Sprint 5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1d5f4306-967d-4927-b4b9-26728cbf0588",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA) Template\n",
    "# 1. Importación de Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c6b86cf0-db64-4194-bb7f-bf697a3bf55a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sweetviz in c:\\users\\javie\\anaconda3\\lib\\site-packages (2.3.1)\n",
      "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from sweetviz) (2.2.2)\n",
      "Requirement already satisfied: numpy>=1.16.0 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from sweetviz) (1.26.4)\n",
      "Requirement already satisfied: matplotlib>=3.1.3 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from sweetviz) (3.8.4)\n",
      "Requirement already satisfied: tqdm>=4.43.0 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from sweetviz) (4.66.4)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from sweetviz) (1.13.1)\n",
      "Requirement already satisfied: jinja2>=2.11.1 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from sweetviz) (3.1.4)\n",
      "Requirement already satisfied: importlib-resources>=1.2.0 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from sweetviz) (6.4.5)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from jinja2>=2.11.1->sweetviz) (2.1.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\javie\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib>=3.1.3->sweetviz) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\javie\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib>=3.1.3->sweetviz) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\javie\\anaconda3\\lib\\site-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2023.3)\n",
      "Requirement already satisfied: colorama in c:\\users\\javie\\appdata\\roaming\\python\\python312\\site-packages (from tqdm>=4.43.0->sweetviz) (0.4.6)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\javie\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib>=3.1.3->sweetviz) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "!pip install sweetviz\n",
    "import sweetviz as sv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "451956f4-7d5a-474e-85fd-d0a8755211be",
   "metadata": {},
   "source": [
    "# Cargar el Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "229dc757-3269-4bac-b508-0a380fe27acb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\javie\\Downloads\\bank_dataset.CSV\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d03fb7b-2242-4676-9c73-feed0ea0d6eb",
   "metadata": {},
   "source": [
    "# Muestro la estructura del DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fea2d502-5833-4b3c-aff1-06e3f73ad738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11162, 17)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d79dd07-65e8-4431-8405-24faa213d535",
   "metadata": {},
   "source": [
    "Se observa que el Df cuenta con un total de 11162 registros (filas) que a su vez cuentan de 17 columnas. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9001e812-7457-43fd-93cd-f43be35357b6",
   "metadata": {},
   "source": [
    "# Muestra las primeras filas del dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27328dbb-3ccd-44f2-8f78-c92827748ce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
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       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.0</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.0</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2476</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age         job  marital  education default  balance housing loan  \\\n",
       "0  59.0      admin.  married  secondary      no     2343     yes   no   \n",
       "1  56.0      admin.  married  secondary      no       45      no   no   \n",
       "2  41.0  technician  married  secondary      no     1270     yes   no   \n",
       "3  55.0    services  married  secondary      no     2476     yes   no   \n",
       "4  54.0      admin.  married   tertiary      no      184      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome deposit  \n",
       "0  unknown    5   may      1042         1     -1         0  unknown     yes  \n",
       "1  unknown    5   may      1467         1     -1         0  unknown     yes  \n",
       "2  unknown    5   may      1389         1     -1         0  unknown     yes  \n",
       "3  unknown    5   may       579         1     -1         0  unknown     yes  \n",
       "4  unknown    5   may       673         2     -1         0  unknown     yes  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6800d5d8-f6e9-41ad-a303-2d7f944fc798",
   "metadata": {},
   "source": [
    "# Información general del Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d9567de-f571-4c05-995c-e8d110422a3c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11162 entries, 0 to 11161\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   age        11152 non-null  float64\n",
      " 1   job        11162 non-null  object \n",
      " 2   marital    11157 non-null  object \n",
      " 3   education  11155 non-null  object \n",
      " 4   default    11162 non-null  object \n",
      " 5   balance    11162 non-null  int64  \n",
      " 6   housing    11162 non-null  object \n",
      " 7   loan       11162 non-null  object \n",
      " 8   contact    11162 non-null  object \n",
      " 9   day        11162 non-null  int64  \n",
      " 10  month      11162 non-null  object \n",
      " 11  duration   11162 non-null  int64  \n",
      " 12  campaign   11162 non-null  int64  \n",
      " 13  pdays      11162 non-null  int64  \n",
      " 14  previous   11162 non-null  int64  \n",
      " 15  poutcome   11162 non-null  object \n",
      " 16  deposit    11162 non-null  object \n",
      "dtypes: float64(1), int64(6), object(10)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4b1a96-6378-46d8-8727-7b63024c9a06",
   "metadata": {},
   "source": [
    "# Descripción estadística del Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6302c0ee-7181-4d4d-8df9-10c2a10add3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11152.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.235384</td>\n",
       "      <td>1528.538524</td>\n",
       "      <td>15.658036</td>\n",
       "      <td>371.993818</td>\n",
       "      <td>2.508421</td>\n",
       "      <td>51.330407</td>\n",
       "      <td>0.832557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.914934</td>\n",
       "      <td>3225.413326</td>\n",
       "      <td>8.420740</td>\n",
       "      <td>347.128386</td>\n",
       "      <td>2.722077</td>\n",
       "      <td>108.758282</td>\n",
       "      <td>2.292007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-6847.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>81204.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3881.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age       balance           day      duration      campaign  \\\n",
       "count  11152.000000  11162.000000  11162.000000  11162.000000  11162.000000   \n",
       "mean      41.235384   1528.538524     15.658036    371.993818      2.508421   \n",
       "std       11.914934   3225.413326      8.420740    347.128386      2.722077   \n",
       "min       18.000000  -6847.000000      1.000000      2.000000      1.000000   \n",
       "25%       32.000000    122.000000      8.000000    138.000000      1.000000   \n",
       "50%       39.000000    550.000000     15.000000    255.000000      2.000000   \n",
       "75%       49.000000   1708.000000     22.000000    496.000000      3.000000   \n",
       "max       95.000000  81204.000000     31.000000   3881.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  11162.000000  11162.000000  \n",
       "mean      51.330407      0.832557  \n",
       "std      108.758282      2.292007  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       20.750000      1.000000  \n",
