Credit_Risk.ipynb

{
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  {
   "cell_type": "markdown",
   "source": [
    "# Challange Credit Risk\n",
    "Este Notebook está enfocada en la resolución de un problema del sector de la banca, tiene el objetivo de la detección de riesgos en la colocación de crétidos "
   ],
   "metadata": {
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   "cell_type": "markdown",
   "source": [
    "## Descripción del problema\n",
    "**RISK MODEL:** El problema se centra en la necesidad de la empresa *X* de poder hacer una clasificación correcta de clientes (buen cliente y mal cliente) que le permite mínimizar los riesgos en la colocación de créditos.\n",
    "\n",
    "**PRIMERA PARTE** se centra en la creación de un modelo que permita minimizar riesgos en la colocación de créditos. Dado la naturaleza de los datos de entrenamiento (los datos ya tienen una etiqueta binaria de clientes pasados donde se ha documentado si fueron \"buenos o malos clientes\") esta etapa se abordará con un modelo de clasificación binaria. Existe una amplia variedad de modelos que podríamos usar para abordar este tipo de problemas (Naive bayes, SVM, Random Forest, Redes neuronales entre otros). Sin embargo, dado que los datos que usaremos son tabulares y el problema es concretamente una clasificación binaria optaremos por explorar los siguientes modelos: \n",
    " \n",
    "- XGboost \n",
    "- Regresión logística \n",
    "\n",
    "**SEGUNDA PARTE** se centra simplemente en una recomendación de como poder ir más allá de este primer modelo y genera una solución que permita asignar tasas de interés a cada cliente."
   ],
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   "cell_type": "markdown",
   "source": [
    "**NOTAS IMPORTANTES:**\n",
    "1) No todos los clientes en el main_dataset tienen historial en el credit_reports"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**CARGA DE LIBRERIAS**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import duckdb # Me permite interactuar con sentencias sql en mis pandas df\n",
    "import plotly.express as px # Visualizaciones\n",
    "import plotly.io as pio\n",
    "from datetime import datetime\n",
    "import xgboost as xgb\n",
    "from xgboost import plot_importance\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pio.renderers.default = 'notebook'\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:12.994936Z",
     "start_time": "2023-10-30T20:11:11.421137Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 1) Lectura de datos y análisis inicial\n"
   ],
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   }
  },
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   "cell_type": "markdown",
   "source": [
    "### 1.1) Lectura de datos\n",
    "**Comentario en la carga y procesamiento de datos:** Usar el nuevo backed de pandas incluido desde pandas 2.0 puede minimizar hasta 3 veces el espacio en memoria que demanda en pandas DF (esto se nota principalmente en archivos grandes). Para procesamiento de volúmenes de datos mayores a memoria podemos herramientas de cómputo distribuido como Dask o Spark de manera local en la nube "
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    {
     "data": {
      "text/plain": "       customer_id  loan_id ACC_CREATION_DATETIME APPLICATION_DATETIME  \\\n0             1223        1   2021-08-23 13:57:56  2022-04-26 07:00:00   \n1             5190        2   2022-04-26 09:57:25  2022-04-26 07:00:00   \n2             5194        3   2022-04-26 12:22:35  2022-04-26 07:00:00   \n3             3978        4   2022-03-09 11:26:55  2022-04-26 07:00:00   \n4             4535        5   2022-04-01 14:28:42  2022-04-26 07:00:00   \n...            ...      ...                   ...                  ...   \n14449        14452    14450   2023-05-15 12:02:51  2023-05-15 07:00:00   \n14450        14453    14451   2023-05-16 12:54:39  2023-05-16 07:00:00   \n14451        14454    14452   2023-05-19 19:55:04  2023-05-19 07:00:00   \n14452          768    14453   2021-06-28 14:36:50  2023-05-22 07:00:00   \n14453        12079    14454   2022-12-24 17:19:26  2023-05-26 07:00:00   \n\n      LOAN_ORIGINATION_DATETIME  max_days_late  target  \\\n0           2022-10-20 18:26:35              5       0   \n1           2022-12-22 18:03:32              2       0   \n2           2022-08-09 17:31:05              4       0   \n3           2022-11-11 19:48:08              2       0   \n4           2022-07-24 12:07:15              2       0   \n...                         ...            ...     ...   \n14449       2023-05-21 14:56:30              5       0   \n14450       2023-05-19 21:07:56              0       0   \n14451       2023-05-22 15:14:50              0       0   \n14452       2023-05-26 07:12:16              0       0   \n14453       2023-05-29 12:18:28              0       0   \n\n       account_to_application_days  n_sf_apps       first_app_date  \\\n0                              245        1.0  2022-04-25 00:00:00   \n1                                0       <NA>                 <NA>   \n2                                0       <NA>                 <NA>   \n3                               47        1.0  2022-03-17 00:00:00   \n4                               24        1.0  2022-04-01 00:00:00   \n...                            ...        ...                  ...   \n14449                            0       <NA>                 <NA>   \n14450                            0       <NA>                 <NA>   \n14451                            0       <NA>                 <NA>   \n14452                          692       <NA>                 <NA>   \n14453                          152        1.0  2022-12-24 00:00:00   \n\n             last_app_date  n_bnpl_apps  n_bnpl_approved_apps  \\\n0      2022-04-25 00:00:00          1.0                   0.0   \n1                     <NA>         <NA>                  <NA>   \n2                     <NA>         <NA>                  <NA>   \n3      2022-03-17 00:00:00          2.0                   0.0   \n4      2022-04-01 00:00:00          1.0                   0.0   \n...                    ...          ...                   ...   \n14449                 <NA>         <NA>                  <NA>   \n14450                 <NA>         <NA>                  <NA>   \n14451                 <NA>         <NA>                  <NA>   \n14452                 <NA>          3.0                   0.0   \n14453  2022-12-24 00:00:00          1.0                   0.0   \n\n              first_bnpl_app_date          last_bnpl_app_date  \\\n0      2022-04-26 02:15:17.742000  2022-04-26 02:15:17.742000   \n1                            <NA>                        <NA>   \n2                            <NA>                        <NA>   \n3      2022-03-19 22:02:50.555000  2022-03-09 16:41:49.675000   \n4      2022-04-01 19:37:56.185000  2022-04-01 19:37:56.185000   \n...                           ...                         ...   \n14449                        <NA>                        <NA>   \n14450                        <NA>                        <NA>   \n14451                        <NA>                        <NA>   \n14452         2023-05-20 17:15:47         2023-05-17 15:20:48   \n14453  2022-12-25 05:05:05.841000  2022-12-25 05:05:05.841000   \n\n       n_inquiries_l3m  n_inquiries_l6m  \n0                 91.0             91.0  \n1                 <NA>             <NA>  \n2                 <NA>             <NA>  \n3                 13.0             13.0  \n4                 21.0             21.0  \n...                ...              ...  \n14449             <NA>             <NA>  \n14450             <NA>             <NA>  \n14451             <NA>             <NA>  \n14452             10.0             10.0  \n14453              0.0             25.0  \n\n[14454 rows x 17 columns]",
      "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>customer_id</th>\n      <th>loan_id</th>\n      <th>ACC_CREATION_DATETIME</th>\n      <th>APPLICATION_DATETIME</th>\n      <th>LOAN_ORIGINATION_DATETIME</th>\n      <th>max_days_late</th>\n      <th>target</th>\n      <th>account_to_application_days</th>\n      <th>n_sf_apps</th>\n      <th>first_app_date</th>\n      <th>last_app_date</th>\n      <th>n_bnpl_apps</th>\n      <th>n_bnpl_approved_apps</th>\n      <th>first_bnpl_app_date</th>\n      <th>last_bnpl_app_date</th>\n      <th>n_inquiries_l3m</th>\n      <th>n_inquiries_l6m</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1223</td>\n      <td>1</td>\n      <td>2021-08-23 13:57:56</td>\n      <td>2022-04-26 07:00:00</td>\n      <td>2022-10-20 18:26:35</td>\n      <td>5</td>\n      <td>0</td>\n      <td>245</td>\n      <td>1.0</td>\n      <td>2022-04-25 00:00:00</td>\n      <td>2022-04-25 00:00:00</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2022-04-26 02:15:17.742000</td>\n      <td>2022-04-26 02:15:17.742000</td>\n      <td>91.0</td>\n      <td>91.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5190</td>\n      <td>2</td>\n      <td>2022-04-26 09:57:25</td>\n      <td>2022-04-26 07:00:00</td>\n      <td>2022-12-22 18:03:32</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5194</td>\n      <td>3</td>\n      <td>2022-04-26 12:22:35</td>\n      <td>2022-04-26 07:00:00</td>\n      <td>2022-08-09 17:31:05</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3978</td>\n      <td>4</td>\n      <td>2022-03-09 11:26:55</td>\n      <td>2022-04-26 07:00:00</td>\n      <td>2022-11-11 19:48:08</td>\n      <td>2</td>\n      <td>0</td>\n      <td>47</td>\n      <td>1.0</td>\n      <td>2022-03-17 00:00:00</td>\n      <td>2022-03-17 00:00:00</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>2022-03-19 22:02:50.555000</td>\n      <td>2022-03-09 16:41:49.675000</td>\n      <td>13.0</td>\n      <td>13.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4535</td>\n      <td>5</td>\n      <td>2022-04-01 14:28:42</td>\n      <td>2022-04-26 07:00:00</td>\n      <td>2022-07-24 12:07:15</td>\n      <td>2</td>\n      <td>0</td>\n      <td>24</td>\n      <td>1.0</td>\n      <td>2022-04-01 00:00:00</td>\n      <td>2022-04-01 00:00:00</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2022-04-01 19:37:56.185000</td>\n      <td>2022-04-01 19:37:56.185000</td>\n      <td>21.0</td>\n      <td>21.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14449</th>\n      <td>14452</td>\n      <td>14450</td>\n      <td>2023-05-15 12:02:51</td>\n      <td>2023-05-15 07:00:00</td>\n      <td>2023-05-21 14:56:30</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n    </tr>\n    <tr>\n      <th>14450</th>\n      <td>14453</td>\n      <td>14451</td>\n      <td>2023-05-16 12:54:39</td>\n      <td>2023-05-16 07:00:00</td>\n      <td>2023-05-19 21:07:56</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n    </tr>\n    <tr>\n      <th>14451</th>\n      <td>14454</td>\n      <td>14452</td>\n      <td>2023-05-19 19:55:04</td>\n      <td>2023-05-19 07:00:00</td>\n      <td>2023-05-22 15:14:50</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n    </tr>\n    <tr>\n      <th>14452</th>\n      <td>768</td>\n      <td>14453</td>\n      <td>2021-06-28 14:36:50</td>\n      <td>2023-05-22 07:00:00</td>\n      <td>2023-05-26 07:12:16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>692</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>&lt;NA&gt;</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>2023-05-20 17:15:47</td>\n      <td>2023-05-17 15:20:48</td>\n      <td>10.0</td>\n      <td>10.0</td>\n    </tr>\n    <tr>\n      <th>14453</th>\n      <td>12079</td>\n      <td>14454</td>\n      <td>2022-12-24 17:19:26</td>\n      <td>2023-05-26 07:00:00</td>\n      <td>2023-05-29 12:18:28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>152</td>\n      <td>1.0</td>\n      <td>2022-12-24 00:00:00</td>\n      <td>2022-12-24 00:00:00</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>2022-12-25 05:05:05.841000</td>\n      <td>2022-12-25 05:05:05.841000</td>\n      <td>0.0</td>\n      <td>25.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>14454 rows × 17 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_dataset   = pd.read_parquet(\"main_dataset.parquet\", dtype_backend=\"pyarrow\") \n",
    "main_dataset"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:13.564905Z",
     "start_time": "2023-10-30T20:11:12.996016Z"
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  },
  {
   "cell_type": "markdown",
   "source": [
    "**BREVE DESCRIPCIÓN:** Cada registro de este dataset corresponde al primer credito de cada cliente, adicionalmente este dataset contiene una variable objetivo, esta variable objetivo es una variable binaria que corresponde a buen o mal cliente"
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   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "        customer_id                      INQUIRY_TIME  \\\n0              4223  2022-04-01 00:32:36.689000+00:00   \n1              4223  2022-04-01 00:32:36.689000+00:00   \n2              4223  2022-04-01 00:32:36.689000+00:00   \n3              3490  2022-02-15 02:30:22.086000+00:00   \n4              6486  2022-06-25 01:57:14.868000+00:00   \n...             ...                               ...   \n287351        12079  2022-12-24 22:21:36.102000+00:00   \n287352        12079  2022-12-24 22:21:36.102000+00:00   \n287353        12079  2022-12-24 22:21:36.102000+00:00   \n287354        12079  2022-12-24 22:21:36.102000+00:00   \n287355        12079  2022-12-24 22:21:36.102000+00:00   \n\n                                      CDC_INQUIRY_ID         INQUIRY_DATE  \\\n0        710278-27993a6e-2885-48d4-a753-9249e7022af1  2022-04-01 00:00:00   \n1        710278-27993a6e-2885-48d4-a753-9249e7022af1  2022-04-01 00:00:00   \n2        710278-27993a6e-2885-48d4-a753-9249e7022af1  2022-04-01 00:00:00   \n3        622857-6b4e9d95-7491-40c3-bccd-442de7f94c58  2022-02-15 00:00:00   \n4        875073-46a5f149-19db-4193-b92a-b8f41fb3e896  2022-06-25 00:00:00   \n...                                              ...                  ...   \n287351  1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a  2022-12-24 00:00:00   \n287352  1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a  2022-12-24 00:00:00   \n287353  1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a  2022-12-24 00:00:00   \n287354  1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a  2022-12-24 00:00:00   \n287355  1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a  2022-12-24 00:00:00   \n\n                    PREVENTION_KEY         CURRENCY  MAX_CREDIT  CREDIT_LIMIT  \\\n0                  SIN OBSERVACION  PESOS MEXICANOS      1244.0        9900.0   \n1                  SIN OBSERVACION  PESOS MEXICANOS      1722.0        9900.0   \n2                  SIN OBSERVACION  PESOS MEXICANOS      6346.0           0.0   \n3                  SIN OBSERVACION  PESOS MEXICANOS     11600.0           0.0   \n4                  SIN OBSERVACION  PESOS MEXICANOS      2452.0       16800.0   \n...                            ...              ...         ...           ...   \n287351             SIN OBSERVACION  PESOS MEXICANOS      1609.0        1609.0   \n287352             SIN OBSERVACION  PESOS MEXICANOS       248.0         248.0   \n287353             SIN OBSERVACION  PESOS MEXICANOS      3267.0        3267.0   \n287354             SIN OBSERVACION  PESOS MEXICANOS       496.0         496.0   \n287355  CUENTA QUE CAUSA QUEBRANTO  PESOS MEXICANOS     26458.0           0.0   \n\n        PAYMENT_AMOUNT          UPDATE_DATE  ... ACCOUNT_TYPE  \\\n0               1244.0  2022-02-28 00:00:00  ...  PAGOS FIJOS   \n1               1722.0  2022-02-28 00:00:00  ...   REVOLVENTE   \n2                482.0  2022-03-28 00:00:00  ...  PAGOS FIJOS   \n3                232.0  2022-02-01 00:00:00  ...  PAGOS FIJOS   \n4               2452.0  2021-09-30 00:00:00  ...   REVOLVENTE   \n...                ...                  ...  ...          ...   \n287351           634.0  2022-12-01 00:00:00  ...  PAGOS FIJOS   \n287352           263.0  2022-12-01 00:00:00  ...  PAGOS FIJOS   \n287353          1243.0  2022-12-01 00:00:00  ...  PAGOS FIJOS   \n287354           189.0  2022-12-01 00:00:00  ...  PAGOS FIJOS   \n287355          9882.0  2022-12-15 00:00:00  ...  PAGOS FIJOS   \n\n         RESPONSABILITY_TYPE TOTAL_PAYMENTS DELAYED_PAYMENTS CURRENT_PAYMENT  \\\n0       INDIVIDUAL (TITULAR)           12.0                7              13   \n1       INDIVIDUAL (TITULAR)            0.0                3              13   \n2       INDIVIDUAL (TITULAR)           16.0                0               V   \n3       INDIVIDUAL (TITULAR)          100.0                4              01   \n4       INDIVIDUAL (TITULAR)            0.0               19              13   \n...                      ...            ...              ...             ...   \n287351  INDIVIDUAL (TITULAR)            3.0               11              01   \n287352  INDIVIDUAL (TITULAR)            1.0               11              01   \n287353  INDIVIDUAL (TITULAR)            3.0               10              03   \n287354  INDIVIDUAL (TITULAR)            3.0               10              03   \n287355  INDIVIDUAL (TITULAR)          102.0               73              84   \n\n       WORST_DELAY TOTAL_REPORTED_PAYMENTS CURRENT_BALANCE BALANCE_DUE  \\\n0             13.0                       0          1244.0      1244.0   \n1             13.0                       0          1722.0      1722.0   \n2              0.0                       0           943.0         0.0   \n3              1.0                       0          6185.0       116.0   \n4             13.0                       0          2452.0      2452.0   \n...            ...                     ...             ...         ...   \n287351         1.0                       0           634.0       634.0   \n287352         1.0                       0           263.0       263.0   \n287353         3.0                       0          3666.0      3666.0   \n287354         3.0                       0           557.0       557.0   \n287355        84.0                       0          9882.0      9882.0   \n\n       BALANCE_DUE_WORST_DELAY  \n0                       1244.0  \n1                       1722.0  \n2                          0.0  \n3                        116.0  \n4                       2452.0  \n...                        ...  \n287351                   634.0  \n287352                   263.0  \n287353                  3666.0  \n287354                   557.0  \n287355                  9882.0  \n\n[287356 rows x 29 columns]",
