Logistic regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "892364cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rc(\"font\", size=14)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "539dd73f",
   "metadata": {},
   "source": [
    "Vamos a importat un dataset que proviene de [UCI Machine Learning Mastery](http://archive.ics.uci.edu/ml/index.php) y que tiene datos relacionados con la dirección de campañas de marketing (llamadas telefónicas) de una institución bancaria portuguesa. La finalidad de la clasificación es predecir si el cliente se suscribirá (1/0) a un depósito determinado (variable y). El dataset os lo proporcionamos nosotros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de9ddcb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\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>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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</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>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</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>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jun</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>services</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>apr</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</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>3</td>\n",
       "      <td>1</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          job  marital          education  default housing loan  \\\n",
       "0   44  blue-collar  married           basic.4y  unknown     yes   no   \n",
       "1   53   technician  married            unknown       no      no   no   \n",
       "2   28   management   single  university.degree       no     yes   no   \n",
       "3   39     services  married        high.school       no      no   no   \n",
       "4   55      retired  married           basic.4y       no     yes   no   \n",
       "\n",
       "    contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
       "0  cellular   aug         thu  ...         1    999         0  nonexistent   \n",
       "1  cellular   nov         fri  ...         1    999         0  nonexistent   \n",
       "2  cellular   jun         thu  ...         3      6         2      success   \n",
       "3  cellular   apr         fri  ...         2    999         0  nonexistent   \n",
       "4  cellular   aug         fri  ...         1      3         1      success   \n",
       "\n",
       "  emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  nr_employed  y  \n",
       "0          1.4          93.444          -36.1      4.963       5228.1  0  \n",
       "1         -0.1          93.200          -42.0      4.021       5195.8  0  \n",
       "2         -1.7          94.055          -39.8      0.729       4991.6  1  \n",
       "3         -1.8          93.075          -47.1      1.405       5099.1  0  \n",
       "4         -2.9          92.201          -31.4      0.869       5076.2  1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = pd.read_csv('data/bank.csv')\n",
    "dt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a1d22125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['basic.4y', 'unknown', 'university.degree', 'high.school',\n",
       "       'basic.9y', 'professional.course', 'basic.6y', 'illiterate'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt['education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "474f2b95",
   "metadata": {},
   "source": [
    "## Análisis descriptivo de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4345aa8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188.00000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>258.285010</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>5167.035911</td>\n",
       "      <td>0.112654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.42125</td>\n",
       "      <td>259.279249</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>72.251528</td>\n",
       "      <td>0.316173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4963.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.00000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>5228.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age      duration      campaign         pdays      previous  \\\n",
       "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
       "mean      40.02406    258.285010      2.567593    962.475454      0.172963   \n",
       "std       10.42125    259.279249      2.770014    186.910907      0.494901   \n",
       "min       17.00000      0.000000      1.000000      0.000000      0.000000   \n",
       "25%       32.00000    102.000000      1.000000    999.000000      0.000000   \n",
       "50%       38.00000    180.000000      2.000000    999.000000      0.000000   \n",
       "75%       47.00000    319.000000      3.000000    999.000000      0.000000   \n",
       "max       98.00000   4918.000000     56.000000    999.000000      7.000000   \n",
       "\n",
       "       emp_var_rate  cons_price_idx  cons_conf_idx     euribor3m  \\\n",
       "count  41188.000000    41188.000000   41188.000000  41188.000000   \n",
       "mean       0.081886       93.575664     -40.502600      3.621291   \n",
       "std        1.570960        0.578840       4.628198      1.734447   \n",
       "min       -3.400000       92.201000     -50.800000      0.634000   \n",
       "25%       -1.800000       93.075000     -42.700000      1.344000   \n",