       "max      854.000000     58.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f85cb4e-d698-4625-8491-8503c34efff9",
   "metadata": {},
   "source": [
    "# Histograma de cada variable numérica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a0595a5-7685-43ac-a871-889e2460afe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=30, figsize=(15, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f5384ca-3f1c-40ff-9573-c5dace5e0c51",
   "metadata": {},
   "source": [
    "# Contar los valores de cada variable categórica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6687949f-ce21-400f-ab44-91f0ce80a42e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La variable JOB tiene categorias: 12    \n",
      "job\n",
      "management       2566\n",
      "blue-collar      1944\n",
      "technician       1823\n",
      "admin.           1334\n",
      "services          923\n",
      "retired           778\n",
      "self-employed     405\n",
      "student           360\n",
      "unemployed        357\n",
      "entrepreneur      328\n",
      "housemaid         274\n",
      "unknown            70\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n",
      "La variable MARITAL tiene categorias: 3    \n",
      "marital\n",
      "married     6349\n",
      "single      3517\n",
      "divorced    1291\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n",
      "La variable EDUCATION tiene categorias: 4    \n",
      "education\n",
      "secondary    5474\n",
      "tertiary     3685\n",
      "primary      1500\n",
      "unknown       496\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n",
      "La variable DEFAULT tiene categorias: 2    \n",
      "default\n",
      "no     10994\n",
      "yes      168\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n",
      "La variable HOUSING tiene categorias: 2    \n",
      "housing\n",
      "no     5881\n",
      "yes    5281\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n",
      "La variable LOAN tiene categorias: 2    \n",
      "loan\n",
      "no     9702\n",
      "yes    1460\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n",
      "La variable CONTACT tiene categorias: 3    \n",
      "contact\n",
      "cellular     8042\n",
      "unknown      2346\n",
      "telephone     774\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n",
      "La variable MONTH tiene categorias: 12    \n",
      "month\n",
      "may    2824\n",
      "aug    1519\n",
      "jul    1514\n",
      "jun    1222\n",
      "nov     943\n",
      "apr     923\n",
      "feb     776\n",
      "oct     392\n",
      "jan     344\n",
      "sep     319\n",
      "mar     276\n",
      "dec     110\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n",
      "La variable POUTCOME tiene categorias: 4    \n",
      "poutcome\n",
      "unknown    8326\n",
      "failure    1228\n",
      "success    1071\n",
      "other       537\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n",
      "La variable DEPOSIT tiene categorias: 2    \n",
      "deposit\n",
      "no     5873\n",
      "yes    5289\n",
      "Name: count, dtype: int64    \n",
      " \n",
      "------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for column in df.select_dtypes(include=['object']).columns:\n",
    "    print(f\"La variable {column.upper()} tiene categorias: {df[column].nunique()}\\\n",
    "    \\n{df[column].value_counts()}\\\n",
    "    \\n \\n------------------------\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e40a45d2-c516-4467-8d0d-011b9f328c33",
   "metadata": {},
   "source": [
    "# Contar los valores nulos por columnas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "156cd70f-f28d-4e97-a2ae-d1371655629a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          10\n",
       "job           0\n",
       "marital       5\n",
       "education     7\n",
       "default       0\n",
       "balance       0\n",
       "housing       0\n",
       "loan          0\n",
       "contact       0\n",
       "day           0\n",
       "month         0\n",
       "duration      0\n",
       "campaign      0\n",
       "pdays         0\n",
       "previous      0\n",
       "poutcome      0\n",
       "deposit       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "031d9123-75c9-441d-9db0-34622d04b029",
   "metadata": {},
   "source": [
    "Observamos que tenemos variables nulas en age, marital y education haciendo un total de 22 nulos que representan < 1% por lo que considero que no representa un valor significativo como para que afecte al modelo o análisis que fuera a hacer con estos datos. Pero a manera de práctica remplazaré esos valores utilizando la mediana y la moda para asemejarlos lo más posible con las coincidencias que crea pueden ser más acertadas, tomando en cuenta el valor de otras columnas como job que en algunos casos nos puede asemejar más el perfil del cliente."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d128fd-122a-4e38-985c-906648e28405",
   "metadata": {},
   "source": [
    "# Visualizar los registros que tienen algún valor nulo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ac6b3286-e7fd-4d13-b05b-33678ed6f433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>53.0</td>\n",
       "      <td>management</td>\n",
       "      <td>NaN</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1004</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>22</td>\n",
       "      <td>dec</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1181</th>\n",
       "      <td>NaN</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>309</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>5</td>\n",
       "      <td>feb</td>\n",
       "      <td>676</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>37.0</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>549</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>2</td>\n",
       "      <td>mar</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1410</th>\n",
       "      <td>NaN</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>4979</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>6</td>\n",
       "      <td>apr</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2943</th>\n",
       "      <td>24.0</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>382</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>13</td>\n",
       "      <td>nov</td>\n",
       "      <td>256</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>3</td>\n",
       "      <td>failure</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3719</th>\n",
       "      <td>68.0</td>\n",
       "      <td>retired</td>\n",
       "      <td>NaN</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>4189</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>14</td>\n",
       "      <td>jul</td>\n",
       "      <td>897</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4019</th>\n",
       "      <td>NaN</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1934</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>29</td>\n",
       "      <td>sep</td>\n",
       "      <td>271</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4737</th>\n",
       "      <td>37.0</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>102</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>6</td>\n",
       "      <td>may</td>\n",
       "      <td>445</td>\n",
       "      <td>1</td>\n",
       "      <td>258</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4854</th>\n",
       "      <td>34.0</td>\n",
       "      <td>management</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>aug</td>\n",
       "      <td>370</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5680</th>\n",
       "      <td>NaN</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>720</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>12</td>\n",
       "      <td>aug</td>\n",