      "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>customer_id</th>\n      <th>INQUIRY_TIME</th>\n      <th>CDC_INQUIRY_ID</th>\n      <th>INQUIRY_DATE</th>\n      <th>PREVENTION_KEY</th>\n      <th>CURRENCY</th>\n      <th>MAX_CREDIT</th>\n      <th>CREDIT_LIMIT</th>\n      <th>PAYMENT_AMOUNT</th>\n      <th>UPDATE_DATE</th>\n      <th>...</th>\n      <th>ACCOUNT_TYPE</th>\n      <th>RESPONSABILITY_TYPE</th>\n      <th>TOTAL_PAYMENTS</th>\n      <th>DELAYED_PAYMENTS</th>\n      <th>CURRENT_PAYMENT</th>\n      <th>WORST_DELAY</th>\n      <th>TOTAL_REPORTED_PAYMENTS</th>\n      <th>CURRENT_BALANCE</th>\n      <th>BALANCE_DUE</th>\n      <th>BALANCE_DUE_WORST_DELAY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>4223</td>\n      <td>2022-04-01 00:32:36.689000+00:00</td>\n      <td>710278-27993a6e-2885-48d4-a753-9249e7022af1</td>\n      <td>2022-04-01 00:00:00</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>1244.0</td>\n      <td>9900.0</td>\n      <td>1244.0</td>\n      <td>2022-02-28 00:00:00</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>12.0</td>\n      <td>7</td>\n      <td>13</td>\n      <td>13.0</td>\n      <td>0</td>\n      <td>1244.0</td>\n      <td>1244.0</td>\n      <td>1244.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4223</td>\n      <td>2022-04-01 00:32:36.689000+00:00</td>\n      <td>710278-27993a6e-2885-48d4-a753-9249e7022af1</td>\n      <td>2022-04-01 00:00:00</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>1722.0</td>\n      <td>9900.0</td>\n      <td>1722.0</td>\n      <td>2022-02-28 00:00:00</td>\n      <td>...</td>\n      <td>REVOLVENTE</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>13</td>\n      <td>13.0</td>\n      <td>0</td>\n      <td>1722.0</td>\n      <td>1722.0</td>\n      <td>1722.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4223</td>\n      <td>2022-04-01 00:32:36.689000+00:00</td>\n      <td>710278-27993a6e-2885-48d4-a753-9249e7022af1</td>\n      <td>2022-04-01 00:00:00</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>6346.0</td>\n      <td>0.0</td>\n      <td>482.0</td>\n      <td>2022-03-28 00:00:00</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>16.0</td>\n      <td>0</td>\n      <td>V</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>943.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3490</td>\n      <td>2022-02-15 02:30:22.086000+00:00</td>\n      <td>622857-6b4e9d95-7491-40c3-bccd-442de7f94c58</td>\n      <td>2022-02-15 00:00:00</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>11600.0</td>\n      <td>0.0</td>\n      <td>232.0</td>\n      <td>2022-02-01 00:00:00</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>100.0</td>\n      <td>4</td>\n      <td>01</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>6185.0</td>\n      <td>116.0</td>\n      <td>116.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6486</td>\n      <td>2022-06-25 01:57:14.868000+00:00</td>\n      <td>875073-46a5f149-19db-4193-b92a-b8f41fb3e896</td>\n      <td>2022-06-25 00:00:00</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>2452.0</td>\n      <td>16800.0</td>\n      <td>2452.0</td>\n      <td>2021-09-30 00:00:00</td>\n      <td>...</td>\n      <td>REVOLVENTE</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>0.0</td>\n      <td>19</td>\n      <td>13</td>\n      <td>13.0</td>\n      <td>0</td>\n      <td>2452.0</td>\n      <td>2452.0</td>\n      <td>2452.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>287351</th>\n      <td>12079</td>\n      <td>2022-12-24 22:21:36.102000+00:00</td>\n      <td>1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a</td>\n      <td>2022-12-24 00:00:00</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>1609.0</td>\n      <td>1609.0</td>\n      <td>634.0</td>\n      <td>2022-12-01 00:00:00</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>3.0</td>\n      <td>11</td>\n      <td>01</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>634.0</td>\n      <td>634.0</td>\n      <td>634.0</td>\n    </tr>\n    <tr>\n      <th>287352</th>\n      <td>12079</td>\n      <td>2022-12-24 22:21:36.102000+00:00</td>\n      <td>1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a</td>\n      <td>2022-12-24 00:00:00</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>248.0</td>\n      <td>248.0</td>\n      <td>263.0</td>\n      <td>2022-12-01 00:00:00</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>1.0</td>\n      <td>11</td>\n      <td>01</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>263.0</td>\n      <td>263.0</td>\n      <td>263.0</td>\n    </tr>\n    <tr>\n      <th>287353</th>\n      <td>12079</td>\n      <td>2022-12-24 22:21:36.102000+00:00</td>\n      <td>1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a</td>\n      <td>2022-12-24 00:00:00</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>3267.0</td>\n      <td>3267.0</td>\n      <td>1243.0</td>\n      <td>2022-12-01 00:00:00</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>3.0</td>\n      <td>10</td>\n      <td>03</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>3666.0</td>\n      <td>3666.0</td>\n      <td>3666.0</td>\n    </tr>\n    <tr>\n      <th>287354</th>\n      <td>12079</td>\n      <td>2022-12-24 22:21:36.102000+00:00</td>\n      <td>1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a</td>\n      <td>2022-12-24 00:00:00</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>496.0</td>\n      <td>496.0</td>\n      <td>189.0</td>\n      <td>2022-12-01 00:00:00</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>3.0</td>\n      <td>10</td>\n      <td>03</td>\n      <td>3.0</td>\n      <td>0</td>\n      <td>557.0</td>\n      <td>557.0</td>\n      <td>557.0</td>\n    </tr>\n    <tr>\n      <th>287355</th>\n      <td>12079</td>\n      <td>2022-12-24 22:21:36.102000+00:00</td>\n      <td>1053785-35891c8d-97ba-4dce-9f5f-e7bf8684473a</td>\n      <td>2022-12-24 00:00:00</td>\n      <td>CUENTA QUE CAUSA QUEBRANTO</td>\n      <td>PESOS MEXICANOS</td>\n      <td>26458.0</td>\n      <td>0.0</td>\n      <td>9882.0</td>\n      <td>2022-12-15 00:00:00</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>102.0</td>\n      <td>73</td>\n      <td>84</td>\n      <td>84.0</td>\n      <td>0</td>\n      <td>9882.0</td>\n      <td>9882.0</td>\n      <td>9882.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>287356 rows × 29 columns</p>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Información externa de buro de crédito\n",
    "credit_reports = pd.read_parquet(\"credit_reports.parquet\", dtype_backend=\"pyarrow\")\n",
    "credit_reports\n",
    "# El cliente ID aparece varias veces, contiene registros históricos lo que puede variar el estatus o calidad crediticia de la persona"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:13.566164Z",
     "start_time": "2023-10-30T20:11:13.083103Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 1.2) Análisis explotario de datos  (AED)\n",
    "Antes de iniciar el tratamiento de los datos  es recomendable hacer una breve inspección y exploración de los datos"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 1.2.1) Coicidencia entre sets de datos\n",
    "Veremos cuantos clientes tiene cada set de datos y en cuantos de ellos hay una coicidencia"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cantidad de clientes en el set de datos 'main_dataset': 14454\n",
      "Cantidad de clientes en el 'credit_report': 9249\n"
     ]
    }
   ],
   "source": [
    "print(\"Cantidad de clientes en el set de datos 'main_dataset':\", len(main_dataset[\"customer_id\"].unique()))\n",
    "print(\"Cantidad de clientes en el 'credit_report':\", len(credit_reports[\"customer_id\"].unique()))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:13.647405Z",
     "start_time": "2023-10-30T20:11:13.552495Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**IMPORTANTE:**  Hay una diferencia importante en el número de clientes sin historial. Voy a tomar la desición de solo continuar con los clientes que cuentan con datos historicos para el proceso de entrenamiento. Esto dado la importancia en los datos contenidos en el credit_report"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 1.2.2) Valores nulos\n",
    "Exploración de registros nulos en ambos set de datos"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "customer_id                       0\nloan_id                           0\nACC_CREATION_DATETIME             0\nAPPLICATION_DATETIME              0\nLOAN_ORIGINATION_DATETIME         0\nmax_days_late                     0\ntarget                            0\naccount_to_application_days       0\nn_sf_apps                      7648\nfirst_app_date                 7648\nlast_app_date                  7648\nn_bnpl_apps                    5715\nn_bnpl_approved_apps           5715\nfirst_bnpl_app_date            5715\nlast_bnpl_app_date             5715\nn_inquiries_l3m                5371\nn_inquiries_l6m                5371\ndtype: int64"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspección de valores nulos en el main dataset (datos internos de la empresa)\n",
    "main_dataset.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:15.976010Z",
     "start_time": "2023-10-30T20:11:15.524712Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "customer_id                     0\nINQUIRY_TIME                    0\nCDC_INQUIRY_ID                 89\nINQUIRY_DATE                   89\nPREVENTION_KEY                 89\nCURRENCY                       89\nMAX_CREDIT                    182\nCREDIT_LIMIT                 8357\nPAYMENT_AMOUNT                 89\nUPDATE_DATE                    89\nLOAN_OPENING_DATE              89\nLOAN_CLOSING_DATE           94747\nWORST_DELAY_DATE           202698\nREPORT_DATE                    89\nLAST_PURCHASE_DATE           4142\nLAST_PAYMENT_DATE           31677\nPAYMENT_FREQUENCY              89\nBUSINESS_TYPE                  89\nCREDIT_TYPE                    89\nACCOUNT_TYPE                   89\nRESPONSABILITY_TYPE            89\nTOTAL_PAYMENTS              18645\nDELAYED_PAYMENTS               89\nCURRENT_PAYMENT                89\nWORST_DELAY                  3210\nTOTAL_REPORTED_PAYMENTS     41941\nCURRENT_BALANCE                89\nBALANCE_DUE                    89\nBALANCE_DUE_WORST_DELAY        89\ndtype: int64"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revision de valores nulos en el dataset de datos externos (buro de credito)\n",
    "credit_reports.isnull().sum()  # Se observa valores nulos en varias columnas"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:16.079782Z",
     "start_time": "2023-10-30T20:11:15.927348Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 1.2.3) Valores duplicados\n",
    "Inspección de valores duplicados en ambos set de datos"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.46 s, sys: 200 ms, total: 5.66 s\n",
      "Wall time: 5.8 s\n"
     ]
    },
    {
     "data": {
      "text/plain": "customer_id                278107\nINQUIRY_TIME               278095\nCDC_INQUIRY_ID             278183\nINQUIRY_DATE               286730\nPREVENTION_KEY             287324\nCURRENCY                   287352\nMAX_CREDIT                 257315\nCREDIT_LIMIT               273117\nPAYMENT_AMOUNT             270117\nUPDATE_DATE                281534\nLOAN_OPENING_DATE          279412\nLOAN_CLOSING_DATE          280188\nWORST_DELAY_DATE           281664\nREPORT_DATE                281542\nLAST_PURCHASE_DATE         279645\nLAST_PAYMENT_DATE          280087\nPAYMENT_FREQUENCY          287344\nBUSINESS_TYPE              287297\nCREDIT_TYPE                287322\nACCOUNT_TYPE               287349\nRESPONSABILITY_TYPE        287350\nTOTAL_PAYMENTS             287182\nDELAYED_PAYMENTS           287258\nCURRENT_PAYMENT            287269\nWORST_DELAY                287270\nTOTAL_REPORTED_PAYMENTS    287354\nCURRENT_BALANCE            263622\nBALANCE_DUE                267812\nBALANCE_DUE_WORST_DELAY    269983\ndtype: int64"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "duplicados_por_columna = credit_reports.apply(lambda col: col.duplicated(keep='first').sum())\n",
    "duplicados_por_columna # Este DF contiene un número importante de valores duplicados, la documentación menciona que"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:22.715031Z",
     "start_time": "2023-10-30T20:11:16.914910Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 1.2.4) Inspección de algunas variables en el dataset de credit_reports\n",
    "Haremos una exploración de un par de variables con el fin de tener una mejor comprensión del comportamiento crediticio de los clientes.\n",
    "\n",
    "Leyendo la documentación proporcionada me surge el interés por ver algunos puntos previos al tratamiento de datos, estos puntos son los siguientes:\n",
    "\n",
    "- Cantidad de prestamos por tipo de credito solicitado (Variable CREDIT_TYPE)\n",
    "- Cantidad de prestamos por plazo solicitados (Variable PAYMENT_FREQUENCY)\n",
    "- Distribución de limites de crédito"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T15:31:41.724173Z",
     "start_time": "2023-10-30T15:31:41.567417Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 1.2.4.1) Cantidad de prestamos por tipo de crédito solicitado"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T15:31:53.338901Z",