       "50%        1.100000       93.749000     -41.800000      4.857000   \n",
       "75%        1.400000       93.994000     -36.400000      4.961000   \n",
       "max        1.400000       94.767000     -26.900000      5.045000   \n",
       "\n",
       "        nr_employed             y  \n",
       "count  41188.000000  41188.000000  \n",
       "mean    5167.035911      0.112654  \n",
       "std       72.251528      0.316173  \n",
       "min     4963.600000      0.000000  \n",
       "25%     5099.100000      0.000000  \n",
       "50%     5191.000000      0.000000  \n",
       "75%     5228.100000      0.000000  \n",
       "max     5228.100000      1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8254c5",
   "metadata": {},
   "source": [
    "Chequeamos el nombre de las variables que tenemos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd91cee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba34bd70",
   "metadata": {},
   "source": [
    "Vamos a comprobar los tipos de variables que tenemos en el dataset. Aquellas categoricas aparecen como object, las variables que toman valores enteros como int64 y las que toman valores con decimales son las que aparecen como float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f23148a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 int64\n",
       "job                object\n",
       "marital            object\n",
       "education          object\n",
       "default            object\n",
       "housing            object\n",
       "loan               object\n",
       "contact            object\n",
       "month              object\n",
       "day_of_week        object\n",
       "duration            int64\n",
       "campaign            int64\n",
       "pdays               int64\n",
       "previous            int64\n",
       "poutcome           object\n",
       "emp_var_rate      float64\n",
       "cons_price_idx    float64\n",
       "cons_conf_idx     float64\n",
       "euribor3m         float64\n",
       "nr_employed       float64\n",
       "y                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5526f798",
   "metadata": {},
   "source": [
    "Comprobamos el tamaño del dataset. Vemos que tenemos 21 variables diferentes y 41188 instancias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9fad57f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e89632aa",
   "metadata": {},
   "source": [
    "Vamos a imprimir los valores posibles de las variables que son categoricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d0b1cdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job \t ['blue-collar' 'technician' 'management' 'services' 'retired' 'admin.'\n",
      " 'housemaid' 'unemployed' 'entrepreneur' 'self-employed' 'unknown'\n",
      " 'student'] \n",
      "\n",
      "marital \t ['married' 'single' 'divorced' 'unknown'] \n",
      "\n",
      "education \t ['basic.4y' 'unknown' 'university.degree' 'high.school' 'basic.9y'\n",
      " 'professional.course' 'basic.6y' 'illiterate'] \n",
      "\n",
      "default \t ['unknown' 'no' 'yes'] \n",
      "\n",
      "housing \t ['yes' 'no' 'unknown'] \n",
      "\n",
      "loan \t ['no' 'yes' 'unknown'] \n",
      "\n",
      "contact \t ['cellular' 'telephone'] \n",
      "\n",
      "month \t ['aug' 'nov' 'jun' 'apr' 'jul' 'may' 'oct' 'mar' 'sep' 'dec'] \n",
      "\n",
      "day_of_week \t ['thu' 'fri' 'tue' 'mon' 'wed'] \n",
      "\n",
      "poutcome \t ['nonexistent' 'success' 'failure'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for var in dt.columns:\n",
    "    if dt[var].dtype == 'object':\n",
    "        print(var, '\\t', dt[var].unique(), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2eb5c576",
   "metadata": {},
   "source": [
    "Nuestra variable predictora, es decir, aquella que deseamos clasificar es la variable y, que nos indica si un cliente se suscribe o no a un depósito. Se trata pues, de una variable binaria, en la que si toma el valor 1 indica que si que se suscribe y si toma el valor 0 indica que no se suscribe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f768b797",
   "metadata": {},
   "source": [
    "En ocasiones, si alguna variables categoricas tienen demasiados valores, se pueden agregar valores para que tomen el mismo valor. Por ejemplo es el caso de la variable de educación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d636cb24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['basic.4y', 'unknown', 'university.degree', 'high.school',\n",
       "       'basic.9y', 'professional.course', 'basic.6y', 'illiterate'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt['education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e80491c3",
   "metadata": {},
   "source": [
    "Vamos a hacer que la variable agrupe los valores “basic.4y”, “basic.9y” y “basic.6y” para que se llamen como 'basic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0bcb3e39",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt['education']=np.where(dt['education'] =='basic.9y', 'Basic', dt['education'])\n",
    "dt['education']=np.where(dt['education'] =='basic.6y', 'Basic', dt['education'])\n",
    "dt['education']=np.where(dt['education'] =='basic.4y', 'Basic', dt['education'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff42674c",
   "metadata": {},
   "source": [
    "Después de agruparlos, la variable tiene los siguientes posibles valores, en los que  “basic.4y”, “basic.9y” y “basic.6y” ya no están, y aparece la nueva variable basic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d80f98b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Basic', 'unknown', 'university.degree', 'high.school',\n",