       "      <td>234</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6701</th>\n",
       "      <td>60.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>-444</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>16</td>\n",
       "      <td>jul</td>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6763</th>\n",
       "      <td>NaN</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>686</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>30</td>\n",
       "      <td>may</td>\n",
       "      <td>98</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6819</th>\n",
       "      <td>32.0</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>-344</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>4</td>\n",
       "      <td>feb</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7121</th>\n",
       "      <td>57.0</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>657</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>12</td>\n",
       "      <td>jun</td>\n",
       "      <td>344</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8419</th>\n",
       "      <td>NaN</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1782</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>19</td>\n",
       "      <td>jun</td>\n",
       "      <td>549</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8592</th>\n",
       "      <td>55.0</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>797</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>29</td>\n",
       "      <td>jul</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9272</th>\n",
       "      <td>30.0</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>35</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>11</td>\n",
       "      <td>jul</td>\n",
       "      <td>366</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9406</th>\n",
       "      <td>32.0</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>696</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>13</td>\n",
       "      <td>aug</td>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "      <td>105</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9470</th>\n",
       "      <td>NaN</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>552</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>28</td>\n",
       "      <td>jul</td>\n",
       "      <td>761</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9552</th>\n",
       "      <td>NaN</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1648</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>nov</td>\n",
       "      <td>172</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9751</th>\n",
       "      <td>NaN</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>2567</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14</td>\n",
       "      <td>may</td>\n",
       "      <td>109</td>\n",
       "      <td>1</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10678</th>\n",
       "      <td>NaN</td>\n",
       "      <td>technician</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>2</td>\n",
       "      <td>jun</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age           job   marital  education default  balance housing loan  \\\n",
       "1088   53.0    management       NaN  secondary      no     1004      no  yes   \n",
       "1181    NaN    management   married   tertiary      no      309     yes   no   \n",
       "1300   37.0    technician   married        NaN      no      549      no   no   \n",
       "1410    NaN   blue-collar   married    primary      no     4979      no   no   \n",
       "2943   24.0       student    single        NaN      no      382      no   no   \n",
       "3719   68.0       retired       NaN  secondary      no     4189      no   no   \n",
       "4019    NaN      services    single  secondary      no     1934      no   no   \n",
       "4737   37.0    management    single        NaN      no      102     yes   no   \n",
       "4854   34.0    management       NaN   tertiary      no        5      no   no   \n",
       "5680    NaN    technician    single   tertiary      no      720      no   no   \n",
       "6701   60.0        admin.       NaN    primary      no     -444      no  yes   \n",
       "6763    NaN    technician    single   tertiary      no      686     yes   no   \n",
       "6819   32.0      services   married        NaN      no     -344      no  yes   \n",
       "7121   57.0  entrepreneur   married        NaN      no      657      no   no   \n",
       "8419    NaN   blue-collar   married  secondary      no     1782     yes   no   \n",
       "8592   55.0    management    single        NaN      no      797      no   no   \n",
       "9272   30.0   blue-collar       NaN    primary      no       35     yes   no   \n",
       "9406   32.0    technician    single        NaN      no      696      no  yes   \n",
       "9470    NaN    management   married   tertiary      no      552      no   no   \n",
       "9552    NaN        admin.   married  secondary      no     1648     yes   no   \n",
       "9751    NaN      services   married    primary      no     2567     yes   no   \n",
       "10678   NaN    technician  divorced   tertiary      no        0     yes   no   \n",
       "\n",
       "         contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
       "1088   telephone   22   dec       119         1     -1         0  unknown   \n",
       "1181    cellular    5   feb       676         1     -1         0  unknown   \n",
       "1300    cellular    2   mar       239         1     -1         0  unknown   \n",
       "1410   telephone    6   apr       180         1     -1         0  unknown   \n",
       "2943    cellular   13   nov       256         2     92         3  failure   \n",
       "3719   telephone   14   jul       897         2     -1         0  unknown   \n",
       "4019    cellular   29   sep       271         1     -1         0  unknown   \n",
       "4737    cellular    6   may       445         1    258         2  failure   \n",
       "4854    cellular   18   aug       370         2     -1         0  unknown   \n",
       "5680    cellular   12   aug       234         4     -1         0  unknown   \n",
       "6701    cellular   16   jul       227         1     -1         0  unknown   \n",
       "6763     unknown   30   may        98         2     -1         0  unknown   \n",
       "6819    cellular    4   feb        44         1     -1         0  unknown   \n",
       "7121     unknown   12   jun       344         1     -1         0  unknown   \n",
       "8419     unknown   19   jun       549         1     -1         0  unknown   \n",
       "8592    cellular   29   jul        24         2     -1         0  unknown   \n",
       "9272    cellular   11   jul       366         2     -1         0  unknown   \n",
       "9406    cellular   13   aug       101         4    105         4  failure   \n",
       "9470    cellular   28   jul       761         8     -1         0  unknown   \n",
       "9552    cellular   19   nov       172         3     -1         0  unknown   \n",
       "9751    cellular   14   may       109         1    360         2  failure   \n",
       "10678   cellular    2   jun        72         1     -1         0  unknown   \n",
       "\n",
       "      deposit  \n",
       "1088      yes  \n",
       "1181      yes  \n",
       "1300      yes  \n",
       "1410      yes  \n",
       "2943      yes  \n",
       "3719      yes  \n",
       "4019      yes  \n",
       "4737      yes  \n",
       "4854      yes  \n",
       "5680       no  \n",
       "6701       no  \n",
       "6763       no  \n",
       "6819       no  \n",
       "7121       no  \n",
       "8419       no  \n",
       "8592       no  \n",
       "9272       no  \n",
       "9406       no  \n",
       "9470       no  \n",
       "9552       no  \n",
       "9751       no  \n",
