Bank_Project_v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projeto para demonstração de conhecimentos no algoritmo de KNN\n",
    "## Vou trabalhar com um dataset disponível na Kaggle: https://www.kaggle.com/henriqueyamahata/bank-marketing?select=bank-additional-names.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importando as principais biliotecas\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carregando a base de Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</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>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</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>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</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    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"bank-additional-full.csv\", delimiter=';')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convertendo a variavel de predição de categórica para numérica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>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",
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       "      <th>y</th>\n",
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       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
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       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</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>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</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    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "0          93.994          -36.4      4.857       5191.0  0  \n",
       "1          93.994          -36.4      4.857       5191.0  0  \n",
       "2          93.994          -36.4      4.857       5191.0  0  \n",
       "3          93.994          -36.4      4.857       5191.0  0  \n",
       "4          93.994          -36.4      4.857       5191.0  0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['y'] = dataset['y'].map({'yes': 1, 'no': 0})\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Information from the Dataset\n",
    "1.  age (numeric)\n",
    "2.  job : type of job (categorical: \"admin.\",\"blue-collar\",\"entrepreneur\",\"housemaid\",\"management\",\"retired\",\"self-employed\",\"services\",\"student\",\"technician\",\"unemployed\",\"unknown\")\n",
    "3.  marital : marital status (categorical: \"divorced\",\"married\",\"single\",\"unknown\"; note: \"divorced\" means divorced or widowed)\n",
    "4.  education (categorical: \"basic.4y\",\"basic.6y\",\"basic.9y\",\"high.school\",\"illiterate\",\"professional.course\",\"university.degree\",\"unknown\")\n",
    "5.  default: has credit in default? (categorical: \"no\",\"yes\",\"unknown\")\n",
    "6.  housing: has housing loan? (categorical: \"no\",\"yes\",\"unknown\")\n",
    "7.  loan: has personal loan? (categorical: \"no\",\"yes\",\"unknown\")\n",
    "# Related with the last contact of the current campaign:\n",
    "8.  contact: contact communication type (categorical: \"cellular\",\"telephone\") \n",
    "9.  month: last contact month of year (categorical: \"jan\", \"feb\", \"mar\", ..., \"nov\", \"dec\")\n",
    "10.  day_of_week: last contact day of the week (categorical: \"mon\",\"tue\",\"wed\",\"thu\",\"fri\")\n",
    "11.  duration: last contact duration, in seconds (numeric). Important note:  this attribute highly affects the output target (e.g., if duration=0 then y=\"no\"). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.\n",
    "# Other attributes:\n",
    "12.  campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "13.  pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "14.  previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "15.  poutcome: outcome of the previous marketing campaign (categorical: \"failure\",\"nonexistent\",\"success\")\n",
    "# Social and economic context attributes\n",
    "16.  emp.var.rate: employment variation rate - quarterly indicator (numeric)\n",
    "17.  cons.price.idx: consumer price index - monthly indicator (numeric)     \n",
    "18.  cons.conf.idx: consumer confidence index - monthly indicator (numeric)     \n",
    "19.  euribor3m: euribor 3 month rate - daily indicator (numeric)\n",
    "20.  nr.employed: number of employees - quarterly indicator (numeric)\n",
    "# Output variable (desired target):\n",
    "21. y - has the client subscribed a term deposit? (binary: \"yes\",\"no\")\n",
    "\n",
    "### Missing Attribute Values: There are several missing values in some categorical attributes, all coded with the \"unknown\" label. These missing values can be treated as a possible class label or using deletion or imputation techniques. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise inicial dos dados "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  float64\n",
      " 20  y               41188 non-null  int64  \n",
      "dtypes: float64(5), int64(6), object(10)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "  </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vamos fazer uma análise das idades dos clientes desse Banco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17a8bf53e50>"
      ]
     },
     "execution_count": 6,
     "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": [
    "sns.distplot(dataset['age'], hist = True, color = \"#5BFF33\", hist_kws = {'edgecolor':'black'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Para melhor avaliar essa base de dados irei criar duas sub bases contendo apenas pessoas \"Yes\" e pessoas \"No\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################################\n",
    "##### Criando um base só com Pessoas \"yes\"  ##############\n",
    "##########################################################\n",
    "\n",
    "dataset_yes=dataset.loc[dataset['y']==1]\n",
    "\n",
    "##########################################################\n",
    "##### Criando um base só com Pessoas \"No\"  ###############\n",
    "##########################################################\n",
    "\n",
    "dataset_no=dataset.loc[dataset['y']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "sns.distplot(dataset_yes['age'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},label=\"idade\");\n",
    "\n",
    "sns.distplot(dataset_no['age'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos observar nos histogramas acima que uma pessoa acima de 60 anos tem a maior probabilidade de aceitar esse produto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizando o Status Civil do Cliente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17a8edcd850>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "\n",
    "# First plot for marital status\n",
    "sns.countplot(x = \"marital\", data = dataset, ax = ax1, color = \"#5BFF33\",order = dataset['marital'].value_counts().index);\n",
    "sns.countplot(x = \"marital\", data = dataset_yes, ax = ax1,color = \"#07247D\",order = dataset['marital'].value_counts().index);\n",
    "ax1.set_title(\"Marital status distribution\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Marital Status\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['All Cases','Yes Cases'])\n",
    "\n",
    "# Second plot for marital status\n",
    "sns.countplot(x = \"marital\", data = dataset, ax = ax2, color = \"#5BFF33\",order = dataset['marital'].value_counts().index);\n",
    "sns.countplot(x = \"marital\", data = dataset_no, ax = ax2,color = \"#FF5733\",order = dataset['marital'].value_counts().index);\n",
    "ax2.set_title(\"Marital status distribution\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Marital Status\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['All Cases','No Cases'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avaliando os histogramas acima, podemos concluir que a base esta desbalanceada uma vez que temos muito mais casos Negativos do que dados Positivos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizando o nível de Instrução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "basic.4y                4176\n",
       "basic.6y                2292\n",
       "basic.9y                6045\n",
       "high.school             9515\n",
       "illiterate                18\n",
       "professional.course     5243\n",
       "university.degree      12168\n",
       "unknown                 1731\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(\"education\")[\"education\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['university.degree', 'high.school', 'basic.9y', 'professional.course',\n",
       "       'basic.4y', 'basic.6y', 'unknown', 'illiterate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[ \"education\"].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17a8eeb36a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "order=['university.degree','professional.course','high.school','basic.9y','basic.6y','basic.4y','illiterate','unknown']\n",
    "# First plot for Education distribution\n",
    "sns.countplot(x = \"education\", data = dataset, ax = ax1, color =\"#5BFF33\",order = order);\n",
    "sns.countplot(x = \"education\", data = dataset_yes, ax = ax1,color = \"#07247D\",order =order );\n",
    "ax1.set_title(\"Education distribution\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Education level\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.set_xticklabels(ax1.get_xticklabels(), rotation = 70);\n",
    "ax1.legend(['All Cases','Yes Cases'])\n",
    "# Second plot for Education distribution\n",
    "sns.countplot(x = \"education\", data = dataset, ax = ax2, color =\"#5BFF33\",order = order);\n",
    "sns.countplot(x = \"education\", data = dataset_no, ax = ax2,color = \"#FF5733\",order = order);\n",
    "ax2.set_title(\"Education distribution \", fontsize = 13);\n",
    "ax2.set_xlabel(\"Education level\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.set_xticklabels(ax2.get_xticklabels(), rotation = 70);\n",
    "ax2.legend(['All Cases','No Cases'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizando os empregos do clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "admin.           10422\n",
       "blue-collar       9254\n",
       "technician        6743\n",
       "services          3969\n",
       "management        2924\n",
       "retired           1720\n",
       "entrepreneur      1456\n",
       "self-employed     1421\n",
       "housemaid         1060\n",
       "unemployed        1014\n",
       "student            875\n",
       "unknown            330\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[ \"job\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['admin.', 'blue-collar', 'technician', 'services', 'management', 'retired', 'entrepreneur', 'self-employed', 'housemaid', 'unemployed', 'student', 'unknown']\n",
      "[ 428  188  473 1031    4  595 1670  251]\n"
     ]
    }
   ],
   "source": [
    "dataset[ \"job\"].value_counts().index\n",
    "labels =['admin.', 'blue-collar', 'technician', 'services', 'management','retired', 'entrepreneur', 'self-employed', 'housemaid', 'unemployed','student', 'unknown']\n",
    "print(labels)\n",
    "men_means = dataset_yes.groupby(\"education\")[\"education\"].count().values\n",
    "print(men_means)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K+HpEbBoRfSm+LJf1pdg+XoyIfhT3KZW11W7nAEdFxEFRPAynV0RsFxHvq1Pn31Is++dz/vdSXKbVUudnc53PiYgBABGxfkR8MK+XWiYCX46IXSOiRz57tFOdvGUzKB7Y8qOI2CjPa+OIOKod07Z4muIszJqltFbbJKdvG8XDG5ZQnMms9/cLPwMOjoj/iOIhD2vFsv/IvBTYMSKOzfMYRfHl8oIO1H9FXETxoKuPR0Tv3OZbRsTYOvnb6lsTgf+MiH/Py9E3InZv57STgROieGhSryj+/3MERdusSF06Wl6t/el1FFc+fDmvs62B/8fy6+Vplg9IVnZ/8Fug5YFh65b2px+slTkito6IsXnf+TrFvjZRBAHtKWsGxcO11oyIQcB/l8reNCIOj4j1UkpLKe6JBVjangXJx4YfAd+PiCG5zHUi4oC8v18zigez9ct5X8j1Lvehj0fEe/Mx8PMUl19el8edBZwexUNeIvepPSI/PIzWt8f2Hj+nUNznfTbFfeblgH0y8IUo/j94TeArFPfxXdfOZWtXO7VRv7a01n61/BA4JYoHe/XMy7FjRIzM41uOl3vl8R+iCKJbtNpnovXvNNIy9a5P9eWrO78o7gN8leLszz8oLtm7hPwAj4p8LfdcfITiPq4lFJfw/Y7iTE1L3okUX0pepPhCMoiK++tyvslU37N4CsWDZF4C/kBx5q9lfMsDVP5BcQnJEbT+gJs1ga9R3Oyf8vthedwh+fMSii8mJ5OPj3n82hT3RCyg+IX1GmBQK+24JsWBdRHFAf//5fLH5/HbUNyb9DJFAHwMxcFoTCtlfiKvj39QnBW4mHwPRh7/PuChvAw3Al+tWP6v5fosBn5DccC9raK9a94XlD9vSRGov5DXZc8abdyXIuB7gSJ4HVdeLmA3intcXgZmUQRuqaUt22q3nGdXisvUnqPY3qYDR7TSbrsCd+d2+VNuh7ml8X0ozkbMptjO5lEEkGvXKS8oLtl7OOd/Evh0nW1uMstv0xtS9Ie5edo5wLml8XMp3SdGRV+hePjHzXm5XyQ/qKa1NqHoQ4/lNn+O4qFCm7TSXntTPKToxbweLiiN2yuXvZjiKoMTS+OW215qLX+t7axi3HLtV0rfjqLPPZ3nfX9Lm1eWSTv6FsW9c/fm6V4g3zPVzmk/Q3HZ4OLcFq312YaWV2t/mtNGALfmZZlLcRlyr9I0H6PYrl8AftbO/cFc6tyzmMe3nJ1cQLFPegT4ep282+d2WJxfdwP/3t6yKO5tnk7Rh6fnNmu5Z7E/xUNTXqDoUw+3tEudulQtF0Xw9N952n9QXDZ5FcWlkGtS3Ff4XJ7/HOBzFeWdRrFvWZKXbeeK8sfl7a2lT90AbF9je2yZd/kevlrru6oPURynExX7QopL6r+e6/lC3k5aHuzW6rLVaLu67dTe/k71PrLV9qtVZk4/kOJe7Bcobo/5I8v3q0+y7Hj5S4qz2T8rja/bZ2jjO40vXy2vSKnqBIqkbiIizqZ4kMftza7LioriEfi9Ukrjm10X6e0oir/O2S6ldEaz66K3p4iYS/HD6cXNrsvb0apqv4i4E7g2pfTNNjNL7eRlqFI3lC/zWYvibND+beWX1D1Fcen6E7gfkLqdfGnyOvky0+MpnkZ9RbPrpe6lV7MrIKlTrENxqc1SisfvS1o9XQe8l+JyTEndyxEU9yD2pLgM/4MppRV+6qxUi5ehSpIkSZKqeBmqJEmSJKmKwaIkSZIkqcpqfc9iv3790qBBg5pdDUmSJElqirvvvvu5lNLGtcat1sHioEGDmDFjRrOrIUmSJElNERFP1BvnZaiSJEmSpCoGi5IkSZKkKgaLkiRJkqQqq/U9i5IkSZK6ptdff5358+fzyiuvNLsq3ULv3r0ZOHAga6yxRrunMViUJEmS1OXMnz+fddddl0GDBhERza7O21pKieeff5758+czePDgdk/nZaiSJEmSupxXXnmFjTbayECxASKCjTbaqMNnaQ0WJUmSJHVJBoqNsyJtabAoSZIkSXVcddVVRASPPPLIW2lz585l2LBhANx222184AMfqDnt9OnTGT16NFtvvTXbbLMNn/jEJ/jnP/+5SurdCN6zKEmSJKnLO3HhQQ0tb2L/a9uV79JLL2WPPfZgypQpnH766e0u/5lnnuHII49kypQp7LrrrqSUuPLKK3nppZfo06fPCtZ61fLMoiRJkiTVsGTJEu644w4uuOACpkyZ0qFpJ06cyLhx49h1112B4jLQI444gk033ZTp06ez22678Z73vIfddtuNRx99FICHHnqIUaNGMWLECIYPH87s2bMBuPjii99KP+GEE1i6dClLly5l/PjxDBs2jO23356zzjqrsQuPZxYlSZIkqabf/OY3jB07lne/+91suOGG3HPPPbz3ve9t17QzZ85k3LhxNcdts802/PGPf6RXr17cdNNNfPnLX+bKK6/k3HPP5eSTT+boo4/mtddeY+nSpcyaNYvLLruMO+64gzXWWINPf/rTXHLJJQwdOpQFCxYwc+ZMAF588cWGLXcLg8U6Gn2au6tp72l3SZIkaXV16aWXcsoppwBw1FFHcemll7Y7WGzN4sWLGTduHLNnzyYieP311wHYddddmTBhAvPnz+ewww5jyJAh3Hzzzdx9993stNNOAPzrX/9ik0024aCDDmLOnDl89rOf5cADD2T//fdf6XpVMliUJEmSpArPP/88t9xyCzNnziQiWLp0KRHBd7/73XZNP3ToUO6++24OOeSQqnGnnnoqe+21F1dddRVz585lzJgxAHz0ox9l55135rrrruOAAw7g/PPPJ6XEuHHj+Na3vlVVzv33388NN9zAxIkTmTp1KpMmTVqpZa7kPYuSJEmSVOGKK67g2GOP5YknnmDu3LnMmzePwYMH86c//ald03/mM5/hwgsvZNq0aW+lXXzxxTz99NMsXryYAQMGADB58uS3xs+ZM4ctt9ySk046iYMPPpgHHniAffbZhyuuuIJnn30WgL///e888cQTPPfcc7z55pscfvjhnHHGGdxzzz2NW/jMYFGSJEmSKlx66aV88IMfXC7t8MMP51e/+lW7pt90002ZMmUKn/vc59h6663Zdtttuf322+nbty9f+MIX+NKXvsTuu+/O0qVL35rmsssuY9iwYYwYMYJHHnmEY489lu22244zzzyT/fffn+HDh7PffvuxcOFCFixYwJgxYxgxYgTjx4+veeZxZUVKqeGFvl2MHDkyzZgxo+Y471mUJEmSmmfWrFlsu+22za5Gt1KrTSPi7pTSyFr5PbMoSZIkSapisChJkiRJqrJKgsWImBQRz0bEzFLahhFxY0TMzu8blMZ9KSIei4hHI+KAUvqOEfFgHnd2REROf0dEXJbTp0XEoFWxXJIkSZLUXa2qM4uTgbEVaV8Ebk4pDQFuzp+JiO2Ao4CheZpzIqJnnuanwPHAkPxqKfM44IWU0ruAs4DvdNqSSJIkSdJqYJUEiymlPwJ/r0g+BLgwD18IHFpKn5JSejWl9DjwGDAqIvoDfVNKd6biqTwXVUzTUtYVwD4tZx0lSZIkSR3XzHsWN00pLQTI75vk9AHAvFK++TltQB6uTF9umpTSG8BiYKNaM42I4yNiRkTMWLRoUYMWRZIkSZK6l674gJtaZwRTK+mtTVOdmNJ5KaWRKaWRG2+88QpWUZIkSVJ3llJijz324Prrr38rberUqYwdW3l3XfssWbKEE044ga222oqhQ4cyevRopk2b1qjqdopeTZz3MxHRP6W0MF9i+mxOnw9sXso3EHgqpw+skV6eZn5E9ALWo/qyV0mSJElvU5sOO7mh5T0z80etjo8Izj33XI488kj22msvli5dyle+8hV+97vfrdD8PvGJTzB48GBmz55Njx49mDNnDrNmzVqhslaVZp5ZvAYYl4fHAVeX0o/KTzgdTPEgm+n5UtWXImKXfD/isRXTtJR1BHBLvq9RkiRJklbIsGHDOOigg/jOd77D17/+dY455hgmTJjATjvtxHve8x6uvroIRx566CFGjRrFiBEjGD58OLNnz16unL/97W9MmzaNM888kx49ihBsyy235MADDwTg0EMPZccdd2To0KGcd955ACxdupTx48czbNgwtt9+e84666y3yho7diw77rgje+65J4888ggAl19+OcOGDWOHHXZg9OjRDVn+VXJmMSIuBcYA/SJiPnAa8G1gakQcBzwJHAmQUnooIqYCDwNvACemlJbmoj5F8WTVtYDr8wvgAuCXEfEYxRnFo1bBYkmSJEnq5k477TTe+973suaaa/KBD3yAvffem0mTJvHiiy8yatQo9t13X84991xOPvlkjj76aF577TWWLl26XBkPPfQQI0aMoGfPnjXnMWnSJDbccEP+9a9/sdNOO3H44Yczd+5cFixYwMyZxb8PvvjiiwAcf/zxnHvuuQwZMoRp06bx6U9/mltuuYVvfOMb3HDDDQwYMOCtvCtrlQSLKaWP1Bm1T538E4AJNdJnAMNqpL9CDjYlSZIkqVHWXnttPvzhD7POOuswdepUrr32Wr7//e8D8Morr/Dkk0+y6667MmHCBObPn89hhx3GkCFDOjSPs88+m6uuugqAefPmMXv2bLbeemvmzJnDZz/7WQ488ED2339/lixZwp///GeOPHJZ6PPqq68CsPvuuzN+/Hg+9KEPcdhhhzVk2Zt5z6IkSZIkdXk9evSgR48epJS48sor2XrrrZcbv+2227Lzzjtz3XXXccABB3D++eez9957vzV+6NCh3H///bz55ptvXYba4rbbbuOmm27izjvvpE+fPowZM4ZXXnmFDTbYgPvvv58