       "       'professional.course', 'illiterate'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt['education'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d71f353",
   "metadata": {},
   "source": [
    "## Exploración de los datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe17d38d",
   "metadata": {},
   "source": [
    "Cuantos datos tengo de cada caso?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03590aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36548\n",
       "1     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b6e08947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='y', data=dt, palette='hls')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f7210f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of no subscription is 88.73\n",
      "percentage of subscription 11.27\n"
     ]
    }
   ],
   "source": [
    "count_no_sub = len(dt[dt['y']==0])\n",
    "count_sub = len(dt[dt['y']==1])\n",
    "pct_of_no_sub = count_no_sub/(count_no_sub+count_sub)\n",
    "print(\"percentage of no subscription is\", round(pct_of_no_sub*100, 2))\n",
    "pct_of_sub = count_sub/(count_no_sub+count_sub)\n",
    "print(\"percentage of subscription\", round(pct_of_sub*100, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5d61192",
   "metadata": {},
   "source": [
    "Tenemos una clase desbalanceada, esto es, que tenemos más casos de un valor que del otro. En este caso, tenemos más casos de no suscripción, a lo que si suscripción."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e617d5f",
   "metadata": {},
   "source": [
    "Vamos a seguir explorando para ver, de media, que tipo de características tiene las otras variables para cada uno de los casos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "552fb325",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-15-185af6871b0a>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  dt.groupby('y').mean()\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <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",
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       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>39.911185</td>\n",
       "      <td>220.844807</td>\n",
       "      <td>2.633085</td>\n",
       "      <td>984.113878</td>\n",
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       "      <td>-40.593097</td>\n",
       "      <td>3.811491</td>\n",
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       "      <th>1</th>\n",
       "      <td>40.913147</td>\n",
       "      <td>553.191164</td>\n",
       "      <td>2.051724</td>\n",
       "      <td>792.035560</td>\n",
       "      <td>0.492672</td>\n",
       "      <td>-1.233448</td>\n",
       "      <td>93.354386</td>\n",
       "      <td>-39.789784</td>\n",
       "      <td>2.123135</td>\n",
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      ],
      "text/plain": [
       "         age    duration  campaign       pdays  previous  emp_var_rate  \\\n",
       "y                                                                        \n",
       "0  39.911185  220.844807  2.633085  984.113878  0.132374      0.248875   \n",
       "1  40.913147  553.191164  2.051724  792.035560  0.492672     -1.233448   \n",
       "\n",
       "   cons_price_idx  cons_conf_idx  euribor3m  nr_employed  \n",
       "y                                                         \n",
       "0       93.603757     -40.593097   3.811491  5176.166600  \n",
       "1       93.354386     -39.789784   2.123135  5095.115991  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.groupby('y').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b4d535b",
   "metadata": {},
   "source": [
    "Observaciones:\n",
    "- La edad media de los clietnes que han comprado el depósito es mayor que los clientes que no han accedido a comprarlo.\n",
    "- Sorprendentemente el número de campañas para los clietnes que han accedido a comprar el depósito es menor que para aquellos que no han accedido"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd543d52",
   "metadata": {},
   "source": [
    "Podemos realizar el mismo estudio para otras variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ce8a787d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-8bd0c490dbe3>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  dt.groupby('job').mean()\n"
     ]
    },
    {
     "data": {
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       "      <th>admin.</th>\n",
       "      <td>38.187296</td>\n",
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       "      <td>4.009645</td>\n",
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       "      <td>0.100000</td>\n",
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       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>42.362859</td>\n",
       "      <td>257.058140</td>\n",
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       "      <td>962.647059</td>\n",
       "      <td>0.185021</td>\n",
       "      <td>-0.012688</td>\n",
       "      <td>93.522755</td>\n",
       "      <td>-40.489466</td>\n",
       "      <td>3.611316</td>\n",
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       "      <td>2.587805</td>\n",
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       "      <td>0.154545</td>\n",
       "      <td>0.357879</td>\n",
       "      <td>93.718942</td>\n",
       "      <td>-38.797879</td>\n",
