T5 - 3- LogisticRegression - Implementación.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regresión logística para predicciones bancarias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"../GitHub/python-ml-course/datasets/bank/bank.csv\",sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>telephone</td>\n",
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       "      <th>4</th>\n",
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       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
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       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.191</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>4114</th>\n",
       "      <td>30</td>\n",
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       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>thu</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.958</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>4115</th>\n",
       "      <td>39</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jul</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.959</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4116</th>\n",
       "      <td>27</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.354</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>no</td>\n",
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       "      <th>4117</th>\n",
       "      <td>58</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.966</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4118</th>\n",
       "      <td>34</td>\n",
       "      <td>management</td>\n",
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       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
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       "      <td>wed</td>\n",
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       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.120</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4119 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age          job  marital          education default  housing     loan  \\\n",
       "0      30  blue-collar  married           basic.9y      no      yes       no   \n",
       "1      39     services   single        high.school      no       no       no   \n",
       "2      25     services  married        high.school      no      yes       no   \n",
       "3      38     services  married           basic.9y      no  unknown  unknown   \n",
       "4      47       admin.  married  university.degree      no      yes       no   \n",
       "...   ...          ...      ...                ...     ...      ...      ...   \n",
       "4114   30       admin.  married           basic.6y      no      yes      yes   \n",
       "4115   39       admin.  married        high.school      no      yes       no   \n",
       "4116   27      student   single        high.school      no       no       no   \n",
       "4117   58       admin.  married        high.school      no       no       no   \n",
       "4118   34   management   single        high.school      no      yes       no   \n",
       "\n",
       "        contact month day_of_week  ...  campaign  pdays  previous  \\\n",
       "0      cellular   may         fri  ...         2    999         0   \n",
       "1     telephone   may         fri  ...         4    999         0   \n",
       "2     telephone   jun         wed  ...         1    999         0   \n",
       "3     telephone   jun         fri  ...         3    999         0   \n",
       "4      cellular   nov         mon  ...         1    999         0   \n",
       "...         ...   ...         ...  ...       ...    ...       ...   \n",
       "4114   cellular   jul         thu  ...         1    999         0   \n",
       "4115  telephone   jul         fri  ...         1    999         0   \n",
       "4116   cellular   may         mon  ...         2    999         1   \n",
       "4117   cellular   aug         fri  ...         1    999         0   \n",
       "4118   cellular   nov         wed  ...         1    999         0   \n",
       "\n",
       "         poutcome emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "0     nonexistent         -1.8          92.893          -46.2      1.313   \n",
       "1     nonexistent          1.1          93.994          -36.4      4.855   \n",
       "2     nonexistent          1.4          94.465          -41.8      4.962   \n",
       "3     nonexistent          1.4          94.465          -41.8      4.959   \n",
       "4     nonexistent         -0.1          93.200          -42.0      4.191   \n",
       "...           ...          ...             ...            ...        ...   \n",
       "4114  nonexistent          1.4          93.918          -42.7      4.958   \n",
       "4115  nonexistent          1.4          93.918          -42.7      4.959   \n",
       "4116      failure         -1.8          92.893          -46.2      1.354   \n",
       "4117  nonexistent          1.4          93.444          -36.1      4.966   \n",
       "4118  nonexistent         -0.1          93.200          -42.0      4.120   \n",
       "\n",
       "      nr.employed   y  \n",
       "0          5099.1  no  \n",
       "1          5191.0  no  \n",
       "2          5228.1  no  \n",
       "3          5228.1  no  \n",
       "4          5195.8  no  \n",
       "...           ...  ..  \n",
       "4114       5228.1  no  \n",
       "4115       5228.1  no  \n",
       "4116       5099.1  no  \n",
       "4117       5228.1  no  \n",
       "4118       5195.8  no  \n",
       "\n",
       "[4119 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns.values"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"y\"]=(data[\"y\"]==\"yes\").astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "    age   job  marital  education  default  housing  loan  contact  month  \\\n",
       "y                                                                           \n",
       "0  3668  3668     3668       3668     3668     3668  3668     3668   3668   \n",
       "1   451   451      451        451      451      451   451      451    451   \n",
       "\n",
