inicial.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Projeto Python IA: Inteligência Artificial e Previsões\n",
    "\n",
    "### Case: Score de Crédito dos Clientes\n",
    "\n",
    "Você foi contratado por um banco para conseguir definir o score de crédito dos clientes. Você precisa analisar todos os clientes do banco e, com base nessa análise, criar um modelo que consiga ler as informações do cliente e dizer automaticamente o score de crédito dele: Ruim, Ok, Bom\n",
    "\n",
    "Arquivos da aula: https://drive.google.com/drive/folders/1FbDqVq4XLvU85VBlVIMJ73p9oOu6u2-J?usp=drive_link"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Passo a passo </h2>\n",
    "<ol>\n",
    "<li> Entender o problema </li>\n",
    "<li> Importar a base de dados </li>\n",
    "<li> Preparar a base de dados pra IA </li>\n",
    "<li> Criar um modelo de IA </li>\n",
    "<li> Escolher o melhor modelo </li>\n",
    "<li> Novas previsões usando a IA escolhida </li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passo 1, 2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_cliente</th>\n",
       "      <th>mes</th>\n",
       "      <th>idade</th>\n",
       "      <th>profissao</th>\n",
       "      <th>salario_anual</th>\n",
       "      <th>num_contas</th>\n",
       "      <th>num_cartoes</th>\n",
       "      <th>juros_emprestimo</th>\n",
       "      <th>num_emprestimos</th>\n",
       "      <th>dias_atraso</th>\n",
       "      <th>...</th>\n",
       "      <th>idade_historico_credito</th>\n",
       "      <th>investimento_mensal</th>\n",
       "      <th>comportamento_pagamento</th>\n",
       "      <th>saldo_final_mes</th>\n",
       "      <th>score_credito</th>\n",
       "      <th>emprestimo_carro</th>\n",
       "      <th>emprestimo_casa</th>\n",
       "      <th>emprestimo_pessoal</th>\n",
       "      <th>emprestimo_credito</th>\n",
       "      <th>emprestimo_estudantil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3392</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>cientista</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>265.0</td>\n",
       "      <td>21.465380</td>\n",
       "      <td>alto_gasto_pagamento_baixos</td>\n",
       "      <td>312.494089</td>\n",
       "      <td>Good</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>3392</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>cientista</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>266.0</td>\n",
       "      <td>21.465380</td>\n",
       "      <td>baixo_gasto_pagamento_alto</td>\n",
       "      <td>284.629162</td>\n",
       "      <td>Good</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3392</td>\n",
       "      <td>3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>cientista</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>267.0</td>\n",
       "      <td>21.465380</td>\n",
       "      <td>baixo_gasto_pagamento_medio</td>\n",
       "      <td>331.209863</td>\n",
       "      <td>Good</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3392</td>\n",
       "      <td>4</td>\n",
       "      <td>23.0</td>\n",
       "      <td>cientista</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>268.0</td>\n",
       "      <td>21.465380</td>\n",
       "      <td>baixo_gasto_pagamento_baixo</td>\n",
       "      <td>223.451310</td>\n",
       "      <td>Good</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>3392</td>\n",
       "      <td>5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>cientista</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>269.0</td>\n",
       "      <td>21.465380</td>\n",
       "      <td>alto_gasto_pagamento_medio</td>\n",
       "      <td>341.489231</td>\n",
       "      <td>Good</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>37932</td>\n",
       "      <td>4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>mecanico</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>...</td>\n",
       "      <td>378.0</td>\n",
       "      <td>24.028477</td>\n",
       "      <td>alto_gasto_pagamento_alto</td>\n",
       "      <td>479.866228</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>37932</td>\n",
       "      <td>5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>mecanico</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>379.0</td>\n",
       "      <td>24.028477</td>\n",
       "      <td>alto_gasto_pagamento_medio</td>\n",