bbriBiRMnMnXqVH74wx+y/vrrc99991XV8dxzz2XatGlcd911jBgxgvvuu4+NNqr5BxHtX+6VmlqSJEmSVhMHHHAAP/7xj2l5PMq9994LwJw5c9hyyy056aSTOPjgg3nggQeWm26rrbZi5MiRnHbaaW9NO3v2bK6++moWL17MBhtsQJ8+fXjkkUe46667AHjuued48803OfzwwznjjDO455576Nu3L4MHD+byyy8Hiie23n///UBxL+POO+/MN77xDfr168e8efNYWQaLkiRJktQOp556Kq+//jrDhw9n2LBhnHrqqQBcdtllDBs2jBEjRvDII49w7LHHVk17/vnn8/TTT/Oud72L7bffnk9+8pNsttlmjB07ljfeeIPhw4dz6qmnsssuuwCwYMECxowZw4gRIxg/fjzf+ta3ALjkkku44IIL2GGHHRg6dOhbD9n5/Oc/z/bbb8+wYcMYPXo0O+yww0ovb6zODw0dOXJkmjFjRs1xJy48aBXXZtWa2P/aZldBkiRJqmvWrFlsu+22za5Gt1KrTSPi7pTSyFr5PbMoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkqUtanZ+v0mgr0pYGi5IkSZK6nN69e/P8888bMDZASonnn3+e3r17d2i6Xp1UH0mSJElaYQMHDmT+/PksWrSo2VXpFnr37s3AgQM7NI3BoiRJkqQuZ4011mDw4MHNrsZqzctQJUmSJElVDBYlSZIkSVW8DFUdcuLCg5pdhU41sf+1za6CJEmS1CV4ZlGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUpenBYkT8V0Q8FBEzI+LSiOgdERtGxI0RMTu/b1DK/6WIeCwiHo2IA0rpO0bEg3nc2RERzVkiSZIkSXr7a2qwGBEDgJOAkSmlYUBP4Cjgi8DNKaUhwM35MxGxXR4/FBgLnBMRPXNxPwWOB4bk19hVuCiSJEmS1K00/cwi0AtYKyJ6AX2Ap4BDgAvz+AuBQ/PwIcCUlNKrKaXHgceAURHRH+ibUrozpZSAi/R4P/UAACAASURBVErTSJIkSZI6qKnBYkppAfB94ElgIbA4pfR7YNOU0sKcZyGwSZ5kADCvVMT8nDYgD1emS5IkSZJWQLMvQ92A4mzhYGAzYO2IOKa1SWqkpVbSa83z+IiYEREzFi1a1NEqS5IkSdJqodmXoe4LPJ5SWpRSeh34NbAb8Ey+tJT8/mzOPx/YvDT9QIrLVufn4cr0Kiml81JKI1NKIzfeeOOGLowkSZIkdRfNDhafBHaJiD756aX7ALOAa4BxOc844Oo8fA1wVES8IyIGUzzIZnq+VPWliNgll3NsaRpJkiRJUgf1aubMU0rTIuIK4B7gDeBe4DxgHWBqRBxHEVAemfM/FBFTgYdz/hNTSktzcZ8CJgNrAdfnlyRJkiRpBTQ1WARIKZ0GnFaR/CrFWcZa+ScAE2qkzwCGNbyCkiRJkrQaavZlqJIkSZKkLshgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVMVgUZIkSZJUxWBRkiRJklTFYFGSJEmSVKVDwWJE7BcRF0TEtfnzyIjYu3OqJkmSJElqll7tzRgRnwVOBs4HjsjJ/wLOBnZrfNWkt48TFx7U7Cp0mon9r212FSRJktQEHTmzeAqwb0rp28CbOe0RYOuG10qSJEmS1FQdCRbXBebl4ZTf1wBea2iNJEmSJElN15Fg8Y/AFyvSTgJubVx1JEmSJEldQbvvWQQ+C1wbEZ8E1o2IR4F/AN33Zi1JkiRJWk21O1hMKS2MiJ2AUcAWFJekTk8pvdn6lJIkSZKkt5uOnFkkpZSAafklSZIkSeqmWg0WI2Ieyx5mU1dKaYuG1UiSJEmS1HRtnVk8pjS8EzCO4n8VnwDeCXwGuKhzqiZJkiRJapZWg8WU0h9ahiNiInBASmlBKe164HfA/3ZaDSVJkiRJq1xH/jpjM2BJRdoSYEDjqiNJkiRJ6go6EixeA1wTEftFxLYRsT9wVU6XJEmSJHUjHQkW/xO4EzgXuCe/T8vpkiRJkqRupCP/s/gK8MX8kiRJkiR1Yx36n8WIWBPYGugHREt6SumWBtdLkiRJktRE7b4MNSL2oPjLjD8ANwJXADcA569MBSJi/Yi4IiIeiYhZEbFrRGwYETdGxOz8vkEp/5ci4rGIeDQiDiil7xgRD+ZxZ0dE1J6jJEmSJKktHbln8SzguymlDYGX8vsZwDkrWYcfAb9LKW0D7ADMorjU9eaU0hDg5vyZiNgOOAoYCowFzomInrmcnwLHA0Pya+xK1kuSJEmSVlsdCRbfTRHYlX0b+K8VnXlE9AVGAxcApJReSym9CBwCXJizXQgcmocPAaaklF5NKT0OPAaMioj+QN+U0p0ppQRcVJpGkiRJktRBHQkWFwN98/DCfJZvA2CdlZj/lsAi4BcRcW9EnB8RawObppQWAuT3TXL+AcC80vTzc9qAPFyZLkmSJElaAR0JFn8NvD8PXwDcCtwNXL4S8+8FvBf4aUrpPcDLtP601Vr3IaZW0qsLiDg+ImZExIxFixZ1tL6SJEmStFroyF9nnFIa/t+ImE5xVvGGlZj/fGB+Smla/nwFRbD4TET0TyktzJeYPlvKv3lp+oHAUzl9YI30WstxHnAewMiRI2sGlJIkSZK0uuvImcXlpJRuTyldn1J6cyXKeBqYFxFb56R9gIeBa4BxOW0ccHUevgY4KiLeERGDKR5kMz1fqvpSROySn4J6bGkaSZIkSVIHtXpmMSJup87lnGUppdErUYfPApfk/3CcA3yMIoidGhHHAU8CR+b5PBQRUykCyjeAE1NKS3M5nwImA2sB1+eXJEmSJGkFtHUZavk/FLcCPk7xdNIngC0ozvpNWpkKpJTuA0bWGLVPnfwTgAk10mcAw1amLpIkSZKkQqvBYkqp5e8riIi7gANSSg+V0n5FESye1mk1lCRJkiStch25Z3Fb4G8VaY8D2zSuOpIkSZKkrqAjweIfgMkRMSQi1oqId1P8hcbtnVM1SZIkSVKzdCRYHJ/fHwKWAA9S/L/hxxpcJ0mSJElSk3Xkfxb/TvG3FT2AjYFFK/O3GZIkSZKkrqutv84YlFKam4e3rBi9dvGXhpBSmtMptZMkSZIkNUVbZxYfBNbNw49R/OdiVORJQM8G10uSJEmS1ERt/XXGuqXhjtzfKEmSJEl6G2t3ABgRZ9dJ/2HjqiNJkiRJ6gpW5Gmolf6jAfWQJEmSJHUhbT4NNSI+3pK3NNxiS+C5htdKkiRJktRU7fnrjJYzh2uy/FnEBDwDjGt0pSRJkiRJzdVmsJhS2gsgIs5MKX2186skSZIkSWq29pxZBKAlUIyITYB1Ksb5P4uSJEmS1I20O1iMiAOASUD/ilH+z6IkSZIkdTMdeRrqOcAZwNoppR6ll4GiJEmSJHUz7T6zCGwA/CyllDqrMpIkSZKkrqEjZxYvAD7WWRWRJEmSJHUdHTmzuAtwUkR8EXi6PCKlNLqhtZIkSZIkNVVHgsXz80uSJEmS1M115K8zLuzMikiSJEmSuo6OnFkkIjYFRgH9gGhJTylNanC9JEmSJElN1JH/WTwUuBiYDQwFHgKGAX+i+P9FSZIkSVI30ZGnoZ4JfCyl9B7g5fx+PHB3p9RMkiRJktQ0HQkWt0gpXV6RdiFwbAPrI0mSJEnqAjoSLD6b71kEmBsRuwJbAT0bXy1JkiRJUjN1JFj8ObBHHj4LuBW4Hzin0ZWSJEmSJDVXR/464zul4Ysi4jZg7ZTSrM6omCRJkiSpeTr01xllKaUnG1kRSZIkSVLX0ZG/zpgHpFrjUkpbNKxGkiRJkqSm68iZxWMqPvcHTgamNK46kiRJkqSuoCP3LP6hMi3ft/g74EcNrJMkSZIkqck68jTUWl4FBjeiIpIkSZKkrqMj9yx+oyKpD/B+4PqG1kiSJEmS1HQduWdxc5Z/wM0/gR8Av2xojSRJkiRJTdeuy1AjoidwK7AmsBmwBnAfcElK6dXOq54kSZIkqRnaDBYjoi9wB/Bd4A3gXmApMAGYHhH/FhEDI+JDnVpTSZIkSdIq057LUL8FPA/sk1J6uSUxItYGpgK/ArYCvtwpNZQkSZIkrXLtCRY/COxSDhQBUkovR8SJwBzg4ymlSzqjgpIkSZKkVa899yyuByyoM24+8GpKaXLDaiRJkiRJarr2BIt/A/auM24f4LHGVUeSJEmS1BW0J1j8AXBRRBweET0AIqJHRBwBTM7jV0pE9IyIeyPit/nzhhFxY0TMzu8blPJ+KSIei4hHI+KAUvqOEfFgHnd2RMTK1kuSJEmSVldtBov5EtPvUwSGr0TEU8ArwC+AH6SUftGAepwMzCp9/iJwc0ppCHBz/kxEbAccBQwFxgLn5L/1APgpcDwwJL/GNqBekiRJkrRaatf/LKaU/pfi/xUPAj6f3weklL63shWIiIHAgcD5peRDgAvz8IXAoaX0KSmlV1NKj1NcAjsqIvoDfVNKd6aUEnBRaRpJkiRJUge152moAKSUXgJu6IQ6/BD4ArBuKW3TlNLCPN+FEbFJTh8A3FXKNz+nvZ6HK9OrRMTxFGcg2WKLLRpRf0mSJEnqdtodLHaGiPgA8GxK6e6IGNOeSWqkpVbSqxNTOg84D2DkyJE180haeScuPKjZVehUE/tf2+wqSJIkdaqmBovA7sDBEfF+oDfQNyIuBp6JiP75rGJ/4Nmcfz6weWn6gcBTOX1gjXRJkiRJ0gpo1z2LnSWl9KWU0sCU0iCKB9fcklI6BrgGGJezjQOuzsPXAEdFxDsiYjDFg2ym50tWX4qIXfJTUI8tTSNJkiRJ6qBmn1ms59vA1Ig4DngSOBIgpfRQREwFHgbeAE5MKS3N03yK4omtawHX55ckSZIkaQV0mWAxpXQbcFsefh7Yp06+CcCEGukzgGGdV0NJkiRJWn009TJUSZIkSVLXZLAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqtKr2RWQpNXJiQsPanYVOtXE/tc2uwqSJKlBPLMoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmq0tRgMSI2j4hbI2JWRDwUESfn9A0j4saImJ3fNyhN86WIeCwiHo2IA0rpO0bEg3nc2RERzVgmSZIkSeoOmn1m8Q3gf1JK2wK7ACdGxHbAF4GbU0pDgJvzZ/K4o4ChwFjgnIjomcv6KXA8MCS/xq7KBZEkSZKk7qSpwWJKaWFK6Z48/BIwCxgAHAJcmLNdCByahw8BpqSUXk0pPQ48BoyKiP5A35TSnSmlBFxUmkaSJEmS1EHNPrP4logYBLwHmAZsmlJaCEVACWySsw0A5pUmm5/TBuThynRJkiRJ0groEsFiRKwDXAmcklL6R2tZa6SlVtJrzev4iJgRETMWLVrU8cpKkiRJ0mqg6cFiRKxBESheklL6dU5+Jl9aSn5/NqfPBzYvTT4QeCqnD6yRXiWldF5KaWRKaeTGG2/cuAWRJEmSpG6k2U9DDeACYFZK6QelUdcA4/LwOODqUvpREfGOiBhM8SCb6flS1ZciYpdc5rGlaSRJkiRJHdSryfPfHfgP4MGIuC+nfRn4NjA1Io4DngSOBEgpPRQRU4GHKZ6kemJKaWme7lPAZGAt4Pr8kiRJkiStgKYGiymlP1H7fkOAfepMMwGYUCN9BjCscbWTJEmSpNVX0+9ZlCRJkiR1PQaLkiRJkqQqzb5nUZIkTlx4ULOr0Kkm9r+22VWQJKnDPLMoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmq4l9nSJLURXXnvxTx70QkqeszWJQkSW8b3TmABoNoSV2LwWIdV+y3ZbOr0Kkmzmx2DSRJkiR1Zd6zKEmSJEmqYrAoSZIkSapisChJkiRJqmKwKEmSJEmqYrAoSZIkSapisChJkiRJquJfZ0iSJL3N+f+TkjqDZxYlSZIkSVU8syhJkqRuyTOu0srxzKIkSZIkqYrBoiRJkiSpipehSpIkSauR7nx5rpfmNpZnFiVJkiRJVQwWJUmSJElVDBYlSZIkSVW8Z1FqgCv227LZVeg0E2c2uwaSJElqBs8sSpIkSZKqeGZRHdKdz6CBZ9EkSZKkFp5ZlCRJkiRVMViUJEmSJFUxWJQkSZIkVTFYlCRJkiRV8QE3kqSm8+FZkiR1PZ5ZlCRJkiRVMViUJEmSJFXxMlRJncLLCmuzXSRJ6npOXHhQs6vQqSb2v3aFpjNYlCRJklYj3fmHS3+0bCwvQ5UkSZIkVTFYlCRJkiRV6VaXoUbEWOBHQE/g/JTSt5tcJUmS1EDd+fI58BK6RnN7kVZOtwkWI6InMBHYD5gP/CUirkkpPdzcmkmStGK68xddv+Q2VnfeVsDtRWqW7nQZ6ijgsZTSnJTSa8AU4JAm10mSJEmS3pa6zZlFYAAwr/R5PrBzk+oiSZIk6W3Cs/O1RUqpsTVpkog4EjggpfSJ/Pk/gFEppc9W5DseOD5/3Bp4dJVWtL5+wHPNrkQXZLtUs01qs11qs11qs12q2Sa12S612S612S7VbJPaulK7vDOltHGtEd3pzOJ8YPPS54HAU5WZUkrnAeetqkq1V0TMSCmNbHY9uhrbpZptUpvtUpvtUpvtUs02qc12qc12qc12qWab1PZ2aZfudM/iX4AhETE4ItYEjgKuaXKdJEmSJOltqducWUwpvRERnwFuoPjrjEkppYeaXC1JkiRJelvqNsEiQErp/4D/a3Y9VlCXuzS2i7Bdqtkmtdkutdkutdku1WyT2myX2myX2myXarZJbW+Lduk2D7iRJEmSJDVOd7pnUZIkSZLUIAaLq0hEjI+In3Rwmv+LiPU7q04rIyIGRUTVP7ZExG0Rscqf7BQRYyLit3m4w23dwHqsHxGfXsFpJ0fEER2c5vyI2K6V8QdHxBdXpD5vRxHxjYjYt9n1WN3l/cNHu0A9luuPEbFZRFyxkmU2ZR9XS0Qc2lr/X52U958RsWdEPBQR90XEWqto/ks6ufx2HR/qHZu7stb6ZVfqbyuq/P2kE+cxNyL6deY8SvM6JSL6rMB0K9xH8ve6zVZ0+lWtmd9DO4PBYheWUnp/SunFZteju4uIRt67uz6wQsHiikgpfSKl9HAr469JKX17VdVnVWhtfaWUvpZSumlV1kc1DQJWWbAYhVrHs+X6Y0rpqZRS1RfuBu8DVqVDgZrB4souUzPapIHzPBr4fkppRErpXw0qU52kXr9Ul3UK0OFgcSWNB942wWJ3Y7DYIBHxm4i4O/+aeXxO+1hE/DUi/gDsXso7OSJ+GhG3RsSciHhfREyKiFkRMbmUb25E9Mu/FM6KiJ/n8n+/qn4tbUOviLgwIh6IiCsqf2kq/4oUEUe0LFtEbBwRV0bEX/Jrd2qIiJ0i4s8RcX9ETI+IdSOid0T8IiIejIh7I2Kv1ioYEQdFxLSc96aI2DSnnx4R50XE74GLVrYhSr4NbJV/0f5eRHw+L+MDEfH1Ur2OzWn3R8QvS9OPzss8p/Qr+Zj86+oVEfFIRFwSEZHHvfWra0SMjYh7cpk357S3ft1qoy0m5bLmRMRJDWyPuiJi7Yi4Ltd3ZkR8OCJ2jIg/5L50Q0T0Ly3nN3Nf+kruGz3yuD4RMS8i1ojlzy7U2n565vXSsk5OyHn7R8Qf83qbGRF7NmgZB+V1dn4u95KI2Dci7oiI2RExKr/+nNfLnyNi6zzt+Ij4dUT8Luf9bqncn0bEjCj2B+Xt6v15fn+KiLNj2dn2tfM6/kuezyGlefwmIq6NiMcj4jMR8d85z10RsWHOt1Wux90RcXtEbJPTJ+f5LLfNUvSDPXN7/lcj2rJO286KiHOAe4BTa/S1yv741lmXvOyXR8S1wO9baaO1ImJKLvcyoFP3vRFxTN5e74uIn+VtdklETMjb8l0RsWlE7AYcDHwv592qop+c3EZ/+mFebzMjYlROX26/GHX21dHKPqNW/XN6vePB5Ij4QUTcCnynoi3avY8oTfMJ4EPA1yLikva0b0v9IuI7udybouiXLct3cM4zPiKuzn3h0Yg4rUb5kbe1mVEcpz6c03/Zsk3lz5dEceVHvX1SRMRPIuLhiLgO2KRdG1ChZ1R8X4iIEXnbeSAiroqIDfJ8yseQfhExNw8PLbXTAxExpAHtNyiK/cc9+bVbKb2lXzasv0XFWdaI+Fzedm/LdZ0exXe0PfP4eutiTN7mpub8346Io/P0D0bEVjnf5Ig4Ny/jXyPiAzXqtGEU+9wH8voYHhE9otjHb5zz9IiIx/L6qNcHN8rr9t6I+BkQK9pObbRhZR88jSJouzWKPtta3x4cEXfmep9RUW7Vd6Oo8103iuPKSOCSWIVXC1TUt0PbUsW0B+Z26Bd1jplRqLXfOKfUf66KiEl5+LiIOLNemzW8AVJKvhrwAjbM72sBM4EBwJPAxsCawB3AT3KeycAUis59CPAPYHuK4P1uYETONxfoR/Er/Rul9KnAMU1e3kFAAnbPnycBnwNuA0bmtCWl/EcAk/Pwr4A98vAWwKwa5a8JzAF2yp/7Ujy993+AX+S0bXIb9wbGAL/N6eNLbb0BvPUgp08A/5uHT89tvVYntMvMPLw/xZOuIq/b3wKjgaHAo0C/im1nMnB5zrsd8FhOHwMsBgbmcXeW2u82ip3oxsA8YHBFme1tiz8D78jb2/PAGqtgGzoc+Hnp83q5Hhvnzx+m+AucluU8p5T3amCvUr7zS214RCvbz/HAV3PaO4AZwOC8XX0lp/cE1m3g9vAGy/fvSSzr+79pqVvOvy9wZWndzcnt0ht4Ati8Yv32zG0zPOcpbwOXsqxPfJO8z6A42/ZXYO08j8eAdfM2tBj4z5zvLOCUPHwzMCQP7wzc0o5t9redvP0MAt4EdqF+XxtE7o81+ud4YH6pLeu10X+zbDscntfnyE5apm2Ba8n9DzgHOJZiX3tQTvtuaRueDBxRmv42cj8B1qD1/vTzPDy61CanU9ovUmdfTZ19Rr365+F6x4PJeX31XMl9xFttUdkubbVvHk7Av+fhq4Df52XaAbivtM0sBDZi2bF+ueNdrvONFH1zU4pjVH/gfcBvSsvxOK3vkw4rlbMZ8GKtZWpln7Pc9wXgAeB9Oe0bwA9L20LLMvQD5ubhHwNH5+E18/KubPv1AXrn4SHAjBr9smH9jer+/zmKbfc2lh3/3g/clIfrrYsxuf375/QFwNdzvpNLbTkZ+B3FPmgIxf6l8vvJj4HT8vDepbY5jWX72/1Zdhyo1wfPBr6Whw/M7d+vE/ZJtfrg3PK8qN+3ryltHyeyrI+0tr+u+V2X0nbajNcKbEvjgZ8AHwRuBzYobSO1jpn19htHAd/LeaYDd+XhXwAHtNZmjXy9XS+96YpOiogP5uHNgf8AbkspLQLIv5C9u5T/2pRSiogHgWdSSg/mfA9RrPz7Ksp/PKXUknZ3ztNs81JKd+Thi4H2npHaF9gu4q0fwvpGxLoppZdKebYGFqaU/gKQUvoHQETsQbGzJaX0SEQ8wfLtWmkgcFkUvz6vSXGAbnFN6txLlPbPr3vz53UoDiA7AFeklJ4DSCn9vTTNb1JKbwIPRz7zl01PKc0HiIj7KNb/n0rjdwH+mFJ6vEaZLVpri+tSSq8Cr0bEsxQ7q/kdX+QOeRD4fkR8h+Jg8QIwDLgxbxs9Kb6YtbisYvjDwK0UO9NzKsqut/3sDwyPZWfA1qNYJ38BJkXEGhTroLL/rYzHK/r3zaW+PyjX4cIofrlPFF+wWtycUlqcp30YeCdFQPihKK5g6EVxQNmO4uAzp2UboAgWj8/D+wMHR8Tn8ufeFF88AG7Nfe+liFhM8WUQivUzPCLWAXYDLi/12XeU6lhvm10Vnkgp3RUR36d2X3uyjelvLPWVem00muKLGSmlByLigUYuQIV9gB2Bv+S2Xgt4FniNoo9Asf/fr5UyWvrJ1rTeny4FSCn9MSL6xrL748v7xZr76jxca59Rr/5tuTyltLRGekf3EW1prX6vUXzRb5nvqyml10v9tMWNKaXnASLi18AeFEFFiz2AS/PyPBPFWd6dUkrXRMTEiNiEIhC8MhX/D11vnzS6VM5TEXFLB5az8vvCVsD6KaX/396dx9xRlXEc//6wyCICFhcsIIuCIohYRIISqBsKSgQFwbC0oChE3IFohIggkigRZS0iUoMWxAARMVAUBAQrSti0SC1gtWItUtqGQoHSPv7xnEvnLnPvu9z3fUv5fZKm7zsz99yZM3POzDnnOfPeUpb9hHxg7WYmGcWxOXBVRMyRNNz8Wxs4V9LOwAo637tHq7xdVf6vPk/VnYtngT9HxHwASQ+RjWHIY61GOF1R6sM5kh4mO7Wr9iAbBkTETWWEcCOyE/GXwPeBo8jGANSXwT3J64iI+LWkRUPJhAFoKoMR8fvKvvTyLsqxApeyKnKg7tnoX6yez7q9dLqWIK+LtwN7N55Bik73zI71BtnQ/KJybvr9wCvKM9zu5DP3JoxCnrmx2AeSJpEFeveIeErSzcADZC9cnWfK/ysrPzd+73ReqtusYIRDoQYoBvH7upWf1yLzqqmhJmkG+cBxJ3mzaE0PBh9qcQ7wvXKjnkT2BDU8Oci0BkvAGRFxYdPCDNnqdGzQfJ5Vs3wF7deIuqTZ0C0veqXfdxHxd0m7kL1xZ5C9arMiYveaj1TP1zXAGcowyV2A1gepuvwQ8LmImNG2QtqT7KG9VNJ3I6Jf4cmt5bta9scBp5ENtgMkbUX2VHb67Aoy9Htrsldz14hYpAz5WZfuZUPAxyJidtNCabcB7N9awOKI2HkAxzcioVBdNK6JurK21QA/30ijUx5B77LVLwJ+EhFfa9mH46N0G9O7fFbzpFt5qquvq3lSV1dD5zqj4/53+L51W9Z1rIuHUEc0kbQFqzo/pvbYv+WVPH6+HETESjXPpex13+tWBi4l51MeQjYIGtu31UmS9u2Q9kC1nptuL8p7jlVTkp4/LxExXdIdZJ04QxneO9z8+xKwgOwwXQt4umaf+lXeqscGzdddI4+q5anuXEyidz3ZMJTrIyJinqQFkt5DRm8cWtZ1K4MjXi+1lkFliHrbZpWfW8t23X24rr5eHZ91YfDXEmRk0DZkp8idHbaHVddDx3ojIh5Rhox/ELgVGE+G2S+NiCckbcIo5JnnLPbHRsCi0lB8EznKsx4wqfQarQ0cNKZ7ODJeJ6lx0/4EzSNdkL0j2yvnlh1QWX4DcFzjl9LLSER8IPKFBJ8iG9sTJO1atnl5ueHcSqlEJW1H9vw3Pdy12IgMGQGYPIRjHKwnyJA+gBnAUWVkBkmblV7lG8mRoU3K8vF9+N6ZwF6lIVGX5mjnRVfKN5s9FRE/Bc4kb5CvalxTyjmIO3T6bEQsJUMyfkD2draOStRdPzOAY0uZRNJ2yjkZWwKPRsRFwMXAxH4fbxfV8zJlANtvSD5cLym9kvuU5Q8A21QaSAdXfBLipwAAB7JJREFUPjMD+Jz0/FzXtw1050qP6D8kHVQ+K0lv7fGxajkYDXVlbTD7UZdH1TpnRzI0bqTcCBxY9r0xv2nLLtt3O77ZdC9PjTkxewBLGiPYLTrW1UPc/7r7Qa3h1BEAETGv3FN2joipPfZvoN5fPrce+YKh21vW3wocrJz/9ipyBOhPZd008uUgRMSssqxjnVTSOaSk81qaR68GawmwSKvmUx0ONEYZ55IdbpAhhJT92IaMVDib7JzbieHn30ZkxMfKsg8v6bBNP8vbAuDV5TlsHaBtDmGLunMxGAcp5xy+nmwotD6fVI9vEvBYZdTpR2SU1hWVe1pdGaymsw85zaTvOpTBibTXO3Vl+3ayYwRWNX6hvr7uZrTvKa0Gey1BTh35KDn/u7aeKrrVGzPJeuNWcqTx+PL/qPHIYn9cDxyjDJeYDfyRDI05hTzJ88kXMHSqGIdF0jEA5UY42v4GTFZOrp4DXADsV1n/VTJ0aB45t2ODsvzzwHklvxoNwGOqCUfEs8oJvueUm/IycvT2fGCqMrTlOWBKRDyj+rCIU8jwuUfI87L1sI64h4hYqHx5yV+B68j5BjPL/i0lY8lnSToduEXSCjIUY8owv/d/yrDEq0qF/SjtoWqnMIp5MQBvIV/OsRJYDhxLntOzlWE548iQnFk1n/85GUo1qXVFl+vnR2SIxl2lUfA/8oFvEnCCpOXkeTqiP4c4IN8hw1C/TPsIaZuIuFfS3WS+PEx5WI2IZco/E3G9pMdYdaOBHL38PnBfOe65DOxm13AocIGkk8hQssuBe7tsfx/wnKR7yfkrZw3iuwYtIm6QtD3tZe2hlvJ4Xpdk6vLoAuCSUl/dQ3O+9vs47i95fEMpx8vJuT51LgcuUkYrNL1NspSBA6kvT4sk/YHsfDiKznrW1QPc/39Sfz/oZrh1xGD2b6BuI0cI3wBMj4g7W9ZfTYaI3UuOqpwYEf8t379A0t/IucoNdXXS1eSctr+Q82dvYXgmk/fO9cl648iy/EzgCkmH01z/HAwcVurE/wKnRsTjw8y/84ErlR1Pv6PziHLfylsJgz0VuIOcdvFAj4/UnYvBmE2eq9eQ87+fbnk+OYVVx/cUzR2315Dhp5dUltWVwW8Cl0m6q3xfr5D7oepUBncHrpM0PyLeTX3Z/gIwXdIXgCsbCdbV1+SoWJ1p5PW7jA4jrSNtCNdS43OzJR1KPnvt12XT2nqDbBjuHREPKqdejWeUG4uNl12YmdkLnKQNImJpedA5D5gz0g01e+FRTpU4vkNDx7qQNIV8ycZxvbat+fz6ZONvYs1Irr2AKacEXBsRQ/pbrsq30p4VEX15G7dZvzgM1cxszXG08gVIs8iQrwt7bG9mo0DS+8jRiHPcULRWkr5Kjr51mg9qNqY8smhmZmZmZmZtPLJoZmZmZmZmbdxYNDMzMzMzszZuLJqZmZmZmVkbNxbNzMzMzMysjRuLZmZmhaS5kpZJWlr5d+5Y75eZmdlYGDfWO2BmZraa2S8ifjtaXyZpXEQ8N1rfZ2ZmNlAeWTQzM+tB0hRJt0s6S9JiSQ9LemdZPk/So5ImV7afJmmqpN9IekLSLZK2rKwPSZ+VNAeYU5Z9WNI9Jf0/SNqpsv1ESXeXtH4h6eeSvlVZf7SkByU9LukaSRPKcpV9flTSEkn3SdpxVDLNzMxe8NxYNDMzG5jdgPuATYDpwOXArsAbgMOAcyVtUNn+UOA04JXAPcDPWtLbv6T5ZkkTgR8DnynpXwhcI2kdSS8FrgamAeOBy4ADGolIeg9wBvBx4LXAP8u+AewN7AlsB2wMHAwsHF42mJnZi4UiYqz3wczMbLUgaS7ZuKuGhZ4ALAe+HhHblu3eQjYcN42IBWXZQuC9EXGPpGnAuhFxSFm3AbAE2Coi5kmKsu1NZf0FwGMRcXJlX2YDnwaCbCBuHuWmLek24OaIOEnSxcDCiDix8l2LgG2BbYCpwBHAnyJiZV8zzMzM1mgeWTQzM2u2f0RsXPl3UVm+oLLNMoBGQ7GyrDqyOK/xQ0QsBR4HJnRaD2wJfKWEoC6WtBjYomw/AXgkmnt3q5+dQI4mVr9rIbBZaYyeC5wHLJD0Q0kb9s4CMzMzNxbNzMxGyhaNH8po33jgP5X1rY2/01saqetHxGXAfGAzSeqUdkmzOh/yZWQo6yMAEXF2ROwC7ECGo57Ql6MzM7M1nhuLZmZmI2NfSXuUOYenAXdExLyabS8CjpG0W3kpzcskfUjSy4GZwArgOEnjJH0EeEfls9OBIyXtLGkd4Nvlu+ZK2rWkuTbwJPB0ScvMzKwnNxbNzMya/arl7yxePcR0pgPfIMNPdyFfeNNRRNwJHE2GjC4CHgSmlHXPAh8FPgksJl+mcy3wTFl/I3AycCU5Cvl64JCS9IZkQ3QRGaq6EDhziMdjZmYvMn7BjZmZWZ+VF9z8OyJOGqH07wCmRsQlI5G+mZkZeGTRzMxstSdpL0mbljDUycBOwPVjvV9mZrZmGzfWO2BmZmY9vRG4gnzb6kPAgRExf2x3yczM1nQOQzUzMzMzM7M2DkM1MzMzMzOzNm4smpmZmZmZWRs3Fs3MzMzMzKyNG4tmZmZmZmbWxo1FMzMzMzMza+PGopmZmZmZmbX5P7sP8QC9UTHOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(15,5)\n",
    "sns.countplot(x = \"job\", data = dataset,color =\"#5BFF33\", order=labels);\n",
    "sns.countplot(x = \"job\", data = dataset_yes,color =\"#07247D\", order=labels);\n",
    "#sns.countplot(x = \"job\", data = dataset_no,color =\"#FF5733\");\n",
    "ax.set_xlabel('Empregos', fontsize = 12);\n",
    "ax.set_ylabel('Quantidade', fontsize = 12);\n",
    "ax.legend(['All Cases','Yes Cases'])\n",
    "ax.set_title(\"Distribuição da quantidade de clientes com relação ao total e seus respectivos empregos\", fontsize = 13);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(15,5)\n",
    "sns.countplot(x = \"job\", data = dataset,color =\"#5BFF33\", order=labels);\n",
    "#sns.countplot(x = \"job\", data = dataset_yes,color =\"#07247D\");\n",
    "sns.countplot(x = \"job\", data = dataset_no,color =\"#FF5733\", order=labels);\n",
    "ax.set_xlabel('Empregos', fontsize = 12);\n",
    "ax.set_ylabel('Quantidade', fontsize = 12);\n",
    "ax.legend(['All Cases','No Cases'])\n",
    "ax.set_title(\"Distribuição da quantidade de clientes com relação ao total e seus respectivos empregos\", fontsize = 13);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Novamente, devido a base estar muito desbalanceada, não podemos retirar nenhuma correlação explicita entre a profissao do usuário e saber se ele vai ser sucetível a campanha ou não "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisando se o Cliente já possuia algum Crédito Aprovado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default\n",
       "no         32588\n",
       "unknown     8597\n",
       "yes            3\n",
       "Name: default, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "\n",
    "# First plot for Credit default\n",
    "sns.countplot(x = \"default\", data = dataset, ax = ax1, color = \"#5BFF33\",order = dataset['default'].value_counts().index);\n",
    "sns.countplot(x = \"default\", data = dataset_yes, ax = ax1,color = \"#07247D\",order = dataset['default'].value_counts().index);\n",