       "   day_of_week  duration  campaign  pdays  previous  poutcome  emp.var.rate  \\\n",
       "y                                                                             \n",
       "0         3668      3668      3668   3668      3668      3668          3668   \n",
       "1          451       451       451    451       451       451           451   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \n",
       "y                                                         \n",
       "0            3668           3668       3668         3668  \n",
       "1             451            451        451          451  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"y\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['basic.9y', 'high.school', 'university.degree',\n",
       "       'professional.course', 'basic.6y', 'basic.4y', 'unknown',\n",
       "       'illiterate'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.education.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.education=np.where(data.education==\"basic.4y\",\"Basic\",data.education)\n",
    "data.education=np.where(data.education==\"basic.6y\",\"Basic\",data.education)\n",
    "data.education=np.where(data.education==\"basic.9y\",\"Basic\",data.education)\n",
    "\n",
    "data.education=np.where(data.education==\"high.school\",\"High School\",data.education)\n",
    "data.education=np.where(data.education==\"professional.course\",\"Professional Course\",data.education)\n",
    "data.education=np.where(data.education==\"university.degree\",\"University Degree\",data.education)\n",
    "data.education=np.where(data.education==\"illiterate\",\"Illiterate\",data.education)\n",
    "data.education=np.where(data.education==\"unknown\",\"Unknown\",data.education)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
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       "  </thead>\n",
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       "      <td>94.465</td>\n",
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       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>services</td>\n",
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       "      <td>Basic</td>\n",
       "      <td>no</td>\n",
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       "      <th>4</th>\n",
       "      <td>47</td>\n",
       "      <td>admin.</td>\n",
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       "      <td>University Degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
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       "      <td>93.200</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>4114</th>\n",
       "      <td>30</td>\n",
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       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jul</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
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       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
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       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4116</th>\n",
       "      <td>27</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>High School</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
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       "      <td>failure</td>\n",
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       "    <tr>\n",
       "      <th>4117</th>\n",
       "      <td>58</td>\n",
       "      <td>admin.</td>\n",
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       "      <td>High School</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
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       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.966</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4118</th>\n",
       "      <td>34</td>\n",
       "      <td>management</td>\n",
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       "      <td>High School</td>\n",
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       "      <td>yes</td>\n",
       "      <td>no</td>\n",
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       "      <td>nov</td>\n",
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       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.120</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4119 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age          job  marital          education default  housing     loan  \\\n",
       "0      30  blue-collar  married              Basic      no      yes       no   \n",
       "1      39     services   single        High School      no       no       no   \n",
       "2      25     services  married        High School      no      yes       no   \n",
       "3      38     services  married              Basic      no  unknown  unknown   \n",
       "4      47       admin.  married  University Degree      no      yes       no   \n",
       "...   ...          ...      ...                ...     ...      ...      ...   \n",
       "4114   30       admin.  married              Basic      no      yes      yes   \n",
       "4115   39       admin.  married        High School      no      yes       no   \n",
       "4116   27      student   single        High School      no       no       no   \n",
       "4117   58       admin.  married        High School      no       no       no   \n",
       "4118   34   management   single        High School      no      yes       no   \n",
       "\n",
       "        contact month day_of_week  ...  campaign  pdays  previous  \\\n",
       "0      cellular   may         fri  ...         2    999         0   \n",
       "1     telephone   may         fri  ...         4    999         0   \n",
       "2     telephone   jun         wed  ...         1    999         0   \n",
       "3     telephone   jun         fri  ...         3    999         0   \n",
       "4      cellular   nov         mon  ...         1    999         0   \n",
       "...         ...   ...         ...  ...       ...    ...       ...   \n",
       "4114   cellular   jul         thu  ...         1    999         0   \n",
       "4115  telephone   jul         fri  ...         1    999         0   \n",
       "4116   cellular   may         mon  ...         2    999         1   \n",
       "4117   cellular   aug         fri  ...         1    999         0   \n",
       "4118   cellular   nov         wed  ...         1    999         0   \n",
       "\n",
       "         poutcome emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "0     nonexistent         -1.8          92.893          -46.2      1.313   \n",
       "1     nonexistent          1.1          93.994          -36.4      4.855   \n",
       "2     nonexistent          1.4          94.465          -41.8      4.962   \n",
       "3     nonexistent          1.4          94.465          -41.8      4.959   \n",
       "4     nonexistent         -0.1          93.200          -42.0      4.191   \n",
       "...           ...          ...             ...            ...        ...   \n",
       "4114  nonexistent          1.4          93.918          -42.7      4.958   \n",
       "4115  nonexistent          1.4          93.918          -42.7      4.959   \n",
       "4116      failure         -1.8          92.893          -46.2      1.354   \n",
       "4117  nonexistent          1.4          93.444          -36.1      4.966   \n",
       "4118  nonexistent         -0.1          93.200          -42.0      4.120   \n",
       "\n",
       "      nr.employed  y  \n",
       "0          5099.1  0  \n",
       "1          5191.0  0  \n",
       "2          5228.1  0  \n",
       "3          5228.1  0  \n",
       "4          5195.8  0  \n",
       "...           ... ..  \n",
       "4114       5228.1  0  \n",
       "4115       5228.1  0  \n",
       "4116       5099.1  0  \n",
       "4117       5228.1  0  \n",
       "4118       5195.8  0  \n",
       "\n",
       "[4119 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3668\n",
       "1     451\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.895311</td>\n",
       "      <td>219.40976</td>\n",
       "      <td>2.605780</td>\n",
       "      <td>982.763086</td>\n",
       "      <td>0.141767</td>\n",
       "      <td>0.240185</td>\n",
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       "      <td>-40.586723</td>\n",
       "      <td>3.802826</td>\n",
       "      <td>5175.502072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41.889135</td>\n",
       "      <td>560.78714</td>\n",
       "      <td>1.980044</td>\n",
       "      <td>778.722838</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>-1.177384</td>\n",
       "      <td>93.417268</td>\n",
       "      <td>-39.786475</td>\n",
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       "      <td>5093.118625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age   duration  campaign       pdays  previous  emp.var.rate  \\\n",
       "y                                                                       \n",
       "0  39.895311  219.40976  2.605780  982.763086  0.141767      0.240185   \n",
       "1  41.889135  560.78714  1.980044  778.722838  0.585366     -1.177384   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \n",
       "y                                                         \n",
       "0       93.599677     -40.586723   3.802826  5175.502072  \n",
       "1       93.417268     -39.786475   2.145448  5093.118625  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"y\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>duration</th>\n",
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       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</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>Basic</th>\n",
       "      <td>42.337124</td>\n",
       "      <td>253.898457</td>\n",
       "      <td>2.429732</td>\n",
       "      <td>978.815597</td>\n",
       "      <td>0.149472</td>\n",
       "      <td>0.237368</td>\n",
       "      <td>93.658600</td>\n",
       "      <td>-41.120552</td>\n",
       "      <td>3.775701</td>\n",
       "      <td>5174.133144</td>\n",
       "      <td>0.079610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High School</th>\n",
       "      <td>38.097720</td>\n",
       "      <td>258.534202</td>\n",
       "      <td>2.630836</td>\n",
       "      <td>958.022801</td>\n",
       "      <td>0.206298</td>\n",
       "      <td>-0.002497</td>\n",
       "      <td>93.564314</td>\n",
       "      <td>-40.995765</td>\n",
       "      <td>3.511732</td>\n",
       "      <td>5163.212595</td>\n",
       "      <td>0.105320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illiterate</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.900000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-31.400000</td>\n",
       "      <td>0.834000</td>\n",
       "      <td>5076.200000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professional Course</th>\n",
       "      <td>40.207477</td>\n",
       "      <td>278.816822</td>\n",
       "      <td>2.512150</td>\n",
       "      <td>958.211215</td>\n",
       "      <td>0.194393</td>\n",
       "      <td>0.163925</td>\n",
       "      <td>93.599630</td>\n",
       "      <td>-40.127664</td>\n",
       "      <td>3.701426</td>\n",
       "      <td>5167.595140</td>\n",
       "      <td>0.121495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>University Degree</th>\n",
       "      <td>39.017405</td>\n",
       "      <td>247.707278</td>\n",
       "      <td>2.583070</td>\n",
       "      <td>947.900316</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>-0.009731</td>\n",
       "      <td>93.499109</td>\n",
       "      <td>-39.830063</td>\n",
       "      <td>3.547132</td>\n",
       "      <td>5163.023180</td>\n",
       "      <td>0.130538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>42.826347</td>\n",
       "      <td>267.281437</td>\n",
       "      <td>2.538922</td>\n",
       "      <td>939.700599</td>\n",
       "      <td>0.263473</td>\n",
       "      <td>-0.074251</td>\n",
       "      <td>93.637455</td>\n",
       "      <td>-39.487425</td>\n",
       "      <td>3.410174</td>\n",
       "      <td>5151.260479</td>\n",
       "      <td>0.155689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           age    duration  campaign       pdays  previous  \\\n",