     "start_time": "2023-10-30T15:31:53.333497Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "        customer_id                     INQUIRY_TIME  \\\n0                 1 2022-06-16 18:06:37.677000-05:00   \n1                 1 2022-06-16 18:06:37.677000-05:00   \n2                 1 2022-06-16 18:06:37.677000-05:00   \n3                 1 2022-06-16 18:06:37.677000-05:00   \n4                 1 2022-06-16 18:06:37.677000-05:00   \n...             ...                              ...   \n262854        14416 2023-04-24 17:36:56.539000-06:00   \n262855        14416 2023-04-24 17:36:56.539000-06:00   \n262856        14416 2023-04-24 17:36:56.539000-06:00   \n262857        14416 2023-04-24 17:36:56.539000-06:00   \n262858        14416 2023-04-24 17:36:56.539000-06:00   \n\n                                      CDC_INQUIRY_ID INQUIRY_DATE  \\\n0           102-b978ce50-aa84-4910-92dd-b3c721e7d831   2022-06-16   \n1           102-b978ce50-aa84-4910-92dd-b3c721e7d831   2022-06-16   \n2           102-b978ce50-aa84-4910-92dd-b3c721e7d831   2022-06-16   \n3           102-b978ce50-aa84-4910-92dd-b3c721e7d831   2022-06-16   \n4           102-b978ce50-aa84-4910-92dd-b3c721e7d831   2022-06-16   \n...                                              ...          ...   \n262854  1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b   2023-04-24   \n262855  1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b   2023-04-24   \n262856  1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b   2023-04-24   \n262857  1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b   2023-04-24   \n262858  1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b   2023-04-24   \n\n                    PREVENTION_KEY         CURRENCY  MAX_CREDIT  CREDIT_LIMIT  \\\n0                  SIN OBSERVACION  PESOS MEXICANOS         0.0           0.0   \n1       CUENTA CANCELADA O CERRADA  PESOS MEXICANOS     26509.0       57340.0   \n2                  SIN OBSERVACION  PESOS MEXICANOS      2675.0       20000.0   \n3                  SIN OBSERVACION  PESOS MEXICANOS     25627.0      134000.0   \n4       CUENTA CANCELADA O CERRADA  PESOS MEXICANOS     22710.0       50000.0   \n...                            ...              ...         ...           ...   \n262854  CUENTA CANCELADA O CERRADA  PESOS MEXICANOS      4999.0        4999.0   \n262855             SIN OBSERVACION  PESOS MEXICANOS    121970.0      121970.0   \n262856             SIN OBSERVACION  PESOS MEXICANOS       716.0       15600.0   \n262857             SIN OBSERVACION  PESOS MEXICANOS      9984.0        9984.0   \n262858             SIN OBSERVACION  PESOS MEXICANOS         0.0         500.0   \n\n        PAYMENT_AMOUNT UPDATE_DATE  ...               ACCOUNT_TYPE  \\\n0                  0.0  2022-05-31  ...  SIN LIMITE PREESTABLECIDO   \n1                  0.0  2018-05-30  ...                 REVOLVENTE   \n2                  0.0  2022-06-10  ...  SIN LIMITE PREESTABLECIDO   \n3                  0.0  2022-05-25  ...                 REVOLVENTE   \n4                  0.0  2018-01-24  ...                 REVOLVENTE   \n...                ...         ...  ...                        ...   \n262854             0.0  2019-12-20  ...                PAGOS FIJOS   \n262855        121970.0  2023-03-31  ...  SIN LIMITE PREESTABLECIDO   \n262856         13457.0  2023-02-28  ...                PAGOS FIJOS   \n262857             0.0  2023-04-02  ...                PAGOS FIJOS   \n262858             0.0  2023-03-31  ...                 REVOLVENTE   \n\n         RESPONSABILITY_TYPE TOTAL_PAYMENTS DELAYED_PAYMENTS CURRENT_PAYMENT  \\\n0       INDIVIDUAL (TITULAR)            1.0              0.0               V   \n1       INDIVIDUAL (TITULAR)            NaN              0.0               V   \n2       INDIVIDUAL (TITULAR)            NaN              0.0               V   \n3       INDIVIDUAL (TITULAR)            NaN              1.0               V   \n4       INDIVIDUAL (TITULAR)            NaN              0.0               V   \n...                      ...            ...              ...             ...   \n262854  INDIVIDUAL (TITULAR)            NaN              6.0               V   \n262855  INDIVIDUAL (TITULAR)            0.0             24.0              53   \n262856  INDIVIDUAL (TITULAR)           12.0              7.0              22   \n262857  INDIVIDUAL (TITULAR)           52.0              0.0               V   \n262858  INDIVIDUAL (TITULAR)            0.0              0.0               V   \n\n       WORST_DELAY TOTAL_REPORTED_PAYMENTS CURRENT_BALANCE BALANCE_DUE  \\\n0              0.0                     0.0             0.0         0.0   \n1              0.0                     NaN             0.0         0.0   \n2              0.0                     NaN             0.0         0.0   \n3              1.0                     NaN             0.0         0.0   \n4              0.0                     NaN             0.0         0.0   \n...            ...                     ...             ...         ...   \n262854         2.0                     NaN             0.0         0.0   \n262855        53.0                     0.0        121970.0    121970.0   \n262856        30.0                     0.0         13457.0     13457.0   \n262857         0.0                     0.0             0.0         0.0   \n262858         0.0                     0.0             0.0         0.0   \n\n       BALANCE_DUE_WORST_DELAY  \n0                          0.0  \n1                          0.0  \n2                          0.0  \n3                          0.0  \n4                          0.0  \n...                        ...  \n262854                     0.0  \n262855                121970.0  \n262856                  4511.0  \n262857                     0.0  \n262858                     0.0  \n\n[262859 rows x 29 columns]",
      "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>customer_id</th>\n      <th>INQUIRY_TIME</th>\n      <th>CDC_INQUIRY_ID</th>\n      <th>INQUIRY_DATE</th>\n      <th>PREVENTION_KEY</th>\n      <th>CURRENCY</th>\n      <th>MAX_CREDIT</th>\n      <th>CREDIT_LIMIT</th>\n      <th>PAYMENT_AMOUNT</th>\n      <th>UPDATE_DATE</th>\n      <th>...</th>\n      <th>ACCOUNT_TYPE</th>\n      <th>RESPONSABILITY_TYPE</th>\n      <th>TOTAL_PAYMENTS</th>\n      <th>DELAYED_PAYMENTS</th>\n      <th>CURRENT_PAYMENT</th>\n      <th>WORST_DELAY</th>\n      <th>TOTAL_REPORTED_PAYMENTS</th>\n      <th>CURRENT_BALANCE</th>\n      <th>BALANCE_DUE</th>\n      <th>BALANCE_DUE_WORST_DELAY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2022-06-16 18:06:37.677000-05:00</td>\n      <td>102-b978ce50-aa84-4910-92dd-b3c721e7d831</td>\n      <td>2022-06-16</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2022-05-31</td>\n      <td>...</td>\n      <td>SIN LIMITE PREESTABLECIDO</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>V</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>2022-06-16 18:06:37.677000-05:00</td>\n      <td>102-b978ce50-aa84-4910-92dd-b3c721e7d831</td>\n      <td>2022-06-16</td>\n      <td>CUENTA CANCELADA O CERRADA</td>\n      <td>PESOS MEXICANOS</td>\n      <td>26509.0</td>\n      <td>57340.0</td>\n      <td>0.0</td>\n      <td>2018-05-30</td>\n      <td>...</td>\n      <td>REVOLVENTE</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>V</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>2022-06-16 18:06:37.677000-05:00</td>\n      <td>102-b978ce50-aa84-4910-92dd-b3c721e7d831</td>\n      <td>2022-06-16</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>2675.0</td>\n      <td>20000.0</td>\n      <td>0.0</td>\n      <td>2022-06-10</td>\n      <td>...</td>\n      <td>SIN LIMITE PREESTABLECIDO</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>V</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>2022-06-16 18:06:37.677000-05:00</td>\n      <td>102-b978ce50-aa84-4910-92dd-b3c721e7d831</td>\n      <td>2022-06-16</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>25627.0</td>\n      <td>134000.0</td>\n      <td>0.0</td>\n      <td>2022-05-25</td>\n      <td>...</td>\n      <td>REVOLVENTE</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>V</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>2022-06-16 18:06:37.677000-05:00</td>\n      <td>102-b978ce50-aa84-4910-92dd-b3c721e7d831</td>\n      <td>2022-06-16</td>\n      <td>CUENTA CANCELADA O CERRADA</td>\n      <td>PESOS MEXICANOS</td>\n      <td>22710.0</td>\n      <td>50000.0</td>\n      <td>0.0</td>\n      <td>2018-01-24</td>\n      <td>...</td>\n      <td>REVOLVENTE</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>V</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>262854</th>\n      <td>14416</td>\n      <td>2023-04-24 17:36:56.539000-06:00</td>\n      <td>1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b</td>\n      <td>2023-04-24</td>\n      <td>CUENTA CANCELADA O CERRADA</td>\n      <td>PESOS MEXICANOS</td>\n      <td>4999.0</td>\n      <td>4999.0</td>\n      <td>0.0</td>\n      <td>2019-12-20</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>NaN</td>\n      <td>6.0</td>\n      <td>V</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>262855</th>\n      <td>14416</td>\n      <td>2023-04-24 17:36:56.539000-06:00</td>\n      <td>1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b</td>\n      <td>2023-04-24</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>121970.0</td>\n      <td>121970.0</td>\n      <td>121970.0</td>\n      <td>2023-03-31</td>\n      <td>...</td>\n      <td>SIN LIMITE PREESTABLECIDO</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>0.0</td>\n      <td>24.0</td>\n      <td>53</td>\n      <td>53.0</td>\n      <td>0.0</td>\n      <td>121970.0</td>\n      <td>121970.0</td>\n      <td>121970.0</td>\n    </tr>\n    <tr>\n      <th>262856</th>\n      <td>14416</td>\n      <td>2023-04-24 17:36:56.539000-06:00</td>\n      <td>1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b</td>\n      <td>2023-04-24</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>716.0</td>\n      <td>15600.0</td>\n      <td>13457.0</td>\n      <td>2023-02-28</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>12.0</td>\n      <td>7.0</td>\n      <td>22</td>\n      <td>30.0</td>\n      <td>0.0</td>\n      <td>13457.0</td>\n      <td>13457.0</td>\n      <td>4511.0</td>\n    </tr>\n    <tr>\n      <th>262857</th>\n      <td>14416</td>\n      <td>2023-04-24 17:36:56.539000-06:00</td>\n      <td>1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b</td>\n      <td>2023-04-24</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>9984.0</td>\n      <td>9984.0</td>\n      <td>0.0</td>\n      <td>2023-04-02</td>\n      <td>...</td>\n      <td>PAGOS FIJOS</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>52.0</td>\n      <td>0.0</td>\n      <td>V</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>262858</th>\n      <td>14416</td>\n      <td>2023-04-24 17:36:56.539000-06:00</td>\n      <td>1169801-55d3f904-d9ce-4ad2-bb39-457595c1859b</td>\n      <td>2023-04-24</td>\n      <td>SIN OBSERVACION</td>\n      <td>PESOS MEXICANOS</td>\n      <td>0.0</td>\n      <td>500.0</td>\n      <td>0.0</td>\n      <td>2023-03-31</td>\n      <td>...</td>\n      <td>REVOLVENTE</td>\n      <td>INDIVIDUAL (TITULAR)</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>V</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>262859 rows × 29 columns</p>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# como no hay un ID del prestamos supondre que la persona abrio solo un credito en un dia por tipo de categoria, plazazo y tipo de crédito esto no es lo más optimo pues puede colarse errores pero me dará una idea de la distribucion de los tipos de crédito (lo hago asi porque no hay un ID_LOAN)\n",
    "\n",
    "tipos_credito = duckdb.sql(\"SELECT DISTINCT ON (customer_id, LOAN_OPENING_DATE, PAYMENT_FREQUENCY, CREDIT_TYPE) * FROM credit_reports ORDER BY customer_id, LOAN_OPENING_DATE, PAYMENT_FREQUENCY, CREDIT_TYPE\").to_df()\n",
    "\n",
    "tipos_credito"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:23.801980Z",
     "start_time": "2023-10-30T20:11:22.719087Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "    Total_Creditos                   CREDIT_TYPE\n0              392           COMPRA DE AUTOMOVIL\n1              511          PRESTAMO GARANTIZADO\n2              639                 BIENES RAICES\n3                5                   DESCONOCIDO\n4                6                     FACTORAJE\n5             9091              APARATOS/MUEBLES\n6              145  FISICA ACTIVIDAD EMPRESARIAL\n7             6778                         OTROS\n8             4503            PRESTAMO DE NOMINA\n9             1151                CREDITO FISCAL\n10              50          PRESTAMO EMPRESARIAL\n11              40        PRESTAMO QUIROGRAFARIO\n12              14         TARJETA DEPARTAMENTAL\n13               2                        FIANZA\n14           21460              LINEA DE CREDITO\n15           11116               GRUPO SOLIDARIO\n16               3                 CONSOLIDACION\n17               7                        EQUIPO\n18               6      PRESTAMO PARA ESTUDIANTE\n19              43       PRESTAMO NO GARANTIZADO\n20             325               BANCA COMERCIAL\n21          120503             PRESTAMO PERSONAL\n22           13915            TARJETA DE CREDITO\n23             371             MEJORAS A LA CASA\n24               2                   FONDEO FIRA\n25               3           AUTOMOTRIZ BANCARIO\n26             640                 ARRENDAMIENTO\n27           59553            CREDITO AL CONSUMO\n28               0                          None\n29              14    PRESTAMO PERSONAL BANCARIO\n30            9080   CREDITO PERSONAL AL CONSUMO\n31            1249                 NO DISPONIBLE\n32            1141        HIPOTECARIO O VIVIENDA\n33              23      ARRENDAMIENTO AUTOMOTRIZ",
      "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>Total_Creditos</th>\n      <th>CREDIT_TYPE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>392</td>\n      <td>COMPRA DE AUTOMOVIL</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>511</td>\n      <td>PRESTAMO GARANTIZADO</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>639</td>\n      <td>BIENES RAICES</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>5</td>\n      <td>DESCONOCIDO</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>FACTORAJE</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>9091</td>\n      <td>APARATOS/MUEBLES</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>145</td>\n      <td>FISICA ACTIVIDAD EMPRESARIAL</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>6778</td>\n      <td>OTROS</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>4503</td>\n      <td>PRESTAMO DE NOMINA</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1151</td>\n      <td>CREDITO FISCAL</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>50</td>\n      <td>PRESTAMO EMPRESARIAL</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>40</td>\n      <td>PRESTAMO QUIROGRAFARIO</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>14</td>\n      <td>TARJETA DEPARTAMENTAL</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2</td>\n      <td>FIANZA</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>21460</td>\n      <td>LINEA DE CREDITO</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>11116</td>\n      <td>GRUPO SOLIDARIO</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>3</td>\n      <td>CONSOLIDACION</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>7</td>\n      <td>EQUIPO</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>6</td>\n      <td>PRESTAMO PARA ESTUDIANTE</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>43</td>\n      <td>PRESTAMO NO GARANTIZADO</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>325</td>\n      <td>BANCA COMERCIAL</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>120503</td>\n      <td>PRESTAMO PERSONAL</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>13915</td>\n      <td>TARJETA DE CREDITO</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>371</td>\n      <td>MEJORAS A LA CASA</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>2</td>\n      <td>FONDEO FIRA</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>3</td>\n      <td>AUTOMOTRIZ BANCARIO</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>640</td>\n      <td>ARRENDAMIENTO</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>59553</td>\n      <td>CREDITO AL CONSUMO</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>0</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>14</td>\n      <td>PRESTAMO PERSONAL BANCARIO</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>9080</td>\n      <td>CREDITO PERSONAL AL CONSUMO</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>1249</td>\n      <td>NO DISPONIBLE</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>1141</td>\n      <td>HIPOTECARIO O VIVIENDA</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>23</td>\n      <td>ARRENDAMIENTO AUTOMOTRIZ</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ahora agrupamos\n",
    "tipos_credito = duckdb.sql(\"select count(CREDIT_TYPE) as Total_Creditos, CREDIT_TYPE from tipos_credito group by CREDIT_TYPE\").to_df()\n",
    "tipos_credito"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:23.802619Z",
     "start_time": "2023-10-30T20:11:23.411881Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualización\n",
    "fig = px.pie(data_frame=tipos_credito, values='Total_Creditos', names='CREDIT_TYPE', title='Distribución por tipos de créditos (estimación)')\n",
    "fig.update_traces(textposition='inside')\n",
    "\n",
    "fig.show(renderer=\"png\") # Para que se pueda ver sin problemas en el repo "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:27.368293Z",
     "start_time": "2023-10-30T20:11:23.426462Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 1.2.4.2) Cantidad de prestamos por plazo solicitado"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "    Frecuencia                     PAYMENT_FREQUENCY\n0         7019  PAGO MINIMO PARA CUENTAS REVOLVENTES\n1       124398                               MENSUAL\n2            0                                  None\n3          378                            TRIMESTRAL\n4        23425                   UNA SOLA EXHIBICION\n5         8768                            CATORCENAL\n6         1395                             BIMESTRAL\n7           22                             SEMESTRAL\n8        87712                               SEMANAL\n9        33675                             QUINCENAL\n10         425                                 ANUAL\n11          50                 DEDUCCION DEL SALARIO",
      "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>Frecuencia</th>\n      <th>PAYMENT_FREQUENCY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>7019</td>\n      <td>PAGO MINIMO PARA CUENTAS REVOLVENTES</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>124398</td>\n      <td>MENSUAL</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>378</td>\n      <td>TRIMESTRAL</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>23425</td>\n      <td>UNA SOLA EXHIBICION</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>8768</td>\n      <td>CATORCENAL</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1395</td>\n      <td>BIMESTRAL</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>22</td>\n      <td>SEMESTRAL</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>87712</td>\n      <td>SEMANAL</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>33675</td>\n      <td>QUINCENAL</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>425</td>\n      <td>ANUAL</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>50</td>\n      <td>DEDUCCION DEL SALARIO</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nuevamente como no cuento con un ID de prestamo tendré que hacer una remoción de duplicados haciendo ciertas suposiciones para dar con un único registro de crédito, eliminando registros duplicados de las variables de: customer_id, loan_opening_date, payment_frecuency & credit_type\n",