       "      <td>3.949033</td>\n",
       "      <td>5172.931818</td>\n",
       "      <td>0.112121</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age    duration  campaign       pdays  previous  \\\n",
       "job                                                                    \n",
       "admin.         38.187296  254.312128  2.623489  954.319229  0.189023   \n",
       "blue-collar    39.555760  264.542360  2.558461  985.160363  0.122542   \n",
       "entrepreneur   41.723214  263.267857  2.535714  981.267170  0.138736   \n",
       "housemaid      45.500000  250.454717  2.639623  960.579245  0.137736   \n",
       "management     42.362859  257.058140  2.476060  962.647059  0.185021   \n",
       "retired        62.027326  273.712209  2.476744  897.936047  0.327326   \n",
       "self-employed  39.949331  264.142153  2.660802  976.621393  0.143561   \n",
       "services       37.926430  258.398085  2.587805  979.974049  0.154951   \n",
       "student        25.894857  283.683429  2.104000  840.217143  0.524571   \n",
       "technician     38.507638  250.232241  2.577339  964.408127  0.153789   \n",
       "unemployed     39.733728  249.451677  2.564103  935.316568  0.199211   \n",
       "unknown        45.563636  239.675758  2.648485  938.727273  0.154545   \n",
       "\n",
       "               emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "job                                                                     \n",
       "admin.             0.015563       93.534054     -40.245433   3.550274   \n",
       "blue-collar        0.248995       93.656656     -41.375816   3.771996   \n",
       "entrepreneur       0.158723       93.605372     -41.283654   3.791120   \n",
       "housemaid          0.433396       93.676576     -39.495283   4.009645   \n",
       "management        -0.012688       93.522755     -40.489466   3.611316   \n",
       "retired           -0.698314       93.430786     -38.573081   2.770066   \n",
       "self-employed      0.094159       93.559982     -40.488107   3.689376   \n",
       "services           0.175359       93.634659     -41.290048   3.699187   \n",
       "student           -1.408000       93.331613     -40.187543   1.884224   \n",
       "technician         0.274566       93.561471     -39.927569   3.820401   \n",
       "unemployed        -0.111736       93.563781     -40.007594   3.466583   \n",
       "unknown            0.357879       93.718942     -38.797879   3.949033   \n",
       "\n",
       "               nr_employed         y  \n",
       "job                                   \n",
       "admin.         5164.125350  0.129726  \n",
       "blue-collar    5175.615150  0.068943  \n",
       "entrepreneur   5176.313530  0.085165  \n",
       "housemaid      5179.529623  0.100000  \n",
       "management     5166.650513  0.112175  \n",
       "retired        5122.262151  0.252326  \n",
       "self-employed  5170.674384  0.104856  \n",
       "services       5171.600126  0.081381  \n",
       "student        5085.939086  0.314286  \n",
       "technician     5175.648391  0.108260  \n",
       "unemployed     5157.156509  0.142012  \n",
       "unknown        5172.931818  0.112121  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.groupby('job').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9b85831f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-a914d679a8eb>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  dt.groupby('marital').mean()\n"
     ]
    },
    {
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       "      <td>937.100000</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>-0.221250</td>\n",
       "      <td>93.471250</td>\n",
       "      <td>-40.820000</td>\n",
       "      <td>3.313038</td>\n",
       "      <td>5157.393750</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age    duration  campaign       pdays  previous  emp_var_rate  \\\n",
       "marital                                                                         \n",
       "divorced  44.899393  253.790330   2.61340  968.639853  0.168690      0.163985   \n",
       "married   42.307165  257.438623   2.57281  967.247673  0.155608      0.183625   \n",
       "single    33.158714  261.524378   2.53380  949.909578  0.211359     -0.167989   \n",
       "unknown   40.275000  312.725000   3.18750  937.100000  0.275000     -0.221250   \n",
       "\n",
       "          cons_price_idx  cons_conf_idx  euribor3m  nr_employed         y  \n",
       "marital                                                                    \n",
       "divorced       93.606563     -40.707069   3.715603  5170.878643  0.103209  \n",
       "married        93.597367     -40.270659   3.745832  5171.848772  0.101573  \n",
       "single         93.517300     -40.918698   3.317447  5155.199265  0.140041  \n",
       "unknown        93.471250     -40.820000   3.313038  5157.393750  0.150000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.groupby('marital').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "196c92a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-18-d7fa08490dd4>:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  dt.groupby('education').mean()\n"
     ]
    },
    {
     "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>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <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>42.163910</td>\n",
       "      <td>263.043874</td>\n",
       "      <td>2.559498</td>\n",
       "      <td>974.877967</td>\n",
       "      <td>0.141053</td>\n",