       "education                                                                    \n",
       "Basic                42.337124  253.898457  2.429732  978.815597  0.149472   \n",
       "High School          38.097720  258.534202  2.630836  958.022801  0.206298   \n",
       "Illiterate           42.000000  146.000000  4.000000  999.000000  0.000000   \n",
       "Professional Course  40.207477  278.816822  2.512150  958.211215  0.194393   \n",
       "University Degree    39.017405  247.707278  2.583070  947.900316  0.207278   \n",
       "Unknown              42.826347  267.281437  2.538922  939.700599  0.263473   \n",
       "\n",
       "                     emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "education                                                                     \n",
       "Basic                    0.237368       93.658600     -41.120552   3.775701   \n",
       "High School             -0.002497       93.564314     -40.995765   3.511732   \n",
       "Illiterate              -2.900000       92.201000     -31.400000   0.834000   \n",
       "Professional Course      0.163925       93.599630     -40.127664   3.701426   \n",
       "University Degree       -0.009731       93.499109     -39.830063   3.547132   \n",
       "Unknown                 -0.074251       93.637455     -39.487425   3.410174   \n",
       "\n",
       "                     nr.employed         y  \n",
       "education                                   \n",
       "Basic                5174.133144  0.079610  \n",
       "High School          5163.212595  0.105320  \n",
       "Illiterate           5076.200000  0.000000  \n",
       "Professional Course  5167.595140  0.121495  \n",
       "University Degree    5163.023180  0.130538  \n",
       "Unknown              5151.260479  0.155689  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(\"education\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frecuencia de compra del producto')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(data.education,data.y).plot(kind=\"bar\")\n",
    "plt.title(\"Frecuencia de compra en nivel de educación\")\n",
    "plt.xlabel(\"Nivel de educación\")\n",
    "plt.ylabel(\"Frecuencia de compra del producto\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proporción de clientes')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.marital,data.y)\n",
    "table.div(table.sum(axis=1).astype(float),axis=0).plot(kind=\"bar\",stacked=True)\n",
    "plt.title(\"Diagrama apilado de estado civil contra nivel de compras\")\n",
    "plt.xlabel(\"Estado civil\")\n",
    "plt.ylabel(\"Proporción de clientes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parece la misma para los diferentes estados, por lo tanto es dificil que se use como variable predictora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proporción de clientes')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.day_of_week,data.y)\n",
    "table.div(table.sum(axis=1).astype(float),axis=0).plot(kind=\"bar\",stacked=True)\n",
    "plt.title(\"Diagrama apilado de día de la semana contra nivel de compras\")\n",
    "plt.xlabel(\"Dia de la semana\")\n",
    "plt.ylabel(\"Proporción de clientes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proporción de clientes')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8kBvuTOPyZgEHSeoELgE+UWI8ZmaD3syZM3nNa17DJptsMmBltvr00RnA7Ig4QdI2wM8kbRIRKxpnlHQYcBjA+PHjKw7TzKyJWaMHuLwnep3lkEMO4fDDD+fggw8esNWWWSNYAozLDXekcXkfBM4HiIjrgRHAOs0Ki4jTImJqREwdO3ZsCeGambW/7bffnrXXXntAyywzEdwETJY0SdJwss7guQ3z/A14O4Ck/0eWCB4pMSYzM2tQWiKIiBeAw4HLgDvJzg66Q9KxkrrukPRp4EOS/gScCxwSEVFWTGZmtqpS+wgi4hKyTuD8uGNyrxcB25YZg5mZ9cxXFpuZ1ZwTgZnZIDJjxgy22WYb7rrrLjo6Ojj99NP7XWarTx81Mxu8CpzuOdDOPffcAS/TNQIzs5pzIjAzqzknAjOzmnMiMDPrg3a/1OnlxOdEYGZW0IgRI1i6dGnbJoOIYOnSpYwYMaJPy/msITOzgjo6Oujs7OSRR9r3TjgjRoygo6OjT8sUSgSSNgQ6I+JZSTsCmwJnRcTjfY7SzGyQGjZsGJMmTWp1GAOuaNPQL4DlkjYCTiO7q+g5pUVlZmaVKZoIVqSbyO0NfD8iPgusW15YZmZWlaKJ4HlJM4D3A79O44aVE5KZmVWpaCL4ALAN8JWIuFfSJOBn5YVlZmZVKdRZHBGLJH0eGJ+G7wW+UWZgZmZWjUI1AknvAhYCl6bhzSU1Pm3MzMwGoaJNQ7OALYHHASJiIbBBSTGZmVmFCncWR0Tj/VZXDHQwZmZWvaJXFt8h6T+AIZImA0cA15UXlpmZVaVojeATwMbAs2QXkj0BHFlWUGZmVp2iNYI9IuJo4OiuEZLeA1xQSlRmZlaZojWCLxQcZ2Zmg0yPNQJJuwG7A+tL+l5u0ijghTIDMzOzavTWNPR3YD4wHbg5N34Z8J9lBWVmZtXpMRFExJ+AP0k6JyKerygmMzOrUNHO4i0lzQImpGUERET4ojIzs0GuaCI4nawp6GZgeXnhmJlZ1Yomgici4relRmJmZi1RNBFcKelbwEVkF5UBEBG3lBKVmZlVpmgi2Cr9n5obF8DbBjYcMzOrWtHnEexUdiBmZtYahRKBpNcCXwXWi4jdJE0BtomI00uNzsysZBOP+k2P0+/7+h4VRdI6RW8xMRu4DFgvDf8F+GQZAZmZWbWKJoJ1IuJ80jMIIuIFfBqpmdlqoWgieErSGLIOYiRtTXYrajMzG+SKnjX0KWAusKGka4GxwH6lRWVmZpUpVCNI1wvsALwF+DCwcUTc2ttyknaVdJekeyQd1c08+0taJOkOSef0JXgzM+u/3m5D/baIuELSPg2TXi+JiLioh2WHAKcA7wA6gZskzY2IRbl5JpM912DbiHhM0mte9jsxM7OXpbemoR2AK4B3NZkWZFcad2dL4J6IWAwgaQ6wF7AoN8+HgFMi4jGAiPhHwbjNzGyA9HYb6i+n/x94GWWvDzyQG+5k5RXKXV4PkPodhgCzIuLSZoVJOgw4DGD8+PEvIxwzM2umt6ahT/U0PSK+MwDrnwzsCHQAf5T0xoh4vMm6TgNOA5g6dWr0c71mZpb01jQ0sh9lLwHG5YY70ri8TuDG9NCbeyX9hSwx3NSP9ZqZDSq9Xd0M5V7h3FvT0H/3o+ybgMmSJpElgAOB/2iY51fADOCnktYhaypa3I91mplZHxW919CZwJFdTTaSXg2cEBEzu1smIl6QdDjZrSmGAGdExB2SjgXmR8TcNO2dkhaRXan82YhY2r+3ZDbIzRrdy3Rfy2kDq+gFZZvm2+3TqZ5b9LZQRFwCXNIw7pjc6yC7WK3HvggzMytP0VtMrJFqAQBIWpviScTMzNpY0R/zE4DrJV2Qht8DfKWckMzMrEpFH0xzlqT5rHwi2T75K4TNzGzwKty8k374/eNvZraaKdpHYGZmqyknAjOzmivcNCRpAjA5In4v6RXA0IhYVl5oVonezlkHn7dutporVCOQ9CHgQuDUNKqD7KpgMzMb5Io2DX0c2BZ4EiAi7gb87AAzs9VA0UTwbEQ81zUgaSjp+cVmZja4FU0EV0v6IvAKSe8ALgD+p7ywzMysKkUTwVHAI8BtZM8svgT4UllBmZlZdYpeWbwC+HH6MzOz1UhvTyi7jR76AiJi0wGPyMzMKtVbjWDP9P/j6f/P0v+DcGexmdlqobcnlN0PIOkdEZF//sDnJd1C1ndgZmaDWNHOYknaNjfwlj4sa2ZmbazoLSY+CJwhqet+BI8D3T6m0szMBo+iZw3dDGzWlQgiwjefMTNbTfTpcZNOAGZmqx+385uZ1ZwTgZlZzfXleQRvASbml4mIs0qIyczMKlQoEUj6GbAhsBBYnkYH4ERgZjbIFa0RTAWmRISvJjYzW80U7SO4HXhdmYGYmVlrFK0RrAMskjQPeLZrZERMLyUqGzATj/pNj9PvG1FRIGbWtoomglllBmFmZq1T9MriqyW9FpiWRs2LiH+UF5aZmVWl2z4CSeNzr/cH5gHvAfYHbpS0X/nhmZlZ2XqqEWwl6T0RcQJwNDCtqxYgaSzwe+DCCmI0M7MSdVsjiIgLgIe65mtoClra07JmZjZ49PZgmrPTy0slXQacm4YPIHuAvZmZDXJFO4s/K2lfoOvhNKdFxC/LC8vMzKpS+F5DEfEL4BclxmJmZi3QYyKQdE1EbCdpGS99WL2AiIhRvSy/K3ASMAT4SUR8vZv59iXreJ4WEfP78gbMBpPeLvADX+Rn1eutj2C79H9kXwuWNAQ4BXgH0AncJGluRCxqmG8kcCRwY1/XYWZm/Vf07qNbA3dExLI0PJLsJnQ9/XhvCdwTEYvTMnOAvYBFDfMdB3wD+GwfYzczq49Zo3uZ/vIfIFn0FNAfAv/KDT+VxvVkfeCB3HBnGvciSW8CxkVE7/VlMzMrRdFEoPwtqCNiBX183vEqBUprAN8BPl1w/sMkzZc0/5FHHunPqs3MLKdoIlgs6QhJw9LfkcDiXpZZAozLDXekcV1GApsAV0m6D9gamCtparPCIuK0iJgaEVPHjh1bMGwzM+tN0UTwEeAtZD/kncBWwGG9LHMTMFnSJEnDgQOBuV0TI+KJiFgnIiZGxETgBmC6zxoyM6tWr8076eyf90bEgX0pOCJekHQ4cBnZ6aNnRMQdko4F5kfE3J5LMDOzKvSaCCJiuaQZwHf7WnhEXELDrSgi4phu5t2xr+WbmVn/Fe3wvVbSycB5ZGcMARARt5QSlZmZVaZoItg8/T82Ny6Atw1sOGZmVrWiN53bqexAzMysNYpeWTwa+DKwfRp1NXBsRLz8S9nMzAaD3q7ohX5d1dsOip4+egawjOwxlfsDTwI/LSsoMzOrTtE+gg0jYt/c8H9LWlhGQGZmVq2iNYKnJW3XNSBpW+DpckIyM7MqFa0RfBQ4M/UVCPgn8P7SojIzs8oUPWtoIbCZpFFp+MlSozIzs8oUahqSNEbS94CrgCslnSRpTKmRmZlZJYr2EcwBHgH2BfZLr88rKygzM6tO0T6CdSPiuNzw8ZIOKCMgMzOrVtEawe8kHShpjfS3P9ldRc3MbJArmgg+BJwDPJf+5gAflrRMkjuOzcwGsaJnDY0sOxAzM2uNws8dljSdlfcauioifl1OSGZmVqWip49+HTgSWJT+jpT0tTIDMzOzahStEewObB4RKwAknQksAL5QVmBmZlaNop3FAGvlXhe4L6uZmQ0GRWsEXwMWSLqS7F5D2wNHlRaVmZlVptdEIEnANcDWwLQ0+vMR8VCZgZmZWTV6TQQREZIuiYg3AnMriMnMzCpUtI/gFknTep/NzMwGm6J9BFsBB0m6D3iKrJ8gImLTsgIzM7NqFE0Eu5QahZmZtUyPiUDSCOAjwEbAbcDpEfFCFYGZmVk1eusjOBOYSpYEdgNOKD0iMzOrVG9NQ1PS2UJIOh2YV35IZmZWpd5qBM93vXCTkJnZ6qm3GsFmuecNCHhFGu46a2hUqdGZmVnpekwEETGkqkDMzKw1+nLTOTMzWw05EZiZ1ZwTgZlZzTkRmJnVXKmJQNKuku6SdI+kVZ5fIOlTkhZJulXSHyRNKDMeMzNbVWmJQNIQ4BSyK5KnADMkTWmYbQEwNd287kLgm2XFY2ZmzZVZI9gSuCciFkfEc8AcYK/8DBFxZUT8Xxq8AegoMR4zM2uizESwPvBAbrgzjevOB4HflhiPmZk1UfQ21KWSdBDZze126GGew4DDAMaPH19RZGZmq78yawRLgHG54Y407iUk7QwcDUyPiGe7KywiTouIqRExdezYsQMerJlZXZWZCG4CJkuaJGk4cCANzzyWtAVwKlkS+EeJsZiZWTdKSwTpbqWHA5cBdwLnR8Qdko6VND3N9i3g34ALJC2UNLeb4szMrCSl9hFExCXAJQ3jjsm93rnM9ZuZWe98ZbGZWc05EZiZ1VxbnD5auVmjC8zzRPlxmJm1AdcIzMxqzonAzKzmnAjMzGrOicDMrOacCMzMas6JwMys5pwIzMxqzonAzKzm6nlBmVkzvtDQaso1AjOzmnMiMDOrOScCM7OacyIwM6s5JwIzs5rzWUOt1NtZKj5Dxcwq4ERgZu3LB0uVcNOQmVnNORGYmdWcm4asPbgJwKxlXCMwM6s5JwIzs5pzIjAzqzn3EZjZqnwn1lpxjcDMrOacCMzMas6JwMys5pwIzMxqzonAzKzmnAjMzGrOicDMrOacCMzMas6JwMys5pwIzMxqrtREIGlXSXdJukfSUU2mrynpvDT9RkkTy4zHzMxWVVoikDQEOAXYDZgCzJA0pWG2DwKPRcRGwHeBb5QVj5mZNVdmjWBL4J6IWBwRzwFzgL0a5tkLODO9vhB4uySVGJOZmTVQRJRTsLQfsGtEHJqG3wdsFRGH5+a5Pc3TmYb/muZ5tEl5hwGHpcE3AHf1I7x1gFXW0QLtEEc7xADtEUc7xADtEUc7xADtEUc7xAD9j2NCRIxtNmHQ3IY6Ik4DThuIsiTNj4ipA1HWYI+jHWJolzjaIYZ2iaMdYmiXONohhrLjKLNpaAkwLjfckcY1nUfSUGA0sLTEmMzMrEGZieAmYLKkSZKGAwcCcxvmmQu8P73eD7giymqrMjOzpkprGoqIFyQdDlwGDAHOiIg7JB0LzI+IucDpwM8k3QP8kyxZVGFAmpgGQDvE0Q4xQHvE0Q4xQHvE0Q4xQHvE0Q4xQIlxlNZZbGZmg4OvLDYzqzknAjOzmnMiMDOruUFzHUF/pNtdHBER3211LHUn6U09TY+IWyqM5fXAZ4EJ5L4LEfG2qmJoJ5LeGBG3tTqOdiBpUkTc29u4imIZBURELCttHXXpLJY0LyK2bHUc7UDS1sAdXTtW2tH+X0TcWMG6r+xhclT5IyzpT8CPgJuB5bkgbq4qhhTHEOD3EbFTlettEsf/AmsCs4GzI+KJFsVxK9ktac6LiL+2KIZbIuJNDeNujog3VxjDNOAMYCQg4HFgZhn7Zy1qBMm1kk4GzgOe6hpZ8RHoMqAx8z4BzAc+HRGLKwrlh0B+J/9Xk3GlaPWPXYMXIuKHrQ4iIpZLWiFpdKt+fFMcb5U0GZgJ3CxpHvDTiLi84lDeBRwAnC9pBdl39vyI+FvZK5b078DGwGhJ++QmjQJGlL3+BqcDH4uI/02xbQf8FNh0oFdUpxpBsyPRqo9AjwM6gXPIMvyBwIbALcBHI2LHiuJYGBGbN4y7NSIGfAfrIYaDm42PiLMqjGEW8A/gl8CzuRj+WVUMuVguBrYALuelBypHtCCWIcC7ge8BT5Ltq1+MiItaEMtk4L+A90bEkArWtxfZe5/OSy+AXQbMiYjryo4hF8uCiNiiYdwqNZUBWVddEkE7kPSniNisYdzCiNi82bQS47gIuIqsFgDwMWCniHh3FetPMXw/NzgCeDtwS0TsV2EMzdp7IyI2qCqGXCzvbzY+Is5sNr6kGDYFPgDsQZaQTo+IWyStB1wfERMqjGUCWa3gALJmu/Mi4oQK179NRFxf1fq6ieFE4BXAuWQtCQcAzwA/h4FtzahNIpA0BvgysB3ZRr0GODYiKru3kaTryZ67cGEatR/wqYjYutlReolxvIbsaO9tZNviD8AnI+IfVay/m5jWIjvi2rVVMdSdpKuBnwAXRlcDC08AAAgISURBVMTTDdPeFxE/qyiOG4FhwAVkCaCqJtN8DGcCR0bE42n41cAJETGzwhgq60+rUyK4HPgjKZsC7wV2jIidK4xhA+AkYBuyH+AbgP8ku/nemyPimqpiaTeShgG3R8QbKlxny5uncrFMBr5G9hCnF9uiW1E7aTVJb4iI/txmfiBiaNYss8q41UWdOovXjYjjcsPHSzqgygDSkc27uplcWRJIp03+EHhtRGySmgSmR8TxFcbwP6zsOB9C9gN4flXrT6blXr/YPAVUngjIOgG/TFZj3ImsiabS63zaKBk9JOk7wPZp+Gqy2nuVHelrSHp1RDwGIGltKv69lPRa4KvAehGxW3rC4zYRcfqAr6tGNYLvAPNY+WOzH7BlRHymwhh+yqpnDVFldTPFcTXZ+fOndh3hSLo9IjapMIYdWLktXgDuj4jG25RXqpXNU12nJkq6LSLemB9XYQzXsDIZvYuUjCLimKpiSHH8AridlU8vfB+wWUTs0/1SAx7DwcDRZL8XIvu9+EpVzWMpht+SHSAcHRGbpVv1L+jaPwZ0XTVKBMuAV7HyfPEhrDw7IyJiVAUx7JsbHAHsDfy96jNDJN0UEdPyVd2q+igkXRMR2+VOpe16NGmkv38C34qIH5QdS5PYKm+eyq37OrL+qwuBK8iaC79ecVNZy5NRWmezs9oq60PLrfMtwFSy/XJ+1Z3HVX5Pa9M0FBEjU/VuMi+t9l5dYQy/yA9LOpcKm4RyHpW0IemIXNljRR+sYsURsV36P7LZ9NSpfx1QeiJok+apLkcCrwSOAI4jax5q2odRomclrQHcnW4hvwT4t4pjAHha0nZdfWaStgWe7mWZASXpSOBQ4CKyg5VTJf04Ir7f85ID6qn0fej6nm5Ndt3RgKtTjeBQsi9bB7AQ2Bq4LiLe3sKY3gD8JiI2qni9G5Dd2/wtwGPAvWTnad9fZRzdkbRuRJSemNqpeUrSVLKmiAlkZ8xAVlOt8tqOacCdwFpkyWgU8M2o4Irzhjg2J2sWGp1GPQa8PyJurTCGW8na459Kw68iO4W2ys/jTcD3gU3ImsrGAvuVsR1qUyMgSwLTgBsiYqd0BeFXqwyg4criAB4GPlfh+j+VG7wEuJKsQ/IpYF/gO1XF0pOyk0BX8xTwaxqapyS1qnnqbLJ+m9uAFRWuNy+An/HSZPRjSriStRd3At8ku9hyLbKj4HcDlSUCsn1ieW54OSv3k6psCOxG9jjffYGtKOk3u06J4JmIeEYSktaMiD+nI/LKdNM8VWWVrKs55g1kSfFisp37fWQd6bXQTs1TOY9E9tS+VmqHZATZfvk42RlcrTqB4KfAjZJ+mYbfTXbLhyr9V0RckK5h2An4NtnZflsN9Irq1DT0S7KzID5JdiHVY8CwiNi9whiaNU9dX+VtLlIcfwT2iJU3nRtJ1kS1fc9L1kdVzVO59b0dmEF2cV/+dheV3dYhV1NqqarPYOshjjeRdeAD/G9ELKh4/QsiYgtJXwNui4hzyrqWoTaJIC+1DY8GLo2I5ypc722sbJ7avKt5qsrT4lIcdwGbRsSzaXhN4NZWnC1jGUk/B/4duIOVR+NR8ZWsLU9GKY7TgO9HzW+JLenXZDWid5DdEPJpYF6UcCuaOjUNvajKM4UatLx5KjkLmNdQ7Z3dgjhspWltkIg/QJaMhpFLRmRnzlRpO+AQZfeCepas+bLSjvM2sT+wK/DtiHhc0rpkTXcDrpY1glZph+apXCxvAt6aBv9YdbXXXipdbPitiFjUwhjuaoNk1HXDuVW0y1ltqyMnghZpVfOUtSdJd5KdJdKyo+B2SEbWGk4EZm2gHY6C2yEZWWs4EZgZ0B7JyFrDicDMrOYqvc2tmZm1HycCM7OacyIw6ydJh0g6ub/zNFlmH0k3SLpI0t79i9Kse7W8oMxsMEhX9FZ9MZfVkGsEttqRNFHSnyXNlvQXSWdL2lnStZLulrRlmu9Vks6QNE/SAkl7pfEbp3ELJd2q7BGOjev4QCp7HrBtbvxYSb+QdFP627Zx2YZytpR0fVr/dV1XmqcaxEWSLk0xfzO3zAxJt0m6XdI3BmizWZ1FhP/8t1r9ARPJni/wRrKDnZuBM8jOi98L+FWa76vAQen1WsBfyJ5i932y5zMADAde0VD+usDfyO4PPxy4Fjg5TTsH2C69Hg/cmV4f0jVPQ1mjgKHp9c7AL3LzLya76HAEcD/Z7YjXy617KNnTzN7d6m3uv8H956YhW13dG+mmZZLuAP4QEZFu/DcxzfNOYLqkrudWjyD78b4eOFpSB3BRRNzdUPZWwFUR8Ugq/zzg9WnazsAU6cVb14+S1NNTvkYDZ6ZaR7DyOQCkmJ9I61hE9pyAMQ3rPpvsIe+/KrBNzJpyIrDV1bO51ytywytYud8L2Dci7mpY9k5JNwJ7AJdI+nBEXFFwvWsAW0fEM/mRucTQ6DjgyojYW9JE4Kpu3sNy/H21kriPwOrsMuATSr/SkroeEL4BsDgivkf2kJTGWyzcCOwgaYyyB96/Jzftd8AnugbSYxd7MpqVD185pEDM89K615E0hOy20a26m66tJpwIrM6OI2uKuTU1Hx2Xxu8P3C5pIdnzYs/KLxTZA2tmkTUhXUv2aMUuRwBTUyfzIuAjvcTwTeBrkhZQ4Ig/rfsosseM/gm4OSIu7m05s574FhNmZjXnGoGZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1dz/B2PJe/gmowPgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.month,data.y)\n",
    "table.div(table.sum(axis=1).astype(float),axis=0).plot(kind=\"bar\",stacked=False)\n",