    "ax1.set_title(\"Credit default distribution \", fontsize = 13);\n",
    "ax1.set_xlabel(\"Credit default Status\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['All Cases','Yes Cases']);\n",
    "\n",
    "# Second plot for Credit default\n",
    "sns.countplot(x = \"default\", data = dataset, ax = ax2, color = \"#5BFF33\",order = dataset['default'].value_counts().index);\n",
    "sns.countplot(x = \"default\", data = dataset_no, ax = ax2,color = \"#FF5733\",order = dataset['default'].value_counts().index);\n",
    "ax2.set_title(\"Credit default distribution\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Credit default Status\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['All Cases','No Cases']);\n",
    "\n",
    "dataset.groupby(\"default\")[\"default\"].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Valores para caso SIM\n",
      "default\n",
      "no         4197\n",
      "unknown     443\n",
      "Name: default, dtype: int64\n",
      "Valores para caso Não\n",
      "default\n",
      "no         28391\n",
      "unknown     8154\n",
      "yes            3\n",
      "Name: default, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Valores para caso SIM')\n",
    "print(dataset_yes.groupby(\"default\")[\"default\"].count())\n",
    "\n",
    "print('Valores para caso Não')\n",
    "print(dataset_no.groupby(\"default\")[\"default\"].count())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentual de Unknown Cases that gives No\n",
      "94.84703966499943\n",
      "Percentual de No Cases that gives No\n",
      "87.1210261445931\n"
     ]
    }
   ],
   "source": [
    "Unknown_cases=(8154/8597)*100\n",
    "No_cases=(28391/32588)*100\n",
    "print('Percentual de Unknown Cases that gives No')\n",
    "print(Unknown_cases)\n",
    "print('Percentual de No Cases that gives No')\n",
    "print(No_cases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisando esse critério, podemos ver novamente o efeito de desbalanceamento da base. Entretanto podemos ver que quase 95% dos casos que eram \"UNKNOWN\" deram não e 87% dos casos que eram \"NO\" deram não."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisando se o Cliente já possuia Crédito imobiliário Aprovado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "housing\n",
       "no         18622\n",
       "unknown      990\n",
       "yes        21576\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "\n",
    "# Second plot for Credit default\n",
    "sns.countplot(x = \"housing\", data = dataset, ax = ax1, color = \"#5BFF33\",order = dataset['housing'].value_counts().index);\n",
    "sns.countplot(x = \"housing\", data = dataset_yes, ax = ax1,color = \"#07247D\",order = dataset['housing'].value_counts().index);\n",
    "ax1.set_title(\"Housing Loan distribution \", fontsize = 13);\n",
    "ax1.set_xlabel(\"Housing Loan Status\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['All Cases','Yes Cases']);\n",
    "\n",
    "# Third plot for Credit default\n",
    "sns.countplot(x = \"housing\", data = dataset, ax = ax2, color = \"#5BFF33\",order = dataset['housing'].value_counts().index);\n",
    "sns.countplot(x = \"housing\", data = dataset_no, ax = ax2,color = \"#FF5733\",order = dataset['housing'].value_counts().index);\n",
    "ax2.set_title(\"Housing Loan distribution \", fontsize = 13);\n",
    "ax2.set_xlabel(\"Housing Loan Status\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['All Cases','No Cases']);\n",
    "\n",
    "dataset.groupby(\"housing\")[\"housing\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisando se o Cliente já possuia Crédito Pessoal Aprovado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "\n",
    "# Second plot for Credit default\n",
    "sns.countplot(x = \"loan\", data = dataset, ax = ax1, color = \"#5BFF33\",order = dataset['loan'].value_counts().index);\n",
    "sns.countplot(x = \"loan\", data = dataset_yes, ax = ax1,color = \"#07247D\",order = dataset['loan'].value_counts().index);\n",
    "ax1.set_title(\"Personal Loan distribution\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Personal Loan Status\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['All Cases','Yes Cases']);\n",
    "\n",
    "# Third plot for Credit default\n",
    "sns.countplot(x = \"loan\", data = dataset, ax = ax2, color = \"#5BFF33\",order = dataset['loan'].value_counts().index);\n",
    "sns.countplot(x = \"loan\", data = dataset_no, ax = ax2,color = \"#FF5733\",order = dataset['loan'].value_counts().index);\n",
    "ax2.set_title(\"Personal Loan distribution\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Personal Loan Status\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['All Cases','No Cases']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisando os Histogramas acima podemos perceber que dos clientes que foram positivos a maioria não tinha nenhum credito pessoal prévio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Meio de contato"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "contact\n",
       "cellular     26144\n",
       "telephone    15044\n",
       "Name: contact, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "\n",
    "# First plot for Credit default\n",
    "sns.countplot(x = \"contact\", data = dataset, ax = ax1, color = \"#5BFF33\",order = dataset['contact'].value_counts().index);\n",
    "sns.countplot(x = \"contact\", data = dataset_yes, ax = ax1,color = \"#07247D\",order = dataset['contact'].value_counts().index);\n",
    "ax1.set_title(\"contact communication distribution\", fontsize = 13);\n",
    "ax1.set_xlabel(\"contact communication Status\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['All Cases','Yes Cases']);\n",
    "\n",
    "# Second plot for Credit default\n",
    "sns.countplot(x = \"contact\", data = dataset, ax = ax2, color = \"#5BFF33\",order = dataset['contact'].value_counts().index);\n",
    "sns.countplot(x = \"contact\", data = dataset_no, ax = ax2,color = \"#FF5733\",order = dataset['contact'].value_counts().index);\n",
    "ax2.set_title(\"contact communication distribution\", fontsize = 13);\n",
    "ax2.set_xlabel(\"contact communication Status\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['All Cases','No Cases']);\n",
    "\n",
    "dataset.groupby(\"contact\")[\"contact\"].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisando esse parâmetro podemos ver que a maioria dos contatos foram feitos pelo celular"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Ultimo mês de contato no ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['month'].value_counts().index\n",
    "labels_months=[ 'mar','apr','may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov',  'dec']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "apr     2632\n",
      "aug     6178\n",
      "dec      182\n",
      "jul     7174\n",
      "jun     5318\n",
      "mar      546\n",
      "may    13769\n",
      "nov     4101\n",
      "oct      718\n",
      "sep      570\n",
      "Name: month, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "\n",
    "# First plot for Credit default\n",
    "sns.countplot(x = \"month\", data = dataset, ax = ax1, color = \"#5BFF33\", order=labels_months);\n",
    "sns.countplot(x = \"month\", data = dataset_yes, ax = ax1,color = \"#07247D\", order=labels_months);\n",
    "ax1.set_title(\"last contact month of year distribution \", fontsize = 13);\n",
    "ax1.set_xlabel(\"Month of Year\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['All Cases','Yes Cases']);\n",
    "\n",
    "# Second plot for Credit default\n",
    "sns.countplot(x = \"month\", data = dataset, ax = ax2, color = \"#5BFF33\", order=labels_months);\n",
    "sns.countplot(x = \"month\", data = dataset_no, ax = ax2,color = \"#FF5733\", order=labels_months);\n",
    "ax2.set_title(\"last contact month of year distribution \", fontsize = 13);\n",
    "ax2.set_xlabel(\"Month of Year\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['All Cases','No Cases']);\n",
    "\n",
    "\n",
    "all_cases=dataset.groupby(\"month\")[\"month\"].count()\n",
    "print(all_cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "yes_cases=dataset_yes.groupby(\"month\")[\"month\"].count()\n",
    "no_cases=dataset_no.groupby(\"month\")[\"month\"].count()\n",
    "#print('Casos Yes')\n",
    "#print(yes_cases)\n",
    "#print('Casos No')\n",
    "#print(no_cases)\n",
    "Result_yes=[]\n",
    "Result_no=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Months</th>\n",
       "      <th>No cases</th>\n",
       "      <th>Yes cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mar</td>\n",
       "      <td>0.494505</td>\n",
       "      <td>0.505495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>apr</td>\n",
       "      <td>0.795213</td>\n",
       "      <td>0.204787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>may</td>\n",
       "      <td>0.935653</td>\n",
       "      <td>0.064347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jun</td>\n",
       "      <td>0.894885</td>\n",
       "      <td>0.105115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>jul</td>\n",
       "      <td>0.909534</td>\n",
       "      <td>0.090466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Months  No cases  Yes cases\n",
       "0    mar  0.494505   0.505495\n",
       "1    apr  0.795213   0.204787\n",
       "2    may  0.935653   0.064347\n",
       "3    jun  0.894885   0.105115\n",
       "4    jul  0.909534   0.090466"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Result_yes=[yes_cases[5]/all_cases[5],yes_cases[0]/all_cases[0],yes_cases[6]/all_cases[6],yes_cases[4]/all_cases[4],yes_cases[3]/all_cases[3],yes_cases[1]/all_cases[1],yes_cases[9]/all_cases[9],yes_cases[8]/all_cases[8],yes_cases[7]/all_cases[7],yes_cases[2]/all_cases[2]]\n",
    "Result_no=[no_cases[5]/all_cases[5],no_cases[0]/all_cases[0],no_cases[6]/all_cases[6],no_cases[4]/all_cases[4],no_cases[3]/all_cases[3],no_cases[1]/all_cases[1],no_cases[9]/all_cases[9],no_cases[8]/all_cases[8],no_cases[7]/all_cases[7],no_cases[2]/all_cases[2]]\n",
    "#Na primeira passada pode ativar essa parte de baixo\n",
    "frame = { 'Months': labels_months, 'No cases': Result_no,'Yes cases': Result_yes  }\n",
    "Month_analisys = pd.DataFrame(frame)\n",
    "Month_analisys.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(Month_analisys['Months'], Month_analisys['No cases']*100,color = \"#FF5733\")\n",
    "ax.plot(Month_analisys['Months'], Month_analisys['Yes cases']*100,color = \"#07247D\")\n",
    "\n",
    "ax.set(xlabel='Months', ylabel='Percentual into the total',\n",
    "       title='Last Contact')\n",
    "ax.grid()\n",
    "ax.legend(['No Cases','Yes Cases']);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Avaliando os meses de ultimo contato com os cliente, é notavel que há uma mudança no perfil de respostas dos clientes nos meses de Agosto a Outubro."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dias da Semana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels_week=[ 'mon','tue','wed', 'thu', 'fri']\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "\n",
    "# First plot for Credit default\n",
    "sns.countplot(x = \"day_of_week\", data = dataset, ax = ax1, color = \"#5BFF33\",order = labels_week);\n",
    "sns.countplot(x = \"day_of_week\", data = dataset_yes, ax = ax1,color = \"#07247D\" ,order = labels_week);\n",
    "ax1.set_title(\"Days of the week distribution \", fontsize = 13);\n",
    "ax1.set_xlabel(\"Days of the week\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['All Cases','Yes Cases']);\n",
    "\n",
    "# Second plot for Credit default\n",
    "sns.countplot(x = \"day_of_week\", data = dataset, ax = ax2, color = \"#5BFF33\",order = labels_week);\n",
    "sns.countplot(x = \"day_of_week\", data = dataset_no, ax = ax2,color = \"#FF5733\",order = labels_week);\n",
    "ax2.set_title(\"Days of the week distribution\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Days of the week\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['All Cases','No Cases']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Avaliando a duração do ultimo contato"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "sns.distplot(dataset_yes['duration'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'});\n",
    "ax1.set_title(\"Last contact duration\", fontsize = 13);\n",
    "ax1.legend(['Yes Cases']);\n",
    "sns.distplot(dataset_no['duration'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'});\n",
    "ax2.set_title(\"Last contact duration\", fontsize = 13);\n",
    "ax2.legend(['No Cases']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Com esses histogramas acima podemos analisar que contatos que passam de 1000 segundos, ou 17 minutos, tem grandes possibilidades de aceitar a proposta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of contacts performed during this campaign and for this client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "#sns.distplot(dataset['campaign'], hist = True, ax = ax1, color = \"#5BFF33\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "sns.distplot(dataset_yes['campaign'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax1.set_title(\"Campaign distribution\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Number of contacts performed during this campaign\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['Yes Cases']);\n",
    "\n",
    "sns.distplot(dataset_no['duration'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax2.set_title(\"Campaign distribution\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Number of contacts performed during this campaign\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['No Cases']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df_diabetes.loc[df_diabetes['Result']==1]\n",
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "sns.distplot(dataset_yes['campaign'].loc[dataset_yes['campaign']<50], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax1.set_title(\"Campaign distribution (less than 100 times)\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Number of contacts performed during this campaign\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['Yes Cases']);\n",
    "\n",
    "sns.distplot(dataset_no['campaign'].loc[dataset_no['campaign']<50], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax2.set_title(\"Campaign distribution (less than 100 times)\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Number of contacts performed during this campaign\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['No Cases']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of days that passed by after the client was last contacted from a previous campaign\n",
    "\n",