       "      <td>496.651610</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>37932</td>\n",
       "      <td>6</td>\n",
       "      <td>25.0</td>\n",
       "      <td>mecanico</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>380.0</td>\n",
       "      <td>24.028477</td>\n",
       "      <td>alto_gasto_pagamento_alto</td>\n",
       "      <td>516.809083</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>37932</td>\n",
       "      <td>7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>mecanico</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>381.0</td>\n",
       "      <td>24.028477</td>\n",
       "      <td>baixo_gasto_pagamento_alto</td>\n",
       "      <td>319.164979</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>37932</td>\n",
       "      <td>8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>mecanico</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>382.0</td>\n",
       "      <td>24.028477</td>\n",
       "      <td>alto_gasto_pagamento_medio</td>\n",
       "      <td>393.673696</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 25 columns</p>\n",
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      "text/plain": [
       "       id_cliente  mes  idade  profissao  salario_anual  num_contas  \\\n",
       "0            3392    1   23.0  cientista       19114.12         3.0   \n",
       "1            3392    2   23.0  cientista       19114.12         3.0   \n",
       "2            3392    3   23.0  cientista       19114.12         3.0   \n",
       "3            3392    4   23.0  cientista       19114.12         3.0   \n",
       "4            3392    5   23.0  cientista       19114.12         3.0   \n",
       "...           ...  ...    ...        ...            ...         ...   \n",
       "99995       37932    4   25.0   mecanico       39628.99         4.0   \n",
       "99996       37932    5   25.0   mecanico       39628.99         4.0   \n",
       "99997       37932    6   25.0   mecanico       39628.99         4.0   \n",
       "99998       37932    7   25.0   mecanico       39628.99         4.0   \n",
       "99999       37932    8   25.0   mecanico       39628.99         4.0   \n",
       "\n",
       "       num_cartoes  juros_emprestimo  num_emprestimos  dias_atraso  ...  \\\n",
       "0              4.0               3.0              4.0          3.0  ...   \n",
       "1              4.0               3.0              4.0          3.0  ...   \n",
       "2              4.0               3.0              4.0          3.0  ...   \n",
       "3              4.0               3.0              4.0          5.0  ...   \n",
       "4              4.0               3.0              4.0          6.0  ...   \n",
       "...            ...               ...              ...          ...  ...   \n",
       "99995          6.0               7.0              2.0         23.0  ...   \n",
       "99996          6.0               7.0              2.0         18.0  ...   \n",
       "99997          6.0               7.0              2.0         27.0  ...   \n",
       "99998          6.0               7.0              2.0         20.0  ...   \n",
       "99999          6.0               7.0              2.0         18.0  ...   \n",
       "\n",
       "       idade_historico_credito  investimento_mensal  \\\n",
       "0                        265.0            21.465380   \n",
       "1                        266.0            21.465380   \n",
       "2                        267.0            21.465380   \n",
       "3                        268.0            21.465380   \n",
       "4                        269.0            21.465380   \n",
       "...                        ...                  ...   \n",
       "99995                    378.0            24.028477   \n",
       "99996                    379.0            24.028477   \n",
       "99997                    380.0            24.028477   \n",
       "99998                    381.0            24.028477   \n",
       "99999                    382.0            24.028477   \n",
       "\n",
       "           comportamento_pagamento  saldo_final_mes  score_credito  \\\n",
       "0      alto_gasto_pagamento_baixos       312.494089           Good   \n",
       "1       baixo_gasto_pagamento_alto       284.629162           Good   \n",
       "2      baixo_gasto_pagamento_medio       331.209863           Good   \n",
       "3      baixo_gasto_pagamento_baixo       223.451310           Good   \n",