    "plt.title(\"Diagrama apilado de mes contra nivel de compras\")\n",
    "plt.xlabel(\"mes del año\")\n",
    "plt.ylabel(\"Proporción de clientes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proporción de clientes')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.age,data.y)\n",
    "table.div(table.sum(axis=1).astype(float),axis=0).plot(kind=\"bar\",stacked=False)\n",
    "plt.title(\"Diagrama apilado de edad contra nivel de compras\")\n",
    "plt.xlabel(\"Edad\")\n",
    "plt.ylabel(\"Proporción de clientes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f67e49d2ba8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.age.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proporción de clientes')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.poutcome,data.y)\n",
    "table.div(table.sum(axis=1).astype(float),axis=0).plot(kind=\"bar\",stacked=False)\n",
    "plt.title(\"Diagrama apilado de compras anteriores contra nivel de compras\")\n",
    "plt.xlabel(\"Compras anteriores\")\n",
    "plt.ylabel(\"Proporción de clientes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conversión de variables categóricas a dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cats=['job', 'marital', 'education', 'default', 'housing', 'loan','contact','month', 'day_of_week','poutcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for category in cats:\n",
    "    cat_list=\"cat\"+\"_\"+category\n",
    "    cat_dummies=pd.get_dummies(data[category],prefix=cat_list)\n",
    "#     print(cat_dummies)\n",
    "    data_new=data.join(cat_dummies)\n",
    "    data=data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vars=data.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_keep=[v for v in data_vars if v not in cats]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_data=data[to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "bank_data_vars=bank_data.columns.values.tolist()\n",
    "Y=[\"y\"] #Columna a predecir\n",
    "X=[v for v in bank_data_vars if v not in Y] #Variables predictoras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selección de rasgos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables más significativas para predecir los valores de salida del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "n=12\n",
    "lr=LogisticRegression()\n",
    "rfe=RFE(lr,n)\n",
    "rfe=rfe.fit(bank_data[X],bank_data[Y].values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False False False  True False False False  True False False False\n",
      "  True False False False  True False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False  True  True  True\n",
      "  True  True False  True False False False False False False  True False\n",
      "  True] [36 44 18 45  1 14 27 25  1 42 22  3  1 38  2 40  1  5 30 34 12 46 19 33\n",
      " 31 50 26 13 48 21 32  8 37 35 49 20  6 10 15  9 23 16  4 41 24  1  1  1\n",
      "  1  1 17  1 47 39 28 43 29 11  1  7  1]\n"
     ]
    }
   ],
   "source": [
    "print(rfe.support_,rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('age', False, 36),\n",
       " ('duration', False, 44),\n",
       " ('campaign', False, 18),\n",
       " ('pdays', False, 45),\n",
       " ('previous', True, 1),\n",
       " ('emp.var.rate', False, 14),\n",
       " ('cons.price.idx', False, 27),\n",
       " ('cons.conf.idx', False, 25),\n",
       " ('euribor3m', True, 1),\n",
       " ('nr.employed', False, 42),\n",
       " ('cat_job_admin.', False, 22),\n",
       " ('cat_job_blue-collar', False, 3),\n",
       " ('cat_job_entrepreneur', True, 1),\n",
       " ('cat_job_housemaid', False, 38),\n",
       " ('cat_job_management', False, 2),\n",
       " ('cat_job_retired', False, 40),\n",
       " ('cat_job_self-employed', True, 1),\n",
       " ('cat_job_services', False, 5),\n",
       " ('cat_job_student', False, 30),\n",
       " ('cat_job_technician', False, 34),\n",
       " ('cat_job_unemployed', False, 12),\n",
       " ('cat_job_unknown', False, 46),\n",
       " ('cat_marital_divorced', False, 19),\n",
       " ('cat_marital_married', False, 33),\n",
       " ('cat_marital_single', False, 31),\n",
       " ('cat_marital_unknown', False, 50),\n",
       " ('cat_education_Basic', False, 26),\n",
       " ('cat_education_High School', False, 13),\n",
       " ('cat_education_Illiterate', False, 48),\n",
       " ('cat_education_Professional Course', False, 21),\n",
       " ('cat_education_University Degree', False, 32),\n",
       " ('cat_education_Unknown', False, 8),\n",
       " ('cat_default_no', False, 37),\n",
       " ('cat_default_unknown', False, 35),\n",
       " ('cat_default_yes', False, 49),\n",
       " ('cat_housing_no', False, 20),\n",
       " ('cat_housing_unknown', False, 6),\n",
       " ('cat_housing_yes', False, 10),\n",
       " ('cat_loan_no', False, 15),\n",
       " ('cat_loan_unknown', False, 9),\n",
       " ('cat_loan_yes', False, 23),\n",
       " ('cat_contact_cellular', False, 16),\n",
       " ('cat_contact_telephone', False, 4),\n",
       " ('cat_month_apr', False, 41),\n",
       " ('cat_month_aug', False, 24),\n",
       " ('cat_month_dec', True, 1),\n",
       " ('cat_month_jul', True, 1),\n",
       " ('cat_month_jun', True, 1),\n",
       " ('cat_month_mar', True, 1),\n",
       " ('cat_month_may', True, 1),\n",
       " ('cat_month_nov', False, 17),\n",
       " ('cat_month_oct', True, 1),\n",
       " ('cat_month_sep', False, 47),\n",
       " ('cat_day_of_week_fri', False, 39),\n",
       " ('cat_day_of_week_mon', False, 28),\n",
       " ('cat_day_of_week_thu', False, 43),\n",
       " ('cat_day_of_week_tue', False, 29),\n",
       " ('cat_day_of_week_wed', False, 11),\n",
       " ('cat_poutcome_failure', True, 1),\n",
       " ('cat_poutcome_nonexistent', False, 7),\n",
       " ('cat_poutcome_success', True, 1)]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(X,rfe.support_,rfe.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_RME=pd.DataFrame(\n",
    "    {\"Vars\":X,\n",
    "     \"Rank\":rfe.ranking_.tolist()\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['previous',\n",
       " 'euribor3m',\n",
       " 'cat_job_entrepreneur',\n",
       " 'cat_job_self-employed',\n",