       "10678      no  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nulos = df['age'].isnull() | df['marital'].isnull() | df['education'].isnull()\n",
    "df[nulos]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c46f5e3-ddff-4bd8-95cf-b97c49d27f4e",
   "metadata": {},
   "source": [
    "# Sustituyo los nulos de la edad por su moda teniendo en cuenta los campos de job, marital y education ajustando los perfiles de coincidencia "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b85b5fb7-3105-4481-be3b-5ba20ec1c99b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mediana_age = df.groupby([\"job\", \"marital\", \"education\"])[\"age\"].transform(lambda x: x.median() if not x.isnull().all() else x)\n",
    "df[\"age\"] = df[\"age\"].fillna(mediana_age.round())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34bc5d8-c109-439a-b855-8e3ba1d9c63e",
   "metadata": {},
   "source": [
    "# Sustituyo los nulos de marital con la moda de los registros teniendo en cuenta la edad y el trabajo para ajustar un poco más la coincidencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d73a79b0-7649-4dbe-8106-d0d0c3896d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "moda_marital = df.groupby([\"job\", \"age\"])[\"marital\"].transform(lambda x: x.mode()[0] if not x.isnull().all() else np.nan)\n",
    "df[\"marital\"] = df[\"marital\"].fillna(moda_marital)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ed7030-aa2c-402a-a8c5-c368feb18fd9",
   "metadata": {},
   "source": [
    "# Sustituyo los nulos de education con la moda de los registros que tengan la misma edad y trabajo para ajustar la coincidencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb0678a1-1375-4312-b139-35073b40c996",
   "metadata": {},
   "outputs": [],
   "source": [
    "moda_education = df.groupby([\"job\", \"age\"])[\"education\"].transform(lambda x: x.mode().iloc[0] if not x.isnull().all() else np.nan)\n",
    "df[\"education\"] = df[\"education\"].fillna(moda_education)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d39a1e3-7631-4657-87a7-09c55ee40e1f",
   "metadata": {},
   "source": [
    "# Vuelvo a contar los valores nulos para comprobar de que han sido sustituidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2c587e7d-5a82-45be-8b94-590e6587acee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "deposit      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8e4de4-6725-4a13-b735-4e641d21be66",
   "metadata": {},
   "source": [
    "# Deteccion de outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d338b430-474b-49f4-9009-eeda226860d4",
   "metadata": {},
   "source": [
    "Esta vez se puede comprobar de que los 22 valores nulos que teniamos anteriormente han sido sustituidos con éxito en el DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4f8d6666-8493-4c19-b09b-8478dfc3e88a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detectados en 'age':\n",
      "        age      job   marital  education default  balance housing loan  \\\n",
      "1162   75.0  retired  divorced    primary      no     3881     yes   no   \n",
      "1236   85.0  retired   married    primary      no    12114      no   no   \n",
      "1243   90.0  retired  divorced  secondary      no        1      no   no   \n",
      "1274   85.0  retired  divorced    primary      no     7613      no   no   \n",
      "1320   83.0  retired  divorced    primary      no     1097      no   no   \n",
      "...     ...      ...       ...        ...     ...      ...     ...  ...   \n",
      "10438  77.0  retired   married    primary      no     1492      no   no   \n",
      "10562  88.0  retired   married    primary      no      648      no   no   \n",
      "10570  77.0  unknown   married    unknown      no      397      no   no   \n",
      "10618  78.0  retired   married    primary      no      680      no   no   \n",
      "10843  86.0  retired  divorced    unknown      no      157      no   no   \n",
      "\n",
      "         contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
      "1162    cellular    4   feb       136         3     -1         0  unknown   \n",
      "1236   telephone   12   feb       165         2     -1         0  unknown   \n",
      "1243    cellular   13   feb       152         3     -1         0  unknown   \n",
      "1274    cellular   18   feb       355         3     -1         0  unknown   \n",
      "1320   telephone    5   mar       181         1     -1         0  unknown   \n",
      "...          ...  ...   ...       ...       ...    ...       ...      ...   \n",
      "10438  telephone    1   sep       663         1    208         2    other   \n",
      "10562  telephone    3   sep       318         1     -1         0  unknown   \n",
      "10570  telephone    8   nov       207         1    185         3  success   \n",
      "10618  telephone   24   may       838         1     89         9  failure   \n",
      "10843  telephone    7   sep       147         1     -1         0  unknown   \n",
      "\n",
      "      deposit  \n",
      "1162      yes  \n",
      "1236      yes  \n",
      "1243      yes  \n",
      "1274      yes  \n",
      "1320      yes  \n",
      "...       ...  \n",
      "10438      no  \n",
      "10562      no  \n",
      "10570      no  \n",
      "10618      no  \n",
      "10843      no  \n",
      "\n",
      "[171 rows x 17 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detectados en 'balance':\n",
      "        age          job   marital  education default  balance housing loan  \\\n",
      "9      28.0     services    single  secondary      no     5090     yes   no   \n",
      "60     51.0  blue-collar   married  secondary      no     7180     yes   no   \n",
      "61     41.0  blue-collar  divorced  secondary      no     5291     yes   no   \n",
      "69     44.0   technician   married   tertiary      no     4580     yes   no   \n",
      "71     29.0   management   married   tertiary      no    10576      no   no   \n",
      "...     ...          ...       ...        ...     ...      ...     ...  ...   \n",
      "11073  34.0  blue-collar   married    primary      no     6718      no   no   \n",
      "11077  54.0      retired  divorced   tertiary      no     6102     yes  yes   \n",
      "11085  60.0   technician   married    primary      no     4243     yes   no   \n",
      "11086  42.0       admin.   married  secondary      no     6445     yes   no   \n",
      "11101  54.0  blue-collar   married    primary      no     6242     yes   no   \n",
      "\n",
      "        contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
      "9       unknown    6   may      1297         3     -1         0  unknown   \n",
      "60      unknown   14   may       927         1     -1         0  unknown   \n",
      "61      unknown   14   may      1423         3     -1         0  unknown   \n",
      "69      unknown   15   may       911         2     -1         0  unknown   \n",
      "71      unknown   15   may      1224         2     -1         0  unknown   \n",
      "...         ...  ...   ...       ...       ...    ...       ...      ...   \n",
      "11073  cellular   13   jan       278         4     97         1    other   \n",
      "11077   unknown   21   may        86         6     -1         0  unknown   \n",
      "11085  cellular    4   feb       387         1    251         1  failure   \n",
      "11086   unknown    2   jun        20         8     -1         0  unknown   \n",
      "11101  cellular    7   may       162         1     -1         0  unknown   \n",
      "\n",
      "      deposit  \n",
      "9         yes  \n",
      "60        yes  \n",
      "61        yes  \n",
      "69        yes  \n",
      "71        yes  \n",
      "...       ...  \n",
      "11073      no  \n",
      "11077      no  \n",
      "11085      no  \n",
      "11086      no  \n",
      "11101      no  \n",
      "\n",
      "[1055 rows x 17 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detectados en 'day':\n",
