n",
       "Sim           1236.569248   1940.604140"
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    {
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       "      <th></th>\n",
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       "      <td>1221.529372</td>\n",
       "      <td>1852.134770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>1193.482083</td>\n",
       "      <td>1875.156854</td>\n",
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      "text/plain": [
       "             BILL_AMT_APRIL              \n",
       "                      Média Desvio Padrão\n",
       "Inadimplente                             \n",
       "Não             1221.529372   1852.134770\n",
       "Sim             1193.482083   1875.156854"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "# Calculando média e desvio padrão dos valores mensais da fatura\n",
    "valores_faturas_meses = ['BILL_AMT_SEPT', 'BILL_AMT_AUG','BILL_AMT_JULY', 'BILL_AMT_JUNE', 'BILL_AMT_MAY', 'BILL_AMT_APRIL']\n",
    "\n",
    "for variavel in valores_faturas_meses:\n",
    "    dados_valor_fatura = df.groupby(['DEFAULT_OCTOBER'])[variavel].agg(['mean', 'std'])\n",
    "    dados_valor_fatura.columns = ((variavel, 'Média'), (variavel, 'Desvio Padrão'))\n",
    "    dados_valor_fatura.index = ['Não', 'Sim']\n",
    "    dados_valor_fatura.index.name = 'Inadimplente'\n",
    "    display(dados_valor_fatura)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "474cb66d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">PAY_AMT_SEPT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>Não</th>\n",
       "      <td>198.431665</td>\n",
       "      <td>568.507991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>107.452519</td>\n",
       "      <td>295.704579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PAY_AMT_SEPT              \n",
       "                    Média Desvio Padrão\n",
       "Inadimplente                           \n",
       "Não            198.431665    568.507991\n",
       "Sim            107.452519    295.704579"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "\n",
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       "\n",
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       "\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">PAY_AMT_AUG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
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       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Não</th>\n",
       "      <td>205.570259</td>\n",
       "      <td>721.648005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>107.347496</td>\n",
       "      <td>383.222488</td>\n",
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      "text/plain": [
       "             PAY_AMT_AUG              \n",
       "                   Média Desvio Padrão\n",
       "Inadimplente                          \n",
       "Não           205.570259    721.648005\n",
       "Sim           107.347496    383.222488"
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">PAY_AMT_JULY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Não</th>\n",
       "      <td>181.068973</td>\n",
       "      <td>569.124801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>105.734180</td>\n",
       "      <td>414.323586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PAY_AMT_JULY              \n",
       "                    Média Desvio Padrão\n",
       "Inadimplente                           \n",
       "Não            181.068973    569.124801\n",
       "Sim            105.734180    414.323586"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">PAY_AMT_JUNE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
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       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Não</th>\n",
       "      <td>167.247435</td>\n",
       "      <td>529.829866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>101.750299</td>\n",
       "      <td>364.367779</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "             PAY_AMT_JUNE              \n",
       "                    Média Desvio Padrão\n",
       "Inadimplente                           \n",
       "Não            167.247435    529.829866\n",
       "Sim            101.750299    364.367779"
      ]
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    {
     "data": {
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       "<div>\n",
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       "      <th colspan=\"2\" halign=\"left\">PAY_AMT_MAY</th>\n",
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       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
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       "      <th>Inadimplente</th>\n",
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       "      <th>Não</th>\n",
       "      <td>165.837890</td>\n",