       "4       alto_gasto_pagamento_medio       341.489231           Good   \n",
       "...                            ...              ...            ...   \n",
       "99995    alto_gasto_pagamento_alto       479.866228           Poor   \n",
       "99996   alto_gasto_pagamento_medio       496.651610           Poor   \n",
       "99997    alto_gasto_pagamento_alto       516.809083           Poor   \n",
       "99998   baixo_gasto_pagamento_alto       319.164979       Standard   \n",
       "99999   alto_gasto_pagamento_medio       393.673696           Poor   \n",
       "\n",
       "       emprestimo_carro  emprestimo_casa emprestimo_pessoal  \\\n",
       "0                     1                1                  1   \n",
       "1                     1                1                  1   \n",
       "2                     1                1                  1   \n",
       "3                     1                1                  1   \n",
       "4                     1                1                  1   \n",
       "...                 ...              ...                ...   \n",
       "99995                 1                0                  0   \n",
       "99996                 1                0                  0   \n",
       "99997                 1                0                  0   \n",
       "99998                 1                0                  0   \n",
       "99999                 1                0                  0   \n",
       "\n",
       "       emprestimo_credito emprestimo_estudantil  \n",
       "0                       1                     0  \n",
       "1                       1                     0  \n",
       "2                       1                     0  \n",
       "3                       1                     0  \n",
       "4                       1                     0  \n",
       "...                   ...                   ...  \n",
       "99995                   0                     1  \n",
       "99996                   0                     1  \n",
       "99997                   0                     1  \n",
       "99998                   0                     1  \n",
       "99999                   0                     1  \n",
       "\n",
       "[100000 rows x 25 columns]"
      ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   id_cliente                100000 non-null  int64  \n",
      " 1   mes                       100000 non-null  int64  \n",
      " 2   idade                     100000 non-null  float64\n",
      " 3   profissao                 100000 non-null  object \n",
      " 4   salario_anual             100000 non-null  float64\n",
      " 5   num_contas                100000 non-null  float64\n",
      " 6   num_cartoes               100000 non-null  float64\n",
      " 7   juros_emprestimo          100000 non-null  float64\n",
      " 8   num_emprestimos           100000 non-null  float64\n",
      " 9   dias_atraso               100000 non-null  float64\n",
      " 10  num_pagamentos_atrasados  100000 non-null  float64\n",
      " 11  num_verificacoes_credito  100000 non-null  float64\n",
      " 12  mix_credito               100000 non-null  object \n",
      " 13  divida_total              100000 non-null  float64\n",
      " 14  taxa_uso_credito          100000 non-null  float64\n",
      " 15  idade_historico_credito   100000 non-null  float64\n",
      " 16  investimento_mensal       100000 non-null  float64\n",
      " 17  comportamento_pagamento   100000 non-null  object \n",
      " 18  saldo_final_mes           100000 non-null  float64\n",
      " 19  score_credito             100000 non-null  object \n",
      " 20  emprestimo_carro          100000 non-null  int64  \n",
      " 21  emprestimo_casa           100000 non-null  int64  \n",
      " 22  emprestimo_pessoal        100000 non-null  int64  \n",
      " 23  emprestimo_credito        100000 non-null  int64  \n",
      " 24  emprestimo_estudantil     100000 non-null  int64  \n",
      "dtypes: float64(14), int64(7), object(4)\n",
      "memory usage: 19.1+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "table = pd.read_csv('clientes.csv')\n",
    "display(table)\n",
    "display(table.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passo 3:\n",
    "\n",
    "<p> IA não reconhece textos, deve-se mudar tudo em números (Label Encoder) </p>\n",