      "Empty DataFrame\n",
      "Columns: [age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome, deposit]\n",
      "Index: []\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detectados en 'duration':\n",
      "        age          job   marital  education default  balance housing loan  \\\n",
      "0      59.0       admin.   married  secondary      no     2343     yes   no   \n",
      "1      56.0       admin.   married  secondary      no       45      no   no   \n",
      "2      41.0   technician   married  secondary      no     1270     yes   no   \n",
      "6      56.0   management   married   tertiary      no      830     yes  yes   \n",
      "9      28.0     services    single  secondary      no     5090     yes   no   \n",
      "...     ...          ...       ...        ...     ...      ...     ...  ...   \n",
      "10647  31.0   technician    single  secondary      no      433      no   no   \n",
      "10788  41.0  blue-collar   married  secondary      no     4910     yes   no   \n",
      "10798  67.0   technician   married  secondary      no        0      no   no   \n",
      "10802  58.0   management  divorced   tertiary      no      577      no   no   \n",
      "11008  56.0     services   married    unknown      no      616      no   no   \n",
      "\n",
      "        contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
      "0       unknown    5   may      1042         1     -1         0  unknown   \n",
      "1       unknown    5   may      1467         1     -1         0  unknown   \n",
      "2       unknown    5   may      1389         1     -1         0  unknown   \n",
      "6       unknown    6   may      1201         1     -1         0  unknown   \n",
      "9       unknown    6   may      1297         3     -1         0  unknown   \n",
      "...         ...  ...   ...       ...       ...    ...       ...      ...   \n",
      "10647  cellular   11   aug      1504         2     -1         0  unknown   \n",
      "10788  cellular    3   apr      1090         1     -1         0  unknown   \n",
      "10798  cellular   13   aug      1152         2    188         1  success   \n",
      "10802   unknown   15   may      1244         3     -1         0  unknown   \n",
      "11008   unknown   18   jun      1812         1     -1         0  unknown   \n",
      "\n",
      "      deposit  \n",
      "0         yes  \n",
      "1         yes  \n",
      "2         yes  \n",
      "6         yes  \n",
      "9         yes  \n",
      "...       ...  \n",
      "10647      no  \n",
      "10788      no  \n",
      "10798      no  \n",
      "10802      no  \n",
      "11008      no  \n",
      "\n",
      "[636 rows x 17 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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AZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGTr0NYT2FrMmTMnVqxY0dbT2GQqKytjyJAhbT0NAGArJSI3wJw5c2K33XbbqDF26FwaZ+1TGT9+ckW8WrVmE81s4zz//PNCEgB4V0TkBqh/BvKmm26KYcOGvasxypf9Xwy770vxySk/iZruu2zK6WWbNWtWTJgwoV09swoAbFkiMsOwYcNi5MiR727jlztF3BcxbOjQiB2Hb9qJAQBsYS6sAQAgm4gEACCbiAQAIJuIBAAgm4gEACCbiAQAIJuIBAAgm4gEACCbiAQAIJuIBAAgm4gEACCbiAQAIJuIBAAgW7uMyOrq6njiiSeiurq6radCO+K8AoB3tMuInD17dowaNSpmz57d1lOhHXFeAcA72mVEAgCweYlIAACyiUgAALKJSAAAsolIAACyiUgAALKJSAAAsolIAACyiUgAALKJSAAAsolIAACyiUgAALJ1aOsJwNbgzTffjNNPPz0iIkaNGtXGs2lZSUlJpJQipdTsYyorK6OqqirWrl0bEREdO3aMiIjVq1c3u02hUIiSkpLo0KFDrF27Nurq6qJDhw5RWloa3bp1i9ra2li1alXU1NREoVCIQqFQ3G7t2rWRUoqysrLo169fLF68ONauXRvdunWLkpKS6NKlS/Tt2zd22GGHWLNmTcyZMycWLVoUHTp0iG7dukVZWVmsXLkyamtro6amJnr37h0LFy6MqqqqiIgYPHhwDB48OCZOnBgHH3xwPPDAA3HppZfGkiVLora2Nnr37h21tbXRo0ePWLVqVYwcOTJ69eoVS5cujZdeein69+8fS5cujWeeeSZeeeWVqKmpia5du8ahhx4al112WXTq1CmmTZsW06ZNi4iIsWPHxoEHHhgzZsyIf/7znzF9+vSYOXNmzJ07N0pLS2Po0KHxxS9+MT760Y9GbW1tnHfeeTFr1qxYtGhR9O3bN3bbbbc4+uij47XXXovFixdHz549Y9GiRbFkyZJ46aWXYuedd45DDz00xo4dG6WlpS1+vdesWRMPPfRQLFiwIPr16xcHHnhgcZuW7mttjIhoctuc/Y0ePToeeuihddatqWPa0HluyPGPHj06ZsyYkTXvxmpra+PKK6+Mhx56KCorK+Okk06KQw89dIPn1BYaHl+fPn2Kn0c0v+5Nbdva2mzoerPxNub7YotJmV5/fVm65+470pe/+Ln04vx5G7RNVVVVOuG4Y1JVVVXu7t6Vxx9/PEVEevzxx9874730dEpfHvTWf9vYpl6f9u79739/iggf2/BHp06d1rutpKSk1e025DEtffTp0yfddtttzZ6bt912Wxo4cOA62wwcODDddtttLd7X2hh9+vRJvXv3Xm/b8847L2t/TR1/796915nDhs5zQ4+/Q4cOWfNu7Lzzzmty3l27dt2gObWFptZhQ8+lnPXf0PV+r67T1mRjvi82Rm6vZUVkdXV1OunE49PX/uNL6YTjjhGROUTkVklAbt0fhULhXW1XWlq63m09evRI999/f7rooota3Lap4IyI1LFjxzRkyJAWt+3cuXOKiLT77ruvcwzN/Q+9UCiko446Kj3yyCNpxYoV6ZFHHklHHXVU8bibu69+vKbG+Na3vlXc97e+9a3itvvtt1+KiLTffvutN2b9POvHuemmm9ZZ+4suuijdf//96YADDijeVh+eLR1DawHdeJ8Rkfr27ZsKhUK66aabWp13432cd955xfntvffe6e67705XXHFF6t69+zrzfi9puA4Nv3Y9evQorn39ujc+3pz1b2q9C4VC6tu3b4qI4npvyNeOlm3M98XG2qwRWVdXl15/fVlatPBVEZlLRG51VqxY0eYR5GPDPpp7xq9QKKSSkpIm728qFCMilZeXp5133jn16dNnvftWrFiRBg4cmI444oj1AvXwww9P48aNSwMGDFjvWbzG2za+b7vttkt9+/ZNgwYNSuPGjUsDBw5MRx55ZCovL0/l5eVp0KBBqa6urnhu1tXVpYEDB6ajjjoqrVmzZp3ztra2NpWXl6eKiopUW1u7zn1r1qxJRx11VBo0aFBatWrVemPUjztu3Lg0bty44n7r6urSgAEDinNsOJfG+6sfo7y8PB155JHrjLNmzZo0bty4VFFRkQYMGNDsMTScZ8N9NXf8DT9fvXr1esfYt2/fNHDgwHXGaryPVatWpdLS0lRSUpLGjRu3zpxWr16d+vTpkwqFwnrjtKWGx11bW5sGDBiQysvL07hx49ZZh9ra2uK6N/yabuj6b+h613+NW/ra0bKcr8vmkNtrWe+JLC0tja5du8XiRQtbfNzq1avXeW9VTU11zm42Wk1NTUREzJo1a5OMVz9O/bhbu029Pu3Vueee29ZTYAPVv7ezsdTCe0P79u0br7zyynq319TUxIsvvhjHH3983Hrrrevcd9JJJ8W8efPimGOOiXvvvXed+77+9a9HSilGjx4d++yzTyxevHid+6+99tqYN29enHfeeettu2rVqrj44ovjzDPPjC984Qtx9913xxe/+MW45557IiJi7ty58dBDD8XYsWMj4q33Ks6bNy9uueWWKClZ9/rI6dOnF7/Hp0+fXtwm4q33y/7Hf/xHjB49Oq6++ur1xmg4bv2x1L+vbv78+XHNNdfEmWeeuc5cGu8vImLevHkREfHVr351nXHGjh0b559/ftx9990xf/78iIgmj6HhPBvuq17j4582bVrx8w4dOqx3jE3Nu/E+nnzyyVizZk1ERFxwwQXrzKlDhw5x8cUXx1lnnRXz5s1rck5toeE6TJ8+vbimF1xwwTrrMH369OK6159LEdHsOdR4bRo/trn1rl+Xlr52tKyl7+3Wvi/awma5sOaO238Vt936P5tj6A1S/xfYhAkTNvm4Y8aM2aRjtoXNtT6wNWkuPOt17tx5vdv+8Y9/RETT/6AcPnx4MVibur9+2/Ly8ib3N27cuHXub/y4BQsWrPfn4cOHrzdOU49rPM+G82k4RsNx64+l4Rj1c2xuH43319Q4jefc1DE0vL2pY2g8VuPPGx9jU/NuvH39Y5ubU/0Yzc2pLTQ87rvuuqt4e+N1WLBgQbPzz1n/1ta7ua8LG66l7+2Gt79X1nazROQxxx4XR4w7pvh5TU11nDPp9M2xqyYNHDgwIiJuuummGDZs2EaPN2vWrJgwYUJx3K3dpl6f9urcc8+NBx98sK2nwWbS+F/5jdVf+d3QrrvuGk8//XSTIfjMM88Ug6mp+3fdddeIaP4Vjbvvvnud+xs/rl+/fuv9+Zlnnon999+/1cc1nmfD+TQco+G49cfScIz6OTa3j8b7a2qc+v03fEzjY2j4uKaOofHxN/688TE2Ne/G+6h/bHNzqh+juTm1hYbH3XBOjdehX79+66x7U49trKn1b229G3+N3yvrtDVp6Xu7/vaGj2tz7+Y1c++JfBe8J3Kr4z2RW8+H90S+w3sivSfSeyK3XlvbeyJF5JYaT0RulVydvXV/bOqrs++777504YUXtrhtS1dnDx48uMVt66/ObngV94ZcnT1jxoy0fPnyNGPGjPWuzm7qvqauzq5/3De/+c3ivr/5zW8Wt214lXPjMevnWT9O46uzL7zwwnTfffe1eHV2S/NsSuNtm7o6u7V5t3R19ogRI9Idd9yRLr/88q3m6uyGX7v6q7MvvPDCNGbMmCbPpZz1b2q9G1+dvaFfO1q2Md8XG2uLRGQuEZlE5FZMSPrYmn5O5KBBg5r9uY3197U2RlM/J3LQoEFN/rzFlvbX1PE3PqYNneeGHn/jn1vY2rwb83Mim1+bDV3v9+o6bU025vtiY+T2WiGlFn6txSZSXV0dp038VPz0hp9HRUXF5t5dPPHEEzFq1Kh4/PHHY+TIke+N8V5+JuKKoyM+c2fEjk2/YXZL2dTrsy14880346CDDoq//e1vbT2VVvmNNX5jjd9Y4zfW+I01W7+2+I01ub0mIrfUeCJyq2fdAGjPcnut5csTAQCgCSISAIBsIhIAgGwiEgCAbCISAIBsIhIAgGwiEgCAbCISAIBsIhIAgGwiEgCAbCISAIBs7TIihw4dGo8//ngMHTq0radCO+K8AoB3dGjrCWwOFRUVMXLkyLaeBu2M8woA3tEun4kEAGDzEpEAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORAABk69DWE9gaVFdXR0TEE0888a7HKF/2fzEsImbNnh01C2s30czenVmzZrXp/gGArZ+I3ACzZ8+OiIgzzjjjXY+xQ+fSOGufyvjxlSfEq1VrNtXUNkplZWVbTwEA2EqJyA0wfvz4iIgYOnRoVFRUbNRYR2+C+WwKlZWVMWTIkLaeBgCwlRKRG6BXr15x+umnt/U0AADeM1xYAwBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBAtg5bYicppYiIqKmp3hK7AwAgU32n1Xdba7ZIRK5cWRMREedMOn1L7A4AgHdp5cqa6Ny5c6uPK6QNzc2NsHbt2li2bGmUlZVHoVDY3LuLmprqOGfS6XHlj66N8vKKzb6/rZE1apn1aZ01ap01apn1aZ01ap01alnO+qSUYuXKmujevUeUlLT+jsct8kxkSUlJ9OzZa0vsah3l5RVRUeGEaok1apn1aZ01ap01apn1aZ01ap01atmGrs+GPANZz4U1AABkE5EAAGRrlxHZsWPH+Pjxn4yOHTu29VTes6xRy6xP66xR66xRy6xP66xR66xRyzbn+myRC2sAAGhf2uUzkQAAbF4iEgCAbCISAIBsW+TnRG5Jj0x/OH7x8xujqurN2O8D+8dpZ0zaZt9s+63/uiiemvlk8fMjjzomDjt8XPzwqsvj//7xQvTrv2OcdfZnYsCAgZFSiltu/lk8+MAfo7S0NMYdPT6OGHd0201+M3njjddj+sN/ij9NeyAmf+ZzsdPOA2LJ4sXZa/LsM0/Hddf+OJYufS32HD4izjr7M9GlS5c2PrpNo6k1mv7wn+LKy79ffEyXysr4yX/fuE2u0V133B733n1nrFq1Mkbss29MOvvf480VK5xDDTS1Ro8/9hfn0NvWrFkT/3PLTfHHP/w+IiL2HblfnDFpcix/4w3nUTS/Pn/9y6POoSb84NLvxJ8fnRG33PqbLf7/s3b1TOSKFcvjR1dPjWOP+0RcdPG3Y+aTT8QD99/X1tNqM8uWLYtTTj8zrr3+prj2+pviEyf8a9x043Wx3XZl8d3vXxE77zwgrvnhlRER8eTfHo8//P638aXzvxann3V23PSz6+LF+fPa9gA2sZqamvjMv50Zj06fHvPnzS3enrsmdXV1cdXUy+L/jTkwvv3dH8TCha/GHb/+VVsd1ibV3Bq9vmxZ7L77sOK5dPmVP46IbW+Nnpr5ZPzPLTfH2Z/5XEz5xiXx3KxZ8dt773IONdDcGjmH3vHwQw/GH/9wX5z3la/GlG9cEs88/VT88Q+/dx69rbn1cQ6t77G//iX++pdHi59v6XOoXT0T+Y8X5kRKEQePPTQKhULsu++omPX3Z+Ojhx3R1lNrE68vWxZ9+vSNzp3f+RfFrL8/GxNPOT169e4dYw/9cEz5+vmxatWqmPX3Z2PIbkNj110HR0REnz5947nZs2LnAQPbaPabXqdOneKKH14TtatWxb9PPqt4e+6alJaWxrJlS+NDH/5odOvePfYfPSaeeOyvbXVYm1Rza/T6smXRvUePdc6liNjm1qhDhw7xqQmfjr1G7B0REf369483Xn/DOdRAc2vUoUMH59DbDh57aBw89tCIiKiqejM6dOgQpaWlzqO3Nbc+ry1Z4hxqoLq6Oq776Y/jY4cfGf97z10RseX/f9aunolcvnx5bFe2XfH3c1d07hzLly9v41m1jbrVq+PNN1fEzTfeEGefeWpc9r1LYsWK5bFi+fIoKyuLiHd+tdGK5ctjeYPbI9rn2pWWlkbXrt3Wuz13TerXpbhNRedY0U7Wqrk1WrZsacx5/rmYfNZp8ZXzPh9PPzUzImKbW6M99hxefAlo/ry58cKc52PMgQc5hxpobo2cQ+v70hc+G2ee+unYoV+/+PBHD3MeNdJ4fZxD67rlpp/FXiP2ieF7jSjetqXPoXb1TGRT3u7JbU+hECefdkbssEP/qKioiKuu+EH86pe/aO6hWbdvC7LXpJ2v1aEf/kjsMXyv2G23ofG7/70npl72vfjhNf/d5GO3hTV67bUl8d1vfyOOHHdMDB6yW5OP2dbPocZr5Bxa35fP/1oseOXlmHrZpfHgtD82+Zht+TxqvD7OoXfMnvX3eOyvf47vfn9qPP/87BYfuznPoXYVkZWVlbGypibWrl0bJSUlUVNdHds38azKtmDt2rWx334fjB49e0ZExH7v/0C8MGdOVFZuH1VVVRHx1vvfIiIqt+8alZWV8dqSxcXta2pqtpm1y12TysrtIyKiqroqysrLo6amOrpu322Lz3tL6td/xxg8eLfotN128bHDj4w/3PfbWLp06Ta5RsuXL49vXnxR7Dl8r/jEif8aEc6hxppaI+fQO+bPnxdvvP56jNh7n+jZs1fsMXx4/P3ZZ5xHb2tuffbe5yTn0Ntuv+3WWLFiRXz2nLNizZq1ERFx2sRPbfFzqF29nL3r4N2ipKQk7r/vd/Hyyy/Fk08+EXvsObytp9UmFr66ICZPOi0emf5wLFz4ajw188nYecCAGLbHnvHQgw/EksWLY9oD98eQIbtHp06dYtgew+P552bHnOefi5lP/i0WvvpqDBu2R1sfxhaRuyY79OsX3bv3iD/87rexeNHCePSRGTGsnZ9n37vkm3HVFT+IpUuXxvSH/xRdulRGjx49trk1WllTE9/55sXRq1evmDDxlKiurorq6mrnUAPNrZFz6B0vzpsbl33v2zHn+efin/98MeY8/1wMHDTIefS25tbHOfSOf5v873HZ1Kvi29+9LCaeenpERHz7u5dt8XOo3f3aw0emPxy33PyzqK6uivd/YP84dRv+ET+//d+7467f3B41NdWx14i944xJ58TKmpr44VVT4x8vzIn+O+4Y/zb532OnnQdESil+cfON8cAf/xClHUrjmPEfj8OOGNfWh7BZLF60MP598lnxnUsvb/AjfvLWpPGPRJg0+TPrvdl7a9Z4jebPmxs/veZHMX/+3OjTd4eYeMrpMXyvEdvcGj047Y/xo6umrnNbr96948Ip33QOva25Nfril853Dr0tpRQ333h9/GnaA5FSiv1Hj4mJp5wery9b5jyK5tfn5Zf+6RxqwhOP/zW+++1vNPgRP1vuHGp3EQkAwObXrl7OBgBgyxCRAABkE5EAAGQTkQAAZBORAABkE5EAAGQTkQAAZBORwDbjPy+8ID5z9hlbZF9/fmRGnHHqSfHCnOe3yP4AtjQRCbAJ/PDKy+PE48cXP99j+PA47YxJsfPOA9puUgCbUYe2ngDAe1lKKVJKUVKS92/uysrtY///N2YzzQqg7fm1h0C79Y8X5sSPf3hlLF60MPYasU8sWvhqVFVXxcFjD43bbv2fmHrVj6N3n74REXHi8ePjoIMPiX8757Pxwysvjz8/+kiMHnNAzJj+cHzpPy6I3XYfFr/8xc3x0J+mRU11dQzaZXCcevqZsdPOA+IzZ58RSxYvLu73axddHIsXLYofXX1FfO2ii2OPPfeKurq6+OUvbo4/TXsgamqqY489h8cpp50Vffq+s/8DDz4kqt58M55+amb06ds3Jp39mRg8ZLc2WTuA1ng5G2iX6urq4rLvXRJLX1sSRx1zbHTs2DHmz5+3wduvWrUyXnxxfhz3iROib98d4vbbbo277rg99t13VPzL8Z+MBa+8HJd975KIiPjkiRNiyJDdIyLi1DMmxQ79dlxvvBtv+O+4647bY9R+749xR42PWX9/Nr7z7f+Kurq64mMeevCB6N69exx97L/EooUL44brrt24RQDYjLycDbRLM598Il57bUmcMWlyHPqhj0RExIIFr8SKFcs3eIzzvzYlKioqIiJi//83OvYasXfstvvQWP7GG/HakiXx+9/eG8vfeCMOOPDgeHrmkzFnznPxkY8ett44NTU18cD998UH9x8dZ0yaHBERXbt1i+t+ek08+bcnYr/3fyAiIvZ7/wfj9LPOjoiIf86fFzOf/NtGrQHA5iQigXbp1QULIiJi8OAhxdvKysqyIrI+ICMievTsGbf/+lfxX1O+HmvWvPPs4apVKyOia4vjvPLyS7F69eoYOmyP4m31f37llZci4gPr7a+srDxWrVq1wXMF2NJEJNAuFUoKERFRUlK6Sca76Ybr4tEZ0+OY8f8SuwweHI/OmB4zpj+0Qdt26Njx7bm88w6i+rejlxS8qwjYOvnbC2iXdtihf0RE/OOFd35O48qamoiIKCsvj4iIqqqqiIiofvu/LfnHC3Ni4MCB8clPTYj3f2D/6NGj5zr3FwpvRevatWubmEu/6NixY8yePat423Nv/3knPwII2Ep5JhJol/beZ9/o2bNX3HjDdfHa0tfipRdfjLlz/y969e4du+761kvc1/30mthr733isT8/2up4O+08IP786Iy49Rc/j5qVNfH73/5vRETUrVkTERFdu3aLiLcuoPnoxw5fZ9vtttsuPnbYkXH3Xb+JivLy6Na9e9xz1x2x084DYvheIzbhUQNsOZ6JBNql0tLS+PwXvxw9evaMO2//ddTW1sbe+4yMiIhhe+wZ4489Lha88krcf9/vYtT7PxhdulS2ON5JE0+NvUbsHffcfWc8P3t2fOywIyIiYtHChRERcehHPhrv22nnePCB+2PZsqXrbf/JE/81Djt8XPz50UfinrvuiOF7jYivnP/1KC3dNC+3A2xpfk4kAADZPBMJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQDYRCQBANhEJAEA2EQkAQLb/D9o0Enw4saIfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detectados en 'campaign':\n",
      "        age            job   marital  education default  balance housing loan  \\\n",
      "125    35.0    blue-collar   married    primary      no      994     yes   no   \n",
      "135    44.0     technician  divorced  secondary      no     1262     yes   no   \n",
      "163    51.0   entrepreneur   married    unknown      no      606     yes   no   \n",
      "186    35.0     technician  divorced  secondary      no     5724     yes   no   \n",
      "187    34.0     technician  divorced  secondary      no     1451     yes   no   \n",
      "...     ...            ...       ...        ...     ...      ...     ...  ...   \n",
      "11057  31.0     management  divorced   tertiary      no     -547     yes  yes   \n",
      "11074  37.0     management   married   tertiary      no       21      no   no   \n",
      "11086  42.0         admin.   married  secondary      no     6445     yes   no   \n",
      "11139  30.0  self-employed    single   tertiary      no     2593      no   no   \n",
      "11140  55.0       services  divorced  secondary      no     2166     yes   no   \n",
      "\n",
      "        contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
      "125     unknown   23   may      1349         8     -1         0  unknown   \n",
      "135     unknown   26   may       788        11     -1         0  unknown   \n",
      "163     unknown   28   may       560         9     -1         0  unknown   \n",
      "186     unknown   30   may       691        10     -1         0  unknown   \n",
      "187     unknown   30   may      1097        15     -1         0  unknown   \n",
      "...         ...  ...   ...       ...       ...    ...       ...      ...   \n",
      "11057  cellular   31   jul       101        10     -1         0  unknown   \n",
      "11074  cellular   21   aug        50        12     -1         0  unknown   \n",
      "11086   unknown    2   jun        20         8     -1         0  unknown   \n",
      "11139   unknown   27   may       662         7     -1         0  unknown   \n",
      "11140   unknown   28   may        23        17     -1         0  unknown   \n",
      "\n",
      "      deposit  \n",
      "125       yes  \n",
      "135       yes  \n",
      "163       yes  \n",
      "186       yes  \n",
      "187       yes  \n",
      "...       ...  \n",
      "11057      no  \n",
      "11074      no  \n",
      "11086      no  \n",
      "11139      no  \n",
      "11140      no  \n",
      "\n",
      "[601 rows x 17 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detectados en 'pdays':\n",
      "        age          job   marital  education default  balance housing loan  \\\n",
      "890    42.0       admin.    single  secondary      no     -247     yes  yes   \n",
      "891    33.0     services   married  secondary      no     3444     yes   no   \n",
      "899    56.0   technician   married  secondary      no      589     yes   no   \n",
      "933    34.0       admin.   married   tertiary      no      899     yes   no   \n",
      "951    53.0      retired   married   tertiary      no     2269      no   no   \n",
      "...     ...          ...       ...        ...     ...      ...     ...  ...   \n",
      "11125  40.0  blue-collar  divorced    primary      no       54     yes   no   \n",
      "11133  37.0   management   married   tertiary      no     1594     yes   no   \n",
      "11145  60.0      retired  divorced   tertiary      no     -134      no   no   \n",
      "11155  35.0  blue-collar   married  secondary      no       80     yes  yes   \n",
      "11160  43.0   technician   married  secondary      no        0      no  yes   \n",
      "\n",
      "         contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
      "890    telephone   21   oct       519         1    166         1    other   \n",
      "891    telephone   21   oct       144         1     91         4  failure   \n",
      "899      unknown   23   oct       518         1    147         2  success   \n",
      "933      unknown   12   nov       114         1    170         3  failure   \n",