       "      <td>505.525496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>100.592503</td>\n",
       "      <td>363.773630</td>\n",
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      "text/plain": [
       "             PAY_AMT_MAY              \n",
       "                   Média Desvio Padrão\n",
       "Inadimplente                          \n",
       "Não           165.837890    505.525496\n",
       "Sim           100.592503    363.773630"
      ]
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       "<div>\n",
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       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">PAY_AMT_APRIL</th>\n",
       "    </tr>\n",
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       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
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       "      <th>Inadimplente</th>\n",
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       "  <tbody>\n",
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       "      <th>Não</th>\n",
       "      <td>180.441758</td>\n",
       "      <td>583.360010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sim</th>\n",
       "      <td>107.699300</td>\n",
       "      <td>413.838909</td>\n",
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       "             PAY_AMT_APRIL              \n",
       "                     Média Desvio Padrão\n",
       "Inadimplente                            \n",
       "Não             180.441758    583.360010\n",
       "Sim             107.699300    413.838909"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculando média e desvio padrão dos valores (da fatura) anteriores ao mês corrente \n",
    "valores_faturas_anteriores = ['PAY_AMT_SEPT', 'PAY_AMT_AUG','PAY_AMT_JULY', 'PAY_AMT_JUNE', 'PAY_AMT_MAY', 'PAY_AMT_APRIL']\n",
    "\n",
    "for variavel in valores_faturas_anteriores:\n",
    "    dados_valor_anterior = df.groupby(['DEFAULT_OCTOBER'])[variavel].agg(['mean', 'std'])\n",
    "    dados_valor_anterior.columns = ((variavel, 'Média'), (variavel, 'Desvio Padrão'))\n",
    "    dados_valor_anterior.index = ['Não', 'Sim']\n",
    "    dados_valor_anterior.index.name = 'Inadimplente'\n",
    "    display(dados_valor_anterior)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "215cb80c-ce5e-401d-93fe-5534e46e9a7e",
   "metadata": {},
   "source": [
    "**Insights**\n",
    "- A média de fatura das pessoas que não pagaram em Outubro (inadimplente) é inferior aqueles que pagaram. Ou seja, mesmo com o valor de fatura menor, não conseguiram pagar.Isso sugere que há outras características associadas a inadimplencia em Outubro. Por exemplo, o limite de crédito das pessoas que ficam inadimplentes em outubro é menor que os adimplentes. Isso pode justificar o fato do valor da fatura ser menor.\n",
    "- Ocorre o mesmo para o caso das variáveis de valores anteriores da fatura."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b1f555f0-9f78-4341-8e73-76325f4d6ae4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inadimplente</th>\n",
       "      <th>Status Setembro</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>Em atraso</th>\n",
       "      <td>1.351648</td>\n",
       "      <td>0.717781</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>Em atraso</th>\n",
       "      <td>1.791544</td>\n",
       "      <td>0.826492</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  mean       std  max\n",
       "Inadimplente Status Setembro                         \n",
       "0            Em atraso        1.351648  0.717781    8\n",
       "1            Em atraso        1.791544  0.826492    8"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando média e desvio padrão da quantidade de meses em atraso por grupo de inadimplentes e adimplentes\n",
    "dados_meses_em_atraso = df.groupby(['DEFAULT_OCTOBER', df['STATUS_PGTO_SEPTEMBER'][df['STATUS_PGTO_SEPTEMBER'] == 'Em atraso']])['QTE_MESES_ATRASADO'].agg(['mean', 'std', 'max'])\n",
    "#dados_meses_em_atraso.columns = (('Meses em atraso', 'Média'), ('Meses em atraso', 'Desvio Padrão'), ('Meses em atraso', 'Máximo'))\n",
    "dados_meses_em_atraso.rename_axis(index={'DEFAULT_OCTOBER': 'Inadimplente', 'STATUS_PGTO_SEPTEMBER': 'Status Setembro'}, inplace=True)\n",
    "#dados_meses_em_atraso.MultiIndex = [('Não', 'Em atraso'), ('Sim', 'Em atraso')]\n",
    "dados_meses_em_atraso"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7892f2af-237f-4692-84ac-6ef6bfd1c171",
   "metadata": {},
   "source": [
    "- A média de meses para o grupo de adimplentes e inadimplentes é semelhante e em ambos há pelo menos uma pessoa que fica 8 meses sem efetura o pagamento.\n",
    "- A quantidade de meses em atraso não parece ser uma variável que podemo contar para desenhar o perfil de 'bom' ou 'mau' pagador."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a84abd3b",
   "metadata": {},
   "source": [