    "*999 means client was not previously contacted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6E5/5zGdYsGABw4cP5yc/+Qlbbrllm31OPvlkjj32WLbbbrvXj/fZz34WgCuvvJJvfetbvPrqqwwbNoyLLrqITTfdlP/+7//myCOPBKoBx5tvvpkNNtiA733ve8ycOZNXXnmF/fffnxNPPJG//OUvfPSjH6WlpYUlS5Zw3HHH8bGPfayO7pEkqWlMNvWy448/nre97W2sueaafPCDH2SPPfbg3HPP5cUXX2SXXXbhve99L2eddRZHHnkkn/zkJ3n11VdZsmRJm2Pcd999jB8/nsGDB3d4jnPPPZeNN96Yv/71r/zd3/0dH/7wh5k3bx5PPPEE9957LwAvvvgiAFOmTOGss85izJgxzJo1i89+9rPceOONfOMb3+Daa69lxIgRr+8rSZLUF02dOpWddtqJL3/5y23KjzjiCA4++GAOOeQQzj33XD7/+c/zy1/+ss0+9957b5vb8Bq9613v4tZbbyUiOOecczj55JP5/ve/zymnnMIZZ5zBrrvuysKFC1l77bW57rrrmDNnDrfddhuZyT777MPNN9/MggUL2HzzzbnqqqsAeOmll3qmEyRJ6kEmm3rZeuutx8c+9jHWX399Zs6cyZVXXskpp5wCVL/Y8thjj/HOd76TadOm0dLSwoc+9KE2s5q64vTTT+c///M/AXj88ceZM2cO2267LY888gif+9zn+MAHPsBee+3FwoUL+Z//+R8OOOCA1+u+8sorAOy666586lOf4qMf/Sgf+tCHarp6SVJ/0ZXb0Vd0i7nULEOHDuXggw/m9NNPZ5111nm9/JZbbuGyyy4D4KCDDlomGbUiLS0tfOxjH2P+/Pm8+uqrjB49GqjiqC9+8Yt88pOf5EMf+hAjR47kuuuu47rrruOtb30rUK2/OWfOHHbbbTe+9KUv8ZWvfIUPfvCD7LbbbjVdtSSpv+nq7ehduc28biabVgODBg1i0KBBZCaXXnop2267bZvt22+/Pe94xzu46qqr2HvvvTnnnHPYY483bvkbO3Ysd911F0uXLn39NrpWN910E7/5zW+45ZZbWHfdddl9991ZtGgRG220EXfddRfXXnstZ5xxBjNnzuS0007jTW96E3feeecybTzrrLOYNWsWV111FePHj+fOO+9k2LBhPdMhkqQ+pyu3o3flFnOpWb7whS/wtre9jU9/+tOd7tPRr76NHTuW22+/nZ133nmZbZ/73Of44he/yD777MNNN930+sLjRx99NB/4wAe4+uqrmThxIr/5zW/ITI455hgOO+ywZY5z++23c/XVV3PMMcew11578fWvf33lL1SS1G919Xb0rtxmXjd/jW41svfee/OjH/2IzATgD3/4AwCPPPIIW221FZ///OfZZ599uPvuu9vU23rrrZkwYQLHH3/863XnzJnD5ZdfzksvvcRGG23Euuuuy4MPPsitt94KwLPPPsvSpUv58Ic/zDe/+U3uuOMOhg4dyujRo/n5z38OVL+Yd9dddwHVulDveMc7+MY3vsEmm2zC448/3pQ+kSRJ6gkbb7wxH/3oR5kxY8brZX//93/PJZdcAsBFF13Eu971rmXq/du//Rvf/va3+eMf/wjA0qVL+cEPfgBUt7yNGDECgPPPP//1Og8//DA77rgjX/nKV5gwYQIPPvgge++9N+eeey4LFy4E4IknnuCZZ57hySefZN111+XAAw/kS1/6EnfccUfPdIAkST3IZNNq5LjjjuO1115jp512Yty4cRx33HEA/OxnP2PcuHGMHz+eBx98kIMPPniZuueccw5PPfUU22yzDTvuuCP//M//zOabb86kSZNYvHgxO+20E8cddxwTJ04EqoBm9913Z/z48XzqU5/iO9/5DlAFVjNmzGDnnXdm7NixXH755UAVWO24446MGzeOd7/73R2O5kmSpOaLiLUj4raIuCsi7ouIE0v5CRHxRETcWR7/0FDnmIiYGxEPRcTeDeVvj4h7yrbTo0ztiYi1IuJnpXxWRIxq9nX2hKOOOopnn3329fenn346P/nJT9hpp5248MIL+eEPf7hMnZ122onTTjuNj3/842y//faMGzeO+fPnA3DCCSdwwAEHsNtuu7X5pbvTTjuNcePGsfPOO7POOuvw/ve/n7322otPfOITvPOd72THHXfkIx/5CC+//DL33HMPu+yyC+PHj2fatGl87Wtf6/mOkCSpZtE6E6Y/mzBhQs6ePbtN2QMPPMD222//+vuurDXRHQNtXYr2/SlJGli+dvzJXbqN7lsndm8NnK6KiNszc0KPHHw1VxJC62XmwohYA/gdcCQwCViYmae0238H4KfALsDmwG+Av83MJRFxW6l7K3A1cHpmXhMRnwV2ysx/iYjJwP6ZudyfSOtK/NXVtSa6qjfWpOgtxl6SpOnTTuzybXRTjj2+R9rQWQzmmk3FQEoMSZKk/iOrkcOF5e0a5bG80cR9gUsy8xXg0YiYC+wSEfOAoZl5C0BEXADsB1xT6pxQ6v8C+L8REbmKo5YDJTEkSdJA4210kiRJfVxEDI6IO4FngOszc1bZdERE3B0R50bERqVsBNC4+GJLKRtRXrcvb1MnMxcDLwH+UogkSeqQySZJkqQ+LjOXZOZ4YCTVLKVxwJnA1sB4YD7w/bL7sj+xVs2E6qx8eXXaiIgpETE7ImYvWLCgm1chSZL6i6Ykm5qxcOXKGAjrVTWD/ShJ0uohM18EbgImZebTJQm1FDibao0mqGYsbdFQbSTwZCkf2UF5mzoRMQTYEHi+g/NPz8wJmTlh+PDhnbVx5S5Or7MPJUmru2bNbHoF2CMzd6YaXZsUERPLtlMzc3x5XA2vL1w5GRhLtbjljyNicNn/TGAKMKY8Jq1Mg9Zee22ee+45/1ivoszkueeeY+211+7tpkiSNCBFxPCIeFN5vQ7wXuDBiNisYbf9gXvL6yuAyeUX5kZTxVO3ZeZ84OWImFgG8w4GLm+oc0h5/RHgxpVZr8n4a9UZe0mS+oKmLBDepIUru2XkyJG0tLTgFO9Vt/baazNy5MgV7yhJknrCZsD5ZWBuEDAzM38VERdGxHiqmGsecBhAZt4XETOB+4HFwNTMXFKOdThwHrAOVXzVGmPNAC4sMdnzVIOC3Wb8VQ9jL0nS6q5pv0ZXAqDbgW2AMzJzVkS8n2rhyoOB2cBRmfkC1SKUtzZUb12g8jU6X7iyW9ZYYw1Gjx69MlUlSZJWG5l5N/DWDsoPWk6dacC0DspnA+M6KF8EHLBqLTX+kiRpoGjaAuFNWLiyDReolCRJkiRJar6m/xpdDy5c2f48K1ygUpIkSZIkSfVq1q/RNWPhSkmSJEmSJPWyZq3Z1IyFKyVJkiRJktTLmvVrdD2+cKUkSZIkSZJ6X9PXbJIkSZIkSVL/ZbJJkiRJkiRJtTHZJEmSJEmSpNqYbJIkSZIkSVJtTDZJkiRJkiSpNiabJEmSJEmSVBuTTZIkSZIkSaqNySZJkiRJkiTVxmSTJEmSJEmSamOySZIkSZIkSbUx2SRJkiRJkqTamGySJEmSJElSbUw2SZIkSZIkqTYmmyRJkiRJklQbk02SJEmSJEmqjckmSZIkSZIk1cZkkyRJkiRJkmpjskmSJEmSJEm1MdkkSZIkSZKk2phskiRJkiRJUm1MNkmSJEmSJKk2JpskSZIkSZJUG5NNkiRJfVhErB0Rt0XEXRFxX0ScWMo3jojrI2JOed6ooc4xETE3Ih6KiL0byt8eEfeUbadHRJTytSLiZ6V8VkSMavZ1SpKkvsNkkyRJUt/2CrBHZu4MjAcmRcRE4GjghswcA9xQ3hMROwCTgbHAJODHETG4HOtMYAowpjwmlfJDgRcycxvgVOCkZlyYJEnqm0w2SZIk9WFZWVjerlEeCewLnF/Kzwf2K6/3BS7JzFcy81FgLrBLRGwGDM3MWzIzgQva1Wk91i+APVtnPUmSJLVnskmSJKmPi4jBEXEn8AxwfWbOAjbNzPkA5fnNZfcRwOMN1VtK2Yjyun15mzqZuRh4CRjWM1cjSZL6OpNNkiRJfVxmLsnM8cBIqllK45aze0czknI55cur0/bAEVMiYnZEzF6wYMGKmi1JkvqppiSbmrFwpSRJ0kCXmS8CN1GttfR0uTWO8vxM2a0F2KKh2kjgyVI+soPyNnUiYgiwIfB8B+efnpkTMnPC8OHDa7oqSZLU1zRrZlMzFq6UJEkacCJieES8qbxeB3gv8CBwBXBI2e0Q4PLy+gpgcvmFudFU8dRt5Va7lyNiYhnMO7hdndZjfQS4sazrJEmStIwhzThJCUY6W7hy91J+PtVI3FdoWLgSeDQiWheunEdZuBIgIloXrrymGdchSZK0GtoMOL8MzA0CZmbmryLiFmBmRBwKPAYcAJCZ90XETOB+YDEwNTOXlGMdDpwHrEMVX7XGWDOAC0tM9jzVoKAkSVKHmpJsgmrhSuB2YBvgjMycFRFtFq6MiMaFK29tqN66QOVrdL5wZfvzTaGaAcWWW25Z56VIkiStNjLzbuCtHZQ/B+zZSZ1pwLQOymcDy6z3lJmLKMkqSZKkFWnaAuFNWLiy/flcM0CSJEmSJKnJmv5rdD24cKUkSZIkSZJ6WbN+ja4ZC1dKkiRJkiSplzVrzaZmLFwpSZIkSZKkXtasX6Pr8YUrJUmSJEmS1PuavmaTJEmSJEmS+i+TTZIkSZIkSaqNySZJkiRJkiTVxmSTJEmSJEmSamOySZIkSZIkSbUx2SRJkiRJkqTamGySJEmSJElSbUw2SZIkSZIkqTYmmyRJkiRJklQbk02SJEmSJEmqjckmSZIkSZIk1cZkkyRJkiRJkmpjskmSJEmSJEm1MdkkSZIkSZKk2phskiRJkiRJUm1MNkmSJEmSJKk2JpskSZIkSZJUG5NNkiRJkiRJqo3JJkmSJEmSJNXGZJMkSZIkSZJqY7JJkiRJkiRJtTHZJEmSJEmSpNqYbJIkSerDImKLiPiviHggIu6LiCNL+QkR8URE3Fke/9BQ55iImBsRD0XE3g3lb4+Ie8q20yMiSvlaEfGzUj4rIkY1+zolSVLfYbJJkiSpb1sMHJWZ2wMTgakRsUPZdmpmji+PqwHKtsnAWGAS8OOIGFz2PxOYAowpj0ml/FDghczcBjgVOKkJ1yVJkvook02SJEl9WGbOz8w7yuuXgQeAEcupsi9wSWa+kpmPAnOBXSJiM2BoZt6SmQlcAOzXUOf88voXwJ6ts54kSZLaM9kkSZLUT5Tb294KzCpFR0TE3RFxbkRsVMpGAI83VGspZSPK6/blbepk5mLgJWBYD1yCJEnqB5qSbGrGWgKSJEkDWUSsD1wKfCEz/0x1S9zWwHhgPvD91l07qJ7LKV9enfZtmBIRsyNi9oIFC7p5BZIkqb9o1symZqwlIEmSNCBFxBpUiaaLMvMygMx8OjOXZOZS4Gxgl7J7C7BFQ/WRwJOlfGQH5W3qRMQQYEPg+fbtyMzpmTkhMycMHz68rsuTJEl9TFOSTU1aS0CSJGnAKbO8ZwAPZOYPGso3a9htf+De8voKYHL5hbnRVIN3t2XmfODliJhYjnkwcHlDnUPK648AN5ZYTJIkaRlDmn3CdmsJ7Eq1lsDBwGyq2U8vUCWibm2o1rpmwGt0vpaAJEnSQLQrcBBwT0TcWcq+Cnw8IsZT3e42DzgMIDPvi4iZwP1Us8+nZuaSUu9w4DxgHeCa8oAqmXVhRMylmtE0uYevSZIk9WFNTTa1X0sgIs4EvkkVBH2Tai2Bz7Byawm0P9cUqtvt2HLLLVe98ZIkSauhzPwdHcdIVy+nzjRgWgfls4FxHZQvAg5YhWZKkqQBpGm/RteEtQTacM0ASZIkSZKk5mvWr9E1Yy0BSZIkSZIk9bJm3UbXjLUEJEmSJEmS1MuakmxqxloCkiRJkiRJ6n1NW7NJkiRJkiRJ/Z/JJkmSJEmSJNXGZJMkSZIkSZJqY7JJkiRJkiRJtTHZJEmSJEmSpNqYbJIkSZIkSVJtTDZJkiRJkiSpNiabJEmSJEmSVBuTTZIkSZIkSaqNySZJkiRJkiTVxmSTJEmSJEmSamOySZIkSZIkSbUx2SRJkiRJkqTamGySJEmSJElSbUw2SZIkSZIkqTYmmyRJkiRJklQbk02SJEmSJEmqjckmSZIkSROTU/0AABn/SURBVJIk1cZkkyRJkiRJkmrT5WRTRBzQSflH6muOJEnSwGOcJUmS+pPuzGya0Un59DoaIkmSNIAZZ0mSpH5jyIp2iIitystBETEaiIbNWwGLeqJhkiRJ/Z1xliRJ6o9WmGwC5gJJFfw83G7bU8AJNbdJkiRpoDDOkiRJ/c4Kb6PLzEGZORj4bXnd+Ng8M53eLUmStBLqiLMiYouI+K+IeCAi7ouII0v5xhFxfUTMKc8bNdQ5JiLmRsRDEbF3Q/nbI+Kesu30iIhSvlZE/KyUz4qIUbV3hiRJ6je6vGZTZv6fnmyIJEnSQLWKcdZi4KjM3B6YCEyNiB2Ao4EbMnMMcEN5T9k2GRgLTAJ+HBGDy7HOBKYAY8pjUik/FHghM7cBTgVOWoX2SpKkfq47v0Y3OiIujoj7I+KxxkdPNlCSJKm/W5U4KzPnZ+Yd5fXLwAPACGBf4Pyy2/nAfuX1vsAlmflKZj5KdSvfLhGxGTA0M2/JzAQuaFen9Vi/APZsnfUkSZLUXlfWbGp1MdVaAkcB/9udk0TEFlQBy1uApcD0zPxhRGwM/AwYBcwDPpqZL5Q6x1CNoi0BPp+Z15bytwPnAesAVwNHloBIkiSpr1rpOKtRub3trcAsYNPMnA9VQioi3lx2GwHc2lCtpZS9Vl63L2+t83g51uKIeAkYBjy7sm2VJEn9V3eSTWOBXTNz6Uqcp3V69x0RsQFwe0RcD3yKanr3dyPiaKrp3V9pN717c+A3EfG3mbmEN6Z330qVbJoEXLMSbZIkSVpdrEqcBUBErA9cCnwhM/+8nIlHHW3I5ZQvr077NkyhitPYcsstV9RkSZLUT3X5NjrgZqqRsm5r0vRuSZKkvmql4yyAiFiDKtF0UWZeVoqfLrET5fmZUt4CbNFQfSTwZCkf2UF5mzoRMQTYEHi+fTsyc3pmTsjMCcOHD1/Zy5EkSX1cd2Y2zQOujYjLqH6K93WZ+fWuHqQHp3dLkiT1VfNYyTirrJ00A3ggM3/QsOkK4BDgu+X58obyiyPiB1QzyMcAt2Xmkoh4OSImUsVpBwM/anesW4CPADe6jIEkSepMd5JN6wFXAmvQdjSsy3p4enf7czmNW5Ik9RWrEmftChwE3BMRd5ayr1IlmWZGxKHAY8ABAJl5X0TMBO6nWupgalmqAOBw3lgb8xreWKpgBnBhRMylmtE0ubsXKEmSBo4uJ5sy89OrcqLlTe8us5pWdXp3+/ZOB6YDTJgwwZE3SZK02lqVOCszf0fHA3IAe3ZSZxowrYPy2cC4DsoXUZJVkiRJK9LlZFNEbNXZtsx8ZAV1mzG9W5IkqU9alThLkiRpddOd2+jmsuytbK0zhgavoG4zpndLkiT1VasSZ0mSJK1WunMbXZtfrouItwDHA7/tQt0en94tSZLUV61KnCVJkrS6GbTiXTqWmU8BXwC+U19zJEmSZJwlSZL6spVONhXbAuvW0RBJkiS1YZwlSZL6pO4sEP5b3lg7AKrgZyzwjbobJUmSNJAYZ0mSpP6kOwuEn9Pu/V+AuzJzTo3tkSRJGoiMsyRJUr/RnQXCz+/JhkiSJA1UxlmSJKk/6fKaTRGxRkScGBGPRMSi8nxiRKzZkw2UJEnq74yzJElSf9Kd2+hOBnYB/gX4E/A3wHHAUOBf62+aJEnSgGGcJUmS+o3uJJsOAHbOzOfK+4ci4g7gLgyCJEmSVoVxliRJ6je6fBsdEN0slyRJUtcYZ0mSpH6jO8mmnwNXRsTeEbF9REwCflnKJUmStPKMsyRJUr/Rndvovgx8DTgD2Bx4Avgp8K0eaJckSdJAYpwlSZL6jRXObIqIXSPipMx8NTO/npnbZOa6mTkGWAt4W883U5Ikqf8xzpIkSf1RV26j+ypwcyfb/gs4tr7mSJIkDSjGWZIkqd/pSrJpPPDrTrb9Bnh7fc2RJEkaUIyzJElSv9OVZNNQYM1Otq0BbFBfcyRJkgYU4yxJktTvdCXZ9CCwVyfb9irbJUmS1H3GWZIkqd/pyq/RnQr8e0QMBn6ZmUsjYhCwH9UvpnyxJxsoSZLUjxlnSZKkfmeFyabMvDgi3gKcD6wVEc8CmwCLgOMz86c93EZJkqR+yThLkiT1R12Z2URm/iAizgHeCQwDngNuycw/92TjJEmS+jvjLEmS1N90KdkEUAKea3uwLZIkSQOScZYkSepPurJAuCRJkiRJktQlJpskSZIkSZJUG5NNkiRJkiRJqo3JJkmSJEmSJNXGZJMkSVIfFxHnRsQzEXFvQ9kJEfFERNxZHv/QsO2YiJgbEQ9FxN4N5W+PiHvKttMjIkr5WhHxs1I+KyJGNfP6JElS32KySZIkqe87D5jUQfmpmTm+PK4GiIgdgMnA2FLnxxExuOx/JjAFGFMercc8FHghM7cBTgVO6qkLkSRJfV9Tkk09PdomSZI0kGXmzcDzXdx9X+CSzHwlMx8F5gK7RMRmwNDMvCUzE7gA2K+hzvnl9S+APY3DJElSZ5o1s+k8ena0TZIkScs6IiLuLgN/G5WyEcDjDfu0lLIR5XX78jZ1MnMx8BIwrCcbLkmS+q6mJJuaMNomSZKkts4EtgbGA/OB75fyjmYk5XLKl1enjYiYEhGzI2L2ggULut9iSZLUL/T2mk11jbZJkiSpQWY+nZlLMnMpcDawS9nUAmzRsOtI4MlSPrKD8jZ1ImIIsCEdDCRm5vTMnJCZE4YPH17n5UiSpD6kN5NNdY62LcORNUmSNJCVWeGt9gda1868AphcfmFuNNXSBLdl5nzg5YiYWNZjOhi4vKHOIeX1R4Aby0xzSZKkZQzprRNn5tOtryPibOBX5e3KjLZ1dPzpwHSACRMmGAxJkqR+KyJ+CuwObBIRLcDxwO4RMZ5qcG4ecBhAZt4XETOB+4HFwNTMXFIOdTjVWpvrANeUB8AM4MKImEs1o2lyz1+VJEnqq3ot2RQRm5URNFh2tO3iiPgBsDlvjLYtiYiXI2IiMItqtO1HzW63JEnS6iYzP95B8Yzl7D8NmNZB+WxgXAfli4ADVqWNkiRp4GhKsqkJo22SJEmSJElaDTQl2dTTo22SJEmSJElaPfT2r9FJkiRJkiSpHzHZJEmSJEmSpNqYbJIkSZIkSVJtTDZJkiRJkiSpNiabJEmSJEmSVBuTTZIkSZIkSaqNySZJkiRJkiTVxmSTJEmSJEmSamOySZIkSZIkSbUx2SRJkiRJkqTamGySJEmSJElSbUw2SZIkSZIkqTYmmyRJkiRJklQbk02SJEmSJEmqjckmSZIkSZIk1cZkkyRJkiRJkmpjskmSJEmSJEm1MdkkSZIkSZKk2phskiRJkiRJUm1MNkmSJEmSJKk2JpskSZIkSZJUG5NNkiRJkiRJqo3JJkmSJEmSJNXGZJMkSZIkSZJqY7JJkiRJkiRJtTHZJEmS1MdFxLkR8UxE3NtQtnFEXB8Rc8rzRg3bjomIuRHxUETs3VD+9oi4p2w7PSKilK8VET8r5bMiYlQzr0+SJPUtJpskSZL6vvOASe3KjgZuyMwxwA3lPRGxAzAZGFvq/DgiBpc6ZwJTgDHl0XrMQ4EXMnMb4FTgpB67EkmS1Oc1JdnU06NtkiRJA1lm3gw83654X+D88vp8YL+G8ksy85XMfBSYC+wSEZsBQzPzlsxM4IJ2dVqP9QtgT+MwSZLUmWbNbDqPnh1tkyRJUlubZuZ8gPL85lI+Ani8Yb+WUjaivG5f3qZOZi4GXgKG9VjLJUlSn9aUZFMTRtskSZLUNR3NSMrllC+vTtsDR0yJiNkRMXvBggWr0ERJktSX9eaaTXWOti3DYEeSJA1wT5fBOsrzM6W8BdiiYb+RwJOlfGQH5W3qRMQQYEOWHUgkM6dn5oTMnDB8+PAaL0WSJPUlq+MC4Ssz2rbsBoMdSZI0sF0BHFJeHwJc3lA+ufzC3GiqpQluK4N/L0fExLIe08Ht6rQe6yPAjWWmuSRJ0jKG9OK5n46IzTJzfg2jbZIkSQNWRPwU2B3YJCJagOOB7wIzI+JQ4DHgAIDMvC8iZgL3A4uBqZm5pBzqcKq1NtcBrikPgBnAhRExl2pG0+QmXJYkSeqjejPZ1DpC9l2WHW27OCJ+AGzOG6NtSyLi5YiYCMyiGm37UfOb3dYZZ85g/lPPLXefzd4yjKmHH9qkFkmSpIEmMz/eyaY9O9l/GjCtg/LZwLgOyhdRklWSJEkr0pRkUxNG23rN/KeeY9T2eyx3n3kP3Nik1kiSJEmSJPWupiSbenq0TZIkSZIkSauH1XGBcEmSJEmSJPVRJpskSZIkSZJUG5NNkiRJkiRJqo3JJkmSJEmSJNXGZJMkSZIkSZJqY7JJkiRJkiRJtTHZJEmSJEmSpNqYbJIkSZIkSVJtTDZJkiRJkiSpNiabJEmSJEmSVBuTTZIkSZIkSaqNySZJkiRJkiTVxmSTJEmSJEmSamOySZIkSZIkSbUx2SRJkiRJkqTamGySJEmSJElSbUw2SZIkSZIkqTYmmyRJkiRJklQbk02SJEmSJEmqjckmSZIkSZIk1cZkkyRJkiRJkmpjskmSJEmSJEm1MdkkSZIkSZKk2phskiRJkiRJUm1MNkmSJEmSJKk2JpskSZL6sYiYFxH3RMSdETG7lG0cEddHxJzyvFHD/sdExNyIeCgi9m4of3s5ztyIOD0iojeuR5Ikrf56PdlUVwAkSZKkTr0nM8dn5oTy/mjghswcA9xQ3hMROwCTgbHAJODHETG41DkTmAKMKY9JTWy/JEnqQ3o92VTUEQBJkiSpa/YFzi+vzwf2ayi/JDNfycxHgbnALhGxGTA0M2/JzAQuaKgjSZLUxuqSbGqvWwFQL7RPkiSpr0jguoi4PSKmlLJNM3M+QHl+cykfATzeULellI0or9uXS5IkLWNIbzeANwKgBP49M6fTLgCKiMYA6NaGugY6kiRJy7drZj5Z4qnrI+LB5ezb0TpMuZzytpWrZNYUgC233HJl2ipJkvqB1WFm066Z+Tbg/cDUiHj3cvbtUqADVbATEbMjYvaCBQvqaKckSVKfk5lPludngP+kmhX+dLk1jvL8TNm9BdiiofpI4MlSPrKD8vbnmp6ZEzJzwvDhw+u+FEmS1Ef0erKppgCoo+Ma7EiSpAEtItaLiA1aXwN7AfcCVwCHlN0OAS4vr68AJkfEWhExmmoh8NvKjPOXI2Ji+RW6gxvqSJIktdGryaa6AqDmtlqSJKnP2BT4XUTcRRUzXZWZvwa+C7wvIuYA7yvvycz7gJnA/cCvgamZuaQc63DgHKo1Mx8GrmnmhUiSpL6jt9ds2hT4z2qAjCHAxZn564j4PTAzIg4FHgMOgCoAiojWAGgxbQMgSZIkNcjMR4CdOyh/DtizkzrTgGkdlM8GxtXdRkmS1P/0arKpzgBIkiRJkiRJva/X12ySJEmSJElS/2GySZIkSZIkSbUx2SRJkiRJkqTamGySJEmSJElSbUw2SZIkSZIkqTYmmyRJkiRJklQbk02SJEmSJEmqjckmSZIkSZIk1cZkkyRJkiRJkmpjskmSJEmSJEm1MdkkSZIkSZKk2phskiRJkiRJUm1MNkmSJEmSJKk2JpskSZIkSZJUG5NNkiRJkiRJqo3JJkmSJEmSJNXGZJMkSZIkSZJqY7JJkiRJkiRJtTHZJEmSJEmSpNqYbJIkSZIkSVJtTDZJkiRJkiSpNiabJEmSJEmSVBuTTZIkSZIkSaqNySZJkiRJkiTVxmSTJEmSJEmSamOySZIkSZIkSbUZ0tsNWBkRMQn4ITAYOCczv9vLTZIkSer3VpcY7OLpZ7FwwdMr3G/94ZvyiSn/0oQWSZKkRn0u2RQRg4EzgPcBLcDvI+KKzLy/d1smSZLUf61OMdjCBU8z5R07rnC/6bPuaUJrJElSe33xNrpdgLmZ+UhmvgpcAuzby22SJEnq74zBJElSl/S5mU3ACODxhvctwDt6qS1dMvv2O/ja8Scvd5/N3jKMqYcf2qQWSZIkdVufi8EkSVLviMzs7TZ0S0QcAOydmf9U3h8E7JKZn2u33xRgSnm7LfBQDzVpE+DZHjq2lmV/N5f93Xz2eXPZ383Vk/39N5k5vIeOLboWgxl/9Wv2eXPZ381lfzeffd5cTY/B+uLMphZgi4b3I4En2++UmdOB6T3dmIiYnZkTevo8qtjfzWV/N5993lz2d3PZ333eCmMw46/+yz5vLvu7uezv5rPPm6s3+rsvrtn0e2BMRIyOiDWBycAVvdwmSZKk/s4YTJIkdUmfm9mUmYsj4gjgWqqf3T03M+/r5WZJkiT1a8ZgkiSpq/pcsgkgM68Gru7tdhQ9PlVcbdjfzWV/N5993lz2d3PZ333cahSD+V1qPvu8uezv5rK/m88+b66m93efWyBckiRJkiRJq6++uGaTJEmSJEmSVlMmm1ZSREyKiIciYm5EHN3b7ekPImKLiPiviHggIu6LiCNL+cYRcX1EzCnPGzXUOaZ8Bg9FxN691/q+KyIGR8QfIuJX5b393YMi4k0R8YuIeLB8199pn/eciPjX8t+TeyPipxGxtv1dr4g4NyKeiYh7G8q63ccR8faIuKdsOz0iotnXor7BGKx+xmC9wxisuYzBmssYrGf1hfjLZNNKiIjBwBnA+4EdgI9HxA6926p+YTFwVGZuD0wEppZ+PRq4ITPHADeU95Rtk4GxwCTgx+WzUfccCTzQ8N7+7lk/BH6dmdsBO1P1vX3eAyJiBPB5YEJmjqNa0Hgy9nfdzqPqr0Yr08dnAlOAMeXR/piSMVjPMQbrHcZgzWUM1iTGYE1xHqt5/GWyaeXsAszNzEcy81XgEmDfXm5Tn5eZ8zPzjvL6Zao/ACOo+vb8stv5wH7l9b7AJZn5SmY+Csyl+mzURRExEvgAcE5Dsf3dQyJiKPBuYAZAZr6amS9in/ekIcA6ETEEWBd4Evu7Vpl5M/B8u+Ju9XFEbAYMzcxbslpM8oKGOlIjY7AeYAzWfMZgzWUM1iuMwXpQX4i/TDatnBHA4w3vW0qZahIRo4C3ArOATTNzPlTBEPDmspufw6o7DfgysLShzP7uOVsBC4CflGnz50TEetjnPSIznwBOAR4D5gMvZeZ12N/N0N0+HlFety+X2vPfaQ8zBmsaY7DmMgZrImOwXrNaxV8mm1ZOR/cx+rN+NYmI9YFLgS9k5p+Xt2sHZX4OXRQRHwSeyczbu1qlgzL7u3uGAG8DzszMtwJ/oUxv7YR9vgrKfer7AqOBzYH1IuLA5VXpoMz+rldnfWzfq6v8rvQgY7DmMAbrFcZgTWQMttrplfjLZNPKaQG2aHg/kmpaoFZRRKxBFeRclJmXleKnyxQ/yvMzpdzPYdXsCuwTEfOobkPYIyL+A/u7J7UALZk5q7z/BVXgY5/3jPcCj2bmgsx8DbgM+Hvs72bobh+3lNfty6X2/HfaQ4zBmsoYrPmMwZrLGKx3rFbxl8mmlfN7YExEjI6INakW27qil9vU55WV72cAD2TmDxo2XQEcUl4fAlzeUD45ItaKiNFUC5rd1qz29nWZeUxmjszMUVTf4Rsz80Ds7x6TmU8Bj0fEtqVoT+B+7POe8hgwMSLWLf992ZNqHRL7u+d1q4/LVO+XI2Ji+awObqgjNTIG6wHGYM1lDNZ8xmBNZwzWO1ar+GtIXQcaSDJzcUQcAVxLtbL+uZl5Xy83qz/YFTgIuCci7ixlXwW+C8yMiEOp/sN1AEBm3hcRM6n+UCwGpmbmkuY3u9+xv3vW54CLyv8kPQJ8mirxb5/XLDNnRcQvgDuo+u8PwHRgfezv2kTET4HdgU0iogU4npX778jhVL+ssg5wTXlIbRiD9RhjsNWD/d2zjMGaxBis5/WF+CuqRcclSZIkSZKkVedtdJIkSZIkSaqNySb9/3buJmTTKQ4D+HUtbJQh2UlWI4UshI2PhWKBshPGyEYoS1koycrCR4kSWfjI186IJRaibNTMwpaImAxjwiDzt3ieqclqvJ73feeZ+f12z33OuTtnc/fvOuc5AAAAACsjbAIAAABgZYRNAAAAAKyMsAkAAACAlRE2AQAAALAywibghNX2y7a/tz3U9ue2n7S9t61vFwDAJlGDAf+XjwVwort5Zs5Icn6Sx5M8lOSl7Z0SAMBJTw0GbJiwCVgLM3NwZvYkuTXJXW0vbntj28/b/tL267aPHu3f9r22Dxz7jrZ7297Shafb/tD24PL5xVu8JACAE54aDNgIYROwVmbmsyTfJLk6ya9Jdic5K8mNSe5re8uy68tJdh0d1/bSJOcmeT/J9UmuSXLBcuytSX7coiUAAKwdNRjwXwibgHX0bZKzZ+ajmdk3M0dmZm+SN5Jcu+zzTpKdbXcuf9+Z5K2Z+TPJX0nOSHJhks7MFzPz3RavAQBg3ajBgOMibALW0blJDrS9su2Hbfe3PZjk3iTnJMnM/JHk7SS7lpdZ3pbk1WXbB0meTfJcku/bvtB2x3YsBABgjajBgOMibALWStvLsyh0Pk7yepI9Sc6bmTOTPJ+kx3R/OckdSa5L8tvMfHq0YWaemZnLklyUxVHuB7dmBQAA60cNBvwXwiZgLbTd0famJG8meW1m9mVxDPvAzBxue0WS248dsyxsjiR5MssdteW7Ll/uyJ2WxZ0Dh5P8vUVLAQBYG2owYCOETcCJ7t22h5J8neThJE8luXvZdn+Sx5btj2RxZPvfXklySZLXjnm2I8mLSX5K8lUWF1M+sSmzBwBYT2owYMM6M9s9B4BN03Z3kntm5qrtngsAwKlCDQanNiebgJNW29Oz2Hl7YbvnAgBwqlCDAcIm4KTU9oYk+5N8n8UllgAAbDI1GJD4Gx0AAAAAK+RkEwAAAAArI2wCAAAAYGWETQAAAACsjLAJAAAAgJURNgEAAACwMsImAAAAAFbmH1A7kDojCDEMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "sns.distplot(dataset_yes['pdays'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax1.set_title(\"Days after the last contact distribution\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Days\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['Yes Cases']);\n",
    "\n",
    "sns.distplot(dataset_no['pdays'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax2.set_title(\"Days after the last contact distribution \", fontsize = 13);\n",
    "ax2.set_xlabel(\"Days\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['No Cases']);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24613     5\n",
       "25519     5\n",
       "25853     6\n",
       "27764    10\n",
       "27768     7\n",
       "Name: pdays, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#avaliando o comando para criar um filtro, uma vez que a o resultado 999, é para referir que o cliente não foi contactado previamente\n",
    "dataset_yes['pdays'].loc[dataset_yes['pdays']<100].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Como era uma informação do Dataset que o numero 999 eram clientes que não foram contactados, fiz esse filtro para avaliar melhor os contactados\n",
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "sns.distplot(dataset_yes['pdays'].loc[dataset_yes['pdays']<200], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax1.set_title(\"Days after the last contact distribution (less than 100days)\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Days\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['Yes Cases']);\n",
    "\n",
    "sns.distplot(dataset_no['pdays'].loc[dataset_no['pdays']<200], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax2.set_title(\"Days after the last contact distribution (less than 100days) \", fontsize = 13);\n",
    "ax2.set_xlabel(\"Days\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['No Cases']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Podemos observar que mesmo a base estando desbalancada, se o contato com o cliente for eficaz, a modo de não distanciar muito entre um contato e outro, temos grandes chances do cliente aceitar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of contacts performed before this campaign and for this client "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "sns.distplot(dataset_yes['previous'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax1.set_title(\"Number of previous contacts distribution\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Contacts Quantity\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['Yes Cases']);\n",