    "\n",
    "plazo_creditos = duckdb.sql(\"SELECT DISTINCT ON (customer_id, LOAN_OPENING_DATE, PAYMENT_FREQUENCY, CREDIT_TYPE) * FROM credit_reports ORDER BY customer_id, LOAN_OPENING_DATE, PAYMENT_FREQUENCY, CREDIT_TYPE\").to_df()\n",
    "\n",
    "plazo_creditos = duckdb.sql(\"select count(PAYMENT_FREQUENCY) as Frecuencia, PAYMENT_FREQUENCY from credit_reports group by PAYMENT_FREQUENCY\").to_df()\n",
    "plazo_creditos"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:11:48.453221Z",
     "start_time": "2023-10-30T20:11:47.871372Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualización\n",
    "fig = px.pie(data_frame=plazo_creditos, values='Frecuencia', names='PAYMENT_FREQUENCY', title='Distribución por plazo de créditos (estimación)')\n",
    "fig.update_traces(textposition='inside')\n",
    "\n",
    "fig.show(renderer=\"png\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:10.817047Z",
     "start_time": "2023-10-30T20:12:10.299900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 1.2.4.3) Distribución de límites de crédito\n",
    "Voy a suponer (no estoy del todo seguro que sea asi) que la variable \"CREDIT_LIMIT\" es independiente a un prestamo concreto, es decir el crédito limite sería el monto máximo de la linea de crétido que tengo  (independientemente si tengo varias lineas de crédito).\n",
    "Otro tema es que en este caso me quedaré con el valor de CREDIT_LIMT de la ultima actualización del cliente ( es decir el esttus más acutal)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "limite_credito = duckdb.sql(\"select * from credit_reports order by customer_id, UPDATE_DATE desc\").to_df()\n",
    "\n",
    "# Boxplot\n",
    "fig = px.box(data_frame=limite_credito, x=\"CREDIT_LIMIT\", title=\"Distribución de límites de crédito\")\n",
    "fig.show(renderer=\"png\")\n",
    "# Es notable ver como hay datos sumamente atipicos dentro del historial de limite de crédito "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:19.507404Z",
     "start_time": "2023-10-30T20:12:17.117045Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "count    2.789990e+05\nmean     5.525842e+03\nstd      2.788269e+04\nmin      0.000000e+00\n25%      0.000000e+00\n50%      4.000000e+02\n75%      3.000000e+03\nmax      1.900000e+06\nName: CREDIT_LIMIT, dtype: double[pyarrow]"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Una inspección estadistica báscia \n",
    "credit_reports[\"CREDIT_LIMIT\"].describe() # No es recomendable ver el promedio  en esta sitaución y tomarlo muy encerio dadolos outlieres tan extremos que hay"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:24.122978Z",
     "start_time": "2023-10-30T20:12:24.094540Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**NOTAS:** De lo observado en esta sección podemos llevarnos varias cosas:\n",
    "\n",
    "- La mayoría de los créditos adquiridos por las personas (el 85.5 % de estos) corresponden a créditos con plazos mensuales, quincenales y semanales\n",
    "- La mayoría de los crédito colocados (76.7%) correspoden a prestamo personal,  crédito al consumo, línea de crédito \n",
    "- La distribución de límites de crédito por persona son muy distintos hay outlieres muy extremos de hasta 1.9 millones de pesos en línea de crédito "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 1.2.5) Inspección de algunas variables en el dataset de main_dataset\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 1.2.5.1) Distribución de inquiries to credit reports from external entities in the last 6 months"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot \n",
    "fig = px.box(data_frame=main_dataset, x=\"n_inquiries_l6m\", title=\"Distribución de cantidad de consultas crediticias\")\n",
    "fig.show(renderer=\"png\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:29.083557Z",
     "start_time": "2023-10-30T20:12:28.948737Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### 1.2.5.2) Distribución de los días desde la creación de la cuenta hasta la solicitud"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.box(data_frame=main_dataset, x=\"account_to_application_days\", title=\"Distribución de  días desde la creación - aplicación\")\n",
    "fig.show(renderer=\"png\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:33.304413Z",
     "start_time": "2023-10-30T20:12:33.201148Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2) Tratamiento de datos\n",
    "En este sección haremos un tratamiento más profundo de los datos, teniendo el objetivo de dejarlos listos para su uso en el modelo que vayamos a usar. El tratamiento ue haremos en este caso se centra principalmente en 2 aspectos los cuales son: \n",
    "\n",
    "- Limpieza de datos \n",
    "- Ingeniería de características (feature engineering)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 2.1) Tratamiento a credit reports"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 2.1.1) Linea máxima de crédito\n",
    "Es la linea máxima de crédito en el historial del cliente, como vimos que esta variable en particular tiene outlieres muy extremos es recomendable hacer un escalamiento de la variable concretamente usando Standar Scalar"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "      customer_id  CREDIT_LIMIT\n0               1      0.735949\n1               2      0.163011\n2               3      3.034093\n3               4     -0.432844\n4               5      0.109040\n...           ...           ...\n9244        14391      0.290040\n9245        14404     -0.418112\n9246        14408     -0.581808\n9247        14410     -0.565438\n9248        14416      0.416495\n\n[9249 rows x 2 columns]",
      "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>customer_id</th>\n      <th>CREDIT_LIMIT</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0.735949</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0.163011</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3.034093</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>-0.432844</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>0.109040</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9244</th>\n      <td>14391</td>\n      <td>0.290040</td>\n    </tr>\n    <tr>\n      <th>9245</th>\n      <td>14404</td>\n      <td>-0.418112</td>\n    </tr>\n    <tr>\n      <th>9246</th>\n      <td>14408</td>\n      <td>-0.581808</td>\n    </tr>\n    <tr>\n      <th>9247</th>\n      <td>14410</td>\n      <td>-0.565438</td>\n    </tr>\n    <tr>\n      <th>9248</th>\n      <td>14416</td>\n      <td>0.416495</td>\n    </tr>\n  </tbody>\n</table>\n<p>9249 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_limit = duckdb.sql(\"select  distinct on (customer_id) customer_id, CREDIT_LIMIT from credit_reports order by customer_id, CREDIT_LIMIT desc\").to_df()\n",
    "\n",
    "# Escalamos la variable\n",
    "scaler = StandardScaler()\n",
    "\n",
    "credit_limit['CREDIT_LIMIT'] = scaler.fit_transform(credit_limit[['CREDIT_LIMIT']]) # Escalamientop de esta variable\n",
    "\n",
    "credit_limit # Este es el limite máximo que han tenido en su historia cada cliente"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:39.114653Z",
     "start_time": "2023-10-30T20:12:38.992801Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 2.1.2) Antiguedad crediticia"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "      customer_id LOAN_OPENING_DATE\n0               1        2003-08-21\n1               2        2009-08-15\n2               3        2014-05-03\n3               4        2020-06-30\n4               5        2002-06-18\n...           ...               ...\n9244        14391        2019-05-31\n9245        14404        2005-05-31\n9246        14408        2019-03-10\n9247        14410        2011-03-25\n9248        14416        2005-11-04\n\n[9249 rows x 2 columns]",
      "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>customer_id</th>\n      <th>LOAN_OPENING_DATE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2003-08-21</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2009-08-15</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2014-05-03</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2020-06-30</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2002-06-18</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9244</th>\n      <td>14391</td>\n      <td>2019-05-31</td>\n    </tr>\n    <tr>\n      <th>9245</th>\n      <td>14404</td>\n      <td>2005-05-31</td>\n    </tr>\n    <tr>\n      <th>9246</th>\n      <td>14408</td>\n      <td>2019-03-10</td>\n    </tr>\n    <tr>\n      <th>9247</th>\n      <td>14410</td>\n      <td>2011-03-25</td>\n    </tr>\n    <tr>\n      <th>9248</th>\n      <td>14416</td>\n      <td>2005-11-04</td>\n    </tr>\n  </tbody>\n</table>\n<p>9249 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionare el registro más antiguo de de variable LOAN_OPENIN_DATE para tomar el primer crédito que se tiene registro de cada cliente y asi estimar su antiguedad con historial crediticio \n",
    "antiguedad_crediticia = duckdb.sql(\"SELECT DISTINCT ON (customer_id) customer_id, LOAN_OPENING_DATE FROM credit_reports ORDER BY customer_id, LOAN_OPENING_DATE ASC\").to_df()\n",
    "antiguedad_crediticia"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:41.209545Z",
     "start_time": "2023-10-30T20:12:41.150611Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "      customer_id  Years_old\n0               1       20.0\n1               2       14.0\n2               3        9.0\n3               4        3.0\n4               5       21.0\n...           ...        ...\n9244        14391        4.0\n9245        14404       18.0\n9246        14408        4.0\n9247        14410       12.0\n9248        14416       17.0\n\n[9249 rows x 2 columns]",
      "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>customer_id</th>\n      <th>Years_old</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>20.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>14.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>9.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>21.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9244</th>\n      <td>14391</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>9245</th>\n      <td>14404</td>\n      <td>18.0</td>\n    </tr>\n    <tr>\n      <th>9246</th>\n      <td>14408</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>9247</th>\n      <td>14410</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>9248</th>\n      <td>14416</td>\n      <td>17.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>9249 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculamos la fecha actual\n",
    "antiguedad_crediticia[\"Fecha_actual\"] = datetime.today().date()\n",
    "\n",
    "# La pasamos de string a formqato fecha\n",
    "antiguedad_crediticia[\"Fecha_actual\"] = antiguedad_crediticia[\"Fecha_actual\"].astype(\"datetime64[ns]\")\n",
    "\n",
    "# Calculamos los años de antiguedad crediticia\n",
    "antiguedad_crediticia[\"Years_old\"] = (antiguedad_crediticia[\"Fecha_actual\"] - antiguedad_crediticia[\"LOAN_OPENING_DATE\"]).dt.days // 365\n",
    "\n",
    "#Nos quedamos con las variables de interés\n",
    "antiguedad_crediticia = antiguedad_crediticia[[\"customer_id\", \"Years_old\"]]\n",
    "antiguedad_crediticia"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:42.580132Z",
     "start_time": "2023-10-30T20:12:42.519264Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 2.1.3) Prestamos totales y categoria  por persona\n",
    "**IMPORTANTE:** Al no tener un ID de cada préstamo estamos haciendo una serie de supuestos para calcular el número total de prestamos  adquiridos a lo largo de la historia de cada persona. Estos supuesto tienen que contabilicemos los registros no duplicados de los siguientes columnas: \n",
    "\n",
    "- customer_id\n",
    "- LOAN_OPENING_DATE\n",
    "- PAYMENT_FREQUENCY \n",
    "- BUSINESS_TYPE\n",
    "- CREDIT_TYPE\n",
    "\n",
    "Básicamente lo que estamos contando son los registros únicos de cada cliente con la fecha de adquisición del crédito (LOAN_OPENING_DATE), la frecuencia de pago de ese crédito, el tipo de negocio y tipo de crédito. \n",
    "\n",
    "**NOTA:** Esta serie de supuestos no es lo más recomendable, ¿qué pasaría si yo como individuo adquiero en el mismo día 2 o más articulos por medio de un crédito en el mismo giro de empresa y con una frecuencia de pago igual?, este método contaría solo como 1 crédito adquirido cuando la realidad fue que pudo haber 2 o más. Sin embargo, dado que no cuento con un ID del prestamos hice este supuesto para estimar la cantidad  total de prestamos adquiridos "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "       customer_id                  CREDIT_TYPE  Total_prestamos_cat\n0                1             LINEA DE CREDITO                    2\n1                1                        OTROS                    1\n2                1           TARJETA DE CREDITO                    3\n3                2     PRESTAMO PARA ESTUDIANTE                    1\n4                2           TARJETA DE CREDITO                    8\n...            ...                          ...                  ...\n47547        14416  CREDITO PERSONAL AL CONSUMO                    2\n47548        14416             LINEA DE CREDITO                    2\n47549        14416                NO DISPONIBLE                    2\n47550        14416            PRESTAMO PERSONAL                    5\n47551        14416           TARJETA DE CREDITO                    2\n\n[47552 rows x 3 columns]",
      "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>customer_id</th>\n      <th>CREDIT_TYPE</th>\n      <th>Total_prestamos_cat</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>LINEA DE CREDITO</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>OTROS</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>TARJETA DE CREDITO</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>PRESTAMO PARA ESTUDIANTE</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>TARJETA DE CREDITO</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>47547</th>\n      <td>14416</td>\n      <td>CREDITO PERSONAL AL CONSUMO</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>47548</th>\n      <td>14416</td>\n      <td>LINEA DE CREDITO</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>47549</th>\n      <td>14416</td>\n      <td>NO DISPONIBLE</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>47550</th>\n      <td>14416</td>\n      <td>PRESTAMO PERSONAL</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>47551</th>\n      <td>14416</td>\n      <td>TARJETA DE CREDITO</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>47552 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1) Dropeamos duplicados (esta es la estrategia seguida para esitmar la cantidad de prestamos)\n",
    "total_prestamos  = credit_reports.drop_duplicates(subset=[\"customer_id\", \"LOAN_OPENING_DATE\", \"PAYMENT_FREQUENCY\", \"BUSINESS_TYPE\", \"CREDIT_TYPE\"]) # Ahora usamos el API de Pandas\n",
    "\n",
    "# 2) Contabilizamos prestamos por tipo de categoria\n",
    "total_prestamos_categoria = total_prestamos.groupby(by=[\"customer_id\", \"CREDIT_TYPE\"]).size().reset_index()\n",
    "total_prestamos_categoria.rename(columns ={0:\"Total_prestamos_cat\"}, inplace=True)\n",
    "total_prestamos_categoria"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:44.170350Z",