       " 'cat_month_dec',\n",
       " 'cat_month_jul',\n",
       " 'cat_month_jun',\n",
       " 'cat_month_mar',\n",
       " 'cat_month_may',\n",
       " 'cat_month_oct',\n",
       " 'cat_poutcome_failure',\n",
       " 'cat_poutcome_success']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lv=df_RME[\"Rank\"]==1\n",
    "cols=df_RME.Vars[lv].tolist()\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=bank_data[cols]\n",
    "Y=bank_data[\"y\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementación del modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Con statsmodel.api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit_model=sm.Logit(np.asarray(Y),X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.279180\n",
      "         Iterations 7\n"
     ]
    }
   ],
   "source": [
    "results=logit_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>    <td>0.192</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Dependent Variable:</td>         <td>y</td>              <td>AIC:</td>         <td>2323.8813</td> \n",
       "</tr>\n",
       "<tr>\n",
       "         <td>Date:</td>        <td>2020-01-05 11:47</td>       <td>BIC:</td>         <td>2399.7617</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>No. Observations:</td>        <td>4119</td>        <td>Log-Likelihood:</td>    <td>-1149.9</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "       <td>Df Model:</td>             <td>11</td>            <td>LL-Null:</td>        <td>-1422.9</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>Df Residuals:</td>          <td>4107</td>         <td>LLR p-value:</td>    <td>4.9751e-110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "      <td>Converged:</td>           <td>1.0000</td>           <td>Scale:</td>         <td>1.0000</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>No. Iterations:</td>        <td>7.0000</td>              <td></td>               <td></td>      \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>             <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>previous</th>              <td>0.3493</td>   <td>0.1411</td>   <td>2.4750</td>  <td>0.0133</td> <td>0.0727</td>  <td>0.6258</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>euribor3m</th>             <td>-0.6253</td>  <td>0.0265</td>  <td>-23.6151</td> <td>0.0000</td> <td>-0.6772</td> <td>-0.5734</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_job_entrepreneur</th>  <td>-0.4842</td>  <td>0.3817</td>   <td>-1.2685</td> <td>0.2046</td> <td>-1.2323</td> <td>0.2639</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_job_self-employed</th> <td>-0.4626</td>  <td>0.3275</td>   <td>-1.4125</td> <td>0.1578</td> <td>-1.1046</td> <td>0.1793</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_month_dec</th>         <td>0.7147</td>   <td>0.4662</td>   <td>1.5331</td>  <td>0.1253</td> <td>-0.1990</td> <td>1.6284</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_month_jul</th>         <td>0.3280</td>   <td>0.1842</td>   <td>1.7808</td>  <td>0.0750</td> <td>-0.0330</td> <td>0.6889</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_month_jun</th>         <td>0.3431</td>   <td>0.1689</td>   <td>2.0310</td>  <td>0.0423</td> <td>0.0120</td>  <td>0.6741</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_month_mar</th>         <td>1.0612</td>   <td>0.3152</td>   <td>3.3666</td>  <td>0.0008</td> <td>0.4434</td>  <td>1.6789</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_month_may</th>         <td>-1.0490</td>  <td>0.1264</td>   <td>-8.3016</td> <td>0.0000</td> <td>-1.2967</td> <td>-0.8014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_month_oct</th>         <td>0.0707</td>   <td>0.2699</td>   <td>0.2621</td>  <td>0.7932</td> <td>-0.4583</td> <td>0.5997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_poutcome_failure</th>  <td>-1.0450</td>  <td>0.2453</td>   <td>-4.2601</td> <td>0.0000</td> <td>-1.5257</td> <td>-0.5642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>cat_poutcome_success</th>  <td>0.7788</td>   <td>0.2888</td>   <td>2.6967</td>  <td>0.0070</td> <td>0.2128</td>  <td>1.3449</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                            Results: Logit\n",
       "======================================================================\n",
       "Model:                Logit             Pseudo R-squared:  0.192      \n",
       "Dependent Variable:   y                 AIC:               2323.8813  \n",
       "Date:                 2020-01-05 11:47  BIC:               2399.7617  \n",
       "No. Observations:     4119              Log-Likelihood:    -1149.9    \n",
       "Df Model:             11                LL-Null:           -1422.9    \n",
       "Df Residuals:         4107              LLR p-value:       4.9751e-110\n",
       "Converged:            1.0000            Scale:             1.0000     \n",
       "No. Iterations:       7.0000                                          \n",
       "----------------------------------------------------------------------\n",
       "                       Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
       "----------------------------------------------------------------------\n",
       "previous               0.3493   0.1411   2.4750 0.0133  0.0727  0.6258\n",
       "euribor3m             -0.6253   0.0265 -23.6151 0.0000 -0.6772 -0.5734\n",
       "cat_job_entrepreneur  -0.4842   0.3817  -1.2685 0.2046 -1.2323  0.2639\n",
       "cat_job_self-employed -0.4626   0.3275  -1.4125 0.1578 -1.1046  0.1793\n",
       "cat_month_dec          0.7147   0.4662   1.5331 0.1253 -0.1990  1.6284\n",
       "cat_month_jul          0.3280   0.1842   1.7808 0.0750 -0.0330  0.6889\n",
       "cat_month_jun          0.3431   0.1689   2.0310 0.0423  0.0120  0.6741\n",
       "cat_month_mar          1.0612   0.3152   3.3666 0.0008  0.4434  1.6789\n",
       "cat_month_may         -1.0490   0.1264  -8.3016 0.0000 -1.2967 -0.8014\n",
       "cat_month_oct          0.0707   0.2699   0.2621 0.7932 -0.4583  0.5997\n",
       "cat_poutcome_failure  -1.0450   0.2453  -4.2601 0.0000 -1.5257 -0.5642\n",
       "cat_poutcome_success   0.7788   0.2888   2.6967 0.0070  0.2128  1.3449\n",