       "      <td>0.191329</td>\n",
       "      <td>93.639933</td>\n",
       "      <td>-40.927595</td>\n",
       "      <td>3.729654</td>\n",
       "      <td>5172.014113</td>\n",
       "      <td>0.087029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high.school</th>\n",
       "      <td>37.998213</td>\n",
       "      <td>260.886810</td>\n",
       "      <td>2.568576</td>\n",
       "      <td>964.358382</td>\n",
       "      <td>0.185917</td>\n",
       "      <td>0.032937</td>\n",
       "      <td>93.584857</td>\n",
       "      <td>-40.940641</td>\n",
       "      <td>3.556157</td>\n",
       "      <td>5164.994735</td>\n",
       "      <td>0.108355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>illiterate</th>\n",
       "      <td>48.500000</td>\n",
       "      <td>276.777778</td>\n",
       "      <td>2.277778</td>\n",
       "      <td>943.833333</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>93.317333</td>\n",
       "      <td>-39.950000</td>\n",
       "      <td>3.516556</td>\n",
       "      <td>5171.777778</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>professional.course</th>\n",
       "      <td>40.080107</td>\n",
       "      <td>252.533855</td>\n",
       "      <td>2.586115</td>\n",
       "      <td>960.765974</td>\n",
       "      <td>0.163075</td>\n",
       "      <td>0.173012</td>\n",
       "      <td>93.569864</td>\n",
       "      <td>-40.124108</td>\n",
       "      <td>3.710457</td>\n",
       "      <td>5170.155979</td>\n",
       "      <td>0.113485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>university.degree</th>\n",
       "      <td>38.879191</td>\n",
       "      <td>253.223373</td>\n",
       "      <td>2.563527</td>\n",
       "      <td>951.807692</td>\n",
       "      <td>0.192390</td>\n",
       "      <td>-0.028090</td>\n",
       "      <td>93.493466</td>\n",
       "      <td>-39.975805</td>\n",
       "      <td>3.529663</td>\n",
       "      <td>5163.226298</td>\n",
       "      <td>0.137245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>43.481225</td>\n",
       "      <td>262.390526</td>\n",
       "      <td>2.596187</td>\n",
       "      <td>942.830734</td>\n",
       "      <td>0.226459</td>\n",
       "      <td>0.059099</td>\n",
       "      <td>93.658615</td>\n",
       "      <td>-39.877816</td>\n",
       "      <td>3.571098</td>\n",
       "      <td>5159.549509</td>\n",
       "      <td>0.145003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           age    duration  campaign       pdays  previous  \\\n",
       "education                                                                    \n",
       "Basic                42.163910  263.043874  2.559498  974.877967  0.141053   \n",
       "high.school          37.998213  260.886810  2.568576  964.358382  0.185917   \n",
       "illiterate           48.500000  276.777778  2.277778  943.833333  0.111111   \n",
       "professional.course  40.080107  252.533855  2.586115  960.765974  0.163075   \n",
       "university.degree    38.879191  253.223373  2.563527  951.807692  0.192390   \n",
       "unknown              43.481225  262.390526  2.596187  942.830734  0.226459   \n",
       "\n",
       "                     emp_var_rate  cons_price_idx  cons_conf_idx  euribor3m  \\\n",
       "education                                                                     \n",
       "Basic                    0.191329       93.639933     -40.927595   3.729654   \n",
       "high.school              0.032937       93.584857     -40.940641   3.556157   \n",
       "illiterate              -0.133333       93.317333     -39.950000   3.516556   \n",
       "professional.course      0.173012       93.569864     -40.124108   3.710457   \n",
       "university.degree       -0.028090       93.493466     -39.975805   3.529663   \n",
       "unknown                  0.059099       93.658615     -39.877816   3.571098   \n",
       "\n",
       "                     nr_employed         y  \n",
       "education                                   \n",
       "Basic                5172.014113  0.087029  \n",
       "high.school          5164.994735  0.108355  \n",
       "illiterate           5171.777778  0.222222  \n",
       "professional.course  5170.155979  0.113485  \n",
       "university.degree    5163.226298  0.137245  \n",
       "unknown              5159.549509  0.145003  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.groupby('education').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5fdf14",
   "metadata": {},
   "source": [
    "## Visualizaciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d7c7e4f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Purchase')"
      ]
     },
     "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": [
    "%matplotlib inline\n",
    "pd.crosstab(dt.job,dt.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Job Title')\n",
    "plt.xlabel('Job')\n",
    "plt.ylabel('Frequency of Purchase')\n",
    "# plt.savefig('purchase_fre_job')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87af07d4",
   "metadata": {},
   "source": [
    "La frecuencia de comprar un depósito depende en gran medida del tipo de trabajo que se tiene. Por tanto, el trabajo puede ser un gran predictor para la variable y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d8647616",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proportion of Customers')"
      ]
     },
     "execution_count": 20,