     "start_time": "2023-10-30T20:12:43.930500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "CREDIT_TYPE  APARATOS/MUEBLES  ARRENDAMIENTO  ARRENDAMIENTO AUTOMOTRIZ  \\\ncustomer_id                                                              \n1                         0.0            0.0                       0.0   \n2                         0.0            0.0                       0.0   \n3                         0.0            0.0                       0.0   \n4                         0.0            0.0                       0.0   \n5                         4.0            0.0                       0.0   \n...                       ...            ...                       ...   \n14391                     0.0            0.0                       0.0   \n14404                     1.0            0.0                       0.0   \n14408                     0.0            0.0                       0.0   \n14410                     0.0            0.0                       0.0   \n14416                    15.0            0.0                       0.0   \n\nCREDIT_TYPE  AUTOMOTRIZ BANCARIO  BANCA COMERCIAL  BIENES RAICES  \\\ncustomer_id                                                        \n1                            0.0              0.0            0.0   \n2                            0.0              0.0            0.0   \n3                            0.0              0.0            1.0   \n4                            0.0              0.0            0.0   \n5                            0.0              0.0            0.0   \n...                          ...              ...            ...   \n14391                        0.0              0.0            0.0   \n14404                        0.0              0.0            0.0   \n14408                        0.0              0.0            0.0   \n14410                        0.0              2.0            0.0   \n14416                        0.0              0.0            0.0   \n\nCREDIT_TYPE  COMPRA DE AUTOMOVIL  CONSOLIDACION  CREDITO AL CONSUMO  \\\ncustomer_id                                                           \n1                            0.0            0.0                 0.0   \n2                            0.0            0.0                 0.0   \n3                            0.0            0.0                10.0   \n4                            0.0            0.0                 2.0   \n5                            1.0            0.0                 1.0   \n...                          ...            ...                 ...   \n14391                        0.0            0.0                 0.0   \n14404                        0.0            0.0                 2.0   \n14408                        0.0            0.0                 1.0   \n14410                        0.0            0.0                 2.0   \n14416                        0.0            0.0                 0.0   \n\nCREDIT_TYPE  CREDITO FISCAL  ...  PRESTAMO DE NOMINA  PRESTAMO EMPRESARIAL  \\\ncustomer_id                  ...                                             \n1                       0.0  ...                 0.0                   0.0   \n2                       0.0  ...                 0.0                   0.0   \n3                       0.0  ...                 1.0                   0.0   \n4                       0.0  ...                 2.0                   0.0   \n5                       3.0  ...                 5.0                   0.0   \n...                     ...  ...                 ...                   ...   \n14391                   0.0  ...                 0.0                   0.0   \n14404                   0.0  ...                 0.0                   0.0   \n14408                   0.0  ...                 0.0                   0.0   \n14410                   0.0  ...                 0.0                   0.0   \n14416                   0.0  ...                 0.0                   0.0   \n\nCREDIT_TYPE  PRESTAMO GARANTIZADO  PRESTAMO NO GARANTIZADO  \\\ncustomer_id                                                  \n1                             0.0                      0.0   \n2                             0.0                      0.0   \n3                             0.0                      0.0   \n4                             0.0                      0.0   \n5                             0.0                      0.0   \n...                           ...                      ...   \n14391                         0.0                      0.0   \n14404                         0.0                      0.0   \n14408                         0.0                      0.0   \n14410                         0.0                      0.0   \n14416                         0.0                      0.0   \n\nCREDIT_TYPE  PRESTAMO PARA ESTUDIANTE  PRESTAMO PERSONAL  \\\ncustomer_id                                                \n1                                 0.0                0.0   \n2                                 1.0                0.0   \n3                                 0.0               19.0   \n4                                 0.0                6.0   \n5                                 0.0                5.0   \n...                               ...                ...   \n14391                             0.0               15.0   \n14404                             0.0               13.0   \n14408                             0.0                2.0   \n14410                             0.0              120.0   \n14416                             0.0                5.0   \n\nCREDIT_TYPE  PRESTAMO PERSONAL BANCARIO  PRESTAMO QUIROGRAFARIO  \\\ncustomer_id                                                       \n1                                   0.0                     0.0   \n2                                   0.0                     0.0   \n3                                   0.0                     0.0   \n4                                   0.0                     0.0   \n5                                   0.0                     0.0   \n...                                 ...                     ...   \n14391                               0.0                     0.0   \n14404                               0.0                     0.0   \n14408                               0.0                     0.0   \n14410                               0.0                     0.0   \n14416                               0.0                     0.0   \n\nCREDIT_TYPE  TARJETA DE CREDITO  TARJETA DEPARTAMENTAL  \ncustomer_id                                             \n1                           3.0                    0.0  \n2                           8.0                    0.0  \n3                           0.0                    0.0  \n4                           3.0                    0.0  \n5                          13.0                    0.0  \n...                         ...                    ...  \n14391                       2.0                    0.0  \n14404                       1.0                    0.0  \n14408                       0.0                    0.0  \n14410                       3.0                    0.0  \n14416                       2.0                    0.0  \n\n[9172 rows x 33 columns]",
      "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>CREDIT_TYPE</th>\n      <th>APARATOS/MUEBLES</th>\n      <th>ARRENDAMIENTO</th>\n      <th>ARRENDAMIENTO AUTOMOTRIZ</th>\n      <th>AUTOMOTRIZ BANCARIO</th>\n      <th>BANCA COMERCIAL</th>\n      <th>BIENES RAICES</th>\n      <th>COMPRA DE AUTOMOVIL</th>\n      <th>CONSOLIDACION</th>\n      <th>CREDITO AL CONSUMO</th>\n      <th>CREDITO FISCAL</th>\n      <th>...</th>\n      <th>PRESTAMO DE NOMINA</th>\n      <th>PRESTAMO EMPRESARIAL</th>\n      <th>PRESTAMO GARANTIZADO</th>\n      <th>PRESTAMO NO GARANTIZADO</th>\n      <th>PRESTAMO PARA ESTUDIANTE</th>\n      <th>PRESTAMO PERSONAL</th>\n      <th>PRESTAMO PERSONAL BANCARIO</th>\n      <th>PRESTAMO QUIROGRAFARIO</th>\n      <th>TARJETA DE CREDITO</th>\n      <th>TARJETA DEPARTAMENTAL</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>19.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>...</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>13.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14391</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>15.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14404</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>13.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14408</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14410</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>120.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14416</th>\n      <td>15.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>9172 rows × 33 columns</p>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3) Pivoteo de la tabla\n",
    "total_prestamos_categoria  = total_prestamos_categoria.pivot(index='customer_id', columns='CREDIT_TYPE', values='Total_prestamos_cat')\n",
    "total_prestamos_categoria.fillna(value=0, inplace =True)\n",
    "total_prestamos_categoria"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:44.838852Z",
     "start_time": "2023-10-30T20:12:44.786748Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "      customer_id  Prestamos_totales\n0               1                  6\n1               2                  9\n2               3                 31\n3               4                 13\n4               5                 41\n...           ...                ...\n9244        14391                 26\n9245        14404                 25\n9246        14408                  4\n9247        14410                130\n9248        14416                 28\n\n[9249 rows x 2 columns]",
      "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>customer_id</th>\n      <th>Prestamos_totales</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9244</th>\n      <td>14391</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>9245</th>\n      <td>14404</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>9246</th>\n      <td>14408</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>9247</th>\n      <td>14410</td>\n      <td>130</td>\n    </tr>\n    <tr>\n      <th>9248</th>\n      <td>14416</td>\n      <td>28</td>\n    </tr>\n  </tbody>\n</table>\n<p>9249 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1) Contabilizamos prestamos  totales por cliente\n",
    "total_prestamos = total_prestamos.groupby(by=[\"customer_id\"]).size().reset_index()\n",
    "\n",
    "# 2) Cambio de nombre\n",
    "total_prestamos.rename(columns = {0:\"Prestamos_totales\"}, inplace=True)\n",
    "total_prestamos # ESTO ES UNA ESTIMACION, NO ES LO MAS OPTMO, NECESITO EL ID DE LOS PRESTAMOS"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:45.947002Z",
     "start_time": "2023-10-30T20:12:45.892124Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 2.1.4) Total de pagos con retraso\n",
    "Seguimo con el problema de que no contamos con un ID de los prestamos en credit_reports. Lo que intentaremos estimar aqui es la cantidad de prestamos con algún retraso en su historial de pago"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 169 ms, sys: 45.4 ms, total: 215 ms\n",
      "Wall time: 217 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": "        customer_id  DELAYED_PAYMENTS\n187528            1                 0\n187529            1                 0\n187530            1                 0\n187527            1                 0\n187526            1                 0\n...             ...               ...\n286521        14416                 0\n286522        14416                 0\n286523        14416                 0\n286508        14416                 0\n286507        14416                 0\n\n[266251 rows x 2 columns]",
      "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>customer_id</th>\n      <th>DELAYED_PAYMENTS</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>187528</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>187529</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>187530</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>187527</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>187526</th>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>286521</th>\n      <td>14416</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>286522</th>\n      <td>14416</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>286523</th>\n      <td>14416</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>286508</th>\n      <td>14416</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>286507</th>\n      <td>14416</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>266251 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# 1) Ordemos los valores más recientes\n",
    "total_retrasos = credit_reports.copy()\n",
    "total_retrasos = total_retrasos.sort_values(by=[\"customer_id\", \"UPDATE_DATE\"],\n",
    "                                            ascending=[True, False])\n",
    "\n",
    "# 2) Drop duplicates\n",
    "total_retrasos.drop_duplicates(subset=[\"customer_id\", \"LOAN_OPENING_DATE\", \"PAYMENT_FREQUENCY\", \"BUSINESS_TYPE\", \"CREDIT_TYPE\"], inplace=True)\n",
    "\n",
    "# 3) Selección de la columnas de interés\n",
    "total_retrasos = total_retrasos[[\"customer_id\", \"DELAYED_PAYMENTS\"]]\n",
    "total_retrasos"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:48.459818Z",
     "start_time": "2023-10-30T20:12:48.236450Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 93.4 ms, sys: 10.5 ms, total: 104 ms\n",
      "Wall time: 105 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": "      customer_id  Porcentaje_retrasos\n0               1            16.666667\n1               2             0.000000\n2               3            32.258065\n3               4            15.384615\n4               5            46.341463\n...           ...                  ...\n9244        14391            34.615385\n9245        14404            52.000000\n9246        14408            50.000000\n9247        14410             7.692308\n9248        14416            14.285714\n\n[9249 rows x 2 columns]",
      "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>customer_id</th>\n      <th>Porcentaje_retrasos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>16.666667</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>32.258065</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>15.384615</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>46.341463</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9244</th>\n      <td>14391</td>\n      <td>34.615385</td>\n    </tr>\n    <tr>\n      <th>9245</th>\n      <td>14404</td>\n      <td>52.000000</td>\n    </tr>\n    <tr>\n      <th>9246</th>\n      <td>14408</td>\n      <td>50.000000</td>\n    </tr>\n    <tr>\n      <th>9247</th>\n      <td>14410</td>\n      <td>7.692308</td>\n    </tr>\n    <tr>\n      <th>9248</th>\n      <td>14416</td>\n      <td>14.285714</td>\n    </tr>\n  </tbody>\n</table>\n<p>9249 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Usaremos la funcion de apply para no meter la siguiente condicionante dentro de un ciclo y aumentar asi el rendimiento del proceso\n",
    "\n",
    "# 1) Definición de la función\n",
    "def retraso (delay):\n",
    "    if delay > 0:\n",
    "        x = 1\n",
    "    else:\n",
    "        x= 0\n",
    "    return x\n",
    "\n",
    "total_retrasos[\"DELAYED_PAYMENTS\"].fillna(value=0, inplace=True)\n",
    "\n",
    "# 2) Aplicamos con apply\n",
    "total_retrasos['Retraso'] = total_retrasos['DELAYED_PAYMENTS'].apply(retraso)\n",
    "\n",
    "# 3) Agrupamos los registros\n",
    "total_retrasos = total_retrasos.groupby(by=[\"customer_id\"])[\"Retraso\"].sum().reset_index()\n",
    "total_retrasos.rename(columns ={\"Retraso\":\"Deudas_con_retraso\"}, inplace= True)\n",
    "\n",
    "# 4) Unimos con el total de deudas adquiridas para sacar % de deudas con algún reistro de retraso\n",
    "total_retrasos = pd.merge (left=total_retrasos, right=total_prestamos, on=\"customer_id\", how=\"left\")\n",
    "\n",
    "# Sacamos porcentaje\n",
    "total_retrasos[\"Porcentaje_retrasos\"] = (total_retrasos[\"Deudas_con_retraso\"] / total_retrasos[\"Prestamos_totales\"])*100\n",
    "\n",
    "# 5) Filtro variables de interes\n",
    "total_retrasos = total_retrasos[[\"customer_id\", \"Porcentaje_retrasos\"]]\n",
    "total_retrasos"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:50.746594Z",
     "start_time": "2023-10-30T20:12:50.601729Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 2.1.5) Prevention keys por cliente\n",