    "<ul> Colunas a mudar:\n",
    "<li> profissao </li>\n",
    "<li> mix_credito </li>\n",
    "<li> comportamento_pagamento </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 25 columns):\n",
      " #   Column                    Non-Null Count   Dtype  \n",
      "---  ------                    --------------   -----  \n",
      " 0   id_cliente                100000 non-null  int64  \n",
      " 1   mes                       100000 non-null  int64  \n",
      " 2   idade                     100000 non-null  float64\n",
      " 3   profissao                 100000 non-null  int32  \n",
      " 4   salario_anual             100000 non-null  float64\n",
      " 5   num_contas                100000 non-null  float64\n",
      " 6   num_cartoes               100000 non-null  float64\n",
      " 7   juros_emprestimo          100000 non-null  float64\n",
      " 8   num_emprestimos           100000 non-null  float64\n",
      " 9   dias_atraso               100000 non-null  float64\n",
      " 10  num_pagamentos_atrasados  100000 non-null  float64\n",
      " 11  num_verificacoes_credito  100000 non-null  float64\n",
      " 12  mix_credito               100000 non-null  int32  \n",
      " 13  divida_total              100000 non-null  float64\n",
      " 14  taxa_uso_credito          100000 non-null  float64\n",
      " 15  idade_historico_credito   100000 non-null  float64\n",
      " 16  investimento_mensal       100000 non-null  float64\n",
      " 17  comportamento_pagamento   100000 non-null  int32  \n",
      " 18  saldo_final_mes           100000 non-null  float64\n",
      " 19  score_credito             100000 non-null  object \n",
      " 20  emprestimo_carro          100000 non-null  int64  \n",
      " 21  emprestimo_casa           100000 non-null  int64  \n",
      " 22  emprestimo_pessoal        100000 non-null  int64  \n",
      " 23  emprestimo_credito        100000 non-null  int64  \n",
      " 24  emprestimo_estudantil     100000 non-null  int64  \n",
      "dtypes: float64(14), int32(3), int64(7), object(1)\n",
      "memory usage: 17.9+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criar label encoder\n",
    "codex = LabelEncoder()\n",
    "\n",
    "# Aplicar o encoder na coluna\n",
    "table['profissao'] = codex.fit_transform(table['profissao'])\n",
    "table['mix_credito'] = codex.fit_transform(table['mix_credito'])\n",
    "table['comportamento_pagamento'] = codex.fit_transform(table['comportamento_pagamento'])\n",
    "display(table.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividir a tabela:\n",
    "# X= quem eu irei usar \n",
    "# Y= quem eu quero prever (score_credito)\n",
    "\n",
    "Y = table['score_credito']\n",
    "X = table.drop(columns=['score_credito', 'id_cliente'])\n",
    "\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.2) # importante manter a ordem das variaveis, test_size = proporção usada entre treino e teste"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passo 4:\n",
    "\n",
    "<ul> Modelos usados:\n",
    "<li> Árvore de decisão </li>\n",
    "<li> KNN </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criar os modelos:\n",
    "Dtree = RandomForestClassifier()\n",
    "KNN = KNeighborsClassifier()\n",
    "\n",
    "# Treinar os modelos\n",
    "Dtree.fit(Xtrain, Ytrain)\n",
    "KNN.fit(Xtrain, Ytrain)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passo 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8327\n",
      "0.74995\n"
     ]
    }
   ],
   "source": [
    "# Testar os modelos\n",
    "predTree = Dtree.predict(Xtest)\n",
    "predKnn = KNN.predict(Xtest)\n",
    "\n",
    "print(accuracy_score(Ytest, predTree))  \n",
    "print(accuracy_score(Ytest, predKnn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passo 6:\n",
    "\n",
    "<p> Melhor modelo dado no passo anterior: Árvore de decisão </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Poor' 'Good' 'Standard']\n"
     ]
    }
   ],
   "source": [
    "newTable = pd.read_csv('novos_clientes.csv')\n",
    "\n",
    "newTable['profissao'] = codex.fit_transform(newTable['profissao'])\n",
    "newTable['mix_credito'] = codex.fit_transform(newTable['mix_credito'])\n",
    "newTable['comportamento_pagamento'] = codex.fit_transform(newTable['comportamento_pagamento'])\n",
    "\n",
    "predNewTable = Dtree.predict(newTable)\n",
    "print(predNewTable)"
   ]
  }
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