    "### Filtrando Outros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4a821b43",
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in df.columns:\n",
    "    df = df[df[column] != 'Outros']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fdcc4c8-d3bd-4561-a3f9-4bb23777ae61",
   "metadata": {},
   "source": [
    "### Analisando o limite de crédito nas variáveis categóricas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "be59e3f5-1c9c-405f-ae24-c601e0da631d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Frequência absoluta</th>\n",
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       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>Mulher</th>\n",
       "      <td>5352.632843</td>\n",
       "      <td>3922.068561</td>\n",
       "      <td>15620</td>\n",
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       "      <th>Homem</th>\n",
       "      <td>5137.189563</td>\n",
       "      <td>4277.640315</td>\n",
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       "       Limite de crédito                                  \n",
       "                   Média Desvio Padrão Frequência absoluta\n",
       "SEX                                                       \n",
       "Mulher       5352.632843   3922.068561               15620\n",
       "Homem        5137.189563   4277.640315               10300"
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       "      <th>EDUCATION</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pós-graduação</th>\n",
       "      <td>6684.789864</td>\n",
       "      <td>4231.543031</td>\n",
       "      <td>9363</td>\n",
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       "    <tr>\n",
       "      <th>Graduação</th>\n",
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       "      <td>4248</td>\n",
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       "              Limite de crédito                                  \n",
       "                          Média Desvio Padrão Frequência absoluta\n",
       "EDUCATION                                                        \n",
       "Pós-graduação       6684.789864   4231.543031                9363\n",
       "Graduação           4623.776302   3783.177775               12309\n",
       "Ensino Médio        4005.985758   3583.607350                4248"
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       "      <td>5703.673337</td>\n",
       "      <td>4178.410774</td>\n",
       "      <td>11968</td>\n",
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       "      <th>Solteiro</th>\n",
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       "         Limite de crédito                                  \n",
       "                     Média Desvio Padrão Frequência absoluta\n",
       "MARRIAGE                                                    \n",
       "Casado         5703.673337   4178.410774               11968\n",
       "Solteiro       4892.460938   3933.265028               13952"
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       "      <th>Desvio Padrão</th>\n",
       "      <th>Frequência absoluta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE_RANGES</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Terceira idade</th>\n",
       "      <td>6844.758065</td>\n",
       "      <td>4418.712265</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adulto</th>\n",
       "      <td>5760.544972</td>\n",
       "      <td>3965.768966</td>\n",
       "      <td>14776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meia-idade</th>\n",
       "      <td>5603.713293</td>\n",
       "      <td>4353.188961</td>\n",
       "      <td>7696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jovem Adulto</th>\n",
       "      <td>2277.384501</td>\n",
       "      <td>2066.513775</td>\n",
       "      <td>3355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Limite de crédito                                  \n",
       "                           Média Desvio Padrão Frequência absoluta\n",
       "AGE_RANGES                                                        \n",
       "Terceira idade       6844.758065   4418.712265                  93\n",
       "Adulto               5760.544972   3965.768966               14776\n",
       "Meia-idade           5603.713293   4353.188961                7696\n",
       "Jovem Adulto         2277.384501   2066.513775                3355"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Limite de crédito</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Média</th>\n",
       "      <th>Desvio Padrão</th>\n",
       "      <th>Frequência absoluta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STATUS_PGTO_SEPTEMBER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abriu conta no mês</th>\n",
       "      <td>7874.921646</td>\n",
       "      <td>3936.206828</td>\n",
       "      <td>2393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pagou totalmente</th>\n",