    "Sacaremos el total de prevention keys en todo el historial del cliente"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "PREVENTION_KEY  ADJUDICACION O APLICACION DE GARANTIA  \\\ncustomer_id                                             \n1                                                 0.0   \n2                                                 0.0   \n3                                                 0.0   \n4                                                 0.0   \n5                                                 0.0   \n...                                               ...   \n14391                                             0.0   \n14404                                             0.0   \n14408                                             0.0   \n14410                                             0.0   \n14416                                             0.0   \n\nPREVENTION_KEY  CARTERA AL CORRIENTE VENDIDA O CEDIDA A UN USUARIO DE UNA SOCIEDAD  \\\ncustomer_id                                                                          \n1                                                             0.0                    \n2                                                             1.0                    \n3                                                             0.0                    \n4                                                             0.0                    \n5                                                             1.0                    \n...                                                           ...                    \n14391                                                         0.0                    \n14404                                                         0.0                    \n14408                                                         0.0                    \n14410                                                         0.0                    \n14416                                                         0.0                    \n\nPREVENTION_KEY  CONVENIO DE FINIQUITO O PAGO MENOR  \\\ncustomer_id                                          \n1                                              0.0   \n2                                              0.0   \n3                                              0.0   \n4                                              0.0   \n5                                              1.0   \n...                                            ...   \n14391                                          0.0   \n14404                                          0.0   \n14408                                          0.0   \n14410                                          0.0   \n14416                                          0.0   \n\nPREVENTION_KEY  CONVENIO Y DISMINUCION DE PAGO  \\\ncustomer_id                                      \n1                                          0.0   \n2                                          0.0   \n3                                          0.0   \n4                                          0.0   \n5                                          0.0   \n...                                        ...   \n14391                                      0.0   \n14404                                      0.0   \n14408                                      0.0   \n14410                                      0.0   \n14416                                      0.0   \n\nPREVENTION_KEY  CUENTA AL CORRIENTE VENDIDA O CEDIDA A UN NO USUARIO DE UNA SOCIEDAD  \\\ncustomer_id                                                                            \n1                                                             0.0                      \n2                                                             0.0                      \n3                                                             0.0                      \n4                                                             0.0                      \n5                                                             0.0                      \n...                                                           ...                      \n14391                                                         0.0                      \n14404                                                         0.0                      \n14408                                                         0.0                      \n14410                                                         0.0                      \n14416                                                         0.0                      \n\nPREVENTION_KEY  CUENTA CANCELADA O CERRADA  \\\ncustomer_id                                  \n1                                      2.0   \n2                                      3.0   \n3                                      6.0   \n4                                      1.0   \n5                                      7.0   \n...                                    ...   \n14391                                 14.0   \n14404                                  6.0   \n14408                                  2.0   \n14410                                  5.0   \n14416                                  2.0   \n\nPREVENTION_KEY  CUENTA CERRADA QUE ESTUVO EN COBRANZA Y FUE PAGADA SIN CAUSAR QUEBRANTO  \\\ncustomer_id                                                                               \n1                                                             0.0                         \n2                                                             0.0                         \n3                                                             0.0                         \n4                                                             0.0                         \n5                                                             0.0                         \n...                                                           ...                         \n14391                                                         0.0                         \n14404                                                         3.0                         \n14408                                                         0.0                         \n14410                                                         1.0                         \n14416                                                         0.0                         \n\nPREVENTION_KEY  CUENTA EN COBRANZA  CUENTA FRAUDULENTA  CUENTA INACTIVA  ...  \\\ncustomer_id                                                              ...   \n1                              0.0                 0.0              0.0  ...   \n2                              0.0                 0.0              0.0  ...   \n3                              0.0                 0.0              0.0  ...   \n4                              0.0                 0.0              0.0  ...   \n5                              2.0                 0.0              1.0  ...   \n...                            ...                 ...              ...  ...   \n14391                          0.0                 0.0              0.0  ...   \n14404                          0.0                 0.0              0.0  ...   \n14408                          0.0                 0.0              0.0  ...   \n14410                          1.0                 0.0              0.0  ...   \n14416                          0.0                 0.0              0.0  ...   \n\nPREVENTION_KEY  FRAUDE NO ATRIBUIBLE AL CONSUMIDOR  \\\ncustomer_id                                          \n1                                              0.0   \n2                                              0.0   \n3                                              0.0   \n4                                              0.0   \n5                                              0.0   \n...                                            ...   \n14391                                          0.0   \n14404                                          0.0   \n14408                                          0.0   \n14410                                          0.0   \n14416                                          0.0   \n\nPREVENTION_KEY  INTEGRANTE CAUSANTE DE MORA  \\\ncustomer_id                                   \n1                                       0.0   \n2                                       0.0   \n3                                       0.0   \n4                                       0.0   \n5                                       0.0   \n...                                     ...   \n14391                                   0.0   \n14404                                   0.0   \n14408                                   0.0   \n14410                                   0.0   \n14416                                   0.0   \n\nPREVENTION_KEY  INTEGRANTE QUE FUE SUBSIDIADO PARA EVITAR MORA  \\\ncustomer_id                                                      \n1                                                          0.0   \n2                                                          0.0   \n3                                                          0.0   \n4                                                          0.0   \n5                                                          0.0   \n...                                                        ...   \n14391                                                      0.0   \n14404                                                      0.0   \n14408                                                      0.0   \n14410                                                      0.0   \n14416                                                      0.0   \n\nPREVENTION_KEY  PEGO MENOR POR PROGRAMA INSTITUCIONAL DE GOBIERNO  \\\ncustomer_id                                                         \n1                                                             0.0   \n2                                                             0.0   \n3                                                             0.0   \n4                                                             0.0   \n5                                                             0.0   \n...                                                           ...   \n14391                                                         0.0   \n14404                                                         0.0   \n14408                                                         0.0   \n14410                                                         0.0   \n14416                                                         0.0   \n\nPREVENTION_KEY  PRORROGA POR DESASTRE NATURAL  PRORROGA POR PERDIDA DE EMPLEO  \\\ncustomer_id                                                                     \n1                                         0.0                             0.0   \n2                                         0.0                             0.0   \n3                                         0.0                             1.0   \n4                                         0.0                             0.0   \n5                                         0.0                             0.0   \n...                                       ...                             ...   \n14391                                     0.0                             0.0   \n14404                                     0.0                             0.0   \n14408                                     0.0                             0.0   \n14410                                     0.0                             0.0   \n14416                                     0.0                             0.0   \n\nPREVENTION_KEY  PRORROGA POR SITUACION ESPECIAL  ROBO DE IDENTIDAD  \\\ncustomer_id                                                          \n1                                           0.0                0.0   \n2                                           0.0                0.0   \n3                                           0.0                0.0   \n4                                           0.0                0.0   \n5                                           0.0                0.0   \n...                                         ...                ...   \n14391                                       0.0                0.0   \n14404                                       0.0                0.0   \n14408                                       0.0                0.0   \n14410                                       0.0                0.0   \n14416                                       0.0                0.0   \n\nPREVENTION_KEY  SIN OBSERVACION  TARJETA DE CREDITO EXTRAVIADA O ROBADA  \ncustomer_id                                                              \n1                           4.0                                     0.0  \n2                           4.0                                     1.0  \n3                          23.0                                     0.0  \n4                          12.0                                     0.0  \n5                          23.0                                     0.0  \n...                         ...                                     ...  \n14391                       9.0                                     0.0  \n14404                      12.0                                     0.0  \n14408                       0.0                                     0.0  \n14410                     122.0                                     0.0  \n14416                      25.0                                     0.0  \n\n[9172 rows x 31 columns]",
      "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>PREVENTION_KEY</th>\n      <th>ADJUDICACION O APLICACION DE GARANTIA</th>\n      <th>CARTERA AL CORRIENTE VENDIDA O CEDIDA A UN USUARIO DE UNA SOCIEDAD</th>\n      <th>CONVENIO DE FINIQUITO O PAGO MENOR</th>\n      <th>CONVENIO Y DISMINUCION DE PAGO</th>\n      <th>CUENTA AL CORRIENTE VENDIDA O CEDIDA A UN NO USUARIO DE UNA SOCIEDAD</th>\n      <th>CUENTA CANCELADA O CERRADA</th>\n      <th>CUENTA CERRADA QUE ESTUVO EN COBRANZA Y FUE PAGADA SIN CAUSAR QUEBRANTO</th>\n      <th>CUENTA EN COBRANZA</th>\n      <th>CUENTA FRAUDULENTA</th>\n      <th>CUENTA INACTIVA</th>\n      <th>...</th>\n      <th>FRAUDE NO ATRIBUIBLE AL CONSUMIDOR</th>\n      <th>INTEGRANTE CAUSANTE DE MORA</th>\n      <th>INTEGRANTE QUE FUE SUBSIDIADO PARA EVITAR MORA</th>\n      <th>PEGO MENOR POR PROGRAMA INSTITUCIONAL DE GOBIERNO</th>\n      <th>PRORROGA POR DESASTRE NATURAL</th>\n      <th>PRORROGA POR PERDIDA DE EMPLEO</th>\n      <th>PRORROGA POR SITUACION ESPECIAL</th>\n      <th>ROBO DE IDENTIDAD</th>\n      <th>SIN OBSERVACION</th>\n      <th>TARJETA DE CREDITO EXTRAVIADA O ROBADA</th>\n    </tr>\n    <tr>\n      <th>customer_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>23.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>7.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>23.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14391</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>14.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14404</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>6.0</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14408</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14410</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>5.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>122.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14416</th>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>25.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>9172 rows × 31 columns</p>\n</div>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1) Dropeamos duplicados (esta es la estrategia seguida para esitmar la cantidad de prestamos)\n",
    "prevention_keys  = credit_reports.drop_duplicates(subset=[\"customer_id\", \"LOAN_OPENING_DATE\", \"PAYMENT_FREQUENCY\", \"BUSINESS_TYPE\", \"CREDIT_TYPE\"]) # Ahora usamos el API de Pandas\n",
    "\n",
    "# 2) Contabilizamos prestamos por tipo de categoria\n",
    "prevention_keys = prevention_keys.groupby(by=[\"customer_id\", \"PREVENTION_KEY\"]).size().reset_index()\n",
    "prevention_keys.rename(columns ={0:\"Prevention_key\"}, inplace=True)\n",
    "\n",
    "# 3) Pivoteo de la tabla\n",
    "prevention_keys  = prevention_keys.pivot(index='customer_id', columns='PREVENTION_KEY', values='Prevention_key')\n",
    "\n",
    "prevention_keys.fillna(value=0, inplace =True)\n",
    "prevention_keys"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:52.717296Z",
     "start_time": "2023-10-30T20:12:52.482209Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 3) Creación del modelo\n",
    "**MUY IMPORTANTE:** En la sección anterior la generación de variables se hizo principalmente a partir del dataset de  *credit_report*. El dataset de *main_dataset* dado que los registros que contiene este dataset se tomaran así como vienen"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.1) Xgboost "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 3.1.1) Tratamiento final de datos\n",
    "**IMPORTANTE:** Se selecionaran las siguientes variables del *main_dataset* debido mi apresiación de que son variables que pueden tener alguna relación con la calidad  de pagos que puede tener una persona. "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "       customer_id  account_to_application_days  n_bnpl_approved_apps  \\\n0             1223                          245                   0.0   \n1             5190                            0                   0.0   \n2             5194                            0                   0.0   \n3             3978                           47                   0.0   \n4             4535                           24                   0.0   \n...            ...                          ...                   ...   \n14449        14452                            0                   0.0   \n14450        14453                            0                   0.0   \n14451        14454                            0                   0.0   \n14452          768                          692                   0.0   \n14453        12079                          152                   0.0   \n\n       n_inquiries_l3m  n_inquiries_l6m  target  \n0                 91.0             91.0       0  \n1                  0.0              0.0       0  \n2                  0.0              0.0       0  \n3                 13.0             13.0       0  \n4                 21.0             21.0       0  \n...                ...              ...     ...  \n14449              0.0              0.0       0  \n14450              0.0              0.0       0  \n14451              0.0              0.0       0  \n14452             10.0             10.0       0  \n14453              0.0             25.0       0  \n\n[14454 rows x 6 columns]",