    "sns.distplot(dataset_no['previous'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax2.set_title(\"Number of previous contacts distribution\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Contacts Quantity\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['No Cases']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisando o histograma acima podemos concluir que clientes que tiveram mais de um contato previo pode aderir mais a esse novo serviço"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resultado de Campanhas de Marketing Anteriores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "poutcome\n",
      "failure         4252\n",
      "nonexistent    35563\n",
      "success         1373\n",
      "Name: poutcome, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "\n",
    "\n",
    "sns.countplot(x = \"poutcome\",ax=ax1, data = dataset,color =\"#5BFF33\");\n",
    "#sns.countplot(x = \"job\", data = dataset_yes,color =\"#07247D\");\n",
    "sns.countplot(x = \"poutcome\",ax=ax1, data = dataset_yes,color =\"#07247D\");\n",
    "ax1.set_xlabel('Previous campaign', fontsize = 12);\n",
    "ax1.set_ylabel('Count', fontsize = 12);\n",
    "ax1.legend(['All Cases','Yes Cases'])\n",
    "ax1.set_title(\"Outcome of the previous marketing campaign\", fontsize = 13);\n",
    "\n",
    "\n",
    "sns.countplot(x = \"poutcome\",ax=ax2, data = dataset,color =\"#5BFF33\");\n",
    "#sns.countplot(x = \"job\", data = dataset_yes,color =\"#07247D\");\n",
    "sns.countplot(x = \"poutcome\",ax=ax2, data = dataset_no,color =\"#FF5733\");\n",
    "ax2.set_xlabel('Previous campaign', fontsize = 12);\n",
    "ax2.set_ylabel('Count', fontsize = 12);\n",
    "ax2.legend(['All Cases','No Cases'])\n",
    "ax2.set_title(\"Outcome of the previous marketing campaign\", fontsize = 13);\n",
    "\n",
    "\n",
    "all_cases_previous=dataset.groupby(\"poutcome\")[\"poutcome\"].count()\n",
    "print(all_cases_previous)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yes cases_previous\n",
      "poutcome\n",
      "failure         605\n",
      "nonexistent    3141\n",
      "success         894\n",
      "Name: poutcome, dtype: int64\n",
      "No cases_previous\n",
      "poutcome\n",
      "failure         3647\n",
      "nonexistent    32422\n",
      "success          479\n",
      "Name: poutcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "yes_cases_previous=dataset_yes.groupby(\"poutcome\")[\"poutcome\"].count()\n",
    "print('Yes cases_previous')\n",
    "print(yes_cases_previous)\n",
    "no_cases_previous=dataset_no.groupby(\"poutcome\")[\"poutcome\"].count()\n",
    "print('No cases_previous')\n",
    "print(no_cases_previous)\n",
    "labels_previous=['nonexistent','failure','sucess']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Previous</th>\n",
       "      <th>No cases</th>\n",
       "      <th>Yes cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nonexistent</td>\n",
       "      <td>0.857714</td>\n",
       "      <td>0.142286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>failure</td>\n",
       "      <td>0.911678</td>\n",
       "      <td>0.088322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sucess</td>\n",
       "      <td>0.348871</td>\n",
       "      <td>0.651129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Previous  No cases  Yes cases\n",
       "0  nonexistent  0.857714   0.142286\n",
       "1      failure  0.911678   0.088322\n",
       "2       sucess  0.348871   0.651129"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Result_yes=[yes_cases_previous[0]/all_cases_previous[0],yes_cases_previous[1]/all_cases_previous[1],yes_cases_previous[2]/all_cases_previous[2]]\n",
    "Result_no=[no_cases_previous[0]/all_cases_previous[0],no_cases_previous[1]/all_cases_previous[1],no_cases_previous[2]/all_cases_previous[2]]\n",
    "frame = { 'Previous': labels_previous, 'No cases': Result_no,'Yes cases': Result_yes  }\n",
    "Previous_analisys = pd.DataFrame(frame)\n",
    "Previous_analisys.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(Previous_analisys['Previous'], Previous_analisys['No cases']*100,color = \"#FF5733\")\n",
    "ax.plot(Previous_analisys['Previous'], Previous_analisys['Yes cases']*100,color = \"#07247D\")\n",
    "\n",
    "ax.set(xlabel='Previous Campaign', ylabel='Percentual into the total',\n",
    "       title='Last Contact ')\n",
    "ax.grid()\n",
    "ax.legend(['No Cases','Yes Cases']);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Se o cliente já tiver casos de contatos bem sucedidos, ele tem grandes chances de aceitar esse novo contato"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisado a Taxa de variação de emprego do cliente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "\n",
    "sns.countplot(x = 'emp.var.rate',ax=ax1, data = dataset,color =\"#5BFF33\");\n",
    "sns.countplot(x = 'emp.var.rate',ax=ax1, data = dataset_yes,color =\"#07247D\");\n",
    "#sns.distplot(dataset['emp.var.rate'], hist = True, ax = ax1, color = \"#5BFF33\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "#sns.distplot(dataset_yes['emp.var.rate'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax1.set_title(\"Employment variation rate\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Campaign Quantity\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['All Cases','Yes Cases']);\n",
    "\n",
    "sns.countplot(x = 'emp.var.rate',ax=ax2, data = dataset,color =\"#5BFF33\");\n",
    "sns.countplot(x = 'emp.var.rate',ax=ax2, data = dataset_no,color =\"#FF5733\");\n",
    "#sns.distplot(dataset_no['emp.var.rate'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax2.set_title(\"Employment variation rate\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Campaign Quantity\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['All Cases','No Cases']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All cases\n",
      "emp.var.rate\n",
      "-3.4     1071\n",
      "-3.0      172\n",
      "-2.9     1663\n",
      "-1.8     9184\n",
      "-1.7      773\n",
      "-1.1      635\n",
      "-0.2       10\n",
      "-0.1     3683\n",
      " 1.1     7763\n",
      " 1.4    16234\n",
      "Name: emp.var.rate, dtype: int64\n",
      "Yes cases\n",
      "emp.var.rate\n",
      "-3.4     454\n",
      "-3.0      88\n",
      "-2.9     594\n",
      "-1.8    1461\n",
      "-1.7     403\n",
      "-1.1     301\n",
      "-0.2       1\n",
      "-0.1     232\n",
      " 1.1     240\n",
      " 1.4     866\n",
      "Name: emp.var.rate, dtype: int64\n",
      "No cases\n",
      "emp.var.rate\n",
      "-3.4      617\n",
      "-3.0       84\n",
      "-2.9     1069\n",
      "-1.8     7723\n",
      "-1.7      370\n",
      "-1.1      334\n",
      "-0.2        9\n",
      "-0.1     3451\n",
      " 1.1     7523\n",
      " 1.4    15368\n",
      "Name: emp.var.rate, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "empvar_total=dataset.groupby('emp.var.rate')['emp.var.rate'].count()\n",
    "empvar_yes=dataset_yes.groupby('emp.var.rate')['emp.var.rate'].count()\n",
    "empvar_no=dataset_no.groupby('emp.var.rate')['emp.var.rate'].count()\n",
    "print('All cases')\n",
    "print(empvar_total)\n",
    "print('Yes cases')\n",
    "print(empvar_yes)\n",
    "print('No cases')\n",
    "print(empvar_no)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Result_empvar_yes=[454/1074,88/172,594/1663,1461/9184,403/773,301/635,1/10,232/3683,240/7763,866/16234]\n",
    "Result_empvar_no=[617/1074,84/172,1069/1663,7723/9184,370/773,334/635,9/10,3451/3683,7523/7763,15368/16234]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Emp_Var_Rate</th>\n",
       "      <th>No cases</th>\n",
       "      <th>Yes cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-3.4</td>\n",
       "      <td>0.574488</td>\n",
       "      <td>0.422719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.488372</td>\n",
       "      <td>0.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.9</td>\n",
       "      <td>0.642814</td>\n",
       "      <td>0.357186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.8</td>\n",
       "      <td>0.840919</td>\n",
       "      <td>0.159081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.7</td>\n",
       "      <td>0.478655</td>\n",
       "      <td>0.521345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.1</td>\n",
       "      <td>0.525984</td>\n",
       "      <td>0.474016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.937008</td>\n",
       "      <td>0.062992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.1</td>\n",
       "      <td>0.969084</td>\n",
       "      <td>0.030916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.946655</td>\n",
       "      <td>0.053345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Emp_Var_Rate  No cases  Yes cases\n",
       "0          -3.4  0.574488   0.422719\n",
       "1          -3.0  0.488372   0.511628\n",
       "2          -2.9  0.642814   0.357186\n",
       "3          -1.8  0.840919   0.159081\n",
       "4          -1.7  0.478655   0.521345\n",
       "5          -1.1  0.525984   0.474016\n",
       "6          -0.2  0.900000   0.100000\n",
       "7          -0.1  0.937008   0.062992\n",
       "8           1.1  0.969084   0.030916\n",
       "9           1.4  0.946655   0.053345"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = { 'Emp_Var_Rate': empvar_total.index, 'No cases': Result_empvar_no,'Yes cases': Result_empvar_yes  }\n",
    "Previous_analisys = pd.DataFrame(frame)\n",
    "Previous_analisys.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(Previous_analisys['Emp_Var_Rate'], Previous_analisys['No cases']*100,color = \"#FF5733\")\n",
    "ax.plot(Previous_analisys['Emp_Var_Rate'], Previous_analisys['Yes cases']*100,color = \"#07247D\")\n",
    "\n",
    "ax.set(xlabel='Emp_Var_Rate', ylabel='Percentual into the total',\n",
    "       title='Employment variation rate ')\n",
    "ax.grid()\n",
    "ax.legend(['No Cases','Yes Cases']);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analisando  as taxas de emprego dos clientes, podemos ver que quanto temos uma valor de -1.7 temos uma inversão na quantidade de não e sim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisando o \"Consumer price index\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows = 2, ncols =1, figsize = (25, 15));\n",
    "sns.distplot(dataset['cons.price.idx'], hist = True, ax = ax1, color = \"#5BFF33\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "sns.distplot(dataset_yes['cons.price.idx'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "#sns.countplot(x = 'cons.price.idx',ax=ax1, data = dataset,color =\"#5BFF33\");\n",
    "#sns.countplot(x = 'cons.price.idx',ax=ax1, data = dataset_yes,color =\"#07247D\");\n",
    "ax1.set_title(\"Consumer price index \", fontsize = 13);\n",
    "ax1.set_xlabel(\"Consumer price index \", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "\n",
    "sns.distplot(dataset['cons.price.idx'], hist = True, ax = ax2, color = \"#5BFF33\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "sns.distplot(dataset_no['cons.price.idx'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax2.set_title(\"Consumer price index\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Consumer price index\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, (ax1, ax2) = plt.subplots(nrows = 2, ncols =1, figsize = (25, 15));\n",
    "#sns.distplot(dataset_yes['cons.conf.idx'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "sns.countplot(x = 'cons.conf.idx',ax=ax1, data = dataset,color =\"#5BFF33\");\n",
    "sns.countplot(x = 'cons.conf.idx',ax=ax1, data = dataset_yes,color =\"#07247D\");\n",
    "ax1.set_title(\"Consumer confidence index\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Consumer confidence index\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "\n",
    "sns.countplot(x = 'cons.conf.idx',ax=ax2, data = dataset,color =\"#5BFF33\");\n",
    "sns.countplot(x = 'cons.conf.idx',ax=ax2, data = dataset_no,color =\"#FF5733\");\n",
    "#sns.distplot(dataset_no['cons.conf.idx'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax2.set_title(\"Consumer confidence index\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Consumer confidence index\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analisando euribor 3 month rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>No cases</th>\n",
       "      <th>Yes cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188.000000</td>\n",
       "      <td>36548.000000</td>\n",
       "      <td>4640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.621291</td>\n",
       "      <td>3.811491</td>\n",
       "      <td>2.123135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.734447</td>\n",
       "      <td>1.638187</td>\n",
       "      <td>1.742598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.634000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>0.634000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.344000</td>\n",
       "      <td>1.405000</td>\n",
       "      <td>0.849000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.857000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>1.266000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.961000</td>\n",
       "      <td>4.962000</td>\n",
       "      <td>4.406000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.045000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5.045000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          euribor3m      No cases    Yes cases\n",
       "count  41188.000000  36548.000000  4640.000000\n",
       "mean       3.621291      3.811491     2.123135\n",
       "std        1.734447      1.638187     1.742598\n",
       "min        0.634000      0.634000     0.634000\n",
       "25%        1.344000      1.405000     0.849000\n",
       "50%        4.857000      4.857000     1.266000\n",
       "75%        4.961000      4.962000     4.406000\n",
       "max        5.045000      5.045000     5.045000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = { 'euribor3m': dataset['euribor3m'], 'No cases': dataset_no['euribor3m'],'Yes cases': dataset_yes['euribor3m']  }\n",
    "Previous_analisys = pd.DataFrame(frame)\n",
    "Previous_analisys.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "ax1.plot(Previous_analisys['euribor3m'], Previous_analisys['Yes cases'],color = \"#FF5733\")\n",
    "#ax.plot(Previous_analisys['Emp_Var_Rate'], Previous_analisys['Yes cases'],color = \"#07247D\")\n",
    "ax1.set(xlabel='euribor3m_YES', ylabel='euribor3m_total',\n",
    "       title='euribor3m ')\n",
    "ax1.grid()\n",
    "ax1.legend(['Yes cases']);\n",
    "\n",
    "\n",
    "ax2.plot(Previous_analisys['euribor3m'], Previous_analisys['No cases'],color = \"#07247D\")\n",
    "ax2.set(xlabel='euribor3m_No', ylabel='euribor3m_total',\n",
    "       title='euribor3m ')\n",
    "ax2.grid()\n",
    "ax2.legend(['No Cases']);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols =2, figsize = (20, 5));\n",
    "#sns.distplot(dataset['euribor3m'], hist = True, ax = ax1, color = \"#5BFF33\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "sns.distplot(dataset_yes['euribor3m'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax1.set_title(\"Euribor 3 month rate\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Campaign Quantity\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax1.legend(['Yes cases']);\n",
    "#sns.countplot(x = 'euribor3m',ax=ax2, data = dataset,color =\"#5BFF33\");\n",
    "#sns.countplot(x = 'euribor3m',ax=ax2, data = dataset_no,color =\"#FF5733\");\n",
    "#sns.distplot(dataset['euribor3m'], hist = True, ax = ax2, color = \"#5BFF33\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "sns.distplot(dataset_no['euribor3m'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "ax2.set_title(\"Campaign distribution (No cases)\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Campaign Quantity\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);\n",