      "951     cellular   17   nov      1091         2    150         1  success   \n",
      "...          ...  ...   ...       ...       ...    ...       ...      ...   \n",
      "11125   cellular   13   may        10         5    362         3    other   \n",
      "11133   cellular   17   apr       110         1    260         5  failure   \n",
      "11145   cellular   12   may       243         1    271         4  failure   \n",
      "11155   cellular   21   nov        38         2    172         2  failure   \n",
      "11160   cellular    8   may         9         2    172         5  failure   \n",
      "\n",
      "      deposit  \n",
      "890       yes  \n",
      "891       yes  \n",
      "899       yes  \n",
      "933       yes  \n",
      "951       yes  \n",
      "...       ...  \n",
      "11125      no  \n",
      "11133      no  \n",
      "11145      no  \n",
      "11155      no  \n",
      "11160      no  \n",
      "\n",
      "[2750 rows x 17 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers detectados en 'previous':\n",
      "        age          job   marital  education default  balance housing loan  \\\n",
      "891    33.0     services   married  secondary      no     3444     yes   no   \n",
      "933    34.0       admin.   married   tertiary      no      899     yes   no   \n",
      "952    37.0   technician   married  secondary      no     5115     yes   no   \n",
      "968    46.0   management   married   tertiary      no        0      no   no   \n",
      "1007   33.0   technician   married   tertiary      no     6843      no   no   \n",
      "...     ...          ...       ...        ...     ...      ...     ...  ...   \n",
      "11092  38.0     services   married   tertiary      no        0     yes   no   \n",
      "11125  40.0  blue-collar  divorced    primary      no       54     yes   no   \n",
      "11133  37.0   management   married   tertiary      no     1594     yes   no   \n",
      "11145  60.0      retired  divorced   tertiary      no     -134      no   no   \n",
      "11160  43.0   technician   married  secondary      no        0      no  yes   \n",
      "\n",
      "         contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
      "891    telephone   21   oct       144         1     91         4  failure   \n",
      "933      unknown   12   nov       114         1    170         3  failure   \n",
      "952     cellular   17   nov      1210         2    171         4  failure   \n",
      "968     cellular   18   nov       716         2    110         3    other   \n",
      "1007    cellular   20   nov       755         1    100        10    other   \n",
      "...          ...  ...   ...       ...       ...    ...       ...      ...   \n",
      "11092   cellular   17   apr       239         1    147         4  failure   \n",
      "11125   cellular   13   may        10         5    362         3    other   \n",
      "11133   cellular   17   apr       110         1    260         5  failure   \n",
      "11145   cellular   12   may       243         1    271         4  failure   \n",
      "11160   cellular    8   may         9         2    172         5  failure   \n",
      "\n",
      "      deposit  \n",
      "891       yes  \n",
      "933       yes  \n",
      "952       yes  \n",
      "968       yes  \n",
      "1007      yes  \n",
      "...       ...  \n",
      "11092      no  \n",
      "11125      no  \n",
      "11133      no  \n",
      "11145      no  \n",
      "11160      no  \n",
      "\n",
      "[1258 rows x 17 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Selecciono solo las columnas numéricas del DataFrame\n",
    "columnas_numericas = df.select_dtypes(include=[np.number]).columns\n",
    "\n",
    "# Bucle for para detectar outliers y crear un boxplot para cada columna numérica\n",
    "for columna in columnas_numericas:\n",
    "    # Cálculo de los límites de outliers usando el IQR\n",
    "    Q1 = df[columna].quantile(0.25)\n",
    "    Q3 = df[columna].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    limite_inferior = Q1 - 1.5 * IQR\n",
    "    limite_superior = Q3 + 1.5 * IQR\n",
    "    \n",
    "    # Filtrado de los outliers\n",
    "    outliers = df[(df[columna] < limite_inferior) | (df[columna] > limite_superior)]\n",
    "    print(f\"Outliers detectados en '{columna}':\")\n",
    "    print(outliers)\n",
    "    \n",
    "    # Creación del boxplot para cada columna \n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.boxplot(df[columna], vert=False)\n",
    "    plt.title(f'Boxplot de {columna}')\n",
    "    plt.xlabel(columna)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "019e210c-71a4-41b7-a790-20fdd4a084a1",
   "metadata": {},
   "source": [
    "Se puede ver que excepto day, las demás variables presentan una dispersion de los datos con varios valores atípico (outliers).  \n",
    "Voy a tratar los valores de age para eliminar los outliers y para no afectar al DF original lo haré en una copia del mismo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f44eaa57-5cd9-419b-96ce-651fb37fd32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats.mstats import winsorize\n",
    "\n",
    "df_copy = df.copy()\n",
    "\n",
    "# Reemplazo los valores extremos en la columna 'age' por los valores de los percentiles 5 y 95.\n",
    "df_copy['age'] = winsorize(df_copy['age'], limits=[0.05, 0.05])\n",
    "\n",
    "\n",
    "# Creación del boxplot de la columna 'age' en el DataFrame winsorizado\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.boxplot(df_copy['age'], vert=False)\n",
    "plt.title('Boxplot de la Edad (Winsorizado)')\n",
    "plt.ylabel('Edad')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318f7059-c271-4744-a41d-2a658605ff7a",
   "metadata": {},
   "source": [
    "# Análisis de desbalance de clases (Variable Resultado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e0dbb351-ca3d-4382-9a91-21d4c13db233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribución de clases:\n",
      "deposit\n",
      "no     5873\n",
      "yes    5289\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "columna_clase = 'deposit'\n",
    "\n",
    "# Conteo de clases\n",
    "conteo_clases = df[columna_clase].value_counts()\n",
    "porcentaje_clases = round(df[columna_clase].value_counts(normalize=True) * 100, 2)\n",
    "\n",
    "# Imprimo el conteo de clases\n",
    "print(\"Distribución de clases:\")\n",
    "print(conteo_clases)\n",
    "\n",
    "# Visualización del desbalance de clases con gráfico de barras\n",
    "plt.figure(figsize=(10, 6))\n",
    "bars = plt.bar(conteo_clases.index, conteo_clases.values, color='blue', edgecolor='black', linewidth=1.2)\n",
    "plt.title(f'Distribución de clases en {columna_clase} (Frecuencia)')\n",
    "plt.xlabel('Clases')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.xticks(rotation=0)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()\n",
    "\n",
    "#Imprimo el porcentaje de las clases\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.pie(\n",
    "    porcentaje_clases,\n",
    "    autopct='%1.1f%%',\n",
    "    startangle=140,\n",
    "    colors=['#66b3ff', '#99ff99', '#ffcc99', '#ff9999'],\n",
    "    wedgeprops={'edgecolor': 'black', 'linewidth': 1.2},\n",
    "    explode=[0.05] * len(porcentaje_clases)  # separación entre segmentos\n",
    ")\n",
    "plt.title(f'Porcentaje de clases en {columna_clase}')\n",
    "plt.legend(porcentaje_clases.index, title='Clases', loc='upper right', bbox_to_anchor=(1.1, 1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e11a6999-f706-4681-838d-89eb918ab9c3",
   "metadata": {},
   "source": [
    "Las clases las tenemos categorizadas en la columna deposit como yes y no . Esta es la información que clasifica a los ususarios en dos grupos, los que tienen un deposito a plazo ya contratado que sería el grupo de yes y los que no lo tienen que son los no.\n",