      "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>customer_id</th>\n      <th>account_to_application_days</th>\n      <th>n_bnpl_approved_apps</th>\n      <th>n_inquiries_l3m</th>\n      <th>n_inquiries_l6m</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1223</td>\n      <td>245</td>\n      <td>0.0</td>\n      <td>91.0</td>\n      <td>91.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5190</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>5194</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3978</td>\n      <td>47</td>\n      <td>0.0</td>\n      <td>13.0</td>\n      <td>13.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4535</td>\n      <td>24</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>21.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>14449</th>\n      <td>14452</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14450</th>\n      <td>14453</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14451</th>\n      <td>14454</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14452</th>\n      <td>768</td>\n      <td>692</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>10.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14453</th>\n      <td>12079</td>\n      <td>152</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>25.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>14454 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = main_dataset.copy()\n",
    "features = duckdb.sql(\"select customer_id, account_to_application_days,n_bnpl_approved_apps, n_inquiries_l3m , n_inquiries_l6m, target from features\").to_df() \n",
    "\n",
    "\n",
    "# Llenamos con 0 los NA\n",
    "features.fillna(value=0,inplace=True)\n",
    "features"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:54.820155Z",
     "start_time": "2023-10-30T20:12:54.788539Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**MERGE CON LOS DATOS GENERADOS A PARTIR DE CREDIT_REPORTS**"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "      customer_id  account_to_application_days  n_bnpl_approved_apps  \\\n0            1223                          245                   0.0   \n1            3978                           47                   0.0   \n2            4535                           24                   0.0   \n3            3604                           72                   0.0   \n4             271                          393                   0.0   \n...           ...                          ...                   ...   \n9244        11725                          151                   0.0   \n9245         7008                          300                   0.0   \n9246        12781                          101                   0.0   \n9247          768                          692                   0.0   \n9248        12079                          152                   0.0   \n\n      n_inquiries_l3m  n_inquiries_l6m  target  Prestamos_totales  Years_old  \\\n0                91.0             91.0       0                 52       18.0   \n1                13.0             13.0       0                  9        6.0   \n2                21.0             21.0       0                 12       13.0   \n3                26.0             26.0       0                 31       10.0   \n4                11.0             11.0       0                 22        7.0   \n...               ...              ...     ...                ...        ...   \n9244              0.0             11.0       0                  5       16.0   \n9245              0.0              0.0       0                 65        9.0   \n9246              0.0              4.0       0                 21       13.0   \n9247             10.0             10.0       0                 20       15.0   \n9248              0.0             25.0       0                 37       15.0   \n\n      CREDIT_LIMIT  Porcentaje_retrasos  \n0        -0.338719            23.076923  \n1        -0.499960            77.777778  \n2         0.604828            50.000000  \n3         2.482673            51.612903  \n4        -0.019011            36.363636  \n...            ...                  ...  \n9244     -0.456580            20.000000  \n9245      0.178562            50.769231  \n9246     -0.455778            14.285714  \n9247     -0.484556            10.000000  \n9248     -0.524514            43.243243  \n\n[9249 rows x 10 columns]",
      "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>customer_id</th>\n      <th>account_to_application_days</th>\n      <th>n_bnpl_approved_apps</th>\n      <th>n_inquiries_l3m</th>\n      <th>n_inquiries_l6m</th>\n      <th>target</th>\n      <th>Prestamos_totales</th>\n      <th>Years_old</th>\n      <th>CREDIT_LIMIT</th>\n      <th>Porcentaje_retrasos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1223</td>\n      <td>245</td>\n      <td>0.0</td>\n      <td>91.0</td>\n      <td>91.0</td>\n      <td>0</td>\n      <td>52</td>\n      <td>18.0</td>\n      <td>-0.338719</td>\n      <td>23.076923</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3978</td>\n      <td>47</td>\n      <td>0.0</td>\n      <td>13.0</td>\n      <td>13.0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>6.0</td>\n      <td>-0.499960</td>\n      <td>77.777778</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4535</td>\n      <td>24</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>21.0</td>\n      <td>0</td>\n      <td>12</td>\n      <td>13.0</td>\n      <td>0.604828</td>\n      <td>50.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3604</td>\n      <td>72</td>\n      <td>0.0</td>\n      <td>26.0</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>31</td>\n      <td>10.0</td>\n      <td>2.482673</td>\n      <td>51.612903</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>271</td>\n      <td>393</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>11.0</td>\n      <td>0</td>\n      <td>22</td>\n      <td>7.0</td>\n      <td>-0.019011</td>\n      <td>36.363636</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9244</th>\n      <td>11725</td>\n      <td>151</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>16.0</td>\n      <td>-0.456580</td>\n      <td>20.000000</td>\n    </tr>\n    <tr>\n      <th>9245</th>\n      <td>7008</td>\n      <td>300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>65</td>\n      <td>9.0</td>\n      <td>0.178562</td>\n      <td>50.769231</td>\n    </tr>\n    <tr>\n      <th>9246</th>\n      <td>12781</td>\n      <td>101</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0</td>\n      <td>21</td>\n      <td>13.0</td>\n      <td>-0.455778</td>\n      <td>14.285714</td>\n    </tr>\n    <tr>\n      <th>9247</th>\n      <td>768</td>\n      <td>692</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>10.0</td>\n      <td>0</td>\n      <td>20</td>\n      <td>15.0</td>\n      <td>-0.484556</td>\n      <td>10.000000</td>\n    </tr>\n    <tr>\n      <th>9248</th>\n      <td>12079</td>\n      <td>152</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>25.0</td>\n      <td>0</td>\n      <td>37</td>\n      <td>15.0</td>\n      <td>-0.524514</td>\n      <td>43.243243</td>\n    </tr>\n  </tbody>\n</table>\n<p>9249 rows × 10 columns</p>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1) Primer merge, total de prestamos \n",
    "features = pd.merge(left=features, right=total_prestamos , on=\"customer_id\", how=\"inner\") # \"inner\" para solo quedarme con los clientes que tienen también datos en el credit_report\n",
    "\n",
    "# 2) Segundo merge, antiguedad (en años) crediticia \n",
    "features = pd.merge(left=features, right=antiguedad_crediticia, on=\"customer_id\", how=\"inner\")\n",
    "\n",
    "# 3) Tercer merge (máximo límite de crédito en la historial de la persona) \n",
    "features = pd.merge(left=features, right=credit_limit, on=\"customer_id\", how=\"inner\")\n",
    "\n",
    "# 4) Cuarto merge (porcentaje de préstamos con retraso)\n",
    "features = pd.merge( left=features, right=total_retrasos, on=\"customer_id\", how=\"inner\")\n",
    "\n",
    "# 5) Quinto merge total de prestamos por tipo de crédito por persona,       NO MEJORO EL MODELO \n",
    "#features = pd.merge (left=features, right=total_prestamos_categoria, on=\"customer_id\", how=\"inner\")\n",
    "\n",
    "# 6) Relfextion keys,                   NO MEJORO EL MODELO \n",
    "#features = pd.merge (left=features, right=prevention_keys, on=\"customer_id\", how=\"inner\")\n",
    "\n",
    "features"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:56.040800Z",
     "start_time": "2023-10-30T20:12:55.991258Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "      account_to_application_days  n_bnpl_approved_apps  n_inquiries_l3m  \\\n0                             245                   0.0             91.0   \n1                              47                   0.0             13.0   \n2                              24                   0.0             21.0   \n3                              72                   0.0             26.0   \n4                             393                   0.0             11.0   \n...                           ...                   ...              ...   \n9244                          151                   0.0              0.0   \n9245                          300                   0.0              0.0   \n9246                          101                   0.0              0.0   \n9247                          692                   0.0             10.0   \n9248                          152                   0.0              0.0   \n\n      n_inquiries_l6m  target  Prestamos_totales  Years_old  CREDIT_LIMIT  \\\n0                91.0       0                 52       18.0     -0.338719   \n1                13.0       0                  9        6.0     -0.499960   \n2                21.0       0                 12       13.0      0.604828   \n3                26.0       0                 31       10.0      2.482673   \n4                11.0       0                 22        7.0     -0.019011   \n...               ...     ...                ...        ...           ...   \n9244             11.0       0                  5       16.0     -0.456580   \n9245              0.0       0                 65        9.0      0.178562   \n9246              4.0       0                 21       13.0     -0.455778   \n9247             10.0       0                 20       15.0     -0.484556   \n9248             25.0       0                 37       15.0     -0.524514   \n\n      Porcentaje_retrasos  \n0               23.076923  \n1               77.777778  \n2               50.000000  \n3               51.612903  \n4               36.363636  \n...                   ...  \n9244            20.000000  \n9245            50.769231  \n9246            14.285714  \n9247            10.000000  \n9248            43.243243  \n\n[9249 rows x 9 columns]",
      "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>account_to_application_days</th>\n      <th>n_bnpl_approved_apps</th>\n      <th>n_inquiries_l3m</th>\n      <th>n_inquiries_l6m</th>\n      <th>target</th>\n      <th>Prestamos_totales</th>\n      <th>Years_old</th>\n      <th>CREDIT_LIMIT</th>\n      <th>Porcentaje_retrasos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>245</td>\n      <td>0.0</td>\n      <td>91.0</td>\n      <td>91.0</td>\n      <td>0</td>\n      <td>52</td>\n      <td>18.0</td>\n      <td>-0.338719</td>\n      <td>23.076923</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>47</td>\n      <td>0.0</td>\n      <td>13.0</td>\n      <td>13.0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>6.0</td>\n      <td>-0.499960</td>\n      <td>77.777778</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>24</td>\n      <td>0.0</td>\n      <td>21.0</td>\n      <td>21.0</td>\n      <td>0</td>\n      <td>12</td>\n      <td>13.0</td>\n      <td>0.604828</td>\n      <td>50.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>72</td>\n      <td>0.0</td>\n      <td>26.0</td>\n      <td>26.0</td>\n      <td>0</td>\n      <td>31</td>\n      <td>10.0</td>\n      <td>2.482673</td>\n      <td>51.612903</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>393</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>11.0</td>\n      <td>0</td>\n      <td>22</td>\n      <td>7.0</td>\n      <td>-0.019011</td>\n      <td>36.363636</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9244</th>\n      <td>151</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>16.0</td>\n      <td>-0.456580</td>\n      <td>20.000000</td>\n    </tr>\n    <tr>\n      <th>9245</th>\n      <td>300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>65</td>\n      <td>9.0</td>\n      <td>0.178562</td>\n      <td>50.769231</td>\n    </tr>\n    <tr>\n      <th>9246</th>\n      <td>101</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>0</td>\n      <td>21</td>\n      <td>13.0</td>\n      <td>-0.455778</td>\n      <td>14.285714</td>\n    </tr>\n    <tr>\n      <th>9247</th>\n      <td>692</td>\n      <td>0.0</td>\n      <td>10.0</td>\n      <td>10.0</td>\n      <td>0</td>\n      <td>20</td>\n      <td>15.0</td>\n      <td>-0.484556</td>\n      <td>10.000000</td>\n    </tr>\n    <tr>\n      <th>9248</th>\n      <td>152</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>25.0</td>\n      <td>0</td>\n      <td>37</td>\n      <td>15.0</td>\n      <td>-0.524514</td>\n      <td>43.243243</td>\n    </tr>\n  </tbody>\n</table>\n<p>9249 rows × 9 columns</p>\n</div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill  NA con 0\n",
    "features.fillna(value=0,inplace=True)\n",
    "\n",
    "# Dreopeo del customer ID\n",
    "features.drop(columns=[\"customer_id\"], inplace=True)\n",
    "features"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:56.731558Z",
     "start_time": "2023-10-30T20:12:56.721382Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "      target\n0          0\n1          0\n2          0\n3          0\n4          0\n...      ...\n9244       0\n9245       0\n9246       0\n9247       0\n9248       0\n\n[9249 rows x 1 columns]",
      "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>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9244</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9245</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9246</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9247</th>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9248</th>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>9249 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variable objetivo\n",
    "target  =features[[\"target\"]]\n",
    "\n",
    "# Dropeo de la vriable target\n",
    "features.drop(columns=[\"target\"], inplace=True)\n",
    "target"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:57.314119Z",
     "start_time": "2023-10-30T20:12:57.281690Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 3.1.2) Definición del conjunto de datos de entrenaimento y de testeo"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "      account_to_application_days  n_bnpl_approved_apps  n_inquiries_l3m  \\\n4002                          658                   0.0              0.0   \n3885                          181                   0.0              0.0   \n9129                          286                   0.0              0.0   \n1597                          162                   1.0              9.0   \n4649                           47                   0.0             38.0   \n...                           ...                   ...              ...   \n5734                          140                   0.0              0.0   \n5191                           69                   0.0             41.0   \n5390                          250                   1.0              0.0   \n860                             3                   0.0             16.0   \n7270                          313                   0.0              0.0   \n\n      n_inquiries_l6m  Prestamos_totales  Years_old  CREDIT_LIMIT  \\\n4002              0.0                  9       23.0     -0.532699   \n3885             31.0                 28        8.0     -0.435071   \n9129              0.0                 72       11.0      0.509721   \n1597              9.0                 38        3.0     -0.336263   \n4649             38.0                 48       17.0     -0.414396   \n...               ...                ...        ...           ...   \n5734             22.0                 39       13.0     -0.335846   \n5191             41.0                 27        4.0     -0.456679   \n5390              0.0                 22       15.0     -0.513882   \n860              16.0                 12        7.0     -0.581808   \n7270              0.0                 16       17.0      1.369430   \n\n      Porcentaje_retrasos  \n4002            22.222222  \n3885            42.857143  \n9129            38.888889  \n1597             2.631579  \n4649            25.000000  \n...                   ...  \n5734            23.076923  \n5191            44.444444  \n5390            40.909091  \n860             41.666667  \n7270            50.000000  \n\n[7399 rows x 8 columns]",