    "ax2.legend(['No Cases']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of employees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 2, ncols =1, figsize = (25, 15));\n",
    "#sns.distplot(dataset_yes['nr.employed'], hist = True, ax = ax1, color = \"#07247D\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "sns.countplot(x = 'nr.employed',ax=ax1, data = dataset,color =\"#5BFF33\");\n",
    "sns.countplot(x = 'nr.employed',ax=ax1, data = dataset_yes,color =\"#07247D\");\n",
    "ax1.set_title(\"Number of employees\", fontsize = 13);\n",
    "ax1.set_xlabel(\"Number of employees\", fontsize = 12);\n",
    "ax1.set_ylabel(\"Count\", fontsize = 12);\n",
    "\n",
    "#sns.distplot(dataset_no['nr.employed'], hist = True, ax = ax2, color = \"#FF5733\", hist_kws = {'edgecolor':'black'},kde = False);\n",
    "sns.countplot(x = 'nr.employed',ax=ax2, data = dataset,color =\"#5BFF33\");\n",
    "sns.countplot(x = 'nr.employed',ax=ax2, data = dataset_no,color =\"#FF5733\");\n",
    "ax2.set_title(\"Number of employees\", fontsize = 13);\n",
    "ax2.set_xlabel(\"Number of employees\", fontsize = 12);\n",
    "ax2.set_ylabel(\"Count\", fontsize = 12);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agora vamos tratar nossas variaveis que são categóricas em nosso DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jobs: \n",
      " ['housemaid' 'services' 'admin.' 'blue-collar' 'technician' 'retired'\n",
      " 'management' 'unemployed' 'self-employed' 'unknown' 'entrepreneur'\n",
      " 'student'] \n",
      "\n",
      "Marital Status: \n",
      " ['married' 'single' 'divorced' 'unknown'] \n",
      "\n",
      "Education: \n",
      " ['basic.4y' 'high.school' 'basic.6y' 'basic.9y' 'professional.course'\n",
      " 'unknown' 'university.degree' 'illiterate'] \n",
      "\n",
      "Default on Credit: \n",
      " ['no' 'unknown' 'yes'] \n",
      "\n",
      "Housing loan: \n",
      " ['no' 'yes' 'unknown'] \n",
      "\n",
      "Loan default: \n",
      " ['no' 'yes' 'unknown'] \n",
      "\n",
      "Contact type: \n",
      " ['telephone' 'cellular'] \n",
      "\n",
      "Months: \n",
      " ['may' 'jun' 'jul' 'aug' 'oct' 'nov' 'dec' 'mar' 'apr' 'sep'] \n",
      "\n",
      "day_of_week: \n",
      " ['mon' 'tue' 'wed' 'thu' 'fri'] \n",
      "\n",
      "Poutcome: \n",
      " ['nonexistent' 'failure' 'success'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Jobs: \\n\", dataset[\"job\"].unique(),'\\n')\n",
    "print(\"Marital Status: \\n\", dataset['marital'].unique(),'\\n')\n",
    "print(\"Education: \\n\", dataset['education'].unique(),'\\n')\n",
    "print(\"Default on Credit: \\n\", dataset['default'].unique(),'\\n')\n",
    "print(\"Housing loan: \\n\", dataset['housing'].unique(),'\\n')\n",
    "print(\"Loan default: \\n\", dataset['loan'].unique(),'\\n')\n",
    "print(\"Contact type: \\n\", dataset['contact'].unique(),'\\n')\n",
    "print(\"Months: \\n\", dataset['month'].unique(),'\\n')\n",
    "print(\"day_of_week: \\n\", dataset['day_of_week'].unique(),'\\n')\n",
    "print(\"Poutcome: \\n\",dataset[\"poutcome\"].unique(),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# transformação de categórico para numérico\n",
    "\n",
    "Para passar para modelos de ML, precisamos transformar as variaveis categóricas para numéricas, para isso podemos utilizar duas abordagens: \"Label Encoder\" or \"One Hot Encoder\"\n",
    "\n",
    "Para ter uma referencia sobre suas respectivas funções utilizei o seguinte artigo: https://medium.com/@contactsunny/label-encoder-vs-one-hot-encoder-in-machine-learning-3fc273365621\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder,  LabelEncoder\n",
    "onehotencoder = OneHotEncoder()\n",
    "labelencoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset[\"job\"] = onehotencoder.fit_transform(dataset[\"job\"])\n",
    "#dataset[\"marital\"] = onehotencoder.fit_transform(dataset[\"marital\"])\n",
    "#dataset[\"education\"] = onehotencoder.fit_transform(dataset[\"education\"])\n",
    "#dataset[\"default\"] = onehotencoder.fit_transform(dataset[\"default\"])\n",
    "#dataset[\"housing\"] = onehotencoder.fit_transform(dataset[\"housing\"])\n",
    "#dataset[\"loan\"] = onehotencoder.fit_transform(dataset[\"loan\"])\n",
    "#dataset[\"contact\"] = onehotencoder.fit_transform(dataset[\"contact\"])\n",
    "#dataset[\"month\"] = onehotencoder.fit_transform(dataset[\"month\"])\n",
    "#dataset[\"day_of_week\"] = onehotencoder.fit_transform(dataset[\"day_of_week\"])\n",
    "#dataset[\"poutcome\"] = onehotencoder.fit_transform(dataset[\"poutcome\"])\n",
    "\n",
    "dataset[\"job\"] = labelencoder.fit_transform(dataset[\"job\"])\n",
    "dataset[\"marital\"] = labelencoder.fit_transform(dataset[\"marital\"])\n",
    "dataset[\"education\"] = labelencoder.fit_transform(dataset[\"education\"])\n",
    "dataset[\"default\"] = labelencoder.fit_transform(dataset[\"default\"])\n",
    "dataset[\"housing\"] = labelencoder.fit_transform(dataset[\"housing\"])\n",
    "dataset[\"loan\"] = labelencoder.fit_transform(dataset[\"loan\"])\n",
    "dataset[\"contact\"] =labelencoder.fit_transform(dataset[\"contact\"])\n",
    "dataset[\"month\"] = labelencoder.fit_transform(dataset[\"month\"])\n",
    "dataset[\"day_of_week\"] = labelencoder.fit_transform(dataset[\"day_of_week\"])\n",
    "dataset[\"poutcome\"] = labelencoder.fit_transform(dataset[\"poutcome\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
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       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
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       "      <th>y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>56</td>\n",
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       "      <td>5191.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>57</td>\n",
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       "      <td>1</td>\n",
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       "      <td>999</td>\n",
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       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>999</td>\n",
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       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>0</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  contact  month  \\\n",
       "0   56    3        1          0        0        0     0        1      6   \n",
       "1   57    7        1          3        1        0     0        1      6   \n",
       "2   37    7        1          3        0        2     0        1      6   \n",
       "3   40    0        1          1        0        0     0        1      6   \n",
       "4   56    7        1          3        0        0     2        1      6   \n",
       "\n",
       "   day_of_week  ...  campaign  pdays  previous  poutcome  emp.var.rate  \\\n",
       "0            1  ...         1    999         0         1           1.1   \n",
       "1            1  ...         1    999         0         1           1.1   \n",
       "2            1  ...         1    999         0         1           1.1   \n",
       "3            1  ...         1    999         0         1           1.1   \n",
       "4            1  ...         1    999         0         1           1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "0          93.994          -36.4      4.857       5191.0  0  \n",
       "1          93.994          -36.4      4.857       5191.0  0  \n",
       "2          93.994          -36.4      4.857       5191.0  0  \n",
       "3          93.994          -36.4      4.857       5191.0  0  \n",
       "4          93.994          -36.4      4.857       5191.0  0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.164965</td>\n",
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       "      <td>0.007021</td>\n",
       "      <td>-0.024877</td>\n",
       "      <td>-0.017572</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004594</td>\n",
       "      <td>-0.034369</td>\n",
       "      <td>0.024365</td>\n",
       "      <td>0.019750</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.129372</td>\n",
       "      <td>0.010767</td>\n",
       "      <td>-0.017725</td>\n",
       "      <td>0.030399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>0.001250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027897</td>\n",
       "      <td>0.134121</td>\n",
       "      <td>-0.028277</td>\n",
       "      <td>0.006962</td>\n",
       "      <td>-0.010209</td>\n",
       "      <td>-0.025132</td>\n",
       "      <td>-0.033213</td>\n",
       "      <td>-0.000844</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>-0.028468</td>\n",
       "      <td>0.020965</td>\n",
       "      <td>0.011504</td>\n",
       "      <td>-0.008271</td>\n",
       "      <td>-0.016017</td>\n",
       "      <td>0.052760</td>\n",
       "      <td>-0.007880</td>\n",
       "      <td>-0.019574</td>\n",
       "      <td>0.025122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <td>-0.389753</td>\n",
       "      <td>0.027897</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109220</td>\n",
       "      <td>-0.079450</td>\n",
       "      <td>0.010467</td>\n",
       "      <td>0.005788</td>\n",
       "      <td>-0.054501</td>\n",
       "      <td>-0.007629</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007240</td>\n",
       "      <td>-0.037942</td>\n",
       "      <td>0.038689</td>\n",
       "      <td>0.001912</td>\n",
       "      <td>-0.084210</td>\n",
       "      <td>-0.057477</td>\n",
       "      <td>-0.033783</td>\n",
       "      <td>-0.091939</td>\n",
       "      <td>-0.086199</td>\n",
       "      <td>0.046203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>-0.117892</td>\n",
       "      <td>0.134121</td>\n",
       "      <td>0.109220</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.186859</td>\n",
       "      <td>0.016825</td>\n",
       "      <td>0.006384</td>\n",
       "      <td>-0.105726</td>\n",
       "      <td>-0.082684</td>\n",
       "      <td>-0.017986</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>-0.046626</td>\n",
       "      <td>0.038831</td>\n",
       "      <td>0.017009</td>\n",
       "      <td>-0.043778</td>\n",
       "      <td>-0.081607</td>\n",
       "      <td>0.078799</td>\n",
       "      <td>-0.036380</td>\n",
       "      <td>-0.041492</td>\n",
       "      <td>0.057799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>0.164965</td>\n",
       "      <td>-0.028277</td>\n",
       "      <td>-0.079450</td>\n",
       "      <td>-0.186859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015815</td>\n",
       "      <td>-0.003782</td>\n",
       "      <td>0.135238</td>\n",
       "      <td>-0.015830</td>\n",
       "      <td>-0.008701</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032825</td>\n",
       "      <td>0.080062</td>\n",
       "      <td>-0.102416</td>\n",
       "      <td>0.023417</td>\n",
       "      <td>0.203263</td>\n",
       "      <td>0.168073</td>\n",
       "      <td>0.026522</td>\n",
       "      <td>0.195336</td>\n",
       "      <td>0.189845</td>\n",
       "      <td>-0.099352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>-0.001603</td>\n",
       "      <td>0.006962</td>\n",
       "      <td>0.010467</td>\n",
       "      <td>0.016825</td>\n",
       "      <td>-0.015815</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044296</td>\n",
       "      <td>-0.082186</td>\n",
       "      <td>-0.018141</td>\n",
       "      <td>0.003339</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011010</td>\n",
       "      <td>-0.010551</td>\n",
       "      <td>0.021314</td>\n",
       "      <td>-0.011783</td>\n",
       "      <td>-0.060196</td>\n",
       "      <td>-0.080504</td>\n",
       "      <td>-0.033845</td>\n",
       "      <td>-0.059277</td>\n",
       "      <td>-0.045862</td>\n",
       "      <td>0.011552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>-0.007368</td>\n",
       "      <td>-0.010209</td>\n",
       "      <td>0.005788</td>\n",
       "      <td>0.006384</td>\n",
       "      <td>-0.003782</td>\n",
       "      <td>0.044296</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008556</td>\n",
       "      <td>-0.005705</td>\n",
       "      <td>-0.009344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005166</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>-0.001327</td>\n",
       "      <td>-0.001511</td>\n",
       "      <td>0.001849</td>\n",
       "      <td>-0.002430</td>\n",
       "      <td>-0.012025</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.003903</td>\n",
       "      <td>-0.004909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact</th>\n",
       "      <td>0.007021</td>\n",
       "      <td>-0.025132</td>\n",
       "      <td>-0.054501</td>\n",
       "      <td>-0.105726</td>\n",
       "      <td>0.135238</td>\n",
       "      <td>-0.082186</td>\n",
       "      <td>-0.008556</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.276565</td>\n",
       "      <td>-0.009575</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077368</td>\n",
       "      <td>0.117970</td>\n",
       "      <td>-0.212848</td>\n",
       "      <td>0.118744</td>\n",
       "      <td>0.393584</td>\n",
       "      <td>0.591474</td>\n",
       "      <td>0.251614</td>\n",
       "      <td>0.399773</td>\n",
       "      <td>0.269155</td>\n",
       "      <td>-0.144773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>-0.024877</td>\n",
       "      <td>-0.033213</td>\n",
       "      <td>-0.007629</td>\n",
       "      <td>-0.082684</td>\n",
       "      <td>-0.015830</td>\n",
       "      <td>-0.018141</td>\n",
       "      <td>-0.005705</td>\n",
       "      <td>0.276565</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.027677</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.062059</td>\n",
       "      <td>-0.047891</td>\n",
       "      <td>0.103157</td>\n",
       "      <td>-0.065012</td>\n",
       "      <td>-0.178782</td>\n",
       "      <td>-0.004239</td>\n",
       "      <td>0.009652</td>\n",
       "      <td>-0.117264</td>\n",
       "      <td>-0.221425</td>\n",
       "      <td>-0.006065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_week</th>\n",
       "      <td>-0.017572</td>\n",
       "      <td>-0.000844</td>\n",
       "      <td>0.002202</td>\n",
       "      <td>-0.017986</td>\n",
       "      <td>-0.008701</td>\n",
       "      <td>0.003339</td>\n",
       "      <td>-0.009344</td>\n",
       "      <td>-0.009575</td>\n",
       "      <td>0.027677</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038288</td>\n",
       "      <td>-0.009531</td>\n",
       "      <td>-0.004102</td>\n",
       "      <td>0.018732</td>\n",
       "      <td>0.033245</td>\n",
       "      <td>0.005644</td>\n",
       "      <td>0.041465</td>\n",
       "      <td>0.039043</td>\n",
       "      <td>0.028380</td>\n",
       "      <td>0.015967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>-0.000866</td>\n",
       "      <td>-0.006490</td>\n",
       "      <td>0.010290</td>\n",
       "      <td>-0.015102</td>\n",
       "      <td>-0.011794</td>\n",
       "      <td>-0.007658</td>\n",
       "      <td>-0.000916</td>\n",
       "      <td>-0.026657</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.021950</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.071699</td>\n",
       "      <td>-0.047577</td>\n",
       "      <td>0.020640</td>\n",
       "      <td>0.033360</td>\n",
       "      <td>-0.027968</td>\n",
       "      <td>0.005312</td>\n",
       "      <td>-0.008173</td>\n",
       "      <td>-0.032897</td>\n",
       "      <td>-0.044703</td>\n",
       "      <td>0.405274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>0.004594</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>-0.007240</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>0.032825</td>\n",