    "Tenemos una gráfica de barras donde se hace un conteo sobre estas clasificasiones y también un pie chart donde se puede visualizar de manera sencilla el porcentaje que representa cada uno de estas clases.\n",
    "No se detectsa una gran desdalance entre las clases así que lo dejaré tal cual, de ser necesario se aplicaria un submuestreo o sobremuestreo para balancear las clases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "704cb31c-bf66-4ab8-8dda-bb3aeb475dae",
   "metadata": {},
   "source": [
    "# Relación entre las variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a9121ae0-f4c1-44a2-8e1f-9788c87edacc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de correlación:\n",
      "               age   balance       day  duration  campaign     pdays  previous\n",
      "age       1.000000  0.112242 -0.000696  0.000169 -0.005383  0.002802  0.020157\n",
      "balance   0.112242  1.000000  0.010467  0.022436 -0.013894  0.017411  0.030805\n",
      "day      -0.000696  0.010467  1.000000 -0.018511  0.137007 -0.077232 -0.058981\n",
      "duration  0.000169  0.022436 -0.018511  1.000000 -0.041557 -0.027392 -0.026716\n",
      "campaign -0.005383 -0.013894  0.137007 -0.041557  1.000000 -0.102726 -0.049699\n",
      "pdays     0.002802  0.017411 -0.077232 -0.027392 -0.102726  1.000000  0.507272\n",
      "previous  0.020157  0.030805 -0.058981 -0.026716 -0.049699  0.507272  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seleccionar solo las columnas numéricas\n",
    "numerical_df = df.select_dtypes(include=['number'])\n",
    "\n",
    "# Calcular la matriz de correlación\n",
    "correlation_matrix = numerical_df.corr()\n",
    "\n",
    "# Imprimir la matriz de correlación\n",
    "print(\"Matriz de correlación:\")\n",
    "print(correlation_matrix)\n",
    "\n",
    "# Visualizar la matriz de correlación con un heatmap\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', square=True, cbar=True)\n",
    "plt.title('Matriz de Correlación')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "955fb95c-f2e5-4d6b-a7a1-12146d1a2123",
   "metadata": {},
   "source": [
    "Obtengo una vista de la correlación de las variables numérica entre si."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36f1dda-2bb4-446e-b326-880a2d0341bc",
   "metadata": {},
   "source": [
    "# Ahora agregaré la columa deposit dando el valor de 0 al no y el valor de 1 a yes para poder incluirla en el mapa de calor y poder analizar la relación que hay ente las variables numéricas y deposit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a24ad7f2-25e5-4df2-b33f-e114a161674b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de correlación:\n",
      "               age   balance       day  duration  campaign     pdays  \\\n",
      "age       1.000000  0.112242 -0.000696  0.000169 -0.005383  0.002802   \n",
      "balance   0.112242  1.000000  0.010467  0.022436 -0.013894  0.017411   \n",
      "day      -0.000696  0.010467  1.000000 -0.018511  0.137007 -0.077232   \n",
      "duration  0.000169  0.022436 -0.018511  1.000000 -0.041557 -0.027392   \n",
      "campaign -0.005383 -0.013894  0.137007 -0.041557  1.000000 -0.102726   \n",
      "pdays     0.002802  0.017411 -0.077232 -0.027392 -0.102726  1.000000   \n",
      "previous  0.020157  0.030805 -0.058981 -0.026716 -0.049699  0.507272   \n",
      "deposit   0.034680  0.081129 -0.056326  0.451919 -0.128081  0.151593   \n",
      "\n",
      "          previous   deposit  \n",
      "age       0.020157  0.034680  \n",
      "balance   0.030805  0.081129  \n",
      "day      -0.058981 -0.056326  \n",
      "duration -0.026716  0.451919  \n",
      "campaign -0.049699 -0.128081  \n",
      "pdays     0.507272  0.151593  \n",
      "previous  1.000000  0.139867  \n",
      "deposit   0.139867  1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrego la columna deposit a la correlacion \n",
    "df['deposit'] = df['deposit'].map({'no': 0, 'yes': 1}).astype(int)\n",
    "# Seleccionar solo las columnas numéricas\n",
    "numerical_df = df.select_dtypes(include=['number'])\n",
    "\n",
    "# Calcular la matriz de correlación\n",
    "correlation_matrix = numerical_df.corr()\n",
    "\n",
    "# Imprimir la matriz de correlación\n",
    "print(\"Matriz de correlación:\")\n",
    "print(correlation_matrix)\n",
    "\n",
    "# Visualizar la matriz de correlación con un heatmap\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, fmt='.2f', cmap='coolwarm', square=True, cbar=True)\n",
    "plt.title('Matriz de Correlación')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "920f8661-e59b-4272-bd17-cb802431f690",
   "metadata": {},
   "source": [
    "Se puede apreciar una relación fuerte entre duration y deposit, lo que sugiere que a mayor duración de la llamada, hay una mayor probabilidad de éxito en la contratación del depósito.\n",
    "\n",
    "Además, se observa una relación moderada entre pdays y deposit, lo que indica que el número de días desde el último contacto también puede influir en la decisión de los clientes de suscribirse a un depósito. Sin embargo, esta relación es menos significativa en comparación con la que se establece entre duration y deposit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4db2606a-e361-4849-8a4c-ef3d4fbe65d0",
   "metadata": {},
   "source": [
    "# EDA automático con Sweetviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2985ff33-fa6f-42f3-a0c8-86abd37277a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cfdab8e6c7ff4f3eb535722e1f9bc4b3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "                                             |          | [  0%]   00:00 -> (? left)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report EDA_report.html was generated! NOTEBOOK/COLAB USERS: the web browser MAY not pop up, regardless, the report IS saved in your notebook/colab files.\n"
     ]
    }
   ],
   "source": [
    "report = sv.analyze(df)\n",
    "report.show_html('EDA_report.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f82473-59db-4c6e-b84f-569535e1ab32",
   "metadata": {},
   "source": [
    "# Conclusiones:  \n",
    " Después de realizar este ejercicio de EDA, puedo concluir que estas herramientas, como Sweetviz, resultan extremadamente útiles para obtener una visión general de los datos, especialmente cuando se trabaja con grandes volúmenes de información. Nos permiten identificar rápidamente características clave sin necesidad de revisar los registros de forma manual.\n",
    "\n",
    " En cuanto a los datos analizados, he observado que hay muy pocos valores nulos en el DataFrame, representando menos del 1% de las entradas totales. Estos valores se encuentran en columnas no críticas, lo que sugiere que no es estrictamente necesario reemplazarlos para continuar con el análisis o la construcción de un modelo de Machine Learning en esta fase.No obstante, he procedido a remplazarlos utilizando la mediana y la moda a manera de práctica.\n",
    "\n",
    " Al revisar los outliers, se observa una alta dispersión en los datos, con muchos valores atípicos. Podemos abordar esto mediante la Winsorización ( como hice con age) para reemplazar estos outliers. Esto nos permitirá seguir un proceso de verificación y aplicar el modelo de manera segura, asegurando que el procesamiento de los datos sea correcto y que el comportamiento de los nuevos datos cumpla con nuestras expectativas.\n",
    "\n",
    " Sobre la correlacion de los datos he tratado solamente las variables numéricas donde he incluido la variable deposit que es la variable resultado , es decir ha donde apuntan los datos como éxito o no del análisis.\n",
    "Según crea necesario podría hacer conversiones para cambiar datos que no sean numéricos por numéricos para poder incluirlos en el mapa de calor sigueindo el mismo proceso que hice con daposit pero de momento no lo veo necesario.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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