     "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(dt.marital,dt.y)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('Stacked Bar Chart of Marital Status vs Purchase')\n",
    "plt.xlabel('Marital Status')\n",
    "plt.ylabel('Proportion of Customers')\n",
    "# plt.savefig('mariral_vs_pur_stack')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6294486",
   "metadata": {},
   "source": [
    "El estatus matrimonial no parece ser significativo a la hora de predecir la variable y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "38ef0f8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proportion of Customers')"
      ]
     },
     "execution_count": 21,
     "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(dt.education,dt.y)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('Stacked Bar Chart of Education vs Purchase')\n",
    "plt.xlabel('Education')\n",
    "plt.ylabel('Proportion of Customers')\n",
    "# plt.savefig('edu_vs_pur_stack')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dd0d40e",
   "metadata": {},
   "source": [
    "El nivel de educación parece ser influyente a la hora de predecir la variable y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "178e5d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Purchase')"
      ]
     },
     "execution_count": 22,
     "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(dt.day_of_week,dt.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Day of Week')\n",
    "plt.xlabel('Day of Week')\n",
    "plt.ylabel('Frequency of Purchase')\n",
    "# plt.savefig('pur_dayofweek_bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baecb2c7",
   "metadata": {},
   "source": [
    "El día de la semana parece no tener mucha relevancia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "feb3477b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Purchase')"
      ]
     },
     "execution_count": 23,
     "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(dt.month,dt.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Month')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Frequency of Purchase')\n",
    "# plt.savefig('pur_fre_month_bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef1f8666",
   "metadata": {},
   "source": [
    "El més si que parece ser una buena variable para usar como predictora de la variable y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "df48dd3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 24,
     "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": [
    "dt.age.hist()\n",
    "plt.title('Histogram of Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "# plt.savefig('hist_age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "219b1a27",
   "metadata": {},
   "source": [
    "Esto representa un histograma de la variable edad en el banco. Podemos decir que es una variable que se aproxima a la forma de una gaussiana con una media centrada en los 30-40 años."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c777e55a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Purchase')"
      ]
     },
     "execution_count": 25,
     "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(dt.poutcome,dt.y).plot(kind='bar')\n",
    "plt.title('Purchase Frequency for Poutcome')\n",
    "plt.xlabel('Poutcome')\n",
    "plt.ylabel('Frequency of Purchase')\n",
    "# plt.savefig('pur_fre_pout_bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f551e94f",
   "metadata": {},
   "source": [
    "Poutcome representa el estado de la anterior campaña. Si la anterior campaña para el cliente fue exitosa entonces no hay mucha varianza. Si fue un fracaso, mayoritariamente no repiten en otra campaña. Si no existe ninguna anterior, entonces lo más probable es que no accedan a otra campaña, aunque hay gente que lo hace. Puede ser un buen indicador para predecir"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b38938f",
   "metadata": {},
   "source": [
    "## Vamos a crear variables dummies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43251108",
   "metadata": {},
   "source": [
    "Vamos a crear un dataset desplegado en el que cada una de las posibles valores de cada variable categórica aparece como una columna, y aquellas instancias que le corresponde ese valor tienen un 1 en dicha variable. Aquellas a las que no les corresponde, entonces tienen un cero. Son, variables dummies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7932c33b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>job_admin.</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>job_entrepreneur</th>\n",
       "      <th>job_housemaid</th>\n",
       "      <th>job_management</th>\n",
       "      <th>job_retired</th>\n",
       "      <th>job_self-employed</th>\n",
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       "      <th>job_student</th>\n",
       "      <th>job_technician</th>\n",
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      ],
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       "       job_admin.  job_blue-collar  job_entrepreneur  job_housemaid  \\\n",
       "0               0                1                 0              0   \n",
       "1               0                0                 0              0   \n",
       "2               0                0                 0              0   \n",
       "3               0                0                 0              0   \n",
       "4               0                0                 0              0   \n",
       "...           ...              ...               ...            ...   \n",