       "      <td>-0.011010</td>\n",
       "      <td>0.005166</td>\n",
       "      <td>0.077368</td>\n",
       "      <td>-0.062059</td>\n",
       "      <td>-0.038288</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052584</td>\n",
       "      <td>-0.079141</td>\n",
       "      <td>0.032586</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.127836</td>\n",
       "      <td>-0.013733</td>\n",
       "      <td>0.135133</td>\n",
       "      <td>0.144095</td>\n",
       "      <td>-0.066357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>-0.034369</td>\n",
       "      <td>-0.028468</td>\n",
       "      <td>-0.037942</td>\n",
       "      <td>-0.046626</td>\n",
       "      <td>0.080062</td>\n",
       "      <td>-0.010551</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.117970</td>\n",
       "      <td>-0.047891</td>\n",
       "      <td>-0.009531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052584</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.587514</td>\n",
       "      <td>-0.475619</td>\n",
       "      <td>0.271004</td>\n",
       "      <td>0.078889</td>\n",
       "      <td>-0.091342</td>\n",
       "      <td>0.296899</td>\n",
       "      <td>0.372605</td>\n",
       "      <td>-0.324914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0.024365</td>\n",
       "      <td>0.020965</td>\n",
       "      <td>0.038689</td>\n",
       "      <td>0.038831</td>\n",
       "      <td>-0.102416</td>\n",
       "      <td>0.021314</td>\n",
       "      <td>-0.001327</td>\n",
       "      <td>-0.212848</td>\n",
       "      <td>0.103157</td>\n",
       "      <td>-0.004102</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.079141</td>\n",
       "      <td>-0.587514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.313110</td>\n",
       "      <td>-0.420489</td>\n",
       "      <td>-0.203130</td>\n",
       "      <td>-0.050936</td>\n",
       "      <td>-0.454494</td>\n",
       "      <td>-0.501333</td>\n",
       "      <td>0.230181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>poutcome</th>\n",
       "      <td>0.019750</td>\n",
       "      <td>0.011504</td>\n",
       "      <td>0.001912</td>\n",
       "      <td>0.017009</td>\n",
       "      <td>0.023417</td>\n",
       "      <td>-0.011783</td>\n",
       "      <td>-0.001511</td>\n",
       "      <td>0.118744</td>\n",
       "      <td>-0.065012</td>\n",
       "      <td>0.018732</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032586</td>\n",
       "      <td>-0.475619</td>\n",
       "      <td>-0.313110</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.192972</td>\n",
       "      <td>0.211330</td>\n",
       "      <td>0.178289</td>\n",
       "      <td>0.184144</td>\n",
       "      <td>0.119689</td>\n",
       "      <td>0.129789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp.var.rate</th>\n",
       "      <td>-0.000371</td>\n",
       "      <td>-0.008271</td>\n",
       "      <td>-0.084210</td>\n",
       "      <td>-0.043778</td>\n",
       "      <td>0.203263</td>\n",
       "      <td>-0.060196</td>\n",
       "      <td>0.001849</td>\n",
       "      <td>0.393584</td>\n",
       "      <td>-0.178782</td>\n",
       "      <td>0.033245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150754</td>\n",
       "      <td>0.271004</td>\n",
       "      <td>-0.420489</td>\n",
       "      <td>0.192972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.775334</td>\n",
       "      <td>0.196041</td>\n",
       "      <td>0.972245</td>\n",
       "      <td>0.906970</td>\n",
       "      <td>-0.298334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.price.idx</th>\n",
       "      <td>0.000857</td>\n",
       "      <td>-0.016017</td>\n",
       "      <td>-0.057477</td>\n",
       "      <td>-0.081607</td>\n",
       "      <td>0.168073</td>\n",
       "      <td>-0.080504</td>\n",
       "      <td>-0.002430</td>\n",
       "      <td>0.591474</td>\n",
       "      <td>-0.004239</td>\n",
       "      <td>0.005644</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127836</td>\n",
       "      <td>0.078889</td>\n",
       "      <td>-0.203130</td>\n",
       "      <td>0.211330</td>\n",
       "      <td>0.775334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058986</td>\n",
       "      <td>0.688230</td>\n",
       "      <td>0.522034</td>\n",
       "      <td>-0.136211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <td>0.129372</td>\n",
       "      <td>0.052760</td>\n",
       "      <td>-0.033783</td>\n",
       "      <td>0.078799</td>\n",
       "      <td>0.026522</td>\n",
       "      <td>-0.033845</td>\n",
       "      <td>-0.012025</td>\n",
       "      <td>0.251614</td>\n",
       "      <td>0.009652</td>\n",
       "      <td>0.041465</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013733</td>\n",
       "      <td>-0.091342</td>\n",
       "      <td>-0.050936</td>\n",
       "      <td>0.178289</td>\n",
       "      <td>0.196041</td>\n",
       "      <td>0.058986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.277686</td>\n",
       "      <td>0.100513</td>\n",
       "      <td>0.054878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>euribor3m</th>\n",
       "      <td>0.010767</td>\n",
       "      <td>-0.007880</td>\n",
       "      <td>-0.091939</td>\n",
       "      <td>-0.036380</td>\n",
       "      <td>0.195336</td>\n",
       "      <td>-0.059277</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>0.399773</td>\n",
       "      <td>-0.117264</td>\n",
       "      <td>0.039043</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135133</td>\n",
       "      <td>0.296899</td>\n",
       "      <td>-0.454494</td>\n",
       "      <td>0.184144</td>\n",
       "      <td>0.972245</td>\n",
       "      <td>0.688230</td>\n",
       "      <td>0.277686</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.945154</td>\n",
       "      <td>-0.307771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nr.employed</th>\n",
       "      <td>-0.017725</td>\n",
       "      <td>-0.019574</td>\n",
       "      <td>-0.086199</td>\n",
       "      <td>-0.041492</td>\n",
       "      <td>0.189845</td>\n",
       "      <td>-0.045862</td>\n",
       "      <td>0.003903</td>\n",
       "      <td>0.269155</td>\n",
       "      <td>-0.221425</td>\n",
       "      <td>0.028380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.144095</td>\n",
       "      <td>0.372605</td>\n",
       "      <td>-0.501333</td>\n",
       "      <td>0.119689</td>\n",
       "      <td>0.906970</td>\n",
       "      <td>0.522034</td>\n",
       "      <td>0.100513</td>\n",
       "      <td>0.945154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.354678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.030399</td>\n",
       "      <td>0.025122</td>\n",
       "      <td>0.046203</td>\n",
       "      <td>0.057799</td>\n",
       "      <td>-0.099352</td>\n",
       "      <td>0.011552</td>\n",
       "      <td>-0.004909</td>\n",
       "      <td>-0.144773</td>\n",
       "      <td>-0.006065</td>\n",
       "      <td>0.015967</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066357</td>\n",
       "      <td>-0.324914</td>\n",
       "      <td>0.230181</td>\n",
       "      <td>0.129789</td>\n",
       "      <td>-0.298334</td>\n",
       "      <td>-0.136211</td>\n",
       "      <td>0.054878</td>\n",
       "      <td>-0.307771</td>\n",
       "      <td>-0.354678</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age       job   marital  education   default   housing  \\\n",
       "age             1.000000  0.001250 -0.389753  -0.117892  0.164965 -0.001603   \n",
       "job             0.001250  1.000000  0.027897   0.134121 -0.028277  0.006962   \n",
       "marital        -0.389753  0.027897  1.000000   0.109220 -0.079450  0.010467   \n",
       "education      -0.117892  0.134121  0.109220   1.000000 -0.186859  0.016825   \n",
       "default         0.164965 -0.028277 -0.079450  -0.186859  1.000000 -0.015815   \n",
       "housing        -0.001603  0.006962  0.010467   0.016825 -0.015815  1.000000   \n",
       "loan           -0.007368 -0.010209  0.005788   0.006384 -0.003782  0.044296   \n",
       "contact         0.007021 -0.025132 -0.054501  -0.105726  0.135238 -0.082186   \n",
       "month          -0.024877 -0.033213 -0.007629  -0.082684 -0.015830 -0.018141   \n",
       "day_of_week    -0.017572 -0.000844  0.002202  -0.017986 -0.008701  0.003339   \n",
       "duration       -0.000866 -0.006490  0.010290  -0.015102 -0.011794 -0.007658   \n",
       "campaign        0.004594 -0.006923 -0.007240   0.000371  0.032825 -0.011010   \n",
       "pdays          -0.034369 -0.028468 -0.037942  -0.046626  0.080062 -0.010551   \n",
       "previous        0.024365  0.020965  0.038689   0.038831 -0.102416  0.021314   \n",
       "poutcome        0.019750  0.011504  0.001912   0.017009  0.023417 -0.011783   \n",
       "emp.var.rate   -0.000371 -0.008271 -0.084210  -0.043778  0.203263 -0.060196   \n",
       "cons.price.idx  0.000857 -0.016017 -0.057477  -0.081607  0.168073 -0.080504   \n",
       "cons.conf.idx   0.129372  0.052760 -0.033783   0.078799  0.026522 -0.033845   \n",
       "euribor3m       0.010767 -0.007880 -0.091939  -0.036380  0.195336 -0.059277   \n",
       "nr.employed    -0.017725 -0.019574 -0.086199  -0.041492  0.189845 -0.045862   \n",
       "y               0.030399  0.025122  0.046203   0.057799 -0.099352  0.011552   \n",
       "\n",
       "                    loan   contact     month  day_of_week  ...  campaign  \\\n",
       "age            -0.007368  0.007021 -0.024877    -0.017572  ...  0.004594   \n",
       "job            -0.010209 -0.025132 -0.033213    -0.000844  ... -0.006923   \n",
       "marital         0.005788 -0.054501 -0.007629     0.002202  ... -0.007240   \n",
       "education       0.006384 -0.105726 -0.082684    -0.017986  ...  0.000371   \n",
       "default        -0.003782  0.135238 -0.015830    -0.008701  ...  0.032825   \n",
       "housing         0.044296 -0.082186 -0.018141     0.003339  ... -0.011010   \n",
       "loan            1.000000 -0.008556 -0.005705    -0.009344  ...  0.005166   \n",
       "contact        -0.008556  1.000000  0.276565    -0.009575  ...  0.077368   \n",
       "month          -0.005705  0.276565  1.000000     0.027677  ... -0.062059   \n",
       "day_of_week    -0.009344 -0.009575  0.027677     1.000000  ... -0.038288   \n",
       "duration       -0.000916 -0.026657  0.003690     0.021950  ... -0.071699   \n",
       "campaign        0.005166  0.077368 -0.062059    -0.038288  ...  1.000000   \n",
       "pdays           0.000345  0.117970 -0.047891    -0.009531  ...  0.052584   \n",
       "previous       -0.001327 -0.212848  0.103157    -0.004102  ... -0.079141   \n",
       "poutcome       -0.001511  0.118744 -0.065012     0.018732  ...  0.032586   \n",
       "emp.var.rate    0.001849  0.393584 -0.178782     0.033245  ...  0.150754   \n",
       "cons.price.idx -0.002430  0.591474 -0.004239     0.005644  ...  0.127836   \n",
       "cons.conf.idx  -0.012025  0.251614  0.009652     0.041465  ... -0.013733   \n",
       "euribor3m       0.000125  0.399773 -0.117264     0.039043  ...  0.135133   \n",
       "nr.employed     0.003903  0.269155 -0.221425     0.028380  ...  0.144095   \n",
       "y              -0.004909 -0.144773 -0.006065     0.015967  ... -0.066357   \n",
       "\n",
       "                   pdays  previous  poutcome  emp.var.rate  cons.price.idx  \\\n",
       "age            -0.034369  0.024365  0.019750     -0.000371        0.000857   \n",
       "job            -0.028468  0.020965  0.011504     -0.008271       -0.016017   \n",
       "marital        -0.037942  0.038689  0.001912     -0.084210       -0.057477   \n",
       "education      -0.046626  0.038831  0.017009     -0.043778       -0.081607   \n",
       "default         0.080062 -0.102416  0.023417      0.203263        0.168073   \n",
       "housing        -0.010551  0.021314 -0.011783     -0.060196       -0.080504   \n",
       "loan            0.000345 -0.001327 -0.001511      0.001849       -0.002430   \n",
       "contact         0.117970 -0.212848  0.118744      0.393584        0.591474   \n",
       "month          -0.047891  0.103157 -0.065012     -0.178782       -0.004239   \n",
       "day_of_week    -0.009531 -0.004102  0.018732      0.033245        0.005644   \n",
       "duration       -0.047577  0.020640  0.033360     -0.027968        0.005312   \n",
       "campaign        0.052584 -0.079141  0.032586      0.150754        0.127836   \n",
       "pdays           1.000000 -0.587514 -0.475619      0.271004        0.078889   \n",
       "previous       -0.587514  1.000000 -0.313110     -0.420489       -0.203130   \n",
       "poutcome       -0.475619 -0.313110  1.000000      0.192972        0.211330   \n",
       "emp.var.rate    0.271004 -0.420489  0.192972      1.000000        0.775334   \n",
       "cons.price.idx  0.078889 -0.203130  0.211330      0.775334        1.000000   \n",
       "cons.conf.idx  -0.091342 -0.050936  0.178289      0.196041        0.058986   \n",
       "euribor3m       0.296899 -0.454494  0.184144      0.972245        0.688230   \n",
       "nr.employed     0.372605 -0.501333  0.119689      0.906970        0.522034   \n",
       "y              -0.324914  0.230181  0.129789     -0.298334       -0.136211   \n",
       "\n",
       "                cons.conf.idx  euribor3m  nr.employed         y  \n",
       "age                  0.129372   0.010767    -0.017725  0.030399  \n",
       "job                  0.052760  -0.007880    -0.019574  0.025122  \n",
       "marital             -0.033783  -0.091939    -0.086199  0.046203  \n",
       "education            0.078799  -0.036380    -0.041492  0.057799  \n",
       "default              0.026522   0.195336     0.189845 -0.099352  \n",
       "housing             -0.033845  -0.059277    -0.045862  0.011552  \n",
       "loan                -0.012025   0.000125     0.003903 -0.004909  \n",
       "contact              0.251614   0.399773     0.269155 -0.144773  \n",
       "month                0.009652  -0.117264    -0.221425 -0.006065  \n",
       "day_of_week          0.041465   0.039043     0.028380  0.015967  \n",
       "duration            -0.008173  -0.032897    -0.044703  0.405274  \n",
       "campaign            -0.013733   0.135133     0.144095 -0.066357  \n",
       "pdays               -0.091342   0.296899     0.372605 -0.324914  \n",
       "previous            -0.050936  -0.454494    -0.501333  0.230181  \n",
       "poutcome             0.178289   0.184144     0.119689  0.129789  \n",
       "emp.var.rate         0.196041   0.972245     0.906970 -0.298334  \n",
       "cons.price.idx       0.058986   0.688230     0.522034 -0.136211  \n",
       "cons.conf.idx        1.000000   0.277686     0.100513  0.054878  \n",
       "euribor3m            0.277686   1.000000     0.945154 -0.307771  \n",
       "nr.employed          0.100513   0.945154     1.000000 -0.354678  \n",
       "y                    0.054878  -0.307771    -0.354678  1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#AVALIAR AS CORRELAÇÔES DA BASE\n",
    "dataset.corr(method ='pearson') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17a8fbd1d90>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.set_theme(style=\"white\")\n",
    "# Compute the correlation matrix\n",
    "corr = dataset.corr()\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criando um Base Line para o método KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dividindo entre dados de Treino e Teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(dataset.drop('y',axis=1),dataset['y'],test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verificando a forma dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28831, 20), (12357, 20))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28831,), (12357,))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Treinando o Algoritmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Executando o método com os dados de Teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado = knn.predict(X_test)\n",