       "      <td>6496.826962</td>\n",
       "      <td>4037.860751</td>\n",
       "      <td>4944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pagou o mínimo</th>\n",
       "      <td>4861.360335</td>\n",
       "      <td>3982.402647</td>\n",
       "      <td>12666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Em atraso</th>\n",
       "      <td>4053.099121</td>\n",
       "      <td>3582.602350</td>\n",
       "      <td>5917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Limite de crédito                                  \n",
       "                                  Média Desvio Padrão Frequência absoluta\n",
       "STATUS_PGTO_SEPTEMBER                                                    \n",
       "Abriu conta no mês          7874.921646   3936.206828                2393\n",
       "Pagou totalmente            6496.826962   4037.860751                4944\n",
       "Pagou o mínimo              4861.360335   3982.402647               12666\n",
       "Em atraso                   4053.099121   3582.602350                5917"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estatísticas de limite de crédito por classes das variáveis categóricas \n",
    "for variavel in ['SEX', 'EDUCATION', 'MARRIAGE', 'AGE_RANGES', 'STATUS_PGTO_SEPTEMBER']:\n",
    "    dados = df.groupby(variavel)['LIMIT_BAL'].agg(['mean', 'std', 'count']) #.sort_values(by='mean', ascending=False))\n",
    "    dados.columns = (('Limite de crédito', 'Média'), ('Limite de crédito', 'Desvio Padrão'), ('Limite de crédito', 'Frequência absoluta'))\n",
    "    display(dados.sort_values(by=('Limite de crédito', 'Média'), ascending=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "482c5997-d432-4c67-a697-729fb5ae956b",
   "metadata": {},
   "source": [
    "- Mulheres, pessoas casadas, pessoas com pós-graduação e pessoas que abriram conta recentemente possuem o maior limite de crédito. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68460669",
   "metadata": {},
   "source": [
    "## Analisando perfís <a id='perfil-pagadores'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d44d8876",
   "metadata": {},
   "source": [
    "#### Função `display_pagadores`\n",
    "\n",
    "Exibe a análise percentual da variável comparativa para diferentes valores da variável de agrupamento, filtrando o DataFrame com base no valor específico de uma coluna.\n",
    "\n",
    "##### Parâmetros\n",
    "\n",
    "- **`df`** (`pd.DataFrame`): \n",
    "  O DataFrame contendo os dados a serem analisados.\n",
    "\n",
    "- **`colunas`** (`list`): \n",
    "  Lista de nomes das colunas a serem consideradas para a análise. Deve incluir a coluna de agrupamento e a variável comparativa.\n",
    "\n",
    "- **`eixo`** (`str`): \n",
    "  Nome da coluna usada para filtrar o DataFrame. A função filtra o DataFrame para manter apenas as linhas onde o valor desta coluna é igual ao valor especificado em `alvo`.\n",
    "\n",
    "- **`alvo`** (`str`): \n",
    "  Valor pelo qual a coluna especificada em `eixo` será filtrada. Apenas as linhas com esse valor serão consideradas na análise.\n",
    "\n",
    "- **`comparativo`** (`str`): \n",
    "  Nome da coluna usada para a variável comparativa. A função calcula a contagem e a porcentagem dos valores desta coluna para cada valor da variável de agrupamento.\n",
    "\n",
    "##### Exceções\n",
    "\n",
    "- **`ValueError`**: \n",
    "  Se qualquer coluna especificada em `colunas` não estiver presente no DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ed9663af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_pagadores(df: pd.DataFrame, colunas: list, eixo: str, alvo: str, comparativo: str):    \n",
    "    for coluna in colunas:\n",
    "        if coluna not in df.columns:\n",
    "            raise ValueError(f\"Coluna {coluna} não encontrada no DataFrame.\")\n",
    "\n",
    "    df_display = df[(df[eixo] == alvo)]\n",
    "    for coluna in colunas:\n",
    "        if (coluna == eixo) or (coluna == comparativo):\n",
    "            continue\n",
    "        \n",
    "        dados = df_display[[coluna, comparativo]]\n",
    "\n",
    "        coluna_rename = f'{coluna}_{alvo.upper()}'\n",
    "        dados = dados.rename(columns={coluna : coluna_rename})\n",
    "        \n",
    "        contagem = dados.groupby([coluna_rename])[comparativo].value_counts().unstack()\n",
    "        porcentagem = ((contagem.divide(contagem.sum(axis=1), axis=0) * 100).round(2)).dropna()\n",
    "\n",
    "        display(porcentagem)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8bb77d",
   "metadata": {},
   "source": [
    "### Perfil dos pagadores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9e496447",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
       "      <th>MARRIAGE</th>\n",
       "      <th>AGE_RANGES</th>\n",