       "41183           0                0                 0              0   \n",
       "41184           0                0                 0              1   \n",
       "41185           1                0                 0              0   \n",
       "41186           0                0                 0              0   \n",
       "41187           0                0                 0              0   \n",
       "\n",
       "       job_management  job_retired  job_self-employed  job_services  \\\n",
       "0                   0            0                  0             0   \n",
       "1                   0            0                  0             0   \n",
       "2                   1            0                  0             0   \n",
       "3                   0            0                  0             1   \n",
       "4                   0            1                  0             0   \n",
       "...               ...          ...                ...           ...   \n",
       "41183               0            1                  0             0   \n",
       "41184               0            0                  0             0   \n",
       "41185               0            0                  0             0   \n",
       "41186               0            0                  0             0   \n",
       "41187               0            0                  0             0   \n",
       "\n",
       "       job_student  job_technician  job_unemployed  job_unknown  \n",
       "0                0               0               0            0  \n",
       "1                0               1               0            0  \n",
       "2                0               0               0            0  \n",
       "3                0               0               0            0  \n",
       "4                0               0               0            0  \n",
       "...            ...             ...             ...          ...  \n",
       "41183            0               0               0            0  \n",
       "41184            0               0               0            0  \n",
       "41185            0               0               0            0  \n",
       "41186            0               1               0            0  \n",
       "41187            1               0               0            0  \n",
       "\n",
       "[41188 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(dt['job'], prefix='job')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2f38d01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']\n",
    "for var in cat_vars:\n",
    "    cat_list='var'+'_'+var\n",
    "    cat_list = pd.get_dummies(dt[var], prefix=var)\n",
    "    dt1=dt.join(cat_list)\n",
    "    dt=dt1\n",
    "    \n",
    "cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome']\n",
    "data_vars=dt.columns.values.tolist()\n",
    "to_keep=[i for i in data_vars if i not in cat_vars]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8471cdcb",
   "metadata": {},
   "source": [
    "Dataset final es tal que:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a166a264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp_var_rate',\n",
       "       'cons_price_idx', 'cons_conf_idx', 'euribor3m', 'nr_employed', 'y',\n",
       "       'job_admin.', 'job_blue-collar', 'job_entrepreneur',\n",
       "       'job_housemaid', 'job_management', 'job_retired',\n",
       "       'job_self-employed', 'job_services', 'job_student',\n",
       "       'job_technician', 'job_unemployed', 'job_unknown',\n",
       "       'marital_divorced', 'marital_married', 'marital_single',\n",
       "       'marital_unknown', 'education_Basic', 'education_high.school',\n",
       "       'education_illiterate', 'education_professional.course',\n",
       "       'education_university.degree', 'education_unknown', 'default_no',\n",
       "       'default_unknown', 'default_yes', 'housing_no', 'housing_unknown',\n",
       "       'housing_yes', 'loan_no', 'loan_unknown', 'loan_yes',\n",
       "       'contact_cellular', 'contact_telephone', 'month_apr', 'month_aug',\n",
       "       'month_dec', 'month_jul', 'month_jun', 'month_mar', 'month_may',\n",
       "       'month_nov', 'month_oct', 'month_sep', 'day_of_week_fri',\n",
       "       'day_of_week_mon', 'day_of_week_thu', 'day_of_week_tue',\n",
       "       'day_of_week_wed', 'poutcome_failure', 'poutcome_nonexistent',\n",
       "       'poutcome_success'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final=dt[to_keep]\n",
    "data_final.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd32d7a7",
   "metadata": {},
   "source": [
    "## SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2c1049a",
   "metadata": {},
   "source": [
    "Con nuestros datos de entrenamiento creados, vamos a usar SMOTE:\n",
    "- Muestrea datos de la clase minoritaria, en este caso, no subscripción\n",
    "- Lo hace de forma aleatoria basándose en el algoritmo KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c7eb2f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imbalanced-learn in c:\\users\\vicen\\anaconda3\\lib\\site-packages (0.10.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\vicen\\anaconda3\\lib\\site-packages (from imbalanced-learn) (3.1.0)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\vicen\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.2.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\vicen\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\vicen\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.22.4)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\vicen\\anaconda3\\lib\\site-packages (from imbalanced-learn) (1.6.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d489ae3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of oversampled data is  51134\n",