       "======================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P valor cuanto más pequeño mucho mayor es la significación en el modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### con scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "logit_model=linear_model.LogisticRegression()\n",
    "logit_model.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9021607186210245"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model.score(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un 10.9% de las personas compran. Si dijeramos que ninguno compraría de forma vanal se obtendría un 89.05%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8905074047098811"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-Y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>previous</td>\n",
       "      <td>[0.3798316142096139]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>euribor3m</td>\n",
       "      <td>[-0.5027490712653142]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cat_job_entrepreneur</td>\n",
       "      <td>[-0.34306615561163367]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cat_job_self-employed</td>\n",
       "      <td>[-0.33506416426093977]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat_month_dec</td>\n",
       "      <td>[0.8733167999349573]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cat_month_jul</td>\n",
       "      <td>[0.3820874456812563]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cat_month_jun</td>\n",
       "      <td>[0.5096949858004949]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cat_month_mar</td>\n",
       "      <td>[1.270361228864782]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cat_month_may</td>\n",
       "      <td>[-0.7430896290668391]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>cat_month_oct</td>\n",
       "      <td>[0.41185574423171945]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cat_poutcome_failure</td>\n",
       "      <td>[-0.7531618690620344]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cat_poutcome_success</td>\n",
       "      <td>[1.077832533480444]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        0                       1\n",
       "0                previous    [0.3798316142096139]\n",
       "1               euribor3m   [-0.5027490712653142]\n",
       "2    cat_job_entrepreneur  [-0.34306615561163367]\n",
       "3   cat_job_self-employed  [-0.33506416426093977]\n",
       "4           cat_month_dec    [0.8733167999349573]\n",
       "5           cat_month_jul    [0.3820874456812563]\n",
       "6           cat_month_jun    [0.5096949858004949]\n",
       "7           cat_month_mar     [1.270361228864782]\n",
       "8           cat_month_may   [-0.7430896290668391]\n",
       "9           cat_month_oct   [0.41185574423171945]\n",
       "10   cat_poutcome_failure   [-0.7531618690620344]\n",
       "11   cat_poutcome_success     [1.077832533480444]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(X.columns,np.transpose(logit_model.coef_))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validación del modelo logístico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm=linear_model.LogisticRegression()\n",
    "lm.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs=lm.predict_proba(X_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93352157, 0.06647843],\n",
       "       [0.88259365, 0.11740635],\n",
       "       [0.93040666, 0.06959334],\n",
       "       ...,\n",
       "       [0.73273217, 0.26726783],\n",
       "       [0.97862459, 0.02137541],\n",
       "       [0.24746608, 0.75253392]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction=lm.predict(X_test)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.06647843, 0.11740635, 0.06959334, ..., 0.26726783, 0.02137541,\n",
       "       0.75253392])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob=probs[:,1]\n",
    "prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1236, 2)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.062666</td>\n",
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       "      <td>0.062606</td>\n",
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       "      <td>0.752534</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1236 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0  Prediction\n",
       "0     0.066478           0\n",
       "1     0.117406           0\n",
       "2     0.069593           0\n",
       "3     0.062666           0\n",
       "4     0.065086           0\n",
       "...        ...         ...\n",
       "1231  0.062666           0\n",
       "1232  0.062606           0\n",
       "1233  0.267268           1\n",
       "1234  0.021375           0\n",
       "1235  0.752534           1\n",
       "\n",
       "[1236 rows x 2 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_df=pd.DataFrame(prob)\n",
    "threshold=0.15\n",
    "prob_df[\"Prediction\"]=np.where(prob_df[0]>threshold,1,0)\n",
    "prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0       count\n",
       "Prediction       \n",
       "0            1019\n",
       "1             217"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(prob_df.Prediction,columns=\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.020226537216828478"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25/len(prob_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9021035598705501"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "metrics.accuracy_score(Y_test,prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Validación cruzada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/home/franklin/my_projects_dir/entorno_jup/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores=cross_val_score(linear_model.LogisticRegression(),X,Y,scoring=\"accuracy\",cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92493947, 0.90048544, 0.90291262, 0.89563107, 0.90291262,\n",
       "       0.90533981, 0.8907767 , 0.89563107, 0.90024331, 0.90024331])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9019115405705567"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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