       "      <th>STATUS_DEFAULT_PAYMENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mulher</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Casado</td>\n",
       "      <td>Jovem Adulto</td>\n",
       "      <td>Inadimplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mulher</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Solteiro</td>\n",
       "      <td>Adulto</td>\n",
       "      <td>Inadimplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mulher</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Solteiro</td>\n",
       "      <td>Adulto</td>\n",
       "      <td>Adimplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mulher</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Casado</td>\n",
       "      <td>Adulto</td>\n",
       "      <td>Adimplente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Homem</td>\n",
       "      <td>Graduação</td>\n",
       "      <td>Casado</td>\n",
       "      <td>Meia-idade</td>\n",
       "      <td>Adimplente</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SEX  EDUCATION  MARRIAGE    AGE_RANGES STATUS_DEFAULT_PAYMENT\n",
       "0  Mulher  Graduação    Casado  Jovem Adulto           Inadimplente\n",
       "1  Mulher  Graduação  Solteiro        Adulto           Inadimplente\n",
       "2  Mulher  Graduação  Solteiro        Adulto             Adimplente\n",
       "3  Mulher  Graduação    Casado        Adulto             Adimplente\n",
       "4   Homem  Graduação    Casado    Meia-idade             Adimplente"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colunas_pagadores = ['SEX', 'EDUCATION', 'MARRIAGE', 'AGE_RANGES', 'STATUS_DEFAULT_PAYMENT']\n",
    "comparativo = colunas_pagadores[-1]\n",
    "df_pagadores = df[colunas_pagadores].copy()\n",
    "\n",
    "df_pagadores.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63f7680b",
   "metadata": {},
   "source": [
    "#### Sexo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "014ebbc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>STATUS_DEFAULT_PAYMENT</th>\n",
       "      <th>Adimplente</th>\n",
       "      <th>Inadimplente</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION_MULHER</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ensino Médio</th>\n",
       "      <td>75.79</td>\n",
       "      <td>24.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduação</th>\n",
       "      <td>77.93</td>\n",
       "      <td>22.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pós-graduação</th>\n",
       "      <td>81.72</td>\n",
       "      <td>18.28</td>\n",
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "EDUCATION_MULHER                                \n",
       "Ensino Médio                 75.79         24.21\n",
       "Graduação                    77.93         22.07\n",
       "Pós-graduação                81.72         18.28"
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       "      <th>Adimplente</th>\n",
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       "      <th>MARRIAGE_MULHER</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>Casado</th>\n",
       "      <td>77.71</td>\n",
       "      <td>22.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solteiro</th>\n",
       "      <td>80.02</td>\n",
       "      <td>19.98</td>\n",
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "MARRIAGE_MULHER                                 \n",
       "Casado                       77.71         22.29\n",
       "Solteiro                     80.02         19.98"
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       "      <th>Adulto</th>\n",
       "      <td>80.90</td>\n",
       "      <td>19.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jovem Adulto</th>\n",
       "      <td>74.12</td>\n",
       "      <td>25.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meia-idade</th>\n",
       "      <td>77.44</td>\n",
       "      <td>22.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terceira idade</th>\n",
       "      <td>78.26</td>\n",
       "      <td>21.74</td>\n",
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "AGE_RANGES_MULHER                               \n",
       "Adulto                       80.90         19.10\n",
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       "Meia-idade                   77.44         22.56\n",
       "Terceira idade               78.26         21.74"
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       "    <tr>\n",
       "      <th>EDUCATION_HOMEM</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ensino Médio</th>\n",
       "      <td>72.27</td>\n",
       "      <td>27.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graduação</th>\n",
       "      <td>74.05</td>\n",
       "      <td>25.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pós-graduação</th>\n",
       "      <td>79.05</td>\n",
       "      <td>20.95</td>\n",
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "EDUCATION_HOMEM                                 \n",