      "Number of no subscription in oversampled data 25567\n",
      "Number of subscription 25567\n",
      "Proportion of no subscription data in oversampled data is  0.5\n",
      "Proportion of subscription data in oversampled data is  0.5\n"
     ]
    }
   ],
   "source": [
    "X = data_final.loc[:, data_final.columns != 'y']\n",
    "y = data_final.loc[:, data_final.columns == 'y']\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "os = SMOTE(random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "columns = X_train.columns\n",
    "os_data_X, os_data_y = os.fit_resample(X_train, y_train)\n",
    "\n",
    "os_data_X = pd.DataFrame(data=os_data_X,columns=columns )\n",
    "os_data_y= pd.DataFrame(data=os_data_y,columns=['y'])\n",
    "\n",
    "# we can Check the numbers of our data\n",
    "print(\"length of oversampled data is \",len(os_data_X))\n",
    "print(\"Number of no subscription in oversampled data\",len(os_data_y[os_data_y['y']==0]))\n",
    "print(\"Number of subscription\",len(os_data_y[os_data_y['y']==1]))\n",
    "print(\"Proportion of no subscription data in oversampled data is \",len(os_data_y[os_data_y['y']==0])/len(os_data_X))\n",
    "print(\"Proportion of subscription data in oversampled data is \",len(os_data_y[os_data_y['y']==1])/len(os_data_X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c1095a2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X train (51134, 61)\n",
      "Y train (51134, 1) \n",
      "\n",
      "X test (12357, 61)\n",
      "Y test (12357, 1)\n"
     ]
    }
   ],
   "source": [
    "print('X train', os_data_X.shape)\n",
    "print('Y train', os_data_y.shape, '\\n')\n",
    "print('X test', X_test.shape)\n",
    "print('Y test', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dfce477",
   "metadata": {},
   "source": [
    "Ahora que tenemos un dataset perfectamente balanceado, te habrás fijado que solo hemos hecho sampling sobre los datos de entrenamiento. Son los datos que usamos para crear el modelo. Los datos de test no es necesario muestrearlos, ya que son con los que vamos a testear el modelo. Ningun dato de test es usado para el entrenamiento."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a28cf4be",
   "metadata": {},
   "source": [
    "## Clasificación"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c670a36a",
   "metadata": {},
   "source": [
    "Vamos a llevar a cabo nuestro modelo de clasificación usando regresión logística sobre los datos de entrenamiento, para luego testear usando los datos de test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6b64c955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(os_data_X, os_data_y.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eee8dc75",
   "metadata": {},
   "source": [
    "Usamos los datos de test para testear el modelo y obtenemos una precisión de 0.91 que es una buena métrica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5df89820",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.90\n"
     ]
    }
   ],
   "source": [
    "y_pred = logreg.predict(X_test)\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2625dd55",
   "metadata": {},
   "source": [
    "Vamos a valorar la matriz de confusión. Recordemos que tenemos ((verdaderos positivos, falsos positivos), (falsos negativos, verdaderos negativos)). De esta forma tenemos 10119 + 912 casos en los que hemos acertado, y 862 + 464 en los que no."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "93c57821",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10213   768]\n",
      " [  523   853]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a42e1d49",
   "metadata": {},
   "source": [
    "Scikit learn tiene una función para crear un report de las medidas de clasificación. Se importa de la siguiente forma:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dc8bf131",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.93      0.94     10981\n",
      "           1       0.53      0.62      0.57      1376\n",
      "\n",
      "    accuracy                           0.90     12357\n",
      "   macro avg       0.74      0.77      0.75     12357\n",
      "weighted avg       0.90      0.90      0.90     12357\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6101b6ad",
   "metadata": {},
   "source": [
    "## Área bajo la curva ROC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99e3b3d0",
   "metadata": {},
   "source": [
    "El área bajo la curva ROC es una herramienta usada para los clasificadores binarios. La línea punteada representa la curva ROC de un clasificador puramente aleatorio. Un buen clasificador se mantiene lo más lejos de la líneal posible. Hacia la esquina superior izquierda."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8cea6dee",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "# plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428657fc",
   "metadata": {},
   "source": [
    "### Ejercicio\n",
    "\n",
    "Aplica lo aprendido sobre regresión logística en un nuevo dataset (wine), y comprueba como de bien se ajusta el modelo. Deja un 30% del dataset para test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "949a48bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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