    "resultado"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testes de validação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Predito      0     1    All\n",
      "Real                                 \n",
      "0                  10466   531  10997\n",
      "1                    689   671   1360\n",
      "All                11155  1202  12357\n"
     ]
    }
   ],
   "source": [
    "print (pd.crosstab(y_test,resultado, rownames=['Real'], colnames=['          Predito'], margins=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avaliando a matriz de confusão acima podemos ver que o desbalanceamento esta afetando muito o resultado, pois o método esta muito bom para achar as negações (0) do que as afirmações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carregando métricas de avaliação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.95      0.94     10997\n",
      "           1       0.56      0.49      0.52      1360\n",
      "\n",
      "    accuracy                           0.90     12357\n",
      "   macro avg       0.75      0.72      0.73     12357\n",
      "weighted avg       0.90      0.90      0.90     12357\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test,resultado))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otimizando o parâmetro K dos vizinhos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de rebalancear a base eu vou fazer um teste, para otimizar a escolha da quantidade de vizinhos (K), para verificar se vamos melhorar muito a precisão do modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_list = list(range(1,21))\n",
    "k_values = dict(n_neighbors=k_list)\n",
    "#k_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(knn, k_values, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(n_neighbors=3),\n",
       "             param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                         13, 14, 15, 16, 17, 18, 19, 20]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(dataset.drop('y',axis=1),dataset['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time', 'param_n_neighbors', 'params', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split3_test_score', 'split4_test_score', 'mean_test_score', 'std_test_score', 'rank_test_score'])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_test_score  :  [0.7489726  0.80525379 0.78901059 0.81416477 0.80476846 0.81921523\n",
      " 0.81431071 0.82256592 0.81787992 0.82436272 0.82135202 0.8258681\n",
      " 0.82295455 0.82640232 0.82496978 0.82710643 0.82574677 0.82771347\n",
      " 0.82681511 0.82873324]\n",
      "std_test_score  :  [0.13052604 0.09868486 0.10891428 0.09885267 0.10519984 0.09770022\n",
      " 0.10212447 0.09750819 0.10123357 0.09829961 0.10085775 0.09878782\n",
      " 0.10096031 0.09800007 0.10002198 0.09723658 0.09907896 0.09644361\n",
      " 0.09815088 0.09549669]\n",
      "param_n_neighbors  :  [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]\n"
     ]
    }
   ],
   "source": [
    "for i in ['mean_test_score', 'std_test_score', 'param_n_neighbors']:\n",
    "        print(i,\" : \",grid.cv_results_[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.cv_results_; #para saber os valores tirar o ; do final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhor valor de k = {'n_neighbors': 20} com o valor 0.828733240012741 de acurácia\n"
     ]
    }
   ],
   "source": [
    "print(\"Melhor valor de k = {} com o valor {} de acurácia\".format(grid.best_params_,grid.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.7489726 , 0.80525379, 0.78901059, 0.81416477, 0.80476846,\n",
       "       0.81921523, 0.81431071, 0.82256592, 0.81787992, 0.82436272,\n",
       "       0.82135202, 0.8258681 , 0.82295455, 0.82640232, 0.82496978,\n",
       "       0.82710643, 0.82574677, 0.82771347, 0.82681511, 0.82873324])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_['mean_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores=[]\n",
    "for score in grid.cv_results_['mean_test_score']:\n",
    "    #print(score)\n",
    "    scores.append(score)\n",
    "\n",
    "#scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(k_list,scores,color='red',linestyle='dashed',marker='o')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AGORA IREMOS REBALANCEAR A BASE E AVALIAR SUA MELHORIA NO MODELO#!pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install imbalanced-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Carregando a biblioteca para fazer o Under Sampling\n",
    "\n",
    "Esse método busca reduzir a base que tem o maior número de individuos de modo a equiparar as quantidades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data set completo\n",
      "(41188, 21)\n",
      "Data set YES\n",
      "(4640, 21)\n",
      "Data set NO\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(36548, 21)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# VERIFICANDO O TAMANHO DAS BASES DESBALANCEADAS\n",
    "print('Data set completo')\n",
    "print(dataset.shape,)\n",
    "print('Data set YES')\n",
    "print(dataset_yes.shape,)\n",
    "print('Data set NO')\n",
    "dataset_no.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fazendo um plot para observar se as bases estão balanceadas agora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    4640\n",
      "0    4640\n",
      "Name: y, dtype: int64\n"
     ]
    },
    {
     "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": [
    "import seaborn as sns\n",
    "# usar técnica under-sampling\n",
    "rus = RandomUnderSampler();\n",
    "X_res, y_res = rus.fit_sample(dataset.drop('y',axis=1), dataset['y']);\n",
    " \n",
    "# ver o balanceamento das classes\n",
    "print(pd.Series(y_res).value_counts());\n",
    " \n",
    "# plotar a nova distribuição de classes\n",
    "sns.countplot(y_res);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agora irei rodar novamente a Base Line, mas considerando uma base balanceada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_res, X_test_res, y_train_res, y_test_res = train_test_split(X_res,y_res,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((6496, 20), (2784, 20))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_res.shape,X_test_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Modelo baseline a ser batido ####\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn_res = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### Treinando o novo Modelo ####\n",
    "knn_res.fit(X_train_res,y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Gerando o resultado com os dados de Teste rebalanceados ####\n",
    "resultado_res = knn_res.predict(X_test_res)\n",
    "#resultado_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Predito_rebalance     0     1   All\n",
      "Real_rebalance                               \n",
      "0                            1135   239  1374\n",
      "1                             189  1221  1410\n",
      "All                          1324  1460  2784\n"
     ]
    }
   ],
   "source": [
    "#### Exibindo a matriz de confusão do novo Modelo ####\n",
    "print (pd.crosstab(y_test_res,resultado_res, rownames=['Real_rebalance'], colnames=['          Predito_rebalance'], margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Predito      0     1    All\n",
      "Real                                 \n",
      "0                  10466   531  10997\n",
      "1                    689   671   1360\n",
      "All                11155  1202  12357\n"
     ]
    }
   ],
   "source": [
    "#### Exibindo a matriz de confusão do Modelo desbalanceado ####\n",
    "print (pd.crosstab(y_test,resultado, rownames=['Real'], colnames=['          Predito'], margins=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Como podemos observar na matriz de confusão depois de ter rebalanceado os dados melhoramos a classificação dos clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.83      0.84      1374\n",
      "           1       0.84      0.87      0.85      1410\n",
      "\n",
      "    accuracy                           0.85      2784\n",
      "   macro avg       0.85      0.85      0.85      2784\n",
      "weighted avg       0.85      0.85      0.85      2784\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test_res,resultado_res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otimizando o parâmetro K para a base de dados rebalanceada "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_list = list(range(1,31))\n",
    "k_values = dict(n_neighbors=k_list)\n",
    "#k_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_res = GridSearchCV(knn_res, k_values, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(n_neighbors=3),\n",
       "             param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                         13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
       "                                         23, 24, 25, 26, 27, 28, 29, 30]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_res.fit(X_res,y_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Melhor valor de k = {'n_neighbors': 27} com o valor 0.815625 de acurácia\n"
     ]
    }
   ],
   "source": [
    "print(\"Melhor valor de k = {} com o valor {} de acurácia\".format(grid_res.best_params_,grid_res.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores=[]\n",
    "for score in grid_res.cv_results_['mean_test_score']:\n",
    "    #print(score)\n",
    "    scores.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(k_list,scores,color='red',linestyle='dashed',marker='o')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Depois de ter rebalanceado os dados obtivemos uma acurácia um pouco menor (82% para 81%), mas em contra partida sabemos classificar melhor nossos tipos de clientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# avaliando o caso inicial sem balanceamento\n",
    "resultado_old = knn_res.predict(X_test)\n",
    "resultado_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.84      0.91     10997\n",
      "           1       0.41      0.91      0.57      1360\n",
      "\n",
      "    accuracy                           0.85     12357\n",
      "   macro avg       0.70      0.87      0.74     12357\n",
      "weighted avg       0.92      0.85      0.87     12357\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test,resultado_old))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Para fazer a utilização do KNN é importante termos uma base balanceada, no entanto para fazer isso abandonamos uma grande quantidade de dados. Por isso irei rebalancear novamente a base, replicando os dados em menor quantidade, e irei aplicar outros modelos para avaliar a ácurácia deles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Carregando a biblioteca necessária\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    36548\n",
      "0    36548\n",
      "Name: y, dtype: int64\n"
     ]
    },
    {
     "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": [
    "# usar técnica over-sampling\n",
    "rus = RandomOverSampler();\n",
    "X_over, y_over = rus.fit_sample(dataset.drop('y',axis=1), dataset['y']);\n",
    " \n",
    "# ver o balanceamento das classes\n",
    "print(pd.Series(y_over).value_counts());\n",
    " \n",
    "# plotar a nova distribuição de classes\n",
    "sns.countplot(y_over);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dividindo novamente em base de treino e base de teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_over, X_test_over, y_train_over, y_test_over = train_test_split(X_over,y_over,test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aplicando o método de MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#definição da biblioteca\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "#Algoritmo Rede MLP\n",
    "clf_mlp = MLPClassifier(solver=\"lbfgs\", alpha=1e-5, hidden_layer_sizes=(3, 3), random_state=1,max_iter=1000)\n",
    "#Realiza o treinamento do classificador\n",
    "clf_mlp= clf_mlp.fit(X_train_over,y_train_over)\n",
    "\n",
    "#Realiza a previsão de classificaçao \n",
    "y_pred = clf_mlp.predict(X_test_over)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Predito_rebalance      0      1    All\n",
      "Real_rebalance                                  \n",
      "0                             9173   1726  10899\n",
      "1                             1258   9772  11030\n",
      "All                          10431  11498  21929\n"
     ]
    }
   ],
   "source": [
    "#### Exibindo a matriz de confusão do novo Modelo ####\n",
    "print (pd.crosstab(y_test_over,y_pred, rownames=['Real_rebalance'], colnames=['          Predito_rebalance'], margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Percentual de acerto de zero foi 82.82% e o de erro foi 17.18%  para o acerto de um tivemos 72.66% e o de erro foi 27.34%\n"
     ]
    }
   ],
   "source": [
    "percentual_acerto_zero=(9074/10956)*100\n",
    "percentual_erro_zero=(1882/10956)*100\n",
    "percentual_acerto_um=(7973/10973)*100\n",
    "percentual_erro_um=(3000/10973)*100\n",
    "\n",
    "print('')\n",
    "print(\"Percentual de acerto de zero foi {:1.2f}% e o de erro foi {:1.2f}%  para o acerto de um tivemos {:1.2f}% e o de erro foi {:1.2f}%\".format(percentual_acerto_zero,percentual_erro_zero,percentual_acerto_um,percentual_erro_um))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.84      0.86     10899\n",
      "           1       0.85      0.89      0.87     11030\n",
      "\n",
      "    accuracy                           0.86     21929\n",
      "   macro avg       0.86      0.86      0.86     21929\n",
      "weighted avg       0.86      0.86      0.86     21929\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test_over,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agora irei fazer um Grid Search para a escolha dos parâmetros das camadas escondidas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#parameter_space = {\n",
    "#    'hidden_layer_sizes': [(10,30,10),(20,)],\n",
    "#    'activation': ['tanh', 'relu'],\n",
    "#    'solver': ['sgd', 'adam'],\n",
    "#    'alpha': [0.0001, 0.05],\n",
    "#    'learning_rate': ['constant','adaptive'],\n",
    "#}\n",
    "#\n",
    "clf_mlp = MLPClassifier(solver=\"lbfgs\", alpha=1e-5, random_state=1,max_iter=1000)\n",
    "parameter_space = {\n",
    "    'hidden_layer_sizes': [(1,1),(2,2),(3,3),(4,4),(5,5),(6,6)],\n",
    "}\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "clf = GridSearchCV(clf_mlp, parameter_space, n_jobs=-1, cv=5)\n",
    "clf_mlp= clf_mlp.fit(X_over,y_over)\n",
    "y_pred = clf_mlp.predict(X_test_over)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Predito_rebalance      0      1    All\n",
      "Real_rebalance                                  \n",
      "0                             7321   3578  10899\n",
      "1                             3637   7393  11030\n",
      "All                          10958  10971  21929\n"
     ]
    }
   ],
   "source": [
    "#### Exibindo a matriz de confusão do novo Modelo ####\n",
    "print (pd.crosstab(y_test_over,y_pred, rownames=['Real_rebalance'], colnames=['          Predito_rebalance'], margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.67      0.67     10899\n",
      "           1       0.67      0.67      0.67     11030\n",
      "\n",
      "    accuracy                           0.67     21929\n",
      "   macro avg       0.67      0.67      0.67     21929\n",
      "weighted avg       0.67      0.67      0.67     21929\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(y_test_over,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores=[]\n",
    "for score in grid_res.cv_results_['mean_test_score']:\n",
    "    #print(score)\n",
    "    scores.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(scores,color='red',linestyle='dashed',marker='o')\n",
    "plt.xlabel('i')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}