       "Ensino Médio                 72.27         27.73\n",
       "Graduação                    74.05         25.95\n",
       "Pós-graduação                79.05         20.95"
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       "      <th>STATUS_DEFAULT_PAYMENT</th>\n",
       "      <th>Adimplente</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Casado</th>\n",
       "      <td>73.94</td>\n",
       "      <td>26.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solteiro</th>\n",
       "      <td>76.95</td>\n",
       "      <td>23.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "MARRIAGE_HOMEM                                  \n",
       "Casado                       73.94         26.06\n",
       "Solteiro                     76.95         23.05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>AGE_RANGES_HOMEM</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adulto</th>\n",
       "      <td>77.26</td>\n",
       "      <td>22.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jovem Adulto</th>\n",
       "      <td>70.71</td>\n",
       "      <td>29.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meia-idade</th>\n",
       "      <td>74.44</td>\n",
       "      <td>25.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terceira idade</th>\n",
       "      <td>63.83</td>\n",
       "      <td>36.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "AGE_RANGES_HOMEM                                \n",
       "Adulto                       77.26         22.74\n",
       "Jovem Adulto                 70.71         29.29\n",
       "Meia-idade                   74.44         25.56\n",
       "Terceira idade               63.83         36.17"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "sexo_valores = df_pagadores['SEX'].unique()\n",
    "for valor in sexo_valores:    \n",
    "    display_pagadores(df_pagadores, colunas_pagadores, 'SEX', valor, comparativo)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbeaf238",
   "metadata": {},
   "source": [
    "#### Educação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "71d01243",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Adimplente</th>\n",
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       "      <th>SEX_GRADUAÇÃO</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Homem</th>\n",
       "      <td>74.05</td>\n",
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       "    <tr>\n",
       "      <th>Mulher</th>\n",
       "      <td>77.93</td>\n",
       "      <td>22.07</td>\n",
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       "</table>\n",
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "SEX_GRADUAÇÃO                                   \n",
       "Homem                        74.05         25.95\n",
       "Mulher                       77.93         22.07"
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       "    <tr>\n",
       "      <th>MARRIAGE_GRADUAÇÃO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>Casado</th>\n",
       "      <td>75.82</td>\n",
       "      <td>24.18</td>\n",
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       "    <tr>\n",
       "      <th>Solteiro</th>\n",
       "      <td>77.05</td>\n",
       "      <td>22.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "MARRIAGE_GRADUAÇÃO                              \n",
       "Casado                       75.82         24.18\n",
       "Solteiro                     77.05         22.95"
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       "      <th>Adulto</th>\n",
       "      <td>78.20</td>\n",
       "      <td>21.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jovem Adulto</th>\n",
       "      <td>71.71</td>\n",
       "      <td>28.29</td>\n",
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       "    <tr>\n",
       "      <th>Meia-idade</th>\n",
       "      <td>75.71</td>\n",
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       "      <th>Terceira idade</th>\n",
       "      <td>69.23</td>\n",
       "      <td>30.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "AGE_RANGES_GRADUAÇÃO                            \n",
       "Adulto                       78.20         21.80\n",
       "Jovem Adulto                 71.71         28.29\n",
       "Meia-idade                   75.71         24.29\n",
       "Terceira idade               69.23         30.77"
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    {
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       "    <tr>\n",
       "      <th>SEX_PÓS-GRADUAÇÃO</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>Homem</th>\n",
       "      <td>79.05</td>\n",
       "      <td>20.95</td>\n",
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       "    <tr>\n",
       "      <th>Mulher</th>\n",
       "      <td>81.72</td>\n",
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "SEX_PÓS-GRADUAÇÃO                               \n",