      "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>account_to_application_days</th>\n      <th>n_bnpl_approved_apps</th>\n      <th>n_inquiries_l3m</th>\n      <th>n_inquiries_l6m</th>\n      <th>Prestamos_totales</th>\n      <th>Years_old</th>\n      <th>CREDIT_LIMIT</th>\n      <th>Porcentaje_retrasos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4002</th>\n      <td>658</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>9</td>\n      <td>23.0</td>\n      <td>-0.532699</td>\n      <td>22.222222</td>\n    </tr>\n    <tr>\n      <th>3885</th>\n      <td>181</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>31.0</td>\n      <td>28</td>\n      <td>8.0</td>\n      <td>-0.435071</td>\n      <td>42.857143</td>\n    </tr>\n    <tr>\n      <th>9129</th>\n      <td>286</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>72</td>\n      <td>11.0</td>\n      <td>0.509721</td>\n      <td>38.888889</td>\n    </tr>\n    <tr>\n      <th>1597</th>\n      <td>162</td>\n      <td>1.0</td>\n      <td>9.0</td>\n      <td>9.0</td>\n      <td>38</td>\n      <td>3.0</td>\n      <td>-0.336263</td>\n      <td>2.631579</td>\n    </tr>\n    <tr>\n      <th>4649</th>\n      <td>47</td>\n      <td>0.0</td>\n      <td>38.0</td>\n      <td>38.0</td>\n      <td>48</td>\n      <td>17.0</td>\n      <td>-0.414396</td>\n      <td>25.000000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5734</th>\n      <td>140</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>22.0</td>\n      <td>39</td>\n      <td>13.0</td>\n      <td>-0.335846</td>\n      <td>23.076923</td>\n    </tr>\n    <tr>\n      <th>5191</th>\n      <td>69</td>\n      <td>0.0</td>\n      <td>41.0</td>\n      <td>41.0</td>\n      <td>27</td>\n      <td>4.0</td>\n      <td>-0.456679</td>\n      <td>44.444444</td>\n    </tr>\n    <tr>\n      <th>5390</th>\n      <td>250</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>22</td>\n      <td>15.0</td>\n      <td>-0.513882</td>\n      <td>40.909091</td>\n    </tr>\n    <tr>\n      <th>860</th>\n      <td>3</td>\n      <td>0.0</td>\n      <td>16.0</td>\n      <td>16.0</td>\n      <td>12</td>\n      <td>7.0</td>\n      <td>-0.581808</td>\n      <td>41.666667</td>\n    </tr>\n    <tr>\n      <th>7270</th>\n      <td>313</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>16</td>\n      <td>17.0</td>\n      <td>1.369430</td>\n      <td>50.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>7399 rows × 8 columns</p>\n</div>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Traning adn testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "X_train"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:58.054756Z",
     "start_time": "2023-10-30T20:12:58.043764Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 3.1.3) Defiinción de la grilla (grid)\n",
    "La grilla la usarmeos para hacer un grid_serach que servira para el tuneo de hiperrametros del modelo"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "# Instancia del modelo\n",
    "model = xgb.XGBClassifier()\n",
    "\n",
    "# Grilla de hiperparámetros\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200], # Número de arboles que  quieres construir\n",
    "    'learning_rate': [0.01, 0.05, 0.1],\n",
    "    'max_depth': [3, 6, 12], # profundidad de los arboles\n",
    "    'colsample_bytree': [0.3, 0.5, 0.9], # Fracción de las características que se pueden seleccionar (de forma aleatoria) para construir un árbol en cada iteración.\n",
    "}\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:12:58.874529Z",
     "start_time": "2023-10-30T20:12:58.846144Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 3.1.4) Entrenamiento y tuneo del modelo"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 747 ms, sys: 477 ms, total: 1.22 s\n",
      "Wall time: 8.41 s\n"
     ]
    },
    {
     "data": {
      "text/plain": "GridSearchCV(cv=5,\n             estimator=XGBClassifier(base_score=None, booster=None,\n                                     callbacks=None, colsample_bylevel=None,\n                                     colsample_bynode=None,\n                                     colsample_bytree=None, device=None,\n                                     early_stopping_rounds=None,\n                                     enable_categorical=False, eval_metric=None,\n                                     feature_types=None, gamma=None,\n                                     grow_policy=None, importance_type=None,\n                                     interaction_constraints=None,\n                                     learning_rate=None,...\n                                     max_delta_step=None, max_depth=None,\n                                     max_leaves=None, min_child_weight=None,\n                                     missing=nan, monotone_constraints=None,\n                                     multi_strategy=None, n_estimators=None,\n                                     n_jobs=None, num_parallel_tree=None,\n                                     random_state=None, ...),\n             n_jobs=-1,\n             param_grid={'colsample_bytree': [0.3, 0.5, 0.9],\n                         'learning_rate': [0.01, 0.05, 0.1],\n                         'max_depth': [3, 6, 12],\n                         'n_estimators': [50, 100, 200]},\n             scoring='accuracy')",
      "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n             estimator=XGBClassifier(base_score=None, booster=None,\n                                     callbacks=None, colsample_bylevel=None,\n                                     colsample_bynode=None,\n                                     colsample_bytree=None, device=None,\n                                     early_stopping_rounds=None,\n                                     enable_categorical=False, eval_metric=None,\n                                     feature_types=None, gamma=None,\n                                     grow_policy=None, importance_type=None,\n                                     interaction_constraints=None,\n                                     learning_rate=None,...\n                                     max_delta_step=None, max_depth=None,\n                                     max_leaves=None, min_child_weight=None,\n                                     missing=nan, monotone_constraints=None,\n                                     multi_strategy=None, n_estimators=None,\n                                     n_jobs=None, num_parallel_tree=None,\n                                     random_state=None, ...),\n             n_jobs=-1,\n             param_grid={&#x27;colsample_bytree&#x27;: [0.3, 0.5, 0.9],\n                         &#x27;learning_rate&#x27;: [0.01, 0.05, 0.1],\n                         &#x27;max_depth&#x27;: [3, 6, 12],\n                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n             scoring=&#x27;accuracy&#x27;)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n             estimator=XGBClassifier(base_score=None, booster=None,\n                                     callbacks=None, colsample_bylevel=None,\n                                     colsample_bynode=None,\n                                     colsample_bytree=None, device=None,\n                                     early_stopping_rounds=None,\n                                     enable_categorical=False, eval_metric=None,\n                                     feature_types=None, gamma=None,\n                                     grow_policy=None, importance_type=None,\n                                     interaction_constraints=None,\n                                     learning_rate=None,...\n                                     max_delta_step=None, max_depth=None,\n                                     max_leaves=None, min_child_weight=None,\n                                     missing=nan, monotone_constraints=None,\n                                     multi_strategy=None, n_estimators=None,\n                                     n_jobs=None, num_parallel_tree=None,\n                                     random_state=None, ...),\n             n_jobs=-1,\n             param_grid={&#x27;colsample_bytree&#x27;: [0.3, 0.5, 0.9],\n                         &#x27;learning_rate&#x27;: [0.01, 0.05, 0.1],\n                         &#x27;max_depth&#x27;: [3, 6, 12],\n                         &#x27;n_estimators&#x27;: [50, 100, 200]},\n             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=None, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=None, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=None, n_jobs=None,\n              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n              colsample_bylevel=None, colsample_bynode=None,\n              colsample_bytree=None, device=None, early_stopping_rounds=None,\n              enable_categorical=False, eval_metric=None, feature_types=None,\n              gamma=None, grow_policy=None, importance_type=None,\n              interaction_constraints=None, learning_rate=None, max_bin=None,\n              max_cat_threshold=None, max_cat_to_onehot=None,\n              max_delta_step=None, max_depth=None, max_leaves=None,\n              min_child_weight=None, missing=nan, monotone_constraints=None,\n              multi_strategy=None, n_estimators=None, n_jobs=None,\n              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "grid_search = GridSearchCV(estimator=model,param_grid= param_grid, cv=5,\n",
    "                           scoring='accuracy', n_jobs=-1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:13:08.066743Z",
     "start_time": "2023-10-30T20:12:59.633873Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 3.1.5) Evaluación del modelo final"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejores hiperparámetros: {'colsample_bytree': 0.5, 'learning_rate': 0.05, 'max_depth': 6, 'n_estimators': 50}\n",
      "Mejor precisión alcanzada: 0.8145696507866893\n"
     ]
    }
   ],
   "source": [
    "print(f\"Mejores hiperparámetros: {grid_search.best_params_}\")\n",
    "print(f\"Mejor precisión alcanzada: {grid_search.best_score_}\")\n",
    "\n",
    "# Mejor modelo\n",
    "best_model = grid_search.best_estimator_"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:13:08.072911Z",
     "start_time": "2023-10-30T20:13:08.054834Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 3.1.6) Análisis de relevancias por variable"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#relevancia de cada variable\n",
    "# Obtener la importancia usando el método 'get_booster().get_score()'\n",
    "importance = best_model.get_booster().get_score(importance_type='weight')\n",
    "\n",
    "# Usar la función plot_importance\n",
    "plot_importance(booster=best_model, importance_type='weight', title='Importance by Weight')\n",
    "plt.show() # Podemos ver que las variables creadas jugaron un rol muy importante "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:13:08.254028Z",
     "start_time": "2023-10-30T20:13:08.063576Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 3.2) Regresión logistica "
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/orlandoandrade/miniconda3/envs/Data_Science/lib/python3.11/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "# Crear una instancia del clasificador y entrenar\n",
    "# Instancia del modelo\n",
    "model = LogisticRegression(max_iter=10000)\n",
    "\n",
    "# Grilla de hiperparámetros\n",
    "param_grid = {\n",
    "    'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "    'penalty': ['l1', 'l2'],\n",
    "    'solver': ['liblinear']  # 'liblinear' es necesario para el penalty 'l1'\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(model, param_grid, cv=5, scoring='accuracy', n_jobs=-1, verbose=1)\n",
    "\n",
    "grid_search.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:16:18.592057Z",
     "start_time": "2023-10-30T20:16:18.297866Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mejores hiperparámetros: {'C': 0.1, 'penalty': 'l1', 'solver': 'liblinear'}\n",
      "Mejor precisión alcanzada: 0.8145695594174296\n"
     ]
    }
   ],
   "source": [
    "print(f\"Mejores hiperparámetros: {grid_search.best_params_}\")\n",
    "print(f\"Mejor precisión alcanzada: {grid_search.best_score_}\")\n",
    "\n",
    "best_model = grid_search.best_estimator_\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-10-30T20:16:21.225941Z",
     "start_time": "2023-10-30T20:16:21.221788Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**NOTA:** Quitar las variables de\n",
    "- Prestamos por Categoria\n",
    "- Prevention keys"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 4) Discuciones\n",
    "En este sección explicaré porque aborde el problema de esta manera y como podría mejorarlo teninedo los mismo datos\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4.1) Por qué lo hice así? y como mejorarlo con los mismo datos \n",
    "El reto y datos planteados están enfocados en una clasificación binaria (buen cliente o mal cliente) por esta razón decide abordarlo de esta forma. Los datos son **tabulares**, es decir se organizan en una estructura de tabla, similar a una hoja de cálculo o una base de datos relacional. Xgboost se un algoritmo muy potente utilizado en problemas de clasificación y regresión, trabaja muy bien en datos tabulares como estos incluso mejor que redes neuronales<sup>1</sup>\n",
    "\n",
    "Por otra parte, plantee un escenario en el que x persona hace la solicitud **DE SU PRIMER CREDITO** a la empresa X, debido a que no se cuenta con un historial interno variables como *max_days_late* del main_dataset no son reales de integrarse para hacer una predicción del **PRIMER CRÉDITO**, estas variables son posible para una persona que está pidiendo su 2, 3 .. crédito en la misma institución. \n",
    "\n",
    "**NOTA:** Una comprensión más profunda de los datos me ayudaría a generar variables nuevas que podrían ser beneficiosas para el modelo, una de estas variables está relacionado al aspecto de BALANCE, me gustaría comnocer más sobre estas variables para poder plantear su uso de manera adecuada \n",
    "\n",
    "Finalmente, una manera *posible* de mejorar el modelo sería con un proceso de **FEATURE SELECTION** más elabordo, en mi caso estuve probando manualmente con algunas de las variables más prometedoras de ambos datasets (y generando variables nuevas) al final me quede con las que se entrena el modelo en este notebook pero prodríamos hacer un proceso más elaborado usando técnicas como **WRAPPED METHODS** para hacer una selección más óptima de variables.  Esto tendría el potencial de mejorar el modelo usando las mismas variables existentes\n",
    "\n",
    "1: Shwartz-Ziv, R. (2021, 6 junio). Tabular data: Deep learning is not all you need. arXiv.org. https://arxiv.org/abs/2106.03253\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4.2) Mejorar el modelo con datos adicionales\n",
    "Crear un modelo de riesgo financiero es crucial para muchas instituciones financieras. La selección de variables es esencial para la eficacia del modelo. Las variables presentadas en ambos conjunto de daatos son acertadas (aunque me gustaría comprender mejor algunas otras variables actuales como la de BALANCE). Sin embargo, el modelo se puede enriquecer y tener una mejora potencial muy significativa con otros datos como lo puede ser: \n",
    "\n",
    "1. **Ingreso mensual**: Una medida de la capacidad del cliente para pagar\n",
    "\n",
    "2. **Ratio de endeudamiento**: Indica cuánto de los ingresos mensuales del cliente ya está comprometido en otros pagos de deuda.\n",
    "\n",
    "3. **Duración del empleo**: Un empleo estable puede ser indicativo de ingresos regulares y estabilidad financiera.\n",
    "\n",
    "4. **Edad**: La edad podría influir en el comportamiento de pago\n",
    "\n",
    "5. **Antigüedad en la vivienda actua/casa propia o renta:** Una residencia estable puede ser un indicativo de estabilidad general en la vida del cliente.\n",
    "\n",
    "6. **Estado civil y dependientes**: Estar casado o tener dependientes puede influir en las responsabilidades financieras y, por lo tanto, en el comportamiento de pago.\n",
    "\n",
    "7. **Tipo de empleo o profesión**: Algunas profesiones pueden estar asociadas con ingresos más estables o más volátiles.\n"
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    "## 5) Tasas de interés a cada cliente \n",
    "**¿Cómo asignaría una tasa de interés a cada cliente?**\n",
    "\n",
    "Abordaría este problema basado en el nivel de riesgos. Para esto haría un etiquedado de multiclases en mi historial interno de registros. Este etiquetado podría poner varios niveles de riesgos como bajo, medio y alto. Cada uno de estas categorias (o más categorias) tendría tasas de interés propia.  \n",
    "\n",
    "Entonces podríamos combinar 2 modelos. El primero  que es un modelo de clasificación binaria me indicaria si se le otorga o no un crédito a una persona y el segundo que sería de una clasificación multiclase me indicaría a que tasa de interés se le podría otorgar el crédito"
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