       "Homem                        79.05         20.95\n",
       "Mulher                       81.72         18.28"
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    {
     "data": {
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       "<div>\n",
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       "      <td>79.10</td>\n",
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       "      <th>Solteiro</th>\n",
       "      <td>81.46</td>\n",
       "      <td>18.54</td>\n",
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "MARRIAGE_PÓS-GRADUAÇÃO                          \n",
       "Casado                       79.10         20.90\n",
       "Solteiro                     81.46         18.54"
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    {
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       "      <th>Adulto</th>\n",
       "      <td>81.92</td>\n",
       "      <td>18.08</td>\n",
       "    </tr>\n",
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       "      <th>Jovem Adulto</th>\n",
       "      <td>76.64</td>\n",
       "      <td>23.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meia-idade</th>\n",
       "      <td>78.82</td>\n",
       "      <td>21.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terceira idade</th>\n",
       "      <td>66.67</td>\n",
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      "text/plain": [
       "STATUS_DEFAULT_PAYMENT    Adimplente  Inadimplente\n",
       "AGE_RANGES_PÓS-GRADUAÇÃO                          \n",
       "Adulto                         81.92         18.08\n",
       "Jovem Adulto                   76.64         23.36\n",
       "Meia-idade                     78.82         21.18\n",
       "Terceira idade                 66.67         33.33"
      ]
     },
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    },
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX_ENSINO MÉDIO</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Homem</th>\n",
       "      <td>72.27</td>\n",
       "      <td>27.73</td>\n",
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       "    <tr>\n",
       "      <th>Mulher</th>\n",
       "      <td>75.79</td>\n",
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "SEX_ENSINO MÉDIO                                \n",
       "Homem                        72.27         27.73\n",
       "Mulher                       75.79         24.21"
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       "      <th>MARRIAGE_ENSINO MÉDIO</th>\n",
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       "      <td>73.71</td>\n",
       "      <td>26.29</td>\n",
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       "      <th>Solteiro</th>\n",
       "      <td>75.35</td>\n",
       "      <td>24.65</td>\n",
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      "text/plain": [
       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "MARRIAGE_ENSINO MÉDIO                           \n",
       "Casado                       73.71         26.29\n",
       "Solteiro                     75.35         24.65"
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    },
    {
     "data": {
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adulto</th>\n",
       "      <td>75.41</td>\n",
       "      <td>24.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jovem Adulto</th>\n",
       "      <td>71.16</td>\n",
       "      <td>28.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meia-idade</th>\n",
       "      <td>74.12</td>\n",
       "      <td>25.88</td>\n",
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       "    <tr>\n",
       "      <th>Terceira idade</th>\n",
       "      <td>74.42</td>\n",
       "      <td>25.58</td>\n",
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      ],
      "text/plain": [
       "STATUS_DEFAULT_PAYMENT   Adimplente  Inadimplente\n",
       "AGE_RANGES_ENSINO MÉDIO                          \n",
       "Adulto                        75.41         24.59\n",
       "Jovem Adulto                  71.16         28.84\n",
       "Meia-idade                    74.12         25.88\n",
       "Terceira idade                74.42         25.58"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "educacao_valores = df_pagadores['EDUCATION'].unique()\n",
    "for valor in educacao_valores:    \n",
    "    display_pagadores(df_pagadores, colunas_pagadores, 'EDUCATION', valor, comparativo)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7388111d",
   "metadata": {},
   "source": [
    "#### Casamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4391b140",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>SEX_CASADO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Homem</th>\n",
       "      <td>73.94</td>\n",
       "      <td>26.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mulher</th>\n",
       "      <td>77.71</td>\n",
       "      <td>22.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "SEX_CASADO                                      \n",
       "Homem                        73.94         26.06\n",
       "Mulher                       77.71         22.29"
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
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       "      <th>Graduação</th>\n",
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       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "EDUCATION_SOLTEIRO                              \n",
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       "      <td>80.78</td>\n",
       "      <td>19.22</td>\n",
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       "      <th>Jovem Adulto</th>\n",
       "      <td>73.58</td>\n",
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       "      <th>Meia-idade</th>\n",
       "      <td>77.27</td>\n",
       "      <td>22.73</td>\n",
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       "      <th>Terceira idade</th>\n",
       "      <td>70.00</td>\n",
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      "text/plain": [
       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "AGE_RANGES_SOLTEIRO                             \n",
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       "Meia-idade                   77.27         22.73\n",
       "Terceira idade               70.00         30.00"
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   ],
   "source": [
    "casamento_valores = df_pagadores['MARRIAGE'].unique()\n",
    "for valor in casamento_valores:    \n",
    "    display_pagadores(df_pagadores, colunas_pagadores, 'MARRIAGE', valor, comparativo)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a1aacc",
   "metadata": {},
   "source": [
    "#### Idades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a29aaa9a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Casado</th>\n",
       "      <td>77.37</td>\n",
       "      <td>22.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solteiro</th>\n",
       "      <td>80.78</td>\n",
       "      <td>19.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "STATUS_DEFAULT_PAYMENT  Adimplente  Inadimplente\n",
       "MARRIAGE_ADULTO                                 \n",
       "Casado                       77.37         22.63\n",
       "Solteiro                     80.78         19.22"
      ]
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       "      <td>74.12</td>\n",
       "      <td>25.88</td>\n",
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       "      <th>Graduação</th>\n",
       "      <td>75.71</td>\n",
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       "      <td>78.82</td>\n",
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       "      <td>69.23</td>\n",
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       "      <th>Solteiro</th>\n",
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   ],
   "source": [
    "educacao_valores = df_pagadores['AGE_RANGES'].unique()\n",
    "for valor in educacao_valores:    \n",
    "    display_pagadores(df_pagadores, colunas_pagadores, 'AGE_RANGES', valor, comparativo)"
   ]
  },
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   "cell_type": "markdown",
   "id": "3733f226-315b-48d4-811d-9e6cf5a2031f",
   "metadata": {},
   "source": [
    "#### Perfil para empresa de concessão de crédito (bons pagadores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c51606",
   "metadata": {},
   "source": [
    "Com base na análise dos dados, identificamos que o perfil do cliente adimplente é composto majoritariamente por mulheres, pós-graduadas, solteiras, e com idade entre 26 e 40 anos. Este perfil demonstra um comportamento financeiro positivo e, portanto, representa um público-alvo ideal para a concessão de novas linhas de crédito ou aumento do limite existente, promovendo assim a fidelização e crescimento do portfólio de clientes adimplentes da empresa."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0cfba0e-64da-42c6-8827-55a578bead6f",
   "metadata": {},
   "source": [
    "#### Perfil para empresa de cobrança (maus pagadores)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e817bddb",
   "metadata": {},
   "source": [
    "A análise dos dados revela que o perfil predominante dos clientes inadimplentes é de homens com ensino médio, casados, e da terceira idade, acima de 65 anos. Este grupo apresenta maior risco de inadimplência, o que sugere a necessidade de estratégias de crédito mais cautelosas e personalizadas para minimizar riscos financeiros. A identificação deste perfil permite ajustar os critérios de avaliação e políticas de crédito, focando em medidas preventivas e ações específicas para melhorar a recuperação de crédito e reduzir o impacto negativo no fluxo de caixa da empresa.\n"
   ]
  }
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